Method and system for optimizing three-dimensional model of dock leveler

Abstract

The invention discloses a dock leveler three-dimensional model optimization method and system, and the method comprises the steps: generating a corresponding model instance through a preset dock leveler structure model according to an application scene demand; according to the model instance and the corresponding optimization target, working condition simulation and topological optimization are carried out on the dock leveler structure model, and a dock leveler simulation model is obtained; performing component decomposition and classification on the dock leveler simulation model to obtain manufacturing process data related to components; and performing damage diagnosis analysis according to the manufacturing process data to generate a structure optimization scheme of the dock leveler. According to the method, the dock leveler model is disassembled and analyzed to optimize the manufacturing process of the components, so that the structural performance of the dock leveler is improved, and the durability of the dock leveler is improved.

Description

Technical Field

[0001] This application belongs to the technical field of boarding bridge model construction, and specifically relates to an optimization method and system for a three-dimensional model of a boarding bridge. Background Art

[0002] A boarding bridge is a retractable device used to assist passengers in getting on and off vehicles and loading and unloading goods. It is mainly applied to transportation means such as airplanes, trains, and passenger ships. Its main functions include providing a safe and convenient boarding and alighting passage for passengers and a passage for loading and unloading luggage and goods. It can also be used in emergency situations such as evacuation and rescue. The design of the boarding bridge needs to meet safety requirements such as load-bearing, anti-slip, and protection against wind and rain, and should have functions such as automatic retraction and angle adjustment to adapt to different transportation tools and environments. Therefore, how to uniformly model the key electromechanical components and structural components of the boarding bridge to meet these safety requirements to the greatest extent is worthy of research.

[0003] Although the existing boarding bridge modeling methods can uniformly model the electromechanical components and structural components, they rarely optimize the three-dimensional modeling process of the boarding bridge for the complex contact, friction, and wear behaviors of multiple moving components. Therefore, the existing three-dimensional models of boarding bridges cannot reasonably handle the contact boundary conditions, friction models, and wear mechanisms between these components, and cannot provide accurate data support for optimizing the performance and stability of the entire boarding bridge system. Summary of the Invention

[0004] This application proposes an optimization method and system for a three-dimensional model of a boarding bridge, which optimizes the manufacturing process of components by disassembling and analyzing the boarding bridge model, improves the structural performance of the boarding bridge, and enhances the durability of the boarding bridge.

[0005] The first aspect of this application provides an optimization method for a three-dimensional model of a boarding bridge, and the method includes:

[0006] According to the requirements of the application scenario, generate a corresponding model instance through a preset boarding bridge structure model;

[0007] According to the model instance and the corresponding optimization goal, perform working condition simulation and topology optimization on the boarding bridge structure model to obtain a boarding bridge simulation model;

[0008] Decompose and classify the components of the boarding bridge simulation model to obtain manufacturing process data related to the components;

[0009] Generate a structural optimization plan for the boarding bridge through damage diagnosis and analysis using the manufacturing process data.

[0010] The above solution constructs a model instance that meets the requirements of the application scenario based on the boarding bridge structure model. Then, in combination with the optimization objectives, the structure of the model instance is refined first to reduce the design error of the model. Then, the optimized model is simulated under working conditions to obtain a boarding bridge simulation model that can be used under various working conditions, so that the subsequent optimization of the boarding bridge can be applicable to all working conditions. Then, the boarding bridge simulation model is decomposed according to components to obtain the models corresponding to the components, and the components are classified according to different functions to obtain the manufacturing process data of the manufacturing components. Based on the manufacturing process data, the damage caused by environmental influences such as temperature and stress during the use of the components is analyzed, and the manufacturable process parameters that can be optimized for each component are obtained according to the analysis results to generate a structural optimization plan for the boarding bridge.

[0011] In a possible implementation method of the first aspect, according to the requirements of the application scenario, a corresponding model instance is generated through a preset boarding bridge structure model. Specifically:

[0012] Attribute labels and structural relationship labels are added to the components in the preset original boarding bridge model to generate the boarding bridge structure model;

[0013] According to the preset boarding bridge design rules, a boarding bridge template library is generated through the boarding bridge structure model, and then the model instance that meets the requirements of the application scenario is selected from the template library.

[0014] In a possible implementation method of the first aspect, it further includes:

[0015] Before generating the boarding bridge template library, the surface of the boarding bridge structure model is reconstructed, and then curve fitting and error adjustment are performed on the reconstructed surface.

[0016] The above solution completes the smoothing process of the surface and curve of the boarding bridge through the surface reconstruction of the boarding bridge structure model, and reduces the structural error of the boarding bridge model through the fitting process of the surface.

[0017] In a possible implementation method of the first aspect, according to the model instance and the corresponding optimization objective, the boarding bridge structure model is simulated under working conditions and topologically optimized to obtain a boarding bridge simulation model. Specifically:

[0018] Component information is extracted from the model instance; among them, the component information includes component geometric information and component material properties;

[0019] According to the component information, a structural model corresponding to each component is generated;

[0020] According to the optimization objective, the structural model is respectively subjected to dimension optimization and material distribution optimization through the component geometric information and the component material properties to obtain a first structural model;

[0021] Under each preset boarding bridge working condition, the load and vibration response of the first structural model are simulated to generate the boarding bridge simulation model.

[0022] The above solution decomposes the boarding bridge structural model into components, and optimizes the dimensions and material density distribution of the decomposed component models according to the set optimization objective, maximizing the structural performance and stiffness of the boarding bridge, and obtaining a first structural model with a better structural shape and dimensions. Then, under different working condition backgrounds, the load and vibration response of the first structural model are simulated, and the change data of the structure in terms of stress, deformation, and vibration mode are collected during the simulation to generate the corresponding boarding bridge simulation model, providing data support for subsequent optimization of the boarding bridge structure in the manufacturing process.

[0023] In a possible implementation method of the first aspect, respectively performing dimension optimization and material distribution optimization on the structural model through the component geometric information and the component material properties specifically includes:

[0024] According to the component geometric information, the dimension parameters of the structural model corresponding to the structural component are adjusted to complete the dimension optimization;

[0025] According to the component material properties, the material density distribution of the structural model corresponding to the thin plate component is adjusted to complete the material distribution optimization.

[0026] In a possible implementation method of the first aspect, under each preset boarding bridge working condition, simulating the load and vibration response of the first structural model to generate the boarding bridge simulation model specifically includes:

[0027] Based on the boarding bridge working condition, set the load parameters and vibration parameters of the first structural model;

[0028] According to the load parameters and vibration parameters, perform an operation simulation on the first structural model, and simultaneously collect the deformation data of the first structural model during the operation simulation;

[0029] Through the deformation data, optimize the first structural model to obtain the boarding bridge simulation model.

[0030] In a possible implementation method of the first aspect, decomposing and classifying the boarding bridge simulation model to obtain manufacturing process data related to the components specifically includes:

[0031] According to the structure of the boarding bridge, decompose the boarding bridge simulation model into different component models, and classify the component models according to the functions of the boarding bridge to obtain a set of component models;

[0032] Screen the set of component models through preset additive manufacturing requirements, and extract the data of additively manufacturable components;

[0033] Classify the data of additively manufacturable components by function and label their attributes to generate the manufacturing process data.

[0034] The above solution first decomposes the boarding bridge simulation model into different component models, and then classifies the component models according to different functions to form a set of component models; select appropriate component data from the set of component models according to additive manufacturing requirements and label them, providing data support for subsequent data evaluation.

[0035] In a possible implementation method of the first aspect, perform damage diagnosis and analysis through the manufacturing process data to generate a structural optimization plan for the boarding bridge, specifically:

[0036] Generate a physical field model of the additive manufacturing process according to the manufacturing process data;

[0037] Based on the physical field model, simulate the environmental conditions during the additive manufacturing process and output the simulation data of the additive manufacturing process;

[0038] Perform damage analysis and defect detection of the material according to the simulation data to obtain the material diagnosis result;

[0039] Modify the parameters of the manufacturing process data according to the material diagnosis result to obtain a structural optimization plan for the boarding bridge.

[0040] The above solution simulates through environmental conditions, detects the influence of changes in temperature and stress on the additive manufacturing process, and performs defect analysis on the components during the manufacturing process according to the obtained influence data, determines how to optimize the manufacturing process parameters of the components to improve the structural stability of the boarding bridge, generates a structural optimization plan to improve the structural performance of the boarding bridge, and makes the boarding bridge more durable and safe in actual use.

[0041] In a possible implementation method of the first aspect, the environmental conditions include: temperature field and stress-strain field.

[0042] The second aspect of this application provides an optimization system for the three-dimensional model of the boarding bridge. The system includes: an instance generation module, a simulation model generation module, a manufacturing process acquisition module, and a boarding bridge model optimization module;

[0043] Among them, the instance generation module is used to generate a corresponding model instance according to the application scenario requirements through a preset boarding bridge structure model;

[0044] The simulation model generation module is used to perform working condition simulation and topology optimization on the boarding bridge structure model according to the model instance and the corresponding optimization objective, so as to obtain a boarding bridge simulation model;

[0045] The manufacturing process acquisition module is used to decompose and classify the components of the boarding bridge simulation model to obtain manufacturing process data related to the components;

[0046] The boarding bridge model optimization module is used to perform damage diagnosis and analysis through the manufacturing process data to generate a structural optimization plan for the boarding bridge. Description of the Drawings

[0047] In order to more clearly illustrate the technical solutions of the present application, the drawings required for the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0048] Figure 1 It is a specific flowchart of an optimization method for a three-dimensional model of a boarding bridge provided by an embodiment of the present application;

[0049] Figure 2 It is a structural diagram of an optimization system for a three-dimensional model of a boarding bridge provided by an embodiment of the present application. Specific Embodiments

[0050] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts fall within the scope of protection of the present application.

[0051] It should be understood that the step numbers used in the text are only for convenience of description and are not intended to limit the order of execution of the steps.

[0052] First Embodiment

[0053] Common types of boarding bridges include: aviation boarding bridges - used for civil aviation aircraft, which can automatically adjust the height and angle to dock with the aircraft cabin door; train boarding bridges - movable passages built between railway station platforms and carriages; cruise ship boarding bridges - facilitating tourists to board and disembark luxury cruise ships, with a certain length and height; trailer boarding bridges - helping truck drivers safely board and disembark the trailer cockpit. The design of the boarding bridge needs to meet safety requirements such as load-bearing, anti-slip, and protection against wind and rain, and also have functions such as automatic telescoping and angle adjustment to adapt to different transportation tools and environments. In these application conditions, friction will occur between the mechanical and electrical components and structural components of the boarding bridge due to externally applied stresses and temperature changes, resulting in wear. These phenomena can cause damage to the boarding bridge, having an adverse impact on the structural performance and durability of the boarding bridge. Therefore, how to optimize the structure between components by modeling the components of the boarding bridge is the main research direction of the embodiments of this application.

[0054] As Figure 1 shown, Figure 1 FIG. is a schematic flow chart of a method for optimizing a three-dimensional model of a boarding bridge provided by an embodiment of this application. The method for optimizing the three-dimensional model of the boarding bridge in this embodiment includes steps S1 to S4, which are described in detail as follows:

[0055] Step S1, according to the application scenario requirements, generate a corresponding model instance through a preset boarding bridge structure model.

[0056] In the embodiments of this application, first obtain the preset boarding bridge structure model. Specifically: formally represent the knowledge in the fields of the structure, materials, control, and electrical of the boarding bridge system based on the RDF language (Resource Description Framework), thereby modeling the boarding bridge to obtain the original boarding bridge model. Then collect the CAD model data of the boarding bridge, which can be obtained from the design team, manufacturer, or relevant databases. Also obtain the CAE analysis model data of the boarding bridge. These data can come from model files generated by finite element analysis software (such as ANSYS, ABAQUS, etc.), including geometric information, boundary conditions, and material properties, as well as obtain the control system model data of the boarding bridge, which can come from model files generated by control system design software (such as Simulink, LabVIEW, etc.), including control algorithms, sensor configurations, and actuator parameters. Construct the heterogeneous model data of the boarding bridge through the collected various model data. Use the heterogeneous model data to perform attribute annotation and structural relationship annotation on the components in the original boarding bridge model to achieve data consistency and mutual association, and construct a boarding bridge structure model that can display different structural attributes of the boarding bridge. The boarding bridge structure model includes information in aspects such as structure, materials, control, and electricity, providing a comprehensive basis for subsequent analysis and optimization.

[0057] Furthermore, the RDF language can be used to map heterogeneous model data onto the original model of the boarding bridge to establish semantic associations.

[0058] In addition, the annotated content can be fused, and the semantic information of different model data can be mapped and aligned to integrate the boarding bridge data from different sources and formats, ensuring consistency and interoperability between models. In some application embodiments, tools provided by the Ontology Alignment Evaluation Initiative (OAEI) are used for concept mapping and relationship matching between models.

[0059] Among them, the RDF language provides a standardized way to describe entities, attributes, and relationships. Specifically, relevant entities and attributes are mapped into the form of RDF triples mainly through the collected knowledge data to establish associations and semantic descriptions between knowledge.

[0060] Then, based on the boarding bridge structure model, pattern mining is performed according to the preset boarding bridge design rules to obtain the boarding bridge ontology knowledge base. This knowledge base includes a large number of boarding bridge instances that can be applied to different working conditions respectively. Then, appropriate model instances are selected from the boarding bridge ontology knowledge base according to the application scenario requirements. These model instances can be the actual boarding bridge design, performance parameters, etc. in a specific scenario. For example, a train boarding bridge is selected as the movable passage built between the railway station platform and the carriage, or a trailer boarding bridge is selected to help truck drivers safely get on and off the trailer cockpit.

[0061] Specifically, first, the boarding bridge structure model is smoothed, and the curved surfaces in the model are reconstructed to obtain a more refined boarding bridge structure model. Then, through Bezier curve fitting tools such as B-Spline and Bezier, the smoothed boarding bridge structure model is accurately approximated, and the curved surfaces and curves in the model are fitted and converted into a smooth geometric body model represented by Bezier curve segments for subsequent processing and analysis. At the same time, error reduction algorithms such as the least squares method and compression algorithms are also used to adjust the error of the boarding bridge structure model, mainly by controlling the threshold and parameters of the surface fitting error to achieve the purpose of simplifying data and improving calculation efficiency.

[0062] Optionally, in the embodiment of the present application, the parametric modeling method of NURBS (Non-Uniform Rational B-Splines) is selected to smooth the boarding bridge structure model and convert it into a smooth geometric body representation. Among them, NURBS is a mathematical representation method suitable for curve and surface modeling, which can provide a highly flexible and accurate geometric shape representation.

[0063] Further, historical boarding bridge design data can be collected through channels such as documents, databases, and file systems. Data cleaning is performed on the historical design data, including operations such as removing duplicate data, handling missing values, and correcting incorrect data, in order to obtain high-quality and standardized historical design data. Then, the preprocessed historical design data is used to perform pattern mining on the boarding bridge structure model represented by Bezier curve segments, discover potential laws, correlations, and important features therein, and then perform knowledge pattern integration to create an ontology model, define classes, attributes, relationships, etc., thereby obtaining the boarding bridge ontology knowledge base.

[0064] Exemplarily, the data mining algorithms and tools used in the embodiments of the present application include association rule mining, clustering analysis, decision trees, etc.

[0065] Step S2, according to the model instance and the corresponding optimization objective, perform working condition simulation and topology optimization on the boarding bridge structure model to obtain a boarding bridge simulation model.

[0066] In the embodiments of the present application, first, data analysis and processing tools are used to extract component information from the model instance, including information on relevant performance models, constraint conditions, design parameters, and evaluation indicators, and then these information are identified and optimized to obtain component geometric information and component material properties.

[0067] Among them, the component material properties are mainly obtained by experimental testing, material databases, or relevant literature to obtain the material property data required for thin plate components in the construction of the boarding bridge, including elastic modulus, Poisson's ratio, density, etc., and then the experimental data is processed and analyzed.

[0068] Then, a corresponding structure model is generated for each component through the component information, and topology optimization based on the structural components is performed on each structure model using a topology optimization tool.

[0069] Specifically, based on the optimization objective, optimization parameters such as design space, loading conditions, and constraint conditions are defined, and a topology optimization algorithm is run to obtain optimal structural topology layout data. Then, based on the structural topology layout data, an isomorphous optimization tool is used to adjust the structure model to maximize the performance and stiffness of the structure, in order to improve the performance and weight ratio of the structure.

[0070] Among them, topology optimization is mainly aimed at structural components such as support beams and frames, and the shape and material distribution of the components are optimized through the geometric information of the structural components to meet target performance such as minimum weight, maximum stiffness, or optimal stress distribution.

[0071] The size optimization and material distribution optimization are also carried out on the structural model. The size optimization mainly further combines the geometric information of the components with the structural model after topology optimization to adjust the size parameters and optimize the size according to the design rules, ensuring its manufacturability in actual production; the material distribution optimization mainly adjusts the density distribution of the structural model after topology optimization in combination with the material properties of the components.

[0072] After topology optimization, size optimization and material distribution optimization, the optimized structural model is mapped into the model instance to obtain the first structural model of the boarding bridge. Then, based on the overall assembly, the load and vibration responses of the first structural model under various working conditions are simulated to generate the simulation model of the boarding bridge.

[0073] Specifically, considering factors such as static load, dynamic load and vibration frequency, the load parameters and vibration parameters under various working conditions are defined. The first structural model is simulated using the load parameters and vibration parameters to obtain information such as the stress, deformation and vibration mode of the structure. Through the collaborative optimization method, considering the structural performance, mechanism performance and working condition requirements comprehensively, the first structural model is further optimized with the information such as the stress, deformation and vibration mode of the collected structure to obtain the simulation model of the boarding bridge.

[0074] Step S3: Decompose and classify the components of the simulation model of the boarding bridge to obtain the manufacturing process data related to the components.

[0075] In the embodiment of the present application, based on knowledge-driven, the components of the simulation model of the boarding bridge are decomposed to obtain the data of the additive components, and then the data of the additive components are functionally classified, classified and marked according to the different functions of the components, which helps to understand the uses and characteristics of the components and provides a basis for subsequent process optimization. Process optimization is carried out according to different functions to obtain the manufacturing process data, providing a basis for optimizing the design of the components considering the requirements and limitations of the manufacturing process to improve the manufacturing efficiency and quality. Among them, the manufacturing process data includes process routes, process parameters, etc., and can be used to guide the actual manufacturing process of the components.

[0076] Specifically, based on the boarding bridge ontology knowledge base, the three-dimensional boarding bridge simulation model is decomposed into different component models for subsequent analysis and processing. Then, according to the structure and function of the boarding bridge, the component models are classified to obtain a set of component models. Based on the requirements and limitations of the additive manufacturing process, the set of component models is analyzed and screened to determine the components that can adopt the additive manufacturing process, and the additive manufacturable component data is obtained. Among them, the additive manufacturing process is a technology that manufactures solid parts by gradually accumulating materials, and can also be called three-dimensional printing technology. It can quickly and precisely manufacture parts with any complex shape, thus realizing the "free manufacturing" of parts, solving the forming of many complex-structured parts, greatly reducing the processing procedures, and shortening the processing cycle. Moreover, the more complex the product structure, the more significant the role of its manufacturing speed.

[0077] The obtained additive manufacturable component data is classified according to the functions and uses of the components, such as support structure components, transmission components, cooling structures, etc. Then, the components are classified and marked according to their different functions, which helps to understand the uses and characteristics of the components and provides a basis for subsequent process optimization. After completing the function classification and attribute annotation, the manufacturing process data related to the components is obtained. Through the manufacturing process data, the dimensions of the components can be optimized during the manufacturing process of the components, and the manufacturing efficiency and quality can be improved.

[0078] Step S4, through the manufacturing process data, perform damage diagnosis analysis to generate a structural optimization plan for the boarding bridge.

[0079] In the embodiment of the present application, according to the manufacturing process data, including laser scanning path, energy parameters, scanning speed, etc., a physical field model of the additive manufacturing process is generated. The physical field model can be established based on numerical methods (such as the finite element method) or analytical methods, and is used to describe physical processes such as melting, solidification, and heat conduction of materials.

[0080] The physical field model is parallelized and distributed to a high-performance computing cluster for finite element analysis to obtain simulation data.

[0081] Specifically, use finite element analysis software (such as ABAQUS, ANSYS, etc.) to perform parallel calculations on the constructed physical field model, simulate the temperature field, stress-strain field, etc. during the additive manufacturing process, and simultaneously extract key information such as the temperature field and stress-strain field, and use appropriate methods and tools to detect and identify defects in the components during the manufacturing process, such as stress concentration, crack formation, etc. Finally, output the simulation data of the additive manufacturing process, including the temperature field, stress-strain field, defect information, etc.

[0082] Then, the corresponding damage diagnosis algorithms and models are used to analyze and diagnose the simulated data to obtain material diagnosis results such as the damage state and defect degree of the material.

[0083] Based on the material diagnosis results, the damage characteristics and defect conditions of the material are analyzed, and the design parameters of the manufacturing process data are corrected accordingly to optimize the model instance corresponding to the boarding bridge design, improve the material properties during the manufacturing process, and reduce the defect rate of components.

[0084] Implementing the embodiments of the present application has the following beneficial effects:

[0085] In the embodiments of the present application, a model instance that meets the requirements of the application scenario is constructed based on the boarding bridge structure model. Then, in combination with the optimization objectives, the structure of the model instance is first refined to reduce the design error of the model. Then, the optimized model is subjected to working condition simulation to obtain a boarding bridge simulation model that can be used under various working conditions, so that the subsequent optimization of the boarding bridge can be applicable to all working conditions. Then, the boarding bridge simulation model is decomposed according to components to obtain the models corresponding to the components, and the components are classified according to different functions to obtain the manufacturing process data of the manufacturing components. Based on the manufacturing process data, the damage caused by environmental influences such as temperature and stress during the use of the components is analyzed, and the manufacturing process parameters that can be optimized during the manufacturing process of each component are obtained according to the analysis results to generate a structural optimization plan for the boarding bridge.

[0086] Further, in order to execute the optimization system of the three-dimensional model of the boarding bridge corresponding to the above method embodiments to achieve the corresponding functions and technical effects, Figure 2 A structural diagram of an optimization system for a three-dimensional model of a boarding bridge is provided. For the convenience of description, only the parts related to this embodiment are shown. The optimization system for the three-dimensional model of the boarding bridge provided by the embodiments of the present application includes:

[0087] An instance generation module 201, configured to generate a corresponding model instance according to the requirements of the application scenario through a preset boarding bridge structure model.

[0088] In the embodiments of the present application, attribute annotation and structural relationship annotation are performed on the components in the preset original model of the boarding bridge to generate the boarding bridge structure model; according to the preset boarding bridge design rules, a boarding bridge template library is generated through the boarding bridge structure model, and then the model instance that meets the requirements of the application scenario is selected from the template library.

[0089] A simulation model generation module 202, configured to perform working condition simulation and topology optimization on the boarding bridge structure model according to the model instance and the corresponding optimization objective to obtain a boarding bridge simulation model.

[0090] In an embodiment of the present application, component information is extracted from the model instance; wherein, the component information includes component geometric information and component material properties;

[0091] According to the component information, a structural model corresponding to each component is generated;

[0092] According to the optimization objective, the structural model is respectively optimized in terms of size and material distribution through the component geometric information and the component material properties to obtain a first structural model;

[0093] Under each preset boarding bridge working condition, the load and vibration response of the first structural model are simulated to generate the boarding bridge simulation model.

[0094] The manufacturing process acquisition module 203 is configured to decompose and classify the components of the boarding bridge simulation model to obtain manufacturing process data related to the components.

[0095] In an embodiment of the present application, according to the structure of the boarding bridge, the boarding bridge simulation model is decomposed into different component models, and the component models are classified according to the functions of the boarding bridge to obtain a component model set;

[0096] The component model set is screened through preset additive manufacturing requirements to extract additively manufacturable component data;

[0097] The additively manufacturable component data is classified by function and labeled with attributes to generate the manufacturing process data.

[0098] The boarding bridge model optimization module 204 is configured to perform damage diagnosis analysis through the manufacturing process data to generate a structural optimization plan for the boarding bridge.

[0099] In an embodiment of the present application, according to the manufacturing process data, a physical field model of the additive manufacturing process is generated;

[0100] Based on the physical field model, the environmental conditions during the additive manufacturing process are simulated, and simulation data of the additive manufacturing process is output;

[0101] According to the simulation data, damage analysis and defect detection of the material are performed to obtain a material diagnosis result;

[0102] According to the material diagnosis result, the parameters of the manufacturing process data are corrected to obtain a structural optimization plan for the boarding bridge.

[0103] In some embodiments, the instance generation module 201 further includes:

[0104] In the embodiments of the present application, first, a preset boarding bridge structure model is obtained. Specifically, based on the Resource Description Framework (RDF), the knowledge in the fields of the structure, materials, control, and electrical of the boarding bridge system is formally represented, so as to model the boarding bridge and obtain the original boarding bridge model. Then, the CAD model data of the boarding bridge is collected, which can be obtained from the design team, manufacturer, or relevant databases. The CAE analysis model data of the boarding bridge is also obtained. These data can come from the model files generated by finite element analysis software (such as ANSYS, ABAQUS, etc.), including geometric information, boundary conditions, and material properties, etc. And the control system model data of the boarding bridge is obtained, which can come from the model files generated by control system design software (such as Simulink, LabVIEW, etc.), including control algorithms, sensor configurations, and actuator parameters, etc. The heterogeneous model data of the boarding bridge is constructed through the collected various model data. The components in the original boarding bridge model are marked with attribute and structural relationship using the heterogeneous model data to achieve data consistency and interconnection, and a boarding bridge structure model that can display different structural attributes of the boarding bridge is constructed. The boarding bridge structure model includes information in aspects such as structure, materials, control, and electricity, providing a comprehensive basis for subsequent analysis and optimization.

[0105] Furthermore, the RDF language can be used to map the heterogeneous model data onto the original boarding bridge model to establish semantic associations.

[0106] In addition, the marked content can be fused, and the semantic information of different model data is mapped and aligned to integrate the boarding bridge data from different sources and formats, so as to ensure the consistency and interoperability between models. In some embodiments of the application, the tools provided by the Ontology Alignment Evaluation Initiative (OAEI) are used to perform concept mapping and relationship matching between models.

[0107] Among them, the RDF language provides a standardized way to describe entities, attributes, and relationships. mainly by mapping the relevant entities and attributes to the form of RDF triples through the collected knowledge data to establish the association and semantic description between knowledge.

[0108] Then, based on the preset boarding bridge design rules, pattern mining is performed based on the boarding bridge structure model to obtain the boarding bridge ontology knowledge base. This knowledge base includes a large number of boarding bridge instances that can be applied to different working conditions respectively. Then, appropriate model instances are selected from the boarding bridge ontology knowledge base according to the application scenario requirements. These model instances can be the actual boarding bridge design, performance parameters, etc. in a specific scenario. For example, a train boarding bridge is selected as the movable passage built between the railway station platform and the carriage, or a trailer boarding bridge is selected to help truck drivers safely get on and off the trailer cockpit.

[0109] Specifically, first, smooth the boarding bridge structure model, reconstruct the curved surfaces in the model to obtain a more refined boarding bridge structure model. Then, through Bezier curve fitting tools, such as B-Spline, Bezier, etc., accurately approximate the smoothed boarding bridge structure model, fit the curved surfaces and curves in the model, and convert it into a smooth geometric body model represented by Bezier curve segments for subsequent processing and analysis. At the same time, error reduction algorithms, such as the least squares method, compression algorithm, etc., are also used to adjust the error of the boarding bridge structure model, mainly by controlling the threshold and parameters of the surface fitting error to achieve the purpose of simplifying data and improving calculation efficiency.

[0110] Optionally, the embodiment of the present application selects the parametric modeling method of NURBS (Non-Uniform Rational B-Splines) to smooth the boarding bridge structure model and convert the boarding bridge structure model into a smooth geometric body representation. Among them, NURBS is a mathematical representation method suitable for the modeling of curves and surfaces, which can provide a highly flexible and accurate geometric shape representation.

[0111] Furthermore, historical boarding bridge design data is collected, which can be obtained through channels such as documents, databases, and file systems. Data cleaning is performed on the historical design data, including operations such as removing duplicate data, handling missing values, and correcting incorrect data, to obtain high-quality and standardized historical design data. Then, the historical design data after preprocessing is used to perform pattern mining on the boarding bridge structure model represented by Bezier curve segments, discover the potential laws, correlations, and important features therein, and then perform knowledge pattern integration to create an ontology model, define classes, attributes, relationships, etc. to obtain the boarding bridge ontology knowledge base.

[0112] Exemplarily, the data mining algorithms and tools used in the embodiment of the present application include association rule mining, clustering analysis, decision trees, etc.

[0113] In some embodiments, the simulation model generation module 202 further includes:

[0114] In the embodiments of the present application, component information is first extracted from the model instance by using data analysis and processing tools, including information on relevant performance models, constraint conditions, design parameters, and evaluation indicators. Then, this information is identified and optimized to obtain component geometric information and component material properties.

[0115] Among them, the component material properties are mainly obtained through experimental tests, material databases, or relevant literature to acquire the material property data required for thin plate components in the boarding bridge construction, including elastic modulus, Poisson's ratio, density, etc. Then, the experimental data is processed and analyzed to obtain them.

[0116] Then, a structural model corresponding to each component is generated through the component information, and topology optimization of the structural components is performed by using a topology optimization tool through each structural model.

[0117] Specifically, based on the optimization objective, optimization parameters such as design space, loading conditions, and constraint conditions are defined, and the topology optimization algorithm is run to obtain the optimal structural topology layout data. Then, based on the structural topology layout data, a homogenization optimization tool is used to adjust the structural model to maximize the performance and stiffness of the structure, so as to improve the performance and weight ratio of the structure.

[0118] Among them, topology optimization is mainly aimed at structural components such as support beams and frames, and the shape and material distribution of the components are optimized through the geometric information of the structural components to meet the target performance, such as minimum weight, maximum stiffness, or optimal stress distribution, etc.

[0119] Size optimization and material distribution optimization are also performed on the structural model. Size optimization mainly further combines the geometric information of the components with the structural model after topology optimization to adjust the size parameters and optimize the size according to the design rules to ensure its manufacturability in actual production; material distribution optimization mainly adjusts the density distribution of the structural model after topology optimization in combination with the component material properties.

[0120] After topology optimization, size optimization, and material distribution optimization, the optimized structural model is mapped into the model instance to obtain the first structural model of the boarding bridge. Then, based on the overall assembly, load and vibration response simulations under various working conditions are performed on the first structural model to generate the simulation model of the boarding bridge.

[0121] Specifically, considering factors such as static load, dynamic load, and vibration frequency, load parameters and vibration parameters under various working conditions are defined. The first structural model is run and simulated using the load parameters and vibration parameters to obtain information such as the stress, deformation, and vibration modes of the structure. Through a collaborative optimization method, considering the structural performance, mechanism performance, and working condition requirements comprehensively, the first structural model is further optimized with the information such as the stress, deformation, and vibration modes of the collected structure to obtain the simulation model of the boarding bridge.

[0122] In some embodiments, the manufacturing process acquisition module 203 further includes:

[0123] Based on knowledge-driven decomposition of the boarding bridge simulation model into component data for additive manufacturing, and then functionally classifying the additive component data, classifying and labeling according to the different functions of the components, which helps to understand the uses and characteristics of the components and provides a basis for subsequent process optimization. Optimize the process according to different functions to obtain manufacturing process data, and provide a basis for optimizing the design of components considering the requirements and limitations of the manufacturing process to improve manufacturing efficiency and quality. Among them, the manufacturing process data includes process routes, process parameters, etc., and can be used to guide the actual manufacturing process of components.

[0124] Specifically, based on the boarding bridge ontology knowledge base, decompose the three-dimensional boarding bridge simulation model into different component models for subsequent analysis and processing. Then, classify the component models according to the structure and function of the boarding bridge to obtain a set of component models. Then, based on the requirements and limitations of the additive manufacturing process, analyze and screen the set of component models to determine the components that can adopt the additive manufacturing process, and obtain additive component data. Among them, the additive manufacturing process is a technology that manufactures solid parts by gradually accumulating materials, also known as 3D printing technology, which can quickly and precisely manufacture parts of any complex shape, thus realizing the "free manufacturing" of parts, solving the forming of many complex structure parts, greatly reducing the processing procedures, and shortening the processing cycle. Moreover, the more complex the product structure, the more significant the role of its manufacturing speed.

[0125] Classify the obtained additive component data according to the functions and uses of the components, such as support structure components, transmission components, cooling structures, etc., and then classify and label according to the different functions of the components, which helps to understand the uses and characteristics of the components and provides a basis for subsequent process optimization. After completing the function classification and attribute annotation, manufacturing process data related to the components is obtained. Through the manufacturing process data, the dimensions of the components can be optimized during the manufacturing process of the components, and the manufacturing efficiency and quality can be improved.

[0126] In some embodiments, the boarding bridge model optimization module 204 further includes:

[0127] Generate a physical field model of the additive manufacturing process according to the manufacturing process data, including laser scanning path, energy parameters, scanning speed, etc. The physical field model can be established based on numerical methods (such as the finite element method) or analytical methods, and is used to describe physical processes such as melting, solidification, and heat conduction of materials.

[0128] Parallelize the physical field model and distribute it to a high-performance computing cluster for finite element analysis to obtain simulation data.

[0129] Specifically, use finite element analysis software (such as ABAQUS, ANSYS, etc.) to perform parallel computing on the constructed physical field model, simulate the temperature field, stress-strain field, etc. during the additive manufacturing process, extract key information such as the temperature field and stress-strain field at the same time, and use appropriate methods and tools to detect and identify defects in the components during the manufacturing process, such as stress concentration, crack formation, etc. Finally, output the simulation data of the additive manufacturing process, including the temperature field, stress-strain field, defect information, etc.

[0130] Then use the corresponding damage diagnosis algorithms and models to analyze and diagnose the simulation data to obtain material diagnosis results such as the damage state and defect degree of the material.

[0131] According to the material diagnosis results, analyze the damage characteristics and defect conditions of the material, and based on this, correct the design parameters of the manufacturing process data to optimize the model instance corresponding to the boarding bridge design, improve the material properties during the manufacturing process, and reduce the defect rate of the components.

[0132] Implementing the embodiments of the present application has the following beneficial effects:

[0133] The embodiments of the present application construct a model instance that meets the requirements of the application scenario based on the boarding bridge structure model. Then, in combination with the optimization goal, first refine the structure of the model instance to reduce the design error of the model, and then simulate the working conditions of the optimized model to obtain a boarding bridge simulation model that can be used under various working conditions, so that the subsequent optimization of the boarding bridge can be applicable to all working conditions. Then decompose the boarding bridge simulation model according to the components to obtain the models corresponding to the components, classify the components according to different functions to obtain the manufacturing process data of the manufacturing components; based on the manufacturing process data, analyze the damage caused by environmental influences such as temperature and stress during the use of the components, and obtain the manufacturable process parameters that can be optimized for each component during the manufacturing process according to the analysis results to generate a structural optimization plan for the boarding bridge.

[0134] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present application. It should be understood that the above are only specific embodiments of the present application and are not used to limit the protection scope of the present application. In particular, for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included within the protection scope of the present application.

Claims

1. A method for optimizing a three-dimensional model of a boarding bridge, characterized in that: include: According to the application scenario requirements, the corresponding model instance is generated through the preset boarding bridge structure model; According to the model instance and the corresponding optimization target, the operating condition simulation and topology optimization are performed on the boarding bridge structure model to obtain a boarding bridge simulation model; Decomposing and classifying the components of the boarding ramp simulation model to obtain manufacturing process data related to the components; Damage diagnosis and analysis are performed using the manufacturing process data to generate a structural optimization solution for the boarding ramp.

2. The optimization method of the three-dimensional model of the boarding bridge according to claim 1 is characterized in that: According to the application scenario requirements, the corresponding model instance is generated through the preset boarding bridge structure model, specifically: Performing attribute annotation and structural relationship annotation on components in the preset original model of the boarding bridge to generate the structural model of the boarding bridge; According to the preset boarding ramp design rules, a boarding ramp template library is generated through the boarding ramp structure model, and then the model instance that meets the application scenario requirements is selected from the template library.

3. The optimization method of the three-dimensional model of the boarding bridge according to claim 2 is characterized in that: Also includes: Before generating the boarding bridge template library, the surface of the boarding bridge structure model is reconstructed, and then the reconstructed surface is subjected to curve fitting and error adjustment.

4. The optimization method of the three-dimensional model of the boarding bridge according to claim 1 is characterized in that: According to the model instance and the corresponding optimization target, the operating condition simulation and topology optimization are performed on the boarding bridge structure model to obtain the boarding bridge simulation model, which is specifically: Extracting component information from the model instance; wherein the component information includes component geometry information and component material properties; Generate a structural model corresponding to each component according to the component information; According to the optimization target, the size optimization and material distribution optimization of the structural model are performed respectively through the component geometric information and the component material properties to obtain a first structural model; Under each preset boarding ramp working condition, the load and vibration response of the first structural model are simulated to generate the boarding ramp simulation model.

5. The optimization method of the three-dimensional model of the boarding bridge according to claim 4 is characterized in that: The size optimization and material distribution optimization of the structural model are performed respectively by using the component geometric information and the component material properties, specifically: According to the component geometric information, the size parameters of the structural model corresponding to the structural component are adjusted to complete the size optimization; According to the material properties of the component, the material density distribution of the structural model corresponding to the thin plate component is adjusted to achieve material distribution optimization.

6. The optimization method of the three-dimensional model of the boarding bridge according to claim 4 is characterized in that: The load and vibration response of the first structural model are simulated under each preset boarding bridge working condition to generate the boarding bridge simulation model, specifically: Based on the working condition of the boarding bridge, setting the load parameters and vibration parameters of the first structural model; Performing an operation simulation on the first structural model according to the load parameters and the vibration parameters, and collecting deformation data of the first structural model in the operation simulation; The first structural model is optimized by using the deformation data to obtain the simulation model of the boarding ramp.

7. The optimization method of the three-dimensional model of the boarding bridge according to claim 1 is characterized in that: The component decomposition and classification of the boarding bridge simulation model is performed to obtain the manufacturing process data related to the components, specifically: According to the structure of the boarding bridge, the boarding bridge simulation model is decomposed into different component models, and the component models are classified according to the functions of the boarding bridge to obtain a component model set; The component model set is screened according to preset additive manufacturing requirements to extract additive component data; Functional classification and attribute annotation are performed on the data of the additive component to generate the manufacturing process data.

8. The optimization method of the three-dimensional model of the boarding bridge according to claim 1 is characterized in that: The damage diagnosis analysis is performed through the manufacturing process data to generate a structural optimization plan for the boarding bridge, specifically: generating a physical field model of the additive manufacturing process according to the manufacturing process data; Based on the physical field model, the environmental conditions in the additive manufacturing process are simulated, and simulation data of the additive manufacturing process is output; Perform material damage analysis and defect detection according to the simulation data to obtain material diagnosis results; The manufacturing process data is parameter-corrected according to the material diagnosis results to obtain a structural optimization solution for the boarding ramp.

9. The optimization method of the three-dimensional model of the boarding bridge according to claim 8 is characterized in that: The environmental conditions include: temperature field and stress-strain field.

10. An optimization system for a three-dimensional model of a boarding bridge, characterized in that: include: Instance generation module, simulation model generation module, manufacturing process acquisition module and ramp model optimization module; Among them, the instance generation module is used to generate corresponding model instances through the preset boarding bridge structure model according to the application scenario requirements; The simulation model generation module is used to perform working condition simulation and topology optimization on the boarding bridge structure model according to the model instance and the corresponding optimization target to obtain a boarding bridge simulation model; The manufacturing process acquisition module is used to decompose and classify the components of the boarding ramp simulation model to obtain manufacturing process data related to the components; The boarding ramp model optimization module is used to perform damage diagnosis analysis through the manufacturing process data and generate a structural optimization solution for the boarding ramp.

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