112 results about "Graph sequence" patented technology

A system and method for implementing a convergent intelligence fabric (CIF) for distributed artificial intelligence operations. The CIF architecture integrates tensor-theoretic foundations, probabilistic cache management, precision-aware memory operations, quantum-resistant security, and neural-based optimization within a unified framework. The system orchestrates asynchronous, multi-hop data flow among computational resources while maintaining data security through per-block encryption and identity-based access control. Key components include a universal multi-model KV cache subsystem, agent-parallel disaggregation pipelines, reinforcement learning-based orchestration, and neuromorphic memory integration. Advanced implementations incorporate graphon-enhanced memory for sparse graph sequences, multi-modal cognitive persistent memory, and quantum-resistant asynchronous multi-domain trust protocols. The system enables efficient cross-agent collaboration, sophisticated knowledge sharing, and secure cross-domain operations while optimizing computational resources and maintaining strict privacy guarantees across distributed AI deployments.

The invention discloses a track representation enhancement method based on a dynamic subgraph. A historical sign-in track of the user is given, semantic track representation aims at learning low-dimensional vectors capable of revealing user behavior patterns and semantic intentions from user track data, and convenience is provided for subsequent prediction tasks. The method comprises the following steps: firstly, constructing a city knowledge graph based on a space-time semantic tetrad, and explicitly modeling the association between a user track and a space-time scene; secondly, extracting a semantic sub-graph sequence taking a user sign-in track as a core from the user sign-in track by using a relation perception dynamic sub-graph extraction mechanism; and then, learning representation of a sub-graph sequence based on an attention aggregation network of a cross-sub-graph, and further obtaining knowledge-enhanced user track representation. And finally, verifying the characterization quality based on a prediction module, and outputting K sign-in points which the user is most likely to be interested in as results. Through the modeling method of the dynamic subgraph sequence, time-space sensitive trajectory semantics are effectively modeled, and the accuracy of a prediction task is improved.

The invention provides a malicious encrypted traffic detection method based on cross-modal alignment and graph sequence fusion, and belongs to the technical field of information security. The invention relates to a multi-modal encrypted traffic detection model GGT-Net, which is a multi-modal encrypted traffic detection model, and is characterized in that the multi-modal encrypted traffic detection model is a multi-modal encrypted traffic detection model GGT-Net which is integrated with a graph convolutional network (GCN) and a gated loop unit (GRU). According to the model, protocol structure features and statistical sequence features of network traffic are independently modeled, multi-modal features are fused through a Transform module, and the specific steps are feature extraction and TLS interaction field analysis, statistical feature standardization and protocol diagram structuring, sequence information processing and diagram convolution feature learning and multi-modal feature fusion. And finally classifying and discriminating. According to the invention, the accuracy and generalization performance of encrypted traffic detection are obviously improved.

The invention discloses a vehicle transaction risk intelligent early warning and credit assessment method based on big data analysis. The method comprises the steps of S1, collecting an original transaction data set; s2, constructing a heterogeneous graph structure based on the original transaction data set; s3, dividing the heterogeneous graph structure into a graph snapshot sequence according to a fixed time window, and generating a time evolution graph sequence with time evolution characteristics; s4, constructing low-dimensional feature representations of the user nodes and the transaction event nodes under each time window; s5, constructing a causal path graph according to the time sequence and behavior dependency relationship of the vehicle transaction record data, and extracting a high-frequency transaction causal chain from the causal path graph; s6, performing joint modeling on the low-dimensional feature representation of the user node and the transaction event node and the high-frequency transaction causal chain; and S7, outputting the risk level label of the target transaction and the credit scoring result of the corresponding user. The method has the advantages of being high in structure expression ability, clear in causal logic and high in risk assessment accuracy.

The invention discloses an urban drainage system multi-target prediction method based on an SWMM and a graph convolutional neural network, and the method comprises the steps: obtaining the actual measurement data of a target urban drainage system pipeline and an inspection well, constructing an SWMM model, and outputting the data; generating a space-time diagram sequence deep learning network GraphSAGE-GRU model on the basis of a graph convolutional neural network GraphSAGE and a gated cycle unit network GRU; and taking the preprocessed data as input, inputting real-time or predicted rainfall data by utilizing the trained model, and synchronously outputting node water head and pipeline load prediction results of the target urban drainage system. According to the method, the problem that the topological structure of an urban drainage system is not considered in a traditional method can be solved, and the hydraulic attributes of the inspection well and the pipeline can be comprehensively output. The problem that a traditional agent model is too black and lacks structural information is solved, and more simulation result output is provided by setting a space-time diagram structure.

The invention belongs to the technical field of knowledge graph construction, and discloses a supply chain knowledge graph construction method based on time sequence dynamic perception and a large language model. Constructing a time sequence dynamic sensing model, and respectively generating a time-sensitive embedding matrix for the entity and relationship of each sub-graph in a historical time window; inputting the time-sensitive embedded matrix into an aggregator to further mine structure and semantic information of entities and relationships; establishing a dependency relationship of an autoregression model learning sub-graph in a time sequence, and generating a time sequence evolution representation; and inputting the time sequence evolution representation into a pre-trained large language model, generating candidate entities or candidate relationships to complement the fact tetrad, and updating the sub-graph sequence of the current timestamp. According to the method disclosed by the invention, the supply chain domain knowledge is adapted while the general semantic understanding capability is reserved, and the balance of dynamic evolution modeling, long-period dependency capture and efficient utilization of the domain knowledge is realized, so that the reliability and interpretability of a construction result are ensured.

The invention relates to the technical field of graph neural network and causal representation learning, in particular to a graph neural network method for separating time-varying and time-invariant causal features, which comprises the following steps: acquiring graph structure data at a plurality of moments, and establishing a dynamic graph sequence; coding the dynamic graph sequence by adopting a time sequence graph neural network to obtain a node representation matrix; respectively adopting a time-varying attention head, a time-invariant attention head and a shortcut attention head to carry out attention score calculation, mask construction and sub-graph decomposition processing on the node representation matrix to obtain a time-varying cause sub-graph, a time-invariant cause sub-graph and a shortcut feature sub-graph; performing independent graph neural network coding and dynamic gating fusion to obtain fusion features; determining a current predicted value according to the fusion feature; according to the method, the causal discovery accuracy, robustness and interpretability can be improved.

The invention discloses an airspace engine 3D printing interlayer metallurgical bonding enhancing method which comprises the following steps: S1, slicing an airspace engine 3D model, and constructing a printing path diagram structure; s2, constructing a lattice consistency map of the current printing layer; s3, performing graph structure alignment on the lattice consistency graphs of the current printing layer and the previous printing layer to generate a cross-layer lattice graph sequence; s4, inputting the cross-layer lattice atlas sequence into the graph neural control body model, obtaining a disturbance sensitivity vector and a disturbance score value of each node, and outputting a correction control strategy vector; s5, executing a cross-layer disturbance projection operation based on the corrected control strategy vector, and generating a control intervention instruction; and S6, repeatedly executing the steps S2 to S5 to form a complete lattice atlas evolution record sequence. According to the method, the lattice atlas modeling and the graph neural control body model are utilized to realize the metallurgical enhancement between the printing layers of the airspace engine, and the method has the advantages of closed loop regulation and control, structural continuity and the like.

The invention relates to the technical field of graph data processing, can be applied to the medical field and the financial science and technology field, and discloses a dynamic feature enhancement method and device based on a graph convolutional network, equipment and a medium, which are applied to a dynamic investment relation network analysis scene or a multi-modal medical knowledge graph scene. Carrying out pretreatment on the raw materials; generating a target dynamic adjacency matrix through the self-supervised graph convolutional network; performing spatial-temporal feature aggregation processing based on the target dynamic adjacency matrix and the target historical graph sequence to obtain a global graph; local neighbor comparison loss calculation and global structure loss calculation are carried out respectively, parameter adjustment and model training are carried out on the self-supervised graph convolutional network according to a local comparison loss value and a global comparison loss value, and a target dynamic feature enhancement model is generated; and outputting a target enhanced feature based on the to-be-processed dynamic graph data through the target dynamic feature enhancement model. According to the method, the comprehensiveness and accuracy of feature extraction are improved.

The invention relates to a power grid topology short-term load prediction method and device based on a graph neural network, and relates to the technical field of power grid prediction, and the method comprises the steps: S1, constructing an initial power grid topological graph, and carrying out the power grid time sequence slicing to generate a power grid topological graph sequence under each timestamp; s2, a power grid space-time topological feature vector set is obtained, topological evolution coupling analysis is carried out based on the power grid space-time topological feature vector set, and corresponding power grid topological evolution feature representation containing node state embedding and edge relation embedding is generated; s3, constructing a power grid topological evolution prediction model based on the power grid topological evolution characteristic representation, and generating a future power grid topological structure evolution prediction result; and S4, calculating a power grid load influence factor based on the real-time operation data corresponding to each node in the power grid, and generating a power grid topology short-term load prediction result in combination with a future power grid topology structure evolution prediction result. Compared with the prior art, the method can achieve the precise load prediction under the topological change of the power grid.

The invention relates to the technical field of social network data mining, and discloses a social network data mining method and system based on a graph neural network, and the method comprises the steps: collecting user interaction data in a social network and a timestamp thereof, employing a time perception graph segmentation algorithm, dividing time slices based on a sliding window, and constructing a dynamic heterogeneous graph sequence. A Transform time decay attention mechanism is adopted, influence weights of different time slices are calculated, and weighted aggregation is carried out on node features of all the time slices; and inputting the weighted time slices into the D-GNN model, generating a time-perceived user embedding vector, and analyzing the influence of the social network. According to the method, the model better conforming to the actual dynamic change of the social network is constructed, and the accuracy and adaptability of data mining are improved. Calculation efficiency is improved on large-scale social network data, and availability of the model in industrial application is ensured. And social influence analysis is carried out, and technical support is provided for practical applications such as precision marketing, public opinion monitoring, social recommendation and the like.

The invention relates to a semi-template drug molecule inverse synthesis prediction method based on graph-sequence pre-training. The method comprises the following steps: selecting a public chemical molecule data set N; selecting a molecular sample from the N, and then extracting a molecular graph and a word segmentation sequence from the molecular sample; obtaining a sequence embedding matrix and a graph embedding matrix of the i by utilizing the word segmentation sequence of the i and the molecular graph; constructing an overall model M and a loss function Ltotal used for training the M; and training M in two stages by using AdamW to obtain a trained model M. By adopting the method disclosed by the invention, reactant atoms for generating a product can be accurately reduced, a starting material of the product is defined, and the research and development success rate of medicines or other products is improved.

The invention discloses a park intelligent operation optimization method based on a graph neural network, and the method comprises the following steps: S1, constructing a park dynamic graph structure, collecting building, equipment, personnel and traffic data, building nodes and edges, and forming a dynamic graph sequence; s2, inputting the dynamic graph sequence into a graph neural network, introducing a causal driven control module, constructing a causal path graph, and generating a causal enhanced graph embedding vector; s3, generating a park operation state prediction index based on the graph embedding vector; s4, according to the prediction indexes, generating a scheduling scheme by adopting an improved self-regulation elastic scheduling system, and issuing and executing the scheduling scheme; and S5, collecting feedback data of each subsystem, updating a dynamic graph structure, a causal path graph weight and a scheduling scheme, and constructing a closed-loop optimization system. According to the invention, the intelligent operation optimization of the park is realized by fusing the graph neural network and the improved self-regulation elastic scheduling system.

The invention discloses a BGP (Border Gateway Protocol) routing anomaly detection method and system based on a graph attention auto-encoder. The method comprises the following steps: step 1, collecting normal BGP data when routing anomaly does not occur on the Internet, and constructing an AS-level topological graph; 2, obtaining AS-level topological graph representation according to a graph attention auto-encoder model encoder; 3, obtaining a reconstructed adjacent matrix according to a graph attention auto-encoder model decoder; step 4, training a graph attention auto-encoder model; 5, obtaining target BGP data and constructing an AS-level topological graph sequence corresponding to the target BGP data; step 6, obtaining AS node embedding of the AS-level topological graph sequence according to an encoder in the pre-trained graph attention auto-encoder model; and 7, embedding and inputting the AS nodes into the multi-layer perceptron model to obtain an anomaly detection result. According to the invention, the key AS is convenient to pay attention, and the accuracy of anomaly detection is improved.

The invention provides a threat detection method and device, and the method comprises the steps: constructing a time sequence diagram sequence corresponding to each time window through the preprocessed multi-source log data of a target network; embedding nodes in the time sequence diagram sequence corresponding to each time window by using a relational graph neural network to obtain an embedded sequence matrix; inputting the embedded sequence matrix into a Transform model, and performing autoregressive prediction to obtain a node prediction matrix of a next sub-graph node; constructing an attention score matrix through the predicted node prediction matrix of the next sub-graph node, so as to predict a connection matrix at the next moment; and calculating an error between the connection matrix at the next moment and the real adjacent matrix at the next moment to determine whether a security threat exists at the next moment. According to the scheme, the technical problems of data imbalance and data annotation in the prior art are solved, and efficient identification of low-frequency and hidden attack behaviors is achieved.

The invention discloses a spherical panoramic image feature matching method and system, a terminal and a storage medium, and the method comprises the steps: obtaining a spherical panoramic image, and carrying out the preprocessing of the spherical panoramic image; screening the preprocessed spherical panoramic image based on a spatial constraint model, and constructing a scene graph based on the screened spherical panoramic image; segmenting the scene graph into a plurality of sub-scene graphs, and performing priority ranking on the sub-scene graphs based on a graph sequence scheduling algorithm; and performing geometric correction and feature extraction on all the screened spherical panoramic images, and executing a feature matching task of the spherical panoramic images in the sub-scene images according to the priority ranking of the sub-scene images. Through a spatial constraint model, a graph order scheduling algorithm, geometric correction and a cascade Hash matching algorithm, the problem that feature matching efficiency and precision are difficult to consider when a spherical image feature matching technology processes large-scale high-resolution spherical images is solved.

The invention relates to a deep learning parallel strategy automatic generation method and system, and the method comprises the steps: carrying out the fine-grained data dependence analysis of a deep learning model calculation graph, and recording the fine-grained data dependence relation and dependence path between tensors; merging the data dependency relationship and the dependency path to form a sub-graph sequence; combining the parallel segmentation strategies of the sub-graphs to construct a sub-graph parallel strategy space; deducing parallel segmentation data dimensions of operators not in the sub-graph sequence, and complementing an operator segmentation mode of a deep learning model calculation graph; and solving a global parallel strategy with the optimal cost. According to the method, the parallel space of the tensor is greatly reduced, and the search time of the parallel decision of the tensor can be remarkably reduced. According to the method, a parallel strategy of coarsely combining each operator when the parallel space of the tensor is constructed is avoided, the operators are combined under the condition that the parallelism is not influenced, and meaningless combination of a large number of operator parallel strategies is removed.

The invention provides a three-dimensional scene understanding method based on a large language model. The three-dimensional scene understanding method comprises the steps of collecting point cloud data and multi-view image information of a scene; processing the point cloud data to obtain finer point cloud data; obtaining a three-dimensional semantic scene graph and a feature vector of a semantic relationship; obtaining a 3D geometric information feature vector and a 2D semantic information feature vector of the object; projecting the feature vector of the semantic relationship, the 3D geometric information feature vector of the object and the 2D semantic information feature vector of the object to an embedding space of a large language model to obtain scene representation with rich semantics; using a k-nearest neighbor algorithm to obtain a sub-graph sequence containing all objects; and training a large language model according to the sub-graph sequence to obtain a language model capable of understanding a three-dimensional scene. According to the three-dimensional scene understanding method, semantic information can be effectively utilized, and the understanding capability and robustness of the three-dimensional scene understanding model are improved.