48 results about "Schema graph" patented technology

The invention belongs to the technical field of compressor maintenance, and discloses a compressor predictive maintenance system and method based on multi-source data fusion, and the method comprises the steps: carrying out the heterogeneous data time-frequency feature analysis of compressor signal data, and generating a multi-dimensional feature spectrum; carrying out load-dependent fault signal separation to generate load decoupling fault feature data; performing environment interference factor elimination analysis to generate a pure fault feature matrix; performing cross-domain signal correlation mapping to generate a multi-source data fusion mode map; constructing a fault feature propagation link, and generating fault evolution path data; carrying out degradation trend prediction under a variable load condition, and generating fault development situation data; constructing a dynamic threshold self-adaptive early warning model, and generating fault early warning critical value data; health state comprehensive evaluation is carried out, a compressor state report is generated, and predictive maintenance implementation is executed; according to the invention, accurate recognition and prediction of the compressor fault are realized, and the equipment reliability and maintenance efficiency are improved.

The invention discloses a large model query generation system and method combining graph structure analysis and execution, and the system comprises a graph mode standardization module which converts a description file into a unified structured representation; the large language model module is used for screening out nodes, edges and sub-graph modes required by query statements according to the natural language problem and the structured representation of the user; the graph mode detection module is used for detecting and modifying the screened nodes, edges and sub-graph modes through scripts; the large language model module generates an initial query statement according to a natural language problem of a user and detected and modified nodes, edges and subgraph modes; the query statement detection module is used for detecting the initial query statement through a script and generating an error report; and the large language model module generates a modified query statement. According to the large model query generation system and method combining graph structure analysis and execution, the accuracy of graph mode selection is remarkably improved through the thinking ability of the large language model and the script error detection ability, and therefore the quality of graph database query statement generation is improved.

The invention relates to the technical field of medical treatment, and discloses a knee joint knee protection treatment personalized planning system based on multi-modal data fusion. A data acquisition and preprocessing module of the system acquires a multi-modal data stream of the knee joint of a patient in real time and generates a standardized data packet; the treatment event identification module is used for automatically identifying and verifying key treatment events from the standardized data packet and generating a treatment event sequence; the historical treatment path query module queries a distributed database and extracts treatment modes of similar patients; the graph matching and path generation module is used for calculating the similarity between the current treatment event sequence and a historical treatment mode, generating a fusion treatment path when the similarity exceeds a threshold value and storing the fusion treatment path to the block chain network; the abnormity monitoring and analysis module monitors abnormal event points in the fusion treatment path in real time, triggers a path decomposition mechanism and performs multi-dimensional performance analysis; and the personalized scheme generation module is used for dynamically adjusting treatment parameters and generating a personalized treatment planning scheme.

The invention relates to the technical field of query characterization, and discloses a database query feature vector generation method, which comprises the following steps of: obtaining a connection bitmap and a database mode graph of a database queried by a target query statement; determining a database mode sub-graph related to the target query statement according to the database mode graph; encoding the database mode sub-graph through a preset gating graph neural network to generate a mode item vector; and determining a query feature vector corresponding to the target query statement according to the connection bitmap and the mode item vector. According to the method, by obtaining the database mode graph and the connection bitmap, the structure association information between the table and the column in the database and the predicate condition features of the query can be synthesized, the semantic association represented by the features is enhanced based on the coding capability of the graph neural network, and the expression capability of the query feature vector for query intention and data association is effectively improved.

The invention provides a cooperative identification method and system for a power line communication signal and an arc fault, and the method comprises the steps: firstly, constructing and training a double-flow communication fault sensor comprising a sensing early-warning sub-module and an active sensing sub-module, then carrying out the communication fault sensing based on the double-flow communication fault sensor, generating a communication fault sensing data set, and transmitting the communication fault sensing data set to a server; then, a graph neural network is adopted to carry out fault mode recognition on the communication fault sensing data set, a communication fault mode graph is generated, then, communication fault positioning is carried out based on the communication fault mode graph, a communication fault positioning data set is generated, and finally, actual feedback data of the communication fault positioning data set is obtained. And the system is updated according to the actual feedback data, so that the fault diagnosis rate and accuracy are improved.

The invention relates to an edge calculation-based equipment fault diagnosis method and system for a battery swap station. The method comprises the following steps of: acquiring data and constructing a regional equipment model; deploying the regional equipment model at an edge computing node, and collecting operation time sequence data in real time; obtaining expected state data through the lightweight simulation model, and comparing the expected state data to generate a residual sequence; obtaining a causal violation event set through a lightweight causal graph model; generating a fault diagnosis result through the fault mode map; in conclusion, the regional equipment model comprising the lightweight causal graph model, the simulation model and the fault mode graph is constructed, real-time data acquisition and closed-loop correction are realized in combination with the edge computing nodes, and the dynamic threshold optimization and fault mode graph fusion mechanism is utilized. The problems that a traditional method is large in response delay, high in false alarm rate and insufficient in diagnosis precision are effectively solved, and the effects of improving fault diagnosis real-time performance, reducing cloud data transmission delay, improving diagnosis precision and reducing the false alarm rate are achieved.

The invention discloses a coarse-grained reconfigurable architecture cyclic mapping method and device based on a graph neural network, and relates to the technical field of computer reconfigurable computing. The method comprises the following steps: based on a priority prediction model, performing list scheduling according to a data flow diagram and hardware information to obtain a list scheduling table; performing modular scheduling according to the data flow diagram and the hardware information; pre-scheduling the data flow diagram on the basis of a list scheduling table according to the modular data flow diagram and the time extension coarse-grained array to obtain a time step attribute data flow diagram; obtaining a mapping result by using a mapping algorithm based on pattern diagram matching according to the time step attribute data flow diagram on the basis of the time extension coarse-grained array; based on the hardware information, processing unit isomorphism verification is carried out according to the mapping result; and compiling the verified mapping result to obtain an executable configuration information file. The invention provides an efficient and accurate coarse-grained reconfigurable architecture cyclic mapping method based on a graph neural network.

The invention relates to the technical field of data analysis, and discloses an electronic terminal MTBF analysis method and device based on an AI model, equipment and a medium, and the method comprises the steps: carrying out an MTBF test through a pre-trained AI test model according to a pre-constructed virtual parameter set, and obtaining an MTBF test report, identifying fault time distribution in the virtual parameter set according to the MTBF test report, identifying a reliable parameter set and a fault-prone parameter set in the virtual parameter set based on a preset threshold value, and constructing a fault mode map according to the virtual parameter set and the MTBF test report, and identifying a sensitive coefficient of each parameter type according to a virtual parameter set and the fault mode atlas based on parameter disturbance, generating a reliable parameter range according to the reliable parameter set, and generating a parameter adjustment suggestion list for each virtual parameter in the virtual parameter set in combination with the sensitive coefficient and the reliable parameter range. And the accuracy of the MTBF test is improved.

Aspects of the disclosed technology provide solutions for extracting subgraph patterns in graph-structured data and encoding them as embeddings using a graph neural network (GNN). In some aspects, a process of the disclosed technology can include steps for receiving an input graph comprising a plurality of nodes and edges, the input graph representing relationships among a plurality of entities, parameterizing a graph neural network model based on a set of pattern graphs, and identifying, for at least a portion of the nodes in the input graph, rooted homomorphisms between the pattern graphs and local subgraphs rooted at the respective nodes. Systems and machine-readable media are also provided.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for linking a database schema to a natural language query. In particular, in some embodiments, the disclosed systems determine, from tables in a database schema, a subset of tables relevant to a natural language query by comparing embeddings for the tables in the database schema and embeddings for the natural language query. Additionally, in some implementations, the disclosed systems select, from a schema graph comprising nodes that represent the tables in the database schema, an additional table along a path between a pair of nodes representing a pair of tables from the subset of tables. Moreover, in some embodiments, the disclosed systems determine a set of relevant tables by appending the additional table to the subset of tables. Furthermore, in some implementations, the disclosed systems generate, from the set of relevant tables, a response for the natural language query.

The present disclosure relates to a static graph mode graph generation system supporting dynamic resolution. The system comprises: an original logic graph construction component, for a first specific graph generation task, tracking original computing logic by executing a construction function of a graph to construct an original logic graph; a logic graph optimization component, forming a first logic graph for the constructed original logic graph; a node insertion component, inserting a communication node and a runtime control node required by distributed computing for the first logic graph to generate a first runtime logic graph for multi-device collaboration; a runtime plan generation component, generating a first runtime execution plan based on a data dependency relationship of the first runtime logic graph; and a runtime initialization component, initializing a first specific graph runtime instance according to the first runtime execution plan to complete dynamic allocation of computing resources and configuration of an execution environment.

The invention belongs to the technical field of medical information and artificial intelligence, and discloses a health trajectory prediction method and system based on double-branch collaborative learning. According to the sequence-graph double-branch collaborative architecture provided by the invention, the limitation of a single modeling view angle is fundamentally changed. The sequence branch focuses on mining a deep time sequence evolution mode in a multi-mode treatment sequence, and the graph branch depicts dynamic topological association among treatment events in a fine manner. The two are fused through a self-adaptive mechanism, high-quality dynamic representation capable of comprehensively reflecting time sequence evolution and structure correlation of a patient is generated, and a solid foundation is laid for accurate prediction.

The embodiment of the invention provides a dynamic graph mode matching method and device. The method comprises the following steps: acquiring an execution plan set for matching a pattern pattern, wherein the execution plan set is obtained by combining equivalent matching sequences in matching sequence groups of all isomorphic edge classes; wherein the isomorphic edge class comprises at least two isomorphic edges which are isomorphic to each other in the pattern pattern; the matching sequence group comprises a matching sequence set taking one isomorphic edge in the isomorphic edge class as a starting edge; a plurality of tasks are generated based on the update edge set and the execution plan set of the dynamic graph, and each task comprises an update edge and an execution plan in the execution plan set; a task load of the tasks is evaluated, and a plurality of tasks are allocated to a plurality of GPUs based on the task load for execution of the tasks by the GPUs using a depth-first search algorithm.

The application discloses a transaction behavior auditing method and device, equipment and medium, and relates to the technical field of financial information. The method comprises the following steps: determining a target violation mode graph based on a mode query instruction, and determining a time window and a matching search tree to determine target flow data based on the time window; constructing a data graph with transaction participants as vertices and transaction behaviors as edges, and determining edges to be matched; matching the edges to be matched with the target violation mode graph, and pruning and updating the matching search tree using a preset pruning strategy when there is a target matching edge to determine whether the current edge to be matched meets the time constraint condition of the target violation mode graph; determining whether a violation mode is detected based on the determination result, and jumping to the step of determining the edge to be matched until several edges of the data graph are matched. Thus, the problem of low auditing efficiency caused by the difficulty in identifying violation operations with time constraints due to the inability to capture complex time sequence relationships between transactions can be solved.

The invention discloses an immersive user experience method and system based on a virtual reality technology, and relates to the technical field of virtual reality, the system is composed of a plurality of functional modules, and the system comprises a map construction module which collects user behavior characteristics through multiple sensors and constructs a group cognition map model; the user behavior characteristics comprise interaction preferences, attention distribution and role behavior modes; the map layer generation module is used for calculating a focusing fixation point of a user node to a virtual scene based on the association strength of the user node and the virtual scene in the group cognition map model, performing labeling initialization and optimizing labeling attributes after effective focusing is judged, and generating a three-dimensional labeling map layer through multi-person collaborative editing; the interactive collaboration module introduces a visual view angle to output a rendering shared view angle on the basis of overlapping the focusing fixation point by expanding multi-dimensional feature fusion; and through the group cognition map model, obtaining the attention distribution weight of the user in the watching area, and outputting the position of a warning icon.

The invention discloses a method and system for extracting target data in a database, and the method comprises the following steps: collecting information in the database, and constructing a database mode graph structure; constructing an enhanced GW-OT model, and initializing a basic cost matrix and a marginal distribution set; in the structure consistency propagation unit, performing consistency propagation calculation; in the cost matrix construction unit, an enhanced cost matrix is constructed; in the optimal transmission solving unit, optimal transmission optimization calculation is executed; in the candidate data generation unit, cross-table and cross-mode matching alignment is carried out; and in the target data extraction unit, performing condition filtering and discrimination, and outputting target data. According to the method, the accuracy and consistency of database target data extraction are improved.

The invention discloses a database user abnormal behavior detection system, which relates to the technical field of database user abnormal behavior detection, and comprises the following steps: constructing a database table association mode graph, and determining a sensitive table set, a sensitive column set and a quasi-identifier combination based on a compliance list; extracting a query sequence and an access table set of the target session, determining a distance drop rate sequence, and calculating a coverage introduction rate in combination with the quasi-identifier combination; calculating sensitive entropy upper impulse based on the sensitive column data features; and constructing a multi-dimensional composite quantity, and judging the composite quantity. According to the method, the recognition capability and the safety protection level of progressive sensitive behaviors in database access can be improved.

This invention discloses a big data analysis-based method and system for art teaching management, relating to the field of educational information technology. The method includes: acquiring multi-source teaching big data for a target art teaching unit; constructing a pattern graph extraction unit and a teaching pattern graph based on the multi-source teaching big data; collecting stage-specific student assignment data and stage-specific student process data for the target art teaching unit according to preset stage window constraints; extracting pattern graphs from the stage-specific student assignment data through the pattern graph extraction unit to obtain a student pattern graph set; comparing the student pattern graph set with the teaching pattern graph; making evaluation decisions based on the comparison results; and implementing stage-specific management of the target art teaching unit based on the evaluation decision report. This invention solves the problems in existing teaching management where teacher evaluation is subjective and difficult to quantify, teaching evaluation lacks process analysis, and lacks the ability to deeply analyze the content of assignments themselves.

The application discloses a kind of efficient subgraph matching query methods across database system, utilize pattern graph cardinality constraint information to reduce the influence of vertex number and edge number increase on subgraph cardinality estimation precision;Utilize sparse sampling graph technology to improve the accuracy of query subgraph matching cardinality estimation with attribute filtering;Based on query subgraph cardinality estimation value calculation execution overhead, from bottom to top search optimal execution plan tree;On the basis of optimal execution plan tree, for different database system, the optimization query statement that can guide execution plan is generated, realizes efficient subgraph matching query across database system.The application solves the problem that existing method is difficult to realize the unified management and optimization across database, cannot give full play to the advantage of each database system, effectively improves the query efficiency when existing database system executes complex graph query, reduces query time.

The invention discloses a stable currency transaction anomaly detection method and system based on a graph neural network, and the method comprises the steps: determining the embedded representation of each node based on a view of each node in an original graph; obtaining a representation mode graph based on each node pair and the corresponding association weight in the original graph; obtaining a behavior pattern graph based on the behavior descriptor of the neighbor node of each node in the original graph; generating a target adjacency matrix based on the original graph, the representation mode graph and the behavior mode graph; and aggregating the embedded representation of each node in the target adjacent matrix by using a graph convolutional neural network model to obtain a final representation of each node. According to the method, manifold connectivity of account transaction data is recovered through a view of a node and a binary decision rule, and a long-range dependency relationship in a transaction network is captured by using a target adjacency matrix, so that the problems of poor model performance and difficulty in capturing a global mode caused by data manifold discontinuity in the prior art are effectively solved.

The application discloses a low-code platform form combined query condition implementation method and system, and belongs to the technical field of data processing. The application comprises the following steps: receiving and analyzing a multi-table combined query condition configured by a user through a visual interface, extracting a data table, a connection condition and a filter predicate; constructing a mode graph with tables as nodes and connection relationships as edges; performing semantic embedding on a character type predicate by using a word vector model, and generating a query feature vector in combination with the mode graph; estimating a query result cardinality by using a lightweight multi-set convolutional neural network; generating an optimized multi-table connection query plan by using reinforcement learning based on the cardinality estimation value and a table importance score; executing the query and monitoring actual performance, feeding an error between a real cardinality and an estimated cardinality back to an adaptive learning module, and incrementally training and closed-loop optimizing a cardinality estimation model, so as to improve the accuracy and execution efficiency of a low-code platform in processing complex combined queries.

Embodiments of the present specification provide a method and device for locating an abnormal root cause of a target system. In the locating method, a service call graph of the target system is obtained. For any first microservice call, a corresponding first call pattern graph is determined, the first microservice call corresponding to a call directed to a first node through a first edge in the service call graph, the first call pattern graph including a predecessor node and a first-order child node of the first node. A target call pattern graph matching the first call pattern graph is determined from each known call pattern graph. Based on a historical execution time distribution of the target call pattern graph, an estimated execution time of the first microservice call is obtained. According to a difference between the estimated execution time and an actual observed execution time of the first microservice call, a first abnormal score of the first microservice call is determined. According to each abnormal score of each microservice call in the service call graph, an abnormal root cause of the target system is determined.

The application discloses a motor fault diagnosis method and system based on a color image fusion symmetry point mode, and the method comprises the following steps: converting vibration signals and electromagnetic signals of a motor into symmetry point mode images respectively, and generating color signal images with fusion feature information; abstracting the color signal images with fusion feature information into nodes and edges in a high-dimensional semantic space respectively, so as to construct graph structure data; using respective capsule graph network models to diagnose and classify the graph structure data of the vibration signals and the electromagnetic signals respectively, and fusing the diagnosis and classification results of the vibration signals and the electromagnetic signals through a voting mechanism to obtain a final diagnosis and classification result. The application aims to convert multi-channel time domain signals into information-intensive and geometric-consistent image expressions, and to realize a new scheme of uniformly modeling features of the images based on a deep graph structure network with topological perception capability, so as to realize motor fault diagnosis with high diagnosis precision and strong robustness.

The application discloses a multi-source time sequence data-oriented abnormal co-debt correlation pattern graph learning and identification system, relates to the technical field of financial data processing, and comprises a data acquisition and preprocessing module, an evidence synchronization construction correlation module, an abnormal co-debt discrimination prediction module and a pattern subgraph learning and identification module.The data acquisition and preprocessing module is used for acquiring transaction debt data and performing preprocessing on the transaction debt data.The evidence synchronization construction correlation module is used for calculating synchronization values between events, screening relevant events and generating an event time sequence correlation graph.The abnormal co-debt discrimination prediction module is used for discriminating abnormal co-debt events, outputting abnormal co-debt prediction values and generating abnormal labels, and writing the abnormal co-debt prediction values and the abnormal labels into the event time sequence correlation graph.The pattern subgraph learning and identification module is used for generating real-time pattern embedding vectors, performing similarity retrieval and edge evidence consistency verification, and outputting abnormal co-debt correlation pattern graph data.The application solves the problem that, in the prior art, the synchronization between events and the transmission quality are not fully considered, resulting in poor accuracy and real-time performance of abnormal co-debt event identification.

Time-series information recorded in log files is converted into a pattern image, and image recognition is used on that pattern image to estimate which module contains the bug from among multiple modules. [Solution] The information processing system 1 includes an acquisition unit 40 that acquires a log file 30 recorded when a software 20 including multiple modules 21 is executed, an image generation unit 50 that generates a pattern image 70 corresponding to the log file 30 by individually assigning a color to each module 21 recorded in chronological order in the log file 30, and an estimation unit 60 that estimates which module 21 has a bug from among the multiple modules 21 based on the pattern image 70.

The application provides an artificial intelligence-based patient screening method and system in clinical research, relates to the technical field of artificial intelligence, and comprises the following steps: obtaining a clinical research enrollment standard text and performing semantic analysis to extract a rule tuple, and converting the rule tuple into a standard schema graph; selecting a disease node in a medical knowledge graph to perform extension matching to obtain an adaptive patient subgraph; performing graph structure alignment to identify a matching substructure, a missing item and a conflict item; and finally calculating the adaptability of a patient to a research project and generating a matching result. The application improves the patient screening efficiency and accuracy, and reduces the clinical trial recruitment cost.

The application provides a graph retrieval method and device, electronic equipment and a storage medium. The method comprises: obtaining question information requiring answer search; determining candidate entities corresponding to the question information and entity categories of the candidate entities; querying a candidate path containing at least one target node in a mode graph according to the entity categories, wherein the target node is a node used to indicate the entity category; generating a target query graph according to all candidate paths and candidate entities corresponding to the entity categories, wherein all target nodes are included in the target query graph; obtaining a query language by parsing the target query graph, and querying an answer corresponding to the question information based on the query language. The method in the embodiment overcomes the technical problems of high cost and poor generalization performance caused by manually defining templates for graph retrieval in the related art.

The application discloses a database user abnormal behavior detection system, and relates to the technical field of database user abnormal behavior detection, and comprises the following steps: constructing a database table association mode graph, determining a sensitive table set, a sensitive column set and a quasi-identifier combination based on a compliance checklist; extracting a query sequence and an access table set of a target session, determining a distance drop rate sequence, and calculating a coverage introduction rate in combination with the quasi-identifier combination; calculating a sensitive entropy impulse based on sensitive column data characteristics; constructing a multi-dimensional composite quantity, and determining the composite quantity. The application can improve the recognition ability and the security protection level of progressive sensitive behavior in database access.

The invention discloses a shield cutter multi-mode damage real-time early warning system based on Internet of Things data, and relates to the technical field of engineering machinery intelligent monitoring and early warning, and the system comprises a data synchronization and time calibration module which receives multi-source asynchronous data from vibration, pressure and temperature sensors, carries out the time scale unified calibration processing of original data, and carries out the real-time early warning of the multi-mode damage of a shield cutter; and generating a time synchronization data set according to the signal response characteristics. According to the method, through time synchronization and calibration processing, the problem of space-time asynchronism during multi-source asynchronous data fusion is solved, and the consistency of data time scales is improved. And through real-time updating of the dynamic damage mode pattern, the system can adapt to the change of the working state of the cutter, and the accuracy of damage mode recognition is improved. Damage classification and prediction rules are dynamically adjusted, the system is allowed to analyze the evolution rule of tool damage according to real-time data, and the reliability of classification and prediction is enhanced.

This invention discloses a data security detection method and system based on big data analysis, comprising the following steps: Step 1: Preprocessing the raw data to be processed and dividing it into multiple temporal subsets; Step 2: Constructing a temporal behavior pattern graph based on the temporal data subsets; Step 3: Extracting behavioral features of nodes from the temporal behavior pattern graph and performing clustering; Step 4: Inputting the attack pattern feature vector set into an improved GATv2 model for propagation analysis to obtain the attack pattern propagation path; Step 5: Updating the attack pattern library based on the attack pattern propagation path; Step 6: Generating different response tasks for different attack patterns and generating a task response strategy set; Step 7: Selecting the corresponding task response strategy to execute the defense task and generating a set of defense response results after execution. This invention achieves efficient and accurate network attack identification and protection through the improved GATv2 model.