47 results about "Relational encoding" patented technology

The invention provides a multi-modal data pairing method and system based on deep learning, and relates to the technical field of data processing, and the method comprises the steps: obtaining a video multi-frame sequence and a target text, and respectively extracting an overlapped frame group set and a standardized text sequence; performing spatio-temporal feature extraction and text dependency relationship coding to obtain a video time sequence vector sequence and a text vector sequence; executing cross-modal alignment search, and constructing a monotonic matching path set; calculating a semantic and action entity relationship consistency score of the paired elements on the path to obtain a comprehensive score; and determining an alignment relationship between the video and the text based on the optimal path. According to the method, accurate matching of the video and the text is realized, and the cross-modal retrieval efficiency is improved.

The invention provides an enhanced cross-document intelligent retrieval method and system based on knowledge memory, relates to the field of information retrieval, and solves the problem of limitation of a traditional retrieval enhanced generation method in cross-document information integration, calculation efficiency and complex question and answer scene processing. The method comprises the following steps of: extracting semantic triples from a plurality of documents by adopting a large language model, and constructing a modelless knowledge graph; a relation encoder is applied to enhance semantic connectivity among different semantic triples, so that information in a plurality of documents is integrated and is used for subsequent comprehensive retrieval; adopting an HITS algorithm to calculate a Hub value and an Authority value of each node in the knowledge graph; a query request of a user is received, the most relevant node is retrieved and determined based on the Hub value and the Authority value of the node, and information matched with the query request is provided; according to the invention, the information retrieval and generation capability of the natural language processing system is effectively improved.

The invention provides a layout generation method and device based on a large model, electronic equipment and a storage medium, relates to the technical field of artificial intelligence, in particular to the technical fields of computer vision, deep learning, the large model, image processing and the like, and can be applied to scenes such as content generation based on artificial intelligence. According to the specific implementation scheme, visual information of an original image is extracted through a target detection model and a semantic segmentation model; converting the visual information into structured data, and converting the structured data into natural language description by utilizing spatial relationship coding; constructing a multi-modal Prompt based on the natural language description and the user instruction; and inputting the constructed multi-modal Prompt into the large model to obtain layout information of the target object output by the large model in the original image. According to the scheme, the layout generation quality and efficiency can be improved.

The invention relates to a vector geographic spatial data relation description method, and belongs to the technical field of spatial data management. According to the method, a relation description data structure comprising relation coding values, relation coordinate values, at least two members and role attributes of each member is constructed, relation data is described by utilizing the structure, and the relation coordinate values are used for describing coordinates displayed in a map or positions where text letters appear. The role attributes are used for describing spatial semantics or other business attribute relations of members in the relations, and the members are point elements, line elements, surface elements or relation data in the vector geographic space data. It can be seen that by means of the constructed relation description data structure, the relation between the geographic entities can be described, the vector geographic space data are made to carry semantic information, the relevance between the vector geographic space data can be described more clearly, and relevance analysis of the geographic space data is facilitated.

The invention discloses a news event prediction method based on heterogeneous evolutionary event clustering, which comprises the following steps: generating event representations based on preliminarily updated entity representations and relationship representations in a constructed entity graph, and constructing an event graph by taking events as nodes and taking heterogeneous relationships between the events as edges; obtaining event clusters through fuzzy clustering and constructing an event cluster graph; using a self-supervised optimization algorithm to optimize the event cluster representation according to the distance and similarity between the event clusters on the event cluster graph; capturing implicit correlation among the event clusters by using an implicit relation encoder, and sequentially updating representation of the event clusters, representation of events, and representation of entities and relations after sparsification and information aggregation; and predicting through the news event model based on convolution. According to the method, the pairwise correlation, the high-order correlation and the multi-step time sequence evolution of the events are effectively modeled, and the method has important application value in the aspects of international situation analysis, social governance, intelligent decision support and the like.

The embodiment of the invention provides a user risk identification method and device, electronic equipment and a storage medium, and relates to the technical field of computers. According to the method, multi-source time series data are fused, local space correlation characteristics of user behaviors are extracted by using a convolutional neural network in sequence, a long-term and short-term memory network captures a long-range time dependency relationship of a behavior sequence, a Transformer encoder mines deep correlation characteristics among key behavior segments, and risk decision is carried out by integrating the three characteristics. According to the method, multi-dimensional collaborative recognition of a complex and hidden risk mode in digital right operation is achieved, the accuracy and robustness of risk recognition are improved, the false alarm rate and the missing report rate are reduced, the limitation of a traditional risk control method or a single model in space-time-semantic joint modeling is overcome, and the method has higher generalization ability and practical application value.

The invention provides a knowledge graph integration method and system based on adaptive embedded code driving, and relates to the technical field of data processing, and the method comprises the steps: constructing a plurality of knowledge graphs according to a plurality of data sources; performing attribute coding on the plurality of knowledge maps according to a pre-training language model, and constructing an attribute encoder; performing relation encoding according to the plurality of knowledge maps, and establishing a relation encoder; carrying out adaptive embedding fusion on the attribute encoder and the relation encoder to generate an entity alignment model; and integrating the plurality of knowledge maps according to the entity alignment model to obtain an integrated knowledge map. According to the knowledge graph integration method and device, the technical problem that in the prior art, due to the fact that data isomerism exists between different graphs, alignment precision is lowered during integration, and knowledge graph integration is difficult can be solved, and the accuracy of knowledge graph integration is improved through self-adaptive embedding fusion of the attribute encoder and the relation encoder.

The present application discloses a network security vulnerability detection method, device, terminal device and medium based on knowledge graph, including: obtaining a time series knowledge graph of a vulnerability; determining the security level corresponding to the vulnerability according to a pre-trained vulnerability detection model and a time series knowledge graph of the vulnerability, wherein the pre-trained vulnerability detection model is obtained by training the TCGFormer model using the time series knowledge graph of the sample vulnerability, the pre-trained vulnerability detection model includes at least an entity encoding module, a relationship encoding module, a joint sequence encoding module and a time series decoder, and the pre-trained vulnerability detection model is determined according to the correlation and dependency of the entity and relationship nodes of the time series knowledge graph of the vulnerability. The present application more effectively captures the correlation of entities and relationship nodes and long-term time dependencies, thereby improving the early warning and response capabilities to potential security threats and improving the accuracy and efficiency of vulnerability detection.

The invention discloses a geospatial data geometric topological relation evaluation method and system based on a large language model, and belongs to the technical field of spatio-temporal information processing, and the method comprises the following steps: S1, data preprocessing and model construction: processing a training data set containing geospatial data and a corresponding geometric topological relation thereof, and constructing a model; the geometric topological relation of the geographic space data is coded into text or vector representation which can be understood by a large language model; the coded training data is utilized to finely adjust a pre-trained large language model, so that the pre-trained large language model can evaluate a topological relation according to input geographic space data description; s2, geographic space data input and feature extraction; and S3, large language model reasoning and topological relation evaluation. According to the method, by combining the language understanding ability of the large model and the spatial topology calculation technology, intelligent and high-precision evaluation of the geographic spatial data geometrical relationship is achieved, and a brand-new and self-adaptive evaluation technology is provided for the field of geographic spatial data processing.

The present invention relates to managing version-controlled datasets. An updated version-controlled dataset is created by applying a structured query to a specific version of a plurality of versions of the version-controlled dataset stored in a backing dataset. The data of the version including the specific version remains unchanged. A version-controlled dataset catalog is updated to include the structured query and lineage associated with the updated dataset version, the lineage indicating that the updated version-controlled dataset includes a successor version of the specific version. A dependency code associated with the updated version-controlled dataset is updated, the dependency code indicating which data of the updated version-controlled dataset matches the data of the specific version and which data of the updated version-controlled dataset is deleted relative to the data of the specific version. A database view of the updated version-controlled dataset is generated by selecting rows of the backing dataset corresponding to the updated version-controlled dataset according to the dependency code and projecting columns.

Disclosed in the present invention are a three-dimensional mesh encoding method and device, and a three-dimensional mesh decoding method and device. The encoding method comprises: performing connection relationship encoding and geometric information encoding on an input three-dimensional mesh to obtain an output code stream, wherein the connection relationship encoding comprises encoding the pattern of a triangle in the three-dimensional mesh; for a preset pattern meeting a condition, the connection relationship encoding further comprises: on the basis of a set threshold corresponding to the preset pattern, determining additional information for indicating the positional relationship of the triangle and encoding the additional information; and the geometric information encoding supports an encoding mode concurrent with the connection relationship encoding. The present invention can improve entropy encoding efficiency, and realize instant stitching of the current triangle during decoding.

The invention discloses a three-dimensional grid coding and decoding method and device, and the coding method comprises the steps: carrying out the connection relation coding and geometric information coding of an input three-dimensional grid, and obtaining an output code stream; wherein the connection relation encoding comprises encoding modes of triangles in a three-dimensional grid; for the preset mode meeting the condition, the connection relation coding further comprises the steps of determining additional information used for indicating the triangular position relation according to a set threshold corresponding to the preset mode and coding the additional information; geometric information coding supports a coding mode parallel to connection relation coding. According to the invention, the entropy coding efficiency can be improved, and the instant stitching of the current triangle is realized in the decoding process.

An inductive link prediction method based on subgraph and path comparative representation learning is characterized in that an overall knowledge graph comprises a plurality of structured triples et = (s, rT, o), surrounding subgraphs containing target nodes are extracted, isolated nodes and a complete neighbor relation are reserved, and node features of complete semantic information between neighbor nodes are obtained. And extracting a topological relation path between the head entity s and the tail entity o from the knowledge graph. Local structures and relation semantics are obtained through node-level relation coding, sub-graph-level aggregation perception message passing and an attention mechanism. And carrying out modeling on paths of adjacent relations of the sub-graphs to obtain features in the sub-graphs. A topological structure of a target map is associated with path representation, marginal-based loss is incorporated into distance scores of a positive path sample and a negative path sample, the positive path sample and the negative path sample which need to be distinguished are compared and learned through semantic information conveyed by a relation path, and a loss function is optimized through joint training.

The invention discloses a multi-relation encoder for 3D human posture estimation, which relates to the technical fields of computer vision and human-computer interaction; the multi-relation encoder adopts a multi-relation encoder network, which includes an attention module and a graph convolution module; the attention module is improved to an adaptive multi-relation attention module, which includes three matrices, namely, an attention matrix, a joint self-similarity matrix and a predefined bone adjacency matrix, which are respectively used to capture the implicit relationship, explicit relationship and inherent physical topological relationship of the joints, and the adaptive multi-relation attention module is used to capture the global features of the joints; the graph convolution network module is obtained by connecting two GCNs in series, as well as layer normalization and residual connection, and the graph convolution network module is used to capture the local features between adjacent joints. The invention realizes the multi-relation representation learning and global-to-local modeling process of human posture, and obtains accurate three-dimensional posture estimation.

This invention proposes a method, system, device, and medium for relation completion in cement-based materials, belonging to the field of cement-based composite materials technology. The method includes: constructing a set of triples for a material spectrum based on original text samples of the cement-based material's formulation, process, and properties; training an encoder based on the triple set to obtain a first relation encoding model; mixing unlabeled samples into the original text samples and using the first relation encoding model as a model base, semi-supervised training of the first relation encoding model using a self-adversarial loss function to obtain a second relation encoding model, and extracting the source node embedding, target node embedding, and relation embedding of all triples; constructing a positive and negative sample pair input self-interference decoder to predict missing relations in the material spectrum; traversing the material spectrum, inputting the embeddings of any two nodes, and completing the missing relations based on the self-interference decoder. This invention achieves relational semantic reasoning for cement-based materials, thereby improving the accuracy of relation completion.

Systems and methods for text processing are described. Embodiments of the present disclosure receive a query comprising a natural language expression; extract a plurality of mentions from the query; generate a relation vector between a pair of the plurality of mentions using a relation encoder network, wherein the relation encoder network is trained using a contrastive learning process where mention pairs from a same document are labeled as positive samples and mention pairs from different documents are labeled as negative samples; combine the plurality of mentions with the relation vector to obtain a virtual knowledge graph of the query; identify a document corresponding to the query by comparing the virtual knowledge graph of the query to a virtual knowledge graph of the document; and transmit a response to the query, wherein the response includes a reference to the document.

The invention discloses a few-sample knowledge graph completion method and system based on semantic driving, and a knowledge graph completion model provided by the method is obtained through the training of the following steps: obtaining a task relationship, and a background triple, a graph entity and a background relationship in a background knowledge graph; according to the background triad and the background relationship, performing neighborhood semantic coding on the map entity to obtain a first embedded representation; performing relation coding on the task relation according to the first embedding representation to obtain a second embedding representation; according to the task relation, strategy negative sampling is carried out on the background triad, and a negative sampling triad is obtained; and according to the second embedded representation, the background triad and the negative sampling triad, performing parameter updating on the initialized knowledge graph completion model to obtain a trained knowledge graph completion model. The method can effectively improve the effect of few-sample knowledge graph completion. The invention relates to the technical field of knowledge graph completion.

The invention relates to the technical field of road disease detection and maintenance, in particular to an enhanced road disease knowledge extraction method and system based on large model self-learning, and the method comprises the following steps: carrying out the entity and relation extraction of a road disease field standard document through a pre-trained large model BERT, and generating an initial knowledge triple; a road disease knowledge extraction decision table is constructed, a hierarchical relation coding rule is defined, an inter-entity relation strength value is quantized, and the relation strength value is calculated through dynamic fusion of an expert initial score, a data-driven co-occurrence frequency and an attention weight; the method has the beneficial effect that the propagation risk of the error relationship is effectively reduced through the constraint of the relationship weight table and an expert rechecking mechanism. In 1000 pieces of test data, the conflict detection module successfully recognizes and corrects 83.2% of semantic conflicts (such as'seam strip 'error association' subgrade subsidence '), and the false alarm rate is lower than 5%.

The application discloses a kind of fusion object position relationship's image description method and device, which comprises the following steps: S1.input image to be described, extract the global feature information of image to be described and candidate frame feature information;S2. according to the feature information extracted, construct object relationship scene graph;S3. the node information of each kind contained in object relationship scene graph is initially encoded;If there is proportion imbalance phenomenon in the position relationship proportion between object nodes in object relationship scene graph compared with the original position relationship of object, then according to the proportion imbalance degree, the object relationship coding feature matrix obtained by initially encoding node information is secondarily encoded;S4. the object relationship scene graph and node information after coding are input into joint decoder for joint decoding, and the text information of the image to be described is predicted.The application can fully fuse the object position relationship in image, realize efficient and accurate image description.

The application discloses a kind of small sample learning method and device based on sample pair relation propagation.The method can obtain better relation coding by explicitly modeling and propagating the relation between the sample pairs of support set-query set.Through the introduction of pseudo-relation node, the characteristic information of the query set sample itself can be effectively retained. In addition, the application further provides an effective transduction learning strategy, which can better mine the relationship information between the query set samples, thereby obtaining more accurate classification results. Compared with the prior art, the present application better mines the potential information contained in the sample pairs of support set-query set in each task, and has higher accuracy and better generalization ability when processing a completely new task.

The invention relates to the field of machine learning, in particular to an industrial internet graph data representation learning method based on edge relation coding, which comprises the following steps of: firstly, randomly initializing embedded vectors with equal dimensions for each node and each edge respectively; constructing a self-center network, updating an edge vector in the self-center network, executing biased random walk on nodes, and sampling a node sequence according to a certain length every time to generate a set; and extracting nodes in a node context window according to the set to generate a context node sequence, and summarizing to obtain a corresponding set. And finally, node vectors are updated in sequence, a final embedding vector is obtained through iteration for a certain number of times, and then a node embedding matrix is constructed. Therefore, the problem that the performance of a downstream task is affected by loss of edge relation information in a graph due to neglect of edge difference in related technologies is solved.

The present invention discloses a log anomaly detection device based on component subsequence correlation perception, which belongs to the field of intelligent operation and maintenance and deep learning technology. The device comprises the following steps: dividing a portion of a log sequence S as a sample, obtaining a time feature embedding representation t and a semantic feature embedding representation V, and obtaining a time feature embedding representation t. c and semantic feature embedding representation V c Based on the proposed method, the system undergoes feature extraction, sequence dependency capture module, latent relation encoding module, graph convolution module, subsequence fusion module, embedding representation concatenation module, and log template prediction module to obtain the probability distribution of the log template in the next log message. All log templates are sorted and the prediction result is the top-k ranked log template set. The prediction result of the next real log template and sample is used to determine whether the log sequence has an anomaly. This effectively captures anomaly information and improves detection efficiency and accuracy.

The invention discloses an automatic framework design for BIM space recognition and classification, which adopts a node feature enhanced self-supervised graph neural network to bride the semantic gap between BIM and graph learning and capture geometric and spatial semantics. The method comprises the following steps: (1) analyzing a functional space representation form and summarizing types, and representing a building space as nodes and a space relationship as edges; (2) before graph propagation, coding a common wall, corridor, corner and open space connection topological relation into weighted edges, and injecting node features through a learnable fusion layer; and (3) training on three self-built building space layout map knowledge bases containing 12 space types and 4 relation characteristics to realize automatic identification of functional space types. According to the method, the space recognition efficiency and the automation level are improved, the high-confidence-coefficient function label corresponding to the IFC standard is generated, design review, facility management and evacuation path optimization can be accelerated, and an extensible basis is provided for ontology driving graph reasoning in multiple building scenes.

The invention belongs to the technical field of visual navigation, and particularly relates to a visual language navigation method and system based on motion feature alignment and a medium, and the method comprises the following steps: coding motion features based on a coordinate relationship between a current position and a candidate position of an intelligent agent; fusing the action features with the seen room features and object features, and updating a topological graph; and weighting words in the instruction based on the fused features to obtain dynamic instruction features. According to the method disclosed by the invention, the action alignment of the visual mode and the instruction mode is fully considered, and the success rate of navigation is greatly improved.

This invention discloses a method and system for constructing relationships between water conservancy objects based on dynamic weights and relational coding, belonging to the field of water conservancy information technology. The method includes: systematically classifying and defining water conservancy objects; constructing a water conservancy-specific relational coding system based on the definitions of water conservancy objects and relationships; performing relational calculations based on a generalized water system map and a business attribution map, and introducing a multi-factor dynamic weight adjustment mechanism; using a spatiotemporal graph database for integrated storage; and achieving iterative optimization of the parameters of the water conservancy object relationship construction system through an online learning and feedback mechanism. This invention achieves knowledge retrieval and matching through a water conservancy-specific relational coding system, introduces a multi-factor dynamic weight model to enable relational calculations to adapt to different hydrological conditions and business scenarios, realizes domain knowledge-driven intelligent reasoning based on a generalized water system map and a business attribution map, and constructs an online learning and feedback mechanism to enable the system to continuously optimize from historical data and real-time monitoring.

The application discloses a rumor detection method based on a graph attention network, belongs to the technical field of image processing, and combines a BERT pre-training model and a graph attention network CNN, wherein the relationship between information is constructed, rumor detection is assisted by means of propagation information, a BERT pre-training model is used for processing data in a tweet coding representation part, and then the feature representation of the CNN is used to complete mapping from word embedding to semantic space; local tweet relationship coding learns the combined representation of each source tweet and related forwarding from each source tweet corresponding forwarding by improving a graph attention network GATv2; global relationship coding indicates how to encode global structure to node representation; and a rumor detection module learns a classification function to predict the label of an original tweet. The original data is processed by using the BERT pre-training method, the information of context is fully considered, the context information is fused by a bidirectional language model, and the representation capability of the model is improved.

The application provides a group chat-oriented dialogue structure analysis model training method, analysis method and device. After obtaining the text representation of each message by using a BERT model, the attention representation of a target message speaker is mined by combining the defined relationship encoding between each speaker through a GRU. According to the reply relationship of the previous message, the corresponding thread is constructed, the target message is connected to each thread, and the content semantic representation of each thread is obtained by inputting the GRU. The discourse structure representation of the target message and the previous message is obtained by a multilayer perceptron. The true reply object of the target message is jointly evaluated, analyzed and judged by combining the attention representation, the content semantic representation and the discourse structure representation, and the accuracy of dialogue structure analysis is greatly improved.

The invention discloses a real-time voice-driven human body posture generation method based on a variational auto-encoder, and belongs to the technical field of digital human interaction, and the method comprises the steps: S1, carrying out the preprocessing and alignment of multi-modal data, and constructing a three-dimensional training sample comprising audio features, emotion labels and posture data; s2, constructing a cross-modal generation model based on a variational auto-encoder, wherein the cross-modal generation model comprises an encoder and a decoder; s3, designing a multi-objective loss function to train and optimize the cross-modal generation model; and S4, inputting the preprocessed audio to be processed into the trained cross-modal generation model, and obtaining a human body posture sequence with a coherent time sequence through real-time reasoning. A variational auto-encoder is applied to a voice-human body posture cross-modal generation scene, the traditional application boundary of the variational auto-encoder is broken, the complex mapping relation between audio features and human body postures is encoded into low-dimensional probability distribution through the probability modeling capability of a potential space of the variational auto-encoder, and a new technical path is provided for posture generation.

The present invention relates to a named entity recognition method based on syntactic dependency relations. In named entity recognition, only when the entity boundaries and types recognized by the model match the boundaries and types of the annotated entities can it be considered a true positive example (TP). In most test samples, false positive examples (FP) and false negative examples (FN) are often caused by incorrect judgment of entity boundaries, that is to say, boundary recognition is much more difficult than type recognition. The present invention proposes to use a self-attention mechanism to weaken the connection between an entity and the words outside the entity and strengthen the relationship between the words inside the entity. Specifically, after a bidirectional long short-term memory network (Bi-LSTM) network, a self-attention mechanism is added to encode the dependency relations between words in the syntactic dependency tree into the context information. Finally, based on the global features provided by the Bi-LSTM network and the local features provided by the syntactic dependency tree, the entity boundaries are jointly judged. The present invention improves the accuracy of named entity recognition.

The invention discloses a method and system for discovering a time-space dependency rule of a cave mural, and relates to the field of time sequence knowledge graph reasoning in a knowledge graph, and the method comprises the steps: obtaining cave mural data needed by initial training, and inputting the data into an initial time sequence knowledge graph reasoning model for training; respectively inputting the trained text data into a relation encoder and an entity encoder to obtain relation embedding and entity embedding, and inputting time information into a time vector generator to obtain a corresponding time vector; performing visual feature extraction on image data in the obtained cave wall painting data; performing feature fusion on relation embedding, entity embedding, time vectors and visual features to generate comprehensive feature vectors; inputting the comprehensive feature vector into a decoder, and calculating a confidence score; and calculating loss according to the confidence score, and further training the time sequence knowledge graph inference model. According to the invention, the reasoning accuracy of the model on the time sequence knowledge graph is improved.