79 results about "Semantic relationship" patented technology

The invention relates to the technical field of text processing, in particular to a text classification method and system based on semantic analysis, and the method comprises the following steps: segmenting semantic units, constructing a direction change sequence, positioning mutation nodes, generating a consistency section, forming a convergence section, and outputting a classification result. According to the method, a continuous change sequence is formed by constructing a semantic embedding vector and calculating a direction difference, a semantic mutation point can be anchored and divided into sections by combining mutation intensity identification and local jump tracking, and a semantic closed structure and a convergence section are extracted by means of context direction consistency judgment and generic label comparison; precise recognition of a semantic relation chain is realized, semantic jump and conflict starting points can be dynamically sensed, the semantic boundary recognition capability is improved, and the understanding and classification capability of a model on semantic attribution in a complex context is enhanced on the premise of not depending on a fixed dictionary and shallow statistics. The problems that a traditional model is slow in response to an abrupt change structure and weak in semantic convergence recognition are effectively solved.

The invention discloses an anti-fact multi-mode dialogue emotion causal reasoning method based on a double-branch hypergraph. The method comprises the following steps: respectively extracting sentence level feature vectors of three modes of text, voice and vision from input multi-mode dialogue data; carrying out modeling on a high-order relationship in the modals and between the modals by utilizing a hypergraph structure, and constructing a dialogue hypergraph containing multi-modal nodes and emotion nodes; introducing a hypergraph attention network on the hypergraph, learning contribution weight of each modal node to a target emotion node, and selecting a candidate reason node set; the candidate reason nodes are intervened, an anti-fact branch is constructed, a fact situation and final node feature representation under the anti-fact situation are calculated, and a causal effect vector is obtained; and designing a joint optimization objective function, and carrying out joint training on emotion recognition loss and causal consistency loss to realize synchronous prediction of emotion categories and emotion reasons. According to the method, a high-order semantic relationship can be effectively modeled in a multi-modal dialogue scene, and a key reason for emotion formation is reasoned.

The invention relates to a multi-modal knowledge representation learning method fusing a multi-attention mechanism and semantic enhancement, and aims to solve the problems that the multi-modal knowledge representation method is insufficient in multi-modal feature fusion and difficult to effectively model complex semantic relationships such as symmetric and anti-symmetric. The method comprises the following steps: firstly, extracting entity image and text features from a multi-modal knowledge graph by using a CLIP model, and extracting entity audio features by using a VGGish model; then image features are enhanced through spatial attention, and image-text feature fusion is realized through cross attention; designing a cross-modal fusion module to dynamically fuse the image-text features and the audio features to obtain multi-modal fusion features; and finally, extracting structured features based on a ComplEx model, integrating the structured features with multi-modal fusion features through an adaptive dual-channel scoring function, and optimizing a training process by adopting a contrast learning loss function. The multi-modal features can be fully fused, the semantic expression ability is enhanced, and the performance of downstream tasks such as intelligent question and answer is improved.

The invention discloses an intelligent dialogue memory management method and system based on the logistics field, and relates to the technical field of logistics intelligent dialogues, and the method comprises the steps: obtaining the historical logistics dialogue data of a target user, generating a user feature-interactive entity-semantic relationship initial knowledge graph based on a QWen2.5-32B large language model, and constructing a Milvus semantic vector library; monitoring a logistics dialogue flow in real time, and starting an adaptive memory management mechanism by taking three rounds of dialogue as a judgment threshold value; extracting user dialogue content attribute features and behavior preferences, and dynamically updating a user feature-interactive entity-semantic relationship initial knowledge graph and a Milvus semantic vector library; and triggering a logistics dialogue according to a target user, starting a multi-modal memory recall mechanism, cooperatively retrieving a user feature-interactive entity-semantic relationship knowledge graph and a Milvus semantic vector library, generating a personalized intelligent dialogue response, and realizing a logistics intelligent dialogue memory management closed loop. The beneficial effect of the invention is that the intelligent and personalized capabilities of the system are enhanced.

The invention discloses a four-dimensional index-based scientific and technical literature knowledge automatic labeling method and system, and relates to the technical field of natural language processing, and the method comprises the following steps: constructing a four-dimensional index architecture; the method comprises the following steps: receiving multi-format scientific and technical literatures through a data input layer, and performing index analysis to obtain a document layer semantic structure, a paragraph layer semantic relation graph, a sentence semantic relation network and an entity semantic relation; performing semantic density calculation and knowledge enrichment region recognition to obtain a knowledge enrichment region recognition result; and establishing a cross-hierarchy semantic association model, performing entity boundary prediction and multi-level labeling on a knowledge enrichment area recognition result to obtain a structured semantic labeling result, and performing visual output and display. The technical problem that in the prior art, scientific and technical literature knowledge recognition is not accurate, hierarchical labeling is insufficient, and consequently the literature processing efficiency and accuracy are low is solved, and the technical effects that precise recognition and multi-hierarchical labeling of scientific and technical literature knowledge are achieved, and the literature processing efficiency and accuracy are improved are achieved.

Disclosed is a system and method for enhancing value for advertisers by helping them select and initiate the purchase of an advertisement associated with advertising (ad) words or phrases that have strong semantic relationships to a given context, but which are not necessarily the most popular ad words or phrases with the highest costs. Advertisements associated with ad words or phrases that have strong semantic relationships to a given context, and yet are still cost effective in that their calculated value exceeds the costs of purchasing the ad keywords, are bid for and bought. The system and method may be adapted to automatically purchase advertisements associated with ad words or phrases when they fall within a desired price range based on their calculated value. As the prices of advertisements associated with these words or phrases fluctuate over time based on their popularity, the automated bidding and buying of advertisements may be used to purchase advertisements associated with words or phrases at a price desirable to a given ad purchaser. By automatically purchasing such advertisements, the return on investment for an advertiser may be improved.

The application discloses a kind of multi-modal video sequence segmentation methods based on multi-scale codec, steps include: extracting image features and text features;Obtain the joint feature representation containing image and language semantic information simultaneously;Extract multi-scale fusion feature sequence under different spatial resolutions;Obtain the feature representation sequence after space-time modeling;Obtain the cross-scale fusion feature representation in unified semantic space;Get foreground features;Visualize segmentation mask to generate semantic segmentation map.The multi-modal video sequence segmentation method can realize the deep interaction of image and language semantics, establish the context and semantic relationship, introduce cross-modal information interaction through multi-modal collaborative mechanism, enhance the robustness and stability of the model in complex dynamic scene, effectively improve the segmentation effect and generalization ability of image sequence segmentation model in segmentation task.

A system and method for an artificially intelligent (AI) platform comprising a Knowledge Net (KN) Platform for automatically creating accessible, transparent knowledge stores which reflect a complete spectrum of semantic relationships between concepts. The system comprises a knowledge discovery engine that creates a machine representation of the semantic knowledge graph of a concept, namely a Concept Semantic Knowledge Graph (CSKG), for explainable, human-analogous understanding of natural language data for the full range of automated natural language understanding tasks—wherein the knowledge discovery engine, in discovering the related concepts, uses a variety of linguistic learning methods.

The application discloses a mineral prediction method based on spatial intelligence, relates to the technical field of spatial intelligence, and comprises the following steps: identifying the spatial relationship of a spatial entity element list, constructing a topological connected graph, extracting connected structure attributes, executing semantic relationship reasoning, and generating an object-level spatial intelligent scene graph; based on the object-level spatial intelligent scene graph, constructing an anisotropic cost field, calculating a minimum cost channel distance, mapping into a channel weight expression, and generating a channel weight layer; performing spatial alignment and multi-modal spatial correlation feature fusion on a multi-source mineralization evidence data set, the object-level spatial intelligent scene graph and the channel weight layer, executing mineral inference through a mineral prediction machine learning model, and generating a mineral prediction layer. The application realizes dynamic modeling of a mineralization path, and improves prediction accuracy and engineering practicability.

The invention relates to the technical field of semantic comprehension, in particular to a water and soil conservation monitoring summary report auxiliary auditing system based on semantic comprehension, which comprises a graph and certificate judgment module, a phrase screening module, an entry matching module, a data error correction module and a revision and collection module. According to the method, the corresponding relation between image components and text main words is extracted, semantic deviation between image-text contents is recognized, a verb group and a time word are combined to carry out sequential relation comparison, the logical sequence in a statement is analyzed to be associated with actions, construction terms are extracted and are associated with standard terms, and phrase contents lacking the orientation of the terms are screened; the method comprises the following steps of: analyzing a connection state of a data segment and text description on a field level by combining a semantic relationship between a field subject and a monitoring unit, constructing a multi-dimensional check chain covering image-text association, a time sequence, term classification and data matching, and promoting collaborative application of semantic processing in structure association, rule matching and logic recognition; and the depth and breadth of text review are expanded.

This invention relates to the field of privacy protection technology, specifically to a method and system for protecting user privacy data based on data security. The method utilizes phrase association analysis of candidate keywords in text to identify core sentences in the user's text; it then filters keyword groups based on their sensitivity within the core sentences to determine noisy placement, and divides text units by combining different semantic relationships between keyword groups; finally, it analyzes the confidentiality requirements of text units based on lexical sensitivity, and clusters text units based on their similarity, adding noise based on the confidentiality requirements of different clusters to obtain noisy text data. This invention combines fine-grained semantic association analysis between words in the text, adaptively adding noise to sensitive parts with varying risks while preserving basic semantics, thus protecting user privacy while ensuring the accuracy and reliability of subsequent data analysis.

The invention provides an application function recommendation method and device, and the method comprises the steps: constructing a text feature vector and a behavior feature vector based on application program data, and carrying out the fusion to obtain a fusion feature vector; determining an embedded vector corresponding to the fusion feature vector by using a heterogeneous graph information network model; inputting the embedded vector into a prediction model, and outputting prediction scores of the multiple functions; according to the prediction scores, the multiple functions are arranged in a descending order, and a preset number of functions with the highest ranking are selected as recommendation results to be output. By constructing a heterogeneous graph information network model with multiple types of nodes and multiple semantic edges and combining multivariate path mining, the accuracy, interpretability and generalization ability of processing a complex semantic relationship between a user and a function are improved. By capturing the attention weights of different relationships and enhancing the distinction degree of feature expression, the method can accurately model user requirements under the condition of data sparsity, and improves the robustness and performance of a recommendation system, thereby meeting the multi-dimensional requirements of different users.

This invention relates to the field of distributed computing technology, specifically to a method and system for multi-agent collaborative task allocation based on a large model. It addresses the technical problem of existing methods struggling to effectively assess and mitigate the risk of blocking propagation along task chains in scenarios with complex task dependencies and semantic relationships, leading to low system resource utilization. The method includes: constructing a semantic blocking risk index based on the semantic blocking strength and semantic transmission capability of all preceding tasks of the task to be allocated, the exclusivity index of candidate agents, and the real-time load data of candidate agents; adjusting the observation window length of the time-series prediction model based on the semantic blocking risk index; predicting the future load and blocking trend of each agent using the adjusted time-series prediction model; and allocating tasks based on the prediction results and the semantic blocking risk index. This invention is applicable to multi-agent collaborative task allocation scenarios.

This invention discloses a method and system for semantic analysis of underwater swimming movements based on multi-agent collaboration, belonging to the field of multi-agent collaboration technology. The method includes identifying and analyzing underwater swimming video datasets based on a visual semantic generation agent model, recognizing natural language commands based on a planning agent, coordinating and interacting with a large language model and multiple agents, outputting kinematic parameter analysis results and personalized training suggestions, and generating an interpretable evaluation report. In this invention, the visual semantic generation agent model is trained using non-uniform soft-label knowledge distillation, so that the output visual semantic vector retains fine-grained inter-class semantic relationships while eliminating redundant information. Furthermore, the multi-agent collaborative architecture, through task orchestration of the planning agent, links structured data retrieval, professional knowledge enhancement, and credibility verification agents, realizing full-process automation from raw video to kinematic parameter analysis and personalized training suggestion generation.

The invention discloses a data-driven agile project management system, and relates to the technical field of intelligent project management, and the system comprises a project management center which is in communication connection with the following modules: a semantic graph construction module which dynamically captures the semantic association of a project task and an activity based on a knowledge graph and natural language processing technology, and stores the semantic association of the project task and the activity; and constructing a project semantic knowledge graph and continuously updating a semantic network between tasks. Through semantic graph construction and a dynamic mapping mechanism, semantic association between a traditional waterfall model and an agile model task can be automatically identified, limitation of traditional static keyword matching is overcome, context changes in task description are captured in real time based on a knowledge graph and a natural language processing technology, and the task description efficiency is improved. The semantic network is updated through incremental learning, the accuracy and adaptability of the mapping relation are ensured, project management can be seamlessly connected in a mixed development mode, and task traceability and management coherence are improved.

This application discloses a multimodal large-scale jailbreak risk detection and defense method and an electronic device. The method acquires original malicious commands and constructs visual and textual embedding features based on these commands, fusing them to form multimodal embedding fusion features. Based on these features, it generates a model response to the original malicious commands and a jailbreak attack evaluation type for the model response. If a successful jailbreak attack is determined, the corresponding attack text and image are used as the jailbreak attack scheme, and then the model jailbreak risk detection and defense processing is performed based on this scheme. This application, through multimodal embedding fusion features, deeply explores the semantic relationships and feature essence of test commands, extracts the attack text and image combinations of successful jailbreak scenarios, and constructs a jailbreak attack scheme that combines concealment and attack. Based on this, targeted model jailbreak risk detection and defense processing is carried out, improving the model's ability to identify and defend against jailbreak attacks in multimodal scenarios.

The invention discloses a document-level event argument extraction method and system based on a semantic fusion graph, and the method comprises the steps: building a semantic mention graph by taking entity mentions in a document as nodes of the graph and semantic relationships as edges, fusing a graph structure into a large language model for embedding representation through a multi-layer encoder, and dynamically updating node and edge information through an attention mechanism. The embedded representation of the enhanced context semantics is obtained; constructing an event graph based on all event structures in the document, modeling a multi-event association relationship, and fusing the multi-event association relationship into a large language model decoder to strengthen the understanding of the model on the association between events; the method comprises the following steps: constructing cue sentences according to event types, synchronously inputting multi-event cue sentences in a training stage, designing a weighted loss function, guiding a model to learn multi-event information interaction, inputting the multi-event cue sentences in a reasoning stage, and only taking a target event argument result as an evaluation standard. According to the method, document noise can be suppressed, the problems of scattered distribution and long-distance dependence of events and arguments are solved, and the accuracy and robustness of argument extraction are improved.

The application discloses a sparse-view three-dimensional Gaussian language field construction method and system for humanoid robot grasping. The method comprises the following steps: acquiring an RGB image set under sparse-view and a natural language instruction of a user; reconstructing a three-dimensional point cloud through stereo matching, initializing a three-dimensional Gaussian primitive field after noise elimination and metric alignment; embedding two-dimensional semantic features in the field, jointly optimizing through a double-path semantic supervision module, and constructing a semantic-consistent three-dimensional Gaussian language field; wherein the double-path semantic supervision comprises an object perception path and a global context path, which respectively constrain local semantic consistency and overall semantic relationship, and the final semantic representation of each Gaussian primitive is obtained through weighted fusion; finally, candidate grasping postures are generated, semantic reordering is performed in combination with the language instruction, and the optimal grasping posture is selected through geometric-semantic joint scoring. The method can improve the execution accuracy of the robot grasping task under complex instructions under the condition of sparse image set input.

The invention belongs to the technical field of electric power equipment discharge fault diagnosis, and discloses a high-voltage electric power equipment partial discharge fault diagnosis method and system.The method deeply fuses ultrahigh frequency and ultrasonic signal features in signal processing, the complementarity of the ultrahigh frequency and ultrasonic signal features under strong electromagnetic interference is utilized, the system still keeps high accuracy in a complex interference scene, and the fault diagnosis accuracy is improved. The environment adaptability limitation is broken through. The convolutional neural network architecture adopts a double-branch 1D-CNN structure to respectively process two signals, enhance the feature extraction capability, accurately capture key features, and avoid feature loss caused by inter-signal interference. A two-way cross attention mechanism is introduced in the signal fusion link, deep semantic association is mined, the advantages of multiple sensors are exerted, and the analysis accuracy is improved. A residual classifier is introduced in the aspect of mode recognition, the gradient problem is relieved, network degradation is dealt with, feature transfer multiplexing is enhanced, system robustness is improved, multi-classification tasks are adapted, and diagnosis accuracy is remarkably improved.

According to aspects of the disclosed subject matter, a digital twin controller, as implemented on a computer system, is presented. With respect to the represented system, the digital twin controller utilizes an entity graph that includes one or more bidirectional, multi-semantic relationships between the graph nodes. Advantageously, the resources to store the entity graph having bidirectional, multi-semantic relationships is reduced. Similarly, this updated entity graph provides for more efficient processing.

The invention discloses an insurance clause accurate retrieval method and system based on large model incremental training, and relates to the technical field of natural language processing, the method comprises the following steps: constructing an insurance clause database, and establishing a perception hierarchy through perception analysis; a semantic label is established through named entity recognition and semantic role labeling under the constraint of the perception level; constructing a secondary retrieval vector by using the two; receiving user input, and calling an intention recognition channel to establish a structured query vector; retrieving the matching and obtaining a result; and after recording feedback, regularly extracting query feedback history and newly-added clause data increment to update a secondary retrieval vector and retrieval matching. According to the method, the technical problems that a retrieval result is one-sided, the matching degree is low and accurate retrieval requirements are difficult to meet due to the fact that semantic association and a hierarchical structure cannot be accurately captured when a traditional data processing mode is used for coping with insurance terms with complex structures and various contents are solved, accurate retrieval of the insurance terms is achieved, and the retrieval efficiency is improved. And the comprehensiveness and the matching degree of retrieval results are improved.

This application relates to the field of computer technology and discloses an image-based test item segmentation method, apparatus, device, and storage medium. The method includes extracting text blocks and their bounding box coordinates from a target test item image; sorting the text blocks according to their bounding box coordinates to obtain a first sorting queue; adjusting the order of the text blocks according to their semantic relationships to obtain a second sorting queue; loading the text blocks in the second sorting queue into a cache queue of a grouping model, whereby the grouping model groups text blocks belonging to the same test item in the cache queue and outputs a first grouping result; retaining the text blocks in the last group in the cache queue; loading the text blocks extracted from the next test item image into the cache queue so that the grouping model outputs a second grouping result; and obtaining the test item segmentation result based on the grouping result. The method of this application achieves the technical effect of improving the accuracy of test item segmentation.

This invention belongs to the field of computer information processing and proposes a document retrieval optimization method based on entity association. It performs secondary optimization while retaining the original retrieval algorithm, which not only improves the accuracy of the retrieval but also avoids the accuracy and efficiency problems caused by algorithm modifications. By performing entity recognition on both the document set and the user's search content, and calculating the association relationships between entities and documents, and entities and search content respectively, and comprehensively applying these association coefficients, it can uncover deep semantic relationships between search content and documents, and accurately locate documents that match the search intent. By calculating entity association degrees and re-ranking them according to these degrees, the accuracy of the search results is improved, resulting in a higher degree of alignment with user needs and allowing users to more easily obtain the information they require.

This invention relates to the field of user interaction technology and discloses an interpretable news recommendation method and apparatus. In this invention, the user preference vector is obtained based on a comprehensive analysis of user attributes, long-term behavior, and short-term behavior, thus providing a more reliable analysis from the perspective of user attributes and behavior. For the semantic features of candidate news, semantic enhancement is performed using a pre-set knowledge base, thereby achieving a higher depth of semantic understanding. Vector similarity provides a basis for news recommendation from the perspective of feature analysis, while semantic relevance provides a basis for news recommendation from the perspective of semantic analysis. The combination of the two can capture the deep semantic relationship between users and news, ultimately improving the reliability of news recommendations. The first matching information consists of mutually matching features, and the second matching information consists of mutually matching text descriptions. The combination of the two can demonstrate the thought process or reasoning behind the news recommendations to the user, making the news recommendation method of this invention interpretable.

This invention discloses an audio forgery detection method based on a heterogeneous graph collaborative attention network. It constructs multi-view feature representations for the input audio and introduces latent style mixing and random feature recombination strategies during the input feature construction stage to weaken style perturbations irrelevant to forgery detection while retaining stable discriminative features across different views. Based on the enhanced multi-view features, a hierarchical heterogeneous graph neural network is constructed to aggregate and model the multi-dimensional semantic relationships between different views. Simultaneously, a bidirectional collaborative attention module is introduced to capture cross-view semantic dependencies, enhance complementary information interaction, and improve the robustness and discriminative ability of feature representations in complex environments. The system identifies real and forged audio based on the output audio forgery probability score. Compared with existing technologies, this invention exhibits superior detection performance, robustness, and generalization ability under unknown attacks, noise interference, and cross-domain testing scenarios.

The invention discloses an image classification method and device for mining a semantic relationship by using a large language model, relates to the technical field of image processing, and can improve the recognition and clustering of images of unknown categories. According to the scheme, the method comprises the following steps: acquiring a training sample set, wherein the training sample set comprises a plurality of first images with labeled categories and a plurality of second images with unlabeled categories; for the labeled categories, generating semantic description of each category in multiple dimensions by using a large language model; based on the semantic description of each category, determining semantic relevancy between the semantics of each category and the semantics of other categories to obtain a semantic relevancy matrix; constructing a target semantic visual contrast loss function based on the semantic relevancy matrix, the feature vector of the first image and the temperature coefficient; and embedding the target semantic visual contrast loss function into an original loss function of the GCD task to obtain a target GCD task, and identifying and clustering the images in the training sample set by using the target GCD task.

This invention belongs to the field of artificial intelligence and data mining technology, and relates to a method for recommending cultural dissemination paths based on spatiotemporal graphs and user behavior modeling. It constructs a spatiotemporal cultural knowledge graph that integrates multi-source data, representing cultural heritage elements and their spatiotemporal semantic relationships using a heterogeneous graph structure. It captures user interaction behavior sequences in real time and generates user intent vectors representing the current exploration intention through graph neural network encoding. Upon receiving a recommendation request, it uses this vector as the core constraint to execute a multi-objective collaborative optimization path search algorithm in the graph, generating a sequence of cultural dissemination paths that considers narrative coherence, spatial feasibility, and temporal order. The paths are then output to the terminal for visualization. This invention enables personalized and coherent cultural dissemination path recommendations based on dynamic intent understanding, significantly improving user experience and cultural dissemination efficiency.

The invention belongs to the technical field of nuclear power, and particularly relates to an asset vulnerability positioning method and device based on bilinear semantic analysis. The semantic matching model is used for replacing traditional rule matching, the speed and accuracy of asset retrieval in the knowledge graph are improved, rapid asset positioning is achieved, and therefore the problems that abnormal asset positioning is difficult and low in efficiency are solved. And through bilinear interactive representation, a complex semantic relationship is captured, and false alarms are reduced. The method has the ability to understand complex semantic contexts, and adapts to diversified expressions of asset names, aliases and contexts. The method is suitable for mapping knowledge domains of different scales, and can be popularized in multiple scenes such as network security and operation and maintenance management.

This application relates to the field of public safety decision-making technology, specifically disclosing an artificial intelligence-driven public safety decision support system. First, it utilizes a security event element extraction module to extract event elements from historical public safety events, and then uses an event spatial distribution clustering module to geographically divide the target area based on a planning map. Next, an event temporal distribution sorting module arranges these event elements chronologically, forming a time series. Subsequently, an event semantic information analysis module uses organic propagation analysis of event semantic information to generate organically encoded representations of the semantic information transmission of public safety events in multiple historical regions, capturing the dynamic semantic relationships between events. Finally, an event risk discrimination module combines the above encoded representations and the planning map to identify the public safety risks in the target area. This effectively integrates information from both temporal and spatial dimensions, enhancing the understanding and prediction capabilities of the complex dynamic interactions of public safety events.

The invention discloses a network threat detection and defense method and device, equipment and a medium, and relates to the field of network security, and the method comprises the steps: carrying out the data cleaning and standardization processing of to-be-detected network threat data, and obtaining target network threat data; performing word segmentation and entity recognition on the target network threat data by using a natural language processing model pre-trained by the corpus in the network security field to obtain a target entity; determining a semantic relationship between the target entities, constructing a target knowledge graph based on the semantic relationship, performing feature extraction according to the target knowledge graph to obtain target network threat features, and converting the target network threat features into feature vectors; inputting the feature vector into a pre-trained target model to obtain a threat judgment result; and generating a threat report and a corresponding defense strategy based on the threat judgment result, and issuing the defense strategy to the corresponding security device for execution. According to the invention, an efficient and intelligent network threat detection and defense system is realized, and the network security level is greatly improved.