764 results about "Model building" patented technology

The invention relates to the technical field of engineering progress management, in particular to an engineering construction digital project management method and system, and the method comprises the steps: database building, real-time data collection, and model building: generating a visual construction progress model; progress deviation judgment: calculating progress deviation and performing judgment; deviation analysis: calculating a resource gap of an affected process, and generating a resource allocation priority list; resource adjustment: pushing an adjustment instruction to the construction terminal according to the priority list; simulation: simulating the adjusted construction progress, and if the deviation is not eliminated, executing a redistribution step; redistribution: executing the deviation analysis step again; and optimization: optimizing subsequent project progress plan generation logic. The system comprises a database building module, a real-time data acquisition module, a model building module, a progress deviation judgment module, a deviation analysis module, a simulation module, a redistribution module and an optimization module. The method and the device have the effect of facilitating fine management of the engineering project.

A BIM-based engineering information management system of the present invention relates to the field of constructional engineering informatization management, and comprises a data processing module, a model construction module, a block chain evidence storage module, a verification module, a data fusion module and an authority management module. The data processing module receives original engineering data and generates a structured data signal. And the model construction module generates a BIM model according to the structured data signal. And the block chain evidence storage module encrypts and stores the metadata signal, generates an evidence storage completion signal and feeds back the evidence storage completion signal to the model construction module. And the verification module analyzes the dynamic operation log signal, and generates a risk early warning signal and a repair suggestion signal when abnormality is detected. And the data fusion module generates a fusion data signal, transmits the fusion data signal to the model construction module and triggers BIM model updating. And the authority management module adjusts the user authority according to the risk early warning signal. According to the invention, the problems of data islands, safety risks, low cooperation efficiency and insufficient intelligence in engineering information management can be solved.

A dynamic optimization system for energy consumption of a refrigeration house based on digital twinning is characterized by comprising a data acquisition module used for acquiring basic structure data of the refrigeration house, technical parameters of a refrigeration system, real-time operation data and historical operation data, preprocessing the data and then outputting a standardized multi-dimensional real-time data stream; the model construction module is used for constructing a 3D geometric model, a thermodynamic transfer model and a refrigeration system mathematical model according to the multi-dimensional real-time data flow, performing machine learning calibration on model parameters through historical operation data, and performing fusion to construct a refrigeration house digital twin model; the prediction analysis module is used for predicting future energy consumption demand and load change according to the refrigeration house digital twin model and the real-time operation data, and outputting an energy consumption prediction result and a load analysis report; a strategy generation module; an execution feedback module; and a learning optimization module. Overall energy consumption of the refrigeration house is reduced, energy utilization efficiency is remarkably improved, and goods storage safety is guaranteed.

The invention discloses a rapid construction method and system for a three-dimensional space model of a multi-level building, and relates to the field of building model construction, and the method comprises the following operation steps: S1, constructing a dynamic demand-oriented detail level model; s2, automatic component modeling based on image segmentation; s3, performing multi-version difference judgment and model updating; and S4, cloud collaboration and load optimization. According to the method and the system for quickly constructing the three-dimensional space model of the multilevel building, the region is dynamically divided through the semantic weight, so that the rendering efficiency during model construction can be effectively improved, the memory can be effectively reduced during rendering, the visual fidelity can be optimized, key curvature characteristics can be forcibly reserved for a high semantic weight region, and the construction efficiency is improved. Based on this, deformation distortion caused by simplification can be avoided, a visible area in the model can be automatically identified, an invisible area can be degraded, only the contour line frame of the building is reserved, and the overall memory occupation can be greatly reduced.

The invention relates to the technical field of three-dimensional modeling in engineering surveying, in particular to a point cloud building component modeling method and system based on feature extraction. Three-dimensional point cloud data of building components are obtained, a multi-scale local neighborhood calculation covariance matrix is constructed with each point as the center, eigenvalues are decomposed, an edge probability graph is generated, corresponding component categories are semantically output, and corresponding key feature point sets are screened; taking the feature point cloud as a control point, constructing a three-level B-spline surface model, and adjusting the corresponding spatial distribution density; a weighted complete graph is formed through a key feature point set, a non-planar area is identified and processed through a Kurtowski theorem, a QEM algorithm is applied to carry out lightweight processing on a three-dimensional model, screening and optimization are carried out through automatically extracting key feature points of building components, and a concise and accurate lightweight model is constructed based on feature point topological optimization and a graph theory algorithm. The automation degree and efficiency of modeling are improved, and the contradiction between model lightweight and precision is effectively solved.

The invention relates to the field of building construction simulation, in particular to a building construction simulation model construction method and system based on a BIM model. The method comprises the following steps of obtaining a multi-source design material of a building project, and performing BIM format heterogeneous conversion so as to generate multi-source heterogeneous conversion BIM data; generating a plurality of component independent sub-models based on the multi-source heterogeneous conversion BIM data; according to the multiple component independent sub-models, building component space position positioning calculation is carried out one by one, global BIM modeling is carried out, and a building global BIM model is constructed; performing inter-component structure collision identification on the building global BIM model, and performing collision conflict optimization so as to construct a conflict optimization BIM model; building construction behaviors are analyzed one by one based on the multi-source heterogeneous conversion BIM data, then construction sequential sequence fitting is conducted, and a construction process behavior sequence is constructed. Through efficient and refined building construction simulation model construction, the construction efficiency is improved, and construction resource allocation is optimized.

The invention relates to a self-adaptive pressure regulation vacuum pump closed-loop control system and method. The system comprises a model building module which is used for acquiring real-time data flow of a vacuum pump and building a nonlinear dynamic model to obtain a pressure-flow-power feature mapping relation; the predictive analysis module calculates a pressure-flow predictive value based on the relationship and compares the pressure-flow predictive value with the real-time power data for analysis. And generating a parameter adjustment instruction once the deviation value exceeds a preset threshold value. And the control scheme generation module corrects the control parameters according to the parameter adjustment instruction and generates an optimized power adjustment scheme. And if the data is abnormal, generating a state update value. And the control scheme updating module fuses the state updating value and the pressure-flow prediction value to obtain an optimized model parameter so as to adjust a weight coefficient of an adaptive control strategy and generate a control scheme. The system effectively improves the accuracy of vacuum pump pressure control, greatly enhances the ability of the system to deal with abnormal conditions and equipment performance changes, and ensures long-term stable operation of the vacuum pump.

The invention provides a fine-grained phytoplankton microscopic image enhancement classification method and a model building method thereof, and belongs to the technical field of underwater image enhancement classification, and the method comprises the following steps: firstly, collecting phytoplankton samples from a plurality of sea areas, and obtaining image data through a microscopic imaging technology; screening, preprocessing and data enhancement are carried out on the images, and a high-quality training set and a high-quality test set are constructed; according to the method, a double-branch deep learning model HySwinFormer is developed, the double-branch deep learning model HySwinFormer comprises a multi-scale context capture branch based on hierarchical vision TransFormer and a local detail enhancement branch designed by adopting a parallel convolution kernel, and joint expression of features is realized through a designed weighted fusion strategy; and training and optimizing the model to obtain a final model. Experimental results show that the classification performance of the HySwinFormer model on multiple data sets is superior to that of an existing mainstream deep learning model, and the classification effect is remarkably improved.

The invention relates to the technical field of finite element simulation modeling, in particular to a model construction method, device and equipment for simulating an air effect in a honeycomb aluminum barrier. The method comprises the following steps: building a target honeycomb block simulation model containing a single-degree-of-freedom discrete beam unit; performing a quasi-static working condition test, and performing benchmarking to construct a honeycomb block static extrusion target simulation model based on a result of the quasi-static working condition test and the target honeycomb block simulation model; a dynamic working condition test is carried out, and a honeycomb block finite element model coupled with the air effect is constructed through benchmarking based on the result of the dynamic working condition test and the honeycomb block static extrusion target simulation model; and on the basis of the honeycomb block finite element model of the coupling air effect, according to a preset skin grid and a preset connection relation, a honeycomb aluminum barrier overall model is generated. Therefore, the problems that in the prior art, the simulation result of automobile collision is inaccurate, and automobile body deformation does not conform to reality are solved, the automobile collision simulation accuracy is improved, and the calculation accuracy and efficiency of the obstacle avoidance working condition are optimized.

The invention relates to the field of milling, in particular to a modeling method and system for a morphology model of a milling surface, and the method comprises the steps of data collection, data preprocessing, feature extraction, model construction and model verification and optimization. According to the modeling method and system, various actual factors in milling are fully considered, including tool abrasion, cutting force vibration, workpiece material non-uniformity and the like, the fitting degree of the model and the actual milling surface appearance is improved, and the microscopic geometrical characteristics of the machining surface can be reflected more accurately; a machine learning algorithm is adopted to construct a model, so that a complex milling process parameter combination relationship can be flexibly processed, the applicability and accuracy of the model under different milling process parameters are improved, and accurate evaluation of part performance and optimization of a machining process are facilitated; the system is complete in structure, all the modules are clear in division of labor, and the construction, verification and optimization process of the morphology model of the milling surface can be efficiently achieved.

The invention discloses a large model construction method for intelligent collaboration and decision-making of a power time sequence, and aims to solve the problems of complex time sequence analysis task, high technical threshold and lack of transparency of a prediction result in a power scene. Comprising the following steps: S1, constructing a diversified power time sequence data set and enhancing characteristics thereof; s2, generating a data set with context description; s3, designing a time sequence thinking chain, and finishing alignment of the power time sequence data and the natural language mode; s4, constructing a flexible cue word template, and generating a diversified question and answer training set; and S5, carrying out model adaptation and supervision fine tuning by adopting a low-rank adaptation technology, and constructing a power time sequence large model with intelligent collaboration and decision-making capabilities. According to the method, the technical threshold of power time sequence analysis is remarkably reduced, the accuracy and transparency of a prediction result are improved, the method can be widely applied to the fields of power load prediction, anomaly detection, energy optimization scheduling and the like, and technical support is provided for intelligent transformation of the power industry.

The invention provides a construction method of a prediction model based on dynamic gating and cross-modal attention fusion, and belongs to the technical field of model construction. Comprising the following steps: constructing a feature coding layer, and respectively coding multi-source input data to obtain each modal feature vector; constructing a multi-source data interaction layer, and performing deep interaction on each modal feature vector; and finally, carrying out weighted fusion on the main modal features and the cross-modal interaction features based on a dynamic gating mechanism. Constructing a feature fusion layer, and performing time sequence pooling and full-connection fusion on the interacted multi-modal features to obtain a fusion feature vector; and constructing a quantile regression layer, and outputting a prediction result based on the fusion feature vector. According to the method, the prediction model is constructed by fusing the dynamic gating mechanism and the cross-modal attention mechanism, so that the problems of an existing prediction model in the aspects of deep fusion of multi-source heterogeneous data, cross-modal dynamic interaction and accurate quantification of tail risks are solved, and then efficient prediction of stock price collapse risks is realized.

The invention relates to the technical field of building information modeling, and discloses a parameterized BIM modeling method, which comprises the following steps: step 1, acquiring specific design parameters of a modeling object in a project design requirement, marking key geometric parameters of the object, including length, width, height, volume and material characteristics, and defining units, physical meanings and application ranges of the parameters in a marking process, a basis is provided for subsequent entity model construction; and 2, based on the geometric parameters marked in the step 1, a solid model is constructed through a BIM modeling tool, and the solid model is a visual model with the shape, the size and the position determined through the geometric parameters in a three-dimensional space. The three-dimensional BIM model is dynamically generated and modified by modifying geometric parameters, model construction is more flexible and efficient due to introduction of parameterization logic, automatic adjustment and rapid iteration of the model are achieved, and the reusability of BIM modeling and the design efficiency of the building model are remarkably improved.

The invention discloses a model construction method and system based on indoor decoration interactive design, and relates to the technical field of intelligent design, and the method comprises the steps: generating a diversified design sketch through an AI generation tool, collecting customer feedback data, determining customer demands, and generating a basic design scheme; creating an interactive ecology home 3D model by using a parameterized design tool, and adjusting a basic design scheme according to customer requirements; based on the adjusted basic design scheme, optimizing visual requirements of the basic design scheme through a V-Ray rendering engine; based on visual requirements, acoustic simulation software and a sensor are utilized to analyze spatial acoustic performance, ecological integration is completed, and intelligent control and AR interaction of an ecological environment are performed in combination with IoT and a user interface. According to the method, the interactive ecology home 3D model is combined with the AI generation tool, the parametric design, the V-Ray rendering engine, the biological feedback technology, the acoustic simulation software and the IoT and AR application, so that an efficient, flexible and personalized design process is realized.

The invention relates to the technical field of seismic signal detection, in particular to a seismic first arrival pick-up deep learning method based on an attention mechanism and a hybrid expert strategy, which comprises three steps of model building, a training stage and first arrival prediction. The core components of the model comprise a front-end feature extraction module, a hybrid expert module and a time domain decoding module. According to the invention, a more efficient expert routing mechanism is adopted in the network to reduce calculation redundancy, a more optimized load balancing strategy is used to improve training stability, and quantification and sparse technologies are combined to further reduce calculation and storage cost. According to the method, high-precision seismic first arrival pickup can still be kept under the conditions of complex seismic phase superposition and regional noise interference, high adaptability and generalization ability to various scenes are achieved, and the computing resource occupancy rate and cost can be greatly reduced under a large number of model parameters.

The invention relates to the technical field of three-dimensional modeling, and discloses a panorama, point cloud and BIM-based model construction method, which comprises the following steps: acquiring multi-view point cloud data of a building, and aligning the multi-view point cloud data to the same coordinate system to obtain three-dimensional point cloud data; acquiring a plurality of panoramic images of the building, and splicing the plurality of panoramic images according to the feature points of the panoramic images to construct three-dimensional panoramic image data; performing standardization processing on the three-dimensional point cloud data and the panoramic image data to obtain standard point cloud data and standard panoramic image data; and obtaining building information model data from preset BIM software, and fusing the standard point cloud data, the standard panoramic image data and the building information model data based on a preset space coordinate system to construct a unified three-dimensional building model. The invention further provides a model construction device and equipment based on the panorama, the point cloud and the BIM and a storage medium. According to the invention, the accuracy of the constructed three-dimensional model can be improved.

The invention discloses a smart park facility predictive maintenance system based on a digital twinborn technology, and relates to the field of smart park facility maintenance. Comprising a data acquisition module, a preprocessing module, a digital twin model construction module, a fault prediction module, a maintenance decision module, a maintenance resource management module, a user interaction module, a system management module, a spatio-temporal data analysis and prediction module and a social-technical system fusion module. The method comprises the following steps: collecting and fusing a risk-dependent frequency modulation rate of quantum sensing, improving speed and precision by means of quantum calculation, fusing a digital twin model into a meta-universe concept and an intelligent agent, predicting a fault by combining quantum machine learning and causal inference, optimizing a maintenance decision based on a game theory and reinforcement learning, and managing resources by using a block chain-Internet of Things fusion technology. The invention discloses a brain-computer interface and holographic projection interaction and quantum encryption dual-protection management system. The system is accurate in data acquisition, vivid in model construction, accurate in fault prediction, scientific in maintenance decision, efficient in resource management, immersive in interactive experience and safe and stable in system, and ensures stable operation of park facilities.

The invention discloses intelligent low-clearance cast-in-place pile construction equipment, system and method. The construction site scanning and model building, paying-off and point position projection and correction, automatic construction path planning, drilling positioning, preparation work before drilling (burying of a soil layer structure integrity evaluation sensor), drilling construction and accurate control of perpendicularity and drilling depth, drilling lifting and in-hole environment detection are sequentially completed. And therefore, the intelligent construction operation of the cast-in-place pile is completed, the equipment has the construction characteristics of miniaturization and large drilling depth, and the intelligent construction operation under the low clearance working condition is perfectly achieved.

The invention provides a digital twin three-dimensional model construction method based on a generative large model, and the method comprises the steps: obtaining multi-source monitoring data of a power distribution network, and processing the multi-source monitoring data into a training data set; the method comprises the following steps: mapping multi-source monitoring data into a multi-scale tensor subspace through tensor wavelet structured transformation, adaptively extracting spatial features through a learnable wavelet kernel, and keeping the structural continuity of a physical field in combination with a geometric prior regular term; constructing and training a generative adversarial network through a training data set; inputting and analyzing the physical parameter vector of the target scene, and if the topological similarity score is lower than a preset threshold value, adjusting noise vector regeneration; and if yes, outputting a three-dimensional model tensor and importing the three-dimensional model tensor into a digital twin platform, and driving real-time physical field visualization. According to the method, characteristics of a multi-scale space structure and a nonlinear physical field can be reserved, the physical rationality and generalization ability of the generated model are remarkably improved, and depth identification of topological attributes (such as hole connectivity and surface defects) and local geometric defects of the three-dimensional model is realized.

The invention provides a construction method and system of a rock-soil shear strength parameter prediction model, and relates to the technical field of model construction.The construction method comprises the steps that CT scanning data of a real rock-soil sample is obtained and preprocessed, and an irregular three-dimensional particle set for geometric modeling is obtained; performing particle surface roughness modeling analysis on the three-dimensional particle body set to obtain a surface roughness coefficient set of the three-dimensional particle body set; performing particle size cumulative distribution function segmentation processing and fractal dimension control on the surface roughness coefficient set to obtain a structure grading sample set; constructing a THMC multi-field coupling model based on the structure grading sample set to obtain a working condition particulate matter physical response parameter set; and performing a simulation experiment on the working condition particle physical response parameter set, and performing deep neural network modeling processing based on an experiment result to obtain a strength response model capable of predicting the cohesive force and the internal friction angle of the rock-soil body under any working condition input. According to the invention, the prediction efficiency and accuracy are significantly improved.

The invention provides a deep tunnel excavation physical simulation test three-dimensional stress field real-time prediction method, and relates to the technical field of tunnel engineering. Comprising two stages of model construction and training and model use. In the model construction and training stage, a physical constraint reference model is constructed, and training optimization is completed; and in the model use stage, real-time prediction of a stress field of a deep engineering physical simulation test is realized through the trained and optimized model. The method has breakthrough advantages in the aspects of physical law constraint, real-time monitoring data fusion, training loss stability, boundary stress condition accuracy and the like, and is a high-reliability three-dimensional stress field prediction method oriented to a deep complex environment.

The invention discloses a CO2 storage multi-field coupling safety evaluation method considering fracture evolution, and relates to the technical field of complex oil and gas reservoir development, and the CO2 storage multi-field coupling safety evaluation method comprises the following steps: s1, constructing and solving a multi-field coupling model; s2, virtual crack grid construction and expansion: according to the updated model stress field parameters, judging whether cracks are generated or not, and if so, constructing and expanding a corresponding virtual crack grid; s3, evaluating the integrity of the cover layer: after each time step is finished, evaluating the integrity of the cover layer; and continuing numerical simulation of the next time step until the simulation is finished. According to the method, comprehensive coupling of multiple physical fields such as crack generation and expansion, CO2 seepage, heat transfer between the injected CO2 fluid and the reservoir and between the injected CO2 fluid and the reservoir fluid, geological stress redistribution and the like in the CO2 storage process is fully considered, so that the spatial and temporal change of each physical field in the reservoir in the CO2 injection process can be truly represented, the simulation precision is greatly improved, and the simulation efficiency is improved. And the uncertainty caused by fixed parameters or simplified processing is effectively reduced.

The invention relates to the technical field of nuclear power modeling, in particular to a nuclear power plant control model construction method, electronic equipment and a storage medium. According to the nuclear power plant control model construction method, a control logic diagram and modeling demand information of a target nuclear power plant control system need to be acquired firstly; performing simulation demand analysis on the control logic diagram based on the modeling demand information to determine a simulation implementation constraint; performing function block icon analysis on the control logic diagram, determining a plurality of target modeling elements, performing function block identifier analysis on the control logic diagram, determining a simulation constant value parameter and a parameter transmission relation, and generating a transmission point-to-point file; constraining the plurality of target modeling elements based on simulation implementation, and generating a simulation control chart; and carrying out model loading on the basis of the simulation control chart, the simulation fixed value parameters, the transmission pair point file and the parameter transmission relation, and generating a nuclear power plant control model. Therefore, the quality and efficiency of the nuclear power plant engineering simulation machine in the modeling link of the nuclear power plant control model can be improved.

The invention discloses a multi-device cooperative control system and method for a construction site intelligent capsule bin, and relates to the technical field of industrial control, and the system comprises a data interface layer, a model construction module, a situation prediction module, a cooperative control unit, a self-adaptive cooperative optimization module, and a safety helmet positioning module. The method comprises a model construction step, a data acquisition step, a simulation prediction step, a situation judgment step, a cooperative control step and a self-adaptive cooperative optimization step. According to the method, unified data fusion and cooperative control are carried out on various devices, dynamic adjustment and strategy optimization of the devices can be carried out in time according to the actual situation of the site, the resource scheduling efficiency is improved, and the utilization efficiency of data is improved.

The invention provides a three-dimensional visualization analysis method for a real scene of an ancient earthquake exploring groove, which belongs to the technical field of seismic geological survey and comprises a data acquisition module used for acquiring point cloud data of target exploring grooves in different regions. And the model building module is used for carrying out scene format conversion on the point cloud data of the target exploring grooves in different regions, and carrying out exploring groove three-dimensional model building by utilizing the converted SLPK file. And the visualization module is used for carrying out live-action three-dimensional display on the three-dimensional models of the target probe grooves in different regions through a three-dimensional scene Web server based on the SLPK files in different regions, and rendering node data of different detail levels of the probe grooves according to user requirements. The ancient earthquake event can be better understood, and a reliable three-dimensional digital file and a new shared analysis normal form are provided for the ancient earthquake.

The invention discloses a multi-precision dynamic loading and performance optimization rendering method for a hydroelectric generating set model. The method comprises three stages of multi-precision model construction, multi-precision model dynamic loading and rejection, and adaptive rendering optimization. And a multi-precision model construction stage: performing component extraction on the original model file to generate high, medium and low resolution multi-precision models, and converting the models into a GLTF format to improve the loading efficiency. And a dynamic loading and rejecting stage: reading the multi-precision GLTF file, generating a geometric model by applying the conversion matrix, constructing an LOD model according to the sight distance, and rejecting only the visible part in the loading visual field through a view cone to reduce the rendering calculation amount. And an adaptive rendering optimization stage: monitoring the frame rate in real time, dynamically adjusting the resolution when the frame rate is relatively low, simplifying material illumination, forbidding special effects such as shadow and the like, ensuring stable rendering performance, and finally outputting an optimized three-dimensional scene. According to the method, the rendering speed and fluency of the water turbine model are remarkably improved, and the method is suitable for the real-time visualization requirement of a complex three-dimensional scene.

The invention discloses an engineering building construction quality analysis system and method based on data processing, and relates to the technical field of construction quality analysis, the system comprises an index and model building unit, a path planning and data acquisition unit, a model training and optimization unit and a real-time evaluation and quality analysis unit; wherein the index and model construction unit is used for determining a quality detection index of a building main body and constructing a quality detection model; the path planning and data acquisition unit is used for optimizing a cruise path by using a guide search algorithm and controlling the unmanned aerial vehicle to acquire construction site data; the model training and optimizing unit is used for optimizing the quality detection model by utilizing a game chess algorithm; and the real-time evaluation and quality analysis unit is used for evaluating the acquired data in real time by using the optimized model and generating a construction quality analysis report. The game chess algorithm is combined with the unmanned aerial vehicle technology, and the speed and accuracy of processing complex construction data are improved.

The invention relates to the technical field of data processing, and discloses a BNCT digital twinborn model construction method, a BNCT digital twinborn model dynamic optimization method, BNCT digital twinborn model dynamic optimization equipment and a medium, and the construction method comprises the following steps: carrying out data assimilation processing on obtained multi-physical field simulation data to generate equivalent measured data matched with physical characteristics of historical measured data, generating a data assimilation training set based on the historical measured data and the equivalent measured data; wherein the multi-physical field simulation data represents simulation data of the accelerator and the beam shaping device in various physical processes; based on the data assimilation training set, a first sub-model used for representing the physical process of the accelerator and a second sub-model used for representing the neutron beam analysis process are obtained through training; and constructing the BNCT digital twinborn model according to the first sub-model and the second sub-model obtained by training. According to the technical scheme provided by the invention, the calculation precision and the processing complexity of the BNCT digital twin system can be improved, and real-time response is ensured.

The invention discloses a tunnel boring machine performance prediction and monitoring system and method based on digital twinning and machine learning. The system comprises a data acquisition module, a data preprocessing module, a model construction module, a performance analysis module and a visual analysis module. The data acquisition module is used for acquiring initial data of a tunnel boring machine on a construction site; the data preprocessing module is used for preprocessing the initial data to obtain preprocessed data; the model construction module constructs a prediction model and a digital twinborn model of the tunnel boring machine based on the preprocessed data; the performance analysis module predicts the tunneling speed and the propelling speed of the tunnel boring machine by using the prediction model to obtain a prediction result, and integrates the prediction result with the digital twinborn model; and the visual analysis module is used for constructing a virtual environment and checking the construction progress and the performance prediction result of the tunnel boring machine in real time in a digital twinborn model in the virtual environment.

The invention discloses a photoresist model optimization and construction method and a photoetching model construction method.The photoresist model optimization method comprises the steps that after iterative optimization of a photoresist threshold model is completed, the coefficient of imaging light intensity curvature in first model parameter data obtained after iterative optimization is reduced to obtain second model parameter data; and iteratively optimizing the second model parameter data again. Compared with a traditional photoresist threshold model which is established only through one-time iterative optimization, the corrected photoresist threshold model obtained through the method can accurately predict the light leakage phenomenon of special patterns in the shape like the Chinese character'hui 'on the wafer on the premise that the model precision is kept, and the situation that the model is excessively corrected is avoided. And meanwhile, only data of the rectangular-ambulatory-plane graph is needed during verification and optimization, so that the data acquisition difficulty is low. It is verified that the photoresist threshold model can more accurately predict the patterns which are represented by the rectangular-ambulatory-plane pattern and have many corners and high density.