880 results about "Modelling analysis" patented technology

The invention relates to the technical field of model analysis, in particular to a time-space fusion neural network line topology analysis method for a power distribution network. The method comprises the following steps: obtaining original line topology data corresponding to a power distribution network, and carrying out structured disassembly and preprocessing to construct a space-time double graph structure; constructing a bidirectional dynamic feature interaction mechanism based on the space-time double graph structure, performing multi-scale topological feature extraction, and generating a space-time separated feature vector set; performing deep coupling fusion on the feature vector set subjected to time-space separation to generate corresponding unified topological feature representation containing abnormal topology; and constructing a dynamic topology state prediction model based on the unified topology feature representation to optimize a space-time joint loss function and output a corresponding real-time topology connection relationship and an equipment state change trend, and meanwhile, performing dynamic topology reconstruction to generate a current-moment reliable topological graph corresponding to potential branch disconnection and temporary tripping. The topology analysis accuracy of the power distribution network can be improved.

To significantly improve the prediction accuracy, response speed and management efficiency of large river bank slope disasters, an integrated bank slope monitoring method based on multi-parameter collaborative recognition is proposed. The solution includes step S1 of synchronously collecting data on bank slope displacement, pore water pressure, inclination angle, vibration frequency and environmental temperature and humidity to form an original monitoring dataset and construct a multi-parameter collaborative recognition network; step S2 of using a multi-modal data fusion algorithm to generate a fusion data matrix including spatiotemporal correlation features and perform spatiotemporal data alignment and outlier cleansing; step S3 of combining a geomechanical parameter library and a past disaster case library to output a risk level map and perform dynamic risk assessment model analysis; and step S4 of triggering a multi-level early warning mechanism and generating linked control commands including treatment suggestions to perform multi-level early warning and linked control.

The invention provides a cast workpiece three-coordinate structured light rapid measurement system, and relates to the technical field of industrial automatic measurement, and the system comprises a calibration parameter module which is used for calibrating a standard ball, building an initial conversion relation between a structured light coordinate system and a three-coordinate measuring machine world coordinate system, and collecting temperature, humidity and vibration data in real time; dynamically updating the conversion relation to form calibration parameters; and the measurement path planning module is used for dividing the casting workpiece into a plurality of measurement areas based on the calibration parameters, planning a motion path of the three-coordinate measuring machine and driving the motion platform to move to each measurement position. According to the invention, full-process automation from calibration, measurement to modeling analysis of the casting workpiece and the advantage of comprehensive quality analysis are realized, and a reliable three-dimensional measurement and quality control solution is provided for the casting industry.

The invention discloses an intelligent legal data processing method and system based on big data, and the method comprises the steps: S1, constructing a legal data collection framework, and carrying out the unified coding and representation of different data forms through a multi-modal fusion technology; s2, performing hierarchical semantic analysis on the cross-domain legal clauses to generate a high-dimensional semantic representation vector with context sensing capability; s3, performing node embedding, association analysis and reasoning optimization on the constructed legal knowledge graph to generate a dynamically updated multi-domain legal association network; s4, mapping and optimization among terms are realized through semantic comparison and multi-level rule verification; s5, performing modeling analysis on the dynamic trend and the potential risk factors of the legal data, and outputting a risk prediction and early warning strategy of the legal event; and S6, improving cross-data-source cooperative computing capability and data analysis efficiency through a distributed optimization strategy. The method has the advantages of high semantic understanding depth, high risk prediction accuracy and high privacy protection and cooperative computing efficiency.

The invention discloses an environment modeling method and system for realizing intelligent security and protection, and relates to the technical field of intelligent security and protection, and the method comprises the steps: collecting a visual image and depth data of a monitoring region, and generating a monitoring scene feature matrix; performing hierarchical feature extraction on the monitoring scene feature matrix by using a deep convolutional neural network, and constructing a scene semantic feature map; projecting the scene semantic feature map to a three-dimensional monitoring space according to the depth data, and constructing a three-dimensional scene grid model; extracting spatial topological characteristics of each grid node in the three-dimensional scene grid model, and calculating association weights among the grid nodes; and constructing a scene dynamic knowledge graph based on the spatial topological features and the associated weights, and carrying out modeling analysis on abnormal events. According to the method, through cooperative collection of the multi-view vision sensor and the depth sensor, in combination with information entropy weighted standardization and principal component analysis, accurate generation of the monitoring scene feature matrix is realized, and the problems of information redundancy and feature coupling in traditional vision data processing are effectively solved.

The invention discloses a modeling analysis method for optimizing an urban water supply pipe network, and belongs to the technical field of intelligent water affairs, and the method comprises the steps: building a comprehensive pipe network database, collecting hydraulic parameters through Internet of Things equipment, and fusing the hydraulic parameters with GIS topological data; constructing a dynamic hydraulic simulation model, coupling real-time data of a pressure sensor with an EPANET model, and correcting a friction resistance coefficient of a pipe section; a hybrid optimization algorithm architecture is designed, a double-target genetic algorithm is adopted for old pipe network transformation, and a constrained particle swarm algorithm is adopted for new pipe network planning; executing multi-stage optimization calculation, generating a pipe diameter feasible solution space in combination with geographical constraints, and iteratively outputting a scheme meeting a pressure threshold value; and generating a pipe network optimal configuration scheme, and outputting an engineering implementation list through spatial overlay analysis and valve regulation logic. According to the method, the problem of modeling distortion caused by a multi-source data island is solved, the defect that a static model is difficult to adapt to dynamic water demands is overcome, and the economical efficiency and the engineering implementability of a pipe network optimization scheme are remarkably improved.

The invention relates to the technical field of data analysis, can be applied to business scenes of medical health, financial science and technology, culture research and the like, and discloses a knowledge graph-based model analysis method, which comprises the following steps of: acquiring and processing target domain data, extracting and labeling keywords, constructing a knowledge graph comprising domain entities, a relational network and attribute characteristics, and analyzing the knowledge graph. Establishing a professional corpus and generating an analysis task, constructing a multi-dimensional analysis index system, inputting the analysis task to a to-be-analyzed model, obtaining a task output result, performing knowledge verification according to the knowledge graph, evaluating the knowledge verification result based on a multi-dimensional analysis index, and generating a final analysis result. According to the method, by fusing the knowledge graph and the multi-dimensional analysis indexes, the accuracy and comprehensiveness of evaluation are enhanced, the evaluation effect of the model in the aspects of professional knowledge understanding, reasoning ability and standard integrating degree is improved, the evaluation result is more reliable, and the application ability of the large model in the specific field is optimized.

The invention relates to the technical field of data analysis, and discloses a data analysis system for multi-joint exercise rehabilitation monitoring. The system collects three-dimensional motion data, myoelectricity activity signals and spatial position data of multiple joints in real time through a multi-mode sensor array. The system comprises a data acquisition module, a preprocessing module, a space-time analysis module and a result output module. The preprocessing module is used for carrying out noise removal, normalization and feature extraction on the original data; the space-time analysis module constructs a monitoring model based on a graph space-time network, and analyzes space-time characteristics of multi-modal data through dynamic joint relation modeling by adopting an intelligent chip. And the result output module generates a visual report including exercise coordination evaluation, injury risk prediction and rehabilitation progress judgment, and feeds back the visual report to the rehabilitation equipment in real time. Besides, the system also has distributed storage and real-time feedback functions, the accuracy and efficiency of rehabilitation monitoring are improved, and the problems of single data processing and inaccurate spatial-temporal feature capture in the prior art are solved.

The invention discloses a grouting method and system for enhancing strip mine slope stability, and belongs to the technical field of slope protection. The grouting method comprises the steps that early-stage data are pre-collected, and an analysis model is established; grouting is designed according to a model analysis result; a pressure-resistant grouting pipe is installed and connected with a grouting pump; grouting parameters are adjusted in real time according to feedback of the sensor; after grouting, all data in the grouting holes are analyzed; outputting a slurry permeation three-dimensional cloud picture, a stress change curve and a stability evaluation index; and storing all monitoring data and model parameters. A cyclic self-adaptive system is formed, it is ensured that the grouting process is efficient and accurate, the overall stability is improved for heterogeneous rock mass, dependence on a static geological report is reduced, and therefore the problem that grouting parameters depend on engineering experience or conservative of the static report is avoided.

The invention belongs to the technical field of thermal circuit model construction, and particularly relates to a high-voltage power cable intermediate connection thermal circuit fault modeling analysis method and device, and the method comprises the steps: building a high-voltage power cable intermediate connection thermal circuit fault circuit model through analyzing a cable intermediate connection temperature inversion principle; the method comprises the following steps: establishing a geometric model for multi-physics field analysis of the intermediate connection of the high-voltage power cable by utilizing Comsol finite element simulation software, setting material parameters of each component, and setting field domain parameters; an electromagnetic field module is added, boundary conditions of electromagnetic field analysis are set, and a cable intermediate connection electromagnetic field simulation model is obtained; adding a solid heat transfer module to obtain a solid temperature field simulation model; obtaining an electromagnetic-thermal coupling model, and solving to obtain the temperature, current and loss of the cable middle connection wire core and the outer surface in different insulation states. According to the method, the cable intermediate connection fault problem can be found and predicted, and the reliability and the safety of the power cable are improved.

The invention provides a digital twinborn model and method for a coal mine disaster scene based on multi-modal data. The method comprises the following steps: acquiring original data in a mine; performing cleaning, normalization and de-noising preprocessing on the original data; fusing different types of preprocessed original data by using a data fusion algorithm, and constructing a comprehensive digital twinborn model; establishing a virtual mirror image of the mine by using a digital twinning technology, and mapping the mine in the real world into a digital twinning model; real-time mapping and simulation analysis are carried out based on the established digital twinborn model, and the disaster development trend is predicted by monitoring the internal environment and the equipment state of the mine; and according to a model analysis result, optimizing prevention and control measures of coal mine disasters, establishing a real-time monitoring and early warning system of the disasters, and taking measures in time when abnormal conditions are found. The system comprises a data acquisition module, a model construction module and a model analysis module. The pertinence and effectiveness of coal mine disaster prevention and control can be improved.

The invention discloses an intelligent course arrangement method based on multi-objective optimization and deep learning, and relates to the technical field of intelligent course arrangement, and the method comprises the steps: collecting multi-source data, and constructing a spatial-temporal feature matrix; mining a historical class schedule conflict mode by using a knowledge graph and constructing an association relationship model; newly-added course constraints are converted into vectors, and a comprehensive constraint model is formed through BERT model analysis. A graph neural network is utilized to capture association of courses, teachers, classrooms and time periods, a comprehensive constraint model is combined to generate candidate schemes meeting hard constraints such as teacher time conflicts and classroom capacity and partial soft constraints such as pre-repair relations and student course selection conflicts, and an optimal course arrangement scheme is obtained through optimization. The problems that an existing system is insufficient in multi-source data application, difficult in association construction, inaccurate in constraint analysis, difficult in complex contact capture, insufficient in multi-target consideration and the like are solved.

The present invention provides a system and method for tail gas emission modeling analysis of a municipal solid waste incineration process. The system comprises a reference working condition multi-software coupling full-process numerical simulation model construction module, a multi-operation working condition simulation mechanism data acquisition module, a MIMO-LRDT-based tail gas emission model construction module and a single / double-factor-based tail gas emission analysis module. The reference working condition multi-software coupling full-process numerical simulation model construction module is connected to the multi-operation working condition simulation mechanism data acquisition module. The multi-operation working condition simulation mechanism data acquisition module is connected to the MIMO-LRDT-based tail gas emission model construction module. The MIMO-LRDT-based tail gas emission model construction module is connected to the single / double-factor-based tail gas emission analysis module. The system and method for tail gas emission modeling analysis of a municipal solid waste incineration process provided by the present invention can achieve tail gas emission modeling analysis of the municipal solid waste incineration process.

The invention relates to the technical field of building construction monitoring, and discloses a method for evaluating mechanical properties of a steel structure building construction whole process based on digital twinning. The method comprises the steps of establishing a digital twin model fusing multi-source information, and performing real-time linkage with a sensor network arranged on site. Collected data such as deformation, temperature and wind speed are processed through dynamic fusion and an anomaly recognition algorithm, model parameters are continuously corrected, and real-time dynamic high-fidelity mapping of the mechanical state in the construction process is achieved. And based on the updated model, the intelligent analysis module performs cooperative calculation, autonomously identifies construction abnormity, quantitatively predicts potential risks, generates a process optimization decision instruction and feeds back the process optimization decision instruction to a site. According to the method, a closed-loop regulation and control mechanism from data perception, model analysis to decision execution is constructed, and accurate online evaluation of construction mechanical properties and active prediction control of safety risks are realized.

The embodiment of the invention relates to the technical field of data modeling analysis, and discloses a tunnel rock stratum large deformation analysis method and system based on three-dimensional modeling, and the method comprises the steps: collecting initial three-dimensional point cloud data through three-dimensional laser scanning; constructing an initial three-dimensional geologic model containing spatial morphological characteristics and a rock stratum interface geometric topological relation through space coordinate matching and curved surface reconstruction; finite element unstructured mesh generation is carried out on the model, and a rock stratum mechanical analysis model is established in combination with physical and mechanical parameters; inputting construction parameters and process information, activating unit nodes in sequence, and solving a force balance equation to simulate stress distribution; identifying a potential deformation area through iterative calculation, and adjusting boundary conditions to predict a deformation trend; the three-dimensional space distribution cloud atlas, the time evolution curve and the position marks are fused to generate multi-view deformation early warning information, the accuracy and the visualization degree of tunnel rock stratum deformation analysis are improved, and support is provided for construction safety.

The invention discloses an abnormal data prediction method and system based on a dynamic threshold value and pattern mining, and the method comprises the steps: carrying out the mining of an association rule and a sequence pattern of historical monitoring data, recognizing the association relationship among a plurality of monitoring indexes under different data collection sources, and extracting a frequently appearing sequence pattern; setting a dynamic threshold range based on the mean value and the standard deviation of the historical monitoring data; dynamically adjusting the threshold range according to the real-time operation state and the change trend of the historical monitoring data; and through matching the real-time data with the historical mode, predicting a potential anomaly, and giving out an early warning. According to the method and the system, through adaptive dynamic threshold and abnormal mode mining, intelligent prediction and early warning of anomalies in a data acquisition process are realized, and post-processing is converted into beforehand early warning; through real-time monitoring and model analysis, the system can detect potential anomalies in advance, optimize data quality, improve operation and maintenance efficiency and reduce manual intervention.

The invention provides a monitoring point and data classification management method based on an intelligent monitoring platform, and belongs to the technical field of data processing, and the method comprises the steps: 1, importing a preset BIM model into a preset intelligent monitoring platform, and carrying out the analysis of the preset BIM model; 2, creating a monitoring project in a preset intelligent monitoring platform based on a model analysis result, and configuring monitoring project information; step 3, acquiring a monitoring demand matched with the monitoring project, determining the type of a monitoring point, analyzing the information of the monitoring project, and creating a plurality of monitoring points; 4, the monitoring equipment based on the monitoring points collects monitoring data in real time, uploads the monitoring data to a preset intelligent monitoring platform through a network, and automatically associates the uploaded real-time data with the corresponding monitoring points; and 5, analyzing the real-time data, and optimizing the deployment of the monitoring points and the data of the monitoring points. And an efficient and intelligent solution is provided for application scenes such as engineering monitoring and environment monitoring.

The invention discloses a person and post matching recommendation method based on BERT and a latent semantic algorithm model, and relates to the technical field of person and post matching recommendation, and the method comprises the steps: obtaining the related data of job hunters and the post recruitment text data of employers; performing word segmentation and part-of-speech tagging on the obtained text data, screening out candidate words and noun phrases, obtaining candidate word and document word embedding vectors by adopting a trained BERT model, calculating semantic similarity of the embedding vectors, and creating BERT bitmap data according to a calculation result; a latent semantic model is adopted to analyze semantic correlation in text data, preference prediction scores of job hunters for posts are calculated, corresponding class vectors are calculated through a deep random forest method, the matching probability of the job hunters and the posts is extracted, and post matching recommendation is carried out according to a probability sorting list. By adopting the hybrid recommendation method based on the latent semantic model and the deep random forest, the recommendation is more accurate, and the efficiency is higher.

The invention discloses a CAD automatic modeling method and system based on parameter driving. The method comprises the steps that model parameter information needing modeling is obtained; generating a CAD (Computer Aided Design) model on the basis of parameter analysis, geometric construction and surface recognition schemes; performing reverse reconstruction of the three-dimensional CAD model based on topology analysis, geometric feature extraction, pattern recognition and parameter relation inference; modeling, optimization and feedback evolution are carried out based on model analysis, feature learning and generation of parametric modeling rules; and according to the obtained data information, CAD automatic modeling based on parameter driving is completed. According to the method, CAD automatic modeling based on parameter driving is achieved, the reliability is higher, the accuracy is better, and the efficiency is higher.

The present application provides a CAD model analysis method and apparatus, a device and a storage medium, which relates to the technical field of industrial manufacturing. The CAD model based on a BRep representation is transformed into a graph structure. The graph structure is input into an analytical model to obtain a modeling sequence of the CAD model. By transforming the CAD model with a BRep representation into the graph structure, the analytical model learns the modeling feature in the graph structure, and the modeling feature type and the feature parameter are obtained, so as to enable the prediction of the model building process of the CAD model and facilitate to subsequently make parametric re-editing of the CAD model

Method and system for analyzing at least one computing system for supply chain vulnerabilities of at least one machine learning model include configuring machine learning model and optionally additional data; decomposing operations of the machine learning model's computational graph into smaller decomposed components; associating properties of each decomposed component with properties of the original operations and associating additional data; detecting the semantic similarity of decomposed component and previously encountered decomposed components; converting decomposed components into a standardized representation; calculating signature of decomposed components; evaluating whether portions of the machine learning model are similar to previously calculated signature; testing for supply chain and model vulnerabilities that exist based on previous signatures; identifying vulnerabilities that persist and correlating defenses; storing the generated signatures, identified vulnerabilities, and identified defenses; and generating report detailing the machine learning model's supply chain, vulnerabilities, and defenses.

The invention belongs to the technical field of railway dispatching management, and provides a railway dispatching safety risk assessment method based on causal inference and a heterogeneous graph neural network in order to solve the technical problem that risk levels cannot be accurately identified and quantified due to the fact that an existing machine learning method cannot cope with complex and changeable railway dispatching environments. Comprising the following steps: S1, constructing a railway dispatching risk index system; s2, constructing a risk event causal graph; s3, calculating a causal effect weight; s4, constructing a heterogeneous graph neural network model based on causal inference; s5, constructing multi-source heterogeneous data; and S6, inputting the multi-source heterogeneous data into the heterogeneous graph neural network model based on causal inference to obtain the risk level of each key risk variable. According to the method, causal inference and the heterogeneous graph neural network are combined, modeling analysis is carried out on complex space-time interaction and node causal relationships in a railway dispatching system, and the accuracy of risk identification and the precision of an evaluation model are improved.

The invention discloses a submarine pipeline state early warning method and system based on scour-vibration coupling sensing, and the method comprises the steps: obtaining the historical marine environment information of a target submarine pipeline region, laying a monitoring sensor array according to the historical marine environment information, collecting multi-source monitoring data, and carrying out the preprocessing of the multi-source monitoring data, thereby forming a multi-source monitoring data set. And constructing a multi-physical field coupling analysis model, inputting the data set into the model to analyze the response condition of the pipeline in the current seabed environment, and obtaining pipeline response simulation information. And analyzing resonance influence caused by environmental scouring in different sections, generating resonance derivative characteristics, and constructing pipeline multi-modal fusion characteristics in combination with monitoring data. Predicting the future response state of the pipeline through the submarine pipeline state analysis model, and carrying out risk early warning prompt; meanwhile, the fatigue life of each section is analyzed based on resonance derivative characteristics, fatigue-prone sections are identified, and maintenance suggestions are generated and pushed. Therefore, the accuracy and timeliness of sensing the state of the submarine pipeline are improved, and the early warning intelligence of the submarine pipeline is effectively improved.

The invention discloses a modeling analysis system based on digital energy configuration, and relates to the technical field of energy consumption management analysis, and the system comprises an energy consumption model which is established based on production data of energy consumption feature point grouping, equipment operation data and equipment operation environment data, and reflects the changes of the production data and the equipment operation data through the energy consumption model, the influence of the equipment operation environment on the energy consumption model is determined, the equipment state is predicted by combining the production data and the change of the equipment operation data, and production optimization suggestions are made; the decision-making model is used for carrying out optimization decision-making according to analysis of the energy consumption model on the energy consumption points, determining decision-making cost according to the decision-making and optimizing calculation of flow energy consumption and efficiency; according to the method, the energy consumption of the production line is predicted, the optimization suggestion for the production line can be made according to the energy consumption of the production line, the energy consumption hot spots are determined according to the energy consumption data corresponding to different equipment operation data and production data, and the process steps are optimized according to the energy consumption.

The invention relates to the technical field of text inspection and analysis, in particular to a localized bidding document error inspection method based on a large language model, which comprises the following steps of: constructing a bidding requirement knowledge graph which comprises a plurality of requirement item nodes, the requirement item node attribute comprises requirement content, a chapter to which the requirement item node attribute belongs and an importance level; establishing a preliminary mapping relationship between the requirement item node and the text content set, and generating a structured bidding file parse body; and carrying out double-round model analysis and error checking, generating an error record for the items judged to be not satisfied, and outputting a structured error list comprising the non-satisfied items and reasons of the non-satisfied items. The method improves the accuracy and coverage of error checking, and is especially suitable for checking scenes of bidding and tendering documents with numerous and jumbled contents and various formats.

The invention discloses a security event traceability system and method based on attack chain analysis, relates to the technical field of data security protection, and is used for realizing multi-level perception of attack activities by building a multi-modal killing chain probe matrix as a structured data basis. Modeling analysis is carried out on attack behaviors of different levels through a deep heterogeneous feature extraction architecture, multi-dimensional features are fused into a cross-layer joint feature vector, a 3D attack deduction sand table is constructed according to the cross-layer joint feature vector, the propagation path and probability of APT attack are simulated, a high-dimensional attack curved surface is drawn, and a 3D attack deduction sand table is constructed according to the cross-layer joint feature vector. And key path nodes and potential transverse diffusion directions are identified, and a defense strategy knowledge graph is constructed according to a modeling result. The traceability system breaks through barriers among threat detection, attack understanding and defense response through a stage linkage mode of'probe sensing-intention recognition-path modeling-defense deduction ', and realizes accurate traceability and efficient disposal of security events.

The invention relates to the technical field of intelligent quantitative research platform systems, in particular to an intelligent quantitative research platform system based on a knowledge graph and a method thereof. The financial knowledge graph construction module is used for constructing a dynamic financial knowledge graph based on the multi-source data; the factor screening and optimizing module is used for extracting and optimizing quantitative factors from the financial knowledge graph and outputting an optimized factor set; the model analysis module is used for analyzing the contribution of the factor output by the factor screening and optimizing module to the income; the back-testing module is used for carrying out strategy back-testing based on the historical data and the factor set and verifying the yield and the risk index; and the result display module is used for displaying and outputting an analysis result of the back-testing module, and the dynamic weighting mechanism effectively improves the screening precision of factors, so that the model can keep higher income stability when facing different market conditions.

The invention provides a data center management and control system and method. The data center management and control system comprises a data layer, an analysis layer, a decision-making layer and an execution layer, the data layer comprises a data acquisition module and a multi-modal feature fusion module, the analysis layer comprises a multi-stage collaborative tracking module, a dynamic prediction and early warning module and a visual large model analysis module, and the decision-making layer comprises a voice interaction processing module, a knowledge base matching module and an operation and maintenance decision-making generation module. And the execution layer comprises a control instruction execution module and an operation and maintenance result feedback module. A complete closed loop of data acquisition-multi-modal feature fusion-analysis decision-execution-feedback optimization is constructed, the timeliness and accuracy of safety early warning are doubly improved, the fault handling efficiency and the intelligent level are remarkably optimized, the energy consumption benefit optimization is remarkably improved, and the edge scene reliability and the operation and maintenance efficiency are cooperatively improved.

The invention discloses a PC box girder fire damage identification and prediction method and a monitoring and early warning system thereof, and belongs to the technical field of bridge safety risk assessment, and the method comprises the steps: selecting and constructing the characteristics of PC box girder fire prestress loss and bending resistance bearing capacity loss; on the basis of the selected features, a PC box girder prestress loss database and an anti-bending bearing capacity loss database under the fire hazard are constructed; preprocessing various types of original data in the prestress loss database and the bending resistance bearing capacity loss database, converting parameters into formats which can be analyzed by a machine learning model, and dividing a data set into a training set and a test set; based on the training set and the test set, respectively optimizing hyper-parameters of the model corresponding to the prestress loss and the anti-bending bearing capacity loss; and performing performance evaluation, result visualization processing and SHAP interpretability analysis on the optimized model, and applying the model to damage identification and prediction under PC box girder fire. The problem of large prediction error in the prior art is solved.

The present invention discloses a modeling and analysis method and system for the correlation between the wear resistance of glaze and process parameters, and relates to the field of data processing technology. It includes model establishment: establishing an analysis model to reflect the correlation between the wear resistance of glaze and process parameters. The present invention sets a multi-level analysis model, which can facilitate users to obtain corresponding parameter data in the analysis model according to their own wear resistance requirements, so that users can understand the correlation between wear resistance and process parameters, and produce products of corresponding requirements according to parameter data, which is conducive to improving work efficiency. Through the set update method, a single analysis model can be optimized and updated. Through the unique multi-level model design and the update method, a single model can be optimized and updated, which can reduce the amount of data processing when training, updating and using the model to improve the response speed and ensure the efficiency of product production. The parameter data does not represent fixed control parameters, which is conducive to the application of different process conditions.