23 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 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 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.

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.

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.

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 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.

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 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.

A technique performs tasks completed by knowledge graphs in a both scalable and resource efficient manner. In some implementations, the techniques identify a source entity having a source-target relationship that connects the source entity to a target entity to be determined. The technique also identifies a source-entity data item that provides a piece of source-entity text related to the source entity. The techniques map source-entity data items to source-entity encoding information using a machine-trained encoder model. The technique then predicts an identification of the target entity based on the source-entity encoding information and based on predicate encoding information encoding the source-target relationship. In some implementations, the techniques also predict the target entity based on consideration of one or more neighbor entities connected to the source entity and their respective source-to-neighbor relationships. The technique also allows for the delivery of knowledge across knowledge graph training phases.

This invention discloses a network data exchange method and system based on data storage, relating to the field of network data exchange technology. The method includes: constructing a field dependency graph by extracting dependencies between data fields in a source data set, and performing hierarchical labeling based on node connectivity; decomposing the data into fragment sets and dividing them into parallel groups according to the field dependency graph; establishing a bidirectional index of physical storage addresses and logical access identifiers, and encoding the topology relationships to generate a storage mapping table; decoding and obtaining the dependency topology relationships and setting access preconditions to generate a synchronization sequence; reading data fragments in the same group in parallel according to the synchronization sequence, and reading data fragments in different groups sequentially; and reassembling the data based on the dependency graph to generate a target set. This invention improves data exchange efficiency and optimizes storage resource utilization.

The application relates to the technical field of natural language processing, and provides a dynamic knowledge graph prediction method and device, electronic equipment and a storage medium, the method acquires historical event corpus; the historical event corpus is input into a graph construction model to obtain a knowledge graph at a current time. The graph construction model realizes extraction and coding of implied relationships in the historical event corpus through an implied relationship extraction module and a relationship coding module, obtains the knowledge graph at the current time through an entity time sequence representation module, so that the obtained knowledge graph not only contains relationship information of each entity that already exists in the historical event corpus, but also contains the association relationship of each entity implied in the historical event corpus, the accuracy of the knowledge graph is higher, and the subsequent application effect of the knowledge graph is better. Moreover, through continuous updating of the historical event corpus, accurate dynamic prediction of the knowledge graph can be realized. The application has been subsidized by a national key research and development plan project (2019YQ1601).

The invention discloses a heterogeneous collaborative recommendation method and system for multi-geometric interactive embedding and graph convolution comparative learning, and the method comprises the steps: constructing a knowledge graph triple and a user-article interactive graph from original data, extracting head entities from the knowledge graph triple and the user-article interactive graph, embedding the head entities, and mapping the head entities to a Euclidean space, a hyperbolic space and a spherical space; after mapping results of different spaces are unified through tangent space projection, fusion of the different spaces is achieved through cross attention; after fusion is completed, a graph attention network is used for executing attention aggregation of message propagation, geometrical relationship coding and structure perception on an interaction graph, and final representations of the user and the article are obtained respectively. And according to the final representations of the users and the articles, generating predicted scores of all the articles by the target user, and sorting the predicted scores to generate a recommendation list to finish article recommendation.

The application discloses a log processing method, device and equipment and a readable storage medium in the computer technical field. The method disclosed by the application comprises the following steps: acquiring homologous IP logs belonging to the same customer, and determining word embedding features corresponding to each alarm type in the homologous IP logs; determining coding features of each alarm type in the homologous IP logs; and performing clustering processing on each alarm type in the homologous IP logs based on the word embedding features and the coding features of each alarm type in the homologous IP logs. The word embedding features can reflect the co-occurrence relationship between different alarm types in the homologous IP logs, and the coding features can reflect the features of each alarm type itself. The clustering processing of each alarm type in combination with the multi-dimensional feature information can improve the accuracy and efficiency of log classification. The log processing device, equipment and readable storage medium provided by the application also have the above technical effects.

The application discloses a drug adverse reaction prediction method based on KEPRF and KGNN-DW fusion, and aims to solve the problems of large noise in unstructured text extraction of pharmacopoeia and difficult prediction of implicit adverse reactions. The technical key points are as follows: a knowledge enhancement extraction framework KEPRF is constructed, visual structural features and progressive reasoning are used to realize high-precision structured extraction, and calibration is carried out in combination with a MedDRA term library; a physical-semantic knowledge graph fusing page code interval features is further constructed, a KGNN-DW model is used for multi-relation coding and attention fusion, different weights are given to different relations, and dynamic weighted fusion of relations is realized; finally, end-to-end prediction and weighted voting strategy are combined to realize accurate prediction. Experiments show that the method can effectively improve the prediction accuracy of implicit adverse reactions and the drug risk identification ability.

The data-driven coding rule, hidden danger management system and method of the present application, the system comprises a data acquisition module for real-time acquisition of multi-dimensional field data related to hidden dangers to generate original characteristic signals; a rule library module for receiving the original characteristic signals and simultaneously adjusting the corresponding relationship between hidden danger coding and investigation items according to the updated dynamic mapping relationship; a coding generation module for receiving coding rule configuration signals, performing multi-dimensional segmented coding mapping on the original characteristic signals according to the coding rule configuration signals to generate hidden danger coding signals containing hidden danger classification and risk level; and a list generation module for receiving the hidden danger coding signals, matching the corresponding inspection items from the corresponding relationship according to the hidden danger coding signals, and generating self-investigation list signals containing hidden danger coding and investigation instructions according to the inspection priority. The present application can solve the problem of static rigidity of coding rules in traditional hidden danger investigation systems, which leads to the disconnection between hidden danger lists and actual situations.

This application discloses a self-supervised group behavior recognition method based on spatiotemporal serial-parallel relationship coding, belonging to the field of video analysis technology. The method includes acquiring group behavior video data; constructing a network structure for a group feature self-learning model based on spatiotemporal serial-parallel attention mechanism relationship prediction coding, the network structure including a group label generator, a serial-parallel Transformer encoder, and an attention mechanism decoder; performing self-supervised training on the network structure to obtain a feature self-learning target network; and fine-tuning the feature self-learning target network based on a small number of samples to obtain a target network for realizing group behavior recognition. This application fully explores the spatial coordination relationships and temporal dynamic changes in the group through spatiotemporal serial-parallel attention mechanism relationship prediction coding, thereby improving the ability to construct state transitions and learn the expression of complex group features. It is suitable for efficient and accurate group behavior recognition in cases lacking annotations.