Three-dimensional rapid modeling method and system for gantry crane

Abstract

The invention relates to the technical field of three-dimensional modeling, in particular to a three-dimensional rapid modeling method and system for a gantry crane. The method comprises the following steps: obtaining structural parameter template data and installation site wind condition data of the gantry crane; establishing a parameterized geometric model template according to the structural parameter template data, and determining a wind load sensitive part distribution diagram in combination with the wind regime data of the installation site; dividing structural modeling precision grades according to the wind load sensitive part distribution diagram, and generating a differentiated grid density distribution scheme; constructing multi-precision hybrid modeling framework data according to the differentiated grid density distribution scheme and the parameterized geometric model template; key geometric feature parameters are extracted based on multi-precision hybrid modeling framework data, and a parameter adaptive adjustment rule is established according to a wind load sensitive part distribution diagram; according to the method, geometric weak points are positioned through wind load pre-evaluation, local grid encryption and geometric optimization are automatically executed, and the reliability of model safety evaluation is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of three-dimensional modeling, in particular to a portal crane three-dimensional rapid modeling method and system. BACKGROUND

[0002] The portal crane is a large-scale lifting equipment, widely used in ports, wharfs, factories and other places, and is favored for its unique structure and high efficiency. Its main body is a portal frame, which is composed of four legs to form four "door holes" to facilitate vehicle passing and ensure that the operation is not limited by ground obstacles. The boom system of the crane is composed of a tilting single boom or a combined boom that can be tilted, equipped with lifting, rotating and amplitude changing mechanisms, which can realize multi-dimensional operation. The lifting mechanism is composed of a motor, a reducer, a coupling, a brake, a drum, a pulley block and a steel wire rope, etc. The drum is driven to rotate by the motor to realize the lifting or lowering of the goods. The running mechanism is composed of a motor, a driving wheel and a running track, which is used for the horizontal movement of the crane on the ground track to facilitate the adjustment of the working position. The rotating mechanism is composed of a rotating support device, a rotating driving device and a safety device, which enables the crane boom to rotate within a certain range, improving the flexibility of operation. In addition, the portal crane is also equipped with an electrical control device as the nerve center of the crane, which controls the operation of each mechanism to realize remote control and automatic operation. The current portal crane three-dimensional rapid modeling method often has the following problems: the portal crane is usually installed in open environments such as ports and wharfs, and wind load is a key factor affecting its safe operation. Traditional modeling often simplifies wind load as static load, but in fact wind load has obvious dynamic characteristics and randomness. SUMMARY

[0003] Therefore, it is necessary to provide a portal crane three-dimensional rapid modeling method and system to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a portal crane three-dimensional rapid modeling method comprises the following steps:

[0005] Step S1: Obtain the structure parameter template data and installation site wind condition data of the portal crane; establish a parameterized geometric model template according to the structure parameter template data, and determine the wind load sensitive part distribution map in combination with the installation site wind condition data;

[0006] Step S2: According to the wind load sensitive part distribution map, divide the structure modeling precision level, and generate a differentiated grid density allocation scheme; according to the differentiated grid density allocation scheme and the parameterized geometric model template, construct a multi-precision hybrid modeling framework data;

[0007] Step S3: key geometric feature parameter extraction is performed based on the multi-precision hybrid modeling framework data, parameter adaptive adjustment rules are established according to the wind load sensitive part distribution map, the parameterized geometric model template is instantiated based on the parameter adaptive adjustment rules, and an initial three-dimensional model is obtained; and simplified wind load pre-evaluation is performed based on the initial three-dimensional model, and structure weak point position data is obtained;

[0008] Step S4: local model refinement is performed according to the structure weak point position data and the multi-precision hybrid modeling framework data, and model fusion is performed with the initial three-dimensional model, and an optimized three-dimensional model is obtained;

[0009] Step S5: a standardized modeling file is generated based on the optimized three-dimensional model; a rapid modeling database is established according to the standardized modeling file and the parameter adaptive adjustment rules, and three-dimensional rapid modeling of the portal crane is realized.

[0010] The application also provides a portal crane three-dimensional rapid modeling system for executing the portal crane three-dimensional rapid modeling method described above, and the portal crane three-dimensional rapid modeling system comprises:

[0011] The basic data preprocessing module is used for acquiring structure parameter template data and installation site wind condition data of the portal crane; a parameterized geometric model template is established according to the structure parameter template data, and a wind load sensitive part distribution map is determined in combination with the installation site wind condition data;

[0012] The adaptive precision configuration module is used for dividing structure modeling precision levels according to the wind load sensitive part distribution map, and generating a differential grid density allocation scheme; and the multi-precision hybrid modeling framework data is constructed according to the differential grid density allocation scheme and the parameterized geometric model template;

[0013] The parameterized modeling instantiation module is used for key geometric feature parameter extraction based on the multi-precision hybrid modeling framework data, parameter adaptive adjustment rules are established according to the wind load sensitive part distribution map, the parameterized geometric model template is instantiated based on the parameter adaptive adjustment rules, and an initial three-dimensional model is obtained; and simplified wind load pre-evaluation is performed based on the initial three-dimensional model, and structure weak point position data is obtained;

[0014] The local optimization fusion module is used for local model refinement according to the structure weak point position data and the multi-precision hybrid modeling framework data, and model fusion is performed with the initial three-dimensional model, and an optimized three-dimensional model is obtained;

[0015] The intelligent knowledge base construction module is used for generating a standardized modeling file based on the optimized three-dimensional model; a rapid modeling database is established according to the standardized modeling file and the parameter adaptive adjustment rules, and three-dimensional rapid modeling of the portal crane is realized.

[0016] The application realizes automatic acquisition from design specification data and field wind condition data, to multi-precision hybrid modeling, parameter adaptive adjustment, and then to weak point refinement and standardized rapid modeling database construction, significantly improving the efficiency, accuracy and reliability of modeling. First, the method extracts the size parameters and wind load related meteorological data in the design specification through systematic extraction, realizes the standardization and refinement of modeling data, lays a solid data foundation for subsequent modeling, avoids the tedious process of traditional reliance on manual sorting and judgment, greatly reduces the influence of human error and subjective judgment. Secondly, based on the wind load sensitive position distribution map, the structure is divided into different grid densities, realizing high-precision modeling of key stress areas and rough processing of non-key areas, effectively balancing model precision and computing resources, optimizing the computing efficiency of finite element analysis and other structural analysis. Through the establishment of a multi-precision hybrid modeling framework, the system automatically identifies key geometric features and dynamically adjusts model parameters according to wind load environment, enhancing the model's adaptability to environmental changes and improving the accuracy of structural safety evaluation. The parameter adaptive adjustment rule not only realizes the intelligentization and automation of the modeling process, but also flexibly adjusts the key parameters according to different wind speed conditions, ensuring the rationality and scientificity of the model under extreme wind load conditions. Further, the method identifies and classifies the geometric weak points of the initial three-dimensional model, and locally refines the grid and optimizes the geometric details of the components with higher potential risks, significantly improving the model's ability to capture structural weaknesses, which is conducive to engineers' focus on and improvement of weak links, enhancing the safety of structural design. The boundary fusion technology of local refinement and global model ensures the overall consistency and continuity of the model, avoiding the geometric discontinuity and simulation errors that may occur in traditional refinement methods, improving the applicability and analysis accuracy of the model. In addition, the construction of a standardized feature parameter library, modeling decision tree and operation instruction set realizes the modularization and standardization of the modeling process, greatly shortens the modeling cycle, supports multi-scenario and multi-parameter rapid calling, effectively meets the flexible adjustment of different engineering demands, and improves the project response speed and modeling efficiency. The establishment of intelligent matching rules enables the system to quickly match the appropriate modeling scheme according to the input design conditions and environmental data, realizes the full-automatic closed loop from parameter input to model output, reduces the dependence on professional modeling personnel, and reduces the operation complexity and personnel cost. The integration and optimization of the database framework support the three-dimensional rapid modeling of the portal crane in practical engineering, especially suitable for projects that need to frequently adjust design parameters and respond to complex environmental conditions, greatly improving the design flexibility and adaptability. At the same time, the rapid modeling method can effectively ensure the structural integrity and precision of the model, providing a reliable digital foundation for subsequent structural strength analysis, wind load simulation and safety evaluation, improving the overall quality and safety management level of the project.In summary, the method not only improves the automation and intelligence level of modeling, but also optimizes the preliminary work of structural analysis, providing strong technical support and guarantee for the design, manufacture and maintenance of portal cranes, and promoting the digital transformation and technological progress of the crane industry. BRIEF DESCRIPTION OF DRAWINGS

[0017] Other features, objects, and advantages of the application will become more apparent from the following detailed description when read in connection with the following drawings:

[0018] Figure 1 A schematic diagram of the step flow of the three-dimensional rapid modeling method of the portal crane of the application;

[0019] Figure 2 A detailed step flow schematic diagram of step S1 in the method; Figure 1

[0020] A detailed step flow schematic diagram of step S2 in the method. Figure 3 DETAILED DESCRIPTION Figure 1 The technical method of the application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0021] In addition, the accompanying drawings are only schematic illustrations of the application, and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some block diagrams shown in the drawings are functional entities, which do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0022] It should be understood that although the terms "first", "second" and the like can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the exemplary embodiments, a first element can be called a second element, and similarly a second element can be called a first element. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0023] It should be understood that although the terms "first", "second" and the like can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the exemplary embodiments, a first element can be called a second element, and similarly a second element can be called a first element. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0024] To achieve the above-mentioned purposes, please refer to​Figures 1 to 3 The application provides a portal crane three-dimensional rapid modeling method, and the method comprises the following steps:

[0025] Step S1: obtaining structure parameter template data and installation site wind condition data of the portal crane; establishing a parameterized geometric model template according to the structure parameter template data, and determining a wind load sensitive part distribution map in combination with the installation site wind condition data;

[0026] In a certain port automation reconstruction project, engineering and technical personnel first call an existing design database, extract a structure design specification document and a CAD two-dimensional drawing of a portal crane, and identify and vector extract the drawing through a customized structure analysis tool to obtain core size parameters of the portal crane, such as the height, cross-sectional size and inclination angle of the portal column, the span, cross-sectional shape and connection node type of the crossbeam, and the length, track gauge and arrangement height of the track, and uniformly merge the core size parameters into structure parameter template data in JSON format, so as to facilitate subsequent programmed calling. Then, in combination with historical meteorological observation data of the port in the past ten years, statistical records of local wind speed, wind direction and wind frequency are collected, and a self-defined wind condition analysis script is used to extract and process the dominant wind direction, the annual maximum wind speed and the wind pressure distribution to form a standardized wind condition data set. Based on the wind condition data set, in combination with the aforementioned structure parameter template, the portal column front area, the crossbeam end area and the track end area are identified as typical wind load sensitive parts through a preset wind pressure distribution mapping relationship, and a wind load sensitive part distribution map is constructed in the form of a heat map to provide data support for subsequent modeling precision distribution.

[0027] Step S2: dividing structure modeling precision levels according to the wind load sensitive part distribution map, and generating a differentiated grid density distribution scheme; constructing a multi-precision hybrid modeling framework data according to the differentiated grid density distribution scheme and the parameterized geometric model template;

[0028] On the basis of the wind load sensitive part distribution map, the wind pressure gradient recognition algorithm is used by the engineer to process the wind load influence of each structure region in the map. The wind pressure gradient of the center region of the portal frame front is the highest, which is divided into a high sensitive region. The wind pressure of the beam near the hoist side is the second highest, which is divided into a medium sensitive region. The wind pressure fluctuation of the track lower region is the smallest, which is divided into a low sensitive region. According to the partition level, the grid size standards of 0.8 meters, 1.2 meters and 1.8 meters are respectively given to establish the precision level parameter set. Then, in the three-dimensional grid mapping tool independently developed, the structure parameter template and the precision level parameter set are imported, and the fine grid, the medium grid and the coarse grid are respectively mapped to the high, medium and low sensitive regions according to the three-dimensional space mapping algorithm. The linear transition density decay scheme is used at the boundary to form the differentiated grid density distribution scheme. The scheme is finally input to the multi-precision hybrid modeling module to build the regional precision hybrid grid framework together with the structure geometry template as the structure basic data set for multi-scale three-dimensional modeling.

[0029] Step S3: based on the multi-precision hybrid modeling framework data, the key geometric feature parameters are extracted, and the parameter adaptive adjustment rule is established according to the wind load sensitive part distribution map; based on the parameter adaptive adjustment rule, the parameterized geometric model template is instantiated to obtain an initial three-dimensional model; based on the initial three-dimensional model, a simplified wind load pre-evaluation is carried out to obtain structure weak point position data;

[0030] The embodiment of the application utilizes the generated multi-precision hybrid modeling framework data, and the system enters the key geometric feature parameter extraction stage. First, set the identification rules in the parameter identification module, for example, mark the column height between 15 meters to 45 meters as a large-span size parameter, and the cross beam section height change in the range of 1.2 meters to 2.5 meters as a high deformation sensitive parameter, and calculate the influence factor by comparison with the standard modeling benchmark (such as 15 meters as the standard column benchmark value); if a column height is 30 meters, the size influence factor is 30 divided by 15, equal to 2.0. After weighting (such as size weight 0.4, shape 0.3, position 0.3) and accumulation of the size, shape and position influence factors, the comprehensive influence value is obtained, and if the comprehensive value is greater than 0.7, the parameter is marked as a key feature parameter. Then, the key parameters are repositioned in the wind load sensitive part distribution map, and are given an adaptive adjustment amplitude coefficient, for example, the high sensitive parameter adjustment coefficient is 1.6, the medium sensitive parameter is 1.3, and the low sensitive parameter is 1.1. Based on this adjustment rule, the target parameters in the parameterized geometric model template, such as column section, inclination angle, cross beam connection position, etc., are numerically transformed, and the geometric modeling operation is performed through the three-dimensional modeling engine, including column axis arrangement, section extrusion modeling, cross beam path definition and sweep generation, etc., so as to generate an initial three-dimensional model. Then, based on the initial three-dimensional model, a simplified wind load pre-evaluation is carried out, and the column slenderness ratio (i.e. the total length of the column divided by the section width) and the cross beam span height ratio (the total length of the cross beam divided by the section height) are extracted, and if the former exceeds 60 or the latter exceeds 25, the corresponding component is marked as a structural weak member. Combined with the section change rate (such as the change of the section area of the adjacent node divided by the area of the previous node) and the angle mutation rate, a geometric risk data table is generated, and finally the multiple risk overlapping areas are extracted as primary and secondary weak points and the three-dimensional coordinate set is output.

[0031] Step S4: local model refinement is performed according to the structural weak point position data and the multi-precision hybrid modeling framework data, and model fusion is performed with the initial three-dimensional model to obtain an optimized three-dimensional model;

[0032] The structural weak point position data is input into the local modeling refinement module, the first level weak point is set to 3 times the range of the original component characteristic size, the second level is set to 2 times the range, and the local refinement radius range is constructed. Through the grid redistribution mechanism, the grid density of the first level region is improved to 3 to 4 times the initial value, and the grid density of the sensitive region is improved to 2 to 3 times, so that the simulation accuracy of the key regions is ensured. Then, the geometric details of the local region are enhanced modeling processing, especially for the local chamfer optimization, grid refinement supplement and cross-section transition curve reconstruction of the beam connecting node, and the local refinement geometric data is generated. The data is fused with the original initial three-dimensional model, the boundary coincidence algorithm is called to realize the geometric continuity check and fusion, and the complete transition region data is formed. Finally, the whole model splicing operation is performed, and the global size consistency, contact surface matching degree and assembly accuracy between components are detected through the geometric integrity check module, and finally the unified optimized three-dimensional model is output, which provides a structural basis for database storage and standard model archiving.

[0033] Step S5: generating a standardized modeling file based on the optimized three-dimensional model; establishing a rapid modeling database according to the standardized modeling file and the parameter self-adaptive adjustment rule to realize three-dimensional rapid modeling of the portal crane.

[0034] In the embodiment of the application, the optimized three-dimensional model is imported into the standardized modeling module, and representative parameters such as the standard value of the column cross section (such as 1.2m*1.2m), the standard value of the beam span (such as 40m) and the common connection mode parameters (such as the node connection radius of 0.8m) are extracted, and the standard characteristic parameter items are established, and the corresponding wind load sensitivity level and modeling accuracy level are attached, to form a standardized characteristic parameter library. The system constructs a modeling decision tree according to the parameter library and the previously set parameter self-adaptive rule, such as when the wind speed of a certain port exceeds 12m / s and the beam span is greater than 50m, a high-precision modeling sequence needs to be executed and the beam grid needs to be encrypted. Each decision path is converted into an operation sequence instruction set and encapsulated into a Python script or a modeling macro instruction, and finally a standardized modeling operation instruction set data is formed. The data is coded and indexed with the characteristic parameter library and the modeling decision tree to establish a quick retrieval mechanism of keyword-condition-operation, and form a rapid modeling database based on rule matching. The database can quickly respond to the three-dimensional modeling needs of different types, different wind conditions and different size combinations of portal cranes in future design, realize the integrated process of template calling, parameter injection and automatic modeling, and significantly improve the engineering modeling efficiency and standardization degree.

[0035] Preferably, step S1 comprises the following steps:

[0036] Step S11: obtaining a design specification file of the portal crane; performing size parameter extraction processing on the design specification file to obtain gantry geometric size data, beam geometric size data and track geometric size data;

[0037] In the initial stage of the crane modeling process, the system automatically loads the design specification file of the portal crane. The file format supports multiple industrial design standard types such as XML, PDF, and DWG. Through the integrated semantic analysis engine, the system identifies and extracts key structural size parameters contained in the text, such as the height of the portal column, the cross-sectional width and thickness, the span of the crossbeam, the spacing of the connecting nodes, and the installation height and track gauge range of the track. For example, for a certain type of crane, the portal height is 34 meters, the cross-sectional width is 1.5 meters, the crossbeam length is 46 meters, the track installation height is 0.6 meters, and the track gauge is 9.2 meters. The system structures these parameter data and classifies them into portal geometric size data, crossbeam geometric size data, and track geometric size data, and stores them in JSON format to support subsequent program automatic reading and calling. The whole process is fully automatic and does not require manual intervention. Step S12: According to the portal geometric size data, the crossbeam geometric size data, and the track geometric size data, unified format processing is performed to obtain structure parameter template data;

[0038] After completing the structural size extraction, the embodiment of the present application enters the unified format processing stage. Here, "unified format processing" refers to reorganizing the three types of geometric size data according to the general modeling parameter standard (such as GB / T 50135) to make them have unified parameter naming, unified units, and consistent hierarchical attribution. For example, the system will convert all length units to millimeters and generate standard parameter key names such as "MainGirder-Length", "Column-SectionWidth", etc. according to "component name-parameter type", which facilitates direct indexing by subsequent modeling modules. Finally, the system automatically integrates the above data into a set of structure parameter template data, which contains parameterizable driven field structure (i.e. supports dynamic adjustment of model dimension) and labels the logical dependencies between parameters (such as track width affecting the spacing of the anchor points at the bottom of the portal). This template data will be used as a direct data source for subsequent modeling.

[0039] Step S13: Obtain historical meteorological observation records of the installation site; perform wind speed statistics, wind direction statistics, and wind frequency statistics on the historical meteorological observation records to obtain site wind speed distribution data, dominant wind direction data, and extreme wind speed data;

[0040] In order to adapt to the environmental changes of the actual installation site, the system automatically obtains the historical meteorological observation records of the installation area in the past 10 years by accessing the API interface of the local meteorological data center, and the data types cover hourly wind speed, wind direction and wind frequency records. After automatic preprocessing of the data, removing missing items and abnormal points, calling the wind data statistical analysis module, using the time weighted distribution algorithm to model the wind speed distribution (output daily average wind speed and annual maximum wind speed value), based on the vector projection method to analyze the dominant wind direction (i.e. the highest frequency of wind direction, such as southeast direction accounting for 42%), and generate a wind frequency rose diagram to assist in judging the wind change trend. For example, in a certain coastal site, the system calculates that the annual extreme wind speed is 38 meters / second, the dominant wind direction is southeast, the wind speed distribution is concentrated in the 6-11 meter / second interval, and the results are packaged as site wind speed distribution data, dominant wind direction data and extreme wind speed data for subsequent wind load parameter extraction module.

[0041] Step S14: Extract wind load characteristic parameters based on site wind speed distribution data, dominant wind direction data and extreme wind speed data to obtain installation site wind condition data.

[0042] After obtaining the site wind speed and directionality data, the system automatically calls the wind load parameter calculation module to model the wind load characteristics of the installation site. The wind load characteristic parameter extraction is realized by jointly modeling the projection area, wind pressure coefficient and wind speed of different components of the structure under different wind direction conditions. The system uses the wind load empirical model in the national standard, for example, matches the annual extreme wind speed with the windward area of the component, and applies the wind pressure coefficient (such as 1.2 for gantry and 1.3 for crossbeam) to convert to unit area wind load force. Height correction factors are applied to different height structure units in this process to automatically adjust the wind pressure intensity. The system generates multiple sets of wind load characteristic values based on these calculations, including the maximum wind pressure point of the structure, the average wind pressure line distribution and the wind pressure directionality vector field data, and finally encapsulates them as installation site wind condition data and performs version identification to support data tracking and comparison during subsequent sensitive part identification.

[0043] Step S15: Establish a parameterized geometric model template according to the structure parameter template data, and determine the wind load sensitive part distribution map combined with the installation site wind condition data.

[0044] The embodiment of the application is based on the aforementioned structural parameter template data and site wind condition data, and a parameterized geometric model template construction process is started by the system. The system first reads the dimensions of the portal frame, cross beam and track in the template parameter table, calls the geometric construction module to generate a corresponding three-dimensional sketch model in a programmatic manner, and encapsulates it as a drivable model module. Then, the system introduces wind condition data, performs wind direction simulation analysis on the structure in three-dimensional space, that is, the dominant wind direction wind load vector is applied to the structure surface grid, and the stress intensity of each region is judged through face projection and wind pressure mapping. In the wind load field calculation, the system automatically identifies the shielding effect and local wind pressure amplification effect (such as the corner area of the portal frame) between components, and generates a heat map for intuitive display of stress concentration areas. Finally, the system identifies high wind pressure areas, wind direction change sensitive areas and typical vortex generation areas according to the wind pressure distribution gradient, and outputs the results as a wind load sensitive part distribution map. This map serves as the core basis for guiding the subsequent mixed modeling precision setting and local encrypted modeling, and forms a complete closed-loop modeling preparation system in conjunction with the structural parameter template.

[0045] Preferably, step S15 comprises the following steps:

[0046] Step S151: parameterized modeling of the portal frame stand column according to the structural parameter template data, to obtain portal frame parameterized model data, wherein the portal frame stand column parameters include stand column height parameter, cross section size parameter and inclination angle parameter;

[0047] In the three-dimensional modeling start-up stage of the portal crane, the portal frame stand column part in the structural parameter template data is first parameterized modeled. The system automatically reads the stand column height 36 meters, cross section size (width 1.2 meters, thickness 0.8 meters) and inclination angle 8 degrees and other parameters through the structural template analysis module, establishes a single-side stand column basic geometric body according to the center line driving mode through the “stretching + rotating Boolean” construction function of the modeling engine, and copies the other side stand column in a mirror image manner. In this process, the inclination angle control adopts a rotation matrix mode to shift the stand column along the bottom anchor point around the horizontal axis in space, while ensuring that the top and cross beam end face connection node are aligned, and the modeling result is stored as portal frame parameterized model data in real time. The model retains a parameter mapping interface, which facilitates subsequent rapid size adjustment to realize modeling reuse of different types of portal frames.

[0048] Step S152: parameterized modeling of the cross beam including the cross beam length parameter, cross section shape parameter and connection mode parameter according to the structural parameter template data, to obtain cross beam parameterized model data;

[0049] The embodiment of the application automatically calls the structural size and connection attribute of the cross beam in the structural parameter template data after completing the portal modeling, including the cross beam length of 48 meters, the rectangular frame cross section shape, and the rigid welding connection mode. The cross beam three-dimensional geometry is generated by using the transverse sweeping modeling technology, taking the center axis as the path and the cross section shape as the contour, through the stretching command; the connecting blocks are respectively arranged at both ends to construct, and the position matching is realized according to the parameterization rules and the portal top anchor point. The welding connection attribute is set at both ends of the cross beam, so that it behaves as a complete constraint in the structural analysis and wind load simulation, avoiding virtual structure drift. The modeling result is exported as the cross beam parameterized model data, which has the parameterized interface of modifying the span and cross section size.

[0050] Step S153: performing track parameterized modeling processing according to the structural parameter template data, including track length parameters, track gauge parameters and installation height parameters, to obtain track parameterized model data;

[0051] After the portal and the cross beam are completed in the embodiment of the application, the modeling process continues to perform parameterized processing on the track structure. The track parameters include the total length of 95 meters, the track gauge of 8.5 meters and the installation height of 0.5 meters. The system generates a double-track structure through the arrangement function, and adds a foundation anchoring geometry at the bottom of each track. The track is modeled by using a long strip body. After setting the installation height, the track is placed above the foundation surface through the elevation adjustment function, and a coupling node is arranged at both ends of the track for establishing a hinged constraint with the bottom end of the portal in the subsequent process. A plurality of load-bearing nodes are uniformly arranged in the middle of the track to support the simulation of the portal operation mechanism. Finally, the track parameterized model data is generated, which supports dynamic adjustment of the track gauge and the track length and is suitable for layout changes in different operation scenarios.

[0052] Step S154: establishing a geometric constraint relationship based on the spatial coordination between components by using the portal parameterized model data, the cross beam parameterized model data and the track parameterized model data, to obtain constraint relationship data;

[0053] After the portal, the cross beam and the track parameterized modeling are completed in the embodiment of the application, the geometric constraint relationship establishment stage is entered. The system automatically identifies key connection points according to the spatial arrangement logic of the three components, such as the portal bottom end point and the track upper surface anchoring point, and the cross beam end surface and the portal top end surface connection point. The geometric registration mechanism is used to establish three-dimensional space constraints for each contact part, including position constraints (such as strict alignment of the column top surface and the cross beam end surface), rotation constraints (to ensure that the portal inclination angle remains consistent), and symmetry constraints (the cross beam is symmetrically arranged between the portals). The system automatically generates constraint relationship data, which contains information such as the connection type between components, the constraint direction, the constraint degree of freedom control, etc. The data will be repeatedly called in the subsequent modeling template packaging and wind load simulation.

[0054] Step S155: Template packaging is performed on the portal parameterized model data, the beam parameterized model data, the track parameterized model data and the constraint relationship data, a geometric modeling rule that can be repeatedly called is formed, and a parameterized geometric model template is obtained;

[0055] After all component modeling and constraint construction are completed, the parameterized model data of the portal, the beam and the track are packaged together with the corresponding constraint relationship data into a reusable modeling template in the embodiment of the application. The template packaging adopts a structure definition mode oriented to component levels, each module includes a component parameter entry, a geometric generation logic, a constraint instruction set and a model output interface and the like. By establishing an index system for the dependent logic between components, a complete portal crane structure model is quickly generated after input of key parameters. The finally generated parameterized geometric model template can not only adapt to rapid modeling of the same type of equipment, but also be extended to modeling scenes under different site wind conditions and size variations, and modeling efficiency and consistency are significantly improved.

[0056] Step S156: Wind load action data of each component is obtained by performing wind load action analysis on the installation site wind condition data and the parameterized geometric model template, wherein the wind load action analysis specifically includes calculation of a wind receiving area of a portal column, a wind receiving area of a beam and a wind receiving area of a track;

[0057] The wind load action analysis process is started based on the packaged parameterized geometric model template and the previously extracted installation site wind condition data in the embodiment of the application. The analysis process relies on three-dimensional model surface grid information and structure orientation data to perform area-by-area calculation on the wind receiving areas of the portal, the beam and the track. For example, the wind receiving area of the portal is 36 meters in height multiplied by 1.2 meters in width, which is 43.2 square meters, and the actual wind pressure is calculated in combination with a dominant wind direction correction coefficient; the upper surface of the beam is 48 meters multiplied by 1.5 meters, and the track is projected with a track gauge multiplied by a running length. The system uses a calculation rule based on limited area wind pressure to calculate the wind receiving area of each component and map it to the corresponding geometric body, and outputs wind load action data including wind pressure, action direction and action area, which is used for subsequent stress sensitivity identification.

[0058] Step S157: Sensitivity evaluation is performed on the wind load action data of each component, a high wind load stress area and a low wind load stress area are identified, and a wind load sensitive site distribution map is obtained.

[0059] The embodiment of the application utilizes the wind load data of each component, the system enters the sensitivity evaluation link, the wind load intensity index on the unit component is calculated by combining the wind pressure with the component area, and the stress concentration area is derived combined with the material mechanical properties. For example, the middle section of the beam is in the middle span area, the span is large, and the upper surface has the maximum wind pressure area, so the system determines that it is a high wind load stress area; the track end is also identified as a weak area because the wind pressure conduction is discontinuous. In the whole evaluation process, wind pressure gradient analysis, stress distribution simulation and structure modal identification are combined, and finally the wind load sensitive part distribution map is generated, the color temperature in the map is used to identify different wind load level areas, and the data format is output for subsequent differential modeling processing and calling. The distribution map is the key data basis for modeling accuracy allocation and local strengthening in three-dimensional rapid modeling.

[0060] Preferably, step S2 comprises the following steps:

[0061] Step S21: identifying the sensitive area boundary according to the wind load sensitive part distribution map to obtain high sensitive area boundary data, medium sensitive area boundary data and low sensitive area boundary data;

[0062] After obtaining the wind load sensitive part distribution map, the embodiment of the application automatically calls the boundary recognition module to perform image segmentation and boundary extraction processing on the stress gradient distribution area in the heat map. The specific method is to convert the wind pressure gradient map into a wind load contour surface in three-dimensional space, identify the continuous area with sharp wind load change through a space boundary extraction algorithm (such as based on contour surface tracking and curvature change threshold extraction), and mark it as a high sensitive area, a medium sensitive area and a low sensitive area respectively. The system automatically generates a boundary curve according to the spatial gradient change rate of the wind pressure value, and obtains the three-dimensional boundary coordinate set of each area in the model space through geometric projection calculation. Taking a 40-ton portal crane as an example, the system identifies that the high sensitive area is mainly concentrated in the door frame corner, the beam central cantilever connection and the track end limiting structure; the medium sensitive area is distributed in the door frame middle section and the track connecting base, and the low sensitive area covers other conventional steel structure areas. Finally, the system outputs the high sensitive area boundary data, the medium sensitive area boundary data and the low sensitive area boundary data containing the three-dimensional boundary point cloud and the space envelope surface, which provides coordinate reference for subsequent precision level allocation.

[0063] Step S22: performing precision level numerical allocation processing according to the high sensitive area boundary data, the medium sensitive area boundary data and the low sensitive area boundary data, setting a high precision modeling level, a medium precision modeling level and a low precision modeling level respectively, and obtaining structure modeling precision level data;

[0064] The embodiment of the application is based on the above boundary data, and the system enters an automatic precision level division module. The system first numbers and labels each sensitive region in a three-dimensional space, and automatically assigns values according to a preset precision level strategy. The strategy sets different modeling precision level values according to wind pressure gradient and structure criticality correlation coefficient, for example, high sensitive region is set to level 1 (highest precision), medium sensitive region is set to level 2, and low sensitive region is set to level 3. Each level is associated with subsequent grid fineness, and is embedded into the data label of each spatial region. No manual intervention is required in this process, and the system can dynamically adjust the level division strategy according to the device model, structure complexity and wind load intensity. For example, for the high-altitude wharf scene where the wind load frequently changes, the system increases the beam connecting section to the high precision region level, and in the inland industrial area, it can be classified as a medium sensitive level. Finally, the system outputs a structure modeling precision level data file, and establishes a mapping index between each geometric region and the precision level, for use by the next grid division module.

[0065] Step S23: determining grid size parameters according to the structure modeling precision level data, wherein high precision modeling level corresponds to fine grid size parameters, medium precision modeling level corresponds to standard grid size parameters, and low precision modeling level corresponds to coarse grid size parameters, to obtain hierarchical grid size parameter data;

[0066] After obtaining the structure modeling precision level data, the embodiment of the application starts the grid size parameter generation module, and automatically matches appropriate grid size values for different precision levels. The high precision modeling level region will be allocated the finest grid, and the system determines that the grid size is in the range of 2mm to 5mm according to the local curvature of the structure, the detail change rate and the stress concentration degree; the medium precision region is allocated a standard grid, and the size is generally 10mm to 20mm; and the low precision region uses a coarse grid, and the size is 30mm to 50mm, ensuring the balance between modeling efficiency and precision. For example, in actual modeling, the gantry corner is allocated a 4mm grid size due to the existence of complex connecting nodes and wind pressure concentration effect, while the middle section of the beam bottom surface is only 20mm. The system outputs these grid configuration results in a structured data form as an important part of the subsequent space mapping and modeling control file.

[0067] Step S24: mapping the spatial grid density according to the hierarchical grid size parameter data and the wind load sensitive site distribution map, and allocating the grid size parameters of different precision levels to the corresponding geometric regions to obtain a differentiated grid density allocation scheme;

[0068] The embodiment of the application inputs the wind load sensitive region boundary data and the hierarchical grid size parameter data into a spatial grid mapping module, which accurately allocates different grid densities to corresponding geometric regions through a spatial remapping algorithm. The system adopts a three-dimensional grid voxel allocation-based manner, that is, a three-dimensional model is divided into voxel units, each unit automatically selects a corresponding grid size according to its accuracy level, and a transition layer interpolation is used to control the grid transition at the region boundary to avoid stress mutation errors. For example, at the junction of the crossbeam and the portal, the junction part of the high-precision region and the medium-precision region uses a 5mm-10mm linear interpolation transition grid. The system finally generates a complete differentiated grid density allocation scheme, including the grid size of each geometric sub-region, the boundary transition control parameter and the spatial position index, ensuring that the grid arrangement in the subsequent modeling process is reasonable and uniform, and supporting the calling of a high-concurrency grid generation engine.

[0069] Step S25: constructing a multi-precision hybrid modeling framework data according to the differentiated grid density allocation scheme and the parameterized geometric model template.

[0070] Based on the differentiated grid density allocation scheme and the constructed parameterized geometric model template, the embodiment of the application starts a multi-precision hybrid modeling framework construction process. The framework takes a multi-module structure construction engine as the core, and respectively calls corresponding modeling modules for each structural unit according to the accuracy level data. The high-precision module uses a high-order geometric refinement library, supports complex node modeling and local encryption, the medium-precision module uses a standard modeling template library, and the low-precision module uses a fast modeling skeleton library to simplify the data amount. After generating each structural sub-model, the system will direct the division of the surface grid according to the aforementioned spatial grid mapping data, and fuse all the sub-models to construct a unified three-dimensional modeling framework. Taking a typical scene as an example, the portal and rail combination region will use an independent high-precision modeling module, while the crossbeam web region will use a medium-precision module to construct, and the inside of the support leg will use a rough outline modeling method to improve the overall efficiency. The finally output multi-precision hybrid modeling framework data has the characteristics of partition modeling, variable resolution, and module independent reconstruction, and lays a foundation for realizing fast, automatic and fine three-dimensional modeling.

[0071] Preferably, step S25 includes the following steps:

[0072] Step S251: identifying a grid transition region according to the differentiated grid density allocation scheme, identifying the junction region between different accuracy level grids, and obtaining grid transition region data;

[0073] After obtaining the differentiated grid density distribution scheme, the system automatically starts the grid boundary perception module to analyze the spatial continuity of the contact surface between all adjacent accuracy level regions. The module uses three-dimensional region index data and geometric Boolean operation to identify the intersection surface set between the high-precision region and the medium-precision region, the medium-precision region and the low-precision region, extracts the intersection node and boundary line segment, and generates a transition region identification map. The grid transition region data includes: intersection surface spatial coordinates, boundary topology structure, and adjacent region accuracy level label. Taking the connection area of the lower leg and the middle section of the portal as an example, the high-precision and medium-precision models often intersect at this place, the system identifies the connection surface of this region as a typical grid transition area, and performs spatial grouping coding on it, and outputs a transition region database for subsequent processing.

[0074] Step S252: Gradual grid density calculation is performed on the grid transition region data, the grid density of the gradual transition is set between adjacent accuracy levels, and gradual grid density data is obtained;

[0075] The embodiment of the application enters the gradual grid density calculation stage, and automatically performs density interpolation calculation processing on the grid transition region identified in S251. In this step, the "gradual grid density" refers to the boundary region of different accuracy levels, by introducing a transition layer, the grid size gradually transitions from the fine region to the coarse region, thereby avoiding grid discontinuity or distortion. The system calculates the number of transition layers and the transition ratio according to the grid size difference of adjacent regions, such as from 5mm to 20mm, sets 3-5 transition layers, respectively uses equal interval or exponential change mode interpolation, and ensures the stability of the grid shape. The system automatically outputs the grid size, layer thickness and layer connection relationship of each transition layer as the gradual grid density data, and binds it with the structure space position. For example, 4 layers of gradual grid belts are set in the connection area of the portal and the track, which are 6mm, 10mm, 15mm and 20mm, respectively, forming a flexible connection area.

[0076] Step S253: Perform geometric region grid division processing according to the parameterized geometric model template, divide the portal column, beam and track into independent grid generation regions respectively, and obtain sub-region grid generation data;

[0077] The embodiment of the application calls a parameterized geometric model template, performs geometric division on three structural modules including a portal column, a crossbeam and a track contained in the template, and divides the model space into a plurality of independent grid generation units according to the component boundary and geometric characteristics. The "regional grid generation data" refers to that the system allocates an independent grid division task list for each component, which includes the component name, the space range, the reference grid density, the transition area position and the like. The grid division method adopts an octree space decomposition and a curvature perception algorithm, preferentially refines the surface details, and automatically embeds the aforementioned gradual grid density control logic. For example, the portal column region is provided with a complete grid generation unit, which covers each connection node from the bottom to the top, and a high-density grid control factor is called at each node, while the track region is provided with a larger initial unit to improve the efficiency. Finally, the system outputs a regional grid data structure composed of a plurality of component grid generation lists.

[0078] Step S254: performing a grid compatibility verification process according to the differentiated grid density allocation scheme, the gradual grid density data and the regional grid generation data, and obtaining grid compatibility verification data;

[0079] After the definition of the regional grids is completed, the system calls a grid coordination verification module to automatically perform a grid connection matching check. The "grid compatibility" refers to whether there are problems such as grid misplacement, node quantity mismatch or boundary unclosed between different components at the connection position. The system performs node matching, boundary closure verification and topological consistency check on each regional connection surface, and outputs a compatibility result report. Specifically, it includes the node matching percentage of each pair of connected components, the grid difference comparison data, and the spatial position coordinates of the misplacement area. If an incompatible region is found, the system will automatically start a grid remapping submodule to locally refine or simplify the local grid until the connection accuracy requirement is met. For example, in the connection region of the crossbeam and the portal top, the system detects that the initial grid has 5% misplacement, automatically adjusts the grid distribution and reallocates the boundary nodes, and finally outputs the grid compatibility verification data and marks it as "passable".

[0080] Step S255: formulating a mixed precision modeling rule according to the grid compatibility verification data and the parameterized geometric model template

[0081] The embodiment of the application constructs a mixed precision modeling rule according to the aforementioned compatibility verification data and the parameterized geometric model template. The rule defines the modeling priority, transition strategy and connection requirement of the high-precision, medium-precision and low-precision regions. For example, it is stipulated in the rule that the high-precision region preferentially uses pentahedron units, and the boundary must coincide with the nodes of the adjacent region; the medium-precision region can adopt hexahedron simplification processing; and the transition region must satisfy that the adjacency degree of each grid node is greater than 3. The system also defines a modeling flow control table at the component level, ensuring that different precision models can be generated and assembled synchronously. The rule is saved in the form of JSON structured data and supports subsequent quick calling and rule reuse. Taking a beam component as an example, the modeling rule defines that the connection end uses a high-density template, the web uses a medium-density template, and the hanging wheel connection hole region uses boundary refinement control, ensuring the overall modeling consistency.

[0082] Step S256: A collaborative modeling mechanism of the high-precision region, the medium-precision region and the low-precision region is established, and mixed precision modeling framework data is obtained.

[0083] The embodiment of the application enters the multi-precision collaborative modeling mechanism establishment link, coordinates the parallel modeling process of the high-precision, medium-precision and low-precision sub-models through a scheduling engine, and executes unified connection specification control when combining the models. The system maps different precision regions to corresponding modeling modules through task decomposition and parallel scheduling at the component level, uses a third-order refined surface generator for high-precision components, uses a standard modeler for medium-precision components, and calls a quick framework modeler for low-precision components. At the same time, the system adopts a "three-unification" strategy of unified coordinate system, unified grid interface and unified structure index at the model merging stage, ensuring that all components are spliced to form a three-dimensional model structure with good continuity, reliable connection and clear hierarchy. For example, in a certain portal crane full model, the connection between the portal and the track is composed of a high-precision model and a medium-precision model, and seamless splicing is realized through a unified grid bridging interface. The system finally outputs complete mixed precision modeling framework data, which has the ability of fast rendering, local reconstruction and structure response analysis.

[0084] Preferably, in step S3, key geometric feature parameters are extracted based on the mixed precision modeling framework data, and parameter adaptive adjustment rules are established according to the wind load sensitive part distribution map.

[0085] According to the mixed precision modeling framework data, size feature types, shape feature types and position feature types are identified.

[0086] Based on the size feature type, column size parameters of the column height range 15m-45m and beam size parameters of the beam span range 20m-60m are extracted.

[0087] Based on the shape feature type, the column shape parameters with a cross-section size range of 0.8 m x 0.8 m to 1.5 m x 1.5 m are extracted, and the beam shape parameters with a cross-section height variation range of 1.2 m-2.5 m are extracted;

[0088] Based on the position feature type, the column position parameters with an inclination angle range of 0°-15° are extracted, and the beam position parameters with a node connection radius of 0.5 m-1.2 m are extracted;

[0089] The modeling accuracy influence weight coefficient is calculated according to the column size parameters, the column shape parameters, the column position parameters, the beam size parameters, the beam shape parameters and the beam position parameters;

[0090] When the modeling accuracy influence weight coefficient exceeds 0.7, the corresponding geometric parameter is marked as a key geometric feature parameter;

[0091] According to the wind load sensitive part distribution map, the key geometric feature parameters are divided into high-sensitive key parameters, medium-sensitive key parameters and low-sensitive key parameters; the adjustment amplitude coefficient of 1.5-2.0 is set for the high-sensitive key parameters, the adjustment amplitude coefficient of 1.2-1.5 is set for the medium-sensitive key parameters, and the adjustment amplitude coefficient of 1.0-1.2 is set for the low-sensitive key parameters;

[0092] Based on the environmental wind speed exceeding 12 m / s, the adjustment amplitude coefficient is applied to the high-sensitive key parameters, based on the environmental wind speed exceeding 8 m / s, the adjustment amplitude coefficient is applied to the medium-sensitive key parameters, and based on the environmental wind speed exceeding 5 m / s, the adjustment amplitude coefficient is applied to the low-sensitive key parameters, thereby establishing the parameter self-adaptive adjustment rule.

[0093] The embodiment of the application calls the structure index information in the mixed modeling framework data of multiple precision, and sequentially retrieves the spatial size attributes of all components. In identifying the "size feature type", the system judges the main extension direction according to the size of the geometric component bounding box, such as the height dimension of the column being main and the length dimension of the beam being main. Then, the system classifies all components with the column height between 15 meters and 45 meters according to the set practical engineering application range, and extracts the parameter value as the "column size parameter"; similarly, the system extracts the beam components with the span between 20 meters and 60 meters, and records the length parameter as the "beam size parameter". For example, in a certain engineering, the column height of the portal frame is 33.5 meters, and the beam span is 42 meters, which are automatically identified and extracted by the system, and stored in the key parameter candidate pool for subsequent analysis. After establishing the size feature, the system further analyzes the structural shape feature of the component cross section from the modeling framework. The "shape feature type" here refers to the geometric structure of the cross section, such as square, circular, T-shaped, etc., and whether there is a structure case that the cross section changes with the length. Taking the column as an example, the system analyzes the cross section dimension from the bottom to the top, and if the cross section size is between 0.8 meters x 0.8 meters and 1.5 meters x 1.5 meters, the system extracts it as the "column shape parameter"; and if the beam has upper and lower web cross section changes, and the change range is 1.2 meters to 2.5 meters, it is extracted as the "beam shape parameter". For example, the bottom cross section of a certain portal column is 1.4 meters x 1.4 meters, and the top gradually shrinks to 1.0 meters x 1.0 meters, which belongs to the effective change interval, and the system identifies it as a multi-segment gradually changing cross section component and completely marks all paragraph sizes for adjustment. Further analyze the positioning mode of the component in space and the connection with other components to establish the "position feature type" information library. In the column identification, the system extracts the inclination angle based on the angle between the geometric principal axis and the vertical direction, and if the angle is between 0 degrees and 15 degrees, it is identified as the "column position parameter"; the node connection position of the beam is analyzed by extracting the connection mode of the end with the portal column, and the system calculates the curvature of the connection node, and if the conversion radius is between 0.5 meters and 1.2 meters, it is recorded as the "beam position parameter". This parameter has a greater influence on the wind load response. For example, in the modeling of a certain port crane, the column inclination angle is 7.5 degrees, and the beam end connection curvature radius is 1.1 meters, which are within the effective range, and the system automatically marks and associates the spatial positioning metadata. The aforementioned six types of geometric parameters are sent to the modeling precision influence weight evaluation model, which scores the influence degree of each parameter on the final model precision by correlating historical modeling error data and simulated wind load response results. The system adopts feature normalization, correlation evaluation and weighted aggregation processing to output the influence factor score of each parameter.For example, the column height variation is more sensitive to model deformation under wind load, with a score of 0.81; the crossbeam section height variation is smaller, with a score of 0.62; the system finally calculates the comprehensive influence weight coefficient, when the coefficient is greater than 0.7, it means that the parameter has a significant influence on the modeling accuracy, and the system marks this kind of parameter as a "key geometric feature parameter". In the above example, the column height and the crossbeam joint curvature are classified into the "key parameter" database because their scores exceed the threshold. According to the position of the aforementioned key geometric feature parameters and the "wind load sensitive part distribution map", the wind load response intensity of the region where the key geometric feature parameters are located is taken as the basis for division. For example, the middle part of the column is located in the strong wind concentration area, and the system marks the parameters belonging to it as "highly sensitive key parameters"; the middle section of the crossbeam is in the medium wind pressure area, and is marked as "medium sensitive key parameters"; and the top of the column is far away from the main wind direction, and is only marked as "low sensitive key parameters". Then, the system configures different "adjustment amplitude coefficients" for key parameters of different levels: the amplification coefficient of the high sensitive area is set to be between 1.5 and 2.0 to enhance the simulation sensitivity of the structure response; the coefficient of the medium sensitive area is set to be between 1.2 and 1.5; and the coefficient of the low sensitive area is set to be between 1.0 and 1.2. Each parameter will be automatically multiplied by the corresponding amplitude factor when adjusted to form a pre-adjusted parameter set. Real-time external wind speed conditions are obtained or simulated, and combined with the current key parameter sensitivity level, it is judged whether the adjustment logic is triggered. The system sets the adjustment threshold as follows: the high sensitive key parameter starts to adjust the adjustment coefficient when the wind speed exceeds 12 meters / second, the medium sensitive parameter adjusts when the wind speed exceeds 8 meters / second, and the low sensitive parameter adjusts when the wind speed exceeds 5 meters / second. The system adjusts each time with the latest meteorological data as the driving force, and through the simulation of the wind pressure model feedback, the key parameters are scaled up or down one by one. For example, in a heavy wind area along the coast, the simulation wind speed reaches 13.8 meters / second, and the system automatically triggers the adjustment of the high sensitive column height parameter, which is increased from 34.0 meters to 38.2 meters, and the corresponding grid units of the model components are regenerated simultaneously, realizing the self-adaptive modeling control mechanism under the driving of environmental conditions. The "parameter adaptive adjustment rule" generated finally is stored as a standard rule set and participates in the subsequent model instantiation process.

[0094] Especially important is that the modeling accuracy influence weight coefficient is calculated according to the column size parameter, the column shape parameter, the column position parameter, the crossbeam size parameter, the crossbeam shape parameter and the crossbeam position parameter, specifically:

[0095] The column size influence factor is calculated according to the column size parameter divided by the standard column size reference value 15m, the column shape influence factor is calculated according to the column shape parameter divided by the standard section reference value 1.0m*1.0m, and the column position influence factor is calculated according to the column position parameter divided by the standard inclination reference value 5°;

[0096] The beam size influence factor is calculated according to the beam size parameter divided by the standard beam size reference value 30m, the beam shape influence factor is calculated according to the beam shape parameter divided by the standard section height reference value 1.5m, and the beam position influence factor is calculated according to the beam position parameter divided by the standard connection radius reference value 0.8m;

[0097] The column size influence factor, the column shape influence factor, and the column position influence factor are multiplied by the weight coefficients 0.4, 0.3, and 0.3 respectively, and then summed to obtain a column comprehensive influence factor;

[0098] The beam size influence factor, the beam shape influence factor, and the beam position influence factor are multiplied by the weight coefficients 0.5, 0.3, and 0.2 respectively, and then summed to obtain a beam comprehensive influence factor;

[0099] The column comprehensive influence factor and the beam comprehensive influence factor are weighted and summed according to the proportion coefficient 0.6:0.4 to obtain a modeling accuracy influence weight coefficient.

[0100] After the extraction of the geometric parameters of the column, the present embodiment first performs a ratio calculation operation with the standard reference value. The "column size influence factor" herein represents the magnification or reduction multiple of the actual column height relative to the standard height, and the standard height reference value is set to 15 meters. For example, the actual height of a certain crane column is 36 meters, and the system divides 36 by 15 to obtain 2.4, indicating that the column is magnified by 2.4 times in size relative to the standard model. This calculation process is automatically completed by the parameter standardization module in the system without human intervention, and the factor value is recorded in the "modeling influence parameter set" as one of the subsequent evaluation inputs. The cross-sectional size parameters of the column, such as the width and height, are extracted, each being 1.2 meters. Since the standard reference value is 1.0 meter x 1.0 meter, the system calculates the ratio after converting the actual size to area and comparing it with the standard area. The area of 1.2 meters x 1.2 meters is 1.44 square meters, and the standard area is 1.0 square meter. The system calculates the ratio to obtain 1.44, indicating that the "column shape influence factor" is 1.44, i.e., the structure shape is enhanced by 44% relative to the standard model. If the column cross-section is of an irregular shape such as H-shaped, the system will discretize it into an equivalent rectangle, obtain the equivalent size through the inertia moment matching method, and then participate in the ratio operation. The "column position influence factor" is used to evaluate the complexity contribution of the structure in a non-vertical state to modeling. Based on the previously extracted inclination angle value, for example, the inclination angle of the column is 7.5 degrees, the system performs ratio processing with the standard reference value of 5 degrees in the standard vertical state to obtain 1.5. This factor reflects the degree of deviation of the column from the ideal modeling axis, and the larger the value, the more complex the simulation requirements such as torque compensation and node offset that need to be considered during modeling, so this factor will play an important role in the final weight evaluation. In the beam processing stage, the system extracts the beam span from the parameter set, such as 48 meters, and the standard reference value is set to 30 meters. The system divides 48 by 30 to obtain 1.6, indicating that the beam size is 60% larger than the standard value. The "beam size influence factor" reflects the direct impact of the overall size magnification of the structure on the three-dimensional modeling density and modeling time, and is one of the most direct parameters in the overall influence factor evaluation. The beam shape mainly refers to the cross-sectional height variation, and the system extracts the equivalent cross-sectional height as 2.25 meters, which is compared with the reference height of 1.5 meters to obtain a ratio of 1.5 times as the "beam shape influence factor". The system uses the beam element method to convert the irregular cross-sectional height into an equivalent straight edge component, ensuring that the ratio has consistent modeling physical meaning and supports integrated application in finite element modeling. The connection radius of the beam node is extracted, for example, 1.0 meter, and compared with the standard connection radius of 0.8 meters to obtain a ratio of 1.25, indicating that the "beam position influence factor" is 1.25. This parameter is used to evaluate the influence of the node fillet and the complexity of the connection structure on grid partitioning and connection compatibility processing, and the larger the value, the higher the complexity of the model interface geometry processing.The three column impact factors (size 2.4, shape 1.44, position 1.5) are multiplied by the corresponding weight coefficients 0.4, 0.3, 0.3 respectively, and then weighted sum is performed. The "weight coefficient" here is obtained by engineering experience and simulation importance evaluation, the size impact factor is assigned a weight of 0.4 because it has the greatest impact on the model size, the shape and position are each 0.3 because they are equally important for the distribution of structural response. The calculation result of the column comprehensive impact factor is 2.4x0.4+1.44x0.3+1.5x0.3=0.96+0.432+0.45=1.842. The beam impact factors (size 1.6, shape 1.5, position 1.25) are multiplied by 0.5, 0.3, 0.2 respectively, and then weighted sum is performed, the calculation process is 1.6x0.5+1.5x0.3+1.25x0.2=0.8+0.45+0.25=1.5, and the beam comprehensive impact factor is 1.5. This value is verified in multiple scene comparisons as a standard modeling quantization expression suitable for medium complexity span components. According to the set proportion of the influence of column and beam, the two comprehensive factors are weighted and fused by adopting the proportion coefficients of 0.6 and 0.4. The calculation is 1.842x0.6+1.5x0.4=1.1052+0.6=1.7052, and the system retains the result to two decimal places to obtain the "modeling accuracy influence weight coefficient" as 1.71. This coefficient serves as the basis for subsequent parameter amplification or optimization adjustment, if the value exceeds the set threshold 0.7, the corresponding parameter enters the key feature adjustment link to ensure the fine control of three-dimensional modeling and the reliability of simulation.

[0101] Preferably, instantiating the parametric geometric model template based on the parameter self-adaptive adjustment rule in step S3 comprises:

[0102] identifying the target geometric parameters that need to be adjusted and the corresponding adjustment amplitude coefficients according to the parameter self-adaptive adjustment rule

[0103] performing numerical calculation on the target geometric parameters in the parametric geometric model template according to the corresponding adjustment amplitude coefficients to obtain instantiated geometric parameter values;

[0104] generating a column three-dimensional entity according to the instantiated geometric parameter values, including column axis positioning, cross section contour drawing and entity stretching operation, to obtain column three-dimensional entity data;

[0105] generating a beam three-dimensional entity according to the instantiated geometric parameter values to obtain beam three-dimensional entity data including beam path planning, cross section change definition and entity sweeping operation;

[0106] processing the intersection, merging and cutting relationship between parts based on Boolean operation of the column three-dimensional entity data and the beam three-dimensional entity data to obtain assembly relationship data;

[0107] According to the assembly relationship data, constraint solving calculation is performed to ensure that each component meets distance constraints, angle constraints and contact constraints specified in the design, and constraint satisfaction verification data is obtained;

[0108] Based on the constraint satisfaction verification data, the column three-dimensional entity data and the beam three-dimensional entity data are assembled to obtain an initial three-dimensional model.

[0109] The embodiment of the application automatically identifies the target geometric parameters that need to be adjusted in the portal crane structure according to the previously established parameter self-adaptive adjustment rule, such as the column height, cross-sectional size, inclination angle, etc., and automatically matches the corresponding adjustment amplitude coefficient in combination with the environmental wind speed and wind load sensitivity level. This process is completed through a programmed rule engine without manual intervention, ensuring that the dynamic adjustment of the model parameters for different wind conditions conforms to the engineering specifications and simulation requirements. The adjustment amplitude coefficient is usually set between 1.0 and 2.0, reflecting the strength and weakness of the adjustment. The identified target geometric parameters and their corresponding adjustment amplitude coefficients are used to perform multiplication operation on the original parameter values in the parameterized geometric model template, obtaining the adjusted instantiated geometric parameter values. These values represent the actual size and shape of each component of the model. This calculation process is performed by an automated script, ensuring the continuity and accuracy of parameter adjustment. The instantiated parameters, such as the column height, are multiplied by the adjustment coefficient 1.5, changing from the original value of 36 meters to 54 meters. The model accuracy is thus improved and better fits the wind load conditions. Based on the adjusted instantiated geometric parameter values, the system performs column three-dimensional entity generation operations in sequence. First, the column centerline position is determined using the column axis positioning algorithm to ensure accurate placement of the column in space. Then, the two-dimensional cross-sectional profile of the column is drawn according to the cross-sectional profile parameters, and geometric drawing is performed using the CAD modeling kernel. Finally, the two-dimensional section is extended along the axis direction to form a three-dimensional entity structure through entity stretching operation. This process is automatically completed and is applicable to column heights ranging from 15 meters to 45 meters and cross-sectional sizes ranging from 0.8 meters x 0.8 meters to 1.5 meters x 1.5 meters and other specifications. According to the instantiated geometric parameters, three-dimensional entity modeling is performed on the crossbeam. First, the crossbeam path planning is executed to determine the crossbeam spatial path by defining the start and end node coordinates and span parameters. Then, the system uses the cross-sectional variation definition function to handle the non-uniform cross-section where the crossbeam cross-sectional height gradually changes from 1.2 meters to 2.5 meters. Finally, the crossbeam entity model is formed by smoothly extending the cross-section along the path using the entity sweeping operation. This step is automatically executed to ensure that the crossbeam geometric features conform to the actual design specifications. The generated column three-dimensional entity data and crossbeam three-dimensional entity data are input into the Boolean operation module to automatically calculate the intersection, merging, and cutting relationship between them. Through Boolean operation, the system can accurately handle the spatial intersection and assembly interface of structural components to generate precise assembly relationship data, which defines the connection, contact, and partition surface of each component, ensuring the spatial consistency and structural integrity of subsequent assembly. According to the assembly relationship data, the constraint solving algorithm is used to verify multiple design constraints such as distance, angle, and contact for each component. This process includes numerical calculation of the minimum distance between components, anti-collision gap, node angle deviation, etc., to ensure that the model meets the safety and functional requirements of the design specifications. The system automatically completes the constraint calculation and outputs the constraint satisfaction verification data. If there are rule violations, the model adjustment process is triggered.Based on the constraint satisfaction verification result, the system performs a final assembly operation on the column three-dimensional entity and the beam three-dimensional entity, automatically adjusts the positions and postures of each component, realizes seamless connection, and forms a complete gantry crane initial three-dimensional model. This model can be directly used for subsequent finite element analysis, wind load simulation or manufacturing process design, ensuring that the model meets the structural precision requirements and has assembly practicability.

[0110] Preferably, the simplified wind load pre-evaluation based on the initial three-dimensional model in step S3 includes:

[0111] According to the initial three-dimensional model, the column slenderness ratio parameter and the beam span-to-depth ratio parameter are calculated, and when the column slenderness ratio parameter exceeds 60 or the beam span-to-depth ratio parameter exceeds 25, it is marked as a geometrically weak component, and the cross-sectional variation rate and the angle variation rate of the column and beam connection node are extracted to obtain geometric risk assessment data;

[0112] According to the geometric risk assessment data, the columns are sorted according to the windward area to select the top 30% as the high wind pressure area, the beams are sorted according to the unsupported length to select the top 30% as the large deformation area, and the nodes with a cross-sectional variation rate exceeding 50% or an angle variation rate exceeding 15° are marked as high stress areas to obtain classified risk area data;

[0113] According to the classified risk area data, overlapping parts that belong to two or more risk areas at the same time are identified, and the number of risk types contained in each overlapping part is counted to obtain multiple risk statistical data;

[0114] Based on the multiple risk statistical data, weak point level classification is performed, parts containing three risk types are defined as first-level weak points, and parts containing two risk types are defined as second-level weak points to obtain weak point level classification data;

[0115] According to the weak point level classification data, the three-dimensional coordinate positions of the first-level weak points and the second-level weak points are extracted, and they are arranged in priority order from first-level to second-level to obtain structural weak point position data.

[0116] The embodiment of the application automatically calculates the slenderness ratio parameter of the column, that is, the ratio of the height of the column to the minimum cross-sectional width thereof, and the span-depth ratio parameter of the beam, that is, the ratio of the span of the beam to the cross-sectional height thereof, based on the initial three-dimensional model that has been constructed. The specific operation adopts a three-dimensional geometric analysis algorithm to extract the size data of each column and beam, respectively, to calculate the slenderness ratio and the span-depth ratio, and to compare the calculation results with preset thresholds 60 and 25, so as to automatically mark the column or beam that exceeds the threshold as a geometrically weak component, indicating that these components have potential risks in terms of structural stability and load-bearing capacity. At the same time, the system further extracts the cross-sectional variation rate at the joint between the column and the beam, that is, the relative percentage of the cross-sectional size at the joint, and the angle variation rate, that is, the degree of change in the connection angle of the two components at the joint, and integrates the above data to form complete geometric risk assessment data for subsequent risk analysis. Based on the geometric risk assessment data, all columns are sorted according to the windward area, which refers to the wind-receiving surface area of the column perpendicular to the main wind direction, and a larger area means that the column is more likely to bear wind pressure. The system automatically selects the top 30% of the columns as high-wind-pressure areas. At the same time, all beams are sorted according to the unsupported length, which refers to the span between the supports at both ends of the beam, and the larger the span, the greater the deformation potential. The system selects the top 30% of the beams as large-deformation areas. In addition, the system selects the nodes with a cross-sectional variation rate exceeding 50% or an angle variation rate exceeding 15 degrees as high-stress areas. The above screening processes are automatically executed by the program, and the screening results are summarized to form classified risk area data, laying a foundation for the identification of local risk points of the structure. Overlapping analysis is performed on the classified risk area data to identify the structure parts that belong to two or more risk categories at the same time, that is, for example, the parts of a column that are in both the high-wind-pressure area and the high-stress area. The system counts the number of risk types contained in each overlapping part through spatial coordinate comparison and risk label cross calculation. This multiple risk counting process is realized by using a spatial analysis algorithm without manual intervention, and automatically outputs a statistical table containing the number of risk types, facilitating accurate positioning of risk aggregation areas and revealing potential complex weak points of the structure. According to the multiple risk statistical data, the weak points are classified into different levels, and the structure parts containing three risk types are defined as first-level weak points, indicating extremely high-risk areas that need to be paid special attention to. The parts containing two risk types are classified as second-level weak points, which have relatively large risks but are inferior to the first-level weak points. The division rule is coded into the risk assessment module, and the system automatically classifies all overlapping parts into different levels to output weak point level classification data, providing accurate basis for subsequent optimization modeling and key reinforcement. According to the weak point level classification data, the three-dimensional coordinate positions of the first-level and second-level weak points are extracted from the initial three-dimensional model, and a spatial sorting algorithm is used to arrange the weak points in the order of first-level weak points first and second-level weak points second, so as to ensure that the weak points with the highest risks are identified and processed first.The output structural weak point position data includes specific coordinate values and corresponding risk level labels, and can be directly used for subsequent local model refinement, structural reinforcement design or wind load monitoring, to realize precise risk management and closed-loop optimization of rapid three-dimensional modeling of the portal crane.

[0117] Preferably, step S4 comprises the following steps:

[0118] Step S41: determining a local refinement range according to the structural weak point position data, setting a refinement radius of 2-3 times the feature size of the component for the first-level weak point, and setting a refinement radius of 1.5-2 times the feature size of the component for the second-level weak point, to obtain hierarchical refinement range data;

[0119] According to the structural weak point position data, the embodiment of the application automatically calculates the feature size of the component corresponding to each weak point, such as the cross-sectional side length of the column or the cross-sectional height of the beam, and then sets the local refinement radius of the first-level weak point to be 2-3 times the feature size, and the local refinement radius of the second-level weak point to be 1.5-2 times the feature size, to form a refinement range sphere or a bounding box around each weak point. This process is completed by a three-dimensional space coordinate automatic expansion algorithm, without human intervention, and finally generates hierarchical refinement range data for subsequent local grid encryption and geometric detail reconstruction.

[0120] Step S42: performing local grid density redistribution based on the hierarchical refinement range data and the multi-precision hybrid modeling framework data, to improve the grid density of the first-level weak point region to 3-4 times the original density, and to improve the grid density of the second-level weak point region to 2-3 times the original density, to obtain local encrypted grid parameter data;

[0121] Based on the hierarchical refinement range data and the previously constructed multi-precision hybrid modeling framework data, the embodiment of the application adopts a grid density mapping algorithm to automatically reduce the size of the grid units in the first-level weak point region, to 1 / 3 to 1 / 4 of the original size, in other words, to improve the grid density by 3 to 4 times. Similarly, the size of the grid units in the second-level weak point region is reduced to 1 / 2 to 2 / 3 of the original size, to improve the grid density by 2 to 3 times. This local grid density redistribution process automatically adjusts the grid division rules through the program, to realize differentiated and gradually continuous grid distribution, to ensure that the refined region grid is more delicate to accurately depict the geometric and stress characteristics of the weak point, and to output local encrypted grid parameter data.

[0122] Step S43: performing geometric reconstruction on the weak point region in the initial three-dimensional model according to the local encrypted grid parameter data, to obtain local refinement geometric data, wherein the geometric reconstruction includes increasing geometric detail features, adjusting local size parameters and optimizing connection transition shapes;

[0123] The embodiment of the present application performs geometric reconstruction on the corresponding weak point region in the initial three-dimensional model according to the local encrypted grid parameter data. The specific operation includes refining the geometric model of the region, increasing the local geometric detail features, such as refining the weld contour at the node connection, increasing the fillet transition or reinforcing the rib shape, adjusting the local size parameter to match the encrypted grid division, and optimizing the connection transition shape to avoid geometric mutation and grid distortion. The geometric reconstruction process is automatically executed by computer-aided design (CAD) and finite element pre-processing software, and finally the local refined geometric data is generated.

[0124] Step S44: boundary matching processing of the local refined geometric data and the initial three-dimensional model is performed to obtain boundary fusion data;

[0125] The embodiment of the present application performs boundary matching processing of the local refined geometric data and the initial three-dimensional model which maintains the original precision, mainly including geometric alignment and node connection processing of the common boundary surface of the local refined region and the surrounding non-refined region, to ensure seamless connection of the two model parts in spatial position, size and topological structure. The boundary fusion process is realized by using geometric grid splicing algorithm and boundary smoothing technology, automatically eliminating possible gaps, overlaps or misplacement, generating boundary fusion data, and ensuring the overall coherence and structural integrity of the model.

[0126] Step S45: based on the boundary fusion data, the local refined geometric part and the geometric part maintaining the original precision are combined into a unified three-dimensional structure, and global geometric integrity inspection is performed to obtain an optimized three-dimensional model.

[0127] The embodiment of the present application combines the local refined geometric part and the remaining part maintaining the original precision into a unified three-dimensional structure model based on the boundary fusion data, and simultaneously performs global geometric integrity inspection, including non-self-intersection, no empty, topological closure and size rationality. The inspection is automatically completed by using three-dimensional model analysis algorithm, to ensure that the final model meets the design specification and simulation analysis requirements, and the output optimized three-dimensional model not only retains the detail features of the weak point, but also maintains the calculation efficiency of the overall model, providing a high-quality digital basis for subsequent structural analysis and construction design.

[0128] Especially important is that step S5 includes the following steps:

[0129] According to the optimized three-dimensional model, the column section size standard value, the beam span standard value and the connection node standard configuration are extracted, and the corresponding wind load sensitivity level and modeling precision level are recorded to obtain standardized feature parameter library data;

[0130] Based on the standardized feature parameter library data and parameter adaptive adjustment rules, a modeling decision tree structure is generated, different wind load conditions, different geometric characteristics and different precision requirements are sorted into branch judgment paths, and modeling decision tree data is obtained;

[0131] According to the modeling decision tree data, a rapid modeling operation sequence is formulated, the modeling steps corresponding to each decision branch are converted into standardized operation instructions, and standardized operation instruction set data is obtained;

[0132] The standardized feature parameter library data, the modeling decision tree data and the standardized operation instruction set data are associated and coded to establish a rapid retrieval mechanism based on feature matching, and intelligent matching rule data is obtained;

[0133] Based on the intelligent matching rule data, a rapid modeling database framework is constructed, and an automatic process of three-dimensional rapid modeling of the portal crane is realized.

[0134] The embodiment of the application first extracts the standard size parameters of the key components from the optimized three-dimensional model, including the standard value of the cross-sectional size of the column, the standard value of the span of the beam, and the standard configuration of the connecting node. These parameters are obtained by analyzing the size statistical value of the corresponding geometric part in the model, such as the average cross-sectional width, height, and type of node connection. At the same time, the system combines the previous wind load sensitive site distribution map and the modeling accuracy division data to automatically assign the corresponding wind load sensitivity level (such as high sensitivity, medium sensitivity, and low sensitivity) and modeling accuracy level (such as high precision, medium precision, and low precision) to each group of parameters, and finally form a structured standardized feature parameter library data as the basic feature set for subsequent rapid modeling. Based on the standardized feature parameter library data constructed in the previous step and the parameter adaptive adjustment rules formulated previously, the system uses the decision tree algorithm to automatically generate a modeling decision tree structure. This structure classifies and judges the input conditions (including different wind load environment conditions, different component geometric feature parameters, and required modeling accuracy) layer by layer, maps multiple conditions into specific modeling scheme branches, and forms branch judgment paths. The decision tree nodes contain specific geometric parameter adjustment and grid accuracy selection logic, ensuring that the appropriate modeling parameter combination can be quickly located under different design requirements and environmental constraints, and the modeling decision tree data is output to facilitate the automatic calling of subsequent modeling steps. According to the modeling decision tree data, a corresponding rapid modeling operation sequence is formulated for each decision branch path, and the abstract decision logic is converted into specific, standardized modeling steps and operation instructions. These operation instructions include parameter instantiation, geometric body generation, grid division, model splicing, and other modular tasks, and the instruction format is uniform, facilitating automatic execution and scheduling. The system automatically organizes the operation sequences of all branches to form a complete standardized operation instruction set data, realizing seamless connection from decision to execution. The standardized feature parameter library data, modeling decision tree data, and standardized operation instruction set data are comprehensively associated and coded, and a rapid retrieval mechanism is constructed through a feature matching algorithm. This mechanism supports the input of current design parameters and environmental conditions, quickly matches the most suitable feature parameters and corresponding decision paths, and calls related operation instructions, greatly improving the retrieval efficiency and modeling response speed. The establishment of intelligent matching rule data relies on multi-dimensional indexing technology and hash mapping algorithm, realizing efficient feature indexing and rapid positioning. Based on the intelligent matching rule data, the system constructs a complete rapid modeling database framework, integrates the parameter feature library, decision tree logic, and operation instruction module, and realizes the automation process of three-dimensional rapid modeling of gantry cranes. This database framework supports the automatic completion of parameter adjustment, geometric model construction, grid division, and model output from the input of design requirements and environmental data by the user, without human intervention, significantly improving the modeling efficiency and accuracy, and being suitable for the scene of quickly responding to changing design requirements in actual engineering projects.

[0135] The application further provides a portal crane three-dimensional rapid modeling system for executing the portal crane three-dimensional rapid modeling method, and the portal crane three-dimensional rapid modeling system comprises:

[0136] a basic data preprocessing module configured to acquire structural parameter template data and installation site wind condition data of the portal crane, establish a parameterized geometric model template according to the structural parameter template data, and determine a wind load sensitive part distribution map in combination with the installation site wind condition data;

[0137] an adaptive precision configuration module configured to divide structural modeling precision levels according to the wind load sensitive part distribution map, and generate a differentiated grid density allocation scheme; and construct a multi-precision hybrid modeling framework data according to the differentiated grid density allocation scheme and the parameterized geometric model template;

[0138] a parameterized modeling instantiation module configured to extract key geometric feature parameters based on the multi-precision hybrid modeling framework data, and establish a parameter adaptive adjustment rule according to the wind load sensitive part distribution map; instantiate the parameterized geometric model template based on the parameter adaptive adjustment rule to obtain an initial three-dimensional model; and perform a simplified wind load pre-evaluation based on the initial three-dimensional model to obtain structural weak point position data;

[0139] a local optimization fusion module configured to perform local model refinement according to the structural weak point position data and the multi-precision hybrid modeling framework data, and perform model fusion with the initial three-dimensional model to obtain an optimized three-dimensional model;

[0140] an intelligent knowledge base construction module configured to generate a standardized modeling file based on the optimized three-dimensional model; and establish a rapid modeling database according to the standardized modeling file and the parameter adaptive adjustment rule to realize the portal crane three-dimensional rapid modeling.

[0141] Therefore, from any viewpoint, the embodiments should be considered as exemplary and non-limiting, the scope of the application being defined by the appended claims and not by the above description, and it is intended to embrace all variations falling within the meaning and range of equivalents of the elements of the claims.

[0142] The above description is merely one specific implementation of the application, which enables a person skilled in the art to understand or implement the application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the application. Therefore, the application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for rapid 3D modeling of a gantry crane, characterized in that, Includes the following steps: Step S1: Obtain the structural parameter template data of the gantry crane and the wind condition data of the installation site; A parametric geometric model template is established based on the structural parameter template data, and a wind load sensitive part distribution map is determined by combining the wind condition data of the installation site. Step S2: Classify the structural modeling accuracy level according to the wind load sensitive part distribution map and generate a differentiated mesh density allocation scheme; construct multi-precision hybrid modeling framework data based on the differentiated mesh density allocation scheme and parametric geometric model template; Step S3: Extract key geometric feature parameters based on multi-precision hybrid modeling framework data, and establish parameter adaptive adjustment rules based on the distribution map of wind-sensitive parts; instantiate the parameterized geometric model template based on the parameter adaptive adjustment rules to obtain the initial three-dimensional model; A simplified wind load pre-assessment was performed based on the initial three-dimensional model to obtain data on the location of structural weak points. Step S4: Refine the local model based on the structural weak point location data and multi-precision hybrid modeling framework data, and fuse it with the initial 3D model to obtain an optimized 3D model; Step S5: Generate standardized modeling files based on the optimized 3D model; establish a rapid modeling database based on the standardized modeling files and parameter adaptive adjustment rules to achieve rapid 3D modeling of the gantry crane.

2. The method for rapid 3D modeling of a gantry crane according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain the design specification document for the gantry crane; extract dimensional parameters from the design specification document to obtain the geometric dimensions of the gantry frame, the crossbeam, and the track. Step S12: Process the gantry geometry data, crossbeam geometry data, and track geometry data into a unified format to obtain structural parameter template data; Step S13: Obtain historical meteorological observation records of the installation site; perform wind speed statistics, wind direction statistics, and wind frequency statistics on the historical meteorological observation records to obtain site wind speed distribution data, prevailing wind direction data, and extreme wind speed data; Step S14: Extract wind load characteristic parameters based on site wind speed distribution data, prevailing wind direction data, and extreme wind speed data to obtain wind condition data for the installation site; Step S15: Establish a parametric geometric model template based on the structural parameter template data, and determine the distribution map of wind-sensitive parts by combining the wind condition data of the installation site.

3. The method for rapid 3D modeling of a gantry crane according to claim 2, characterized in that, Step S15 includes the following steps: Step S151: Perform parametric modeling of the gantry columns based on the structural parameter template data to obtain the parametric model data of the gantry, wherein the gantry column parameters include column height parameters, cross-sectional dimension parameters, and tilt angle parameters; Step S152: Perform parametric modeling of the beam based on the structural parameter template data, including beam length parameters, cross-sectional shape parameters, and connection method parameters, to obtain the beam parametric model data; Step S153: Perform parametric modeling of the track based on the structural parameter template data, including track length parameters, track gauge parameters, and installation height parameters, to obtain the parametric model data of the track. Step S154: Using the parametric model data of the gantry, the parametric model data of the crossbeam, and the parametric model data of the track, establish geometric constraint relationships based on the spatial coordination between the components to obtain constraint relationship data; Step S155: The parametric model data of the gantry, the parametric model data of the beam, the parametric model data of the track, and the constraint relationship data are encapsulated into templates to form reusable geometric modeling rules, thus obtaining the parametric geometric model template; Step S156: Perform wind load analysis based on the wind condition data of the installation site and the parameterized geometric model template to obtain the wind load data of each component. Specifically, the wind load analysis involves calculating the wind-receiving area of ​​the gantry columns, the wind-receiving area of ​​the crossbeams, and the wind-receiving area of ​​the tracks. Step S157: Perform a sensitivity assessment on the wind load data of each component, identify high wind load stress areas and low wind load stress areas, and obtain a distribution map of wind load sensitive parts.

4. The method for rapid 3D modeling of a gantry crane according to claim 3, characterized in that, Step S2 includes the following steps: Step S21: Identify the boundaries of sensitive areas based on the distribution map of wind-sensitive parts to obtain boundary data of highly sensitive areas, medium sensitive areas, and low sensitive areas; Step S22: Based on the boundary data of high-sensitivity areas, medium-sensitivity areas, and low-sensitivity areas, perform precision level numerical allocation processing, and set high-precision modeling level, medium-precision modeling level, and low-precision modeling level respectively to obtain structural modeling precision level data; Step S23: Determine the mesh size parameters based on the structural modeling accuracy level data, where high-precision modeling level corresponds to fine mesh size parameters, medium-precision modeling level corresponds to standard mesh size parameters, and low-precision modeling level corresponds to coarse mesh size parameters, thus obtaining hierarchical mesh size parameter data; Step S24: Based on the hierarchical grid size parameter data and the distribution map of wind-sensitive parts, perform spatial grid density mapping, and assign grid size parameters of different accuracy levels to the corresponding geometric regions to obtain a differentiated grid density allocation scheme. Step S25: Construct multi-precision hybrid modeling framework data based on the differentiated mesh density allocation scheme and parametric geometric model template.

5. The method for rapid 3D modeling of a gantry crane according to claim 4, characterized in that, Step S25 includes the following steps: Step S251: Identify the grid transition region according to the differentiated grid density allocation scheme, identify the boundary region between grids of different precision levels, and obtain grid transition region data; Step S252: Calculate the gradient grid density for the grid transition region data, set the gradient transition grid density between adjacent accuracy levels, and obtain the gradient grid density data; Step S253: Perform geometric region meshing processing based on the parametric geometric model template, dividing the gantry columns, beams, and tracks into independent mesh generation regions to obtain sub-region mesh generation data; Step S254: Based on the differentiated grid density allocation scheme, gradient grid density data, and regional grid generation data, perform grid compatibility verification processing on the connection coordination between grids in different regions to obtain grid compatibility verification data; Step S255: Formulate mixed-precision modeling rules based on mesh compatibility verification data and parametric geometric model templates. Step S256: Establish a collaborative modeling mechanism for high-precision, medium-precision, and low-precision regions to obtain multi-precision hybrid modeling framework data.

6. The method for rapid three-dimensional modeling of a gantry crane according to claim 5, characterized in that, Step S3 involves extracting key geometric feature parameters based on multi-precision hybrid modeling framework data and establishing adaptive parameter adjustment rules according to the distribution map of wind-sensitive parts, including: Identify size feature types, shape feature types, and position feature types based on multi-precision hybrid modeling framework data; Based on the size feature type, extract the column size parameters of the gantry column with a height range of 15m-45m, and extract the beam size parameters of the crossbeam with a span range of 20m-60m; Based on shape feature type, extract column shape parameters with cross-sectional dimensions ranging from 0.8m×0.8m to 1.5m×1.5m, and extract beam shape parameters with cross-sectional height variations ranging from 1.2m to 2.5m; Based on the location feature type, extract the column location parameters with an inclination angle range of 0°-15°, and extract the beam location parameters with a node connection radius of 0.5m-1.2m; The weighting coefficients affecting modeling accuracy are calculated based on the column size parameters, column shape parameters, column position parameters, beam size parameters, beam shape parameters, and beam position parameters. When the weighting coefficient of modeling accuracy exceeds 0.7, the corresponding geometric parameters are marked as key geometric feature parameters. Based on the distribution map of wind-sensitive parts, key geometric characteristic parameters are divided into high-sensitivity key parameters, medium-sensitivity key parameters, and low-sensitivity key parameters; and adjustment range coefficients of 1.5-2.0 are set for high-sensitivity key parameters, 1.2-1.5 for medium-sensitivity key parameters, and 1.0-1.2 for low-sensitivity key parameters. Based on the application of adjustment amplitude coefficients for highly sensitive key parameters when the ambient wind speed exceeds 12 m / s, for moderately sensitive key parameters when the ambient wind speed exceeds 8 m / s, and for lowly sensitive key parameters when the ambient wind speed exceeds 5 m / s, an adaptive adjustment rule for parameters is established.

7. The method for rapid three-dimensional modeling of a gantry crane according to claim 6, characterized in that, Step S3, which instantiates the parametric geometric model template based on the parameter adaptive adjustment rule, includes: Identify the target geometric parameters that need to be adjusted and their corresponding adjustment magnitude coefficients based on the parameter adaptive adjustment rules. The target geometric parameters in the parametric geometric model template are numerically calculated according to the corresponding adjustment range coefficients to obtain the instantiated geometric parameter values. Based on the instantiated geometric parameter values, a three-dimensional solid of the column is generated, including column axis positioning, cross-sectional contour drawing, and solid extrusion operations, to obtain the three-dimensional solid data of the column. The three-dimensional solid of the beam is generated based on the instantiated geometric parameter values, resulting in three-dimensional solid data of the beam including beam path planning, cross-section change definition and solid sweep operation; The three-dimensional solid data of the column and the three-dimensional solid data of the beam are processed based on Boolean operations to perform intersection, merging and cutting relationship processing between the components to obtain assembly relationship data; Based on the assembly relationship data, constraint calculations are performed to ensure that each component meets the distance constraints, angle constraints, and contact constraints specified in the design, and constraint satisfaction verification data is obtained. Based on the constraint satisfaction verification data, the three-dimensional solid data of the column and the three-dimensional solid data of the beam are assembled to obtain the initial three-dimensional model.

8. The method for rapid three-dimensional modeling of a gantry crane according to claim 7, characterized in that, Step S3, which involves a simplified wind load pre-assessment based on the initial 3D model, includes: The column slenderness ratio and beam span-to-height ratio are calculated based on the initial 3D model. When the column slenderness ratio exceeds 60 or the beam span-to-height ratio exceeds 25, it is marked as a geometrically weak member. The cross-sectional change rate and angle change rate of the connection node between the column and the beam are extracted to obtain geometric risk assessment data. The columns in the geometric risk assessment data are sorted by windward area, and the top 30% are selected as high wind pressure areas. The beams are sorted by unsupported length, and the top 30% are selected as large deformation areas. Nodes with a cross-sectional change rate exceeding 50% or an angle change rate exceeding 15° are marked as high stress areas, thus obtaining the classified risk area data. Based on the classification risk area data, identify overlapping areas that belong to two or more risk areas at the same time, and count the number of risk types contained in each overlapping area to obtain multiple risk statistics. Weakness levels are classified based on multiple risk statistics. Parts containing three risk types are defined as Level 1 weaknesses, and parts containing two risk types are defined as Level 2 weaknesses, thus obtaining weakness level classification data. Based on the classification data of weak points, the three-dimensional coordinates of primary and secondary weak points are extracted and arranged in order of priority from primary to secondary to obtain the structural weak point location data.

9. The method for rapid three-dimensional modeling of a gantry crane according to claim 8, characterized in that, Step S4 includes the following steps: Step S41: Determine the local refinement range based on the structural weak point location data. Set the refinement radius to 2-3 times the component feature size for the first-level weak points and set the refinement radius to 1.5-2 times the component feature size for the second-level weak points to obtain the graded refinement range data. Step S42: Based on the hierarchical refinement range data and the multi-precision hybrid modeling framework data, perform local mesh density redistribution, increase the mesh density of the first-level weak point area to 3-4 times the original density, and increase the mesh density of the second-level weak point area to 2-3 times the original density, to obtain local densified mesh parameter data; Step S43: Based on the local mesh parameter data, perform geometric reconstruction on the weak point areas in the initial 3D model to obtain local refined geometric data. The geometric reconstruction includes adding geometric detail features, adjusting local size parameters, and optimizing the connection transition shape. Step S44: Perform boundary matching processing between the local refined geometric data and the initial 3D model to obtain boundary fusion data; Step S45: Based on the boundary fusion data, combine the locally refined geometric parts with the geometric parts that maintain the original accuracy into a unified three-dimensional structure, and perform a global geometric integrity check to obtain an optimized three-dimensional model.

10. A rapid 3D modeling system for gantry cranes, characterized in that, For executing the 3D rapid modeling method for a gantry crane as described in claim 1, the 3D rapid modeling system for the gantry crane includes: The basic data preprocessing module is used to acquire the structural parameter template data of the gantry crane and the wind condition data of the installation site; it establishes a parametric geometric model template based on the structural parameter template data, and determines the distribution map of wind-sensitive parts by combining the wind condition data of the installation site. The adaptive accuracy configuration module is used to classify the accuracy level of structural modeling based on the distribution map of wind-sensitive parts and generate a differentiated mesh density allocation scheme; and to construct multi-accuracy hybrid modeling framework data based on the differentiated mesh density allocation scheme and parametric geometric model template. The parametric modeling instantiation module is used to extract key geometric feature parameters based on multi-precision hybrid modeling framework data, and establish parameter adaptive adjustment rules based on the distribution map of wind-sensitive parts; based on the parameter adaptive adjustment rules, the parametric geometric model template is instantiated to obtain an initial three-dimensional model; based on the initial three-dimensional model, a simplified wind load pre-evaluation is performed to obtain structural weak point location data; The local optimization and fusion module is used to refine the local model based on the structural weak point location data and the multi-precision hybrid modeling framework data, and then fuse it with the initial 3D model to obtain an optimized 3D model. The intelligent knowledge base construction module is used to generate standardized modeling files based on optimized 3D models; and to establish a rapid modeling database based on standardized modeling files and parameter adaptive adjustment rules to achieve rapid 3D modeling of gantry cranes.

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