573 results about "Relational model" patented technology

The invention relates to the technical field of fault detection, in particular to an industrial fault diagnosis method and system based on intelligent causal correction. The method comprises the following steps: acquiring monitoring data and user task requirements; constructing task structure information based on the obtained user task demand; processing the monitoring data through a three-layer cascaded framework to generate metadata; constructing an initial causal graph structure by utilizing the task structure information and the metadata; performing deep optimization on the initial causal graph structure through a graph neural network to obtain a causal composite relation graph; fault diagnosis and information retrieval are carried out by utilizing the causal composite relation graph and combining a knowledge base; and generating a structured diagnostic report. Through an innovative structure integrating the LLM and the graph neural network, the system can dynamically optimize a causal relationship model, automatically learn and correct fault association, enhance the interpretability of a causal graph by using semantic reasoning of the LLM and a graph attention mechanism, and significantly improve the robustness and accuracy of diagnosis.

The present invention relates to an automatic speed regulation and grouting regulation system for construction of a deep mixing pile in a reclamation area, the main steps comprising: first, configuring and installing a multi-modal sensing system (S101) to monitor key parameters, torque, energy consumption, grouting pressure and stratum permeability of a construction site in real time; constructing a data acquisition and fusion module (S102), integrating multi-sensor data, and forming real-time mapping of stratum-construction parameters; establishing a relation model of formation input and formation response through an extended Kalman filter (EKF) and predictive control (MPC) algorithm (S103), and intelligently evaluating formation compactness and grouting quality; on the basis of a feedback closed-loop regulation and control system (S104), the stirring speed, the grouting amount and the construction pressure are adaptively adjusted in real time, and precise control is achieved; through construction data storage and self-learning optimization (S105), the system records all construction data, and through a deep reinforcement learning optimization control algorithm, the long-term construction precision is improved.

The invention discloses a power distribution operation and maintenance inspection track intelligent planning and optimizing method, and relates to the technical field of power distribution network intelligent operation and maintenance, and the method comprises the steps: collecting the topological data and equipment state information of a power distribution network, and constructing a power equipment association relationship model through an intelligent analysis model; on the basis of the incidence relation model, the inspection importance degree of the power equipment is calculated, and an initial inspection sequence is generated according to the inspection importance degree; and in combination with the initial inspection sequence and the real-time road condition information, constructing a dynamic inspection track network, and generating a final inspection path through an intelligent optimization algorithm. The problems of inaccurate equipment state evaluation, unreasonable routing inspection task planning, insufficient routing inspection path optimization and the like in the prior art are solved. The method has the main beneficial effects that the utilization efficiency of inspection resources is improved, the adaptability of an inspection strategy is enhanced, and the scientificity of an inspection path is optimized.

The invention discloses a distributed power supply access-oriented power distribution network fault distance measurement method and system, and relates to the technical field of power systems, and the method comprises the steps: collecting the electrical parameters and operation states of distributed power supply access nodes in a power distribution network in real time, building a dynamic manifold model based on an ecological niche theory, and carrying out the calculation of the dynamic manifold model; adaptively adjusting manifold learning neighborhood parameters according to power fluctuation data included in the electrical parameters, and updating node ecological niches to reconstruct a dynamic manifold model; based on the reconstructed dynamic manifold model, fault features are extracted from three scales of a current harmonic component, a feed line inter-harmonic propagation path and whole network voltage influence, and a three-dimensional feature vector is generated through fusion of a graph correlation algorithm; based on the expanded fault sample library and the power fluctuation data, constructing a fault transfer relation model to predict a ground fault and a short circuit risk area; a fault source is modeled by adopting a topological neural network, and a fault point distance measurement value is output through state prediction and strategy deduction.

The invention discloses an intelligent multi-gas detection module data processing system and method based on NDIR, and relates to the technical field of gas detection.The method comprises the steps that a multi-wavelength NDIR sensor is used for obtaining light intensity changes of a gas sample under different wavelengths, and original spectral signals are generated; performing denoising processing on the spectral signal by adopting wavelet transform, and performing zero calibration and dynamic baseline deduction; based on the Beer-Lambert law in combination with environmental factors, establishing a relation model between gas absorption and spectral signals; updating model parameters by using a recursive least square algorithm, and continuously optimizing gas concentration prediction; separating gas signals by adopting a non-negative matrix factorization algorithm, and predicting the concentration of each gas; and the edge end operates the lightweight model in real time, and regularly uploads the model to the cloud end for global optimization and federated learning. The method can effectively improve the condition that the precision is insufficient when the model is used for a long time in the prior art.

The invention relates to an intelligent maintenance decision method and system based on a knowledge graph, and relates to the technical field of intelligent operation and maintenance and decision support and information, and the method comprises the steps: obtaining the operation state data and historical maintenance data of industrial equipment; constructing an industrial knowledge graph and generating an incidence relation model; an optimal maintenance path is obtained based on the analysis of the relationship between the fault type and the maintenance path; optimizing a maintenance task allocation scheme through the optimal maintenance path, the incidence relation model and the maintenance resource constraint condition; carrying out maintenance time window dynamic adjustment analysis to obtain an optimal maintenance strategy; and finally, performing multi-objective optimization iterative analysis to obtain a final maintenance decision. According to the scheme, various factors can be comprehensively considered, and the scientificity and the accuracy of maintenance decision making of the industrial equipment are improved.

The invention provides a power grid cable loss analysis method and device and a storage medium, and the method comprises the steps: obtaining cable topology data, extracting parameter characteristics, and generating a parameter database; performing matrix conversion based on the parameter database to establish a network calculation model, executing load flow calculation, and outputting voltage power distribution data; establishing a temperature and loss coupling relation model according to the voltage power distribution data, calculating cable temperature distribution, and generating time-varying loss data; and performing multi-dimensional feature analysis on the time-varying loss data, calculating a loss risk index, identifying loss hotspots and generating an optimization scheme. According to the method, the dynamic relation model of the cable parameters and the loss is established, and the cable shielding layer characteristics and the temperature change factors are combined, so that accurate analysis of the power grid cable loss is realized, technical support is provided for identifying loss hotspots and formulating a targeted optimization scheme, and the power grid loss calculation deviation is effectively reduced.

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 relates to the field of industrial automation control, and discloses an intelligent control system and method based on multi-parameter coupling analysis and predictive maintenance. According to the method, flow, temperature and water quality parameters are collected in real time through a sensor network, and after data cleaning and standardization processing, a parameter coupling relation model is constructed; an LSTM neural network is adopted to predict a system energy consumption trend, and a time sequence regression model is combined to analyze equipment performance attenuation characteristics; and when the predicted value exceeds a threshold value, dynamically adjusting operation parameters and optimizing load distribution to form closed-loop control. Accurate energy-saving control under complex working conditions is achieved, energy consumption can be reduced by 8%-12% through tests, the equipment maintenance cost is reduced by 15%-20%, and the system operation efficiency is remarkably improved.

The invention belongs to the technical field of bridge engineering, and particularly discloses a bridge safety early warning method and system based on fusion of a data model and a physical model. Comprising the following steps: in a first stage, on the basis of a neural network monitoring site excitation-response mapping relation model established by an initial state full-bridge finite element model, calculating response information corresponding to field excitation, and comparing response monitoring signals to determine whether a structure is damaged from a full-bridge angle; in the second stage, through data expansion from a monitoring point position to a non-monitoring point position, a set threshold value is compared to determine a damage position and a damage grade; and in the third stage, adjusting parameters of the model are calculated according to excitation-response signals of a monitoring point position sensor, the finite element structure model is updated, the residual bearing capacity of the structure is calculated by combining state information borne by the structure model, and the service life of the component is determined. The invention provides a safety assessment method fusing monitoring data and a physical model for structural health monitoring, and the method is clear in physical significance, high in real-time performance and reliable in result.

The invention relates to the field of waterproof sockets, and discloses a multi-scene adaptive intelligent waterproof socket remote regulation and control system and method, and the method comprises the steps: obtaining multi-source sensing data of a socket deployment environment, and constructing a multi-scene adaptive input data set in combination with a parameter fusion strategy and a signal interference compensation mechanism; environment feature recognition is carried out on the multi-scene adaptive input data set, and typical application modes in different scenes are extracted by using a socket state tag extraction algorithm; based on the state mapping relation model, a remote regulation and control strategy optimization algorithm is adopted to dynamically analyze the operation state of the socket, and the matching degree of the current operation mode and the environment scene is identified; an intelligent judgment mechanism is introduced, pre-execution verification is carried out on the multi-scene regulation and control suggestion set, and potential control conflicts are recognized in combination with historical operation data and real-time control instructions; and performing instruction issuing management on the remote control behavior of the socket based on the optimized execution strategy. The invention has the advantage of improving the universality of the intelligent waterproof socket.

The invention discloses an intelligent diagnosis and maintenance system for power equipment, and relates to the technical field of power equipment detection, and the system comprises the following components: an array microphone module which is composed of a plurality of microphones, is used for accurately calculating the spacing, is arranged in a three-dimensional or plane matrix manner, and is used for collecting sound signals generated in the operation process of the power equipment; according to the invention, the array microphone module is combined with a beam forming technology, the sound signals are collected efficiently, a voiceprint topological map is constructed, sound source distribution and energy conduction paths of all parts of the equipment are represented in a visual form, and the sound source distribution and energy conduction paths of all the parts of the equipment are displayed in a visual manner. Meanwhile, voiceprint features and vibration modal features are extracted through a feature extraction module, and a joint inversion module is used for performing joint analysis on the two features based on a mapping relation model and a machine learning algorithm and in combination with a modal database, so that the damage degree of the mechanical part of the power equipment is accurately inverted.

The invention provides a network flow regulation and control system and method and a storage medium, and relates to the technical field of network communication. The network traffic regulation and control system comprises: an information collection unit, which is used for obtaining historical traffic data of all media plane network elements connected with a control plane network element and a topological relation of the media plane network elements; the AI model updating unit is used for updating the traffic regulation and control model of the control plane network element according to the historical traffic data and the topological relation, and the strategy generation unit is used for obtaining a traffic distribution strategy of the control plane network element; and the strategy execution unit is used for dynamically adjusting the media traffic of each media plane network element according to the traffic distribution strategy. According to the method, the network data is collected and analyzed in real time, and dynamic updating of the AI model is combined, so that dynamic regulation and control of the network traffic are realized, the method can quickly adapt to changes of network conditions, and the flexibility and efficiency of traffic regulation and control are improved.

The invention discloses a high-voltage direct current transmission converter thyristor fault identification method based on reliability evaluation. The method comprises the following steps: constructing a stress-degradation-life mapping relation model through a stepped temperature-voltage cooperative stress test; establishing a three-stage Wiener degradation model, and predicting a critical failure point in combination with a dynamic threshold jump mechanism; an FPGA + CPU heterogeneous computing platform is adopted to execute an EM-MCMC joint estimation algorithm in parallel, and parameter updating is accelerated through assembly line MCMC sampling; designing a dynamic correction factor optimization reliability index based on the buffer layer parameters; constructing a dual-channel LSTM network to extract degradation features and generate a dynamic failure threshold value; a converter topology is reconstructed through a graph neural network, and fault positioning and damage grading are realized in combination with a three-level early warning mechanism. According to the method, fault identification is carried out through combination of multi-stress coupling modeling and a dynamic threshold value, the real-time performance is improved through a heterogeneous computing architecture, the false alarm rate is reduced through combination of a dynamic correction and topology reconstruction technology, and accurate identification and operation and maintenance decision support of the thyristor fault are realized.

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 belongs to the technical field of information, and discloses an IT performance index early warning method based on time sequence analysis, and the method comprises the steps: S1, inputting historical data; s2, risk detection; and S3, outputting early warning. Through fusion of statistical learning and machine learning technologies, accurate identification and early warning of three scenes of slow degradation of IT performance indexes, mode switching and capacity bottleneck are realized. The method specifically aims to improve the accuracy of performance index trend identification through a variable-point enhanced segmentation regression algorithm, and help an I T manager to more accurately evaluate the influence and trend of performance changes. Designing a cycle-adaptive double-sample hypothesis testing mechanism, and detecting and identifying switching of different load modes, especially significant changes caused by special or emergency events; and constructing a multivariable autoregressive VAR model, establishing a relation model of performance indexes and capacity indexes, evaluating capacity bottleneck conditions, and performing early warning in time.

The invention relates to an intelligent IT asset management system, and the system comprises a configuration asset management module which is used for automatically collecting IT asset information in a network, building a complete configuration item relation model, and supporting the dependency relation mapping and impact analysis between configuration items; the service desk management module is used for establishing a service catalog, defining a processing flow, response time and a service level protocol, automatically carrying out classification and priority evaluation on the received service requests through an intelligent classification engine, and distributing the service requests to proper processing personnel according to a preset rule; the contract supplier management module is used for full-life-cycle management of suppliers and contracts; the IT service financial module is used for multi-dimensional budget management and fine management of IT cost; and the multi-tenant management module is used for realizing entity separation and authority control. The problems of a traditional IT asset management system in the aspects of asset tracking, service management, cost control and the like are solved, and a set of complete IT service management solution is provided for enterprises.

The invention relates to the technical field of software and hardware compatibility testing, in particular to a domestic software and hardware compatibility intelligent evaluation system and method, and the system comprises a compatibility analysis engine, a compatibility detection task execution engine, a software behavior analysis module, a fault positioning module, a knowledge graph module, a performance optimization suggestion module and a visual interface. The software behavior analysis module constructs a high-dimensional feature space of a software running state through deep learning, and extracts abnormal features based on a topological space mapping technology; the fault positioning module constructs a dynamic causal network based on a probability graph model, and identifies a root node through a chaos sensitivity analysis algorithm; the knowledge graph module constructs a software and hardware dependency relationship model and provides priori knowledge for fault location; and the performance optimization suggestion module generates an optimization scheme based on the root positioning result, so that the functions of automatically executing a compatibility test, accurately positioning the root of the compatibility problem and providing targeted optimization suggestions are realized, and the adaptation efficiency of domestic software and hardware is greatly improved.

The invention relates to an intelligent integrated warehouse logistics management method based on an electronic tag, and the method comprises the steps: obtaining real-time humidity monitoring data of a warehouse environment, and generating an environment parameter matrix in combination with temperature and ventilation conditions; according to the environment parameter matrix, a mapping relation model of humidity and label performance is trained, and the influence weight of the humidity on the label performance is obtained; according to the label performance comprehensive score, adopting a support vector machine algorithm to judge whether the label performance is close to a failure threshold value, and generating a performance early warning signal; when the performance early warning signal is triggered, predicting a label performance change trend through time sequence analysis, and generating a performance degradation curve; according to the performance degradation curve, a Bayesian network model is adopted, label performance evaluation model parameters are updated, and model prediction precision is optimized; and applying the updated model parameters to the dynamic performance evaluation model, and outputting a label health state diagnosis result in real time in combination with the real-time environment parameter matrix.

The invention discloses a recirculating ball type electric power steering system fault self-checking method and system and a readable medium, and relates to the technical field of vehicles, and the method comprises the steps: storing a signal transmission path and each function node, determining the type of an influence factor which influences the instruction execution accuracy of the function node, and determining the instruction execution accuracy of the function node; analyzing and acquiring the relationship between the fluctuation amplitude of the data signal at each function node and the numerical value of the influence factor, storing the relationship as a correction model, and storing a first relationship model for reflecting the data association relationship among the function nodes on the same signal transmission path; acquiring real-time data and source data of various impact factors, and generating theoretical response data according to the first relation model, the real-time impact factor data and the correction model; the actual response data is collected, the deviation amplitude between the actual response data and the theoretical response data is calculated, whether the function node breaks down or has a potential fault is judged according to the deviation amplitude, early warning is output, and the fault detection accuracy and the potential fault pre-judgment capability are greatly improved through the technical scheme.

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 construction method and system of a settlement landscape evolution database based on multivariate spatial-temporal characteristics. According to the method, historical literatures, aerial photography, satellite images and landscape data are integrated to form a multivariate data set; constructing a dynamically updated database by using MySQL, and realizing data source associated storage by using a relational model; establishing a settlement landscape state machine model, and extracting spatio-temporal data to identify typical states and conversion conditions; constructing a state transition matrix, and analyzing the influence of social economic and environmental changes on evolution; the database is updated through field investigation, and a visual tool is developed to display the evolution process. According to the method, full-process dynamic analysis of settlement landscape evolution is realized, and efficient and accurate support is provided for decision making.

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.

The invention discloses a cutting process parameter optimization method and system based on deep reinforcement learning, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: constructing a relation model between process parameters and process indexes for a multi-step cutting task; a cutting machining environment is built based on the relation model, the machining state in the current environment is obtained, and a technological parameter optimization problem is formalized into a Markov decision process; in the Markov decision process, an improved soft decision-evaluation algorithm is adopted as a process parameter optimizer, a process agent is generated, and an optimal process parameter combination of multiple steps is output through training; wherein the improved soft decision-evaluation algorithm is characterized in that a neural network is introduced into a Critic module and an Actor module; and executing an actual cutting task according to the optimal step number and the process parameter combination output by the process agent. The process parameter optimization performance can be improved, and the part machining quality is improved.

The invention discloses a TSV-based multi-layer microsystem interconnection module total dose effect simulation modeling method, and relates to the technical field of three-dimensional integrated microsystems, and the method comprises the steps: obtaining geometric parameters and material parameters of a multi-layer three-dimensional microsystem interconnection module, building a finite element model of a microsystem module based on the geometric parameters and the material parameters, and carrying out the simulation modeling of the TSV-based multi-layer microsystem interconnection module total dose effect. Setting boundary conditions and port excitation; scattering parameters under different material conditions in the finite element model are extracted, frequency points are fixed, and a relational expression of the scattering parameters and material parameters is established; an RLC equivalent circuit model is constructed, and an equivalent circuit extraction method is obtained; according to an equivalent circuit extraction method, in combination with a relational expression of scattering parameters and material parameters, establishing a relationship between return loss parameters and equivalent circuit parameters; and establishing a relation model between the radiation dose and the resistance, inductance and capacitance in the equivalent circuit according to the relation between the return loss parameter and the equivalent circuit parameter. According to the invention, a scientific basis is provided for the reliability design of the three-dimensional microsystem in the radiation environment.

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.

The invention relates to the field of network control system security, in particular to an asynchronous processing method for resisting zero dynamic attacks in a non-uniform sampling control system. Comprising the following steps: comparing and analyzing an attacked system and an unattacked system, and establishing a dynamic relation model between an output signal difference value and an attack signal; based on the relation between the zero-dynamic attack signal and the system state space, a zero-dynamic attack signal design strategy which is kept invisible at the non-uniform sampling moment is given; active introduction of a time difference into a controller of the sampling control system is considered, and a dynamic relation between a corresponding attack signal and an output difference value is reconstructed; active introduction of a time difference into a controller of a sampling control system is considered, and a dynamic relation between an output signal difference value containing the time difference and an attack signal is reconstructed; the existence of the time difference is determined by constructing a system containing the time difference and combining with a zero dynamic attack definition; on the basis of the given time difference, the condition for detecting the zero dynamic attack in the non-uniform sampling control system is determined, so that the system is protected; the method is used for enhancing the detection capability and defense effect of the non-uniform sampling control system on the zero dynamic attack.

The invention relates to a surface roughness nondestructive testing method based on machine learning. The method comprises the following steps: measuring the surface roughness of a sample, acquiring surface roughness data, acquiring an SEM image and a metallographic image of the sample, preprocessing the image, extracting a texture feature value in the image, and forming a data set with the surface roughness data; establishing a linear mapping relation model between the surface roughness and the texture feature value, training the linear mapping relation model by adopting the training set, and verifying the accuracy of the linear mapping relation model by adopting the test set; and simulating a to-be-measured object by using the adjusted linear mapping relation model to obtain the surface roughness of the to-be-measured object. Compared with the prior art, the method has the advantages that the linear mapping relation model between the object image feature value and the roughness is constructed by using the machine learning technology, the current situation of discreteness or lack of a mapping model in a traditional detection method is changed, non-contact and rapid measurement of the surface roughness of the object is realized, and the detection cost is reduced.

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.