618 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 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 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 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 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 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 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 relates to the technical field of data processing, in particular to a three-dimensional live-action model construction system for building structure demolition and reconstruction. The system extracts event combinations with obvious transfer correlation features for localization and analysis of dangerous sound sources. Through simulation of transmission of test waves in an initial acoustic slowness field, a most probable dangerous sound source position for generating a current event combination under each test wave is obtained by using an objective function optimization solution method. And further performing statistics on simulation information of all test waves, establishing a residual curve, screening out a brittleness event position based on feature extraction, and adjusting a slowness value in a current voxel through various parameters corresponding to the brittleness event position in an initial acoustic slowness field. Through the dynamic correction method, the obtained corrected acoustic slowness field can more effectively represent the specific change of the building structure in the dismantling process, so that the internal damage can be more clearly quantified.

The invention discloses a cross-modal large model construction method and system based on track spatio-temporal characteristics, and belongs to the crossing field of artificial intelligence and dynamic spatio-temporal data processing, and the method comprises the steps: carrying out the sliding sampling and spatial distribution difference judgment through the semantic dynamic segmentation of multi-scale track spatio-temporal data, and generating spatio-temporal data blocks with consistent semantics; designing a space-time encoder of a hybrid architecture, extracting track time sequence association and spatial features, and unifying dimensions; constructing a text space-time fusion mechanism, and dynamically adapting cross-modal features by means of an anchor interface and gating fusion; a staged instruction fine tuning strategy is adopted, semantic alignment of space-time and text features is optimized firstly, then model top-layer parameters are trained cooperatively, complex scene adaptation is enhanced in combination with instruction difficulty progression and hard sample mining, and space-time constraint regular terms are introduced to guarantee output rationality. According to the method, high-precision cross-modal reasoning capability is provided for scenes such as track analysis and track prediction.

The invention discloses a coal seam fully-mechanized mining hydraulic support robot digital twinborn teaching system, and relates to the technical field of coal industry teaching, and the system comprises the following components: a model construction module, a virtual simulation and operation module, a fault simulation and diagnosis module, a data interaction and remote monitoring module, and a teaching management and evaluation module. According to the invention, a comprehensive and systematized scheme is provided for the teaching of the coal seam fully-mechanized mining hydraulic support robot through integrating a plurality of modules of model construction, virtual simulation and operation, fault simulation and diagnosis, data interaction and remote monitoring, and teaching management and evaluation; students can perform operation practice and troubleshooting training of equipment in a virtual environment without contacting actual equipment, so that teaching cost is reduced, safety is improved, the system can feed back operation results and evaluation scores of the students in real time, teachers can know learning conditions of the students in time, targeted teaching guidance is performed, and teaching efficiency is improved. And the teaching effect and the learning efficiency are obviously improved.

The invention relates to the technical field of water resource management and forecasting, in particular to a deep learning model building and forecasting method based on hydrological mechanism fusion. The method specifically comprises the following steps: constructing a snow melting calculation module and a soil calculation module, splicing to form a runoff production model, constructing a confluence calculation module, executing confluence evolution simulation of a calculation result of the runoff production module by using the confluence calculation module, and constructing a confluence model. In confluence model simulation calculation, according to the number of calculation units, a broadcast calculation strategy and an array-based ordinary differential equation solving method are adopted, parallel calculation of the multiple calculation units is achieved, and runoff production calculation results of the multiple units are obtained; and integrating and calculating runoff production calculation results of each unit through a confluence model to obtain a runoff prediction result of the modeled drainage basin after confluence. The method provided by the invention solves the problems of low model construction efficiency and poor practical application effect faced by the application of the deep learning technology in the hydrological model at present.

The invention relates to the technical field of model construction, can be applied to business scenes such as financial science and technology, and discloses a double-track evaluation driven optimization processing method, device and equipment and a medium, and the method comprises the steps: constructing a double-track evaluation system, analyzing training data, generating optimized training data, and building a target model; generating a capability index by utilizing a double-track evaluation system and multi-role agent simulation, and generating an alignment analysis result in combination with reinforcement learning performance; obtaining real service feedback indexes to form a training evaluation index set, and determining an attribution link; and generating training adjustment information based on the attribution link and driving the target model to be retrained to obtain an optimized target model for processing the service input data and outputting a service processing result. According to the method, the evaluation result and the service feedback are linked to realize a training closed loop, so that the capability gap of the model can be identified and corrected, and the compliance, the reliability and the application effect of the model in the actual service are improved.

The invention relates to the technical field of elevator safety monitoring, and discloses an elevator safety detection system based on vision and multi-modal data fusion, comprising: a data preprocessing module for collecting and preprocessing original multi-modal sensor data of elevator operation; the feature extraction module is used for identifying operation conditions and extracting multi-scale features for different conditions; the multi-modal fusion module is used for calculating the reliability coefficient of each modal and carrying out adaptive fusion; the anomaly detection module is used for extracting transient characteristics of working condition switching, carrying out anomaly judgment and mining an anomaly evolution trend; the sample enhancement module is used for extracting normal samples and rare fault samples, generating synthetic samples and performing quality screening; the model construction module is used for training a multi-level fault identifier and fusing a multi-modal feature library to carry out optimization learning; the intelligent early warning module is used for calculating a safety level and reasoning a fault reason and a disposal measure; accurate dynamic safety monitoring of the super high-rise building elevator under the extreme load switching working condition is achieved.

The invention relates to an end-to-end point cloud denoising model construction method and system, belongs to the technical field of deep learning, and solves the problems that an existing denoising model is insufficient in generalization ability and output point cloud geometric features are not high in fidelity. Comprising the steps that after collected clean point clouds are preprocessed, point cloud samples are generated by injecting noise, and a sample set is constructed; constructing a deep learning model, and constructing a combined loss function by fusing multiple supervision signals; the deep learning model sequentially comprises a feature extraction module, a denoising guide prediction module and a point cloud attribute prediction module, and is used for outputting a prediction coordinate and a prediction normal vector of a denoised point cloud; and performing end-to-end training on the deep learning model based on the sample set and the combined loss function, and taking the trained deep learning model as a point cloud denoising model. The generalization ability of the denoising model and the denoising effect are improved.

The invention discloses a material level intelligent sensing and early warning system in the silo unloading process, and relates to the technical field of silo material level intelligent sensing. The method comprises the following steps: a data acquisition module used for acquiring a multi-modal data stream; the model construction module is used for constructing a normal unloading model based on the multi-modal data flow and a preset physical mechanism constraint; the state judgment module is used for extracting a current feature vector from the multi-modal data stream; comparing the current feature vector with a feature distribution range in a multi-dimensional feature space determined by a normal unloading model, and judging a current material flow state; according to the invention, material flow abnormal symptoms can be captured in time, early warning of the blocking risk is realized, the production rhythm is prevented from being disturbed by material flow interruption, the potential safety hazard caused by manual entering into the silo for troubleshooting is reduced, the grain loss and equipment damage risks caused by long-term retention and mildew of materials are reduced, and the storage safety and the operation stability of a grain storage system are guaranteed.

The invention discloses an industry exclusive customization model generation method based on a large model, and the method comprises the following key steps: firstly, collecting structured and semi-structured multi-modal data containing key entities, concepts and relationships in a target industry; then, constructing a knowledge graph through a deep learning method; in a model construction stage, carefully selecting a pre-trained large language model, and carrying out personalized adjustment on feature engineering and a model architecture according to industry characteristics; on the basis of the constructed knowledge graph, a retrieval enhancement technology based on the knowledge graph is adopted, the answer content of the large language model is optimized, and target industry domain knowledge is injected; and finally, by using a reinforcement learning algorithm, optimizing model output by training a reward model. According to the method, specialized customization of the industry exclusive model is realized, the question and answer ability of the large language model in the target industry field is remarkably improved, and high accuracy and high correlation of the model when the model answers related questions of the target industry are ensured.

The invention discloses a water quality probability forecasting method based on Bayesian multi-time-sequence deep learning. The method comprises the following steps: S1, determining a forecasted water environment water ecological index, a driving index and a forecasting day number; s2, collecting time sequence data monitored by the forecasting indexes and the driving indexes, and after data preprocessing, constructing a data set required by model construction; s3, carrying out data division on the time sequence data, constructing a driving index forecasting model by adopting a multi-time sequence deep learning method, and carrying out parameter learning by selecting a Bayesian random discarding method; s4, performing effect evaluation on the accuracy and precision of the model, and adopting a hyper-parameter optimization method to improve the simulation forecast effect; s5, carrying out model training by adopting all data without segmenting the training set and the test set, carrying out water quality probability forecasting by utilizing the trained model, and outputting a forecasting mean value and a confidence interval; according to the method, the confidence interval is output while high-precision prediction is provided, and the scientificity and stability of prediction are improved.

The invention relates to the technical field of structural design and optimization, and particularly discloses a structural error tolerance model construction method for compartment connection positioning, which comprises the following steps of: obtaining real point cloud data of a bolt connection pair through three-dimensional laser scanning, and realizing parameterized digital geometric modeling in combination with a deep neural network; material attributes are given, and self-adaptive grid division is carried out; generating a dynamic load sequence based on a vehicle driving condition, applying the dynamic load sequence to the model, and extracting full-field displacement vector data by using a digital image correlation technology; further identifying a micro-slip area of the contact interface, and calculating an energy dissipation index of the micro-slip area; a quantitative mapping relation between energy dissipation and fatigue life is established through experimental calibration, key design parameters are iteratively optimized by adopting a pattern search method with the target life as a constraint, and finally a connection structure scheme meeting the durability requirement is output.