804 results about "Smart data" patented technology

A system and method for adaptive real-time multi-modal compression with dynamic resource allocation provides intelligent compression optimization based on continuously monitored device conditions. The system monitors battery level, CPU utilization, and memory availability while classifying incoming multi-modal data streams comprising image, audio, text, and sensor data to determine processing priorities. Multi-objective optimization balances compression efficiency, reconstruction quality, and energy consumption using evolutionary algorithms that generate optimal parameters for an adaptive variational autoencoder. The autoencoder features dynamically selectable processing complexity, adjustable latent space dimensionality, and modality-specific processing layers. The system automatically switches between operational modes including emergency mode triggered by resource constraints, which applies maximum compression settings and intelligent data triage. Continuous learning adapts compression parameters based on observed performance outcomes, improving future optimization decisions. The system enables homomorphic operations on compressed data and provides enhanced compression performance under varying resource constraints across diverse edge computing applications.

The invention relates to the technical field of intelligent data fusion deduction, in particular to a task planning method and system based on intelligent deduction, and the method comprises the steps: constructing a rule knowledge graph with weight annotation through integrating a real-time battlefield situation and a preset rule library, and carrying out the task planning through the real-time battlefield situation; a large-model intention understanding engine is used for analyzing a commander instruction to generate a five-dimensional intention vector, an initial scheme is generated by combining a double-engine mechanism of a rule hard constraint engine and a large-model soft constraint engine, and graph weights are dynamically adjusted through reinforcement learning to optimize a rule path. The system monitors node conflict frequency in real time, adopts a near-end strategy optimization algorithm to re-distribute weights for high-frequency conflicts, activates a new rule injection mechanism when the intention conformity is insufficient, and finally outputs a three-level structured scheme including a main scheme, an alternative scheme set and a risk early warning report, thereby realizing an intelligent closed loop from situation awareness to scheme generation. And the command decision-making efficiency and the combat collaboration are obviously improved.

The invention discloses a cloud edge cooperation method for AI intelligent Internet of Things equipment data processing, and relates to the technical field of data processing, and the method comprises the steps: S1, intelligent data collection, S2, edge side AI preprocessing, S3, edge-cloud end cooperation reasoning, S4, intelligent data transmission, S5, cloud end AI big data analysis, S6, real-time feedback and self-optimization, S7, adaptive resource scheduling, and S8, full-link visualization. Through AI-driven dynamic sampling and multi-modal data fusion, the efficiency and precision of data acquisition are optimized, redundancy or omission caused by fixed sampling is avoided, meanwhile, the transmission load is reduced, layered task dynamic unloading and intelligent transmission protocol optimization are achieved, the flexibility and stability of cloud edge collaboration are improved, and the cloud edge collaboration efficiency is improved. Manual intervention is reduced through a real-time feedback and self-optimization mechanism, the autonomy of the system is enhanced, and the interpretability and fault diagnosis capability of the system are remarkably improved through a visual panel and a causal reasoning model.

The invention provides an intelligent data query method based on a natural language, and relates to the technical field of intelligent data processing and natural language interaction.The intelligent data query method comprises the steps that firstly, enterprise original data is subjected to standard treatment, and a standardized theme database and a data directory and index definition document are constructed; key semantics are extracted based on unstructured knowledge, and a domain knowledge vector library is fused and constructed by combining document text fragments and vector representation of a mapping relation between historical questions of a user and an SQL (Structured Query Language). And after receiving a natural language question of a user, calling a large language model to identify a task type, and distinguishing knowledge questions and answers, data query and complex analysis. Executing corresponding operations according to different types: directly retrieving a vector library by knowledge questions and answers to generate answers; extracting keywords in data query and generating a query request in combination with context; and in the complex analysis, predefined workflow is judged and executed or intelligent agent processing is called, and query or analysis requirements are output.

The invention discloses an artificial intelligence data analysis method and system based on machine learning, and the method comprises the following steps: 1, collecting and standardizing original data, and carrying out the embedding processing, and obtaining a data feature vector; 2, mapping the data feature vector into a Riemannian manifold space; 3, calculating the distance between every two sample points by using Gromov-Hausdorff measurement, and establishing an initial causal latent map; 4, constructing a causal potential energy tensor field; 5, obtaining an evolved causal latent map by adopting a Ricci curvature disturbance mechanism; 6, calculating a geodesic distance in the evolved causal latent map as an information propagation path; 7, constructing a causal relationship model; and 8, executing anti-fact reasoning based on the causal relationship model, simulating the causal influence of input variable disturbance on an output result, and performing visual output. According to the method, Riemannian manifold embedding and curvature evolution methods are fused, and intelligent data analysis of causal structure modeling is realized.

The invention provides an intelligent data labeling method and system based on multi-modal fusion and large model verification, belongs to the field of artificial intelligence and data processing, and innovatively fuses multi-modal information such as an OCR recognition result, a layout structure, original image visual features and deep semantic analysis of a large language model (LLM). And a precise automatic labeling result credibility evaluation mechanism is constructed. According to the method, various errors in automatic labeling can be accurately recognized and adaptively corrected, and the errors comprise conventional error correction based on hard coding rules and complex semantic error correction driven by LLM. Meanwhile, the system can continuously optimize the data labeling capability of the system through an efficient man-machine cooperation and closed-loop feedback learning mechanism, and automatically precipitate domain knowledge assets. The invention aims to solve the problems of recognition accuracy bottleneck, heavy manual proofreading burden, lack of intelligent judgment and error correction, knowledge accumulation lag and the like in traditional document data labeling, so that the efficiency, accuracy and automation level of document data labeling are remarkably improved.

The disclosed embodiments provide systems and methods for performing queries via an intelligent query engine. Various embodiments include receiving an input query and parsing the input query into a query representation object; generating an evaluation plan based on the query representation object, wherein the evaluation plan comprises a graph of computation nodes, each computation node specifying a granularity for grouping, associated filters, an aggregation schema, and join dependencies required for node computation, and wherein the evaluation plan is ordered to account for hierarchical dependencies among the computation nodes; translating the evaluation plan into an executable query optimized for a specific target data store, wherein the translation adapts query syntax and join structures to capabilities of a target data store; and executing the translated query on the target data store, resolving dependencies and aggregations according to the evaluation plan to generate query results satisfying the input query.

The invention discloses an intelligent storage system supporting multi-source heterogeneous data fusion management, which relates to the technical field of multi-source heterogeneous data fusion, and comprises a data source adaptation module, an intelligent data source adapter is configured in a DataWorks data integration module, multi-source heterogeneous data is accessed through the intelligent data source adapter, and the data source adaptation module is connected with the DataWorks data integration module; extracting a semantic feature vector and a technical feature set; the neural symbol hybrid inference module inputs the semantic feature vector and the technical feature set into a neural symbol hybrid inference engine, calculates a similarity matrix between multi-source heterogeneous data fields through a BERT-based neural network, and imports the technical feature set into a field knowledge graph constructed by Neo4j for symbol logic verification to generate a unified metadata model; according to the method, a semantic similarity matrix calculated by a BERT-based neural network is combined with symbol logic verification of a Neo4j knowledge graph through a neural symbol hybrid inference engine, so that automatic semantic alignment and logic consistency verification of multi-source heterogeneous data are realized.

The invention relates to the technical field of automatic data analysis, in particular to an intelligent data report generation method and system based on an MCP protocol. The method comprises the following steps: constructing a semantic intention vector through a context semantic structure according to an obtained natural language instruction of a user; constructing a semantic task path graph based on the semantic intention vector by utilizing a knowledge graph; according to the constructed semantic task path diagram, performing intention path and tool capability mapping based on an MCP protocol; constructing a task flow based on an MCP protocol and performing task scheduling optimization; carrying out data source unified modeling based on the constructed task flow, and generating a stable and controllable calculation task flow; and visual expression generation from data to graphs is realized through an intelligent recommendation and structural modeling mechanism. According to the method, a semantic-driven MCP plug-in tool chain and a chart capability component index are constructed, so that a full-automatic closed loop from a natural language intention to data analysis and chart generation is realized, and meanwhile, the use threshold of non-technical users is reduced.

The invention provides a one-way isolation network intelligent data analysis method and device based on a large model and a medium, and belongs to the technical field of data processing and network security. Matching an accessible node area list according to a preset organization structure level; the system receives and analyzes the instruction to determine a query intention and an associated data range; the system converts a natural language query instruction into a cross-node SQL statement through a large model module, and verifies the legality of the SQL statement based on user permission; and the system unidirectionally transmits the encrypted SQL instruction to a target node area through a hardware optical shutter, triggers the node area to execute local statistical operation and returns a structured result. Dynamic data pulling in a one-way network environment is achieved through a hardware optical shutter and data pump technology, natural language query is converted into SQL in real time in combination with distributed query optimized by a large model, and the problems that data statistics is lagged and user operation intervention is needed in a traditional isolation network are solved.

The invention discloses an intelligent data stream processing system based on Flink and an implementation method thereof, and belongs to the technical field of distributed streaming data processing, and the implementation method comprises the following steps: a data source access layer uniformly accesses multi-source data; the entity monitoring layer captures service entity change in a non-intrusive manner based on JPA, generates a standardized message body and asynchronously delivers the standardized message body to double channels; the unified event model layer maps the data into a standardized event and expands the standardized event; the Flink stream processing engine layer is used for complex event processing, state management and window calculation, and supports declarative pipeline definition and dynamic construction; the anomaly detection layer performs multi-dimensional anomaly detection; the distributed task scheduling layer schedules tasks according to a DAG model, and realizes load balancing through hotspot splitting and state transition; and the result storage and visualization layer adopts a cold and hot separation strategy to store data, and provides a dynamic visualization interface. The real-time performance, the consistency and the intelligent level of data processing are remarkably improved, and the development and maintenance cost is reduced.

The invention provides an intelligent data security exposed surface risk assessment method, system and device and a medium, and relates to the technical field of information security, and the method comprises the steps: constructing an exposed surface asset map through an asset fingerprint recognition engine, and calculating an asset exposure index; constructing a vulnerability knowledge base, and identifying potential vulnerabilities; based on the standard vulnerability score, the vulnerability utilization tool activeness and the attack event frequency in the set time period, calculating the triggering probability of the potential vulnerability as a dynamic vulnerability score; malicious IP access frequency and leakage record matching degree are obtained through firewall logs and dark web monitoring data, and threat intelligence factors are obtained through calculation; calculating an exposed surface risk value in combination with the asset exposure index, the dynamic vulnerability score and the threat intelligence factor; and based on a time decay model, according to the exposed surface risk value and the business influence factor, calculating a residual risk value, and obtaining an exposed surface risk assessment result. The accuracy of risk assessment is further improved through multi-dimensional data fusion.

Techniques include generating, by an artificial intelligence agent, a request for artificial intelligence data. The techniques further include presenting the request via a user interface. The techniques further include receiving the artificial intelligence data. The techniques further include providing the artificial intelligence data to the artificial intelligence agent for at least one of training or inference.

The invention relates to the technical field of intelligent control of automobile air conditioners, and discloses an intelligent regulation and control method of an automobile air conditioner controller based on a self-adaptive neural network, which realizes comprehensive perception of environment information inside and outside an automobile through distributed multi-source data intelligent fusion processing. Establishing a data quality evaluation model and an intelligent data interpolation algorithm to ensure data reliability; constructing a lightweight dual-path adaptive neural network model by adopting a knowledge distillation technology; establishing a hierarchical multi-objective optimization control architecture which comprises a three-layer optimization structure of a strategic layer, a tactical layer and an operation layer, and realizing organic combination of long-term planning, medium-term prediction and real-time control; the method further comprises key technologies such as multi-level data missing fault-tolerant processing, actuator self-adaptive compensation control, system-level fault self-healing and degradation control and the like, and stable and reliable intelligent regulation and control can be achieved under the extreme environment condition. According to the invention, the control precision, the energy consumption optimization and the user experience in extreme environments such as polar regions, plateaus and deserts are improved.

The invention discloses an intelligent data query system and method based on natural language processing, and relates to the technical field of natural language processing and database query. According to the method, natural language input and database mode information are received, a historical query log is combined to construct mode knowledge representation, a query skeleton is generated on the basis, a fine-tuned large language model is called to generate candidate SQL statements, error detection and ambiguity recognition are carried out on the candidate statements, and the query result is obtained. And if necessary, triggering interaction clarification and updating a query result according to user feedback. And meanwhile, the wrong clauses are locally repaired through a gating mechanism, and the clauses are returned and regenerated when multiple times of repair fails, so that the correctness of the query statement is ensured. And finally, after the SQL is executed in the database, a result is fed back to the user. Besides, the system records generation and repair tracks in the operation process, and continuously optimizes the model based on reward shaping, comparative learning and self-game training, so as to improve the generalization ability in different business scenes.

The invention provides an intelligent data alignment method and system based on time sequence dynamic multi-source embedding mapping. The method belongs to the technical field of multi-modal data fusion and spatio-temporal information processing. The method comprises the following steps: performing time sequence dynamic feature extraction on a multi-source heterogeneous data source to generate a heterogeneous data sequence containing a time dependency relationship; and constructing a time sequence dynamic multi-source embedded manifold space based on a manifold learning theory, mapping a heterogeneous data sequence to a unified evolution geometric structure representation space, and generating embedded manifold data. Through the method, heterogeneous data from various different data sources can be effectively processed, unified mapping is carried out through time sequence dynamic feature extraction and a manifold learning technology, cross-source alignment of the data is achieved, and the method is particularly suitable for a data scene needing to consider a time dependency relationship.

The invention discloses an automatic data modeling and optimizing system and method fusing a knowledge graph and ChatBI, and relates to the technical field of data analysis. In order to solve the problems of high interaction threshold and insufficient analysis depth of a traditional BI tool, the scheme adopted by the invention comprises a data layer which has the capabilities of multi-source data access, domain knowledge graph construction, intelligent mapping recommendation, federal calculation and data mild governance, and realizes data integration and semantic unification; the analysis layer realizes accurate conversion from a natural language to an SQL and semantic reasoning of a knowledge graph through graph vectorization, multi-model cooperation, natural language understanding, intelligent SQL generation and graph dynamic updating, and supports efficient data query and analysis; and the application layer has the functions of natural language interaction, visual recommendation and generation, root cause analysis and intelligent early warning, and provides a visual interaction interface and data display service for a user. According to the invention, intelligent data analysis and visualization based on natural language interaction can be realized.

The invention provides an SMT patch correction system linked with AOI and a method thereof. The SMT patch correction system comprises a stokehole AOI detection module, a stokehole AOI detection module, a multi-dimensional sensing module, an intelligent data center module, an AI decision optimization module and a process simulation verification module. According to the method, the AOI detection data in front of and behind the furnace are fused through the intelligent data center module, the mapping relation of knowledge graph associated elements, the technology and offset data is constructed, a linkage closed loop is formed, and offset source tracing and full-process dynamic deviation correction are achieved; environmental parameters such as temperature, vibration and suction nozzle pressure are collected in real time through the multi-dimensional sensing module, and the environmental parameters are standardized through an intelligent data platform and then are included into correction logic of the AI decision optimization module, so that dynamic compensation of the influence of environmental factors on the mounting precision is realized; a deep learning and reinforcement learning cooperation strategy is adopted through the AI decision optimization module, parameters are dynamically optimized and corrected in combination with thermal deformation simulation and other multi-source information of the process simulation verification module, and the adaptability to different element types and production scenes is improved.

The invention discloses an intelligent data anomaly detection method and system based on an Internet of Things integrated management and control technology, and the method comprises the steps: S1, collecting and preprocessing intelligent data, and constructing a standardized data set; s2, performing preliminary anomaly detection on the standardized data set by adopting an isolated forest algorithm, and screening out potential abnormal data points; s3, constructing a self-supervised training sample, performing enhancement detection on the abnormal data point set, and generating a correction factor; s4, recalculating an abnormal score for each abnormal data point based on the correction factor; s5, abnormal data points output through re-correction are classified and stored, and log information is generated; and S6, dynamically optimizing the model parameters according to a detection result fed back in real time. According to the method, an efficient and scientific optimization scheme can be provided in Internet of Things intelligent data anomaly detection, and remarkable technical values and economic benefits are brought to practical application.

The invention discloses an intelligent data acquisition system for a coal mine operation robot, and relates to the field of intelligent data acquisition, the intelligent data acquisition system comprises a heterogeneous fusion module, an obstacle avoidance decision module, a sampling optimization module, a dynamic sensing module and an evaluation decision module, multi-source heterogeneous sensing fusion is carried out by obtaining original sensor data, a binary compression feature vector is obtained, and the binary compression feature vector is extracted; performing autonomous obstacle avoidance decision analysis on the basis of binary compression feature vectors to obtain a joint angle instruction, performing sampling planning and path optimization on the joint angle instruction to obtain a global optimal moving path, performing adaptive sampling control and multi-source data real-time fusion on the global optimal moving path and an original sensor data stream to obtain an enhanced data set, and performing multi-source data real-time fusion on the enhanced data set. And performing geological safety index and action instruction analysis on the enhanced data set to obtain an autonomous action instruction, and dynamically compensating motion distortion and sensor time delay through a displacement vector and a real-time speed to improve subsequent fusion precision.

The invention relates to an optical data network (10-1) for connecting a plurality of data network nodes (N1-1, N2-1, N3-1, N4-1). The optical data network (10-1) comprises: i) the plurality of data network nodes (N1-1, N2-1, N3-1, N4-1), the data (99) comprising data packets (99p), wherein each data network node (N1-1, N2-1, N3-1, N4-1) is provided with a network interface (Nl); and ii) a data connection network (100-1) comprising at least one data switch (SW) and a plurality of optical connections (50) for allowing establishment of data links (DL1, DL2) between pairs of said plurality of data network nodes (N1-1, N2-1, N3-1, N4-1). At least one of the at least one data switch (SW) is a smart data switch (SSW) comprising at least one packet switch (PS, EPS) and at least one circuit switch (CS, OCS), At least two network interfaces are smart network interfaces (SN, SNC) comprising a data classifier (25) configured for classifying the data packets (99p) into at least two traffic classes including first traffic class (D1) and a second traffic class (D2) in accordance with a classification scheme. The smart data switch (SSW) and the network interfaces (SN, SNC) are configured to route the data packets (99p) of the first traffic class (D1) to the packet switch (PS, EPS) and to route the data packets (99p) of the second traffic class (D2) to the circuit switch (CS, OCS).

The invention relates to a database table field description intelligent generation and dynamic maintenance method and device based on RAG and a large model. The method comprises the following steps: constructing a domain-based dynamic knowledge base system through multi-source corpus collection, domain adaptation vectorization model processing and a dynamic knowledge base construction and updating process; receiving a description generation request of a table or a field, recalling related knowledge from the knowledge base through an RAG technology, assembling cue words, and then calling a large model for reasoning to generate a description result; the generated description result is confirmed through manual auditing, the knowledge base is updated according to confirmation feedback, and continuous optimization of the knowledge base is achieved. According to the method provided by the invention, the problems of low accuracy, high maintenance cost, poor interpretability and the like in a traditional method are effectively solved by constructing a field-enhanced RAG architecture and combining a three-layer knowledge system and a self-adaptive prompt framework which can be dynamically adjusted according to a business scene, and a new technical implementation path is provided for table and field description in intelligent data governance.

The invention discloses an intelligent data question answering and visualization system and method for leader decision making, and relates to the technical field of artificial intelligence, and the system comprises a knowledge base unit, an intelligent question answering module, a dynamic visualization unit, a quality control unit and a performance optimization unit. According to the method, the customized knowledge base is constructed through the knowledge base unit, cross-modal alignment and layered long context processing are weakly supervised during data fusion, the defects that cross-modal data labeling cost is high and long context processing is redundant can be overcome, and an interaction platform of the knowledge base and a data engine in the knowledge base unit is constructed through the intelligent question and answer module; the model mixing module mixes an NLP model, constructs a combined architecture of an LLM + field fine tuning model, ensures understanding of natural languages and intention analysis precision, introduces an online expert error correction mechanism, and reduces misjudgment during question answering, so that the processing capacity of long texts or complex logic can be ensured, missing of key information in question answering is avoided, and then the answering quality is ensured.

Provided are a driving state monitoring and feedback method and system based on multi-modal human-factor intelligent data analysis, and an edge computing terminal device. The method includes: receiving multi-modal human-factor data collected in real time from a tested driver; preprocessing the multi-modal human-factor data; inputting the preprocessed multi-modal human-factor data to a pre-trained first state identification model to obtain a driver state identified in real-time, the driver state including a normal state and a plurality of abnormal states; and generating, in response to the driver state being identified as an abnormal state, a driving state feedback instruction for the category of the abnormal state and sending the driving state feedback instruction to a driving intervention system, to cause the driving intervention system to perform state feedback adjustment on the driver based on the received driving state feedback instruction.

The embodiment of the invention relates to the technical field of information, and discloses an intelligent data acquisition method and device, a medium and a product, the intelligent data acquisition method comprises the following steps: acquiring artificial demonstration data through an exoskeleton system, the artificial demonstration data comprising video stream data, motion data and force sense data; importing the video stream data into a first large language-vision model, segmenting the video stream data into a plurality of independent task fragments, and generating independent task fragment descriptions; importing the independent task fragment description into a large language model, and generating a task label based on natural language description; and exporting the independent task segments, the task labels and the corresponding motion data and force sense data to generate intelligent data. Therefore, a low-cost, high-efficiency and large-scale intelligent data acquisition and processing method is established, and automatic conversion from human operation data to high-quality VLA training data is realized.

The invention belongs to the technical field of data centers, discloses an energy efficiency evaluation method of an artificial intelligence data center, and solves the problem that a static evaluation method is disjointed with the real-time operation state of the data center in the prior art by constructing a dynamic feature library and an AI adaptive weight model. The dynamic feature library collects and associates service scene labels and time-space coupling data in real time, and provides a scene basis for an AI adaptive weight model, so that weight adjustment is converted from passive response index mutation in the prior art to active adaptive service scene change. Nonlinear influences of dynamic factors such as server load fluctuation and environment adjustment on energy efficiency are effectively captured, so that an energy efficiency evaluation result better fits the actual operation state of the data center, energy efficiency factor relevance changes caused by service switching of the AI data center are adapted, scene-based self-adaptive adjustment of weights is achieved through a deep learning model, and energy efficiency evaluation accuracy is improved. The limitation that the fixed weight distribution ignores the difference of different service scenes in the prior art is solved.

The invention relates to a BI intelligent question answering system and method based on business rule retrieval and AI workflow, and belongs to the field of intelligent data processing. The system comprises a user interaction module, a knowledge base module and an AI workflow module. The method comprises the following steps: (1) business rule retrieval: taking a BI analysis problem written by a user interaction module as a target, retrieving business data table information from a knowledge base module, judging a business data table of the user problem in combination with a big language model, obtaining business indexes contained in the table according to the business table, retrieving BI business information related to the user problem, and performing business rule retrieval; and (2) AI workflow execution: taking a BI analysis problem as input, gradually scheduling workflow nodes of business rule retrieval, cue word construction, large language model interaction and database interaction, realizing an accurate Text-to-SQL function and a data visualization function, and further realizing a question-answer ability of converting the BI analysis problem input by a user into a BI analysis result.

The invention belongs to the technical field of intelligent document processing, and discloses an intelligent data processing method for collected files based on artificial intelligence, which comprises the following steps: acquiring a multi-source multi-version file to carry out layout rectification and OCR (Optical Character Recognition) to obtain a page fragment object set; using a controlled decoding language model to extract clause records according to preset Schema to form a clause record set; performing cross-version alignment on homonymous or synonymous terms, and constructing a time sequence knowledge graph; compiling the cross-document consistency rule into an executable rule set which can be evaluated on the time sequence knowledge graph by adopting an executable collection constraint language; evaluating the scope of action of each rule to generate conflict candidates; and calculating a minimum hit set to obtain a minimum conflict interpretation set, positioning a root cause term instance, and generating a minimum repair suggestion according to a normalization result of the operator and the node violating the rule. According to the method, the unification of clauses and calibers and the automatic judgment of the latest effective state, the accurate positioning of conflicts and the minimum cost repair are realized.

The invention discloses an underground artificial chamber gas storage sealing layer leakage monitoring device and method, and belongs to the technical field of compressed air energy storage. Comprising a distributed optical fiber sensing network which is spirally wound on the outer surface of a sealing layer of an underground artificial chamber gas storage at an interval of 1m and is used for monitoring temperature distribution, the intelligent data processing unit integrates an AI algorithm and a GIS system and is used for analyzing temperature gradient abrupt change and positioning a leakage point, and the early warning and linkage module is used for triggering emergency response according to a positioning result. And the monitoring efficiency and safety are improved.

The invention provides an intelligent data consanguinity tracking and visualization method based on graph calculation, and the method comprises the steps: carrying out the data structure analysis and metadata extraction of original data assets, so as to generate a standardized data asset package; performing graph node attribute definition on the data entities in the standardized data asset package, and performing graph edge attribute definition on the association relationship between the data entities to generate a first data blood relationship model; performing blood relationship path mining on graph nodes and graph edges in the first data blood relationship graph model to obtain a basic blood relationship path set, and performing quantitative calculation and feature labeling on path association strength in the basic blood relationship path set to generate a second data blood relationship graph model; and performing visual rule mapping on graph node attributes and graph edge attributes in the second data blood relationship graph model to construct a standardized visual data set and generate a data blood relationship visual interaction interface according to the standardized visual data set.