321 results about "Relational graph" patented technology

The invention belongs to the field of natural language processing, and provides a task complexity driven graph semantic multi-agent collaborative decision-making method and system.The task complexity driven graph semantic multi-agent collaborative decision-making method comprises the steps that a task text is obtained and subjected to semantic coding to obtain a task semantic vector, evaluation is conducted based on the task semantic vector to obtain a complexity vector, and a task complexity score of the complexity vector is calculated; the task semantic vector and the complexity vector are fused to obtain a task representation vector, an agent capability relation graph is constructed, the participation probability of each agent node is obtained according to the task representation vector and the agent capability relation graph, and a dynamic agent combination scheme is formed; and performing task decomposition according to the agent combination scheme, constructing a sub-task dependency graph, scheduling the execution sequence of the sub-tasks through topological sorting, realizing cooperative execution of the agents, and generating a task result. According to the method, precise matching and efficient cooperation of the agent combination are realized, and the capability of processing complex tasks and the resource utilization efficiency of the multi-agent system are remarkably improved.

The invention discloses an energy consumption model intelligent construction system and method based on artificial intelligence, and relates to the technical field of artificial intelligence, and the system comprises an Internet of Things multi-source data collection module, a data cleaning and space-time calibration module, a multi-source data semantic fusion module, a dynamic energy consumption relation graph construction module, an intelligent decision engine module and an edge-cloud collaborative deployment module. According to the method, multi-source data are fused through a Transform multi-head self-attention mechanism, a dynamic energy consumption relation graph is constructed by using a graph neural network, and dynamic modeling and intelligent regulation and control of energy consumption are realized in combination with an edge-cloud hierarchical decision architecture; the method comprises the steps of data acquisition and standardization, cleaning calibration, semantic fusion, graph modeling, hierarchical decision making and collaborative execution. According to the method, the problems of insufficient data integration and model staticization of a traditional system are solved, the accuracy, real-time performance and global optimization capability of energy consumption management are improved, the method is suitable for scenes such as intelligent buildings, the energy efficiency is remarkably improved, and the data security is guaranteed.

The invention relates to a water quality prediction method and system based on gating residual enhancement and feature fusion, and belongs to the technical field of water environment intelligent analysis and deep learning. Taking each water quality index as a node of the graph, and constructing two complementary variable relation graph structures by utilizing a Pearson's correlation coefficient and mutual information; respectively inputting the two graph structures into a graph convolutional network, extracting deep dependency features among indexes, and splicing and fusing the deep dependency features. A multi-head attention mechanism is used as a trunk to extract global time dependence, a GRU network is introduced to extract local time sequence features, GRU output is used as an adjustable residual term to be injected into the attention trunk through a residual gating mechanism, self-adaptive enhancement of local dynamic features is achieved, and finally a self-adaptive fusion mechanism is introduced to generate comprehensive representation. According to the method, the complex dependency relationship between the water quality indexes and the time dynamic evolution process can be modeled in a collaborative manner, the response capability to key local change and sudden change events is remarkably enhanced, and the accuracy and robustness of water quality prediction are improved.

The invention relates to the technical field of database management, in particular to a two-way linkage database table and supervision submission form field synchronous construction method which is applied to a supervision data submission scene. According to the scheme, the method comprises the steps that physical structure metadata of a database table and definition metadata of a supervision submission form are obtained, a semantic vector set is formed by combining metadata semantic analysis and semantic coding, and a bidirectional mapping relation graph is constructed through relation weaving; constructing a linkage propagation path according to a field change event, realizing adaptive bidirectional structure adjustment, generating a field synchronous construction scheme, and driving bidirectional mapping relation graph evolution optimization through feedback learning; according to the method, the bidirectional mapping relation graph and self-adaptive bidirectional structure adjustment are creatively combined, and efficient and self-adaptive bidirectional field synchronous construction is realized.

The invention discloses an agent inference system based on multiple maps, and relates to the technical field of artificial intelligence, and the system comprises the steps: based on industry report, academic literature and business manual multi-source data, extracting entity-relationship-attribute, and constructing a knowledge map; dynamically capturing cooperation, report and task allocation relationships among entities, and constructing a production relationship graph; constructing a decision graph based on a field expert heuristic rule; based on the thinking engineering theory, human thinking modes and emotional states are analyzed, and a thinking map is constructed; integrating a knowledge graph, a production relation graph, a decision graph and a thinking graph, performing entity and mode alignment, mapping multi-source nodes and edges into the same vector space, introducing conflict resolution, and constructing a unified multi-mode heterogeneous knowledge graph; and guiding a large language model to generate a reasoning direction through path cue word injection, querying a constraint reasoning boundary, outputting an optimal reasoning result, and realizing agent reasoning of multiple maps. The method has the beneficial effect that the reasoning accuracy is improved.

The invention discloses a multi-priori knowledge fused cerebral apoplexy rehabilitation map convolutional network evaluation method, and belongs to the technical field of cerebral apoplexy rehabilitation evaluation. Firstly, a high-precision prior information matrix is automatically generated through a collaborative and causal relationship automatic reasoning method based on Riemannian manifold geometry and transfer entropy, the problem that in the prior art, engineering depends on artificial features is effectively solved, and interpretable physical prior guidance is provided for a model. Then, through a multi-relation graph construction and attention weighting multi-modal adaptive fusion method, spatio-temporal features and priori knowledge are adaptively fused, an optimized graph structure is constructed, and the representation ability and interpretability of the model to complex joint interaction are improved. And finally, through a space-time diagram convolutional network guided by prior information and a comparative learning collaborative optimization method, the generalization ability and evaluation precision of the model in a small sample scene are remarkably improved through data enhancement and loss function optimization.

The invention relates to the technical field of knowledge maps, and discloses a knowledge map completion method based on semantic-structure multi-level fusion. According to the technical scheme, for a training set positive example triple, a structured rationality score and a semantic correlation score are fused to screen high-quality training samples; in order to complement a query search relation path, screening a guiding path through a uniqueness index; integrating the query text, the entity description, the neighbor facts and the guide path to construct an enhanced input prompt; dynamic structure embedding is generated by using a relational graph convolutional network, the dynamic structure is mapped by an adapter module and then injected into a large language model, and completion prediction is completed by combining a fusion input fine tuning model. According to the method, through multi-level fusion of semantics and structural information, intelligent sample screening, prompt enhancement construction and structural information dynamic injection are achieved, the accuracy, reasoning ability and training efficiency of a large language model in a knowledge graph completion task are effectively improved, and the method is especially excellent in performance in complex relation reasoning and inductive completion scenes.

The invention discloses an operation and maintenance workflow cooperation system and method, and relates to the technical field of business process.The method comprises the steps that after a natural language operation and maintenance requirement is received, a subtask set containing task attributes is extracted through a semantic model built based on a pre-training operation and maintenance field language model; inputting the sub-tasks into a causal mining model, capturing an implicit dependency relationship between the tasks through an attention mechanism which takes task types and resource demands as weight regulation factors, and generating an operation and maintenance relationship graph which contains dependency confidence coefficients and dependency types and does not have cyclic conflicts; splitting the atlas into a task chain set and a free task point set by adopting a causal-oriented greedy pruning algorithm based on a directed edge association subtask maximum aggregation and task chain set scale minimization principle; task chains are distributed through a weighted matching algorithm in combination with the chain overlap ratio and the to-be-handled task amount of the intelligent agent, remaining free task points are distributed according to the balance principle, and accurate disassembly and efficient cooperation of operation and maintenance tasks are achieved.

The invention relates to the technical field of data intelligent traceability, and discloses a whole-process business data intelligent traceability method and system. The method comprises the following steps: collecting first data of a preset business link and creating a semantic tag to form a semantic tag data set; the semantic tag data set is stored in a Merkle tree structure, and a service block chain is constructed; extracting second data of each business link from the business block chain, and generating a fusion data set based on the second data; and establishing a relation graph according to the fused data set, after receiving a traceability query request, starting to search a traceability path from a starting service point in the relation graph, and outputting a traceability result, thereby ensuring the reliability of the traceability result, and solving the problems of serious data island, missing association relationship and low traceability precision of the traditional traceability technology.

The invention discloses a financial knowledge graph construction method and system based on artificial intelligence, and relates to the field of artificial intelligence data processing. The method comprises the following steps: performing multi-dimensional semantic analysis on a heterogeneous financial data source, and extracting a structured semantic fragment; constructing a financial entity perception unit, identifying a multi-granularity entity and generating a unique code; generating a preliminary relation graph based on the event cascade relation and the attachment structure, and injecting a semantic translation label; normalizing the atlas relationship through semantic separation and a label reconstruction mechanism to form a financial relationship network with consistent semantics; executing evolution increment iteration in combination with the newly added corpus, and dynamically updating nodes and edge sets; and performing semantic consistency and structural integrity evaluation on an iteration result, and outputting a stable financial knowledge graph structural body. By introducing a multi-factor semantic analysis model, a causal relationship modeling mechanism and a graph evolution iteration strategy, systematic improvement of the financial knowledge graph in the aspects of structural expression precision, semantic reasoning ability and dynamic adaptability is achieved.

The invention discloses a scientific and technological achievement analysis and prediction method and system based on big data, and relates to the technical field of machine learning and big data analysis, and the method comprises the steps: collecting and preprocessing multi-source scientific and technological achievement semantic data, and constructing a scientific and technological concept relation graph; the method comprises the following steps: performing training by taking a time sequence diagram convolutional network as a basic framework and taking a scientific and technological concept relation graph as a training sample, constructing a dynamic knowledge flow semantic model, performing evolution feature extraction on the scientific and technological concept relation graph by utilizing the dynamic knowledge flow semantic model, and outputting a knowledge flow feature vector; and inputting the causal enhanced space-time diagram into a space-time diagram neural network, aggregating semantic association and causal relationships among the scientific and technological achievements in a space dimension, capturing a dynamic change mode of scientific and technological achievement characteristics in a time dimension, and outputting a scientific and technological concept time sequence predicted value sequence. According to the method, the causal enhancement space-time diagram is constructed, so that trend deduction and causal traceability analysis are carried out for the time dimension, and the accuracy of scientific and technological achievement development trend prediction is improved.

The invention discloses a graph structure-based domain penetration attack path generation method, which belongs to the technical field of network security, and comprises the following steps of: constructing a multi-level relation graph comprising a host node, a service node and a user node through automatic detection by taking any host in a domain as a starting point; assigning a weight attribute to the atlas edge based on a vulnerability library and an attack pattern library; an improved heuristic graph search algorithm is adopted, all feasible attack paths and threat scores thereof are generated by integrating the path length, attack difficulty and permission improvement effect, and the problem that the threat scores of all the feasible attack paths are influenced in various scenes and dynamic change domain environments is solved. The technical problems of realizing comprehensive automatic penetration testing, accurately identifying potential attack paths and establishing a systematic threat assessment mechanism are solved, the automation, intelligence and high-efficiency level of domain penetration testing is remarkably improved, and the method has good adaptability, expansibility and practical value and is suitable for popularization and application. And attack path discovery and risk early warning work in a dynamic network environment with high security requirements can be effectively supported.

The invention discloses a signaling road network matching method based on a heterogeneous graph convolutional network and an attention mechanism, and belongs to the crossing field of mobile communication and intelligent traffic technologies. According to the method, a multi-relation graph containing a road segment set and a base station sampling point set is constructed, wherein an edge set covers associated edges of base station sampling points and road segments, topological edges of the road segments and time edges of the base station sampling points; performing feature extraction on the multi-relation graph by adopting a 3-5-layer heterogeneous graph convolutional network to obtain low-dimensional vector representation of nodes; dynamically calculating an observation probability and a transition probability based on an attention mechanism; and performing path search by using an improved Viterbi algorithm in combination with a backtracking mechanism and a pruning strategy, and outputting a path with the highest score as a matching result. The method can effectively solve the problems of poor adaptability to complex road networks, insufficient robustness to high positioning error signaling data, low precision caused by matching probability immobilization and the like in the prior art, significantly improves the accuracy and robustness of signaling road network matching in complex scenes, and can be applied to the fields of traffic flow monitoring, travel mode analysis, urban planning and the like.

The invention discloses an intelligent manufacturing feeding method and system fusing three-dimensional vision and robot cooperation, and belongs to the technical field of robot automation control. Three-dimensional point cloud data in a feeding area is acquired, and a workpiece space topological relation graph is constructed through adaptive clustering and boundary extraction; generating a task association tensor in combination with the physical attribute of the workpiece and a scheduling rule; the method comprises the following steps: collecting operation state information of a plurality of industrial robots, and constructing an action capability tensor; based on the task association tensor and the action capability tensor, an optimal task allocation matrix is generated through a multi-target collaborative optimization algorithm, and dynamic matching between the robot and the workpiece is achieved; in combination with real-time point cloud feedback, the initial path is corrected and the tail end is adjusted, and the robot is driven to accurately execute a grabbing task; the intelligent feeding system has the advantages of being high in environment adaptability, high in dispatching intelligence degree, excellent in path control precision and the like, and is suitable for high-dynamic complex manufacturing scenes.

The invention discloses an industrial design-oriented drawing semantic analysis and structured conversion method and system. The method comprises the following steps: acquiring an input image, carrying out different-scale coding on image features through an image coding backbone network, selecting a specific layer to extract a multi-scale feature map, and fusing to generate multi-scale features; inputting the multi-scale features into a geometric primitive detection network and an other element detection network, respectively identifying geometric primitives and non-geometric primitives, and carrying out positioning and classification; analyzing mutual relations between geometric elements and other elements contained in the graph through an element relation graph network, and generating formalized language description based on a matching rule; a Prompt template is constructed, geometric information is supplemented by using a multi-modal large model, the rationality of the supplemented information is verified through a geometric attribute relationship verifier, and complete formalized language description is obtained. According to the method, the industrial drawing image containing the complex constraint relation can be efficiently and accurately converted into machine-readable structured data.

The invention provides a product recommendation method and device, electronic equipment, a medium and a program product, and can be applied to the technical field of artificial intelligence, the technical field of big data, the technical field of block chains and the technical field of privacy computing. The method comprises the following steps: acquiring structured data and unstructured data of a target user; extracting preference features based on the unstructured data, and obtaining a time sequence preference portrait by using a time sequence attention model; obtaining a target user portrait based on the time sequence preference portrait and the structured data; obtaining a multi-dimensional sequential relation graph, and reasoning the multi-dimensional sequential relation graph by using a graph neural network to obtain relation representation; executing collaborative filtering scoring based on the target user portrait and the relationship representation, and generating an intermediate product score; taking the generated intermediate product score as an input, and outputting a candidate product score based on a recommendation optimization function containing a space-time weight; and performing product recommendation based on the candidate product scores.

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 relates to the technical field of price monitoring, in particular to a building material multi-source price anomaly detection method, which comprises the following steps: firstly, uniformly metering and pricing calibers, learning a conversion coefficient, and constructing a replaceable relation graph; multi-source distribution is aligned through optimal transmission, residual errors and shadow prices are obtained based on structure invariants and variational inequality, and abnormal evidences are formed through hypergraph propagation and persistent coherence; generating a valence band reference in combination with a convex hull method and distribution robust optimization under the equilibrium clearing of graph regularization; a feasible set is defined by price bands and constraints, a weighted maximum satisfactory model is constructed to position a minimum default set, a minimum correction suggestion is generated by using vector optimal transmission and packet sparsity, and an executable closed loop is realized through satisfactory model theory verification.

System and method for aspect-level sentiment classification. The system includes a computing device, the computing device has a processer and a storage device storing computer executable code. The computer executable code is configured to: receive an aspect term-sentence pair; embed the aspect term-sentence pair; parse the sentence using multiple parsers to obtain dependency trees, and perform edge union to obtain a merged graph; combine the embedding and the merged graph to obtain a relation graph; perform a relation graph neural network on the relation graph; extract hidden representation of the aspect term from updated relation neural network; and classify the aspect term based on the extracted representation to obtain a predicted classification label of the aspect term. During training, the computer executable code is further configured to calculate a loss function based on the predicted label and the ground truth label, and adjust parameters of models.

The invention discloses an Internet of Things terminal anomaly clustering method based on a graph neural network. The Internet of Things terminal anomaly clustering method comprises the following steps that data are collected and preprocessed to form time series data; constructing an Internet of Things terminal relation graph, and representing time sequence correlation, spatial proximity and protocol layer interaction characteristics by using a multi-scale edge weight factor; weighting by using a feature attention mechanism to generate an abnormal sensitive input vector; performing time modeling by adopting phase-aligned expansion causal time sequence convolution to obtain time channel representation; an edge-time double-attention coupling mechanism is introduced into the improved ST-GNN model, edge attention and time attention are calculated, and an embedded vector is generated; executing density clustering and correcting isolated nodes and small-scale clusters through semi-supervised label propagation to obtain a correction result; and carrying out dynamic evolution analysis, identifying growing, merging and disappearing abnormal clusters, and feeding back to optimize modeling. According to the invention, the precision and robustness of Internet of Things anomaly detection are improved, and the method is suitable for large-scale Internet of Things safety monitoring.

The invention discloses a joint object attitude generation method based on physical perception and graph diffusion, and relates to the field of computer vision. The method comprises the following steps: firstly, reconstructing componentized geometric and physical attributes of an object from an image through a double-branch neural implicit network, and initializing a component connection relationship; then, a component relation graph is refined through kinematics fitting and time sequence consistency test, and attitude prior distribution of the component on the SE (3) manifold is inferred by utilizing a physically enhanced graph diffusion process; and finally, constructing a conditional diffusion model on the SE (3) manifold under the condition of prior and reconstruction information, and performing reverse sampling through a physically guided two-step reverse sampling framework to generate a group of diversified and physically reasonable joint object attitude hypothesis sets. According to the method, efficient generation of diversified and high-physical-rationality postures of a joint object is realized through tight coupling of physical rules and data driving generation.

The invention discloses a zero-sample composite fault diagnosis method based on semantic graph embedding and multi-stage fusion, and the method comprises the following steps: firstly, generating an initial semantic descriptor through manual definition or statistical features according to known fault types, and constructing a fault relation structure diagram to represent the association between types; combining reconstruction, semantic comparison and propagation loss by using a graph convolutional network, and fusing the initial semantics and the relation graph to generate enhanced semantic features; meanwhile, a multi-modal model is constructed to extract vibration, temperature, acoustics and other signal features, and after multi-stage fusion of input-stage cross-modal attention, feature-stage Transform and output-stage semantic alignment, a semantic feature supervision training network is jointly enhanced; and finally, extracting unseen fault features in a zero sample scene and carrying out classified diagnosis. According to the method, through a multi-modal fusion and semantic enhancement strategy, the precision and generalization ability of zero-sample composite fault diagnosis are remarkably improved.

The invention discloses a multi-platform log anomaly recognition and analysis method based on self-supervised learning, which comprises the following steps: collecting and preprocessing multi-platform data, constructing a call chain relation graph, and generating a standardized log sequence; carrying out conversational splicing to generate a conversational log sequence; inputting the conversational log sequence into an extended LogBERT model, and generating a multi-granularity log representation vector; cross-platform domain alignment processing is carried out through adversarial training based on gradient inversion and cross-platform comparison alignment, and cross-platform log representation in the unified embedding space is generated; an abnormal detection result is determined by adopting a one-class classification detection method and confidence interval calibration; generating a root cause analysis result by utilizing a graph attention mechanism and time sequence correlation analysis; and generating an exception report according to an exception detection result and a root cause analysis result. According to the method, the accuracy of multi-platform log anomaly detection and the precision of root cause analysis are improved, and the method is suitable for cross-platform operation and maintenance and monitoring scenes.

The invention discloses an event entity prediction method based on perceptual contrast learning, which comprises the following steps: obtaining a time sequence knowledge graph of all events, each event being represented by a subject, a relationship, an object and a time tetrad; performing reverse operation on the tetrad to obtain a reverse tetrad, and respectively constructing a global historical graph and a local historical graph for all events in any day; then constructing an event entity prediction model, and inputting the global historical graph and the local historical graph into the event entity prediction model to train the model; and finally, predicting the event entity by using the trained event entity prediction model. According to the method, the accuracy of event entity prediction is improved through multi-tense dynamic embedding and relational graph comparative learning, the influence of time on event evolution can be more accurately captured by adopting the multi-tense dynamic embedding, and a dynamic context is provided for local historical node feature updating; the problem that the flexibility is insufficient due to the fact that event prediction excessively depends on recent events is solved.

The invention belongs to the technical field of computer big data, and discloses a relation graph construction and layout method, device and system based on spectral clustering and a storage medium, a clustering center is initialized through a genetic algorithm, the clustering center serves as genetic information and is coded into a character string, the operation time can be shortened, and the classification precision can be improved; furthermore, a weighted Euclidean distance is constructed as a distance function of a K-means algorithm, mutual relation weighting between the features can be reflected, features of different weights are counted into the distance, the classification precision can be effectively improved, the loss is reduced, and the classification efficiency is improved. According to the method, an initial similarity matrix, obtained through a traditional similarity calculation method, between XML documents is corrected through an affinity propagation algorithm, the similarity between the hidden similar XML documents can be reflected, on the basis, the correct clustering number and the correct clustering result are obtained by applying a multi-path spectral clustering method NJW, the method is irrelevant to the sequence of the XML documents, and the method has the advantages of being high in practicability and easy to popularize. The method is suitable for clustering the retrieval results of the XML documents arranged in any sequence.

In some embodiments, the present disclosure provides an exemplary technically improved computer-based method utilizing an element level mapping module that determines a correlative relationships of interconnected nodes and edges in relation to an output data element and an input data element, and determines an appropriate graph of the relationships.

The invention discloses a radar working mode recognition method and system based on a graph neural network, and relates to the related technical field of radar signal processing, and the method comprises the steps: carrying out the pulse arrival time detection of a received radar signal, and constructing a pulse parameter set; extracting a multi-dimensional pulse feature vector; constructing a pulse parameter similarity graph; inputting into a multi-relation graph neural network to execute graph feature aggregation, and outputting a high-dimensional embedding representation of each pulse node; dividing the high-dimensional embedding representation into N time sub-blocks based on a pulse time sequence, and executing weighted aggregation of graph embedding pooling results; and performing confidence identification according to global pulse sequence embedding, and outputting a working mode identification result. The technical problems of insufficient multi-mode radar signal classification precision and poor working mode recognition accuracy caused by difficulty in effective modeling of inter-pulse relevance and insufficient anti-interference capability in the prior art are solved, and the technical effect of improving the multi-mode radar signal classification precision and the working mode recognition accuracy is achieved.

The invention relates to the technical field of computers, and discloses a visual operation and maintenance supervision platform based on a digital twinning technology, and the platform comprises a data preprocessing module which is used for preprocessing a multi-source heterogeneous monitoring data flow; the data fusion module is used for constructing an equipment topological relation graph, extracting an initial feature vector set of equipment nodes and carrying out space-time fusion; the digital twin modeling module is used for performing parametric modeling and semantic annotation; the real-time synchronization module is used for extracting a model updating instruction set and carrying out asynchronous message transmission and parallel updating; the anomaly detection module is used for training an anomaly detection model and analyzing anomaly types and anomaly reasons; the root cause tracing module is used for constructing an associated sub-graph of abnormal equipment, predicting a root cause candidate equipment list and carrying out hybrid reasoning and evidence fusion; the visual presentation module is used for visualizing a fault influence range, dynamically displaying network traffic and performing multi-view roaming and information drilling; according to the invention, the fault positioning efficiency and the operation and maintenance management level are improved.

The invention discloses an industrial element association reasoning method based on a space-time knowledge graph. The method comprises the following steps: S1, encoding space-time characteristics of industrial association information; s2, constructing a spatio-temporal enhanced relation graph; s3, topological feature representation under the space-time condition; and S4, performing entity-level dynamic reasoning on the industrial elements. According to the method, the structural generalization ability of the pre-trained knowledge graph basic model is fused, a spatial-temporal feature coding mechanism is introduced, dynamic reasoning of industrial evolution paths in different regions and different periods is realized by constructing the relation graph with spatial-temporal attributes, and the method can be widely applied to scenes such as urban industrial planning, regional economic evaluation and industrial chain optimization and has a wide application prospect. The intelligent level of government decision-making and the efficiency of enterprise strategy deployment are improved, and accurate decision-making support is provided for industrial analysis, policy making and the like.

The invention discloses a graph theory analysis-based power grid project supplier equity relationship intelligent identification method. The method comprises the following steps of S1, collecting supplier relationship data; s2, stock equity structure features and transaction features of suppliers in the standardized data set are extracted; s3, constructing a supplier equity relation graph, traversing the supplier equity relation graph by adopting a depth-first search algorithm and a breadth-first search algorithm, and calculating a control path length and an equity control weight; s4, optimizing the stock right control relation identification of the suppliers by using an improved Grapporter network, and predicting the stock right control relation strength between the suppliers; s5, calculating the risk scores of the suppliers, setting a threshold value according to the risk scores, and classifying the risk levels of the suppliers; and S6, an incremental learning method is adopted to optimize the Grapher network. According to the method, graph theory analysis and Grapher network improvement are combined, the supplier stock equity control relation is intelligently identified, the risk is dynamically evaluated, and the method has the advantages of being accurate in identification, high in adaptability and high in real-time performance.