81 results about "Graph similarity" patented technology

The invention discloses an APT attack path reconstruction method based on time sequence diagram comparison clustering and a medium. A security event standardized data set is obtained; mapping each security event into a multi-modal node through a heterogeneous time sequence diagram set construction method, and generating a directed edge to construct and complete a heterogeneous time sequence diagram set; a stage embedding time sequence diagram set is obtained through the joint attack stage set; outputting a time sequence diagram similarity matrix by adopting a multi-scale diagram similarity algorithm of time alignment perception; generating an event semantic sparse matrix based on the threat intelligence knowledge graph; obtaining an image clustering result set; uncertain samples in the graph clustering result set are obtained and processed, and an APT attack path reconstruction result is obtained. The problem that an APT attack path reconstruction method mainly depends on rule matching, single-dimensional feature comparison and manual analysis of security logs or alarm streams is solved, the interpretability of APT traceability is greatly enhanced, and subjective errors of manual research and judgment are reduced.

The invention provides a multi-modal geographic positioning method based on a knowledge graph. The method comprises the following steps: graph construction: constructing a geographic knowledge graph; image positioning: calculating the entity similarity between the to-be-queried image and each sub-image so as to select an image positioning candidate sub-image, calculating the image matching similarity between the to-be-queried image and each image positioning candidate sub-image so as to determine an image positioning target sub-image, and taking the latitude and longitude coordinates of each image positioning target sub-image as a positioning result; text positioning: converting a to-be-queried text into map representation, calculating the map similarity between a text query sub-graph and each sub-graph to select a text positioning candidate sub-graph, and calculating the semantic similarity between the to-be-queried text and each text positioning candidate sub-graph, and calculating a text matching similarity corresponding to each text positioning candidate sub-graph based on the graph similarity and the semantic similarity corresponding to each text positioning candidate sub-graph so as to determine a text positioning target sub-graph, and taking latitude and longitude coordinates of each text positioning target sub-graph as a positioning result.

The invention is suitable for the technical field of geological disasters, and provides a geological disaster emergency response scheme generation method based on a domain knowledge graph, and the method comprises the steps: determining an optimal path set of each entity of a target geological disaster event in a geological disaster emergency response knowledge graph, and determining the weight of each entity; screening out a plurality of reference geological disaster events of the target geological disaster event from historical geological disaster events used when the geological disaster emergency response knowledge map is constructed based on weights by taking geological disaster mechanism and effect similarity as constraints; screening out a matched geological disaster event of the target geological disaster event from the plurality of reference geological disaster events based on the graph similarity; and generating an emergency response scheme of the target geological disaster event based on the geological disaster emergency response knowledge map and the matched geological disaster event. According to the invention, the matching precision of the geological disaster event can be improved, and the emergency response scheme with high feasibility can be automatically generated based on the matching result.

The invention discloses a semantic auxiliary vision SLAM loopback detection method based on topological graph matching, and belongs to the field of computer vision. The implementation method comprises the following steps of: screening out candidate key frames without a common-view relationship by judging whether the common-view relationship exists between a current frame and each frame in a historical key frame sequence or not; and constructing a semantic topological graph, namely constructing nodes and undirected weighted edges in the topological graph and calculating node descriptors, and realizing topological expression of the key frame image by constructing the semantic topological graph and calculating a feature descriptor for each node in the topological graph. And calculating an affinity matrix, constructing the affinity matrix between the topological graphs by combining the similarity between the weights of the node descriptors and the edges in the topological graph of the current key frame and the topological graph of the candidate key frame, and calculating an incidence matrix between the nodes of the topological graphs at the same time. And calculating the topological graph similarity through the affinity matrix and the incidence matrix. And judging whether loopback detection succeeds or not by judging whether the topological graph similarity reaches a loopback detection threshold value or not.

The invention discloses a multi-view clustering method based on tensor feature extraction, and the method comprises the steps: inputting a multi-view data matrix, constructing a similarity matrix of each view through a K-NN algorithm and a Gaussian kernel function, and carrying out the spectral clustering to obtain a sample embedding matrix; performing singular value decomposition on an original data matrix of each view, taking first c left singular vectors to construct a feature embedding matrix, applying 2, 1 norm group sparse constraint on the feature embedding matrix, and connecting a sample embedding matrix through a bigraph to extract features; and normalizing the sample embedded matrix, and reconstructing a block diagonal matrix into a third-order tensor. And integrating the sample embedding matrix, the feature embedding matrix and global tensor learning to construct a target function, and optimizing through an alternating direction multiplier method until convergence. And finally, the normalized samples are embedded into the matrix to form block diagonals to form a consistent similarity graph, and a clustering result is obtained by using an N-Cut or k-means algorithm.

There are provided systems and methods for pairwise graph querying, merging, and computing for account linking. A service provider may provide an account graph system to identify pairwise similarities between different accounts based on shared data that may be identified through one or more linking characteristics. When providing pairwise graph similarities, a service provider may receive a query identifying two or more accounts and / or an account with a parameter for graph exploration and querying. The service provider may utilize connection, link, or relationship graphs, queried and generated using a graph database, to determine pairwise similarities between the designated seed account and one or more selected accounts. The graph may include vertices for different queried data points and edges connecting such queries, where directionality of the edges or other vectors may be used to identify links or hops between accounts for data querying and exploration.

The invention discloses a three-level interactive fusion graph similarity learning method, which comprises the following steps: learning node embedding through a multi-layer GIN in jump connection, and designing a style-based multi-head attention mechanism to capture fine-grained node-node interaction information; a coarse and fine granularity aggregation network is combined with different attention mechanisms to generate graph embedding features of two granularities, and the graph embedding features are fused; generating new node embedding through fusing cross-node-graph interaction information by adopting a node-graph interaction comparison network, and finally learning comparison characteristics of original node embedding and new node embedding; and performing global-level graph-graph interaction modeling on a relationship between input graphs by fusing comparison features and aggregation features generated by a graph interaction learning module and utilizing a full connection layer based on multi-level features of two input graphs, and converting the relationship into a graph-graph similarity score. Learning of enhanced node embedding is focused on, and rich graph interaction features are generated through a more effective graph interaction learning mode so as to promote modeling of a graph similarity relationship.

The invention relates to a power grid work ticket automatic generation method, terminal equipment and a storage medium. The method comprises the following steps: constructing a weekly plan corresponding to a to-be-generated work ticket; extracting the weekly plans and the sub-graphs of the historical work tickets, and calculating the sub-graph similarity between the weekly plans and the historical work tickets; comprehensive similarity is calculated in combination with the semantic similarity, and the historical work ticket with the highest comprehensive similarity is extracted; acquiring K images of each category of equipment to form a training image set, and recording category labels; obtaining a visual feature matrix and a one-hot coding label matrix of the training image set; taking images in the week plan as test images and extracting visual features; constructing a visual adapter, and predicting the category of each test image; and obtaining a work ticket corresponding to the weekly plan after the weekly plan, the extracted historical work ticket and the category predicted by the visual adapter are jointly used as a query input large language model. According to the invention, information of different modes can be effectively integrated to complete a work ticket generation task.

The invention provides a three-dimensional part retrieval method based on graph similarity search, relates to the field of computer graphics, and solves the technical problems that in the prior art, geometric and design semantic information of CAD cannot be fully utilized, and a topological relation is difficult to capture and display, so that the retrieval efficiency is low. The method comprises the following steps: acquiring part three-dimensional data of a computer-aided design (CAD) model, and constructing a training data set; constructing an edge-surface connection diagram based on the three-dimensional data of the part; calculating a graph editing distance (GED) matrix of all edge surface connection graphs in the training data set as a supervision signal; based on the GED matrix, training a sorting model, mapping an edge-surface connection graph to a hidden space, and constructing a part vector database according to an output graph-level embedding vector; the sorting model is constructed based on a graph attention network; and inputting a to-be-queried CAD part into the trained sorting model to obtain a feature vector, carrying out nearest neighbor search in the part vector database, and returning a similar part result.

The invention relates to the technical field of artificial intelligence, and discloses a model training method, and clients generate different aggregation strategies for each client on the premise of not sharing privacy information by uploading key model parameters and graph-level samples. A representative sample with privacy protection property is constructed at a client side through an adversarial learning mode, and the representative sample is uploaded to a server for subsequent personalized model aggregation. According to the personalized global aggregation strategy, graph-level representation is extracted from uploaded information on a server side, and graph similarity between clients is estimated, so that a personalized aggregation strategy is formulated through collaborative integration of multiple clients, the clustering performance of a local model is enhanced, the relationship between the clients can be accurately captured, and the clustering effect is improved. When personalized recommendation is carried out on a trained clustering model, the accuracy of personalized recommendation is improved. The invention further discloses a model training device, a storage medium and a program product.

The invention discloses a digital human virtual human setting generation method, and relates to the technical field of data processing, and the method comprises the steps: responding to an input task theme, a target region identifier and an industry classification identifier, and generating a human setting genotype structure according to the target region identifier and the industry classification identifier; constructing and generating an environment control file based on the human-set genotype structure; loading the generation environment control file into a pre-trained generation model, and generating differentiated contents according to the task theme; the cognitive homogenization risk of the differentiated content is analyzed through a graph isomorphism detection algorithm; and if the graph similarity of the cognitive homogenization risk exceeds a preset threshold value, updating the human genotype structure. According to the method, the homogenization degree of the output content of the large model is reduced.

The application discloses a graph similarity search method based on subgraph matching, divides data graphs into a plurality of mutually exclusive half-edge graphs, constructs an inverted index by taking subgraph embedding as a key and a data graph set containing the subgraph as a value, constructs a coordinate index based on size information of the data graph, realizes hierarchical filtering by using a mapping relationship between the two indexes, processes the half-edge by using a self-loop or loop-free strategy, designs a sequential embedding model, so that embeddings of two graphs with subgraph isomorphism have the same partial order position in a high-dimensional space, inputs a query graph, screens out a preliminary candidate set through the coordinate index, further screens the candidate set through the inverted index, accurately calculates a graph edit distance between the query graph and the candidate graph, and obtains a final result set, which not only avoids traditional subgraph isomorphism testing, but also can generate subgraph embedding in an offline stage, greatly shortens inference time of the model.

This invention discloses a multi-view subspace clustering method based on diversity graph fusion. The method comprises the following steps: Step 1: Acquire multi-view data and perform preprocessing; Step 2: By adding regularization terms for multi-view consistency and diversity, introduce self-expression learning to explore the intrinsic structure of the multi-view data, and introduce low-rank and sparse constraints on the consistency expression matrix, thereby obtaining a highly reliable and robust similarity matrix; Step 3: Using an induced self-weighting approach, fuse the view similarity matrices obtained in Step 2 to form a final consistent similarity matrix, which serves as the input of a spectral clustering algorithm and outputs the clustering results. This method can improve clustering performance and achieve optimal clustering results.

The invention discloses a vehicle cabin sound field reconstruction method based on space energy diagram constraint and deep optimization. The method comprises the following steps: 1) measuring the impulse response of a vehicle-mounted loudspeaker array in a target vehicle cabin in a listening area; (2) space scanning is conducted on the impulse response and the impulse response simulated in the ideal anechoic chamber scene through a delay and beam forming method, sound energy distribution in all preset directions is calculated, and a space energy diagram of an actual sound field and a space energy diagram of a target ideal sound field are generated; 3) calculating the space energy diagram similarity between the two space energy diagrams, and constructing a target function; 4) constructing a neural network, taking random noise as input, outputting a control filter coefficient, and iteratively optimizing the neural network by using a target function; and 5) according to the neural network after iterative optimization, outputting a control filter coefficient of each loudspeaker channel, and loading the control filter coefficient to a sound system in the target vehicle cabin to realize sound field reconstruction in the target vehicle cabin. The invention has better subjective tone quality.

The application provides a three-dimensional part retrieval method based on graph similarity search, relates to the field of computer graphics, and solves the technical problem that the prior art cannot fully utilize the geometry and design semantic information of CAD, and it is difficult to capture and display the topological relationship, resulting in low retrieval efficiency. The method comprises the following steps: obtaining part three-dimensional data of a computer-aided design (CAD) model, and constructing a training data set; constructing an edge-face connection graph based on the part three-dimensional data; calculating a graph edit distance (GED) matrix of all edge-face connection graphs in the training data set as a supervision signal; training a ranking model based on the GED matrix, mapping the edge-face connection graph to a hidden space, and constructing a part vector database according to the output graph-level embedding vector; the ranking model is constructed based on a graph attention network; inputting a CAD part to be queried into the trained ranking model to obtain a feature vector, performing nearest neighbor search in the part vector database, and returning a similar part result.

The invention relates to software quality verification, in particular to a software quality verification system and method for logic alignment of an execution path and a design blueprint, a design logic atlas construction module is used for converting extracted blueprint logic elements into a design logic atlas in a digraph form and providing a standardized design reference for logic alignment; the execution path atlas construction module is used for converting the collected key program execution track data into an execution path atlas consistent with the design logic atlas in structure; the logic alignment algorithm module is used for calculating map similarity based on a map editing distance, quantitatively evaluating logic consistency between a design logic map and an execution path map, and outputting a logic alignment score; the difference analysis engine is used for carrying out deep analysis on parts which are not successfully logically aligned, identifying logic difference types and carrying out risk level evaluation; according to the technical scheme provided by the invention, the defect that logic consistency verification is difficult to carry out on the program execution logic and the original design intention in the prior art can be effectively overcome.

The invention belongs to the technical field of educational competition, and particularly relates to an improved educational competition optimization algorithm based on a distribution estimation algorithm, which adopts a contour comparison method to extract graphic key feature points, establishes feature point matching pairs through a K-nearest neighbor matching algorithm, and optimizes the educational competition. The contour similarity is calculated in combination with the number of matched pairs, the average distance of standard graphic feature points and the coordinate deviation value, accurate quantization of graphic similarity is achieved, modeling data abnormal value detection is conducted on modeling data through a box graph method, reasonable discrete data and unreasonable data are effectively distinguished in combination with the quartile distance characteristic of the data, and the accuracy of the data is improved. Different feature point extraction modes and different data models are adopted for comparison of different types of data, it can be guaranteed that the data models are more practical, and the extracted data have higher reliability.

The invention provides a regulation query method and device based on a coal mine regulation knowledge graph, and relates to the technical field of coal mine safety. The method comprises the steps of obtaining a to-be-queried coal mine case text, a coal mine case set and a coal mine regulation knowledge graph; performing semantic similarity calculation on the case text and each historical case, and taking the obtained semantic similarity as a semantic similarity component of a corresponding regulation; calculating the graph similarity between the case text and each node of the knowledge graph to obtain the graph similarity component of each regulation; and fusing the semantics and the graph similarity component to select a target regulation and generate a reply text. According to the method, semantic matching and graph structure association are fused, so that target terms which are related to case text semantics and are closely structurally associated can be accurately screened from an overall regulation system, and end-to-end accurate query from case description to regulation matching is realized; and the accuracy and integrity of regulation basis acquisition in coal mine safety supervision are obviously improved.

The present disclosure provides a knowledge graph-based industrial knowledge mining modeling method and system, relating to data mining technology, the method comprising: obtaining basic information of a pre-constructed knowledge graph; determining data collection type and data collection amount; collecting relevant data on multiple same-type equipment products of a target device to construct multiple target knowledge graph sets; based on device usage time nodes, traversing and comparing the multiple target knowledge graph sets to obtain a graph similarity feature set; constructing a target device twin model and embedding graph similarity features in the graph similarity feature set into the target device twin model according to device usage time nodes; and performing fault early warning on the target device usage process according to the target device twin model. The present disclosure can solve the technical problem of low accuracy and timeliness of existing industrial equipment fault early warning methods, and can improve the accuracy and efficiency of industrial equipment fault early warning, thereby timely discovering and handling fault hazards.

The application discloses a mountainous area image geographical positioning method and system based on a scene semantic graph. In view of the problems of lack of reference images in mountainous areas and weak target texture of mountainous area images and the characteristics that it is difficult to match and position, a mountainous area image scene information extraction and modeling method is provided to obtain a mountainous area scene semantic graph; a mountainous area scene semantic graph matching method is provided to realize mountainous area image geographical positioning. Compared with the prior art, the application constructs a reference image library of mountainous area rendering images, and provides a mountainous area scene semantic graph similarity matching and positioning algorithm based on a graph editing distance, so that the positioning precision and speed are good, and the application has wide application value.

The invention discloses an intelligent process generation method and device based on knowledge graph embedding similarity matching, and belongs to the technical field of intelligent manufacturing process planning, and the method comprises the steps: constructing an assembly process knowledge graph; an assembly process sub-graph is extracted with the process as the center and serialized; the method comprises the following steps of: obtaining node-level embedding through a twin Transform encoder with shared parameters; obtaining graph-level embedding through batch embedding splicing and a pooling layer; performing bilinear interaction on an assembly subject, a relationship and an object by using an assembly element interaction neural tensor network, and extracting deep semantic features; outputting a similarity score of the two sub-graphs; retrieving the most similar process node based on the score; and executing shortest path search on the directed acyclic graph to complement missing nodes, and generating a target assembly process path. According to the method, rapid matching and automatic generation of different and semantically similar processes are realized, and the process reuse rate and the generation efficiency in a multi-variety, small-batch and rapid iterative manufacturing environment are remarkably improved.

This application relates to a method, apparatus, device, medium, and product for generating a product recommendation list. The method includes: acquiring an interaction data graph, with products as nodes and the number of users sharing the same interactive behavior event between any two nodes as edges; acquiring a product information graph, with products as nodes and the correlation degree corresponding to the common attributes between any two nodes as edges; determining the single-graph similarity matrix between products in each graph, and summing all single-graph similarity matrices to obtain a comprehensive similarity matrix; and determining products similar to the specified product from the comprehensive similarity matrix, based on the specified product, for extracting a product recommendation list from a product candidate library. This application can obtain a product recommendation list composed of similar products matching the specified product with lower system overhead, higher execution efficiency, lower implementation cost, and is suitable for deployment on independent websites.

Embodiments of the present application disclose a knowledge graph construction method and related equipment, the method comprises: obtaining target data of a preset scene, wherein the target data comprises at least one of entity data, attribute data and relationship data; the target data is associated with preset rules and time sequence information according to the corresponding data type, node data is generated and stored; a knowledge graph is constructed based on the stored node data, by adding a time dimension in the knowledge graph data, the user can use the time sequence analysis technology and the graph similarity technology to analyze the change and trend of the graph structure with time, so as to master more information.

The invention relates to a similarity calculation method of a affair graph and a related product. The method comprises the following steps: acquiring a plurality of affair maps; based on different types of information similarity models, respectively analyzing the similarity of the target information between the affair maps to obtain different types of information similarity features; on the basis of domain weights, the information similar features are fused, associated features are obtained, and the domain weights are determined according to domain knowledge; fusing the weight of each matching edge under the maximum weight matching of the bipartite graph to obtain a graph similarity; the bipartite graph is determined according to the associated features. According to the scheme provided by the invention, the domain weight is determined through the domain knowledge, and the fusion process of the information similar features is guided to improve the influence of key elements in the information similar features on the associated features, so that the related parameters of the bipartite graph more accurately reflect the similarity degree between the two affair maps, and accurate calculation of the map similarity is facilitated.

The invention relates to generating violation exemption terms for static lint checks. In one example, a method includes converting source code for register transfer level design into a directed graph; acquiring a first violation generated by analyzing the source code; identifying a violation statement sub-graph associated with the first violation in the directed graph; extracting a reduced sub-graph representing the first violation from the directed graph, wherein the violation statement sub-graph comprises a starting point of the reduced sub-graph; converting the reduced sub-graph into a first vector; calculating a graph similarity between the first vector and a second vector representing a second violation of the source code for which an existing exempt item has been generated; determining, by a processing device, that the graph similarity satisfies a threshold similarity; and generating an exemption item for the first violation in response to the determination.

The invention discloses a new equipment starting scheduling method and system based on a graph neural network and a knowledge graph, and relates to the technical field of power scheduling, and the method comprises the steps: constructing a power grid equipment knowledge graph according to power grid physical equipment and related knowledge elements; generating starting scheme power grid model data based on the power grid equipment knowledge graph; converting the generated starting scheme power grid model data into graph vector data by adopting a graph embedding method, and determining a historical typical starting scheme which is most matched with the new equipment starting scheme by adopting a graph similarity method; and on the basis of the historical typical starting scheme, a node alignment method is adopted to search for a device node pair, corresponding to the historical matching device and the new device, of the historical matching device, the new device node is replaced with the historical matching device node based on the device node pair, and a new device starting scheme is obtained. The method can effectively simulate the complex topological structure of the power grid and the mutual dependence relation between elements, accurately measures the operation characteristics of the power grid, and achieves the intelligent compilation of a new equipment starting scheme to reduce the burden of power grid operation personnel.

The application discloses an agent-based task processing method and device, equipment and medium, and belongs to the field of artificial intelligence. The method comprises the following steps: obtaining an initial anchor point vector according to the original input of a user in a dialogue; obtaining N sub-tasks contained in a task indicated by the original input based on the original input by an agent; obtaining a similarity parameter corresponding to the sub-task and a skill sub-graph based on the text information of the sub-task, the anchor point vector and a pre-obtained skill knowledge graph, wherein the similarity parameter represents the similarity between the semantic vector of the sub-task and the anchor point vector, and the skill sub-graph is a subset of the skill knowledge graph, comprising a skill node with an authority level and a risk parameter within the safety range of the sub-task; and in the case that the similarity parameter meets the intention consistency condition and the skill corresponding to the sub-task is in the skill sub-graph of the sub-task, the corresponding skill is allowed to be called to execute the sub-task. According to the embodiment of the application, the safety of the agent in processing the task can be improved.

The present invention discloses a heterogeneous information similarity matching method and system based on graph representation, including the steps of: extracting a heterogeneous graph of heterogeneous information; converting the high-dimensional information of nodes in the heterogeneous graph into a low-dimensional information vector space, and obtaining a node-level graph embedding vector after splicing; extracting a similarity feature vector of graph-graph nodes in an alignment similarity matrix; using a cross-attention mechanism to aggregate the embedding vectors of graph pairs to obtain an aggregated graph-level embedding vector; inputting the similarity feature vector of graph-graph nodes and the aggregated graph-level embedding vector into a multi-layer perceptron to obtain a prediction result of the graph-graph similarity score. In the present invention, the graph-based representation method can map heterogeneous information into a unified low-dimensional space, making similarity matching more efficient and accurate, reducing the dependence on feature engineering, and having a good matching effect when dealing with various types of data and the relationships between them.

The invention discloses a feature extraction method based on dynamic graph learning and sub-manifold preservation, and belongs to the technical field of image processing. The method specifically comprises the following steps: S1, constructing a joint optimization framework: integrating subspace learning, label learning and graph learning tasks, and cooperatively updating a projection matrix, a label matrix and a graph similarity matrix through an alternate optimization strategy; s2, robust sparse projection learning: adopting an l2, 1 norm to restrain a loss function, suppressing noise interference and realizing feature line sparsity; a high-quality neighbor graph is dynamically constructed by utilizing neighbor information of a sample in a projection feature space, a connectivity graph structure of approximate data is established based on a maximum entropy principle, local geometric structure information of a data bottom layer is captured, the problem of structure loss caused by neglecting a non-linear sub-manifold relationship in a traditional method is avoided, and the reliability of the method is improved. Laplacian regularization further smooths manifold distribution, it is ensured that unlabeled samples and adjacent labeled samples share label information, and the discriminability and robustness of subspace learning are enhanced.

The application discloses a residential building function relationship graph recommendation method based on graph similarity and graph edit distance, and comprises the following steps: 1) establishing a residential function relationship graph database; 2) obtaining a function relationship graph input by a user; 4) obtaining a function relationship graph to be matched; 5) searching for residential function relationship graphs with the same number of vertices as the function relationship graph to be matched, and respectively calculating the similarity between the function relationship graph to be matched and the residential function relationship graphs; 6) based on the similarity, arranging the searched residential function relationship graphs in descending order, and then calculating the graph edit distance between the function relationship graph to be matched and the residential function relationship graphs; 7) based on the graph edit distance, arranging the searched residential function relationship graphs in ascending order, and selecting K residential function relationship graphs as recommended residential function relationship graphs. The application realizes the recommendation of the residential building function relationship graph, the recommended scheme is accurate and diverse, can be used in various building auxiliary design software, and has high portability.