106 results about "Graph match" patented technology

The invention provides a deep learning-based dynamic early warning analysis method for disseminated intravascular coagulation, which comprises the following steps of: acquiring and structuring a core physiological mechanism knowledge graph of the disseminated intravascular coagulation, preprocessing standardized and clean multi-modal clinical time sequence data, generating a physiological consistency constraint vector by utilizing a dynamic graph matching and graph embedding technology, and performing dynamic early warning analysis on the disseminated intravascular coagulation core physiological mechanism knowledge graph. Inputting the medical logic correlation variable into a time sequence encoder fused with a knowledge graph attention mechanism to realize efficient modeling of the medical logic correlation variable; anti-factual reasoning is carried out based on model output, the risk of dispersive intravascular coagulation of a patient is dynamically scored, a grading early warning signal is triggered, and clinical decision making is supported; the model can update the knowledge graph and optimize parameters according to new cases and clinical feedback increments, the adaptability and generalization are enhanced, and the accuracy, early warning ability and medical interpretation of risk assessment of disseminated intravascular coagulation are effectively improved.

The invention discloses a timing sequence statement positioning model training method and device based on proposal selection and anchor point distribution, and relates to the technical field of timing sequence statement positioning. The method comprises the following steps: initializing a fixed number of learnable queries according to a first static anchor point set; on the basis of the first learnable query, according to the unpruned long video and the natural language description text, performing proposal generation through a time sequence statement positioning model; based on the proposal selection module, redundant proposal filtering is carried out by using a non-maximum suppression algorithm; based on an anchor point distribution module, according to the untrimmed long video, the first static anchor point set and the candidate proposal set, performing bipartite graph matching by using a Hungary algorithm; and carrying out loss function weighting calculation according to the unpruned long video and the optimal proposal set, and carrying out parameter optimization on the time sequence statement positioning model to obtain an optimized time sequence statement positioning model. The method is an efficient and accurate time sequence statement positioning model training method combining proposal selection and anchor point distribution.

Systems and methods for entity linking using a graph neural network are disclosed. In one aspect, a method for entity linking can include extracting a first attribute set of an unknown entity from an information source and retrieving second attribute sets of known entities from a database, wherein each of the second attribute sets corresponds to one of the known entities. The method can further include generating an unknown entity graph based on the first attribute set, generating known entity graphs based on the second attribute sets, generating an unknown entity graph embedding by applying the unknown entity graph to a graph neural network, and generating known entity graph embeddings by applying the known entity graphs to the graph neural network. The method can further include assigning the information source to one of the known entities based on the unknown entity graph embedding and the known entity graph embeddings.

The invention discloses a sheet metal part manufacturability reasoning method based on space-semantic map alignment, and relates to the field of manufacturing-oriented design evaluation and industrial knowledge reasoning, and the method comprises the following steps: carrying out geometric analysis on a CAD geometric model of a sheet metal part to be evaluated; abstracting the geometric features and the topological / metric spatial relationship thereof into a computable spatial semantic graph; performing semantic analysis on the process specification described by a natural language, converting the process rule into formalized logic check expression by using a large language model through context learning, and generating an executable domain-specific language check script; executing the script on a spatial semantic graph, realizing deterministic reasoning through graph matching and attribute verification, and completing accurate mapping and violation detection of text rules and geometric features; and outputting an interpretable diagnosis result containing violation feature positioning, triggering rules and numerical evidence. In order to solve the problems that a process rule'natural language-geometric model 'has a semantic gap, a traditional rule system is poor in adaptability, and an end-to-end learning method is high in data dependence and cannot be explained, a new rule can be quickly adapted under the condition that a large amount of data does not need to be labeled and a model does not need to be retrained; the method can accurately identify the violation of the micro-size and spatial relationship, and has reasoning preciseness, interpretability and engineering availability.

The invention discloses a traditional Chinese medicinal material defect detection method based on machine vision, relates to the technical field of intelligent detection, and solves the problems that in the prior art, a threshold cannot be migrated, so that an inter-batch judgment standard is unstable, and high-resolution block reasoning causes boundary pseudo defects and instance wrong affiliation. According to the method, a reference area is constructed in a batch domain, a statistical descriptor is extracted, and monotone mapping calibration is performed on defect scores so as to realize cross-batch unified judgment; meanwhile, a seam continuity field is introduced in the image block splicing process, defect connected domains are fused through a cross-window adjacency relation, and accurate attribution of defects and medicinal material pieces is achieved based on bipartite graph matching; according to the method, the judgment stability and the statistical reliability in the traditional Chinese medicinal material defect detection process are remarkably improved.

The invention discloses a vehicle path problem solving method, system and equipment and a medium, and particularly relates to a vehicle path problem solving method based on clustering decomposition and graph matching, which comprises the following steps: S1, receiving a large-scale vehicle path problem instance to be solved, modeling into a weighted undirected graph, and combining a series of constraint conditions and optimization targets; s2, constructing an offline knowledge base with a diversified structure, inputting a group of preset parameters in the step, and outputting a knowledge base stored in the memory; s3, aiming at the large-scale vehicle path problem instance to be solved, executing initialization operation to generate a global initial solution; and S4, by taking the global initial solution as a starting point, executing an iterative local search framework to carry out deep optimization on the solution until a preset termination bar is met. The technical problems that in the prior art, when a large-scale complex logistics network is processed, the calculation time consumption is long, the planning cost is high, and the result is unstable are solved.

The application discloses a mathematical expression diagnosis method and device based on an abstract syntax tree and a semantic network, and belongs to the technical field of mathematical expression processing. The method comprises the following steps: establishing formal grammar rules of a mathematical expression based on an extended Backus-Naur form; performing lexical analysis on an input mathematical expression to generate a token stream; performing syntax analysis on the token stream based on the formal grammar rules to construct an abstract syntax tree; in the process of constructing the abstract syntax tree, running a structural diagnosis process, wherein the structural diagnosis process comprises constructing a semantic network based on a mathematical knowledge base, and performing graph matching on nodes in the abstract syntax tree and the semantic network to identify structural errors in the mathematical expression; and generating error correction suggestions based on the identified structural errors. The application can realize deep analysis from syntax correctness checking to mathematical rationality, and improve the accuracy of mathematical expression processing.

The invention provides a graph matching network-based vulnerability and patch semantic association evaluation method, which comprises the following steps of S1, performing standardization processing on a vulnerability report and patch description to obtain a preprocessed text; s2, constructing a word co-occurrence graph of the preprocessed text by utilizing a sliding window mechanism; s3, coding initial features of nodes and edges of the word co-occurrence graph into hidden vectors by using a multi-layer perceptron, and generating a vulnerability graph and a patch graph; s4, introducing an intra-graph attention mechanism and a cross-graph attention mechanism, and aggregating neighbor information in and between the vulnerability graph and the patch graph; and S5, respectively aggregating the vulnerability graph and the patch graph into graph-level representation vectors by using the patch correctness evaluation model, and calculating the Euclidean distance between the vulnerability graph and the patch graph. According to the method, the graph structure representation of the vulnerability report and the patch description is constructed, and the cross-graph attention mechanism is introduced to carry out joint semantic modeling, so that the problems of text structure information missing and lack of joint reasoning in the existing method are effectively solved.

The invention discloses a sub-graph matching method and device, and the method comprises the steps: obtaining a query graph containing N query nodes and a data graph containing M data nodes, and determining a candidate set, containing candidate data nodes corresponding to the query node in the data graph, of each query node; according to a first sequence used for traversing the N query nodes, the candidate sets of the N query nodes are filtered in sequence, and the step of filtering the first candidate set of the first query node comprises the substeps that a first set of all second query nodes which are contained in the query graph and are adjacent to the first query node is determined; for each candidate data node in the first candidate set, determining whether the candidate data node has at least one connection with the candidate set of each second query node in the first set through a plurality of threads; if the check result is no, removing the candidate data nodes from the first candidate set to update the first candidate set; the updated first candidate set is used for determining sub-graphs isomorphic to the query graph in the data graph.

The invention relates to the technical field of tower identification, and discloses a similar tower retrieval method and system based on vertex neighborhood attributes, and the method comprises the steps: obtaining vertex coordinate data of a tower model, calculating the vertex neighborhood attributes according to the vertex coordinate data, determining the neighborhood attribute information entropy of vertexes, and screening structural key points based on the neighborhood attribute information entropy; constructing a local reference frame for the structure key points, converting coordinates of each point in a neighborhood of the structure key points to the local reference frame, and generating a rotation invariant descriptor; constructing a local adjacency graph based on the structure key points and the rotation invariant descriptors; and obtaining a local adjacency graph of the tower to be retrieved and a local adjacency graph of a target tower in a preset database, performing sub-graph matching on the local adjacency graph of the tower to be retrieved and the local adjacency graph of the target tower, and determining a similar tower retrieval result according to a sub-graph matching result. By replacing original point cloud data with vertex neighborhood attributes, similar structures can be quickly positioned.

The invention provides a territorial space knowledge evolution model construction method, and belongs to the technical field of territorial space knowledge evolution models.The territorial space knowledge evolution model construction method includes the steps that a distributed graph storage framework is constructed, sub-graph partitions are divided through a community detection algorithm, and external event information is collected and converted into a graph operation instruction sequence; positioning a k-hop neighborhood sub-graph by using an incremental sub-graph matching algorithm and executing local updating to generate an intermediate knowledge graph, calculating node state embedding representation of a fusion time coding vector, capturing a time sequence dependence mode through a time perception graph attention aggregation algorithm, executing semantic consistency constraint detection to identify conflicting nodes, and obtaining a time perception graph attention aggregation algorithm; when the conflict degree exceeds a threshold value, the multi-version concurrency control protocol is triggered to roll back to the snapshot version, otherwise, the intermediate knowledge graph is reserved as final output, and the problem that it is difficult to give consideration to updating efficiency and semantic consistency guarantee when the territorial space knowledge graph responds to an external event for evolution updating is solved.

The invention belongs to the related technical field of large-scale graph matching, and particularly relates to a suspicious transaction sub-graph matching calculation method based on UPMEM storage and calculation integrated equipment, which comprises the following steps: generating sub-tasks according to the granularity of candidate vertex-neighbor vertex sub-segments, estimating the cost of the sub-tasks by using a recursive price expansion model, and executing logic segmentation on high-out-degree candidates; and then statically distributing the fine-grained subtasks to a plurality of DPUs by adopting a greedy method. In a single DPU, threads are specially divided into a loading type and a computing type, and asynchronous data flow is achieved through DMA prefetching and lockless annular FIFO; and in the set intersection stage, distributing to microkernels such as bitmap parallelism, bipartite detection and double-pointer merging according to vertex degree topology. Through strict evaluation, the scheme effectively relieves load unevenness and access delay, reduces control divergence, is suitable for multi-seed graph mode query, and has the advantages of high throughput, expandability and stability.

The invention provides a sub-graph matching method based on decomposition query, which relates to the technical field of data processing, and comprises the following steps: decomposing a query graph and a data graph into a plurality of query sub-graphs and data sub-graphs; establishing a filtering strategy based on the feature vectors, filtering the data sub-graphs based on the filtering strategy, and obtaining a first candidate data sub-graph set which comprises a plurality of candidate data sub-graphs; generating an R tree based on the candidate data sub-graphs, and excluding unmatched candidate data sub-graphs in the first candidate data sub-graph set to obtain a candidate result set; and calculating a matching sequence based on the candidate result set, obtaining a preliminary matching result of the query sub-graph, combining the preliminary matching result into a candidate sub-graph, and taking the verified matching sub-graph as a final sub-graph matching result through refinement and verification. A data graph and a query graph are decomposed into a plurality of sub-graphs for efficient query of vertexes in the graphs. The generated candidate vertex set can provide more accurate information for the sorting method.

The invention discloses a paper classification method based on graph matching and self-supervised graph learning, and relates to the technical field of document classification based on deep learning. According to the method, literature data is represented by adopting a literature relation graph, a graph learning model ConGM based on the literature relation graph is constructed, and reference and theme association between literatures are mined through sub-graph sampling and data enhancement, linear node matching, secondary edge alignment and double-layer negative sample selection, so that precise classification of fields to which papers belong is realized.

The invention belongs to the technical field related to information analysis, and discloses a flow graph processing method and processing system based on sub-graph hybrid computation.The processing method comprises the steps that an original flow graph in a graph database is divided into a plurality of sub-graphs with the vertex total weight distributed in a balanced mode; executing a graph updating operation on the sub-graphs; matching a corresponding graph calculation mode for each updated sub-graph, matching the corresponding updated sub-graph with a full-amount calculation mode when the proportion of the vertexes influenced by the updating operation in the updated sub-graphs exceeds a preset proportion, and otherwise, matching with an incremental calculation mode; extracting a dependency relationship between the sub-graphs; performing graph calculation on each updated sub-graph in parallel by adopting the matched graph calculation mode to respectively obtain an intermediate result of each sub-graph; and transmitting and updating the intermediate result of each sub-graph based on the dependency relationship, and outputting a global processing result of the flow graph. According to the processing mode, the locality characteristic of the graph can be fully utilized, the calculation overhead is reduced, and the accuracy of a calculation result is ensured.

The application belongs to the field of circuit design, and particularly relates to a subgraph matching method based on an enhanced VF3 algorithm, a storage medium and equipment. The method first generates a type of graph structure data of a pattern circuit and type II graph structure data of the pattern circuit and a target circuit according to a netlist file, and establishes a mapping between nodes in different graph structure data. The type II graph structure data takes element ports as nodes, and takes electrical connection relationships between nodes, signal transmission directions and control logic as edges. Then, key nodes in the pattern circuit are identified and the priority of the remaining nodes is calculated. A priority queue is generated in which the key nodes are in front and sorted by priority. Finally, the priority queue is taken as a node search sequence, and a subgraph matching algorithm is used to match nodes in the pattern circuit and the target circuit in the type II graph structure data, so as to determine the pattern circuit contained in the target circuit. The application solves the problem of low search efficiency and error identification of the existing subgraph matching scheme.

The invention discloses a cutting track planning method and device and a storage medium, and the method comprises the steps: building a bipartite graph model of a workpiece graph set and a threading hole set, and building a weight matrix based on an edge set in the bipartite graph model; based on the weight matrix, performing optimal matching on a workpiece graph node set and a threading hole node set by adopting a bipartite graph matching algorithm, and determining a matching pair between the workpiece graph and the threading hole according to nodes which can be matched with each other; performing type division on the workpiece graph according to the matching pair to obtain a male die and female die workpiece graph set, and combining the cutting starting point position of the workpiece graph to obtain a first corresponding relation between the female die workpiece graph and the cutting starting point position and a second corresponding relation between the male die workpiece graph and the cutting starting point position; and according to the obtained first and second corresponding relations, carrying out cutting track planning on the workpiece graphs in the female die and male die workpiece graph set. Therefore, batch programming of a plurality of male dies and female dies is realized, and the efficiency of generating a machining program is improved.

This invention relates to the fields of data processing and talent matching technology, and in particular to a two-way matching method for university-enterprise talent based on financial capability profiling. The method includes: constructing a corporate financial capability profiling indicator system, including indicators of corporate financial stability and job financial capacity; constructing a talent financial capability profiling indicator system, including indicators of financial knowledge reserves, financial practice skills, financial risk awareness, and financial digitalization capabilities; collecting and preprocessing data from both the corporate and talent sides; calculating a comprehensive evaluation value based on a combined weighting method using an improved analytic hierarchy process (AHP) and entropy weighting method; constructing a corporate job demand vector and a talent supply vector; solving for the optimal matching scheme based on a bipartite graph matching algorithm, with the objective function of maximizing two-way matching satisfaction; and outputting the matching results. By constructing a two-dimensional evaluation system of corporate and talent financial capabilities, and using an improved weighting method and matching algorithm, a precise two-way matching of university and enterprise talent is achieved, improving the scientific nature of the matching.

This invention discloses a method for detecting building change regions in remote sensing images, relating to the field of region detection technology. The method includes the following steps: collecting and preprocessing remote sensing images of buildings to obtain preprocessed remote sensing images; generating a spatial layout structure field of buildings based on the morphological and structural patterns of buildings in the preprocessed remote sensing images; performing cross-temporal reasoning on the spatial layout structure field of buildings using a graph neural network structure graph matching algorithm to obtain a cross-temporal building structure relationship graph; constructing a joint determination mechanism for building change regions and performing consistency judgment on the cross-temporal building structure relationship graph to determine the building change regions; and reconstructing the building change regions using a level set method to obtain the reconstructed building change regions. This invention utilizes the stable boundary, roof, and group arrangement morphological and structural patterns of buildings to construct a robust foundation for change detection.

The present application relates to the technical field of multispectral image fusion, and discloses a real-time multispectral image fusion system based on deep learning. The system comprises a multispectral image atlas module, a flow atlas synthesis module, a personalized flow extraction module, a flow vertex labeling module and a fusion flow verification module. The multispectral image atlas module stores a plurality of image category templates of graph structures, each template corresponding to a fusion flow graph containing processing vertices and connecting edges; the flow atlas synthesis module fuses a plurality of templates into a unified hypergraph through vertex clustering and edge redirection; the personalized flow extraction module analyzes user demand text and converts it into a demand atlas, and matches a subgraph from the unified hypergraph as a basic fusion flow; the flow vertex labeling module identifies adjustable vertices and adds labels; and the fusion flow verification module verifies logic through a deep learning model, detects abnormal vertices and generates a report. The system is suitable for multiple scenarios and improves the flexibility and reliability of the fusion flow.

The invention belongs to the technical field of power system network security and intelligent power grid operation monitoring, and discloses a power grid penetration level coupling alarm association method, system and device and a medium, and the method comprises the steps: obtaining a suspicious range of an alarm log based on a monitoring host and a time label; constructing an alarm association graph, and performing extraction processing in a local adjacent sub-graph mode to obtain an attack path; constructing a natural language processing mechanism in a classified manner to obtain an attack database; matching and identifying by using a graph matching algorithm with label classification to obtain a network information layer attack event; carrying out delay sampling on abnormal data, carrying out state aggregation by utilizing a prior fault knowledge base, constructing an initial state sequence, and carrying out compression and merging to obtain a target state sequence; and carrying out hierarchical coupling analysis on the network information layer attack event and the target state sequence to obtain an attack event sequence after alarm association. According to the invention, cross-level and multi-stage abnormal alarm association of power grid penetration attacks is realized, and a complete link can be identified and tracked.

This invention provides a continuous subgraph matching method for social networks based on dynamic pruning, belonging to the field of social network behavior detection. The invention proposes a dynamic pruning algorithm that establishes local equivalence theory by calculating the backward neighbors of query points and candidate points, defines subtree-level equivalence criteria in dynamic graph environments, and is applicable to highly isomorphic behavior patterns such as zombie account groups and terminal nodes of propagation chains. The dynamic pruning algorithm skips isomorphic subtree structures to achieve the pruning effect. A query graph decomposition and sorting mechanism is proposed to remove the binding constraint of the matching order on the update edges and candidate sets, actively identifying and prioritizing highly redundant candidate set vertices, further reducing the search space. Ultimately, while ensuring result completeness, it achieves a 16.9-fold performance improvement over RapidFlow and a 100% completion rate for complex queries, providing sub-second decision support for abnormal behavior detection in social networks and promoting the application of dynamic graph analysis in resource-constrained environments such as edge computing.

The invention discloses a video material automatic matching result analysis method. The method comprises the following steps: obtaining text features according to an obtained query text; obtaining multi-modal fusion video features of each video material in the video material library; inputting the text features and the multi-modal fusion video features of each video material into a pre-constructed cross-modal routing interaction network model for interaction to generate text enhancement features and corresponding multi-modal video fusion enhancement features, based on the text enhancement features and the corresponding multi-modal video fusion enhancement features, respectively constructing a text semantic graph and a corresponding video semantic graph; and calculating a graph structure similarity between the text semantic graph and the video semantic graph of each video material based on a graph matching network, and outputting the video material matched with the query text based on a graph structure similarity calculation result. The problems of inaccurate video material matching result, large error and the like in the prior art can be solved.

The invention discloses a multi-modal data association rule learning method based on deep learning, and the method comprises the following steps: collecting multi-modal data, and obtaining a modal sample sequence; constructing a modal diagram structure according to the similarity between the time label and the modal; inputting to an improved GMN model, and generating a modal sub-graph set; performing node fusion, edge feature aggregation and label alignment operations to generate a regular triple set; constructing a causal path diagram based on the modal combination sequence and weight information; and calculating a residual error change rate between the predicted label and the real label to obtain a consistency score, and outputting a rule result set according to a comparison result of the consistency score and a preset score threshold. According to the method, the modal diagram modeling and the improved GMN model are introduced to perform sub-graph matching, and the rule triples are dynamically screened in combination with a consistency scoring mechanism, so that the modeling accuracy of the causal association relationship between the multi-modal data and the reliability of rule generation are improved.

This invention discloses a real-time dynamic trajectory tracking method and system for millimeter-wave radar gesture recognition, belonging to the field of gesture recognition and tracking technology. The method includes: receiving echo signals reflected from gestures, extracting potential target point clouds, and clustering to generate gesture point cloud sequences; establishing a multimodal motion model library, dynamically selecting the optimal motion model using graph matching, and generating predicted states based on the gesture point cloud sequences; dynamically adjusting observation weights and optimizing the observation point clouds based on the signal-to-noise ratio and spatial distribution of the current gesture point cloud sequences using a Poisson-Doubouli hybrid filtering framework, and performing optimal association using Mahalanobis distance combined with dynamic time warping; maintaining trajectory hypotheses using a multi-hypothesis tracking strategy, selecting the optimal trajectory through a trajectory scoring mechanism, and smoothing the optimal trajectory using Kalman filtering. This invention achieves high-precision, low-latency tracking of gesture movements, adapting to different users' gesture habits and complex environmental interference while maintaining high trajectory accuracy.

The application discloses a photovoltaic array fault detection and positioning method based on bipartite graph matching, comprising the following steps: 1) based on the connection structure of a photovoltaic array, a photovoltaic array bipartite graph is established, and the connection points of components on the same photovoltaic component string constitute an independent node set in a sub-bipartite graph; 2) under the complete matching rule of the bipartite graph, the minimum edge covering rule and the constraint that equal-weight points in the bipartite graph are not connected, edge matching of the bipartite graph is derived, the edge is converted into a voltage sensor, and optimal configuration of the voltage sensor is obtained; 3) the fault voltage threshold of the photovoltaic component under different faults is calculated, the actual measured value of the photovoltaic component voltage collected by the voltage sensor is compared with the fault voltage threshold, and photovoltaic array fault detection and positioning are realized. The application realizes accurate detection and positioning of open-circuit, short-circuit, aging and shading faults of each photovoltaic component by using fewer voltage sensors, and the voltage sensor only needs to be connected between adjacent photovoltaic component strings, and is easy to operate.

The application provides a subgraph matching method and device. In the execution method, first, store the query graph and the data graph processed into a preset format; then, filter the data vertices in the query graph twice to generate a query vertex candidate set; then, connect the candidate vertices in the query vertex candidate set in a preset order to obtain a subgraph with the same structure as the query graph, and the preset order is to connect the core vertices in the candidate vertices in the query vertex candidate set first, and then connect the leaf vertices in the candidate vertices in the query vertex candidate set. The application filters the data vertices in the query graph twice, so that fewer invalid vertices are screened, and in the connection process, the vertices with a degree of one are connected first, and then the vertices with a degree of one are connected, which produces fewer invalid intermediate results, speeds up the subgraph matching speed, avoids occupying too much GPU memory, and further avoids the problem of excessive GPU memory load.

This invention discloses a client selection method and system for multi-task federated learning. Addressing the shortcomings of client selection and insufficient consideration of task urgency in multi-task dynamic federated learning scenarios, this invention first constructs a multi-task federated learning system model, defining a utility function that includes learning quality and penalty terms. Second, it establishes fairness constraints and introduces a fairness queue to transform the problem into a queue stability problem. Then, based on Lyapunov optimization theory, it constructs a drift-plus-utility function, transforming a long-term stochastic optimization problem into a deterministic optimization problem for each round of communication by minimizing its upper bound. Finally, it constructs an auxiliary bipartite graph to transform client selection into a minimum-weight bipartite graph matching problem. This invention, by jointly optimizing fairness, learning quality, and task urgency through the Lyapunov framework, transforms long-term constraints into a solvable problem for each round, reducing computational complexity and achieving efficient and fair dynamic client selection.

The application provides an AI-based object-oriented business object modeling method, medium and system, belongs to the technical field of AI-based object-oriented business object modeling methods, media and systems, and solves the technical problem of low business object recognition accuracy caused by low-frequency long-tail distribution of professional terms in natural language text of industrial business requirements by receiving industrial business requirement text, using natural language processing technology and an industrial vertical field continuous pre-training corpus to complete standardized mapping, inputting standardized text into a hierarchical gated knowledge injection double-flow encoding model to identify core business objects, attributes and classification labels, and then automatically generating a business object model of a unified modeling language specification through an artificial intelligence model combined with a Monte Carlo tree search heuristic sampling strategy and a VF2++ graph matching algorithm, and finally calculating a field bias comprehensive score according to verification feedback of business personnel and adaptively fine-tuning model parameters.