227 results about "Semantic context" patented technology

The invention discloses an intelligent interactive enterprise management simulation system and a method thereof. The method comprises the following steps: constructing a multi-level state causal graph model with a multi-dimensional feature tag; fusing interaction data of the user, constructing a behavior intention tensor, and identifying a situation transition intention through a multi-modal fusion reasoning algorithm; a personalized interaction strategy is dynamically generated by a context-driven interaction optimization generation algorithm; a context semantic tensor is constructed based on a semantic context dynamic matching algorithm, a semantic compression response is realized in combination with a current intention and a historical information path, and a future strategy plan is actively generated through a causal relationship backstepping inference device; and establishing a situation feedback learning and weight updating mechanism, and dynamically adjusting a state causal graph, an intention tensor and an interaction strategy parameter to realize self-evolution closed-loop optimization of the model. Context changes can be perceived in real time, and the decision intention of the user can be deduced deeply.

Methods, systems, and techniques for data searching and querying. A query for retrieving information is received, the information corresponds to a subset of data comprised in a dataset. A response to the query is generated, comprising the information retrieved from the dataset using a series of machine learning models configured to parse datasets. The generating comprises: processing the query; generating an instruction for parsing the dataset according to the processing; and parsing the database using the instruction to return the information. Semantic context for the query can be determined using a machine learning model. Search results determined by parsing the database can be translated into natural language using a large language model. The instructions and search results can be evaluated using one or more arrays of large language models.

The invention discloses a person and post accurate matching system based on an artificial intelligence technology, and relates to the technical field of human resource recruitment applied to the artificial intelligence technology, and the system comprises a first-layer feature vector matching module which is used for inputting text information of job seekers and enterprise posts, analyzing the text information through a natural language processing NLP technology, and obtaining a second-layer feature vector matching module; identifying and extracting structured features, outputting a structured feature set, and mapping the structured feature set into feature vectors by adopting a pre-training model; according to the method, through a triple matching mode of combining vector matching, semantic analysis matching and five-dimensional rule matching, the vector matching speed is high, and a large number of candidates can be quickly screened out; semantic analysis matching can understand the semantic context of the text, and supplement the deviation of vector matching in the aspect of semantic understanding; the five-dimensional rule matching can be comprehensively and carefully evaluated according to different recruitment conditions and post requirements, and the accuracy and reliability of a final matching result are ensured.

A data processing system implements receiving content and a call requesting a generative model to generate a video summary of the content; constructing a prompt including the content and instructions to the model to identify semantic context of the content, to identify a text data item, an audio data item, and / or a video data item embedded in the content to generate a text transcript of the audio data item and / or the video data item, or a textual description of the video data item, to summarize the text data item, the text transcripts, and / or the textual description as a summary of the content based on the semantic context, and to generate the video summary based on the summary and a portion of the text data item, the audio data item, and / or the video data item; providing the first prompt to the generative model; providing the video summary to a client device for presentation.

The invention relates to the technical field of natural language processing, in particular to a multi-modal interactive intelligent NPC dialogue intention matching method and system. The method comprises the following steps: collecting multi-modal data in real time, and generating multi-modal semantic features through sub-modal preprocessing; a cross-modal fusion module based on semantic association is adopted to integrate each modal semantic feature, and an intention candidate set is generated based on semantic context representation and in combination with a semantic analysis module and a predefined intention template; through a continuous semantic learning mechanism and an interactive memory module, in combination with a Bayesian updating method, tracking user intention change in real time, dynamically adjusting the confidence of each intention in the intention candidate set, and screening a final intention; constructing an NPC semantic cognition model, and performing semantic check on user input and an NPC dialogue state through semantic consistency analysis; generating a dialogue strategy in combination with the final intention and a decision engine, and outputting synchronous response content; according to the invention, the precision of intelligent dialogue intention dynamic matching is improved.

The invention discloses a camouflage target detection method based on feature selection attention and frequency domain edge guidance. According to the method, four-level features of a camouflage target image are extracted through a backbone network SMT and are respectively screened; the high-level features are input into a semantic information supplement module, and after semantic features are enhanced, the high-level features and the trunk features are sent into a spatial feature enhancement module together. And inputting the obtained fine-grained features into an edge feature sensing module, and finally fusing multi-scale features through a multi-scale jump connection technology to generate a mask pattern with higher discrimination. The method has the advantages that the network parameter quantity is reduced and key information is reserved through a feature selection mechanism; a spatial feature enhancement module is used for enhancing multi-scale feature representation and remote dependence modeling; the dilution of the semantic context is relieved by means of a semantic supplement module so as to improve the positioning precision; and an edge feature enhancement module is adopted to enhance edge semantic perception and improve boundary integrity. According to the method, the camouflage target detection performance is remarkably improved with relatively low calculation cost.

A data processing system implements receiving content and a call requesting a generative model to generate a video summary of the content; constructing a prompt including the content and instructions to the model to identify semantic context of the content, to identify a text data item, an audio data item, and / or a video data item embedded in the content to generate a text transcript of the audio data item and / or the video data item, or a textual description of the video data item, to summarize the text data item, the text transcripts, and / or the textual description as a summary of the content based on the semantic context, and to generate the video summary based on the summary and a portion of the text data item, the audio data item, and / or the video data item; providing the first prompt to the generative model; providing the video summary to a client device for presentation.

A device includes a memory and one or more processors configured to process sensor data to determine a semantical context associated with the sensor data. The one or more processors are also configured to generate adjusted face data based on the determined semantical context and face data. The adjusted face data includes an avatar facial expression that corresponds to the semantical context.

The invention discloses a remote sensing image semantic segmentation method and device based on wavelet transform convolution. The method comprises the following steps: firstly, extracting semantic feature information in a remote sensing image based on a wavelet transform convolution encoder; secondly, the encoder and the decoder are connected through double-cross attention, the attention uses channel cross attention and space cross attention through series connection, feature information output by the encoder is enhanced, the validity of jump connection between the encoder and the decoder is enhanced, and the semantic gap between the encoder and the decoder is reduced; and finally, a traditional CNN decoder is used for the feature map so as to obtain a prediction result, and the prediction result is the semantic segmentation node of the remote sensing image. According to the method, semantic context information in the remote sensing image can be efficiently captured, the response of the model to shape features is enhanced, and the semantic segmentation precision is improved.

A system (100) for adaptive data blending across heterogeneous BI platforms for real-time decision making, comprising: (a) a data source abstraction module configured to establish standardised and secure connections with several different Business Intelligence (BI) platforms and convert their native data structures into a unified intermediate schema; (b) a real-time data ingestion and synchronisation module operable to continuously monitor and retrieve data updates from the BI platforms using event-driven mechanisms and configurable synchronisation policies; c) an adaptive data mapping and transformation module designed to intelligently harmonize, adapt, and semantically translate disparate data formats using rule-based logic and AI-assisted schema mapping; (d) a semantic context and metadata management module configured to maintain a unified data dictionary, track data sequence and maintain semantic consistency across platforms; (e) a cross-platform query orchestration engine designed to decompose common user queries, translate them into platform-specific query languages, execute them across the relevant BI platforms, and aggregate the results into a coherent output; (f) a decision intelligence and recommendation module that analyses the mixed data to generate real-time insights, alerts and contextual recommendations for decision support; and (g) a security, governance and compliance module configured to enforce access controls, maintain audit trails and ensure compliance with data protection regulations; h) the system provides seamless, real-time and secure data blending to enable consistent analytics and decision-making across heterogeneous BI environments.

The invention discloses an archive arrangement system and method capable of achieving multilingual conversion, and relates to the technical field of archive arrangement. The system comprises a multilingual archive database, a semantic analysis module, a retrieval matching module and a user interaction interface; according to the method, a multi-language association retrieval system based on semantic understanding is constructed, a semantic deep mining algorithm, a cross-language semantic association algorithm and a semantic association degree algorithm are applied, the effect of accurately matching semantic similar contents in files of different languages is achieved, and automatic index updating and incremental updating algorithms are designed for a multi-language file database, so that the accuracy of the multi-language file database is improved. According to the method, the effect of efficient file storage and query is achieved, complexity and resource waste of index updating during new file input or information change are avoided, the effect of extracting semantic features more accurately is achieved by customizing preprocessing processes for different language texts and combining a word vector generation algorithm based on semantic context, and the efficiency of file storage and query is improved. And the accuracy and efficiency of multi-language archive arrangement are improved.

The invention discloses a personalized semantic understanding system for assisting an autonomous mobile platform, and the system comprises a user personalized word meaning library which is used for recording the mapping relation between the user habit expression and the corresponding operation; the fuzzy semantic analysis module is used for identifying fuzzy expression in a natural language instruction of a user and generating a plurality of candidate targets or operation options of semantic generalization; the multi-round context modeling module is used for establishing a semantic context based on session history and performing disambiguation and complementation on current semantics; and the intention generation module is used for converting the processed semantic information into a structured instruction. The personalized semantic understanding system is reasonable in structural design, all the modules cooperate with one another, the effect that 1 + 1 is larger than 2 can be achieved, fuzzy semantic understanding and multi-round context modeling can be conducted, the personalized requirement of a user can be met, semantic understanding precision is high, user adaptability is good, and the personalized semantic understanding system is suitable for man-machine interaction of an autonomous mobile platform.

The invention discloses an internet mass data accurate search method and system based on an AI technology, and relates to the technical field of internet, the method comprises the following steps: receiving a query text input by a user, using a natural language processing technology to identify the input query text, and generating a structured query vector; based on the structured query vector, constructing a knowledge graph to obtain a rich semantic context graph; a knowledge transfer learning technology is introduced, historical search data and behavior characteristics of a user are analyzed, and a personalized search model is constructed; monitoring the real-time interaction behavior of the user, judging the cognitive load of the user, and generating an adaptive search interface; initiating a plurality of query requests by using the personalized search model, and outputting a personalized search result set through filtering and sorting; according to the method, the knowledge graph is constructed and the rich semantic context graph is generated, so that the system can identify entities and multiple relationships thereof, more relevant search results are provided, and a user is promoted to discover potential requirements.

The invention discloses a network threat intelligence analysis attack scene graph (ASG) generation method based on deep learning and natural language processing. The method combines a deep learning model and a natural language processing technology, and improves the efficiency and accuracy of attack scene graph generation. The method comprises the steps that network threat intelligence is collected, an improved BERT-BiLSTM-CRF model is used for entity recognition, and threat attack entities are recognized in combination with a regular expression; carrying out core anaphora analysis by adopting a CR-M-SpanBERT model, and capturing a semantic context dependency relationship between entities; extracting a bidirectional context semantic dependency relationship of the text by using a dependency analysis technology, and generating an embedded representation in combination with RoBERTa-BiGRU; and finally, triple information is extracted based on a BERT-RE model, and an attack scene graph is generated. According to the method, the processing capability of the complex network threat intelligence data is improved, the robustness and adaptability are high, and the method is particularly suitable for multi-mode threat intelligence.

The invention discloses a high-precision geographical name translation method integrating artificial intelligence and multi-language syllable segmentation, and relates to the technical field of geographical name translations, the method comprises the following steps: firstly splitting an input geographical name into a sub-word Token sequence, and using a context coding model of a word embedding model to realize semantic context associated coding of a to-be-translated geographical name; and then modeling internal structured information and a dependency relationship in context semantics of the token to be translated in a local semantic relevance reconstruction enhancement mode, identifying recessive syllable relevance in compound words and adhesive words, and enhancing local morphological characteristics near syllable boundaries, so as to solve the problems of irregular spelling and cross-language interference, and improve the translation efficiency of the token to be translated. And further decoding and outputting the geographical name character string after syllable segmentation to carry out geographical name translation, generating a corresponding geographical name target language translation text, and realizing end-to-end effective conversion from an original geographical name to a target translation name.

The invention relates to the technical field of equipment maintenance and overhaul decision, and discloses a knowledge graph driven two-stage classification equipment condition-based overhaul decision method, which comprises the following steps: acquiring multi-source text data; performing first-stage classification on the equipment based on the equipment importance evaluation model, and performing second-stage classification on the parts based on the maintenance strategy; a device entity, a fault mode entity and a maintenance measure entity are taken as knowledge ontologies, state variable nodes are embedded, and a knowledge graph fusing state variables is constructed by using a long-short-term memory network; in combination with a semantic context sensing mechanism and a graph structure stability constraint mechanism, outputting candidate fault nodes; candidate maintenance measures corresponding to the candidate fault nodes are retrieved in the knowledge graph, candidate maintenance measure verification is carried out in combination with soft constraints and hard constraints, and a maintenance measure list is converted into an equipment maintenance decision scheme by utilizing an execution arrangement generator, so that intelligence of the maintenance decision scheme is realized in combination with the knowledge graph; and decision support is provided for maintenance personnel.

The invention discloses an attention-based context-aware sparse hybrid expert model routing method, which comprises the following steps of: encoding prompt information input by a user to obtain context vector representation of the prompt information, and introducing a multi-head attention mechanism to obtain multi-scale semantic interaction information; further, constructing a gating network based on attention output, and dynamically selecting Top-K expert networks for reasoning; by introducing a self-adaptive neighbor attention weight and a fusion gating mechanism, expert dispatching and weight fusion of a token level are realized; combining with the sub-output of each expert network, and aggregating according to the weight to obtain the final model output; according to the method, the understanding ability of the model for different semantic contexts is enhanced through a multi-expert structure and a dynamic routing mechanism, and the method is adaptive to multiple rounds of token generation processes, so that the accuracy and diversity of generated texts can be improved.

The invention discloses a traffic accident risk prediction method and system inspired by driving attention, and the method comprises the steps: obtaining a driving scene image, preprocessing the image, and converting the image into continuous image frames; based on attention mechanism text-to-video attention transfer fusion, text information is converted into video representation, and cross-modal features with coherent semantics are generated; semantic context migration is realized on the cross-modal features through a graph convolutional network, and a dual-path model is constructed on the basis of a generated semantic context feature matrix; and a multi-task learning strategy is adopted, and a potential accident risk caused by distraction of the driver is identified and predicted through a dual-path model. According to the invention, key semantic clues are identified in each module by using an attention mechanism, so that the robustness of accident prediction is enhanced, and the driving safety is remarkably improved.

The invention discloses a spoken language understanding joint model method based on label attention and a window mechanism. The spoken language understanding joint model method is used for improving the effects of intention recognition and slot filling. The method comprises the following steps: firstly, performing semantic coding on a statement input by a user by adopting a self-attention mechanism and Bi-LSTM coding to generate basic semantic representation; then, dynamically adjusting attention distribution of each lexical element through a tag attention mechanism, extracting sentence-level intention and slot tag semantics, and constructing an overall semantic context, so as to form an intention tag attention module and a slot tag attention module; the intention module preliminarily predicts the intention of a statement by using a block-level sliding window, and the slot module preliminarily predicts slot position information by means of a slot classifier. And then, through a graph convolution layer, carrying out adaptive fusion on the preliminarily predicted intention and slot position information, and realizing information interaction between nodes to obtain an updated label embedding representation. And finally, decoding the embedded representation through a classifier module, and generating a final intention and slot position recognition result.

Aspects of the present disclosure relate to systems and methods for generating one or more prompts based on an input and the semantic context associated with the input. In examples, the prompts may be provided as input to one or more general ML models to provide a semantic context around the input and / or output of the model. The prompt simulates training and fine-tuned specialization of the general ML model without the need to use a fine-tuning process to actually train the general ML model into a fine-tuned state. Additionally, the model output may be evaluated for responsiveness to the input prior to being returned to the user. An advantage of the present disclosure is that it allows a general ML model to be applied to a plurality of applications without the need for expensive and time-consuming training to fine-tune the ML model.

The invention discloses an SAR directed target detection method based on multi-scale context sensing, and belongs to the technical field of remote sensing image processing and computer vision. According to the method, firstly, through a scattering characteristic guided multi-scale dynamic characteristic enhancement network, a multi-branch structure and a dynamic fusion mechanism are utilized to enhance characteristic expressions of targets of different scales; and secondly, designing a task-adaptive regional context sensing module, fusing local details and semantic contexts, and improving the target discrimination capability under a complex background. And a decoupling type progressive refining detection head is adopted to predict a target category, a directed bounding box and a direction angle, and the positioning precision is optimized through progressive regression. Semantic consistency loss is introduced in the training stage, and end-to-end optimization is achieved in combination with a multi-task joint strategy. According to the method, the problems of large target scale difference, strong background interference, changeable directions and the like in the SAR image are effectively solved, and the accuracy and robustness of directed target detection are remarkably improved.

A data processing system implements receiving a user prompt requesting a diagram representing digital content; constructing a prompt including the user prompt, the digital content, and instructions to a generative model to identify semantic context of the digital content, to identify a text data item, an audio data item, a video data item, and / or a structured file item embedded in the digital content to generate at least one of a text transcript of the audio / video / structure file item, and / or a text description of the audio / video / structure file item, to semantically analyze and extract diagram data from the text data item, the text transcripts, and / or the textual descriptions based on the semantic context, and to generate the diagram of the digital content based on the diagram data; providing the prompt to the generative model and receive the diagram; and providing the diagram to the client device for display.

To mine and visualize related topics in a knowledge base, where a knowledge base is mined for related topics to create a knowledge graph that is output as a visualization display of automatically suggested related topics. To mine the knowledge base an approach has been developed which incorporates user personalized results in addition to semantic context. The results are displayed in a visualization display for user interaction. While interacting with a suggested topic the user can view and select related topic information which enables users to discover other similar or related topics they would be interested in gaining additional context about. Thus, the related topics and visualization display according to aspects described herein may serve the purpose of more effective utilization and exploration of the knowledge base.

The invention discloses an automatic advertisement creativity generation method and system based on natural language generation. According to the invention, through establishing a keyword and context construction module, a brand semantic modeling and control module, an industry trend extraction module, an advertisement generation engine, a diversity control module and a putting adaptation and post-processing module, automatic generation of advertisement originality is realized. The system firstly receives a keyword and a mood instruction input by an advertiser, and constructs an initial semantic context; and then, coding a brand sample text by utilizing a pre-training model, and generating a brand semantic center vector as a prompt embedding so as to guide the generated content to accord with brand tonality. Meanwhile, through a keyword reservation supervision mechanism, it is ensured that core keywords appear naturally and at high frequency in the process of generating the copywriting. In the generation process, the system also uses a sampling strategy through a diversity control module and uses a semantic vector to remove duplication, and finally outputs diversified and non-redundant high-quality advertisement copywriting.

The invention belongs to the technical field of digital image processing, and particularly relates to a knowledge distillation-based cross-modal fusion lightweight defect detection method, which comprises the following steps of S10, cross-modal fusion distillation; through a bidirectional vision-language alignment mechanism, the frozen multi-modal knowledge of a teacher model vision-language basic model VLM is migrated to a lightweight student model, and the dual-path fusion module comprises text condition region representation injected with semantic context and region anchoring semantic embedding fused with spatial vision clues; step S20, cross-header word-region alignment is carried out; embedding the fusion visual features generated by the two-way fusion module and the enhanced text to generate cross-head prediction so as to simulate the semantic-space association capability of a teacher model; s30, knowledge distillation loss is fused; according to the method, the multi-modal basic model is fused and distilled into the lightweight single-modal detection model, the detection performance in a defect detection scene can be improved, and compared with the basic model, the reasoning speed is greatly improved, and the parameter quantity is reduced.

The invention discloses a low illumination enhancement method based on event and image bidirectional collaborative guidance, and belongs to the technical field of computer vision. The method comprises an event feature extraction branch and an image-event fusion branch which are respectively provided with an image-guided event enhancement module and a Mama-based image-event fusion module; the two modules realize cross-modal interaction through bidirectional cooperative guidance. The image-guided event enhancement module suppresses event noise and compensates for context loss by using semantic context information of an image. The Mama-based image-event fusion module adjusts the global illumination and contrast of the image by using the global representation capability of Mama and the characteristics of linear space-time complexity, and fuses event data to the image in combination with a signal-to-noise ratio map to supplement structural information. According to the method, enhancement and fusion of the image and event information are realized through a bidirectional collaborative guidance mechanism, and the enhancement problems of image structure deficiency and illumination contrast degradation in a low-illumination scene are solved.

The invention discloses a power dispatching monitoring data anomaly detection method based on artificial intelligence, and relates to the field of data anomaly detection, and the method comprises the steps: firstly carrying out the multi-modal alignment and preprocessing of high-frequency time sequence monitoring data and sparse log text of power dispatching, and constructing the association mapping of heterogeneous data under a time window; a retrieval enhancement generation technology is utilized to deeply parse a log text to construct a global semantic context, and time series data is synchronously subjected to double-flow coding to extract inherent fluctuation features. Based on this, a cross-modal semantic gating feature modulation mechanism is established, and a dynamic reweighting sequential sequence is guided by using log semantics, so that when a semantic background of a specific scheduling operation is perceived, the attention on compliance data mutation is automatically reduced. And finally, performing signal reconstruction and adaptive anomaly judgment based on the feature sequence under semantic guidance. In this way, false alarms can be significantly reduced while the real fault sensing capability is guaranteed, and the intelligence and robustness of the power monitoring system are improved.

The invention discloses a file digital circulation privacy protection method and system based on a zero-trust architecture, and relates to the technical field of data security, and the method comprises the steps: building a mapping table of semantics and encryption fragments based on a semantic context tag set and a dynamic authorization intention in combination with the operation granularity in a homomorphic encryption domain, generating a homomorphic operation plan and a re-encryption credential set; according to the homomorphic operation plan and the re-encryption credential set, executing logic fragmentation and homomorphic encryption on the to-be-transferred file to form a cryptographic container set, obtaining an access commitment instance, and constructing an access commitment chain initial fragment; homomorphic cooperation operation is executed in the secret state container set, the access commitment chain initial fragment is checked in real time, node revocation and authorization reversal are executed according to the commitment chain state, and an updated access commitment chain and an instant trust reconstruction record are generated. According to the invention, the security, controllability and traceability of the file collaboration process are improved.

The invention discloses a three-dimensional point cloud semantic segmentation method based on cross-scale feature fusion. In order to solve the problems that a decoder of an MLP-based point cloud semantic segmentation algorithm supplements features only through jump links, semantic and detail information of different levels cannot be aggregated, and features extracted by an encoder cannot be better analyzed, a cross-scale feature fusion module is provided; semantic context information of a high resolution level and geometric detail information of a low resolution level are reserved. According to the three-dimensional point cloud semantic segmentation method based on cross-scale feature fusion, features of three different scales are selected, and feature channels of the features are compressed into 1, so that the problem of information coincidence caused by direct element-by-element summation and element-by-element multiplication is solved, and the effect of unifying dimensions without convolution operation is achieved; then multiplying the high-scale features by the basic scale features element by element, adding the low-scale features by the basic scale features element by element, and finally applying the element-by-element results or the added results to the high-scale features or the low-scale features respectively, and adding the high-scale features or the low-scale features with the original basic scale features to form cross-scale features. And the decoder can obtain finer features for analysis.

A fast fusion network driver attention prediction method in a dangerous driving scene comprises the following steps: step 1, segmenting an RGB set in a traffic accident video set into semantic images with different semantic features frame by frame, and extracting spatio-temporal features and semantic features of the semantic images; 2, fusing the spatio-temporal features and the semantic context features of the image extracted in the step 1 by using an attention strategy; step 3, constructing an attention fusion module by using an AC-mix module to quickly identify a key object or region attracting the attention of the driver; and 4, converting the potential driver attention map obtained in the step 3 into a final driver attention map by using an attention map decoding module. According to the method, the semantic context related to the driving scene is comprehensively fused, so that the prediction accuracy is improved; meanwhile, an AC-mix module is integrated, and the global perception capability and the local feature extraction capability are combined, so that the path calculation efficiency is improved, and the calculation complexity in the prediction process is reduced.