1030 results about "Data node" patented technology

The invention provides a self-adaptive adjustment Internet operation and maintenance strategy generation method and system, and relates to the technical field of Internet, and the method comprises the steps: 1, dynamically collecting the operation state data of a target operation and maintenance environment through a distributed sensor and an edge node, and generating a multi-dimensional time series data set; 2, performing space-time correlation analysis on the multi-dimensional time sequence data set, determining a reference data node, constructing a two-dimensional correlation structure, and generating a dynamic judgment interval; and step 3, respectively selecting monitoring sample sets in the inner domain and the outer domain of the dynamic judgment interval, generating a trajectory feature sequence according to the time evolution relationship of the sample sets, and calculating a dynamic correction coefficient based on the trajectory feature sequence. According to the method, the self-adaptive circulation control is formed by dynamically adjusting the threshold baseline, the judgment interval and the strategy generation rule, the accuracy and effectiveness of the internet operation and maintenance strategy are improved, and the stability of internet operation is enhanced.

The invention provides a multi-branch network and cross attention multi-source data fusion slope displacement prediction method, which comprises the steps of collecting meteorological data, GNSS node data and radar three-dimensional data, and performing preprocessing and standardization processing on multi-source data; constructing a multi-branch network to perform feature extraction on each type of data; fusing the feature vectors of the multi-source data through a cross attention mechanism; a recent trend is captured by combining a multi-scale memory network with LSTM, periodic features are extracted by a one-dimensional expansion convolutional neural network, and a displacement predicted value is obtained through adaptive fusion; and training the model by adopting a course learning mechanism, and outputting a displacement predicted value. According to the method, the multi-branch network is constructed to carry out targeted feature extraction on different modal data, deep fusion is carried out on multi-source data by using a cross attention mechanism, different modal specific feature extraction, cross-modal association modeling and multi-scale prediction of the multi-source data are realized, potential information in the multi-source data is fully mined, and the multi-source data extraction efficiency is improved. And the accuracy and reliability of slope displacement prediction are improved.

The present application discloses a distributed database-oriented compensating transaction implementation method. The method specifically comprises: a client initiates a transaction request, and determines, by means of an execution directory, whether a transaction SQL exceeds a limit value (S1); a coordination node allocates a timestamp to a transaction initiated by the client in step S1, and simultaneously sends a related operation of the transaction to a data node (S2); upon receiving the transaction request, the data node determines whether to commit or roll back the transaction, and returns an execution result to the coordination node for confirmation and verification, and if the verification fails, the coordination node notifies the data node and performs a compensation operation (S3); and when the operation is completed, the coordination node commits the transaction, performs regular cleaning, and returns the execution result to the client (S4).

The invention relates to the field of data management and knowledge maps, and provides a civil administration data consanguinity knowledge map construction method and device based on a large model, and the method comprises the steps: constructing a basic entity network based on the metadata feature extraction of civil administration data; based on the basic entity network, analyzing the data table structure to generate a data table structure relation model; analyzing the complex structured query language statement, identifying field-level blood relationship, and generating a preliminary blood relationship graph; all physical source tables are traversed, virtual table redundancy and data node repetition are eliminated, and an optimized blood relationship graph is generated; and based on the optimized blood relationship graph, identifying public data nodes and combining field-level data blood relationships to generate a final civil administration data blood relationship graph. According to the method, the problems of difficulty in accurately analyzing complex SQL statements, processing virtual table redundancy and data node repetition in the prior art are solved, and high-precision and automatic civil administration data consanguinity knowledge graph construction is realized.

The invention belongs to the technical field of project management, and discloses a project management big data dynamic optimization method, which comprises the following steps: acquiring project optimization information; constructing a project knowledge graph, and performing graph traversal on the project knowledge graph based on the real-time task set of the project needing to be managed to obtain a corresponding dynamic sub-graph; image reasoning is carried out based on the obtained dynamic subgraph, corresponding project expected information is obtained, whether project risks exist in the project needing to be managed or not is judged based on the project expected information, and if the risks exist, corresponding risk early warning information is generated; and constructing a standby coping strategy set based on the risk early warning information, performing strategy selection optimization based on the dependency relationship among the data nodes in the dynamic subgraph to obtain a corresponding optimal coping strategy, performing real-time monitoring on the execution process of the corresponding optimal coping strategy, and performing information feedback based on the monitoring result. According to the invention, the overall level and efficiency of project management are significantly improved.

The invention relates to a brain inspired data service system and a brain inspired data service method, which are characterized in that a layered architecture is constructed, the layered architecture comprises a data node cluster deployed in a mixed manner, the data node cluster specifically comprises a core node, an acquisition node and a gateway node, and the core node, the acquisition node and the gateway node are respectively used for processing core service data, real-time edge data and cross-domain connection; the small-world connection layer realizes same-domain efficient communication through high-clustering local connection, and breaks a data island through dynamic long-range cross-domain connection; the intelligent control layer integrates a DMN control center, a collaborative state monitoring engine, a prediction analysis module and a reinforcement learning driven resource scheduler. The system initializes a topological structure through a small-world network manager, and when the communication frequency of a cross-domain node exceeds a threshold value, long-range connection is dynamically inserted to ensure that the hop count of a cross-domain path is stably not higher than a preset value. The DMN control center realizes dual-mode switching based on a system load threshold; in an idle period, data pre-cleaning, knowledge graph construction, predictive pre-fetching and resource pre-allocation are executed; and optimizing routing in combination with a graph traversal algorithm in a task period.

The invention relates to the technical field of electronic detection, and discloses an electronic component dispensing detection method. The method comprises the following steps: acquiring dispensing image data of the surface of the electronic component, and generating a standardized dispensing data matrix containing glue position, thickness and uniformity characteristics through standardized preprocessing; constructing a dispensing correction matrix based on an adaptive window frame, and performing spatial reference dynamic correction on the standardized data matrix to obtain a spatial correction dispensing data matrix; inputting the data into a multi-layer sensor fusion network for feature fusion, and outputting a multi-source fusion dispensing data set; constructing a multi-dimensional abnormal feature incidence matrix based on the data set, and identifying abnormal dispensing data nodes by using a dynamic threshold detection algorithm; performing parameter optimization iteration on the multi-source fusion data set by using a gradient descent optimization algorithm to generate an optimized dispensing parameter set; and finally, constructing a three-dimensional visual dispensing quality model, and establishing a dynamic mapping relationship between model parameters and glue physical characteristics. The method can more comprehensively detect the glue quality.

The invention relates to the technical field of power grid data analysis, in particular to a power consumer anomaly detection and risk assessment method, system and device based on spatial-temporal feature fusion and a storage medium. The method comprises the following steps: acquiring multi-source power consumption data of various power consumers, constructing a space-time correlation feature extraction model, and establishing a user behavior space-time coupling relationship through time sequence analysis and spatial distribution characteristic analysis; constructing a multi-dimensional risk mapping model based on a long short term memory network and a convolutional neural network, establishing a nonlinear mapping relation from feature information to a risk assessment result, and identifying an abnormal power consumption behavior; establishing a distributed node credibility evaluation system, and performing credibility quantitative analysis on the data nodes in combination with a fuzzy comprehensive evaluation method; and based on an abnormal detection result and credibility evaluation, intelligent analysis and dynamic early warning of the electric charge management and control risk are realized. The time-space coupling characteristic of power consumption data can be fully mined, and high-precision identification of abnormal power consumption behaviors and accurate detection of non-technical power loss are realized.

The invention discloses a GoldenDB database change risk management and control method and system. The method comprises the steps that change scheme data is obtained, a coding format is recognized, effective change content is extracted, the change content is disassembled into single transactions, semantic analysis is executed, and change types are classified based on keyword matching and a rule base; wherein the change types comprise data change, structure change, parameter change and permission change; generating a distributed execution plan according to the change types, analyzing the distributed execution plan, executing information in parallel, and determining data nodes and computing nodes corresponding to the four change types respectively; respectively constructing risk assessment models in the execution domains corresponding to the four change types; and converting the risk assessment result of each node into a standardized data format, aggregating and analyzing the cross-node common risk, generating a father-child tree management and control report, and executing blocking, rollback or alarm operation according to the risk level.

The invention discloses a visual interaction artificial intelligence algorithm automatic arrangement and deployment method, and belongs to the technical field of artificial intelligence development and deployment. The method comprises the steps that after a training data set is uploaded, a system automatically analyzes a format and generates a visual analysis report; importing an algorithm operation environment and codes, and configuring parameters and a required data set through a visual interface; after a user inputs a training task, a system dynamically schedules an algorithm and a data node automatically arranges a process and generates a directed acyclic graph; the intelligent resource scheduler allocates computing resources and monitors a training state; and after the training is completed, deploying a pipeline publishing model API through servitization. According to the method, the training process of the algorithm can be automatically arranged, the training process of the algorithm is greatly shortened, and the problems that in the prior art, an automatic arrangement function is lacked, automatic parameter mapping is not supported, the resource scheduling strategy is single, and the multi-mechanism joint training requirement cannot be met are solved.

The invention relates to the technical field of transformers, and particularly discloses a transformer monitoring data processing method and system, and the method comprises the steps: obtaining the monitoring data of each part in a target transformer and a corresponding transformer state correlation graph; preprocessing the monitoring data; associating the preprocessed monitoring data to data nodes in a transformer state association graph; when abnormal data nodes occur, associated data nodes are tracked and acquired along causal influence edges based on the abnormal data nodes, and the abnormal data nodes are data nodes with monitoring data deviating from a preset normal range; analyzing the change characteristics of the monitoring data of the abnormal data nodes and the associated data nodes, and obtaining an abnormal mode; generating early warning information according to the abnormal mode, component nodes in the transformer state association graph and physical connection edges; according to the method, timely early warning is realized, the equipment damage risk is reduced, and the safe and stable operation level of a power system is improved.

The invention provides a cloud police service analysis method and system based on big data analysis, and the method comprises the steps: obtaining a real-time police service data flow and a historical police service database of a target region, and carrying out the fusion to obtain a dynamic police service feature set; screening out a key police service feature subset according to a preset police service analysis model training rule to generate a multi-level police service analysis model; inputting the real-time police service data flow into a first police service data node of a real-time analysis layer, and carrying out initial classification on the real-time police service data flow based on a first type of analysis model to obtain an initial classification result set; triggering a second police service data node of a deep verification layer according to the initial classification result set, and performing data verification on the initial classification result set through a second type of analysis model to generate multi-level police service early warning signals; and adjusting analysis model parameters of at least one police service data node in the real-time analysis layer and the deep verification layer, and outputting an optimized police service strategy. According to the invention, the timeliness and accuracy of police service strategy optimization can be improved.

The invention discloses a data query method and device based on a database, electronic equipment and a storage medium. The method comprises the following steps: receiving a data query message sent by a client, and generating a target execution plan based on the data query message; wherein the data query message comprises an execution message; determining the type of the Portal object based on the execution message; and under the condition that the type of the Portal object is anonymous Portal, constructing a first message based on the first data volume and the target execution plan and sending the first message to the target data node, so that the target data node executes the target execution plan and returns query results, and query is stopped immediately until the number of the returned query results meets the first data volume. According to the scheme, the message is constructed according to the data volume and the execution plan under the condition that the type of the Portal object is the anonymous Portal, and the target data node is controlled to only query and return the data meeting the data volume according to the message, so that the waste of database communication and memory resources is avoided, and the database performance is improved.

A system including a media data optimization engine (MDOE) and a method for automatically creating structured data objects for media content rendered in one or more languages in an electronic document of a business entity are provided. The MDOE identifies non-textual objects including media content rendered in one or more languages in the electronic document and generates textual objects in the corresponding language(s) therefrom. The MDOE transforms the textual objects into structured data objects based on configurable criteria and generates a dynamic index-oriented object for the structured data objects specific to the business entity. The MDOE connects the structured data objects to the dynamic index-oriented object by creating linked data nodes therefrom with the dynamic index-oriented object as a core. The MDOE connects the dynamic index-oriented object with the linked data nodes to the electronic document, thereby facilitating dynamic changes to the electronic document and dynamically optimizing the electronic document.

The embodiment of the invention provides a concurrent access control and data processing method and device, a storage medium and a program product. In the embodiment of the invention, a concurrent access control scheme based on a snapshot isolation technology is provided for a database, and the concepts of two-level timestamps, namely a system-level timestamp and a user-level timestamp, are put forward in the concurrent access control scheme; the user-level timestamp performs concurrent access control in a memory state based on the user-level timestamp for data items in a database memory, and the system-level timestamp performs concurrent access control in a system state based on the system-level timestamp for data nodes in an external memory of the database. In the embodiment of the invention, the two layers of timestamps are fused with the snapshot isolation technology, while the advantages of the snapshot isolation technology are exerted, the implementation of concurrent access control can be simplified based on the two layers of timestamps, the concurrent access control can be realized in the memory state and the system state at the same time, and the concurrent performance of the database can be improved.

The embodiment of the invention provides a protocol converter and method for a network card and electronic equipment. The protocol converter comprises a protocol controller, a crossbar switch and a high-speed serial computer extension bus PCIe interface. The protocol controller is provided with a transmit-receive interface which is connected with the data node. The data node is not provided with a processor; the PCIe interface is connected with the RDMA network card and is used for transmitting data between the data node and the RDMA network card; the protocol controller further comprises a sending management unit and a receiving management unit, and the sending management unit is used for receiving data sent by the data node and sending the data to the PCIe interface through the crossbar switch in a message mechanism; and the receiving management unit is used for receiving data transmitted by the PCIe interface through the crossbar switch. By adopting the protocol converter, a data node without a processor can realize data transmission through the RDMA network card, and the data transmission rate of a common node is improved.

The invention discloses a transformer state prediction method fusing vibration and temperature data, and relates to the technical field of data processing. The method comprises the following steps: interacting fault operation and maintenance records of a transformer, and mining a fault associated network; arranging a front-end sensing array, carrying out operation monitoring sampling on a transformer operation scene, and determining sensing sampling data; a state prediction model is supervised and trained by taking a fault association network as a criterion and subjective and objective weighting based on fault features as a criterion, prediction is carried out based on a time sequence data chain, and the time sequence data chain is updated through average weakening processing of data nodes; sending back the sensing sampling data, and combining the state prediction model to update a time sequence data chain and predict the state of the transformer to determine the state of the transformer; and carrying out operation, maintenance, debugging and control on the transformer by taking the transformer state as a reference. The technical problem of low operation and maintenance efficiency caused by inaccurate transformer state prediction in the prior art is solved, and the technical effect of improving the transformer state prediction precision is achieved.

The invention discloses a data protection method and system based on federated learning and homomorphic encryption, belongs to the technical field of data security, and aims to solve the technical problem of how to effectively protect user privacy while ensuring data availability. Comprising the following steps: a medical data node performs model training on a local AI model deployed locally based on a local data set, and uploads an encryption model gradient to a federal coordination server; the global parameters issued by the federal coordination server are decrypted, and gradient updating is performed on the local AI model based on the decryption result of the global parameters; calling the trained local AI model to perform model reasoning on the basis of the reasoning request for the third-party user who passes the identity authentication and has the access permission, and returning a reasoning result to the third-party user; and based on the auditing log, analyzing the running state and resource consumption of the medical data node according to a predefined auditing rule, and generating alarm information based on the abnormal operation.

The invention discloses a data value evaluation method based on a data blood relationship, and relates to the technical field of data processing. The method comprises the following steps: constructing data blood relationship map data based on sensor metadata, communication protocol characteristics and a data blood relationship; obtaining a data value dynamic sorting result; constructing a monitoring index, importing the monitoring index into a tool to configure a data quality automatic monitoring tool, and obtaining a data exception intelligent detection result of the focused data node by using the data quality automatic monitoring tool; and performing data error repair and data processing flow optimization by using the block chain consensus and generative adversarial network to obtain a final data value evaluation result. According to the method, the data blood relationship graph can be efficiently constructed, the data value can be evaluated, the data quality can be monitored, and the data management strategy can be optimized, so that more accurate and reliable data support can be provided for decision making in intelligent traffic.

The invention relates to the technical field of data processing, in particular to a distributed data storage system and method based on artificial intelligence, and the method comprises the steps: obtaining multi-source data information of an enterprise, and numbering the multi-source data information of the enterprise; segmenting the multi-source data, creating abstracts for data types, and calculating weight values of the data types; cleaning different types of data, integrating the cleaned data, and distributing a transmission sequence of the data; segmenting to-be-transmitted data into a plurality of data blocks, and after a response instruction of the distributed data node is received, sending the data blocks one by one; the method comprises the following steps: monitoring the operation state of a distributed data node which performs data transmission, constructing an operation state set of the distributed data node, and identifying an abnormal condition in a data transmission process by comparing historical data with a preset threshold value. The data transmission completeness can be ensured through data breakpoint transmission, and the working efficiency of data transmission can be improved.

The invention discloses a multi-dimensional time series data dynamic evolution analysis method and device, a medium and electronic equipment. The method comprises the steps that time series data are obtained and subjected to structured preprocessing; performing multi-dimensional quantization and attribute endowing on each piece of time series data, and constructing a three-dimensional data node containing a space coordinate, an initial weight and a timestamp; based on the set of the three-dimensional data nodes in a specific time window, constructing a three-dimensional data density field capable of representing data hotspot distribution and intensity in the time window; in the three-dimensional data density field, identifying a density peak region as a core data cluster, calculating the cluster mass according to the density and the coverage range of the core data cluster, and calculating the association strength among different core data clusters; and serializing a plurality of continuous time window data field state snapshots to form a dynamic evolution view capable of showing data hot spot generation and extinguishing and data cluster fusion and splitting processes. According to the invention, abstract multi-dimensional data is converted into a visual three-dimensional density field.

The invention belongs to the technical field of essence and fragrance quality monitoring, and particularly relates to an essence and fragrance quality monitoring method and system based on supply chain traceability, and the method comprises the steps: executing supply chain traceability when the monitoring data of an essence and fragrance processed finished product meets a preset abnormality judgment condition, according to supply chain backtracking, a sample to be retested and a node to be retested are determined, quality detection is carried out, so that an original data node causing abnormity is positioned, and the node is determined as an abnormal node; after the abnormal node is positioned, the deviation value of the abnormal node is calculated based on the reference datum, and the data of the abnormal node is corrected to generate corrected data; after the corrected data is subjected to simulation evaluation, updating the abnormal node by using the corrected data; according to the method, the abnormal nodes are accurately positioned through supply chain backtracking, and the reliability and credibility of monitoring system data are ensured by means of quantitative analysis and correction of abnormal data and by adopting a simulation evaluation and closed-loop updating process.

The invention relates to the technical field of block chains, and particularly provides an Internet of Vehicles data security sharing method, system and device, and a storage medium, and the method comprises the steps: a data node extracts a feature anchor point from original data of the data node, and obtains a feature vector of the original data; coordinating a plurality of data nodes by using a block chain to execute federated learning based on own feature vectors, and aggregating learning results into a global feature map through a security aggregation protocol; generating a data directory based on the global feature map, and generating utility proof for the data directory by using a zero-knowledge-range proof technology; and uploading the data directory with the valid proof to a shared block chain, wherein the shared block chain is used for providing the data directory for the data user. According to the invention, the sharing efficiency of the Internet of Vehicles data is improved, and meanwhile, the security of privacy data is protected.

The invention discloses a carbon emission abnormity traceability method and device, a medium and equipment, and belongs to the field of carbon emission, and the method comprises the steps: receiving a traceability request containing a to-be-verified data identifier, positioning target link data from a block chain storage, and executing signature verification and Hash consistency verification, thereby guaranteeing the authenticity and consistency of the data. And if the verification of the target link data is passed, verifying the inclusion relation of Merkle tree root hash of each link upwards layer by layer to confirm the integrity of the full-chain data. And if verification failure of any level is found, the feature data of the level is combined with an anomaly detection model based on an isolated forest algorithm, and an abnormal data node is positioned. And then, according to the signature information of the abnormal data node and the preset tracking secret key, a group signature verification and private key derivation process is applied, the responsibility participant is traced, and the tracing process of the carbon emission abnormity is completed, so that the problem that the carbon emission abnormal point cannot be accurately and efficiently traced in the prior art is effectively solved.

A system for event-driven distributed data network analysis, the system includes: a multitude of domain data nodes, each comprising a stream storage, a multitude of transformation pods, and a product API, with each domain data node ingesting heterogeneous event streams, validating schema compliance, performing enrichments, and making data products available under versioned schema contracts; each domain data node further comprises a hardware-based edge intelligence appliance comprising an enclosure, a compute module with heterogeneous processing units selected from CPUs, GPUs, and TPUs, a secure enclave module for confidential execution of transformation code on encrypted data, a protocol-structured stream storage medium supporting multi-level retention, and industrial I / O interfaces configured for direct interaction with sensors, programmable logic controllers, and cloud-native services; an event mesh backbone processor configured to connect the multitude of domain data nodes via a publish-subscribe architecture, with the backbone guaranteeing exactly one-time delivery, partition-level sorting, and geo-replicated durability; a Contextual Intelligence Engine coupled with the Event Mesh Backbone processor, the engine comprising a semantic graph memory and a Streaming Graph Reasoner configured to map raw events into an ontology, perform graph joins in real time, and recognize composite causal patterns across multiple domain data nodes; a governance and policy control unit integrated into both the data and control layers. This layer enforces zero-trust security, role-based access control, real-time provenance tracking, and regulatory compliance; and An actuation interface coupled with the contextual intelligence engine and the governance layer. The actuation interface is configured to generate control commands for operational technology devices and digital workflows, thus closing the loop from data acquisition to autonomous decision-making.

The invention provides a ship inspection data processing method and a ship inspection data processing system. The ship inspection data processing method comprises the following steps: constructing an inspection data storage structure tree; numbering all nodes in the test data storage structure tree; generating file identification information for the file nodes; generating data identification information for the data nodes; obtaining an inspection file of an electronic edition and basic inspection data, and then respectively adding corresponding file identification information and data identification information into the inspection file and the basic inspection data; storing the inspection files to corresponding file nodes according to the file identification information; and storing the basic inspection data to the corresponding data nodes according to the data identification information. According to the technical scheme, structured and electronic storage of the inspection file and the basic inspection data can be achieved, the whole process of all the inspection data is traceable, the efficiency of inspection personnel is improved, and deep integration of the inspection data and other management systems is facilitated.

A heterogeneous multi-hop wireless network is provided. The network consists of single-link data nodes, multi-link data nodes and data centers. The single-link data node supports one communication interface, the multi-link data node supports two communication interfaces and the data center is considered as multi-link node. The routes from all data nodes to data centers are given. The network is configured to schedule data transmissions from all data nodes to data centers without transmission interference and channel access delay. The routing link scheduling problem is formulated as an optimization problem with constraints. Due to the NP-Hard complexity of the formulated optimization problem, the scheduling policies are then parameterized for the application of graph neural network techniques. The parameterized optimization problem is solved using primal-dual approach with zero duality gap. A heterogeneous graph neural network (HetGNN) algorithm is provided to train the primal-dual problems.

Embodiments of the present disclosure provide a concurrent access control method, a data processing method, a device, a storage medium, and a program product. In the embodiments of the present disclosure, a concurrent access control scheme based on snapshot isolation technology is provided for a database. In the concurrent access control scheme, the concept of two-level timestamps is introduced: system-level timestamps and user-level timestamps; the user-level timestamps are applied to data items in a memory of a database, and concurrent access control is performed in a memory mode on the basis of the user-level timestamps; and the system-level timestamps are applied to data nodes in a persistent storage of the database, and concurrent access control is performed in a kernel mode on the basis of the system-level timestamps. In the embodiments of the present disclosure, the integration of the dual-level timestamps with the snapshot isolation technology can simplify the implementation of concurrent access control on the basis of the dual-level timestamps while the advantages of the snapshot isolation technology are exerted, and concurrent access control can be realized in both the memory mode and the kernel mode, thereby improving the concurrency performance of databases.

The invention discloses a distributed cloud native storage-oriented small file merging optimization method, which comprises the following steps of: converting data into a structure suitable for analysis by analyzing an HDFS (Hadoop Distributed File System) access log, and proposing a relevance analysis algorithm based on a user access mode. And discovering association rules from the file access records by using an FP-Growth algorithm: compressing transaction data and determining a frequent item set by constructing an FP tree, and generating the association rules conforming to the support degree and the confidence coefficient from the frequent item set. Respectively processing the small files with different relevance according to the generated rule; searching the small files with strong relevance, and merging the files by adopting a small file merging strategy based on a Huffman tree; small files with weak relevance are continuously added into a waiting queue, and when the queue meets the HDFS data node storage size condition, merging storage is carried out. According to the method, the file storage space of the HDFS is optimized, and the access efficiency of massive small files is improved. The method belongs to the field of correlation analysis and small file storage.

The invention relates to the technical field of data networks and data security, and discloses a data asset value evaluation method and system based on dynamic network analysis, and the method comprises the following steps: S1, constructing a data asset catalog, extracting a data feature vector of each data node, forming a data network, connecting the data nodes through an association relationship, distributing a weight for the incidence relation between the data nodes; s2, performing quality evaluation on each data node, calculating a quality value, evaluating a matching value based on a matching degree between the data node and a user demand, and calculating an access frequency of each data node, the access frequency being used for subsequent value calculation; and S3, based on the historical data and the access log, training the graph neural network model. According to the method, the value evaluation theory based on the data relation is adopted, engineering implementation is integrated, and the effect of more scientifically and reasonably evaluating the value when the data enters the table is achieved.