758 results about "Graph structured data" patented technology

The invention relates to the technical field of red tide anomaly detection, in particular to an ocean red tide anomaly detection method and system fusing multi-source remote sensing and a graph neural network. The method comprises the following steps: acquiring remote sensing image data, unmanned aerial vehicle image data and monitoring data of a monitoring point; performing data preprocessing on the acquired remote sensing image data and unmanned aerial vehicle image data; performing feature extraction and feature fusion on the remote sensing image and the unmanned aerial vehicle image to obtain remote sensing feature data; constructing a space-time diagram structure based on the monitoring data of the monitoring points to obtain diagram structure data; based on a cross-modal comparison self-supervised learning mechanism, carrying out consistency representation learning on a remote sensing feature mode and a graph structure feature mode; by introducing multi-source heterogeneous data and fusing a graph neural network modeling means, the limitation of a single data driving method in the aspects of coarse red tide recognition granularity, low space-time precision and the like is effectively broken through, and the meticulous property and global perception ability of red tide feature modeling are remarkably improved.

The invention discloses a slope deformation monitoring and dynamic early warning method and system based on multi-sensor data, and relates to the technical field of slope monitoring, and the method comprises the steps: collecting multi-source sensor data by using pre-deployed multi-class sensors, constructing graph structure data according to the sensor distribution and the pre-processed multi-source sensor data, and carrying out the graph structure data; a graph convolutional network is used for modeling, and a slope deformation monitoring model is constructed; introducing a clustering federation learning strategy to carry out joint training on the slope deformation monitoring models of the plurality of sites, and carrying out risk grade division by using the trained slope deformation monitoring models; key influence factors of landslide disasters are extracted, an improved firefly algorithm is introduced to dynamically optimize an early warning threshold value, the optimized early warning threshold value and the current risk level are used for judgment, and early warning information is generated. According to the invention, the reliability of monitoring and the timeliness of early warning are improved through multi-source data fusion and intelligent analysis, and the crossing of slope deformation monitoring from single-point static state to networked intelligence is realized.

The invention discloses a mountain torrent disaster risk level prediction method based on a knowledge graph and a graph neural network, and relates to the technical field of risk level prediction, which is used for collecting data related to mountain torrent disaster risks, carrying out data preprocessing operation, constructing a risk level prediction model combining the knowledge graph and the graph neural network and training the risk level prediction model. According to the mountain torrent disaster risk level prediction method based on the knowledge graph and the graph neural network, entities, relationships and attributes can be represented in a graph form through the knowledge graph, complex knowledge and information can be effectively integrated and expressed, graph structure data can be effectively processed through the graph neural network, relationships between nodes can be captured, and the risk level of mountain torrent disasters can be predicted. Modeling and analysis of a complex system are achieved, a knowledge graph and a graph neural network are introduced into mountain torrent disaster risk level prediction, and the method aims at fully utilizing multi-source data, mining potential risk modes and laws, improving prediction accuracy and reliability and providing scientific basis and decision support for disaster prevention work.

The invention relates to the technical field of electric power operation and maintenance, and particularly discloses a power distribution network fault line selection method considering topological structure change, and the method comprises the steps: collecting zero-sequence current data, converting the zero-sequence current data into graph structure data, and providing input information representing the topology and current characteristics of a power distribution network for a model; learning the adjacency matrix and the node features by using a graph attention network until the model can accurately predict the probability of the single-phase earth fault of the power distribution network; based on the node fault probability output by the model, a threshold value is set to identify and position a specific fault line; and inputting field zero-sequence current data into the trained model so as to quickly and accurately judge the fault position of the power distribution network. According to the power distribution network fault line selection method considering topological structure changes, the efficiency and accuracy of fault line selection can be improved, and therefore effective fault monitoring can be achieved in a wider power distribution network environment.

The invention discloses a point cloud data processing method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring initial point cloud data to be sent; dividing the initial point cloud data into a group of point cloud blocks; performing graphical representation on the basis of the point cloud data in each point cloud block in the group of point cloud blocks to generate a group of graph structure data, one graph structure data in the group of graph structure data corresponding to one point cloud block in the group of point cloud blocks, and the graph structure data being used for representing features of the corresponding point cloud block; and encoding the group of graph structure data to obtain a group of data packets, and sending the group of data packets to a target terminal so as to perform decoding reconstruction at the target terminal to obtain target point cloud data. The technical problems that the processing efficiency of the point cloud data is low and the effect cannot be quickly achieved in the related technology are solved.

The invention relates to the technical field of fault detection, and particularly discloses a prediction reconstruction framework causal perception space-time network for explaining anomaly monitoring in a complex industrial process, comprising the following steps: S01, constructing graph data E (V) and a causal graph; automatically adjusting the fusion proportion of the time-frequency characteristics according to the data characteristics so as to ensure that the model can comprehensively capture the information of the data in the time domain and the frequency domain; secondly, introducing a residual image attention network (RGAT), and converting the image data E (V) into image structure data G (S (V), E (V)); and S03, reconstructing a prediction error by adopting a variational automatic encoder (VAE), learning an error mode of normal data, providing an anomaly judgment AD (V) for anomaly detection, analyzing a causal relationship between data in combination with a causal graph, and positioning an anomaly reason according to an anomaly score, so as to form a prediction result. The network solves the problem that a traditional monitoring network is high in false alarm rate.

The invention requests to protect a rotating machine noise robust fault diagnosis method based on a mixed domain graph neural network. Comprising the following steps: collecting vibration data, and dividing an original vibration signal into a plurality of sections of subsamples with the length of 1024 by using non-overlapping sampling; converting the constructed subsample signal vibration signal into a frequency domain signal by using fast Fourier transform (FFT); taking the subsample frequency domain signals as nodes, the frequency spectrums as node features and the fault types as node labels, and then constructing the nodes into a graph structure; dividing the generated graph structure data set into a training set and a test set; and performing fault diagnosis on a test set by using the trained model, realizing self-adaptive weight adjustment by using a gating mechanism to fuse mechanical features, outputting a diagnosis result, calculating a performance index and performing result visualization analysis. The result shows that compared with a single-domain graph neural network, the method has higher accuracy and noise immunity, and end-to-end rotating machine fault diagnosis under the strong noise condition can be effectively achieved.

The invention discloses an intelligent construction and retrieval method for a medical knowledge graph based on GraphRAG and LLM. The intelligent construction method of the medical knowledge graph comprises the steps of data collection and processing, text segmentation, entity and relation extraction, entity analysis, weak connected component identification, word distance filtering, LLM evaluation, element abstract generation, graph community generation and community abstract generation. The intelligent retrieval method comprises the steps of knowledge graph vector library construction, text vector library construction, user question entity extraction and vectorization processing, multi-layer entity and relation retrieval, text retrieval, LLM summarization, result output and user feedback collection. According to the method, the GraphRAG and the LLM large model technology are combined, and efficient retrieval and accurate presentation of the medical information are achieved. According to the system, the accuracy and efficiency of medical literature retrieval are remarkably improved through deep integration of graph structured data and natural language processing capacity.

The invention discloses a head-mounted brain body workability assessment method based on a space-time dynamic fusion network, belongs to the technical field of artificial intelligence and human body signal acquisition, and solves the problems that an existing method cannot comprehensively reflect the comprehensive state of an individual, and interaction and potential risks among multiple factors are difficult to reveal. The method comprises the steps that physiological parameter signals are collected based on wearable equipment, and an attention and fatigue prediction result is output by an LSTM prediction model; according to the method, the graph structure data packet is analyzed and processed based on the pre-constructed space-time dynamic fusion network model, the working state signal characteristics of the plateau high-risk operating personnel can be accurately evaluated, the space-time graph neural network (STGNN) can capture the complex relation, the high-dimensional topological characteristics are extracted, and the high-risk working personnel can be accurately evaluated. The dynamic event-driven pulse neural network (deSNN) can convert the pulse sequence features into pulse sequence features so as to describe feature values more accurately, iterative optimization enables the pulse sequence features and the pulse sequence features to cooperate, and the accuracy and reliability of time-space dynamic fusion network model evaluation are improved.

The invention relates to the technical field of machine learning, and discloses a decision tree data model establishment method, which comprises the following steps of: acquiring heterogeneous data sources such as a structured data table, a time sequence data stream and graph structure data through distributed nodes, sampling the time sequence data stream by using a dynamic sliding window, and vectorizing the graph structure data through a graph embedding algorithm; a multi-stage feature selection model is constructed to screen features, and a dynamic decision tree generation framework adopting an adaptive splitting criterion is established based on the features. A tree structure is adjusted by applying a multi-objective optimization algorithm, and the performance is improved by introducing an incremental pruning mechanism. And the online model updating module monitors data distribution change, reconstructs a local sub-tree in good time, and injects noise to protect data privacy in combination with a differential privacy protection mechanism. According to the method, heterogeneous data is effectively processed, the model classification precision is improved, the complexity is reduced, the generalization ability is enhanced, the model can be updated online, and the data privacy is protected. The electronic equipment calls related instructions to execute the method, and efficient data processing and analysis can be achieved.

The invention discloses an intelligent management system and method for knowledge graph quality evaluation and self-repairing, belongs to the technical field of knowledge graphs, and aims to solve the problems that in traditional knowledge graph management, manual auditing efficiency is low, an effective automatic repairing means is lacked, and data complexity and real-time changes are difficult to deal with. The system firstly collects multi-source heterogeneous data in a target field, cleans the data through a deep learning noise recognition model, extracts entities and relationships by using a natural language processing technology, and adds metadata to convert the entities and relationships into graph structure data; then, a graph framework is defined based on the ontology, entity semantic alignment is achieved in combination with a graph neural network, and a knowledge graph is constructed by complementing implicit relations with the help of a pre-training language model. Then, the quality of the atlas is quantitatively evaluated through a four-layer quality evaluation system, meanwhile, a repair scheme is generated based on vulnerability feature extraction, knowledge base matching and decision fusion, and intelligent self-repair is achieved; the map can be monitored in real time and evaluated regularly, a repair strategy and a knowledge base are optimized through reinforcement learning, it is ensured that the map is kept accurate and time-efficient for a long time, and the practical value is improved.

The invention relates to a multi-modal trajectory prediction method based on vehicle interaction diagram space-time decoupling coding, and belongs to the technical field of automatic driving automobiles. The method comprises the following steps: generating graph structure data according to a traffic scene trajectory data set; constructing an interaction graph considering multiple traffic factors based on the graph structure data; when the characteristics of the interaction graph are updated, capturing the change of the node characteristics of the interaction graph on a time sequence through a time sequence coding module; multi-modal prediction is completed by constructing a trajectory decoding module and a fractional decoding module; and designing a prediction model loss objective function and carrying out training. According to the method, the influence of multi-source factors on motion prediction is combined through a heterogeneous graph attention method, decoupling coding is carried out on spatial-temporal characteristics, and the characteristics on a time sequence are better captured. Compared with a traditional vehicle track prediction method, the method can complete more refined prediction, and is better in scene generalization.

The invention provides a network traffic data security assessment method and system based on deep learning. The method comprises the following steps: converting original network traffic data into a graph structure data set comprising a topological structure, node attributes and time sequence behaviors; in the process, the time-space fusion input tensor is formed through the association strength between adjacent matrix and Laplacian matrix coding network entities and the fusion of time sequence characteristics extracted by time window slices. Compared with traditional flow analysis which only pays attention to a single protocol or a rate threshold value, the method achieves global relevance expression of network behaviors through graph structure modeling. Through graph structure modeling, multi-dimensional feature fusion and probabilistic risk assessment, the method can adapt to dynamic change of network topology and continuous evolution of an attack mode, so that a final assessment result is more accurate.

The invention relates to the technical field of industrial Internet of Things detection, in particular to an industrial Internet of Things equipment detection method and system based on a federal map neural network. The method comprises the steps of obtaining real-time operation state data of each device and a measurement value of a sensor, constructing a topological graph by using input data as device node features, introducing a knowledge graph based on the topological graph, fusing a client device state and knowledge graph information, and obtaining a client device state information fusion result. Introducing an attention mechanism to adaptively adjust the weight of the local model of the client; the method comprises the following steps: fusing node features, performing feature enhancement based on the fused node features, establishing a federated graph neural network client dynamic scheduling strategy based on reinforcement learning, and performing a node classification task of graph structure data by using a global model. The method improves the performance of the model in the absence of annotation data, enables the model to learn more robust features, and improves the judgment accuracy of equipment faults.

The invention discloses an alarm data generation method and device and electronic equipment, and the method is applied to the field of public safety protection, and the method comprises the steps: collecting the multi-modal data of each region through a sensor, extracting the feature data of the multi-modal data, and obtaining a feature data set; performing feature fusion on the feature data set, converting fused feature data into graph structure data, and inputting the graph structure data into a target graph model to establish a target relation graph; adopting a quantum search algorithm to match abnormal objects in the target relation graph to obtain a matching result; and generating alarm data based on the matching result and the target processing request. Through application of the method and the device, the problems that data processing is delayed and public security risks are increased especially under the condition of large data volume or poor network conditions due to the fact that data collected by each area needs to be sent to a central server for processing and analysis when the public security condition is monitored and analyzed in real time in related technologies are solved.

The invention discloses a bank anti-money laundering monitoring method based on a graph neural network. The method comprises the following steps: S1, collecting data from a transaction database of a bank; s2, integrating all the collected data into multi-dimensional graph structure data by using a data fusion technology; s3, constructing an enhanced transaction network graph based on the multi-dimensional graph structure data; s4, using a depth map neural network model to carry out deep learning on the enhanced transaction network graph; s5, performing intelligent monitoring and analysis on new or continuously performed transaction activities by using the trained depth map neural network model; s6, according to a monitoring result in the step S5, continuously optimizing and updating the graph neural network model by using a real-time feedback loop and a theme perception adaptive mechanism; and S7, generating a report. According to the invention, the depth map neural network model is introduced, and the map neural network model considers the complexity and dynamic nature of a transaction network, so that money laundering behaviors can be identified more accurately.

The invention provides an electric power work ticket content intelligent verification method and system, and the method comprises the steps: obtaining the target content information of an electric power work ticket, and determining the target graph structure data corresponding to the target content information according to the target content information of the electric power work ticket, finally, inputting the target graph structure data into a pre-trained dependency verification model to obtain a first verification result; the dependency relationship verification model is obtained by training graph structure data corresponding to the historical power work ticket and a verification result corresponding to the graph structure data, and is used for obtaining a verification model of the dependency relationship among the entities in the target content information. According to the technical scheme, the dependency relationship between the entities in the power work ticket can be determined by using the dependency relationship verification model, and whether the power work ticket has logic errors, dependency relationship missing or dependency relationship conflicts or not is judged, so that a user can be efficiently assisted to determine the accuracy of the power work ticket based on the dependency relationship missing or the dependency relationship conflicts.

The invention discloses a service database dialogue method and device, electronic equipment and a medium, and the method comprises the steps: carrying out the cleaning and partitioning of original knowledge data, and obtaining text blocks; carrying out data extraction and construction by utilizing a large language model to obtain graph structure data; detecting and dividing the graph structure data through a community detection algorithm to obtain a plurality of communities; generating a corresponding community abstract for each community through a large language model; performing embedding processing on the community abstract to obtain a first vector; in response to an input question of the target object, performing similarity matching on the first vector to obtain candidate communities, generating an intermediate answer for each candidate community, and obtaining a correlation score of each intermediate answer and the input question; taking the candidate community with the highest correlation score as a target community; and based on the node attribute information corresponding to the target community, generating a target answer by using a large language model. Business database dialogues can be efficiently achieved, and the method can be widely applied to the technical field of data processing.

The invention discloses an equipment operation and maintenance decision-making method based on large-scale knowledge graph multi-granularity learning reasoning, which comprises the following steps of: firstly, extracting a multi-granularity sub-graph of a subject entity in an equipment operation and maintenance knowledge graph, and integrating multilevel information such as an equipment state, a fault mode and an environmental factor to form a relation path set; secondly, performing joint coding on a question text and a relation path through an attention enhanced word embedding module based on a graph convolutional network, forming a word embedding vector, calculating a similarity score between a relation in the path and the question, pruning a sub-graph, and reducing the calculation amount; and finally, fusing a plurality of particle size sub-graphs after pruning processing, inputting the fused particle size sub-graphs into a reasoning decision module, extracting feature information from graph structure data by the reasoning decision module through graph convolution operation, dynamically adjusting a GCN learning process in combination with LSTM, finally realizing question reasoning and answer generation, and ensuring the accuracy of an equipment operation and maintenance decision.

The invention provides a graph representation learning method based on sparse and far and adjacent node enhancement, and relates to the technical field of data enhancement. The method comprises the following steps: obtaining an initial node feature matrix, an adjacent matrix and an original classification label according to an obtained to-be-enhanced graph structure data set, and dividing a training set and a test set; reinforcing node features of the training set by using a feature enhancement technology to obtain a sparse enhanced graph structure data set; then, in combination with a node mixing technology, mixing of features and classification labels is performed on intra-class nodes and inter-class nodes, so that the accessibility of far-adjacent nodes is increased, and a far-adjacent enhanced graph structure data set is obtained; inputting the far-adjacent enhanced graph structure data set into the graph neural network model to carry out graph representation learning and training so as to obtain optimal node representation and a trained graph representation learning model; and finally, performing model test evaluation to obtain an evaluated model. According to the method, the defects of a traditional graph neural network in processing information aggregation of sparse nodes and far and adjacent nodes are overcome.

The invention discloses an electric power Internet of Things equipment anomaly detection method and system based on federal map learning. The method comprises the following steps: constructing graph structure data; in a client layer, using a graph neural network as a local node classifier of a downstream task, and using an adaptive graph interpolation generator to repair sub-graphs and lost cross-sub-graph connections; adopting a plurality of servers as central nodes, and uploading obtained model parameters to the central server of a local area after a client is locally trained to converge; in the server layer, potential global features of covered clients are obtained according to an automatic encoder, model parameters are allowed to be transmitted between adjacent servers, and a global model is obtained by adopting an aggregation algorithm and issued to each client; and after each client updates the model, the next round of training is continued until the optimal detection model is obtained. Potential connections between sub-graphs are mined by using an adaptive graph interpolation generator, and the blank of lack of cross-client topological information is filled.

The invention discloses a multi-layer network flow dynamic monitoring and analysis method based on a graph neural network. The method comprises the following steps: S1, collecting data of each node in real time and preprocessing the data; s2, dividing the preprocessed data based on a preset time window, and constructing graph structure data; s3, carrying out topology learning on the constructed graph structure data by adopting an improved EvolveGCN, and generating final feature representation of dynamic change of a network topology structure; s4, carrying out modeling on the preprocessed data by utilizing Graph WaveNet, and extracting final feature representations of space-time dependency and local spatial correlation; s5, performing feature fusion to generate a comprehensive feature vector; s6, generating a final monitoring result based on the comprehensive feature vector; and S7, transmitting the final monitoring result to a network flow management platform for data processing and analysis. According to the method, an efficient and scientific optimization scheme can be provided in dynamic monitoring of the network traffic, and remarkable technical values and economic benefits are brought to practical application.

The invention provides an insurance product pushing method and system for modeling by using user interests, which can accurately capture relevance and characteristics between user behaviors by constructing target interaction behavior graph structure data, and perform user interest modeling by using a logistic regression network and a random forest tree network respectively, so as to improve the user interests. And two types of modeling data are fused, so that the interests, preferences and potential demands of the user can be reflected more comprehensively. In addition, network parameter learning is carried out by adopting different example product interaction behavior data sequences, so that the accuracy and robustness of user interest modeling are further improved. Finally, insurance product pushing is carried out based on the target user interest modeling data, the pushing accuracy and the user satisfaction degree can be remarkably improved, and data support is provided for personalized and intelligent recommendation of insurance products.

The invention provides an industrial time sequence anomaly detection method and device and computer equipment, and belongs to the technical field of industrial time sequence anomaly detection. The method comprises the following steps: acquiring industrial time series data; the time sequence data are converted into graph structure data and then serve as model input data to be input into a reconstruction model, reconstruction data are obtained, and the reconstruction model comprises a digital composite graph attention network; and if the degree of difference between the reconstruction variable data of a certain timestamp in the reconstruction data and the original variable data corresponding to the timestamp in the model input data reaches a preset abnormal threshold, determining that the state of the original variable data corresponding to the timestamp is abnormal. The above reconstruction model extracts the superior representation of the variable interaction network based on knowledge according to the knowledge graph related to the industrial system, and fuses the superior representation with the time sequence dependency based on the sequence, thereby reducing the risk that the model learns pseudo-correlation, and improving the anomaly detection performance and anomaly interpretability of the industrial time sequence.

The invention relates to the technical field of mining equipment management and maintenance scheduling, in particular to a mining equipment maintenance task priority intelligent control method and system. Comprising the steps of performing data preprocessing on acquired mining equipment operation parameters; generating graph structure data based on the preprocessed mining equipment operation parameters; performing feature representation on the graph structure data by using a multilayer graph convolutional neural network, and outputting a health score; calculating a resource constraint score according to the obtained resource data; calculating a maintenance task priority based on the health score and the resource constraint score; carrying out descending sorting on the maintenance tasks according to the priority; and carrying out dynamic planning optimization based on a multi-stage decision, and solving to obtain a global optimal scheduling scheme. According to the method, GNN is introduced into mining equipment health state evaluation for the first time, and the evaluation precision is remarkably improved through equipment associated information modeling and node embedding representation learning.

The embodiment of the invention discloses a power distribution network fault positioning method and device, electronic equipment and a storage medium. The method comprises the following steps: obtaining first graph structure data of a target power distribution network, the first graph structure data comprising first zero sequence current data of each test node in the target power distribution network and a first adjacent matrix corresponding to the target power distribution network, the first adjacency matrix is obtained based on the connection relation of the test nodes; inputting the first graph structure data into a pre-trained fault positioning model to obtain a fault probability of each test node; and obtaining a fault section of the target power distribution network according to each fault probability. According to the technical scheme provided by the embodiment of the invention, the fault section in the power distribution network can be quickly and accurately positioned.

The invention relates to the technical field of unmanned aerial vehicle anti-collision path planning, in particular to an unmanned aerial vehicle ad hoc network anti-collision path planning method based on a graph neural network, and the method comprises the steps: obtaining the topographic data of a task airspace, recording the initial positions, target positions and motion constraint conditions of all unmanned aerial vehicles, and constructing unmanned aerial vehicle graph structure data; according to the unmanned aerial vehicle graph structure data, taking each unmanned aerial vehicle as a unit node, and representing a potential collision relationship between two adjacent unmanned aerial vehicles by a side between the nodes; initializing features of the nodes and the edges, and completing training of an unmanned aerial vehicle graph neural network model based on the input features of the nodes and the edges as input of the preset unmanned aerial vehicle graph neural network model; and performing collision risk prediction based on the unmanned aerial vehicle graph neural network model, and generating and outputting an optimal flight path. According to the invention, through the graph neural network and multi-objective optimization, intelligent cooperative flight of the unmanned aerial vehicle group is realized, the collision risk and energy consumption are significantly reduced, and the network connectivity and task efficiency are ensured at the same time.

The invention discloses a transient power angle stability evaluation method, system and device for a power system and a medium. The method comprises the following steps: S1, generating a simulation result of an operation scene by using a time domain simulation function of power system simulation software as a sample, and incorporating the simulation result into a sample set; s2, sample data processing is carried out, bus types in the power system are marked, sample labels are marked, and the power system and simulation results generated in the step S1 are converted into graph structure data; s3, dividing the sample set into a training set, a verification set and a test set; s4, constructing a heterogeneous space-time neural network model; s5, training the heterogeneous space-time neural network model; and S6, inputting data of an actual to-be-evaluated scene into the trained model for evaluation to obtain a stability result. According to the method, the bus type and the heterogeneous space-time neural network model are marked, the difference of transient characteristics of different types of equipment in the power system is considered, and high-precision evaluation of transient power angle stability under the double-high power system is realized.

The invention provides a TSP path combinatorial optimization method and system based on reinforcement learning, and the method comprises the steps: constructing an anisotropic graph neural network as a backbone network of a diffusion model through the combination of solution distribution learning and combinatorial optimization target learning, capturing a complex relation in graph structure data through the representation capability of the network, and carrying out the optimization of a TSP path. And probability distribution is modeled by using a diffusion model in a single-to-Markov forward process. In addition, two kinds of accelerated sampling methods DDIM and DPM-solver of diffusion models are introduced, the sampling process of denoising is accelerated, and the training efficiency is improved. According to the method, direct preference optimization can be introduced into a diffusion model to further provide preference-guided combinatorial optimization (PGCO), so that the generalization ability and adaptability of a traveling salesman problem (TSP) solving model are improved, and a more efficient and high-quality model for solving large-scale combinatorial optimization is provided.

The invention belongs to the field of big data, and relates to a data processing method which comprises the following steps: acquiring initial data; sending the initial data to a pre-constructed real-time queue system, wherein the real-time queue system is used for caching and scheduling the received data; according to a preset priority strategy and a preset scheduling strategy, performing real-time processing on the initial data in the real-time queue system to obtain candidate data; analyzing the candidate data by adopting a preset graph data model to obtain graph structure data corresponding to the initial data; and importing the graph structure data into a graph database. The invention further provides a data processing device, equipment and a medium. In addition, the invention further relates to a block chain technology, and the initial data, the candidate data and the graph structure data can be stored in a block chain. According to the method, the real-time performance, accuracy and efficiency of importing the data into the graph database can be improved.