340 results about "Bipartite graph" patented technology

The invention provides a resource scheduling control method and system for a big data server, and the method comprises the steps: constructing a multi-dimensional resource portrait module, collecting the CPU, memory, network, storage I / O load and task queue length of each node in real time, and predicting a resource demand trend through a time sequence algorithm; extracting characteristics such as calculation intensity, data dependence, memory requirements, network transmission quantity and the like; adjusting the weight coefficients of the resource utilization rate, the task completion time and the energy consumption efficiency according to the system load and the historical effect; establishing a bipartite graph model by taking a resource trend as a node feature and a task vector as an edge feature, and calculating a matching score through graph convolution and a multi-objective optimization function; the scheduling scheme is synchronized by adopting a consistency algorithm; automatic rollback and reallocation are carried out when resources are detected to be insufficient; and optimizing a weight coefficient and a network parameter through reinforcement learning. Through the method, the system resource utilization rate can be improved, the task execution efficiency is improved, the overall scheduling effect stability is improved, and the system fault recovery time is shortened.

The invention discloses a multi-agent cooperation enhancement method, system and equipment based on a knowledge graph, and the method comprises the steps: obtaining original data in an external environment, carrying out the preprocessing and feature extraction of the original data, generating a knowledge triple, storing the knowledge triple in a local knowledge graph, and submitting the knowledge triple to a shared knowledge graph for knowledge updating; when a to-be-executed task is received, decomposing the to-be-executed task by utilizing the large language model and querying global knowledge in the shared knowledge graph and local knowledge in the local knowledge graph to obtain a plurality of sub-tasks; a bipartite graph minimum cost matching algorithm is adopted to match a plurality of sub-tasks with the capability and availability of each agent to generate a preliminary task allocation scheme, and a large language model is utilized to optimize the preliminary task allocation scheme to generate an optimal task allocation scheme; and sending each task allocation knowledge fragment in the optimal task allocation scheme to a corresponding agent for collaborative execution through a semantic communication protocol.

The invention discloses a three-dimensional shielding target tracking method based on multi-modal space-time interaction, and relates to the technical field of target tracking. The method comprises the following steps: acquiring a point cloud and an image and preprocessing to obtain global fusion features; obtaining an initial detection frame and region-of-interest features through region proposal network processing; projecting the non-empty voxel point cloud to the image features, and reconstructing shielded target features; convolution and neural network processing are utilized to obtain a refined detection frame; screening legal detection frames through distance calculation and legality judgment; the bipartite graph and the self-adaptive channel graph are adopted for convolution, and appearance correlation scores are calculated; and matching the detection frame and the trajectory based on a Hungary algorithm to realize whole-course tracking. The target identification accuracy and robustness are improved, the shielding problem is solved, the accuracy of the detection frame is ensured, the correlation accuracy is improved by using the bipartite graph and the adaptive convolution, the nodes are matched in combination with the geometric cost matrix, and whole-course tracking and error calibration are realized.

The invention provides a self-adaptive intent perception enhanced graph contrast learning recommendation method, which comprises the following steps of: constructing a user-article bipartite graph, carrying out multi-layer embedded coding on a user and an article by adopting a graph neural network, and obtaining a multi-intention embedded representation of Gaussian distribution through a variational auto-encoder in combination with a multi-intention hypothesis; noise disturbance is adaptively added to intention embedding so as to enhance feature robustness, and the method respectively implements comparative learning of isomorphic and heterogeneous nodes in a node interaction space domain and an intention perception domain, so that the problems of data sparsity and intention entanglement are effectively relieved; and finally, carrying out joint optimization on recommendation task loss, KL divergence loss and double-domain comparison loss, and realizing accurate modeling and recommendation of the personalized preference of the user. Experimental results show that the method is superior to a mainstream recommendation system on a plurality of real data sets, and has strong generalization ability and robustness.

The invention relates to the technical field of recommendation algorithms, provides a graph collaborative filtering course recommendation method based on interactive attention and comparative learning, and aims to solve the problem that a traditional recommendation system is insufficient in modeling ability in a sparse interaction scene. The method comprises the following steps: firstly, constructing a user-course bipartite graph, and utilizing a dynamic attention mechanism guided by an interactive opposite-end node: carrying out vector dot product through original embedding of the opposite-end node (for example, course embedding is used during user aggregation) and current embedding of a neighbor node, and generating an attention coefficient in combination with temperature parameter normalization; and multi-level structure information aggregation is realized. Afterwards, local context features are fused through a multilayer graph convolutional network, random noise disturbance is introduced to generate a multi-view comparison sample, the consistency of positive samples is maximized in combination with an InfoNCE loss function, and the robustness of the model to noise and sparse data is enhanced; and finally, optimizing user-course embedding in combination with Bayesian personalized sorting loss and comparison loss, and generating a personalized recommendation list. According to the method, the key interaction relationship is screened through guided attention, the representation discrimination is improved in combination with comparative learning, and the recommendation precision in cold start and data sparse scenes can be improved.

The invention relates to the technical field of multi-view clustering, in particular to a multi-view clustering method and device, and can solve the problem that the overall effect of an existing method in a large-scale clustering task is limited due to the fact that the existing method has problems in the aspects of calculation efficiency, robustness and multi-view information integration to a certain extent. The method comprises the following steps: dynamically learning an anchor matrix and a projection matrix for each view, and constructing a bipartite graph to generate a similarity matrix; calculating a graph Laplacian matrix based on the similarity matrix of each view, and extracting spectrum embedding; the spectrums of multiple views are embedded and stacked into a third-order tensor, and cross-view shared information is extracted by using a low-rank tensor constraint; multi-view atlas embedding is aligned through a spectrum rotation technology, and a discrete clustering indication matrix is directly output.

The invention discloses a large language model semantic enhancement collaborative filtering recommendation method based on comparative learning, and belongs to the field of recommendation systems.The method comprises the steps that a user-item bigraph is constructed, user-item semantic information collaborative information is provided through GNN, user-item semantic information is extracted through cue words, a deterministic topology view enhancement strategy is adopted, and user-item semantic information collaborative filtering recommendation is achieved. According to the method, a semantic neighbor extension view is generated, a semantic neighbor reconstruction view is generated, collaboration and semantic information alignment are performed, double-view structure alignment is performed, and a loss function is integrally trained, so that the accuracy and the cold start capability of a recommendation system are remarkably improved through collaborative graph structure learning and semantic enhancement.

The invention discloses a smart building energy consumption data monitoring management method and system, and relates to the technical field of smart building and energy management, and the method comprises the steps: constructing a dynamic graph structure, extracting joint features through employing a graph attention mechanism, weighting the Mahalanobis distance of a node through employing a batch average attention coefficient, and obtaining an abnormal scene feature vector; a target classification function is defined, an EPC-PSO algorithm is used for optimization, and a Softmax classifier is used for classifying abnormal scene feature vectors; defining an energy consumption efficiency objective function, decomposing into a sub-problem of each device by using Lagrange, outputting a global initial strategy vector by using a gradient descent method, defining a smart building task, constructing a matrix of a comprehensive benefit weight, converting the device and the task into a bipartite graph problem, and solving by using a Hungary KM algorithm; the batch average attention coefficient weights the mahalanobis distance, the robustness of anomaly detection is enhanced, and the comprehensive benefit of resources is improved by using Lagrange decomposition, a gradient descent method and a Hungary KM algorithm.

The invention discloses a multi-modal collaborative recommendation method, system and device, a medium and a program product, and relates to the technical field of multi-modal recommendation, and the method comprises the steps: carrying out the feature enhancement of a user portrait and a commodity attribute through a large-scale language model, and obtaining a more comprehensive and precise text modal representation through generating more abundant semantic description; a modal reference vector is introduced, and the similarity between the modal features and the modal reference vector is calculated to evaluate the importance of different modals, so that modal noise is filtered, a user-commodity bipartite graph and a commodity-commodity similarity graph based on the modal features are constructed, and user preferences are captured from user-commodity interaction and commodity-commodity semantic relationships; by integrating a difference perception attention mechanism and a dynamic modal preference gating mechanism, fine-grained fusion of multi-modal features is realized, so that the model can adaptively adjust the importance of each modal according to different scenes.

A method for selecting training data for training a deep learning model is provided. The method includes steps of: (a) obtaining one or more individual attributes each of which corresponds to each of a plurality of training data included in total training data, and generating a bipartite graph by matching each of the plurality of training data included in the total training data with the individual attributes; and (b) selecting n training data among the total training data, by referring to the bipartite graph, wherein the n is a target number of the training data to be used for training the deep learning model, and wherein the training data selecting device selects the n training data to be used for training the deep learning model such that each cardinal number of each of the individual attributes matched with the n training data is within a predetermined deviation threshold.

The invention relates to an identification method for matching scene behaviors by using multi-modal features, and belongs to the technical field of scene behavior matching. The method comprises the following steps: acquiring multi-modal scene behavior data, and carrying out noise self-adaptive purification processing on the multi-modal scene behavior data to obtain a preprocessed scene image, scene audio data and a scene label text; secondly, performing feature collaborative extraction on the preprocessed data to obtain visual features, audio features and text features, inputting the cooperatively extracted features into a scene behavior matching network, and performing scene feature fusion and behavior feature fusion respectively to obtain a scene feature vector and a behavior feature vector; and constructing a bipartite graph according to the scene feature vector and the behavior feature vector, calculating the semantic similarity between nodes of the bipartite graph, dynamically updating the edge weight of the bipartite graph according to the semantic similarity between the nodes, and normalizing the updated bipartite graph to obtain a scene behavior matching result. According to the method, the association degree of the scene and the behavior can be accurately quantified, and the accuracy of a matching result is greatly improved.

The invention provides a method for detecting the quality of a foundation steel bar welded joint, and belongs to the technical field of steel bar welded joint quality detection.The method comprises the steps that a stress distribution matrix is obtained through ultrasonic detection equipment, and a fatigue distribution matrix is established in combination with a fatigue analysis algorithm; a stress fatigue correlation analysis method and a bipartite graph maximum matching algorithm are used to construct a stress fatigue correlation matrix, a damage evaluation deep learning model based on a Transform architecture is used to generate a damage distribution matrix, and a time sequence prediction algorithm is used to consider a fatigue center change effect to establish a damage expansion trend matrix. A damage prediction matrix is deduced through a damage prediction deep learning model of a bidirectional long-short-term memory network, and finally, a quality detection result matrix is comprehensively evaluated and output by utilizing a game model, a multi-objective optimization algorithm and a gating weighting function, so that the technical problem that the quality detection accuracy of the steel bar welded joint is not enough is solved.

The invention belongs to the technical field of data processing, and particularly provides a bipartite graph subgraph mining-based product quality problem correlation analysis method, which comprises the following steps of: obtaining a product and quality-related data; formatting and cleaning the data; extracting data related to quality to perform label extraction; establishing a bipartite graph model according to the association relationship between the product and the quality label; and performing correlation analysis on the product quality problem based on a bipartite graph sub-graph mining method. According to the method, data resources are integrated, and a bipartite graph sub-graph mining algorithm is optimized, so that the system is suitable for large-scale data analysis, experts can conveniently carry out correlation analysis and optimize a manufacturing process, and tracing of product quality problems and mining of products with potential quality problems are facilitated. In addition, the method is also helpful for predicting and identifying products with possible quality problems, and the product quality is improved.

The invention discloses a non-coding RNA (Ribonucleic Acid) and disease association prediction method based on a graph neural network, and belongs to a biomolecule association prediction technology in the field of biological information. The invention breaks through the limitation of a single task of a conventional method, and provides a novel multi-task learning framework based on task migration. Potential LDA, MDA and LMI can be deduced by effectively utilizing the known complex relationship among lncRNA, miRNA and diseases. A plurality of graph structures are constructed, including a bipartite graph, a feature structure graph and a meta-path graph. The synergistic effect of the multi-graph structure significantly enriches the context information of the nodes, and effectively improves the prediction accuracy of the model. Pairwise attribute learning is introduced and is used for capturing the direct relationship among lncRNA, miRNA and diseases. According to the method, the prediction performance is improved, overfitting is effectively reduced, and the robust generalization ability of the model is ensured.

The invention discloses a complex network importance node evaluation method based on group interaction. The method comprises the following specific steps: step 1, carrying out community division on a network through a Louvain algorithm; step 2, constructing a Louvain bipartite graph; step 3, obtaining an outgoing transfer matrix and an incoming transfer matrix; step 4, obtaining an extended transfer matrix according to the outgoing transfer matrix and the incoming transfer matrix; 5, expanding the transfer matrix to obtain an enhanced transfer matrix; step 6, calculating the importance score of each node; 7, arranging the nodes in the network in a descending order according to the importance score of each node; and step 8, identifying important nodes of the network through a network disintegration algorithm. According to the method, based on group interaction, the interaction between nodes which are not directly connected is considered, the random walk is expanded to the Louvain bipartite graph, and the concepts of outgoing walk and incoming walk are added; and in combination with a network disintegration algorithm, the key nodes in the network are efficiently and accurately identified, and the robustness and the stability are high.

The invention relates to an APT attack detection and machine learning technology, in particular to an APT attack detection method fusing comparative learning and cross-domain recommendation. A traceability graph is constructed based on a data set, and a bipartite graph is constructed according to the traceability graph; generating an r-ego network for all nodes in the bipartite graph; generating positive and negative sample pre-training graph encoders in the source domain, and transferring the pre-trained graph encoders to the target domain; generating initialization embedding in a target domain by using a pre-trained graph encoder; the matrix decomposition model is finely adjusted by using the initialized embedding, and the final embedding of each node in the target domain is obtained by using the finely-adjusted matrix decomposition model; and predicting whether the two nodes interact based on the final embedding. According to the method, the high-order connectivity is formed by using the side information of the system entities to predict the possibility of interaction between the entities. And meanwhile, a cross-domain recommendation method is used, so that the problem of data sparsity of APT attacks is relieved while the recommendation performance on a target domain is improved.

Methods and systems for learning representations for nodes of temporal bipartite graph. Method performed by server system includes accessing temporal bipartite graph including first nodes, and second nodes. Each first node has first features and each second node has second features. Method includes generating, by Graph Neural Network (GNN) model, first interim representations for each first node based on first features corresponding to each of a set of temporal two-hop neighbor nodes of each first node. Method includes computing first homogeneous representation for each first node based on first interim representations and first features of each first node. Method includes computing first global homogeneous representation for each first node based on first homogeneous representation for each first node. Method includes computing first local heterogeneous representation for each first node based on first homogeneous representation for each first node.

The invention discloses a recommendation system noise trimming and long tail enhancement method based on two-stage graph optimization. In order to solve the problems of noise interaction and long-tail user data sparsity in an implicit feedback recommendation system, the method comprises the following steps: firstly, constructing a user-article interaction bipartite graph, and initializing a graph convolutional network model to generate a preliminary embedded representation; in the first stage, the reliability of an interaction edge is evaluated through a node similarity index (Nsim), a noise edge is trimmed in combination with a dynamic threshold strategy, and a de-noised subgraph is generated to improve the embedding quality. And in the second stage, for the long-tail user, a probability sampling mechanism is adopted to add a high-confidence potential interaction edge, and an enhanced sub-graph is generated to improve the long-tail recommendation effect. Finally, Bayesian personalized ranking (BPR) loss is optimized through iterative training, and an accurate personalized recommendation result is generated. The accuracy and fairness of the recommendation system are remarkably improved, and the method is suitable for application scenes such as e-commerce, social media and content recommendation.

The invention relates to a joint simulation model execution sequence determination method based on an improved Tarjan algorithm. The joint simulation model execution sequence determination method comprises the following steps: constructing a weighted bigraph representing a dependency relationship between models; applying a multi-level weighted Tarjan algorithm to the weighted bipartite graph to identify strong connected components; processing the identified strongly connected components to eliminate loop dependencies; performing weighted topological sorting on the processed graph without the strong connected component; carrying out key path analysis; and generating an optimized model execution sequence based on a weighted topological sorting result and a critical path analysis result. According to the method, the requirement of strong-coupling multi-model joint simulation can be better met, and the execution sequence of the models is effectively determined.

The invention discloses a multi-view self-representation and clustering joint optimization deep clustering method, which comprises the following steps of: firstly, acquiring data samples of a plurality of views, and selecting a most representative sample from each view as an anchor point by adopting a VDA algorithm; constructing and pre-training an auto-encoder network; on this basis, a self-representation module is introduced, shared self-representation and view unique self-representation are learned at the same time, and self-representation of each view is constructed through the similarity between an anchor point and a sample; constructing comprehensive self-representation based on sharing and unique self-representation, constructing a bipartite graph affinity matrix, and obtaining an initial clustering result of samples and anchor points by adopting a bipartite graph clustering algorithm; a clustering result is fed back to the self-representation module, and the representation is iteratively corrected; and finally, sharing and view unique self-representation are fused, comprehensive self-representation is obtained and used for spectral clustering, and a final clustering result is obtained. According to the method, the consistency and diversity characteristics between the views are effectively combined, and the problems that a traditional method is insufficient in structure modeling and low in optimization efficiency are solved.

The invention discloses an underwater hyperspectral clustering method based on a bipartite graph, and relates to the technical field of underwater image processing. The method comprises the following steps: acquiring spectral images of a to-be-processed image in different spectral intervals, taking the spectral image of each spectral interval as a view, and constructing hyperspectral data based on the acquired views; constructing a hyperspectral clustering model based on subspace clustering learning, and processing the hyperspectral image based on the hyperspectral clustering model; the hyperspectral clustering model comprises an adaptive dynamic anchor point selection module, an anchor point centroid learning module and a bipartite graph decomposition module; and iterating the hyperspectral clustering model based on an alternative update variable strategy until an iteration stop condition is met, and outputting a clustering result based on the clustering indication index matrix. According to the method, a model based on subspace clustering learning is constructed, uniform self-adaptive anchor points are dynamically learned in all pixel points, and the optimal cluster with accurate pixel division can be directly obtained from bipartite graph decomposition.

The invention provides a multi-feature factor matching and interrupt processing method for storm tracking. The method comprises the following steps: calculating storm features required by tracking; carrying out discretization processing and coding on the storm features, and constructing a sparse matrix; constructing an auto-encoder model, performing data reconstruction on the sparse matrix based on the auto-encoder model, and calculating main features of the single storm; on the basis of the main features, calculating the feature vector distance between the previous and later time secondary storms, and constructing a feature space distance matrix; a bipartite graph algorithm is adopted, the incidence relation of the previous and later time-order storms is obtained based on the feature space distance matrix, and a storm matching result is constructed; identifying an interrupted storm based on a storm matching result and carrying out secondary matching; and constructing a storm trajectory based on a secondary matching result and removing a repeated storm trajectory. According to the method, the movement track of the storm can be accurately tracked from formation to extinction of the storm, reliable data support is provided for meteorological monitoring and forecasting, and the timeliness and accuracy of severe convective weather early warning are improved.

The invention discloses an optimization solution system for a large-scale mixed integer linear programming problem, and the system is characterized in that the system comprises a problem decomposition module which is used for modeling the large-scale mixed integer linear programming problem into a bipartite graph for representation, and dividing the bipartite graph into a plurality of sub-graphs through a graph decomposition algorithm; the problem reduction module is used for obtaining a feature code of each sub-graph node, inputting the feature code into a pre-training neural network to obtain a variable prediction value, reducing the scale of the large-scale mixed integer linear programming problem through a redundancy constraint removal and coefficient priority variable fixing strategy based on the variable prediction value, and then outputting the problem; and the efficient solving module is used for solving the large-scale mixed integer linear programming problem based on the result output by the problem reduction module and weighted subgraph division. According to the method, the high-quality solution of the large-scale mixed integer linear programming problem can be quickly given, so that the high-quality feasible solution can be obtained within the acceptable time.

The invention provides a personalized recommendation method and system based on a large model. According to the method, the content data stream is disassembled into independent feature packets in different modals through the multi-channel acquisition server; and analyzing a cross-modal semantic relationship among the feature packets by using a large model, and establishing a cross-modal similarity corresponding table. And generating a dynamic compensation coefficient through deviation analysis in combination with a cross-modal conversion record in a historical behavior. And constructing a bipartite graph structure of the content and the user, converting a compensation coefficient into a connection strength adjustment factor, and integrating weights of adjacent nodes to generate an expression vector. And finally, extracting a user purpose, content association and aging characteristic value of the vector, constructing a dynamic weight model in combination with a preference change track, integrating multiple characteristics to generate a decision vector, realizing cross-modal matching sorting, and outputting a personalized recommendation result. According to the method, the cross-modal data deviation is dynamically compensated and the user track is fused, so that high-precision matching recommendation of the multi-modal content and the user demand is realized.

The invention discloses a medical image clustering method based on correlation entropy and an adaptive bipartite graph, and the method comprises the steps: collecting image data of medical imaging equipment, and forming a sample data matrix; selecting anchor point samples from the sample data matrix, and establishing an original sample low-dimensional representation-anchor point low-dimensional representation graph for capturing a local geometric structure of an embedded space; introducing an orthogonal basis matrix; carrying out joint modeling on the sample reconstruction error, the structure retentivity, the adaptive graph constraint and the discriminative features by adopting a joint objective function; iteratively updating the joint objective function; and inputting the finally obtained low-dimensional representation of the original sample as an embedded representation into a clustering device of K-means, and completing final clustering label distribution. According to the method, more stable, efficient and interpretable unsupervised clustering is realized, the clustering precision and generalization ability are improved, and the method is particularly suitable for medical image data analysis tasks of high-dimensional and complex structures.

The invention discloses a drug recommendation model construction method for relieving entity sparsity based on a hierarchical algorithm, and belongs to the technical field of drug recommendation, and the method comprises the steps: building a pre-training module, and obtaining the health representation of a patient; performing correlation layering; constructing a dual-attribute graph network representation stage; aggregating the drug graph representing the safety and the bipartite graph used for representing the drug disease / operation accuracy into dual-attribute representation in a graph network; recommending related medicine combinations to adapt to long-term health conditions; constructing a loss function, wherein the loss function comprises a prediction probability based on recommended drugs and cross entropy loss and DDI loss of a real label; and training the deep learning model to obtain predicted drug recommendation representation. By layering historical record data of a patient, capturing a sequence relation and forming an accurate graph structure and a safe graph structure for drugs, diseases and operations, the drug recommendation performance can be improved, and the calculation amount is reduced.

The invention provides a personalized semantic understanding learning method, medium and system under an AI platform, and belongs to the technical field of semantic understanding. According to the technical scheme, the personalized semantic understanding learning method comprises the steps that a personalized semantic file recording user question and answer habits and personal terms is constructed, and dual time decay values are set; a semantic understanding optimization model is established based on an attention mechanism, personalized feature vectors and input semantic vectors are fused, a concept drift detection algorithm adopting bipartite graph maximum matching and a Hungary algorithm is designed to monitor semantic habit changes in real time, and an incremental learning mechanism based on a variable sliding window is established to calculate a semantic difference matrix. A forgetting function based on a Gaussian kernel is applied to adjust the historical semantic feature weight according to the time distance and the use frequency, and a reinforcement learning feedback module is constructed to collect user satisfaction evaluation and generate reward signal optimization model parameters; and executing a self-iterative optimization process to periodically update semantic archives and models so as to continuously adapt to personalized semantic requirements of users.

The invention provides a confidence-guided adaptive graph representation reinforcement learning method, equipment and a medium, and relates to the technical field of label prediction and electric data processing. The method comprises the following steps: acquiring a crowdsourcing data set composed of multi-source heterogeneous electronic data, and inputting an adaptive graph representation learning model; mapping the crowdsourcing data set to construct an original bipartite graph; based on the original bipartite graph, generating a trimmed view and an enhanced view through dynamic confidence trimming and conservative edge addition; performing feature extraction, adaptive information enhancement and learning on the worker node and the task node through a dual-channel heterogeneous adaptive network, and performing prediction through a predictor to obtain an initial prediction label; and in combination with the original bipartite graph, performing label correction on the initial prediction label to obtain a final prediction label. The method can effectively suppress noise interference, capture local features and adapt to a dynamic environment, is remarkably superior to the prior art in the aspects of accuracy, robustness and calculation efficiency, and is suitable for the fields of multi-source heterogeneous intelligent labeling and the like.

The invention provides a method and a system for accurately converting a large-model natural language into an SQL (Structured Query Language) based on multi-modal fusion. The method comprises the following steps: S1, performing named entity recognition and dependency syntax analysis on a natural language text, performing vision-text dual-path processing on image data, and constructing a bipartite graph structure for table data so as to obtain a vision text composite feature and a table feature; s2, designing a modal attention controller and calculating the weight of each modal through a gating network; s3, constructing a knowledge graph, clearly recording all legal table names and column names in a database, and retrieving the knowledge graph in real time when a decoder generates the table names or the column names; s4, generating an SQL draft; s5, performing bidirectional verification correction on the SQL statement; and S6, outputting the legal SQL statement. According to the method, multi-modal information can be integrated, information loss and semantic deviation are reduced, illegal statements are avoided through grammar and semantic dual verification, and the overall reliability of SQL statement generation is improved.

The present application discloses a risk account determination method and apparatus, a device, a storage medium and a program product. The risk account determination method comprises: constructing a transfer network graph on the basis of transfer information among a plurality of accounts, wherein the plurality of accounts comprise at least one known risk account; converting the transfer network graph into a corresponding bipartite graph, and constructing a quantum walk space on the basis of the bipartite graph; using a plurality of quantum bits to perform quantum state preparation on each node among a plurality of nodes, so as to obtain an initial quantum state corresponding to each node; on the basis of the initial quantum state, taking each node as a starting point respectively to perform a quantum walk in the quantum walk space, so as to obtain a final quantum state corresponding to each node; on the basis of the final quantum state, determining embedded information corresponding to each node; and on the basis of the embedded information, determining, from among the plurality of accounts, an account similar to the known risk account as a risk account.