252 results about "Graph encoding" patented technology

The invention belongs to the field of bioinformatics, and relates to a molecular property prediction method based on multi-modal gating and comparative learning, which comprises the technologies of comparative learning, graph neural network, cross-modal alignment, gating attention and the like. Firstly, data standardization and graph construction are carried out, and molecular fingerprint embedding is extracted; secondly, a heterogeneous dual-channel graph coding architecture is adopted, one path captures atom short-range interaction through an attention mechanism, the other path integrates a molecular global structure and long-range dependence, and complementary molecular representation is generated; then, a cross-modal attention mechanism is introduced, bidirectional association of graph and fingerprint features is achieved, and modal weights are adaptively and dynamically distributed through a gating fusion module; and finally, a comparison pre-training strategy is adopted, a molecular graph and fingerprints are utilized to construct a sample pair, and discriminative molecular representation is learned on unlabeled data. According to the method, the accuracy of molecular property prediction is remarkably improved, and an efficient and reliable calculation tool is provided for virtual drug screening and lead compound optimization.

The invention provides a geological data driving path optimization and settlement prediction method for pipe jacking construction, and relates to the technical field of artificial intelligence. The method comprises the following steps: firstly, acquiring sparse drilling point location geological data, constructing a geological data set including soil layer types, forming continuous geological feature tensors through multi-dimensional interpolation, and generating a path vector sequence by combining starting and ending points and building distribution to represent crossing tracks of different paths under geological conditions; and constructing a path graph topological structure through a graph coding neural network, performing feature propagation, obtaining a path comprehensive score vector, and screening an optimal path meeting structural integrity and settlement response constraints. Further identifying settlement trend sensitive points by utilizing the settlement risk prediction values and the accessibility scores of the nodes, constructing a settlement trend map and calculating settlement transaction coefficients. And the transaction coefficient is fed back to a path generation link and is used for iteratively optimizing path sampling density and node distribution so as to realize dynamic correction and stable convergence of a path scheme.

The invention relates to the technical field of slope catastrophe intelligent early warning and data modeling, and particularly discloses a strip mine high and steep slope multi-source monitoring and early warning method based on a graph neural network and Transform, and the method comprises the following steps: S01, carrying out the data preprocessing and disturbance variable construction of monitoring data; s02, constructing a heterogeneous space diagram structure by taking the monitoring points as nodes and taking geography, lithology and dynamic response relationships as edges; s03, constructing a space-time end-to-end multilayer coding framework based on the graph attention network and the integrated deep neural structure; s04, on the basis of graph coding and time sequence output, introducing a disturbance variable embedding mechanism, and designing a joint attention fusion structure; and S05, generating a deformation trend prediction value of the slope in a future period of time and performing corresponding risk grade judgment. The invention aims to solve the key technical problem of weak adaptability and interpretability of an early warning system.

The invention provides a method and equipment for constructing a text entity recognition model based on large model data enhancement. The method comprises the following steps: firstly, constructing an initial model comprising a preprocessing unit, a syntax dependency analysis unit, a data correction enhancement unit, a context coding unit, a syntax enhancement unit, an expression fusion unit and a sequence decoding processing unit; preprocessing the training sample to obtain a preprocessed text, and establishing a preliminary dependency graph through syntactic analysis; correcting and enhancing the text and the dependency graph by the large language model to obtain a text sequence and a dependency graph for subsequent use; encoding the text sequence to obtain an initial representation vector, and enhancing the vector in combination with the dependency graph; after fusion, decoding and outputting a prediction label containing a lexical entity label; and calculating loss by using a function containing conditional random field structure loss, judging convergence, and if not, updating parameters and continuing training until a target model is obtained. According to the method, data are optimized and expanded by means of a large language model, syntactic dependency enhancement features are combined, and the recognition capability of the complex text named entities is improved.

The invention discloses a copywriting generation method based on artificial intelligence. The method comprises the following steps: constructing a propagation technique knowledge base and a creation technique knowledge base of an advertisement copywriting according to demand information; retrieving a matched creative strategy combination from the creative technique knowledge base, and encoding the creative strategy combination as a first cue word fragment; outputting a Top-K sub-graph, and encoding the Top-K sub-graph into a second cue word fragment; splicing the first cue word segment, the second cue word segment and the target audience portrait and style control parameters, generating a complete cue word, and generating an advertisement copywriting first draft; performing quality evaluation and generating a hallucination probability graph; and outputting the finally generated advertisement copywriting until the illusion probability output by the rewritten advertisement copywriting meets the requirement. According to the method, a closed loop of copywriting generation-evaluation-optimization is realized, and the quality of the copywriting can be effectively improved through a small number of iterations.

The invention discloses a multi-language code generation method based on self-supervised pre-training, which comprises the following steps: acquiring and cleaning multi-language code data to form a training corpus; the method comprises the following steps: representing code data as an abstract syntax tree, extracting a control flow diagram and a data flow diagram of the code data, and obtaining unified semantic representation through combination of a diagram encoder and a sequence encoder; designing a self-supervised pre-training task, and pre-training the semantic representation based on the training corpus; constructing a multi-language pre-training model based on the structure-improved recurrent neural tensor network and the multi-language embedding matrix; when a user inputs a natural language, generating a target language code by using the multi-language pre-training model; and target language code correction is carried out through conventional function testing and grammar checking. According to the method, multi-channel recursive combination and a hierarchical recursive expansion mechanism are combined with self-supervised pre-training, so that accurate generation and performability improvement of cross-language codes are realized.

The invention provides an enhanced generation method based on topology perception graph coding and adaptive sub-graph retrieval, which comprises the steps of multi-granularity semantic perception segmentation and knowledge graph construction, structural feature matrix construction and explicit topology position code generation, and constrained sub-graph diffusion and dynamic pruning based on semantic-topology joint scoring. Generating a graph-text consistency loss constraint based on attention matrix structured alignment; explicit topological position coding is adopted in a coding layer to avoid overhead and excessive smoothness caused by online GNN aggregation; in a retrieval layer, a connected sub-graph instead of a fragment node is used as an enhanced context; in an alignment layer, an internal attention matrix of a large language model is directly constrained to be consistent with a sub-graph adjacent matrix, and structured guidance is realized from a reasoning mechanism level, so that logic illusion is inhibited, and the multi-hop reasoning accuracy is improved.

The invention discloses a multi-lane heterogeneous traffic joint modeling method and system based on a GNN-PINN coupling architecture. The method comprises the following steps: constructing a space-time dynamic graph coding lane changing rule through a GNN, embedding a traffic flow physical constraint through a PINN, and solving a source item; according to the method, CAV behaviors are described by using improved IDM in the mid-microscopic view, an LWR equation subjected to GNN lane change flow correction is solved by using PINN in the macro view, GNN and PINN closed-loop optimization is realized by combining a double-path back propagation mechanism, and a model is optimized based on a residual loss function fusing physical loss, data driving items and boundary constraints. The system comprises a dynamic graph topology module, a GNN coding module, a PINN solving module and the like, the multi-lane traffic dynamic description precision can be improved, the multi-scale characteristic is considered, real-time simulation is supported, and the system is suitable for heterogeneous traffic modeling.

The invention discloses a structure entropy model for detecting a social robot in real time, particularly relates to the technical field of big data mining, and is used for solving the problem that an existing social robot detection method is difficult to balance between detection precision and processing efficiency. User data is processed through a feature selection weighting module to screen key features and distribute weights, a feature stability evaluation module analyzes the contribution degree of the key features to a community structure to screen features with the most distinguishing power, and a multi-relation graph construction module constructs a user similarity graph based on the key features and the weights thereof. The coding tree community division module initializes a coding tree on the user similarity graph and generates a hierarchical community division structure through iterative calculation of structure entropy change; and the community level classification module forms a comprehensive score for each community fusion structure entropy and behavior statistical characteristics and judges a social robot community through comparison with an optimization threshold.

The invention belongs to the technical field of image data processing, and particularly relates to a wood surface defect detection method based on multi-view coding and a feature memory bank. The method comprises the steps that an original wood data set is acquired, an original wood image in the original wood data set is preprocessed, and the original wood image is converted into a Lab image; a wood surface defect detection model is constructed to process the Lab image, and a normal feature core set is obtained; after an anomaly detection framework is constructed and a feature core set is obtained, inputting a to-be-detected wood surface image into a wood surface defect detection model to extract local features, and calculating a nearest neighbor distance between each local feature and features in the normal feature core set; and generating an abnormal heat map of the to-be-detected image based on the nearest neighbor distance so as to realize defect detection and pixel-level positioning. The problems that the wood surface defect types are diversified, abnormal samples are difficult to cover, and the marking cost is high are solved.

The invention discloses a sub-graph reasoning method fusing logic rule learning and attack semantic enhancement. The method comprises the steps that an input layer dynamically integrates knowledge graph topology and an AMIE rule base, an initial k-hop sub-graph is generated, and structured input is provided for attack chain mining; the sub-graph extraction module is used for executing double confidence filtering, screening high-value attack chains and applying dictionary filtering to enhance semantic reliability; the sub-graph coding module adopts an entity perception update layer and a relationship aggregation evolution layer of a dual-channel mechanism to collaboratively model the spatial-temporal characteristics of an attack chain; the relation reasoning optimization module is used for dynamically injecting high confidence rules and optimizing triple scores; and the training optimization module is used for implementing task perception negative sampling. According to the subgraph reasoning method fusing logic rule learning and attack semantic enhancement, based on inductive reasoning and semantic perception modeling, by taking subgraph modeling guided by a logic path as a core, attack chain rules with high confidence in a training graph are mined, and the understanding ability of a model structure is improved.

This invention discloses a multimodal traffic flow prediction method based on multi-source data feature fusion, comprising: acquiring traffic data; dividing the traffic data into training and testing sets; constructing a traffic prediction model and training the traffic prediction model using the training set; and validating the traffic prediction model using the testing set. The traffic prediction model includes: a cloud map encoder for extracting features from cloud map data to obtain cloud map features; a spatiotemporal encoder for extracting features from spatiotemporal data to obtain spatiotemporal features; a fusion module for fusing cloud map features and spatiotemporal features to obtain fused features; and adding the cloud map features, spatiotemporal features, and fused features, inputting the sum to the spatiotemporal decoder for prediction to obtain the final prediction result. This invention integrates multi-dimensional and multi-faceted information, simplifies redundant information in the spatiotemporal feature extraction process, and achieves efficient and high-precision prediction, which can be widely applied in the field of traffic flow prediction technology.

The invention discloses a dynamic graph distribution external detection method and system based on spectrum sensing enhanced evidence learning, and belongs to the field of graph data mining and anomaly detection. In order to solve the problem that distribution outside samples in a dynamic graph structure are difficult to accurately identify, a virtual negative sample generation method based on graph spectrum disturbance is mainly adopted, Dirichlet posterior distribution is modeled in combination with a dynamic graph encoder and an evidence neural network, and the distribution outside samples are discriminated by using uncertainty measurement. According to the method, the distributed external detection performance in a dynamic graph environment can be effectively improved, and the method has relatively high robustness and generalization ability.

The invention relates to the field of abnormal state detection, and particularly discloses an electric power informatization data flow abnormal state detection system, which comprises the following steps: firstly, acquiring heterogeneous OT monitoring data and IT service data in real time, and generating a unified feature matrix and event flow; and then, taking a basic knowledge graph containing entity assets, a topological structure and a business process as a skeleton, dynamically attaching real-time features to graph nodes, and generating a dynamic association edge according to an event flow, thereby constructing a time sequence graph sequence capable of continuously evolving. On the basis, a time sequence diagram coding prediction technology is utilized to carry out spatio-temporal feature learning on a diagram sequence to calculate a node anomaly score. Once an anomaly is detected, the system performs reverse traceability based on a time sequence diagram structure, and utilizes clear physical and logic connection paths in a graph to accurately position potential root cause nodes causing a fault chain reaction, thereby realizing causal association analysis of cross-domain data streams and full-link abnormal state perception.

According to various embodiments, a method for predicting trajectories of traffic participants is provided, comprising representing a traffic scene as a knowledge graph, processing the knowledge graph by means of a neural graph encoding network to determine encodings of nodes of the knowledge graph and predicting at least one trajectory of the target vehicle from the encodings.

The invention relates to the technical field of social robot detection, in particular to a social robot detection method and system based on causal intervention and mixed experts, and the method comprises the steps: constructing a CusBot model which comprises a pseudo-environment estimator and a mixed expert mechanism enhanced graph Transform encoder; estimating the distribution of a potential environment to which each node belongs through a pseudo environment estimator, and generating an environment perception weight based on a Gumbel-Softmax relaxation technology; a graph Transform encoder enhanced by a hybrid expert mechanism is utilized to perform multi-level encoding on node features under the regulation and control of an environment perception weight so as to extract cross-environment invariant causal features; and training the CusBot model by adopting a joint optimization objective function, wherein the objective function comprises a supervision loss item and a KL divergence regularization item. According to the method, environment pseudo-correlation factors are effectively decoupled, cross-environment invariant causal features are extracted, and the stability and accuracy of social robot detection in cross-platform, cross-period and other distributed migration scenes are remarkably improved.

The invention discloses a feature fusion optimization-based multi-modal map enhancement retrieval method and a dialogue system. The method comprises the following steps of: respectively carrying out pre-training and fine tuning on a visual model and a language model by utilizing a domain image and text data; constructing a knowledge graph based on the text data in the knowledge base and constructing a vector database containing associated image data; performing semantic analysis and optimization on the original query of the user by using the language model and forming a structured retrieval intention; searching related sub-graphs, text semantic vector information and associated image data based on the search intention; encoding the sub-images into knowledge contexts, inputting the knowledge contexts into a dynamic prompt generator to generate visual prompts, and extracting enhanced visual features from the associated image data through a visual model; and inputting the subgraph, the text semantic vector information and the enhanced visual features into a language model for collaborative reasoning, and generating and outputting a final answer. According to the method, deep fusion and accurate retrieval of multi-modal knowledge can be realized, and the accuracy and efficiency are remarkably improved.

Some embodiments of a method may include a learning-based point cloud geometry processing block method, the method including: accessing a first feature map, wherein the first feature map has a quantity of C channels and is an input to the processing block, and wherein the first feature map is generated by a first set of neural network layers; accessing a set of distribution parameters; transforming the first feature map to a second feature map based on the set of distribution parameters; and encoding the second feature map into a bitstream. These example processes may be applicable to both the encoder and the decoder of an AI-based point cloud compression (PCC) framework.

The present disclosure relates to efficient signaling of feature map information for a system employing a neural network. In particular, at the decoder side, a presence indicator is obtained based on information parsed from a bitstream. Based on the value of the obtained presence indicator, further data related to a feature map region are parsed or the parsing is bypassed. The presence indicator may be, for instance, a region presence indicator indicating whether feature map data is included in the bitstream or may be a side information presence indicator indicating whether a side information related to the feature map data is included in the bitstream. Similarly, an encoding method, as well as encoding and decoding devices, are provided. Accordingly, feature map data may be processed more efficiently, by reducing decoding complexity, and the amount of transmitted data can be reduced by applying the bypassing.

The invention discloses a molecular multi-mode characterization method based on attention fusion, and the method comprises the following steps: firstly, carrying out the independent feature extraction of the one-dimensional, two-dimensional and three-dimensional modes of a molecule through a molecular sequence encoder, a molecular map encoder and a molecular conformation encoder, and carrying out the feature extraction of the one-dimensional, two-dimensional and three-dimensional features of the molecule; based on natural language text description, multi-level contrast learning is adopted, and gradual alignment of each mode and the text is achieved; secondly, multi-modal consistency constraint is introduced, the characterization distance of the same molecule in different modals is shortened, and semantic consistency is ensured; thirdly, adaptively integrating multi-modal features through a modal attention fusion module to obtain uniform molecular semantic representation, and further performing final alignment with a text embedding space; and finally, completing tasks such as molecule-text bidirectional retrieval and molecule attribute prediction by utilizing fusion representation. According to the method, under the condition that the model does not need to be retrained for different tasks, natural language-driven molecular retrieval, editing and property prediction can be realized, and the method has relatively high robustness, expansibility and universality.

The invention provides a double-layer safe trainable quantum machine learning method based on polynomial dynamics Lie algebra, and the method comprises the following steps: 1, constructing core variation simulation meeting the constraint of the polynomial dynamics Lie algebra, and guaranteeing the trainability of a model; 2, performing truncated Chebyshev graph coding at an input end, and constructing a rugged loss function landscape by using graph state entanglement and a Chebyshev tower strategy to prevent a snapshot inversion attack; 3, executing dynamic local scrambling at an output end, applying time-varying random local unitary transformation before measurement, and confusing a linear relation between gradient and a snapshot to prevent recovery attack of the snapshot; and 4, measuring and calculating a loss function, and updating parameters. According to the method, an orthogonal decoupling strategy is adopted, a privacy protection mechanism is externally arranged on an input / output interface, and trainability is anchored to core configuration, so that the capability of resisting algebraic attacks is remarkably improved while model convergence is ensured.

The invention discloses an anomaly detection method and device based on financial transaction behavior attribute graph data, and the method comprises the steps: determining a positive subgraph and a negative subgraph corresponding to a node for each node in a constructed static attribute graph; inputting the sub-graph corresponding to the node into a transaction anomaly detection model, and determining a curvature space weight corresponding to the node based on a weight network; hybrid curvature embedding corresponding to the nodes is determined based on a hybrid curvature graph encoder; performing feature extraction on the mixed curvature embedding corresponding to the nodes based on the comparison network to obtain feature representations corresponding to the nodes, and obtaining positive pair scores and negative pair scores of the nodes in various curvature spaces through discriminators corresponding to the various curvature spaces by the feature representations; according to the curvature space weights corresponding to the nodes and the positive pair scores and the negative pair scores of the nodes in various curvature spaces, determining transaction anomaly probability values of the nodes; and determining whether the node is an abnormal transaction node according to the abnormal transaction probability value of the node. And the accuracy of transaction anomaly detection is improved.

The application discloses a kind of multilingual code generation methods based on self-supervised pre-training, comprising the following steps: obtaining and cleaning multilingual code data, forming training corpus;Code data is represented as abstract syntax tree, the control flow graph and data flow graph of code data are extracted, and unified semantic representation is obtained by combining graph encoder and sequence encoder;Self-supervised pre-training task is designed, and the semantic representation is pre-trained based on the training corpus;Multilingual pre-training model is constructed based on the recursive neural tensor network and multilingual embedding matrix improved in structure;When natural language is input by user, the multilingual pre-training model is used to generate target language code;Target language code is corrected by routine function test and syntax check.The application realizes accurate generation and executable improvement of cross-language code by combining self-supervised pre-training with multichannel recursive combination and hierarchical recursive development mechanism.

To provide a device for efficiently encoding / decoding a feature map with a small processing amount.SOLUTION: The feature map decoding device 200 includes a feature map internal decoding unit for decoding the first single-scale feature maps that have been packed into frames and encoded to generate integer-type packed feature frames, and a feature map internal decoding unit for converting elements of the integer-type packed feature frames into decimal values to generate decimal-type packed feature frames. A feature map inverse transformation unit for generating a second single-scale feature map by dividing the second single-scale feature map into one or more packing groups based on information about the packing groups and unpacking the second single-scale feature map, a single-scale feature map refinement unit for generating a third single-scale feature map by calculating a distribution characteristic of the second single-scale feature map and refining the second single-scale feature map, and a feature map restoration unit for generating a multi-scale feature map by transforming the third single-scale feature map.SELECTED DRAWING: Figure 2

Methods and apparatus are provided for unified significance map coding. An apparatus includes a video encoder (400) for encoding transform coefficients for at least a portion of a picture. The transform coefficients are obtained using a plurality of transforms. One or more context sharing maps are generated for the transform coefficients based on a unified rule. The one or more context sharing maps are for providing at least one context that is shared among at least some of the transform coefficients obtained from at least two different ones of the plurality of transforms.

The application discloses a kind of based on graph coding's diagnosis and surgical matching check method and system, the method includes: obtaining the diagnosis data and surgical plan data of target object;Based on feature extraction algorithm, extract the diagnosis change feature and surgical step feature in the diagnosis data and the surgical plan data;Based on graph network coding algorithm, the diagnosis change feature and surgical step feature are encoded into corresponding node relationship graph;The node relationship graph is input into trained graph neural network to obtain the matching degree parameter of output diagnosis and surgical plan.It can be seen that the present application can realize accurate medical diagnosis and surgery matching evaluation based on graph structure feature association, improve the scientificity and adaptability of surgical plan selection, reduce the surgical risk and treatment deviation caused by mismatch between scheme and diagnosis.

The invention provides a method, a device, equipment and a medium for identifying abnormal behaviors of people crossing boundaries, and the method comprises the steps: collecting T frames of video images based on a target monitoring video, dividing each frame of video image into a plurality of independent image blocks, and converting the independent image blocks into image block feature vectors; generating a boundary distance map based on each frame of video image and the spatial boundary model; encoding the generated boundary distance map into a boundary sensing position embedding vector consistent with the feature vector dimension of the image block; adding the boundary sensing position embedding vector and the original space-time position code corresponding to each frame of image block to obtain an enhanced position code fused with boundary information; fusing the feature vectors of the image blocks of the enhanced position coding, and obtaining a feature map of enhanced spatial correlation information by using a spatial attention branch in a Division Attention structure; the feature map of the enhanced space correlation information utilizes a time attention branch in a Division Attention structure to obtain features fusing time sequence and motion information; and the space-time fusion feature identifies a border-crossing abnormal behavior based on an abnormal behavior identification model.

The invention discloses a graph anomaly detection method and system based on graph contrast learning, and relates to the technical field of graph neural networks. The method comprises the following steps: constructing a graph encoder DP-GNN, and obtaining effective node representation in an attribute graph for a subsequent graph anomaly detection task; a cross-scale contrast learning model is constructed, and joint detection of attribute anomaly and structure anomaly is realized by maximizing semantic distances of positive and negative samples in an attribute space and a structure space; and constructing a feature screening framework of global search and local optimization, and realizing screening of key feature subsets through a hybrid genetic algorithm. According to the method, joint detection of the node attribute anomaly and the structure anomaly can be realized, and the anomaly detection accuracy is improved.

The invention provides a social function identification method and system based on field sensing and semantic conflict detection, and relates to the technical field of multi-modal content understanding, computational social science and content function identification. The method aims at solving the problems that in the prior art, multi-modal semantic conflict recognition fails, social field domain contexts are lost, and generative model input is uncontrollable. According to the method, to-be-recognized social media content is acquired, multi-view coding and self-adaptive gating fusion are performed on the to-be-recognized social media content, different expert agents are called for reasoning through a neural routing network based on fusion feature vectors and gating weights, semantic judgment vectors are generated, the semantic judgment vectors are matched with a pre-constructed social function anchor point library, a target anchor point is determined, and the social media content is identified. And performing offset correction on the vocabulary probability related to the target anchor point by adopting a Logits offset dry pre-algorithm, and outputting a function label and an interpretation text. According to the method, the problems in the prior art are solved, and automatic and high-certainty identification required by industrial-grade content risk control is realized.