2911 results about "Data association" patented technology

The invention discloses an aerospace intelligent manufacturing large model construction method, which comprises the steps of collecting original data, performing preprocessing and data association, and constructing an aerospace intelligent manufacturing database; establishing a knowledge acquisition and structured conversion assembly line, a multi-dimensional associated domain knowledge graph, a knowledge quality control system and a dynamic updating mechanism, and constructing a professional knowledge base; aligning the cross-modal manufacturing data to generate a corpus; combining base general large model pre-training, injecting terminology semantics and multi-modal association capability, and completing knowledge migration; based on the pre-trained aerospace intelligent manufacturing large model, constructing an aerospace manufacturing cognitive agent, and forming a complex engineering problem solving framework; professional ability is optimized through a two-stage progressive multi-task training strategy, and dynamic adaptation of a production environment is realized in combination with an online learning and incremental updating mechanism. The intelligent level of aerospace intelligent manufacturing is remarkably improved.

Disclosed are a high-performance loosely-coupled multi-modal data fusion system for a smart driving environmental perception system and a vehicle-mounted device, comprising: a fusion detection model based on a modality-independent feature interaction strategy, which is configured for converting a LiDAR point cloud, a camera image, and a millimeter-wave radar point cloud into a unified bird's-eye view representation, and performing multi-modal fusion; and a fusion tracking model based on a motion-appearance feature cascaded coupling data association strategy, which is configured for performing subsequent trajectory tracking and matching according to multi-modal fusion feature information. A VoD data set and a K-Radar data set are selected for training, verifying, and testing the comprehensive performance of the models, and a TensorRT accelerated inference model is applied, then quantized, and deployed to a vehicle-mounted computational testing platform. The present invention is compatible with mainstream sensor deployment solutions, and achieves the efficient complementary fusion of multi-source heterogeneous sensor information, significantly improving the reliability, accuracy, and adaptability of vehicle-mounted perception systems, thereby effectively responding to extreme operating conditions such as complex traffic scenarios and inclement weather.

The invention discloses a data exception traceability tracking and positioning method, system and device and a storage medium. Relates to the technical field of power system data management. At present, the problems of insufficient data anomaly detection accuracy, low traceability positioning efficiency, incomplete knowledge association and the like in analysis and application links of power marketing data are solved. The method comprises the following steps: carrying out preprocessing and feature extraction on power marketing data, constructing a data association relationship network through a clustering analysis and statistical method, analyzing the difference of data features, analyzing the propagation path of an abnormal point identified in a data stream by adopting a path reasoning technology, and constructing a data abnormity tracing positioning model. A semi-supervised learning method is adopted to improve the accuracy of the model, result optimization and hyper-parameter adjustment are carried out, and multi-condition factors influencing business data information are introduced into the model as hyper-parameters. According to the technical scheme, the anomaly detection and traceability positioning capability of the power marketing data is greatly improved.

The invention provides an intelligent traffic data processing method and system based on edge computing, and the method comprises the steps: collecting multi-source traffic data through an edge computing node, carrying out the preprocessing of the data, and obtaining the preprocessing data; and performing space-time alignment and data association processing on the preprocessed data to generate an associated data set, and completing feature extraction and preliminary analysis at an edge computing node to obtain a local analysis result. The local analysis result is transmitted to the cloud server, deep data mining and mode recognition are carried out, and a global analysis result is generated. And based on the global analysis result, generating a local traffic control strategy at the edge computing node, and generating a global traffic optimization scheme at the cloud server. The local traffic control strategy is issued to traffic facilities for execution, and the global optimization scheme is distributed to each edge computing node to realize cooperative control. The advantages of edge computing and cloud processing are combined, and the real-time performance of traffic data processing and the overall optimization capability of an intelligent traffic system are effectively improved.

The invention discloses a data analysis platform management method and system, and belongs to the technical field of computer data processing, and the method comprises the steps: obtaining multi-source heterogeneous data from a medical information system; establishing a cross-system data association model, performing dynamic feature extraction and weight distribution on multi-source heterogeneous data, and outputting a resource supply-demand relationship graph; analyzing the resource supply-demand relation graph, and identifying business conflicts in medical resource scheduling; generating a conflict solution of the business conflict based on a multi-objective optimization algorithm, and generating an execution instruction; and issuing an execution instruction to the target medical system, verifying an execution result of the execution instruction, and updating system parameters. According to the method, a cross-system dynamic modeling and multi-objective optimization strategy is adopted, and a closed-loop verification mechanism and a multi-source heterogeneous data fusion technology are combined, so that intelligent prediction, accurate scheduling and self-learning optimization of medical resource conflicts can be realized, full-process traceable execution of high-value consumables is ensured, and the resource utilization efficiency and the emergency response capability are comprehensively improved.

The invention relates to the technical field of tunnel power supply and distribution, in particular to a tunnel modular prefabricated cabin power supply and distribution intelligent substation self-adaptive regulation and control system based on edge calculation. Comprising an edge calculation and AI decision-making unit which is used for realizing rapid acquisition, processing and instant decision-making of tunnel power supply and distribution multi-dimensional data, generating a power supply and distribution adaptive regulation and control strategy by deploying calculation resources and a machine learning algorithm at edge nodes close to a data source, and converting the strategy into an executable regulation and control instruction; a cloud platform collaborative management unit; and an intelligent sensing and internet-of-things unit. According to the invention, hierarchical decision control of the tunnel power supply and distribution system is realized by constructing a hybrid architecture of edge computing and cloud platform collaboration and a priority judgment mechanism; according to the invention, multi-modal data are integrated through the multi-protocol communication link module and the full-scene data fusion analysis module, and data association analysis is realized through Kalman filtering, D-S evidence theory and other algorithms.

The invention discloses an Internet of Things data integration method and system based on dynamic semantic atlas and edge collaboration, and the method comprises the steps: carrying out the equipment access and protocol conversion, capturing the original data of equipment through an edge node, analyzing the average communication interval of 100ms + / -20ms through an LSTM model, generating an equipment fingerprint hash value, analyzing a message structure through a TinyBERT model, and extracting a key field, a temperature value and a timestamp. The method comprises the following steps: establishing a dynamic semantic graph, converting the dynamic semantic graph into a standard JSON format, uploading the standard JSON format to a cloud end through a service grid, constructing a dynamic semantic graph, collecting multi-source metadata by the cloud end, generating an entity relationship by utilizing a Transform model, dynamically optimizing a mapping rule based on reinforcement learning, automatically associating a Pressures field to a database column PSI, and carrying out collaborative optimization by an edge cloud; according to the method, the knowledge graph and the Transform model are combined, the data entity relation is updated in real time, the semantic drift problem of a traditional static graph is solved, the cross-domain data association accuracy is improved by 35%, the unique device fingerprint is generated through the message frequency and load distribution, and dynamic tracking of the mobile device is supported.

The invention relates to the technical field of data processing, and discloses a data processing method and system for cloud computing and a storage medium. The method comprises the following steps: acquiring preprocessed data through a distributed acquisition node to obtain a structured data packet; performing classification analysis through mixed feature extraction to obtain a safety disaster recovery data set; analyzing the incidence relation through a heterogeneous fusion network to generate a knowledge base; detecting abnormity through a self-adaptive threshold algorithm to form a risk library; determining a recovery scheme through a multi-objective decision to generate an execution log; and a protection strategy is formed through multi-angle comparison and optimization of parameters. According to the method, the data association relationship is mined and analyzed by constructing the multi-level heterogeneous fusion network, the optimal disaster recovery scheme is automatically determined through the multi-target decision process, and meanwhile, a continuous learning optimization mechanism is introduced; the intelligent level of data processing in the cloud computing environment, the timeliness of disaster recovery response and the stability of system operation are remarkably improved.

The invention provides a multi-source data real-time fusion processing method and system for a mobile intelligent device, and relates to the technical field of data processing.The method comprises the steps that 1, multi-dimensional original data streams are collected in real time through a heterogeneous sensor array integrated by the mobile intelligent device, data streams of different sensors are aligned by applying a space-time synchronization mechanism, and the data streams of different sensors are obtained; generating an original data set with consistent time and space; 2, dynamic interpolation compensation operation is executed on the original data set, and a dynamic calibration framework is constructed based on the internal topological relation of the data flow to form a dynamic sensing domain; and generating an evolution sequence according to the data unit evolution behavior of the domain boundary, generating a space correction value through the evolution sequence and the offset feature of the preset reference, and generating preprocessed data fused with the space correction value in combination with real-time data correlation analysis. According to the method, dynamic adjustment is triggered through anomaly detection, the fusion parameters are updated through the sliding window, and real-time efficient fusion processing of multi-source data of the mobile intelligent device is achieved.

The invention provides an unmanned aerial vehicle multi-target tracking method and system based on visual and millimeter-wave radar information fusion, and the method comprises the steps: obtaining an association result through employing a space-time consistency model, constructing a self-adaptive joint probability data association frame according to the association result, so as to obtain an initial target state estimation value, and carrying out the recognition of the initial target state estimation value. A prospective path planning algorithm is adopted to obtain an optimal observation angle flight path, a Bayesian network is utilized to obtain initial observation data, based on the initial observation data, an optimal target state estimation value is generated, a distributed cooperative sensing mechanism is used to generate an initial tracking task allocation scheme, and an intelligent resource allocation strategy is implemented. Generating an initial resource allocation scheme, starting a quick response protocol to obtain an optimal tracking task allocation scheme, and generating an optimal resource allocation scheme based on the optimal tracking task allocation scheme; according to the technical scheme provided by the invention, the accuracy of target identification is improved, the environmental adaptability is enhanced, the tracking efficiency is improved, and the working time is prolonged.

The invention provides a Python-based automatic system configuration and monitoring method and system, and relates to the technical field of automatic system configuration and monitoring, and the method comprises the steps: constructing a multi-level service dependence model and a service node affinity matrix, generating a configuration parameter priority queue, and completing the initial configuration of a system through a distributed transaction coordinator. When the system runs, the monitoring agent program collects system running data, calculates a node health degree index and shares the node health degree index through the net-shaped sharing channel. And the core node performs data association analysis and updates a node load value and a health degree index. And when the node health degree is lower than a preset threshold value, triggering an alarm processing module, selecting an optimal node based on a node health state evaluation report to execute reconfiguration, updating a service registration center and broadcasting a configuration updating message. According to the invention, automatic system configuration and monitoring can be realized, and the system reliability and stability are improved.

The invention relates to the technical field of big data storage, in particular to a storage control method and device based on big data. The method comprises the following steps: acquiring an original data stream and application demand information; performing data dynamic correlation analysis on the original data flow according to the application demand information to obtain a dynamic update data correlation graph; performing feature vector generation on the dynamic update data association graph to obtain a data feature vector; performing intelligent quality evaluation on the data feature vector to obtain a data quality label; performing timeliness evaluation according to the data feature vector, and performing access popularity evaluation to obtain a timeliness score and an access popularity score; and acquiring system resource load condition information. The storage resource utilization rate, the data access performance and the overall efficiency of the system are improved by solving the limitation of a traditional method in the aspects of static data management and resource allocation.

The invention relates to the technical field of data processing, and discloses a data processing method and system for a smart city and a storage medium. The method comprises the following steps: acquiring city multi-source data through a distributed sensor network, and performing data cleaning and standardization processing to obtain standardized city data; establishing a domain concept system for semantic mapping to obtain semantic association data; analyzing the spatial-temporal characteristic and regional interaction relationship to obtain dynamic characteristic data; constructing a multi-level abnormal feature library for event recognition to obtain abnormal mode data; carrying out distributed optimization decision making to obtain management decision data; and establishing an evaluation system for strategy optimization to obtain a city management scheme. According to the method, efficient processing and intelligent analysis of the city data are realized, specifically, through a distributed computing architecture and a multi-level data processing method, the problems of low data processing efficiency, insufficient data association analysis, low computing resource utilization rate and the like are solved, and thus the overall performance of smart city data processing is improved.

The invention provides a numerical control machining model verification method and system combined with digital twinning, and the method comprises the steps: firstly obtaining a numerical control machining twinning data association set which is correspondingly formed by a physical machining process actual measurement data sequence and a virtual twinning model simulation data sequence according to a machining stage identifier; the actual measurement data sequence and the simulation data sequence both comprise processing action and material response feature sequences, and then carrying out feature space unification processing on the twin data association set to generate a verification feature corresponding set comprising actual measurement comprehensive features and simulation comprehensive features; performing deviation analysis on the verification feature corresponding set by using a pre-trained model deviation analysis model to obtain a feature matching and association influence relationship, and deducing and determining a specific deviation position of the virtual twin model in a processing action and material response modeling link based on an analysis result; and model optimization guidance information for defining the parameter adjustment direction and the structure correction content is generated, so that the precision and the reliability of the virtual twin model are effectively improved.

The invention provides an electronic information data association method and system based on the Internet of Things. The method comprises the steps that a parallel processing architecture is adopted to execute rapid protocol conversion on a high-priority data packet, data recombination is directly carried out through a pre-compiled conversion rule template, a complex format verification link is skipped, and the integrity and format correctness of the converted data packet are determined; response time and error rate statistical information in the data transmission process are obtained, protocol conversion rules and scheduling strategies are optimized through a feedback mechanism, and if the success rate of certain protocol conversion is lower than a standard value, corresponding mapping rules and verification algorithms are updated; and carrying out standardized packaging on all converted data by adopting a unified data exchange standard, adding an equipment identifier, a timestamp and a data type label, establishing a cross-protocol general data format, and carrying out final distribution after judging whether a data packet meets a standard format requirement.

The invention discloses a multi-source heterogeneous data fusion processing and intelligent analysis method, device and equipment, and relates to the field of data fusion processing.The method comprises the steps that firstly, data in data sources of corresponding types are collected through different collection strategies; performing data preprocessing on the collected data to obtain standard data in a unified format; performing data fusion on the standard data based on the structural features of the data to obtain a fused data set; and determining a target analysis model according to the data analysis target, and performing data analysis on the fused data set. According to the method, various types of data formats can be processed, multi-source heterogeneous data fusion is realized, and through data semantic understanding and a feature-based fusion algorithm, the accuracy and quality of data fusion are improved, and correct data association is established on the semantic level.

The invention relates to an intelligent monitoring method and system for an air environment monitoring station, and belongs to the technical field of environment monitoring. The method comprises the following steps: acquiring multi-modal monitoring data, performing data association analysis on the multi-modal monitoring data by adopting a multi-source data association model, and establishing an association relationship among the multi-modal monitoring data; performing data exception monitoring on the multi-modal monitoring data, and triggering and starting an exception type diagnosis event when data exception is monitored; extracting a first data subset related to an exception type from the multi-modal monitoring data; extracting a second data subset associated with the first data subset from multi-modal monitoring data; and performing fault diagnosis based on the first data subset and the second data subset, and outputting an abnormality diagnosis result. According to the method, the multi-source data association model is utilized, the association between different types of data can be effectively analyzed, detailed fault analysis and diagnosis are carried out, the fault response time is shortened, and the operation and maintenance efficiency is improved.

The invention belongs to the technical field of data storage, and discloses a multi-source marine environment data storage method and system based on HDF5. According to the method, a hierarchical storage framework is constructed, a domain-type-source three-level directory architecture is constructed, a space-time dimension dynamic extension mechanism is executed, and space-time composite coding is performed; designing an HDF5 nested packet storage architecture, wherein grid topological structure definition, dynamic resolution self-adaptive storage, multi-source data processing and metadata management are carried out; constructing distributed data association and indexes, including data standardization preprocessing and cross-source data association; the storage performance is optimized and expanded, wherein density sensitive blocking and layered compression are carried out. According to the method, redundant storage of a high-latitude region is greatly reduced, and dynamic adaptation of a local encryption grid is supported. According to the invention, open circulation and collaborative utilization of interdisciplinary data resources are facilitated.

The invention relates to the field of internet attack detection, in particular to a data security event real-time monitoring method and system, and the method comprises data collection, multi-modal fusion, threat detection, causal reasoning, dynamic response and feedback optimization. Compared with the traditional security analysis which depends on isolated data dimension or simple rule association, is difficult to capture a cross-data-source complex attack mode, and is faced with the limitations of low calculation efficiency, slow link traceability, storage access bottleneck and the like in mass data association analysis, the scheme integrates multi-source heterogeneous data into a dynamic association network through graph structure modeling, so that the security analysis efficiency is improved. Hidden association and behavior patterns among users, equipment and IPs are deeply mined by utilizing a GNN framework, the suspicious degree among entities can be accurately quantified, and hidden attack chains can be identified; and meanwhile, a hybrid storage architecture and a query optimization technology are adopted, so that a security analyst can backtrack a complex attack path in a second level while breaking through the bottleneck of large-scale graph data access performance, and the threat hunting efficiency and the high-level attack traceability are remarkably improved.

The invention provides a data association maintenance processing method and system based on file reference, and relates to the technical field of data association maintenance, which comprises the following steps: constructing a directed reference graph, identifying direct and indirect reference paths, and constructing a reference dependence intensity matrix in combination with a time sequence, an interaction amount and a reference propagation probability. And carrying out file clustering based on the matrix, and determining a core reference file set. And when the core file is updated, acquiring incremental update data by adopting data block level difference comparison, and merging the data blocks based on the content similarity and the semantic association degree. After the merged data blocks are segmented according to threshold values and semantic boundaries, updating is propagated to a target node through a multi-path updating propagation network, and the data consistency is ensured through version consistency check and a compensation updating mechanism. According to the method, data association between files can be effectively maintained, and the data updating efficiency and consistency are improved.

Systems and methods are disclosed for condition identification. One or more processors may receive a member data object with indicators and dimensions, access a member-specific graph network with nodes representing attributes and weighted edges indicating associations, modify the nodes and edges based on the member data object, generate a multi-modal graph database by combining the modified member-specific graph network and a disease graph network, apply the multi-modal graph database to an attention-based graph neural network (GNN) that identifies associations between nodes by dynamically allocating attention weights to edges, generate an embedding data object with node identifiers and vectors representing features and relationships, select a target node associated with condition data, apply the embedding data object to a classification layer that outputs predicted conditions for the target node, and generate the probability of predicted conditions appearing in the target node.

The invention relates to the technical field of breeding planning, in particular to an intelligent breeding planning and decision-making method and system based on a large model. The method comprises the following steps: acquiring a multi-source breeding data set; constructing a structured breeding knowledge graph based on the multi-source breeding data set; performing breeding data association on the structured breeding knowledge graph according to a preset large model to generate a special breeding basic model; obtaining a breeding instruction input by a user; performing user semantic recognition on a breeding instruction input by a user to generate breeding semantic recognition data; inputting the breeding semantic recognition data into a breeding special basic model for breeding intention analysis, and generating user breeding intention data; and determining data information needing to be called based on the breeding intention data of the user, analyzing and screening to generate germplasm resource screening data and a breeding plan / breeding decision scheme. According to the method, the intelligence and operability of breeding planning are improved through integration of multi-source data, intelligent semantic recognition, combined genetic analysis and executable evaluation.

The invention provides a big data aggregation analysis method and system based on a typical service scene. The method comprises the following steps: receiving multi-dimensional service data streams from a plurality of heterogeneous data sources; carrying out deep mining processing on time characteristics and causal relationships of data by utilizing the multi-dimensional service data flow and combining time sequence analysis and causal inference technologies to obtain a cross-domain data association network; based on the cross-domain data association network, performing deep learning processing on features of nodes and edges in the network by adopting a graph neural network algorithm to obtain key data nodes and a combination mode thereof; according to the key data nodes and the combination mode thereof, dynamically adjusting a selection strategy of the key data nodes through a Q-learning algorithm to obtain an optimized data aggregation analysis model; and performing comprehensive analysis processing on the service data based on the optimized data aggregation analysis model to generate a comprehensive service insight report. According to the technical scheme provided by the invention, the efficiency and accuracy of big data aggregation analysis can be remarkably improved.

The invention relates to the technical field of intelligent process monitoring, and discloses an intelligent process monitoring and feedback method and system for resourceful treatment of construction waste, and the method comprises the steps: carrying out the process parameter collection and standardization processing of a plurality of monitoring points of a construction waste treatment production line, and obtaining a preprocessing data set; constructing a graph data structure, calculating a correlation coefficient between nodes to determine a node connection relationship, and performing dynamic optimization through a capability awareness graph neural network to obtain a process data association graph; inputting the process data association graph into a process parameter association model for graph convolution operation and attention feature extraction to obtain a process parameter feature vector; the technological parameter feature vectors are input into a multi-constraint non-convex optimizer, combined optimization of the processing efficiency, the resource utilization rate and the product quality is calculated, and a technological parameter regulation and control strategy is obtained, intelligent technological monitoring of construction waste resourceful treatment is achieved, and the optimal control strategy under different working conditions is effectively integrated.

The invention discloses a multi-source business data collaborative acquisition method for subcompartment change, and relates to the technical field of forest resource management, and the method comprises the following steps: S1, capturing subcompartment boundary, ownership and operation attribute change events in real time through a dynamic topology perception layer, and generating a dynamic topology map; according to the multi-source business data collaborative acquisition method oriented to the subcompartment change, a dynamic topology sensing mechanism and a standardized acquisition process are constructed, so that the problem of multi-source data splitting caused by frequent subcompartment change is solved, and real-time collaborative acquisition and consistency guarantee of multi-source heterogeneous data are realized. A dynamic topological graph accurately senses boundary adjustment and ownership change events, and a double-channel analyzer is combined to automatically map service rules into equipment instructions, so that the data association error rate is remarkably reduced; the bimodal verification mechanism effectively eliminates version conflicts and business rule contradictions through time sequence alignment and sandbox simulation, and improves the accuracy of forest resource assessment.

The invention relates to the technical field of business data insight, and discloses a method for disassembling insight business data through indexes. The method comprises the following steps: firstly, constructing a multi-dimensional index system, determining key business indexes and associated dimensions, and outputting an index disassembling framework; the method comprises the following steps: collecting multi-source data in a business process, performing cleaning and standardization processing, extracting index characteristics and dimension attributes, and constructing a business data association graph; introducing an adaptive weight distribution mechanism and a hierarchical iterative algorithm to optimize a preset attribution model, and generating a preliminary index contribution degree sequence based on an index disassembly framework; setting a monitoring threshold value of each dimension index, and tracking service data in real time; dynamically adjusting an index weight and a model parameter according to a tracking result, and recalculating and updating an index contribution degree sequence; and evaluating the service health degree, identifying abnormal index nodes, and starting a root cause analysis process for the abnormal index nodes. According to the method, multi-dimensional and dynamic insight of the business data can be realized.

The invention provides a network security data association analysis method and apparatus, an electronic device and a storage medium. The method comprises the steps of obtaining target network security data; constructing a knowledge graph based on the target network security data; performing network security risk analysis based on the knowledge graph to obtain a risk analysis result; and constructing a multi-dimensional analysis view based on the knowledge graph and the risk analysis result, and visually displaying the multi-dimensional analysis view. According to the network security data association analysis method provided by the invention, a closed-loop analysis result is formed with vulnerability data, a three-layer knowledge graph of the vulnerability data, asset data and threat data is constructed, the problem of data islands caused by data acquisition of different data sources is broken through, deep integration and dynamic analysis of multi-dimensional data are realized, and the network security data association analysis efficiency is improved. The network security risk analysis is further realized through the association relationship in the knowledge graph, and the intelligent level of the network security data association analysis is improved, so that the efficiency and accuracy of the network security data association analysis are improved.

The invention discloses a network threat deduction system and method based on Transform and a graph attention network model. The system mainly comprises four modules including a threat intelligence extraction module, a network threat entity construction module, a network threat deduction module and a deduction result application module. The method comprises the following steps: preprocessing and mining network security threat intelligence data by utilizing a graph attention network (GAT) model, constructing a complete network threat intelligence knowledge graph based on a data association relationship, and determining a vulnerability utilization weight in the graph; modeling is carried out on a reasoned path sequence based on a Transform model, a next reasoning sequence is predicted in a generative mode, and rapid and efficient network threat intelligence reasoning is achieved. According to the method, the complexity of attack deduction calculation is reduced, the deduction efficiency is improved, and security risk investigation and early warning can be carried out for a large-scale network environment.

The invention relates to the technical field of medical health big data analysis, in particular to a diabetic nephropathy risk identification method and system based on big data analysis, and the method comprises the following steps: screening key index features of risk patients through big data analysis based on health risk data of diabetic nephropathy patients; association of key indexes of the risk patients is analyzed, risk index interaction features are extracted, risk feature constraint conditions are determined, virtual sample parameters are identified through multi-dimensional data association, and a virtual sample set of the risk patients is established. According to the method, interaction characteristic values are extracted by analyzing key indexes of risk patients, high-risk index association is accurately captured, coverage and balance are enhanced based on multi-dimensional data association, non-stationary influence is solved by combining time sequence key change rate and shear point identification, and accurate matching is realized by analyzing offset rate and change trend. And the overall risk level is quantitatively evaluated by integrating interval data weighting, so that the risk identification comprehensiveness and the result reliability are improved.

The invention provides an intelligent inspection method and device for a database, and belongs to the technical field of databases. The method comprises the following steps: acquiring a multi-modal data set corresponding to the operation state of the inspection database by presetting a multi-source heterogeneous data acquisition mode; based on a cross-modal attention mechanism and a self-learning model, establishing a multi-dimensional data association relationship corresponding to the multi-modal data set, and in combination with historical time sequence data and unsupervised anomaly detection, determining a database health value and anomaly early warning information; wherein the multi-dimensional data association relationship is at least established among the SQL performance, the resource consumption, the transaction behavior and the business load. And generating a database optimization scheme according to the database health value, the abnormal early warning information and a preset feedback optimization mechanism, and sending the database optimization scheme to the user terminal. According to the scheme, the database is comprehensively inspected in combination with the cross-modal attention mechanism, the self-learning model, the knowledge graph association reasoning model and the time sequence prediction model, and the automation, dynamic and intelligent levels of inspection are improved.