41 results about "Graph patterns" patented technology

The invention relates to the technical field of big data, in particular to an intelligent operation and maintenance management method based on a big data algorithm, and the method comprises the steps: constructing and continuously updating a dynamic fault association graph through inputting multi-source heterogeneous operation and maintenance data; starting full-graph scanning based on a predefined period, detecting an abnormal topological structure through a graph pattern recognition algorithm, and marking potential risk nodes; executing dynamic influence diffusion simulation on the potential risk nodes, calculating a business influence severity quantized value after the fault, and marking fault propagation vulnerabilities according to the quantized value; taking the potential risk node as a starting point, executing a reverse traceability algorithm for preferentially exploring a path pointing to a fault propagation vulnerable point, and outputting a fault propagation path and a source fault node identifier; and finally generating and executing a fault processing strategy. The process solves the problem that traditional operation and maintenance cannot quantitatively evaluate and discriminate the highest priority disposal object from numerous potential risks, and realizes accurate positioning and active prevention and control of weak links of fault propagation.

The invention discloses a feature fusion processing method for anesthesia depth multi-modal data, and belongs to the technical field of graphic data processing and pattern recognition, and the method comprises the steps: obtaining a multi-modal physiological signal and an electromyographic signal of a patient; performing time axis calibration on the physiological signal to generate an alignment signal; extracting a multi-modal feature vector and calculating an anesthesia depth index; performing deviation analysis on the basis of the electromyographic signal and the index to obtain an electromyographic response deviation index; performing graphical feature mapping on the real-time electroencephalogram signal to generate a real-time time-frequency map; when the deviation index exceeds a safety threshold value, performing graph pattern matching with a pattern template library to calculate a similarity score; and outputting the current anesthesia state mode in a classified manner. According to the method, a multi-modal signal graphical feature fusion technology is adopted, and a dynamic map generation and pattern matching mechanism is combined, so that the problem of complex pattern recognition of physiological signal graphic data can be solved, and the accuracy and timeliness of anesthesia state classification are improved.

This application is directed to automated graph schema generation. A schema generation method includes receiving a graph schema having vertices and edges associated with a plurality of data tables. The method also includes automatically generating, based on the plurality of data tables and the graph schema, one or more suggestions using a computational model. The one or more suggestions identify one or more vertices and / or one or more edges. The one or more suggestions include a respective suggestion that identifies a respective subset of the one or more vertices and / or the one or more edges. The method further includes receiving a first user input that is a selection of the respective suggestion from the one or more suggestions. The method further includes updating, based on the first user input, the graph schema. The method further includes outputting the updated graph schema.

The invention discloses a big language model Cypher generation method and system fusing graph mode selection and reinforcement learning, and belongs to the technical field of graph databases and big models. The method comprises the following steps: firstly, constructing training data, carrying out reinforcement learning training fused with graph mode selection on a large model, selecting a sub-graph mode related to a problem from a graph database mode, and verifying the sub-graph mode; and then, taking the selected sub-graph mode as a core context, and carrying out Cypher query generation. The large model is trained through a specially-designed reinforcement learning normal form, a selection strategy of a graph mode can be learned and optimized, and a selection result is recursively used for guiding and correcting a Cypher generation process. According to the method, a graph mode selection process is used as a key reasoning step and is seamlessly integrated into a Cypher generation process, so that the focusing capability and reasoning capability of the model when facing a complex and huge graph mode are remarkably improved, and the accuracy and practicability of Cypher query are greatly improved.

Systems and methods for identifying and remediating architecture risk are disclosed. In one aspect, a method includes generating a first chaos graph pattern and a second chaos graph pattern; training a machine learning model to recognize the first chaos graph pattern and the second chaos graph pattern; identifying an architecture graph pattern of an evaluated architecture; including the architecture graph pattern in an architecture testing graph; recognizing by the machine learning model that a shape of the architecture graph pattern is similar to a shape of the first chaos graph pattern and that the shape of the architecture graph pattern is similar to a shape of the second chaos graph pattern; and predicting a remedial reconfiguration, wherein the remedial reconfiguration includes a reconfiguration of a design of the evaluated architecture.

The invention discloses a graph pattern generation method based on a Clarii graph, and relates to the technical field of information, and the method comprises the steps: constructing an extended fluorescent material calibration module, introducing a saturation critical light intensity reference value, and carrying out the calibration of the Clarii graph under the dual constraints of color temperature deviation distribution and brightness standard deviation; according to the method, the pattern contrast change condition is recognized through the adjusted pattern contrast, a correction index is formed, the LED backlight wavelength is corrected for the second time according to the correction index, pattern structure deviation information is fed back to a spectrum control link, and the color uniformity of the LED backlight is improved. According to the scheme, on the premise of ensuring uniform fluorescence response and inhibiting bright spots, dark spots and stripes, the Clariy pattern can be kept stable, clear and layered in an actual application environment.

The invention discloses a big language model Cypher generation method and system based on graph pattern alignment enhancement, and the method comprises the steps: constructing a data set which comprises a plurality of types of training data; supervising and finely tuning the large language model; sampling the supervised and fine-tuned large language model to obtain a sampling data set; training the large language model after supervision and fine tuning; obtaining a question and a graph database mode input by a user; related sub-graph modes are selected from the complete graph modes according to user questions and are verified; the large language model generates a corresponding Cypher query using a selected mode and a given question. According to the big language model Cypher generation method and system based on graph pattern alignment enhancement, after the big language model is enabled to better adapt to a Cypher generation task through supervision and fine tuning of various data, the model is trained by model sampling after supervision and fine tuning, the capability of the big language model to generate the Cypher is enhanced, and the big language model and the graph pattern are effectively aligned.

The application provides a weakly supervised knowledge graph automatic construction method for professional text, relates to the technical field of knowledge graph construction, and comprises the following steps: converting a professional text into a structured text format to generate a preprocessed text set; based on a preset domain graph pattern, constructing a structured knowledge unit by using a large language model, identifying a pattern conflict, and generating a candidate new pattern description document; evaluating the comprehensive confidence of the candidate new pattern description document through a multi-dimensional confidence evaluation mechanism, and dynamically updating the domain graph pattern; optimizing and reconstructing the structured knowledge unit, calculating the quality index of the reconstructed weakly supervised knowledge graph, repeatedly constructing the weakly supervised knowledge graph until the quality index of the weakly supervised knowledge graph reaches a preset quality stability threshold, and obtaining a weakly supervised knowledge graph of a professional text. The technical problem that the preset domain graph pattern in the prior art is rigid and difficult to adapt to emerging knowledge types in the construction of a weakly supervised knowledge graph is solved.

The application is suitable for the field of model explanation, and relates to a rule mining method and device for recommending model explanation, equipment and medium. For any user-item pair in graph data, a corresponding neighborhood graph and a connected subgraph are determined, a candidate subgraph is determined from the connected subgraph, a target subgraph is determined from the candidate subgraph according to a graph evaluation score, a target graph pattern is extracted from the target subgraph, for any variable in a pattern path of the target graph pattern, a target predicate is determined from all predicates corresponding to the variable, a candidate premise condition is formed by the variable and the target predicate, a candidate explanation rule is obtained according to the target graph pattern and a candidate premise condition set corresponding to all pattern paths in the target graph pattern, and a candidate explanation rule satisfying a preset condition is determined as a target explanation rule from all candidate explanation rules. The target explanation rule reflecting the prediction principle of the recommendation model is mined from the graph data as the global explanation of the recommendation model, and the effectiveness of the explanation is improved.

The invention discloses a multi-modal-based graph model identification method, which comprises the following stages: (1) a preprocessing stage: preprocessing a training set; (2) a model training stage: inputting a training set to train an existing network model to obtain a common primitive recognition model, a character recognition model, a module division model and an anomaly analysis model; (3) an image recognition stage: completing primitive recognition, character recognition and wiring relation recognition for the to-be-recognized plant station wiring diagram; (4) a module division stage: obtaining a plurality of functional modules through a module division model; (5) a post-processing stage: completing a series of downstream tasks; and (6) an output stage: through analysis of the anomaly analysis model, an output module can output the CIM / SVG file meeting the standard. According to the method, accurate identification can be realized, obvious problems can be found and marked in time when the circuit of the plant station wiring diagram has the obvious problems, and the standard CIM / SVG file can be rapidly and accurately output.

A computer-implemented method for generating by a query engine a graph of operators for a SPARQL query over an RDF graph. The method includes obtaining a graph of operators executable by the query engine, the graph comprising a plurality of basic operators, at least two of said operators being of a first type each configured to find RDF triples of the RDF graph that match a respective basic graph pattern. The method further comprises identifying a group of operators among the at least two basic operators of the graph which are of the first type. The respective basic graph patterns of the group of operators have same subject and / or predicate and / or object and the identified group of operators is replaced in the graph by an equivalent operator configured to find RDF triples of the RDF graph that match the respective basic graph patterns of the group of operators.

The invention provides a professional text-oriented weak supervision knowledge graph automatic construction method, and relates to the technical field of knowledge graph construction, and the method comprises the following steps: converting a professional text into a structured text format, and generating a preprocessed text set; based on a preset domain graph mode, constructing a structured knowledge unit by using a large language model, identifying mode conflicts, and generating a candidate new mode description document; evaluating the comprehensive confidence of the candidate new mode description document through a multi-dimensional confidence evaluation mechanism, and dynamically updating the domain atlas mode; optimizing and reconstructing the structured knowledge unit, and calculating a quality index of the reconstructed weak supervision knowledge graph; and repeatedly constructing the weakly supervised knowledge graph until the quality index of the weakly supervised knowledge graph reaches a preset quality stability threshold, and obtaining the professional text weakly supervised knowledge graph. The technical problem that in weak supervision knowledge graph construction in the prior art, a preset domain graph mode is rigid and is difficult to adapt to emerging knowledge types is solved.

This invention relates to a method, device, and medium for retrieving problem-related subgraphs based on evidence graph patterns. It retrieves a small-scale evidence subgraph from a graph-structured knowledge base that provides factual support for solving natural language problems. First, the natural language problem is transformed into a vector representation using an association resource pair retrieval model, and highly relevant candidates are retrieved from an offline-built association resource pair vector library. Then, all possible evidence graph patterns are enumerated based on the association resource pairs. Finally, all evidence graph patterns are uniformly ranked using a ranking model, and the top-ranked patterns are used to derive the problem-related subgraph. This invention transforms the retrieval of problem-related subgraphs into the retrieval of a small number of evidence graph patterns, avoiding the pruning overhead caused by iterative subgraph expansion with pruning on large-scale graph data, and effectively improving subgraph retrieval performance for multi-hop problems.

The application provides a method and device for event prediction based on a timing diagram rule, the method comprising: obtaining timing diagram data, and adding event prediction results of a machine learning model to the timing diagram data to obtain timing diagram extension data; extracting a subgraph for verifying prediction rule reliability from the timing diagram extension data according to a verification ratio; training a rule creator according to the timing diagram extension data and the subgraph to obtain a timing event prediction rule set; and obtaining a prediction event set according to the timing event prediction rule set and the timing diagram data. By embedding the machine learning model for event prediction into a rule-based association relationship system as a predicate, the association rule can not only use the existing machine learning model event prediction result, but also use a logical condition to improve the prediction result of the machine learning model, has stronger expression ability, and does not have to constrain constant interval time or have a common focus constraint on a graph pattern.

The present invention discloses a method and device for pre-calculating subgraph queries based on graph summarization technology. The present invention performs local graph summarization on a graph in real time, thereby reducing the number of entities contained in the graph structure in order to improve the efficiency of graph queries. In the pre-summarization stage, a simplified multi-hop summary is introduced. These summaries summarize the similarities between entities in a multi-hop neighborhood and a specific entity through a small graph pattern that contains rich information. In the graph query stage, the present invention uses a designed graph summary quality scoring function to sort, maintain and update the summary queue in real time. The graph query is covered by obtaining summaries from the graph summary queue, and necessary entities are obtained from the original graph, thereby improving the efficiency of the graph query. The present invention avoids the difficulties of directly performing graph queries by pre-calculating graph summaries, thereby enhancing the effectiveness and efficiency of graph queries.

The invention provides a fund transaction association relationship mining and abnormal behavior identification method based on a knowledge graph, and relates to the technical field of knowledge graphs, and the method comprises the steps: collecting fund transaction flow data, and carrying out the cleaning and time window division, and obtaining a standardized transaction data set; constructing a time sequence dynamic knowledge graph; defining a rule type exception graph mode, extracting exception sub-graphs from historical exception case data, selecting a prototype mode, and combining the prototype mode and the rule type exception graph mode to form an exception transaction graph mode library; executing graph pattern matching to obtain an abnormal sub-graph instance set; and calculating an abnormal score of each abnormal sub-graph instance in the abnormal sub-graph instance set, performing score propagation based on an account node overlapping relationship between the abnormal sub-graph instances, and aggregating the abnormal scores of all the abnormal sub-graph instances in which each account node participates for each account node to obtain an account-level abnormal score. According to the invention, the recognition accuracy of the complex hidden abnormal transaction network can be improved.

The application relates to a social network repeated event detection method based on repeated subgraph pattern mining. The method comprises the following steps: obtaining a social network snapshot graph of a current timestamp, mapping an m-edge subgraph related to a newly inserted edge e in the social network snapshot graph into a corresponding bucket of a first auxiliary data structure by using a first hash function, and updating the frequency and frequency counter state of a subgraph pattern in the corresponding bucket; obtaining the frequency of the updated subgraph pattern, and if the frequency is equal to a support threshold value sigma, mapping the corresponding four-element information of the subgraph pattern into a corresponding unit of a second auxiliary data structure by using a second hash function, and updating the four-element information in the corresponding unit; and obtaining repeated events in the current social network according to the events corresponding to the subgraph pattern in the updated second auxiliary data structure. The method can efficiently detect repeated events on a social network stream graph.

The present application is applicable to the field of graph data technology, and relates to a graph association rule mining method, apparatus, device and medium. Among them, the graph data of the business system is obtained, and the target attribute corresponding to the target task and the target node label corresponding to the target attribute are determined; a reference graph pattern is obtained according to the target attribute and the target node label; target subgraph data that matches the reference graph pattern is determined from the graph data; graph features of the target subgraph data are obtained, and the target attribute is used as a training label, and a decision tree is trained according to the graph features to obtain a reference decision tree; association rules are extracted from the reference decision tree to obtain target association rules. The present application can improve the mining efficiency of graph association rules.

A computer-implemented method for generating a summary of a graph database comprising a set of RDF tuples including obtaining the graph database and generating a summary having a set of probabilistic filters. Each probabilistic filter of the set determines if at least one RDF tuple existing in the graph database corresponds to a respective basic graph pattern of the probabilistic filter with a possibility of false positive.

The present invention discloses a graph index migration method and device based on DCG. DCG is a method for constructing indexes in a dynamic graph pattern matching algorithm. Index migration can migrate valid indexes in the original DCG to the DCG corresponding to the query graph and discard invalid indexes. The graph index migration method based on DCG is mainly divided into the following four parts: generation of a query tree, determination of a matching order, construction of an index, adjustment of a query tree and migration of an index. The method proposed in the present invention is mainly used in network security APT attack pattern recognition scenarios, and can be used when the initial data is small. Therefore, the method can ensure its effectiveness for a long time, ensure millisecond-level return results, and save a lot of storage space.

Techniques and systems disclosed herein relate to optimizing the repeat patten graph mapping for coarse-grained reconfigurable processors. For example, a method of mapping a dataflow graph onto a coarse-grained reconfigurable (CGR) processor including one or more arrays of CGR units may include receiving a dataflow graph of a high-level program and detecting one or more repeated sub-graph patterns within the dataflow graph, each repeated sub-graph pattern comprising a set of operations that recurs across multiple instances in the dataflow graph. The method may then include generating configuration data for the CGR processor including assigning multiple instances of a detected repeated sub-graph pattern to a set of CGR units of the CGR processor.

A method for modeling and analysing single-phase or multiphase digital signals in an angular coordinate axis defined depending on the signal's arguments and phase angles in a device having information processing capability is disclosed, a system comprising instructions operating on a device having a processor for applying the method of the invention to various signals and various angular axes, and a novel method for estimating signal vectors for multiphase signals at angular phases not provided as input signals. In the inventive method, single-phase or multi-phase digital signals received from a data source are displayed on an angular coordinate axis defined depending on the independent variables and phase angles of the signal. Depending on signal variables such as resolution amounts of the parameters desired to be displayed on the angular coordinate axis in the signal, linear or non-linear increase amounts, lower and upper limit values, a graphic pattern is created that is divided into sections called segments and layers (grids) on the angular coordinate axis. The signal is then expressed in segments and layers of the graph on the angular coordinate axis with numerical values, colours, symbols, signs or other visual elements. The method of the present invention can be used in devices with information processing capacity and can be used in modeling all kinds of signals. The method was implemented in a signal modeling system designed using the method, and various single-phase and multi-phase signals were tested on this system.

The invention discloses a cross-individual emotion online monitoring system based on electroencephalogram and map deep learning, and relates to the technical field of biomedical engineering. The system comprises an emotion induction module, a data collection module, a data processing module, a model calculation module and a stimulation presentation module, emotions are induced through video stimulation, multi-guide electroencephalogram signals are collected in real time, an electroencephalogram network connection matrix and multi-band differential entropy features are extracted after preprocessing, a multi-view space-time diagram mode is extracted by means of a graph neural network double-branch structure, and a multi-view space-time diagram mode is obtained. And in combination with transfer learning, realizing cross-individual emotion real-time decoding, and finally visually presenting a result through a graphical user interface. The system is divided into training and testing stages, the problems of poor robustness, insufficient cross-individual generalization and the like in the prior art are solved, multiple types of emotions can be accurately and dynamically monitored, and the method is suitable for actual scene application.

The invention relates to a static graph mode graph generation system supporting dynamic resolution. The system comprises an original logic diagram construction component which is used for constructing an original logic diagram by tracking original calculation logic by executing a construction function of a diagram aiming at a first specific diagram generation task; the logic diagram optimization component is used for forming a first logic diagram according to the constructed original logic diagram; the node insertion component is used for inserting communication nodes and runtime control nodes required by distributed calculation according to the first logic diagram, and generating a first runtime logic diagram for multi-device collaboration; the operation plan generation component is used for generating a first runtime execution plan based on the data dependency relationship of the first runtime logic diagram; and the runtime initialization component is used for initializing the runtime instance of the first specific diagram according to the first runtime execution plan so as to complete dynamic allocation of computing resources and configuration of an execution environment.

The invention discloses a dynamic graph data storage method and device oriented to hybrid workloads. According to the method, aiming at the problem that various graph workloads are difficult to efficiently support in a single system in the prior art, a hash-based index construction, dynamic hierarchy edge storage design and a fine-grained concurrency control mechanism are provided. According to the method, efficient indexing of the vertexes is realized by adopting the hash table, and hash conflicts are solved in combination with a hash bucket strategy, so that the data query performance is improved. In the aspect of edge data storage, a compact array structure with a continuous memory is adopted, so that the traversal efficiency of graph data is optimized; meanwhile, aiming at the power law distribution characteristic of the graph data, the invention provides a dynamic hierarchical storage structure to improve the storage and updating efficiency, and an ordered storage strategy is adopted for the edge data of the vertex of the height number to efficiently support the graph pattern matching type workload. The invention provides a control mechanism, which improves the throughput of the system under a more novel working load while ensuring the data consistency.

Systems and methods for identifying and remediating architecture design defects are disclosed. In one aspect, a method includes generating a new architecture graph pattern based on an architecture design document of an evaluated architecture; determining a model graph pattern, wherein a shape of the model graph pattern is similar to a shape of the architecture graph pattern; determining, based on a comparison of the shape of the model graph pattern with the shape of the new architecture graph pattern, that the new architecture graph pattern includes a design defect; generating, based on the shape of the model graph pattern, a remediated graph pattern; and determining, based on the differences between the remediated graph pattern and the new architecture graph pattern, a suggested remedial change to the architecture design document.

The application discloses a financial risk reasoning method and system based on positive and negative association, an electronic device and a computer readable storage medium. The method comprises the following steps: acquiring a financial extension graph containing positive association information and negative association information, and a financial reasoning rule set containing a graph pattern, an antecedent predicate set and a consequent predicate; matching the rule graph pattern in the financial extension graph to obtain a matching instance; checking whether the matching instance meets the antecedent predicate, instantiating the matching instance that passes the check according to the consequent predicate to generate a candidate reasoning result including a positive or negative conclusion; performing conflict detection and resolution on the candidate reasoning result to obtain a target risk reasoning result; and outputting the target risk reasoning result and reasoning explanation information. The positive and negative evidence fusion reasoning is realized, the false positive rate is reduced through conflict detection and multi-dimensional priority resolution, and auditable explanation information is outputted, so that the fine risk control and strong supervision scene demand are met.

Systems and methods to identify blockchain anomalies include an AI tool comprising a processor, a GPU, and a GNN model to extract graph parameters from a block transactions graph of a blockchain block, generate statistical approximations of the graph based on the graph parameters, compare the statistical approximations to at least one anomaly threshold, detect an irregular graph pattern in the graph when the statistical approximations exceed the at least one anomaly threshold, identify via the GNN model an anomaly within the block transactions graph based on the irregular graph pattern, generate via the GPU an address graph based on the block transactions graph when the anomaly is identified to display one or more addresses associated with the anomaly, and generate an alert when the anomaly is identified.

The invention relates to the technical field of supply chain intelligent prediction, and discloses a supply chain demand prediction method and system based on artificial intelligence. The method comprises the steps of obtaining a complete transaction record in a target time period, forming a material flow time sequence, and screening flow subsequences according to a predicted target material variety, a time span and a geographical range. And generating a material flow change map based on the subsequences, and visually describing a flow dynamic fluctuation track. Scanning the atlas by using a preset atlas feature recognition rule, recognizing a specific traffic change form, marking the specific traffic change form as a to-be-analyzed feature, and extracting a corresponding original transaction record subset; constructing a deep neural network, taking the to-be-analyzed features and the transaction record subsets as input, and training the network to learn a mapping relation between the to-be-analyzed features and the transaction record subsets and subsequent material demands; according to the method, complex non-stationary demand fluctuation is captured through combination of map form identification and transaction detail multi-modal learning, and prediction accuracy and business interpretability are improved.

The invention is suitable for the technical field of graph data, and relates to an anti-fact interpretation generation method and device, equipment and a storage medium. Wherein a negative prediction rule of the graph neural network is obtained, and the negative prediction rule is composed of a reference graph mode and a rule attribute predicate set; a candidate space is constructed by taking a current node for performing negative prediction on the graph data by the graph neural network as a pivot, the candidate space comprises candidate node subsets corresponding to other mode variables except the current node of the reference graph mode, and the candidate node subsets comprise candidate nodes simultaneously matched with the reference graph mode and a rule attribute predicate set in the graph data; searching a candidate node with the highest perturbation cost performance and a corresponding graph perturbation operation in the candidate space, and updating the candidate space after executing the graph perturbation operation; iteratively executing until at least one candidate node subset in the candidate space is empty; and generating an anti-fact explanation corresponding to the current node according to the graph perturbation operation searched in the iteration process. According to the invention, the generation efficiency of anti-fact explanation is improved.