21 results about "Bridging model" patented technology

The invention discloses a structural semantic guided bridge point cloud Gaussian splash modeling and rendering method, and belongs to the technical field of computer vision and bridge engineering digitization. The method comprises the steps of collecting a multi-view image of a bridge, and obtaining a sparse point cloud and a camera pose through a motion recovery structure technology; dense point clouds are generated, deep learning semantic segmentation is carried out, and key components such as piers, bridge floors and cables are identified; mapping the semantic tag back to the sparse point cloud; performing differential Gaussian element initialization on the sparse point cloud based on semantic tags, and endowing different parts with initial shapes conforming to geometric characteristics of the different parts; a geometric constraint loss function is introduced in the optimization process for joint optimization; and finally, outputting a Gaussian splash model of a high-fidelity and accurate geometric structure. According to the method, the problems of model redundancy and geometric distortion when a traditional Gaussian splashing technology is used for processing a bridge scene are solved, and lightweight and high-precision reconstruction and real-time rendering of the bridge model are realized.

The invention relates to the technical field of infrastructure operation and maintenance, and provides a bridge digital twin modeling system for structure detection, which comprises a parametric modeling rule base, an input interface, a parameter processing module, a geometry generation engine and an output module, and is characterized in that a user only needs to provide a bridge type and a small amount of key geometry parameters through the input interface; the parameter processing module can automatically derive and supplement all parameters required for generating a complete model according to a pre-stored rule base; and the geometric generation engine then programmatically generates a component section and a three-dimensional component according to the complete parameter set and performs integral assembly, and finally outputs a complete bridge three-dimensional model. According to the method, the defects that in the prior art, all accurate parameters are input manually, and modeling efficiency is low are overcome, the high-fidelity bridge model can be rapidly and automatically generated on a detection site only by means of limited data, modeling efficiency and feasibility are remarkably improved, and iterative optimization and integrated application of the model are naturally supported.

The application provides a bridge process information generation method and device, electronic equipment and storage medium, and relates to the technical field of bridge information processing.The method comprises the following steps: cutting a pre-constructed bridge model based on a preset segmentation strategy to obtain multiple segments of a rod piece; determining the contact information between the parts in each segment of the rod piece based on the preset constraint information corresponding to the bridge model; obtaining the segmentation process information corresponding to each segment of the rod piece according to the contact information corresponding to each segment of the rod piece, and obtaining the bridge process information based on each segmentation process information; wherein the segmentation process information comprises welding information determined based on the line contact information and / or coating information determined based on the surface contact information.The application determines the contact information between the parts based on the preset constraint information, provides accurate and reliable reference for the generation of process information, and is beneficial to avoiding the problems of low efficiency and the like when relying on manual acquisition of bridge process information.

The present disclosure relates to a bridge information processing method, device, equipment and storage medium. The method comprises: determining a plurality of structure information corresponding to a plurality of bridge structures in the bridge, and a plurality of component positions corresponding to a plurality of bridge components in each bridge structure; for each bridge structure, generating a component identifier corresponding to each component type in the bridge structure according to the assembly information and the component detail information, and assigning the component identifier to the corresponding bridge component according to the component type and the component position; determining a plurality of component models according to the component types corresponding to a plurality of bridge components, and adding the plurality of component models to the corresponding component positions to obtain a bridge model corresponding to the bridge; establishing a component tree diagram of the bridge according to the component identifier, and associating and displaying the component tree diagram and the bridge model. Thus, the belonging relationship of the bridge components is intuitively displayed through the component tree diagram, and the unified collaborative management of information is realized.

This invention discloses a method for constructing a bridge digital twin health monitoring system, belonging to the field of civil engineering and smart infrastructure technology. The method includes: constructing a three-dimensional geometric model of the bridge; establishing an initial finite element model based on the bridge's three-dimensional geometric model, and using a genetic algorithm to optimize and correct its key parameters to obtain a physical simulation model; acquiring structural response sample data based on the physical simulation model, and constructing an RBF surrogate model using the response surface methodology or radial basis function neural network method to obtain a digital twin bridge model; integrating the digital twin bridge model into a three-dimensional visualization platform to realize visual perception, interactive operation, and remote management of the bridge structural status, thereby constructing a bridge digital twin health monitoring system.

The application provides a bridge data conversion method and system of BIM and a finite element model, and relates to the technical field of bridge model optimization. Based on Revit API, component traversal extraction and attribute information extraction are performed on the BIM model to generate a structured data table; a first classification model is used to automatically identify the component type, attribute supplementation is performed in combination with a second rule mapping process, and an optimized structured data table is obtained; subsequently, mesh division and quality optimization processing are performed to obtain high-quality mesh division information; finally, a target finite element model is constructed through material attribute mapping and information addition. The application realizes seamless connection of the BIM model and finite element analysis, provides efficient and accurate technical support for bridge structure design and analysis, effectively solves the problems of low model conversion efficiency and modification difficulty in bridge structure analysis, and improves the informatization and intelligentization level of bridge engineering.

This invention discloses a heterogeneous federated cross-domain recommendation system based on hypernetworks and its implementation method. This method is designed for heterogeneous federated cross-domain recommendation scenarios, allowing participating clients to flexibly deploy personalized recommendation models based on their local data scale. The method includes performing multi-objective collaborative pre-training within each single domain to obtain high-quality initial embedding representations; and constructing a hypernetwork-based bridging model generator on the server side. By fusing the architecture description of the client's local bridging model with specific user preference information, it dynamically generates weight parameters that match the client's model structure. The client uses these parameters for local fine-tuning and sends gradient updates back to the server for global parameter iteration. This invention effectively overcomes the challenge of directly aggregating model parameters due to heterogeneous embedding layer dimensions, and achieves efficient knowledge sharing and cross-domain collaborative training among heterogeneous clients while strictly protecting user privacy.

The invention discloses an urban bridge model generation method based on parameterization rule driving, and belongs to the technical field of bridge model generation. Comprising the following steps: S1, judging a design mode of a workflow, and generating a skeleton model by adopting different generation modes based on different design modes; s2, inputting the skeleton model into urban three-dimensional modeling software, and using a CGA rule to drive the skeleton model to generate a bridge superstructure three-dimensional model; s3, inputting the skeleton model into urban three-dimensional modeling software, and using a CGA rule to drive the skeleton model to generate a bridge substructure three-dimensional model; s4, dynamically adjusting parameters of the bridge superstructure three-dimensional model and the bridge substructure three-dimensional model by using an interactive parameter adjustment interface; and S5, outputting the adjusted three-dimensional model of the bridge superstructure, the adjusted three-dimensional model of the bridge substructure and the adjusted topographic map layer as scene assets. According to the method, the CGA rule code is adopted to realize automatic modeling, so that the model generation efficiency and precision are improved, and meanwhile, the modeling cost and the data delivery difficulty are reduced.

The invention discloses a monitoring data and natural language driven bridge finite element model rapid construction method, system and device and a storage medium, and belongs to the technical field of bridge models.The method comprises the following steps that a bridge initial finite element model is constructed based on Midas Civil NX and a natural language; optimized and corrected bridge finite element model parameters are obtained based on the bridge initial finite element model and the PSO optimization model architecture, the bridge initial finite element model is corrected, and a bridge reference finite element model capable of reflecting the real state of the structure is obtained; and importing the obtained bridge key monitoring data into the bridge reference finite element model in real time, calculating an accurate bridge structure response and verification coefficient, and carrying out rapid verification and visual display on the safety state of the bridge structure. According to the method, efficient construction of the bridge finite element model is achieved, the method is particularly suitable for large-quantity and wide-range bridge groups with similar structural forms, and technical support is provided for scheme design and safety inspection of a bridge monitoring system.

The application discloses a kind of composite material multiscale analysis method and application considering manufacturing defect, belong to composite material performance analysis technical field.The method is by constructing microcosmic, mesoscopic and macro scale model introducing manufacturing defect;Damage model of each scale model is constructed, the damage model includes the constitutive relation of describing material linear elastic behavior, initial failure criterion judging damage initiation, and damage evolution criterion describing stiffness degradation after damage occurs;Through bridging model, the stress, strain and damage information between microcosmic and mesoscopic scale are bidirectionally transferred, and the equivalent performance of composite material is obtained;Equivalent performance is endowed to macro scale model for structure analysis.The application can be applied to performance prediction, defect tolerance evaluation and structure design optimization of composite material by multiscale coupling and defect introduction, accurately predicts the mechanical properties and failure mechanism of composite material containing manufacturing defect.

The embodiment of the specification provides a method for training a student model, a behavior prediction method and device, the method trains the student model based on a teacher model and a bridge model, the method comprises the following steps: inputting original feature data into a first embedding layer to obtain first embedding features and inputting the first embedding features into the trained teacher model and the bridge model to obtain first and second prediction results and updating the bridge model according to the first and second prediction results; inputting the first embedding features into a bridge feature extraction network included in the bridge model and a student feature extraction network included in the student model to obtain a first bridge extraction vector and a student extraction vector, and determining an interactive distillation loss according to the first bridge extraction vector and the student extraction vector; inputting the original feature data into a second embedding layer to obtain second embedding features and inputting the second embedding features into the bridge feature extraction network to obtain a second bridge extraction vector; determining an embedding distillation loss according to the first and second bridge extraction vectors; and determining a comprehensive loss according to at least the interactive distillation loss and the embedding distillation loss and updating the student model according to the comprehensive loss.

The application relates to a bridge model construction method and system, a storage medium and an intelligent terminal, relates to the field of object modeling technology, and comprises the following steps: acquiring bridge liquid distance information and bridge deck width information; determining bridge under-path information according to the bridge liquid distance information and an occupancy value; determining side path information according to the bridge deck width information and an interval value; combining the bridge under-path information, the side path information and a bridge over-path to determine detection track information; controlling a UAV to work at a starting point on a track corresponding to the detection track information to acquire bridge image information, outputting a completion signal when the UAV moves to the starting point again, and determining bridge model information according to the bridge image information; after the completion signal is output, controlling the UAV to move along a fixed direction by a fixed distance, and updating the starting point and the detection track information until the UAV moves to an ending point. The application has the effect of facilitating the construction of a bridge model.

The application discloses a bridge detection information visualization method based on a blender three-dimensional model, and the method comprises the following steps: creating a three-dimensional model of a bridge to be detected by using Blender combined with a Python script; importing and attaching an image of apparent disease information of the bridge after image processing on the model by using a blende Import-Export:Import Images as Planes plug-in; and realizing roaming interaction through a mouse and a keyboard by using a built-in Logic editor of Blender combined with a Python script. The application can realize the visualization of apparent disease detection information of the bridge, solves the positioning and identification difficulties of traditional disease information, and realizes multifunctional interaction of a full-bridge model in view of the problem of lacking roaming interaction function of the full-bridge model in the past.

The invention discloses a stay cable operation period behavior intelligent evaluation and abnormity identification method and system. The method comprises the following steps: completing the construction of a multi-source data acquisition system; data preprocessing is completed, and a data time axis unified template is established; feature extraction and fusion are carried out on the data, and the data are recombined according to typical performance dimensions of the stay cable and based on theme types; performing standardization and structuring processing on all the sensing data; a dynamic input vector containing multi-source heterogeneous data space-time correlation features is constructed through A / B type core indexes, nonlinear features are mapped through a self-adaptive optimized kernel function, and the parameter optimization efficiency is improved by adopting a mode of fusing cross validation and Bayesian optimization so as to complete model training; and completing stay cable state evaluation and output based on the trained model, binding with a bridge model, and automatically completing visual land disease marking. And a scientific basis is provided for the full-life-cycle management of the stay cable.

This application belongs to the field of artificial intelligence and numerical weather prediction fusion technology. Addressing the technical problems in existing technologies where there is insufficient physical consistency, poor multivariate synergy, inadequate observation fusion, and difficulty in effectively suppressing systematic biases between the output of large-scale AI weather prediction models and the input of regional numerical models, this application proposes a physical perception fusion bridging model, a weather prediction method, and related devices. A multi-scale encoder extracts multi-scale meteorological field features from the output of the large-scale AI weather prediction model; a physical constraint decoder decodes the meteorological field features to generate a high-resolution meteorological field that conforms to physical conservation laws, with a physical conservation penalty term introduced during training; and an observation fusion layer fuses multi-source observation data and the high-resolution meteorological field to obtain a high-resolution initial field. This application achieves seamless integration between the large-scale AI weather prediction model and the regional numerical model, ensuring physical consistency and fusing multi-source observation data.

This invention discloses a method for generating urban bridge models based on parametric rule-driven principles, belonging to the field of bridge model generation technology. The method includes: S1, determining the workflow design mode and generating a skeleton model using different generation methods based on different design modes; S2, inputting the skeleton model into urban 3D modeling software and using CGA rules to drive the skeleton model to generate a 3D model of the bridge superstructure; S3, inputting the skeleton model into urban 3D modeling software and using CGA rules to drive the skeleton model to generate a 3D model of the bridge substructure; S4, using an interactive parameter adjustment interface to dynamically adjust the parameters of the 3D models of the bridge superstructure and substructure; and S5, outputting the adjusted 3D models of the bridge superstructure, substructure, and terrain layer as scene assets. This invention uses CGA rule code to achieve automated modeling, improving model generation efficiency and accuracy while reducing modeling costs and data delivery difficulty.

This disclosure relates to a method, system, and storage medium for predicting the elastic compression loss of prestressed steel bars in bridges. The method includes: simulating a bridge model using finite element software; calculating the sum of the prestressed steel bar elements and the temperature load applied to each element; applying the temperature load to each element sequentially according to the prestressing order and the birth-death element method; constructing and preprocessing an original dataset to obtain training, validation, and test sets; constructing an MLP (Multilayer Perceptron) neural network model as a prediction model for the elastic compression loss of prestressed steel bars on concrete, and training the prediction model; and using the trained prediction model to predict the elastic compression loss of prestressed concrete caused by the tensioning of other prestressed steel bars. This disclosure enables rapid and accurate prediction of the elastic compression loss of prestressed steel bars in bridges.

The invention provides a BIM and finite element model bridge data conversion method and system, and relates to the technical field of bridge model optimization. Component traversal extraction and attribute information extraction are carried out on the BIM model based on Revit API, and a structured data table is generated; component types are automatically identified through a first classification model, attribute supplementation is carried out in combination with second rule mapping processing, and an optimized structured data table is obtained; performing grid division and quality optimization processing to obtain high-quality grid division information; and finally, constructing a target finite element model through material attribute mapping and information addition. According to the method, seamless connection of the BIM model and finite element analysis is realized, efficient and accurate technical support is provided for bridge structure design and analysis, the problems of low model conversion efficiency, difficulty in modification and the like in bridge structure analysis are effectively solved, and the informatization and intelligence levels of bridge engineering are improved.

The application discloses a cable-stayed bridge completion cable force optimization method and device and a medium, wherein the method comprises the following steps: establishing a cable-stayed bridge model; determining the upper and lower bounds of the solution space of an optimization problem, and calculating an initial data set DB; obtaining a training set according to the data set DB, and training a radial basis neural network as a surrogate model; based on the surrogate model, using a social learning particle swarm optimization algorithm as an optimizer to optimize the prediction function g(x) in the solution space; after the optimizer reaches a convergence threshold, selecting multiple individuals in the last population for real solution evaluation, and adding the evaluated solutions to the data set DB; and outputting the data set DB. By adopting the radial basis neural network assisted particle swarm optimization algorithm, the application can achieve good optimization effect within a limited number of solution evaluations, and obtain an excellent feasible solution in the cable-stayed bridge completion cable force optimization problem at a lower cost. The application can be widely applied to the fields of bridge design and intelligent optimization algorithms.

The application relates to a bridge model design method and system based on static and dynamic information fusion, and belongs to the technical field of bridge design. The bridge model design method based on static and dynamic information fusion comprises the following steps: obtaining physical quantities of a prototype and a scale model of a bridge; determining static dimensionless parameters based on the physical quantities and a dimensional analysis method; establishing a multi-degree-of-freedom motion equation; determining dynamic dimensionless parameters based on the multi-degree-of-freedom motion equation and the physical quantities; determining a similarity relationship between the prototype and the scale model based on the static dimensionless parameters and the dynamic dimensionless parameters; performing distortion analysis on the scale model based on the similarity relationship, determining an actual similarity relationship between the prototype and the scale model, and completing scale model design based on the actual similarity relationship.

The application discloses a visual navigation method and system based on a denoising diffusion bridge model. The method comprises the following steps: acquiring initial image data; performing feature coding processing on the initial image data to generate a context vector; performing linear modulation processing on the context vector to generate a conditional variable; generating a prior action according to the context vector and a preset motion rule; inputting the prior action and the conditional variable into the denoising diffusion bridge model for diffusion training to generate a target navigation action; and wherein the prior action is used as an initial state of the denoising diffusion bridge model when performing reverse denoising processing. The application can effectively reduce the redundant iteration in the diffusion process, reduce the cumulative error, greatly improve the efficiency and stability of action generation, and can be widely applied in the field of computer technology.