292 results about "Relational model" patented technology

The invention discloses a substation switching misoperation risk intelligent control method and system, and relates to the technical field of power system safety production, and the method comprises the steps: collecting multi-dimensional real-time risk factor data, building a dynamic probability dependency relationship model based on the risk factor data, quantifying the correlation influence between different dimensions, and updating the model parameters, feature information of a current operation scene is converted into a standardized vector, similarity matching is carried out on the standardized vector and a scene template, an adaptive basic strategy is screened, the basic strategy is dynamically adjusted according to a real-time risk assessment result, and a scenarized risk management and control strategy is generated; and implementing a scenarized risk management and control strategy, collecting multi-dimensional feedback data in an execution process, generating a new generation of optimization strategy based on the feedback data, and updating the strategy library. The risk management and control strategy is generated and adjusted according to the actual situation, the strategy is continuously optimized, the safety and reliability of switching operation of the transformer substation are improved, accidents caused by misoperation are reduced, and stable operation of a power grid is guaranteed.

The invention discloses a substation operation and maintenance task risk digital assessment method and system, and belongs to the technical field of task risk digital assessment, and the method comprises the steps: carrying out the hidden risk dominant modeling based on the structural data of a data lake, generating a personnel and equipment dynamic coupling risk coefficient, and carrying out the hidden risk dominant modeling; the space-time diagram neural network captures a personnel-equipment-environment coupling relationship by fusing spatial topology and time sequence features, the model can be associated with a historical fault mode of adjacent equipment, potential risks are identified in advance, and the causal model can identify the potential risks in advance by calculating causal strength between nodes and revealing hidden risk driving factors. The attention weight mechanism dynamically adjusts the fusion proportion of the space and time features, the method adapts to the complex scene of the transformer substation, the dynamic coupling risk coefficient is combined with the frequency and similarity of the historical accident library, the risk weight is quantified, and the probability of artificial misjudgment is reduced.

The invention provides a pollutant traceability method, system and device based on multiple media and multiple links and a medium, and belongs to the technical field of target area pollution abatement, and the method comprises the steps: preprocessing pollutant monitoring data to obtain a standardized data set; the pollutant monitoring data comprises data of pollutants in a plurality of links and a plurality of media in a target area, and the standardized data set comprises a plurality of standardized data; based on the standardized data set, main characteristic components representing pollutant distribution and evolution rules are extracted; constructing a transfer coupling relation model based on the main characteristic components, and obtaining a transfer path and a coupling strength coefficient of the pollutants according to the transfer coupling relation model; based on the transmission path and the coupling strength coefficient, constructing a causal model and carrying out joint verification; and when the verification is passed, outputting a pollution traceability result. According to the invention, the scientificity, the accuracy and the decision support reliability of mine pollution traceability are obviously improved.

The invention provides a power grid mountain fire prediction method based on causal driving and a space-time diagram convolutional network, and belongs to the technical field of mountain fire prediction. A dynamic feature encoder and a static feature encoder are designed to extract high-dimensional spatial-temporal features of meteorological time sequence information, geographic space environment and power transmission line distribution multi-source heterogeneous data, a causal discovery algorithm is adopted to construct a dynamically evolved causal graph topology, a real causal driven relationship between variables is identified, a causal intensity matrix is decoupled into positive and negative adjacent matrixes, and the dynamic evolved multi-source heterogeneous data is obtained. And designing a causal constrained graph convolution module to aggregate and propagate high-order information, and finally realizing accurate prediction of the power grid forest fire. According to the method, the PCMCI causal discovery algorithm is introduced, so that the real causal driven relationship among multivariate time sequence factors is effectively identified; a causal GCN layer in the CSTGCN model extracts spatial dependence features by using an adjacent matrix constrained by a causal structure, the CSTGCN model embeds causal structure information into a spatio-temporal feature learning framework, and higher prediction precision and generalization performance are achieved.

The invention discloses a pipeline topological relation construction method based on a graph neural network. The method comprises the following steps: collecting and preprocessing original spatial data of a pipeline system; utilizing feature extraction to generate a node feature matrix and an edge feature matrix; constructing a pipeline diagram structure; index alignment and size verification are executed, and a pipeline diagram input matrix is generated; inputting to an improved EvolveGCN model, and introducing a graph attention control evolution mechanism to obtain a time sequence embedding feature matrix of each pipeline node object; generating a predicted pipeline topology connection set; carrying out topological consistency verification to obtain a final pipeline topological relation structure; a pipeline topological relation model file is formed, automatic modeling and optimization of a pipeline system are achieved, and the recognition precision and modeling efficiency of a pipeline topological structure are improved.

Techniques for an email-security detection system to analyze incoming emails for Business Email Comprise (BEC) attacks of targeted new email users in an enterprise based on peer models of email recipients in an enterprise and the conversation history of recipients. A method is disclosed that includes analyzing an incoming email and identifying one or more recipients of the incoming email in an enterprise network; analyzing contextual information in the incoming email for the email intent, and associating the email with a target recipient; applying a relational model associated with peers of the target recipient for detecting whether the incoming email is a business email compromise (BEC) attack directed to the target recipient; applying a relationship model of other recipients of the incoming email for detecting whether the BEC attack is associated with the target recipient; and determining, whether the incoming email is a BEC attack.

Methods, systems, apparatuses, and computer program products are described. A system may obtain a set of metadata associated with multiple entities of a datastore, the multiple entities including at least two different data types. The system may augment, using a generative artificial intelligence (AI) model, the set of metadata resulting in a set of augmented metadata for the multiple entities of the datastore. The system may generate, using the set of augmented metadata, a set of entity objects for the multiple entities of the datastore and in accordance with a reference schema. The system may identify, using the generative AI model and the set of entity objects as inputs into the generative AI model, first relationships between two or more first entities of the multiple entities of the datastore. Based on the identified relationships and a reference schema relationship model, the system may generate a schema relationship model.

The invention relates to the technical field of medical data processing, and provides a medical image data processing method based on deep learning, which comprises the following steps of: acquiring data according to an acquisition template, extracting a pulse time sequence by self-adaptive threshold peak detection, calculating an instantaneous phase according to linear interpolation, calculating a statistical magnitude, comparing a quantitative index with a preset threshold value, and calculating a pulse time sequence according to the statistical magnitude. Judging a steady state by combining a peak loss rate and an abrupt change detection rule, and calculating phase consistency between channels for verification; the method comprises the following steps of: splitting acquired data according to a concept entity to form a data relation model, implementing rapid global rigid estimation and applying affine transformation, estimating a pixel-level displacement field by adopting a pyramid dense optical flow network, applying the displacement field to an original pixel, and performing time domain fusion by taking optical flow confidence and a registration residual error as weights; and cutting the short-time image stabilization sequence after registration compensation, and outputting a pixel-level risk thermodynamic diagram, a candidate focus list and each output confidence interval by taking a hybrid network of a convolution front end and a space-time Transform backbone as a prediction model.

The invention discloses a plain small watershed nitrogen and phosphorus pollutant collaborative source analysis method, which belongs to the technical field of environmental pollution traceability, and comprises the following steps: 1, collecting multi-source basic data in a target plain small watershed, carrying out unified coordinate system, format and precision preprocessing, and constructing a watershed basic database; and 2, constructing a response relation model of various pollution sources and potential pollution loads on a GIS platform based on a drainage basin basic database, qualitatively identifying main pollution sources through spatial visualization analysis, and delineating key risk areas. According to the method, macroscopic spatial analysis, microchemical tracing and mathematical model calculation are organically fused, high-precision spatial data, hydrochemical and isotope analysis, multi-model quantitative analysis and uncertainty quantification are integrated, and high-precision and high-reliability cooperative source analysis of nitrogen and phosphorus pollutants is achieved.

The invention discloses a tunnel precise drilling and blasting design method integrating multi-source intelligence and closed-loop optimization. The method comprises the steps that geological parameters are collected through geological detection equipment; based on fracture mechanics and rock mass damage theories, constructing a quantitative relation model reflecting joint network distribution, stress wave propagation characteristics and a rock mass crushing effect coupling mechanism; inputting the geological parameters into a pre-constructed multi-dimensional knowledge base, integrating historical engineering data association rules by the multi-dimensional knowledge base by using a graph database, and extracting geological features; based on a deep learning model, analyzing the mapping relation between the geological features and the blasting parameters, and generating a preliminary blasting scheme including the blast hole distance, the charging structure and the detonating mode; according to the method, a closed-loop optimization system of geologic features-blasting parameters-effect evaluation is constructed, so that dynamic intelligent decision-making and construction self-adaptive regulation and control of tunnel drilling and blasting design are realized, and the contour forming precision and the blasting efficiency are remarkably improved.

The invention discloses a human and house data comprehensive treatment method, system and equipment based on a multi-level address model, and a medium, belongs to the technical field of data treatment, and aims to solve the technical problem of how to effectively integrate human and house data sources of multiple parties and improve the accuracy, consistency and availability of human and house data. The defects that in the prior art, people and house data are scattered, address description is not standard, the incidence relation is inaccurate, data quality is uneven and a unified identification system is lacked are overcome. According to the technical scheme, the method comprises the steps that a five-level core data table comprising a community table, a building table, a house table, a people and house relation table and a personnel information table is constructed; the method comprises the following steps: accessing original human and house data from a plurality of data sources, performing intelligent analysis and standardization processing on address information, and obtaining data after standardization processing; establishing a house unique identification system and a human-house multi-dimensional relation model based on the standardized data; evaluating and repairing the data quality; and standardized human and house data service is provided for the outside through the data service interface.

The invention provides a medical quality dynamic evaluation method and system based on machine learning. The method comprises the following steps: acquiring a cross-mechanism desensitization data set, constructing a medical quality causal diagram, generating an anti-fact intervention scene by using Do calculation, and outputting an interpretable causal relationship model; constructing a federated learning optimization framework, aggregating causal relationship model parameters of all medical institutions, introducing a doctor crowdsourcing labeling mechanism to correct a result, and generating a global medical quality dynamic evaluation model; and constructing a surgical digital twin model, mapping dynamic data, and generating a dynamic quality score. Through a federated learning framework, the limitation of single mechanism data is broken through, parameters are optimized, and the performance is improved; causal graph construction and anti-fact intervention scene generation are combined, causal logic is mined, and the result value is enhanced; mapping dynamic data based on a digital twinborn model to realize accurate dynamic scoring; a doctor crowdsourcing labeling mechanism is introduced, a closed-loop optimization system is formed, and finally a high-precision medical quality dynamic evaluation result is output.

The invention provides an auxiliary rock core geological logging method based on drilling parameters, and relates to the technical field of geological investigation, the method is applied to a logging control terminal, and the method mainly comprises the following steps: obtaining regional typical rock core samples and five types of drilling parameter signals, classifying and extracting static characteristics of the rock core samples, and carrying out parallel noise reduction analysis on the parameter signals; obtaining a matching relationship between the rock core and the parameter signal, and constructing a lithologic parameter bidirectional association reference library; on the basis of the matching relation of the lithologic parameter bidirectional association reference library, the model is embedded into an incremental learning module to update the weight, and a subsection lithologic pre-judgment result is output; and calling a preset catalog template to load a pre-judgment result, generating a catalog report after man-machine interaction recheck and correction, collecting correction data, reversely transmitting the correction data back to the reference library to update a matching relationship, and triggering incremental learning of the model to complete a closed loop. The technical problems that in traditional rock core logging, regional lithology adaptation is poor, parameter interference is large, a model is not dynamically optimized, the deep operation risk is high, and efficiency is low are effectively solved.

The invention discloses an industrial Internet of Things sensor fault monitoring method, system and device and a medium, and relates to the technical field of sensor fault monitoring. The invention provides an industrial Internet of Things sensor fault monitoring method, which comprises the steps of obtaining a target sensor and at least one associated sensor associated with the target sensor, and constructing a relation model based on the target sensor and the at least one associated sensor; according to the relation model and the actual reading sequence of the at least one associated sensor in the preset time period, obtaining at least one predicted value sequence of the target sensor in the preset time period, and calculating a fault feature sequence between the at least one predicted value sequence of the target sensor and the actual value sequence of the target sensor; and obtaining the relationship health degree of the target sensor according to each fault feature sequence, if at least one relationship health degree is lower than a preset threshold, generating a relationship abnormality report, and sending the relationship abnormality report to the target user.

The invention relates to a desensitization method, system, equipment and medium for industrial data, and the method comprises the steps: building and dynamically maintaining an industrial field standard data variable rule table, and defining a desensitization strategy and a sensitivity level matched with specific industrial variables such as temperature, rotating speed and the like in physical meanings, time scenes and node levels of the specific industrial variables; the method comprises the following steps: adding a multi-dimensional identifier to original industrial data, establishing a six-dimensional binding relation model of data variable type-time dimension-node hierarchy-cluster role-sensitive level-rule entry, driving the data to execute a time-coordinated stepped desensitization process in four node hierarchies of a workshop, an enterprise, a park and a cluster, according to the method, refined access control and data decryption fusing time, space and role dimensions are realized according to the model and a full-link tracing log, and the problems that rules and industrial scenes are disjointed, dynamic time adjustment is lacked, cross-node collaboration is insufficient and authority control coarse granularity is caused in the prior art are effectively solved.

In an identity governance system, processes enable secure delegation of authority to create and manage identities for users of a computer network and to provision access entitlements to users to network resources in a manner that ensures compliance with security and access policies. The provisioning of access entitlements to users is controlled based on a model of relationships between business entities that the computer network serves. Identities of users are assigned to a node of the model. Provisioning of entitlements for coarse and fine-grain access is restricted based on this assignment. The node of the business relationship model limits the scope of what user access entitlements can be assigned, to whom they may be assigned, and who may assign them.

The invention discloses an overhead working truck safety early warning method and system and a storage medium. The method comprises the steps that real-time load, height and inclination angle data of an overhead working truck are acquired through a multi-source data acquisition module and transmitted to a data analysis decision module; calculating the maximum allowable load at the current height through a table look-up method or a height-load relation model based on the collected load and operation height data, and dynamically adjusting a load alarm threshold value; independently judging the overload risk and the inclination risk to generate a control instruction; operation height, load and inclination angle data are fused, a linkage risk index is calculated through a weight-inclination angle-height dynamic correlation model, a composite risk is identified, and a control instruction is generated; and the early warning and execution control module receives the control instruction to control the equipment to act and feeds back an execution result. Dynamic overload threshold adjustment, inclination grading response control, multi-source data fusion overload and anti-overturning system cooperative judgment of composite risks are realized, and the flexibility and effectiveness of risk early warning are enhanced.

The invention discloses a gas detection precision improving method based on a multi-algorithm fusion framework. A photonic crystal resonant cavity and a tunable band-pass filtering structure are integrated in a Fourier transform spectrometer light path; collecting a wide-spectrum light intensity signal, and constructing a spectral signal database in combination with the enhancement characteristic and the filtering characteristic of the resonant cavity; zero calibration and dynamic baseline deduction operation are carried out, and self-adaptive variational mode decomposition and wavelet transform are adopted to carry out signal denoising on the spectral signals; an environment compensation model is established, and an Arrhenius type correction factor is adopted to suppress water vapor cross interference; constructing an RLS and fuzzy control combined hybrid adaptive filter; separating aliasing spectral signals by adopting a non-negative matrix factorization algorithm; establishing a quantitative relation model; in the online concentration prediction process, the initial concentration value is subjected to recursive optimization by using a hybrid adaptive filter, the deviation is continuously corrected through an environment compensation model, and an accurate concentration value is output. The method can significantly improve the accuracy and stability of multi-gas detection.

A transformer neural network may include a model. The model may receive a plurality of feature groups, the plurality of feature groups including a plurality of features. The model may process the feature groups, for example at a first layer of the transformer neural network to determine a relationship between the plurality of feature groups. The model may process the plurality of features, for example at a second layer of the transformer neural network to determine a relationship between the plurality of features. The plurality of feature groups may include feature groups associated with a candidate for a job. The feature may include features associated with the candidate for the job. The model may output an indication of the relationship between the plurality of feature groups and / or the plurality of features. For example, the indication may include a score associated with a suitability of the candidate for the job.

The invention discloses a drainage pipe network multi-modal data analysis method for urban inland inundation prevention and control, and belongs to the technical field of urban inland inundation prevention and control. According to the method, satellite remote sensing surface ponding images, drainage pipe network liquid levels, regional meteorology, public ponding crowdsourcing information and terrain elevation and pipe network attribute data are collected and integrated into a multi-dimensional original data set; after cleaning and standardization processing, mining waterlogging associated features by using an adaptation algorithm, and establishing a coupling relation model; waterlogging risk reasoning is carried out based on the model, and graded early warning information and pump station scheduling, pipe network dredging and emergency dredging path schemes are generated; and performing visual analysis on a result, and dynamically optimizing a model and a scheme according to real-time updated data. According to the scheme, waterlogging influence factors are comprehensively covered, the accuracy of risk assessment and prevention and control decision is improved, dynamic changes of waterlogging are adapted, and the initiative and systematicness of urban waterlogging prevention and control are enhanced.

The invention relates to the technical field of data simulation analysis, in particular to a simulation data driving-based loader load process prediction method. The method comprises the following steps that a loading lifting capacity optimization mechanical relation model is established based on structure parameters of the skid steer loader; establishing a multi-domain loader oil way-dynamics simulation coupling model; and carrying out loading machine lifting mechanical collaborative optimization processing on the multi-domain loading machine oil way-dynamics simulation coupling model through the loading lifting capacity optimization mechanical relation model so as to obtain a loading machine working condition multi-domain collaborative simulation model. Transmitting the loading machine working condition multi-source load data to a loading machine working condition multi-domain collaborative simulation model to analyze loading machine multi-domain collaborative simulation load process characteristic data; and based on the loader multi-domain collaborative simulation load history characteristic data, establishing a loader multi-domain collaborative precision optimization load history prediction mapping relationship, and generating an optimized loader multi-domain collaborative load history prediction model. According to the invention, accurate prediction of the loading process of the loader is realized.

The invention relates to the technical field of photovoltaic power station intelligent operation and maintenance and equipment health management, and discloses a photovoltaic equipment residual life prediction method based on degradation data, which comprises the following steps of: firstly, comprehensively obtaining multi-source heterogeneous operation data of photovoltaic equipment by constructing a full life cycle information model; and then, processing the preprocessed data by using a model for deeply fusing a convolutional neural network and a long-short-term memory network. Wherein the CNN module is used for automatically extracting local coupling features in multi-channel time sequence data, the LSTM module is used for accurately capturing a long-term time sequence dependency relationship of equipment degradation, and the model finally outputs a degradation index sequence capable of quantifying a health state. According to the method, through collaborative modeling of multi-scale features, the accuracy and reliability of prediction are remarkably improved, and powerful technical support is provided for predictive maintenance of the photovoltaic power station and reduction of operation and maintenance cost.

The invention relates to an anti-misoperation intelligent inspection method and system for secondary equipment of a transformer substation and a medium. The method comprises the following steps: acquiring operation state data of a secondary soft pressing plate and primary equipment; establishing an equipment relation model based on a power dispatching procedure, a relay protection procedure and a substation field operation procedure; constructing an anti-error rule base; when a switching operation instruction of the transformer substation is received, simulation rehearsal is carried out on the operation sequence based on the anti-error rule base and the operation state data of the secondary soft pressing plate and the primary equipment; the primary equipment and the secondary equipment are operated based on the operation sequence of the simulation rehearsal, and an inspection report is generated; model decomposition, graph-model matching, rule calling and the like of the transformer substation are achieved through the topology technology, misjudgment prevention of primary and secondary equipment operation is further achieved, and it is effectively avoided that a protection pressing plate is missed in the primary equipment operation process; the method has the advantages that the equipment relation model and the anti-error rule base are established, the substation model decomposition and anti-error judgment are realized, and the protection pressing plate is effectively prevented from being missed to be put and withdrawn.

Methods, systems, and computer-readable media are used in graph-based machine learning of analog integrated circuits (ICs). In a first aspect, functional device pairs in transistor-level circuits are detected and classified using a hybrid approach combining a subgraph isomorphism algorithm, such as VF2, with a trained relational-graph convolutional network (RGCN). The VF2 algorithm identifies candidate pairs and initial categories, while the RGCN filters false positives using link prediction scores, preserving category labels for validated pairs. In a second aspect, a relational GraphSAGE model performs multi-class link prediction on a heterogeneous graph with netlist and functional relation edge types, labeling device pairs into analog primitive categories without technology-dependent features and merging overlapping pairs into larger functional groups. In a third aspect, circuit performance is predicted using a hierarchy-aware graph neural network comprising an edge-conditioned convolution (ECC) layer and multiple Circuit graph isomorphism network layers corresponding to hierarchy levels of device groupings.

The invention discloses a power grid operation and maintenance service management method based on an informatization means, and belongs to the field of network operation and maintenance, and the method comprises the steps: S1, building power grid operation and maintenance service integrated data information, and generating a multi-source data flow with a timestamp; s2, constructing an operation inspection operation relation model of the hierarchical knowledge graph, and carrying out semantic modeling on equipment, personnel and operation scenes to realize task and risk matching identification; s3, executing task dynamic assignment and resource reconstruction, and performing priority optimization and cross-region scheduling; s4, carrying out digital twin operation inspection site virtual replication, synchronously mapping sensing data and operation images, and realizing track, working condition and risk node visualization; s5, performing abnormal working condition closed-loop management, pushing abnormal data through event triggering, and forming iterative optimization in combination with historical feedback; and S6, forming comprehensive performance evaluation and data archiving, and uniformly incorporating task execution, risk response and equipment maintenance into an intelligent archiving system. The method has the beneficial effects that the power grid operation inspection efficiency, accuracy and traceability are improved.

The invention belongs to the technical field of oilfield development, and discloses an oil and gas reservoir modeling processing system based on clastic rocks and a use method of the oil and gas reservoir modeling processing system. The data processing system is used for processing fault and logging data, geological stratification data and well perforation data, the grid management system is used for dividing and encrypting grid types, the model building system is used for building a digital-analog model and an oil-gas reservoir relation model, and the digital-analog management system is used for outputting the built model. And the established digital model is convenient to modify and reconstruct, so that the digital model pretreatment work requirement of complex oil and gas reservoir types is met, the historical fitting speed of subsequently realizing numerical simulation is improved, and the modeling treatment of clastic rock oil and gas reservoirs is facilitated.

The invention discloses an arterial lesion treatment scheme auxiliary generation system based on ultrasonic and radiography data, and belongs to the technical field of data processing. Comprising a data model storage unit used for storing relation models, an acquisition unit used for acquiring medical record data, and a reference medical record database generation unit used for screening medical records similar to a current patient and storing the medical record data of the medical records as a reference medical record database. The system comprises a data acquisition unit used for acquiring detection data, a data abnormity screening unit used for screening and outputting abnormal data and missing data, a reference scheme generation unit used for confirming and outputting a reference treatment scheme, and an interaction unit used for realizing data and information interaction between the system and a user. Meanwhile, a reference treatment scheme is automatically generated based on a case big database, risk points in an operation are locked, powerful assistance and support are provided for accurate treatment of doctors, and the treatment effect of arterial lesion is improved.

According to the load aggregation method and device based on the network load graph, by constructing the dynamically updated network load graph and establishing the network load coupling relation model, the constraint of a power grid topological structure on load adjustment is accurately quantified, and matching of a regulation and control strategy and a real-time system state is ensured; by introducing a multi-dimensional comprehensive aggregation evaluation system, the differences of the load in the aspects of characteristics, economy, response speed and the like are fully considered, and refined portraying and accurate aggregation of load resources are realized; and finally, through combination of multi-time scale aggregation and a multi-objective optimization algorithm, system operation indexes which conflict with each other are effectively coordinated, and an optimal load aggregation scheme which can quickly respond to system changes and can comprehensively optimize the regulation and control effect, the economic cost and the response speed is generated.

The invention discloses a data warehouse model design method, and belongs to the technical field of data warehouse modeling. According to the method, a dimension modeling method is adopted to construct and analyze a mart model, data is organized into a non-standardized relation model, and the relation between tables is defined through keywords and foreign keys. And designing a dimension table system comprising a product dimension, a calendar day dimension, an institution dimension, an insurance policy attribute dimension and a customer dimension, and a fact table system comprising an insurance claim settlement analysis fact table, a payment analysis fact table, a claim settlement efficiency analysis fact table and a corresponding intermediate fact table. The analysis subject domain data is formatted into a star model or snowflake model, where the analysis fact table is a lightly summarized transactional fact table, and the intermediate fact table is a versioned historical table. According to the method, repeated data construction is avoided through a unified modeling method, and the cost is reduced; an efficient query analysis function is provided by optimizing a model structure; and the data traceability is ensured through versioning management.

The application relates to a data processing method and device based on a configuration management system, computer equipment and a storage medium. The method comprises the following steps: acquiring an object model, an attribute model and a relationship model in the configuration management system; determining a first association relationship between the object model and the attribute model and a second association relationship between the object model and the relationship model based on object coding corresponding to each object; constructing a relationship architecture of the configuration management system based on the first association relationship and the second association relationship; and performing data processing on the configuration management system based on data processing parameters corresponding to a data processing operation and the relationship architecture. Through the above method, data management is directly performed based on the data processing operation, and the data management of the configuration management system is simplified.