A load data determination method, device, apparatus and storage medium

Abstract

The application discloses a load data determination method and device, equipment and a storage medium, comprising: obtaining initial parameter data of a steel skeleton light plate, and performing data processing on the initial parameter data to obtain target parameter data; according to the target parameter data, a macro model and a micro model for the steel skeleton light plate are established through a finite element network; the macro model and the micro model are coupled and iteratively processed, and when a coupling end condition is met, target load data is obtained based on the coupled macro model and micro model. The above technical solution realizes safe, reliable and accurate model construction analysis and numerical simulation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of building and civil engineering, and in particular to a load data determination method, device, equipment and storage medium. BACKGROUND

[0002] As a new type of light energy-saving and environment-friendly material, steel skeleton light plate is widely used in industrial and civil building fields such as coal conveying trestle. Its unique structure and performance characteristics, such as light weight, load bearing, energy saving, fireproofing, etc., make it an indispensable part of modern construction engineering.

[0003] With the continuous improvement of the performance requirements of the building industry on materials, it is particularly important to conduct in-depth research and analysis on steel skeleton light plate. SUMMARY

[0004] The present application provides a load data determination method, device, equipment and storage medium, which realizes safe, reliable and accurate model construction analysis and numerical simulation.

[0005] In a first aspect, the present application provides a load data determination method, comprising:

[0006] Obtaining initial parameter data of a steel skeleton light plate, and performing data processing on the initial parameter data to obtain target parameter data;

[0007] According to the target parameter data, a macroscopic model and a microscopic model for the steel skeleton light plate are established through a finite element network;

[0008] The macroscopic model and the microscopic model are coupled and iteratively processed, and when the coupling end condition is met, target load data is obtained based on the coupled macroscopic model and the microscopic model.

[0009] In a second aspect, the present application provides a load data determination device, comprising:

[0010] A parameter data determination module is configured to obtain initial parameter data of a steel skeleton light plate, and perform data processing on the initial parameter data to obtain target parameter data;

[0011] A model establishment module is configured to establish a macroscopic model and a microscopic model for the steel skeleton light plate according to the target parameter data through a finite element network;

[0012] A load data determination module is configured to couple and iteratively process the macroscopic model and the microscopic model, and when the coupling end condition is met, target load data is obtained based on the coupled macroscopic model and the microscopic model.

[0013] In a third aspect, the present application provides an electronic device, comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected with the at least one processor; wherein

[0016] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the load data determination method provided in the first aspect.

[0017] In a fourth aspect, the embodiments of the present disclosure provide a computer readable storage medium, which stores computer instructions, and the computer instructions are used to enable a processor to perform the load data determination method provided in the first aspect when executed.

[0018] The load data determination method, device, equipment and storage medium provided by the embodiments of the present disclosure obtain initial parameter data of a steel skeleton light plate, perform data processing on the initial parameter data to obtain target parameter data, establish a macro model and a micro model for the steel skeleton light plate according to the target parameter data through a finite element network, perform coupling iteration processing on the macro model and the micro model, and obtain target load data based on the coupled macro model and micro model when a coupling end condition is met. The above technical solution realizes safe, reliable and accurate model construction analysis and numerical simulation.

[0019] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0021] Figure 1 is a flowchart of a load data determination method provided by the first embodiment of the present application;

[0022] Figure 2 is a flowchart of a load data determination method provided by the second embodiment of the present application;

[0023] Figure 3 is a stress nephogram of a stiffening rib provided by the second embodiment of the present application;

[0024] Figure 4 is a face load-plastic strain curve provided by the second embodiment of the present application;

[0025] Figure 5 is a structural schematic diagram of a load data determination device provided by the third embodiment of the present application;

[0026] Figure 6 is a structural schematic diagram of an electronic device provided by the fourth embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to make the personnel in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by the person of ordinary skill in the art without making creative labor should belong to the protection scope of the present application.

[0028] It should be noted that the terms "first", "second" and "target" and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0029] Steel skeleton light plate is widely used in the field of building and civil engineering due to its advantages of light weight, high strength, convenient construction and so on. However, in the process of structural design and bearing capacity analysis of steel skeleton light plate, it is usually dependent on the finite element analysis (FEA) method. Traditional finite element analysis usually uses a single scale model, which cannot fully consider the multi-scale characteristics of steel skeleton light plate, such as the interaction of different structural parts such as steel skeleton, reinforcing rib and core material. This single scale analysis method has limitations in simulating complex structural behavior, resulting in a large deviation between the simulation results and the actual situation, and further affecting the reliability and safety of structural design. In addition, in the process of load test, the existing technology cannot accurately predict the response of the structure under different load conditions, increasing the uncertainty and potential risk of design. Therefore, it is urgent to develop a finite element analysis method that can accurately simulate the multi-scale behavior of steel skeleton light plate to improve the accuracy and safety of structural design. Thus, the present application provides a kind of multi-scale finite element simulation method for load test of steel skeleton light plate to solve the above technical problems.

[0030] Embodiment one

[0031] Figure 1 is a flow chart of a load data determination method provided by the embodiment one of the present application, the embodiment can be applicable to the multi-scale finite element simulation of steel skeleton light plate for load test, the method can be executed by load data determination device, the load data determination device can be realized in the form of hardware and / or software.

[0032] As shown in Figure 1 , the method comprises:

[0033] S101, obtain the initial parameter data of the steel skeleton light plate, and process the initial parameter data to obtain the target parameter data.

[0034] In the embodiment, the initial parameter data can be understood as direct parameter information of the steel skeleton light plate, including geometric parameters, initial load parameters and performance parameters, wherein the geometric parameters include plate length, plate width, plate edge height, core plate thickness, main rib size, end rib size and reinforcing rib size; the initial load parameters at least include material density, steel plate volume, load partial coefficient, load standard value, load index, dead weight coefficient, load standard value coefficient and load combination coefficient, and the performance parameters are technical performance index parameters of the core material, including dry apparent density, cubic strength, splitting tensile strength, elastic modulus, thermal conductivity and heat storage coefficient. The target parameter data can be understood as parameter information used for constructing the steel skeleton light plate model, determined by the initial parameter data, and the target parameter data includes geometric parameters, target load parameters and performance parameters, wherein the geometric parameters and the performance parameters are the same as those in the initial parameter data, and the target load parameters include plate dead weight standard value, external load standard combination value design value and external load basic combination value design value, which are obtained from the initial load parameters.

[0035] Specifically, the geometric parameters, the initial load parameters and the performance parameters of the steel skeleton light plate are obtained, each parameter in the initial load parameters is taken as an input variable, target load parameters such as dead weight standard value, external load standard combination design value and external load basic combination design value are obtained based on corresponding formulas, and the geometric parameters, the target load parameters and the performance parameters are determined as the target parameter data.

[0036] S102, according to the target parameter data, a macro model and a micro model for the steel skeleton light plate are established through finite element network.

[0037] In the embodiment, the macro model can be understood as a steel skeleton light plate simulation model at a macro level, and the micro model can be understood as a steel skeleton light plate simulation model at a micro level.

[0038] Specifically, a geometric model at a macro level is established based on the geometric parameters in the target parameter data, the geometric model is meshed according to different grid densities, smaller grid units are divided in stress concentrated areas, larger grid units are divided in units with smaller stress changes, boundary conditions are applied to each grid based on the target load parameters in the target parameter data, and the support type and support nodes of the model are determined, corresponding constraints are applied to selected process nodes, and the establishment of the macro model is completed. After the establishment of the macro model, load parameter information (stress, strain and displacement) of the macro model is solved, and size information of key areas in the macro model is extracted, a geometric model at a micro level is established based on the size information, the geometric model is meshed according to different grid densities, smaller grid units are divided in key areas, loads are applied to the micro model based on the load parameter information determined by the macro model, and corresponding boundary conditions are applied to the micro model to apply constraints, and the establishment of the micro model is completed.

[0039] S103, coupling iteration processing is performed on the macro model and the micro model, and when a coupling end condition is met, target load data is obtained based on the coupled macro model and the micro model.

[0040] In the embodiment, the coupling end condition can be understood as a condition for ending the coupling optimization iteration of the macro model and the micro model, for example, a difference between load parameter information (for example, stress) extracted in two adjacent iterations is less than a specific convergence threshold. The target load data can be understood as a basic combined design value of the uniform load of the steel skeleton light plate.

[0041] Specifically, after the micro model is established, the load parameter information (stress, strain and displacement) of the micro model is solved, the load parameter information is fed back to the macro model, the macro model is regulated and controlled according to the corresponding boundary condition, and the load parameter information of the macro model after regulation and control is solved. The micro model is regulated and controlled based on the load parameter information of the new macro model, and the load parameter information of the updated micro model is solved. In this way, the coupling iteration of the macro model and the micro model is realized, until the difference between the stresses solved by the macro model in two adjacent iterations is less than the set convergence threshold, and it is determined that the coupling end condition is met. At this time, the element with the earliest plastic strain is determined, the load-carrying capacity limit state point of the model is determined according to the element with the earliest plastic strain, the finite element network analysis is performed, the corresponding external surface load is read from the analysis result, the external surface load of the load-carrying capacity limit state point is determined as the external surface load carrying capacity of the steel skeleton prefabricated slab, and the numerical simulation uniform load basic combined design value, that is, the target load parameter, is calculated based on the external surface load carrying capacity.

[0042] The load data determination method provided by the embodiment of the application comprises: obtaining initial parameter data of a steel skeleton light plate, and performing data processing on the initial parameter data to obtain target parameter data; establishing a macro model and a micro model for the steel skeleton light plate through finite element network according to the target parameter data; and performing coupling iteration processing on the macro model and the micro model, and obtaining target load data based on the coupled macro model and micro model when a coupling end condition is met. The above technical solution realizes safe, reliable and accurate model construction analysis and numerical simulation.

[0043] As a first optional embodiment of the embodiment, the method further comprises:

[0044] a, determining the difference between the target load data and the ultimate load-carrying capacity of the steel skeleton light plate.

[0045] In the embodiment, after simulating the target load parameter, the consistency of the target load parameter is verified based on the actual experimental data. First, the difference between the simulated target load parameter and the actual ultimate bearing capacity of the steel skeleton light plate is determined.

[0046] Specifically, according to the basic combination design value of the numerical simulation of the uniform load, the actual load test is carried out, and the displacement, stress and strain data in the loading process are recorded. According to the displacement, stress and strain data, the actual bearing capacity data is obtained.

[0047] Specifically, the stress sensor, strain gauge and displacement sensor are installed at the key positions of the steel skeleton light plate to obtain the initial stress, strain and displacement before the load is applied. The load is increased step by step, and the corresponding stress, strain and displacement are obtained once the load value is increased by one level, as shown in the following formula, wherein, is the load value of the first level, is the load value of the first level, is the load value of the first level, is the load value of the first level, design At this time, the corresponding displacement, stress and strain data are {δ design , σ design , ∈ design}. The load-stress curve is generated, and the load-stress curve is analyzed to determine the ultimate bearing capacity of the test piece, that is, the uniform load when the test piece reaches the limit state, and the ultimate bearing capacity q actual of the steel skeleton light plate is obtained. According to q cz =q actual -q design , the difference q cz between the target load parameter and the ultimate bearing capacity of the steel skeleton light plate is determined, wherein q design is the basic combination design value of the uniform load (target load data).

[0048] b. Take the ratio of the difference value to the target load parameter, compare the ratio with the set consistency threshold value, and obtain the numerical simulation consistency result.

[0049] In the embodiment, the consistency threshold value can be understood as a pre-set consistency coefficient boundary value. The numerical simulation consistency result can be understood as information for representing the accuracy of the simulation result of the steel skeleton light plate model.

[0050] Specifically, by Determine the consistency coefficient T of the steel skeleton light plate simulation, compare the consistency coefficient T of the steel skeleton light plate simulation with the set consistency threshold value yz, judge the consistency degree of the numerical simulation and the actual test result, when T> yz, the error exceeds the acceptable range, then determine that the numerical simulation consistency result is low consistency degree, when T≤ yz, the error is within the acceptable range, then determine that the numerical simulation consistency result is high consistency degree.

[0051] The above technical content verifies the consistency of the model by comparing and analyzing the simulation results with the actual load test results, and provides reliable technical support for actual engineering application.

[0052] Embodiment two

[0053] Figure 2 is a flowchart of a load data determination method provided by the embodiment two of the present application, the embodiment is a further optimization of any of the above embodiments, which can be applicable to the multi-scale finite element simulation of the steel skeleton light plate facing the load test, the method can be executed by a load data determination device, which can be realized in the form of hardware and / or software.

[0054] As Figure 2 shown, the method comprises:

[0055] S201, obtain the initial parameter data of the steel skeleton light plate, the initial parameter data comprising geometric parameters, initial load parameters and performance parameters.

[0056] In the embodiment, the geometric parameters, initial load parameters and performance parameters of the steel skeleton light plate are obtained. The geometric parameters include plate length, plate width, plate edge height, core plate thickness, main rib size, end rib size and reinforcing rib size; the initial load parameters at least include material density, steel plate volume, load partial coefficient, load standard value, load index, self-weight coefficient, load standard value coefficient and load combination coefficient; the performance parameters are technical performance index parameters of the core material, including dry apparent density, cubic strength, splitting tensile strength, elastic modulus, thermal conductivity and heat storage coefficient.

[0057] S202, calculate the self-weight standard value of the steel skeleton light plate according to the material density and the steel plate volume in the initial load parameters.

[0058] In the embodiment, the self-weight standard value represents the weight of the steel skeleton light plate itself.

[0059] Specifically, the self-weight standard value G of the steel skeleton light plate is calculated by G=ρ·V·g according to the material density ρ and the steel plate volume V in the initial load parameters, wherein g is the acceleration of gravity.

[0060] S203, calculating the external load standard combined design value of the steel skeleton light plate according to the load partial coefficient, the load standard value and the load index in the initial load parameter.

[0061] In the embodiment, the external load standard combined value design value refers to the sum of various load combinations considered in the design.

[0062] Specifically, the external load standard combined value design value of the steel skeleton light plate is calculated according to the load partial coefficient γ θ , the load standard value Q θ and the load index θ in the initial load parameter. d The load standard value Q θ includes the live load, the wind load and the snow load, and the load index θ has a value range of 1, 2,..., N.

[0063] S204, calculating the external load basic combined design value of the steel skeleton light plate according to the self-weight coefficient, the load standard value coefficient and the load combination coefficient in the initial load parameter.

[0064] In the embodiment, the external load basic combined value design value includes the combination of the permanent load (such as the self-weight) and the variable load (such as the live load and the wind load). The self-weight coefficient is the coefficient of the self-weight standard value of the steel skeleton light plate, and the load standard value coefficient is the coefficient of the load standard value.

[0065] Specifically, the external load basic combined value design value of the steel skeleton light plate is calculated according to the self-weight coefficient γ G , the load standard value coefficient γ Qi and the load combination coefficient ψ. c

[0066] S205, determining the self-weight standard value, the external load standard combined design value and the external load basic combined design value as the target load parameter, and determining the geometric parameter, the target load parameter and the performance parameter as the target parameter data.

[0067] In the embodiment, after the self-weight standard value, the external load standard combined design value and the external load basic combined design value are determined as the target load parameter based on the initial load parameter, the target load parameter replaces the initial load parameter, so that the geometric parameter, the target load parameter and the performance parameter are combined to form the target parameter data.

[0068] S206, constructing the macro model for the steel skeleton light plate according to the target parameter data, wherein the macro model is composed of a plurality of finite element network modules.

[0069] ​​​In the embodiment, the finite element network module can be understood as a grid divided in the macro model based on the finite element network.

[0070] Specifically, the geometric parameters and the target load parameters in the target parameter data are processed, and a macro model of the steel skeleton light plate is constructed by using the finite element network.

[0071] Optionally, the macro model for the steel skeleton light plate is constructed according to the target parameter data, and the macro model includes:

[0072] S2061, a first geometric model of the steel skeleton light plate is established according to the geometric parameters in the target parameter data.

[0073] In the embodiment, the first geometric model is a macro geometric model of the steel skeleton light plate, which is a basic framework of the complete macro model.

[0074] Specifically, the material properties are defined, and the material properties usually include the elastic modulus and the Poisson's ratio. The first geometric model of the steel skeleton light plate is created by using the geometric parameters such as the plate length L, the plate width W, the plate edge height H, the core plate thickness t, the main rib size D m , the end rib size D Φ and the reinforcing rib size υ, and the first geometric model is represented as Ω ma =(L, W, H, t, D m , D Φ , υ).

[0075] S2062, the first grid density corresponding to each type of region is determined, the first geometric model is meshed based on the first grid density, and a plurality of finite element network modules are formed.

[0076] In the embodiment, the first grid density can be understood as the grid density when the macro model is divided, and is associated with the position of the steel skeleton light plate. When the divided type region is a key region such as a connection, the first grid density is large, and when the divided type region is a non-key region such as a large-area plate body, the first grid density is small.

[0077] Specifically, a suitable element type is selected according to the structural characteristics, such as a solid element; the grid is automatically generated by using the finite element network, and the first geometric model is meshed. The geometric domain of the macro structure is divided into a plurality of macro finite element network modules e i . In the formula, Ω ma is the first geometric model of the macro, e i is the i-th macro finite element network module, i is the index of the macro finite element, the value range of i is 1, 2,..., n, and n is the number of macro finite element units.

[0078] In the process of meshing, the first mesh density is controlled to ensure that finer meshes are used in critical areas (such as connections), i.e. smaller elements are used in areas where stress is concentrated to improve calculation accuracy; larger elements are used in areas where stress changes less to reduce calculation amount, wherein the determination of the first mesh density is: wherein s mesh is the first mesh density, fine is a fine mesh, which means more nodes and elements for higher precision calculation, usually applied in critical areas (such as connections), and coarse is a coarse mesh, which means fewer nodes and elements for reducing calculation cost, usually applied in non-critical areas.

[0079] S2063, according to the target load parameter in the target parameter data, load is applied to each finite element network module, and the boundary conditions of the macro model are determined, and each finite element network module is constrained based on the boundary conditions.

[0080] In this embodiment, load is applied to the constructed macro model according to the target load parameter in the target parameter data, and appropriate boundary conditions are set to realize the constraint of the macro model and prepare for subsequent model regulation.

[0081] Specifically, according to the target load parameter in the target parameter data, total load is applied to the finite element network module, and the total load q total is calculated as follows: q total =G+Q d +Q c , wherein G is the self-weight load, Q d is the personnel load, and Q c is the wind load; according to the actual working condition (such as dead load or live load), load is applied, wherein F ma is the load vector applied to the macro model, and F i is the force applied to the i-th macro finite element unit.

[0082] The support type is determined and the node is selected, the boundary condition BC ma is set on the selected support node to apply corresponding constraints to simulate the real support condition, specifically, the simply supported support is selected as the support node, the translational freedom degree of the simply supported support is constrained, but the rotational freedom degree is not constrained; the corresponding node is selected in the finite element network, usually, these nodes are located at the edge or specific position of the structure; for the node, the simply supported support condition can be expressed as: When u x =0 or u y =0, the displacement of the structure in the x direction and the y direction in the horizontal plane is limited, and when u z =0, the displacement of the structure in the vertical direction is limited.

[0083] S207. Extract the regional parameters of the target region from the macroscopic model, and establish a microscopic model for the steel frame lightweight panel based on the regional parameters. The microscopic model consists of multiple finite element network elements.

[0084] In this embodiment, the target region can be understood as the key area of ​​the steel-framed lightweight panel, such as the connection points. Region parameters can be understood as the geometric dimensions, shape, stress, strain, and displacement parameters of the key region. Finite element network elements can be understood as meshes generated in the microscopic model based on a finite element network.

[0085] In the finite element method (FEM) network, the technical performance parameters of the core material are input into the macroscopic model of the steel-framed lightweight panel. Specifically, the apparent density, cubic strength, splitting tensile strength, elastic modulus, thermal conductivity, heat storage coefficient, and Poisson's ratio are assigned to the core material portion of the macroscopic model. Regional parameter data at the connection points between the stiffening ribs and the steel frame are extracted from the macroscopic model of the steel-framed lightweight panel. Based on these regional parameter data, a microscopic model is established using the finite element method (FEM).

[0086] Optionally, regional parameters of the target region are extracted from the macroscopic model, and a microscopic model for the steel-framed lightweight panel is established based on the regional parameters, including:

[0087] S2071. Extract the geometric dimensions and shape of the target region from the macro model, and establish a second geometric model based on the geometric dimensions and shape.

[0088] In this embodiment, the second geometric model is a micro-geometric model of a steel-framed lightweight plate, which is the basic framework of the complete micro-model.

[0089] In this embodiment, before establishing the microscopic model, a finite element solution is run, using σ... ma =R∈ ma , and Obtain the stress vector σ of the macroscopic model of the steel frame lightweight panel. ma strain vector ∈ ma and displacement vector u ma In the formula, K ma Let F be the stiffness matrix of the macroscopic model. ma R represents the load vector of the macroscopic model, and R is the constitutive matrix of the material. Stress, strain, and displacement data (σ) at the connection between the stiffening rib and the steel frame (target region) are extracted from the solution results. key ,∈ key ,u key ).

[0090] extracting the geometric size and shape of the target region from the macroscopic model, and establishing a second geometric model, i.e., a microscopic model Ω, according to the geometric size and shape of the target region mi .

[0091] S2072, determining a second grid density corresponding to each type of region, performing grid division on the second geometric model based on the second grid density, and forming a plurality of finite element network units.

[0092] In this embodiment, the second grid density can be understood as the grid density when the microscopic model is divided, and is also associated with the position of the steel skeleton light plate. When the divided type region is a key region such as a connection, the second grid density is large, and when the divided type region is a non-key region such as a large-area plate body, the first grid density is small.

[0093] Specifically, the second grid density of each region is determined, the second grid density of a key target region (such as a connection) is large, and the second grid density of a non-key region is small, based on and the second grid density is used to perform grid division on the second geometric model, to ensure that the grid density of the key region is high enough, and the second geometric model of the microscopic structure is divided into a plurality of microscopic finite element network units e j , wherein e j is the jth microscopic finite element unit, j is the index of the microscopic finite element, and j takes a value in the range of 1, 2, …, m, and m is the number of microscopic finite element units.

[0094] S2073, determining first load data of the target region in the macroscopic model, applying a load to each finite element network unit based on the first load data, and determining boundary conditions of the microscopic model, and constraining each finite element network unit based on the boundary conditions.

[0095] In this embodiment, the first load data can be understood as stress, strain and displacement data of the target region solved by the macroscopic model, i.e., the stress, strain and displacement data (σ key , key u key ) solved in step S2071.

[0096] Specifically, according to the stress σ key extracted from the target region in the macroscopic model, a corresponding load is applied to the microscopic model based on , wherein F mi is a load vector applied to the microscopic model, V j is the volume of e j of the microscopic model, and K is a derivative matrix used to convert stress into node force.

[0097] The displacement data u key, through BC mi，τ = u key，τ Boundary conditions are formed and applied to the boundary nodes of the micro model, wherein BC mi，τ is the displacement boundary condition of the micro model boundary node τ, u key，τ is the displacement vector of the key node τ, τ ranges from 1, 2,..., Y, and Y is the number of key nodes.

[0098] S208, determining second load data of the micro model, feeding back the macro model based on the second load data to obtain updated first load data of the macro model.

[0099] In this embodiment, the second load data can be understood as stress, strain and displacement data solved by the micro model.

[0100] Specifically, the micro model finite element solution is run to obtain the latest second load data of the micro model, that is, stress, strain and displacement data (σ' key / σ mi , ∈' key / ε mi , u' key / u mi ). The second load data is fed back to the macro model through and to obtain the boundary condition BC' mi of the stress vector σ ma of the adjusted macro model, the boundary condition BC' mi of the strain vector ε ma of the adjusted macro model and the boundary condition BC' mi of the displacement vector u ma of the adjusted macro model, wherein V i is the volume of e i of the micro model. After one round of regulation of the macro model, the finite element solution for the macro model is run again to obtain the updated first load data of the overall macro structure, that is, the stress σ ma , the strain ∈ ma and the displacement u ma data.

[0101] S209, feeding back the micro model based on the updated first load data to obtain the updated second load data of the micro model.

[0102] In this embodiment, for the updated macro model, new first load data σ ma / σ key , ∈ ma / ∈ key and uma / u key , based on and updating boundary conditions and loads of the micro model, wherein, BC' mi (σ) is the boundary condition of the stress vector of the adjusted micro model, BC' mi (∈) is the boundary condition of the strain vector of the adjusted micro model, BC' mi (u) is the boundary condition of the displacement stress vector of the adjusted micro model. After completing a round of regulation and control of the micro model, the finite element solution for the micro model is run again to obtain the second load data of the updated microstructure, that is, stress, strain and displacement data (σ' key / σ mi ,∈' key / ε mi ,u' key / u mi ).

[0103] S210, based on the updated second load data, return to feedback regulation of the macro model, and iteratively update the macro model and the micro model until the coupling end condition is met, to determine the corresponding target macro model and target micro model.

[0104] In this embodiment, the target macro model can be understood as the macro model of the last iteration before the end of coupling, and the target micro model can be understood as the micro model of the last iteration before the end of coupling.

[0105] Specifically, a macro model is established, first load parameters of the macro model are determined, a micro model is created / updated based on the first load parameters, second load parameters of the micro model are solved, the macro model is updated based on the second load parameters, and the first load parameters of the macro model are solved. The coupling update of the macro model and the micro model is iterated continuously, until the results of the macro and micro models meet wherein, is the stress of the macro model obtained in the ξ+1th iteration, is the stress of the macro model obtained in the ξth iteration, ξ is the index of iteration, and the value range of ξ is 1, 2,..., α, and α is the number of iterations, is the convergence threshold.

[0106] S211, based on the target macro model and the target micro model, extracting plastic strain data, and determining target load data of the steel skeleton light plate according to the plastic strain data.

[0107] In this embodiment, the plastic strain data can be understood as the force when the steel skeleton light plate appears plastic deformation.

[0108] Specifically, for each dangerous unit of the target region, based on determining its plastic strain at each loading time point, wherein, ∈ pl，β is the plastic strain of the βth dangerous unit, ∈ β is the strain of the βth dangerous unit, σ β is the stress of the βth dangerous unit, E is the elastic modulus of the material, β is the index of the dangerous unit, β ranges from 1, 2, …, B, B is the number of dangerous units. Traverse all dangerous units, at each loading time point, detect whether the plastic strain ∈ pl，β is greater than the preset plastic strain threshold value, and record the unit that first appears the plastic strain. According to the unit that first appears the plastic strain, determine the bearing capacity limit state point of the model, perform finite element network analysis, read the corresponding surface load from the analysis result, and determine the surface load bearing capacity of the steel skeleton light plate as the surface load, determine the basic combination design value of the uniform load of the numerical simulation, that is, the target load data.

[0109] For example, perform finite element network analysis, record the strain data of each unit, especially the change of plastic strain, record the strain data-time curve, analyze the strain data, find the unit that first appears the plastic strain and the corresponding time point by analyzing the plastic strain-loading time curve, Figure 3 is a stiffening rib stress cloud diagram provided by the second embodiment of the present application, as shown by the arrow of Figure 3 , the unit that first appears the plastic strain is the connection between the stiffening rib and the steel skeleton (red unit), continue to load until the plastic strain of the unit reaches the preset limit value, record the time point at this time, when the plastic strain grows to a certain preset limit value (for example, the yield strain or failure strain of the material), it is considered that the model reaches the bearing capacity limit state, and the loading time point at this time is the bearing capacity limit state point of the model, then the surface load at the bearing capacity limit state point is obtained. Figure 4 is a surface load-plastic strain curve diagram provided by the second embodiment of the present application, the vertical coordinate is the surface load, and the horizontal coordinate is the plastic strain, as shown by Figure 4 , the surface load-plastic strain curve shows that the surface load bearing capacity of the steel skeleton light plate is 23 kN / m2, and the surface load at the limit state point is converted into the basic combination design value of the uniform load, then according to and q design = q limit · xs, the target load data of the numerical simulation, that is, the basic combination design value of the uniform load q design is determined, wherein q limit is the surface load per unit area, and P limitFor the external surface load, A is the loaded area, xs is the load combination coefficient, xs e [0.5, 1.2].

[0110] The embodiment of the present application provides a load data determination method, which comprises the following steps: obtaining initial parameter data of a steel skeleton light plate, wherein the initial parameter data comprises geometric parameters, initial load parameters and performance parameters; calculating a dead load standard value of the steel skeleton light plate according to material density and steel plate volume in the initial load parameters; calculating an additional load standard combined design value of the steel skeleton light plate according to a load subitem coefficient, a load standard value and a load index in the initial load parameters; calculating an additional load basic combined design value of the steel skeleton light plate according to a dead load coefficient, a load standard value coefficient and a load combination coefficient in the initial load parameters; determining the dead load standard value, the additional load standard combined design value and the additional load basic combined design value as target load parameters, and determining the geometric parameters, the target load parameters and the performance parameters as target parameter data; constructing a macro model for the steel skeleton light plate according to the target parameter data, wherein the macro model is composed of multiple finite element network modules; extracting regional parameters of a target region from the macro model, and establishing a micro model for the steel skeleton light plate based on the regional parameters, wherein the micro model is composed of multiple finite element network units; determining second load data of the micro model, feeding back and regulating the macro model based on the second load data to obtain updated first load data of the macro model; feeding back and regulating the micro model based on the updated first load data to obtain updated second load data of the micro model; returning to feed back and regulate the macro model based on the updated second load data, and coupling and iteratively updating the macro model and the micro model until a coupling end condition is met, to determine a target macro model and a target micro model; and extracting plastic strain data based on the target macro model and the target micro model, and determining target load data of the steel skeleton light plate according to the plastic strain data. The above technical solution comprises the following steps: collecting detailed geometric parameters, load parameters and core material technical performance index parameters, generating a geometric model and a load distribution, and defining grid division and material properties by using a finite element network to construct a macro and micro model of the steel skeleton light plate; coupling analysis and iterative solution are performed to accurately extract a plastic strain-loading time curve of each part of a dangerous unit, determine the unit and its position where the plastic strain first appears, determine a bearing capacity limit state point of the model according to the unit where the plastic strain first appears, perform finite element network analysis, read the corresponding additional surface load from the analysis result, determine the additional surface load of the bearing capacity limit state point as the additional surface load bearing capacity of the steel skeleton prefabricated plate, obtain the additional surface load of the bearing capacity limit state point, convert the additional surface load of the limit state point into a uniform load basic combined design value, obtain a numerical simulation uniform load basic combined design value, perform an actual load test according to the numerical simulation additional uniform load basic combined design value, compare and analyze the numerical simulation additional uniform load basic combined design value and the ultimate bearing capacity of the test piece, and judge the consistency of the steel skeleton light plate simulation.Therefore, the application can effectively improve the accuracy of simulation results, and significantly improve the design reliability and safety of the steel skeleton light plate.

[0111] Embodiment three

[0112] Figure 5 is a structural schematic diagram of a load data determination device provided by the third embodiment of the application. As shown in the figure, the device comprises: Figure 5

[0113] a parameter data determination module 31, configured to acquire initial parameter data of a steel skeleton light plate, and perform data processing on the initial parameter data to obtain target parameter data;

[0114] a model establishment module 32, configured to establish a macro model and a micro model for the steel skeleton light plate through finite element network according to the target parameter data;

[0115] a load data determination module 33, configured to perform coupling iterative processing on the macro model and the micro model, and obtain target load data based on the coupled macro model and micro model when a coupling end condition is met.

[0116] The load data determination device adopted in the technical solution realizes safe, reliable and accurate model construction analysis and numerical simulation.

[0117] Optionally, the parameter data determination module 31 is specifically configured to:

[0118] acquire initial parameter data of a steel skeleton light plate, wherein the initial parameter data comprises geometric parameters, initial load parameters and performance parameters;

[0119] calculate a self-weight standard value of the steel skeleton light plate according to material density and steel plate volume in the initial load parameters;

[0120] calculate an external load standard combined design value of the steel skeleton light plate according to a load sub-item coefficient, a load standard value and a load index in the initial load parameters;

[0121] calculate an external load basic combined design value of the steel skeleton light plate according to a self-weight coefficient, a load standard value coefficient and a load combination coefficient in the initial load parameters;

[0122] determine the self-weight standard value, the external load standard combined design value and the external load basic combined design value as target load parameters, and determine the geometric parameters, the target load parameters and the performance parameters as target parameter data.

[0123] Optionally, the model establishment module 32 comprises: ​

[0124] a macro model establishing unit configured to construct a macro model for the steel skeleton light plate according to the target parameter data, wherein the macro model is composed of a plurality of finite element network modules;

[0125] a micro model establishing unit configured to extract region parameters of a target region from the macro model, and establish a micro model for the steel skeleton light plate based on the region parameters, wherein the micro model is composed of a plurality of finite element network units.

[0126] Optionally, the macro model establishing unit is specifically configured to:

[0127] establish a first geometric model of the steel skeleton light plate according to geometric parameters in the target parameter data;

[0128] determine a first grid density corresponding to each type of region, perform grid division on the first geometric model based on the first grid density, and form a plurality of finite element network modules;

[0129] apply a load to each of the finite element network modules according to target load parameters in the target parameter data, and determine boundary conditions of the macro model, and constrain each of the finite element network modules based on the boundary conditions.

[0130] Optionally, the micro model establishing unit is specifically configured to:

[0131] extract geometric size and shape of a target region from the macro model, and establish a second geometric model according to the geometric size and shape;

[0132] determine a second grid density corresponding to each type of region, perform grid division on the second geometric model based on the second grid density, and form a plurality of finite element network units;

[0133] determine first load data of a target region in the macro model, apply a load to each of the finite element network units based on the first load data, and determine boundary conditions of the micro model, and constrain each of the finite element network units based on the boundary conditions.

[0134] Optionally, the load data determining module 33 is specifically configured to:

[0135] determine second load data of the micro model, perform feedback regulation on the macro model based on the second load data, and obtain updated first load data of the macro model;

[0136] perform feedback regulation on the micro model based on the updated first load data, and obtain updated second load data of the micro model;

[0137] Return to feedback regulation of the macro model based on the updated second load data, coupling and iteratively updating the macro model and the micro model until a coupling end condition is met, to determine a target macro model and a target micro model;

[0138] Based on the target macro model and the target micro model, plastic strain data is extracted, and the target load data of the steel skeleton light plate is determined according to the plastic strain data.

[0139] Optionally, the device further comprises a consistency verification module for:

[0140] Determining the difference between the target load data and the ultimate bearing capacity of the steel skeleton light plate;

[0141] Taking the ratio of the difference and the target load data, comparing the ratio with a set consistency threshold to obtain a numerical simulation consistency result.

[0142] The load data determination device provided in the embodiments of the present application can execute the load data determination method provided in any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0143] Embodiment four

[0144] Figure 6 is a structural schematic diagram of an electronic device provided in Embodiment Four of the present application. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are merely examples, and are not intended to limit the implementations described and / or claimed in this document.

[0145] As Figure 6As shown, the electronic device 40 includes at least one processor 41, and a memory, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., connected to the at least one processor 41 in communication. The memory stores computer programs executable by the at least one processor 41, and the processor 41 can perform various appropriate actions and processes according to the computer programs stored in the read-only memory (ROM) 42 or loaded into the random access memory (RAM) 43 from the storage unit 48. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other through a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0146] Various components in the electronic device 40 are connected to the I / O interface 45, including an input unit 46, such as a keyboard, a mouse, etc., an output unit 47, such as various types of displays, a speaker, etc., a storage unit 48, such as a magnetic disk, an optical disk, etc., and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0147] The processor 41 can be various general and / or special-purpose processing components having processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 41 performs various methods and processes described above, such as the load data determination method.

[0148] In some embodiments, the load data determination method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the load data determination method described above can be performed. Alternatively, in other embodiments, the processor 41 can be configured to perform the load data determination method by any other appropriate means, such as by means of firmware.

[0149] The various embodiments of the systems and techniques described above can be implemented in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a load programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0150] Computer programs used to implement the processes of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program, when executed, can cause instructions defined in the flow charts and / or block diagrams to be implemented. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as a standalone software package and partially on a remote machine or entirely on a remote machine or server.

[0151] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store computer programs for use by or in connection with an instruction execution system, apparatus, or device. Computer-readable storage media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0152] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0153] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0154] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0155] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be executed in parallel, executed in sequence, or executed in a different order, as long as the desired results of the present disclosure are achieved, and the present disclosure is not limited herein.

[0156] The specific embodiments described above are not intended to be limiting, and persons skilled in the art will appreciate that various modifications, combinations, sub-combinations and alternatives can be made to the specific embodiments without departing from the spirit and principles of the disclosure. Accordingly, the disclosure is not limited to the specific embodiments described above, but only by the scope of the appended claims.

Claims

1. A load data determination method characterized by, The method comprises the following steps: obtaining initial parameter data of the steel skeleton light plate, and performing data processing on the initial parameter data to obtain target parameter data; establishing a macro model and a micro model for the steel skeleton light plate through a finite element network according to the target parameter data; performing coupling iteration processing on the macro model and the micro model, and obtaining target load data based on the coupled macro model and micro model when the coupling end condition is met; wherein, the method for obtaining initial parameter data of the steel skeleton light plate and performing data processing on the initial parameter data to obtain target parameter data comprises: obtaining initial parameter data of the steel skeleton light plate, wherein the initial parameter data comprises geometric parameters, initial load parameters and performance parameters; calculating a self-weight standard value of the steel skeleton light plate according to the material density and the steel plate volume in the initial load parameters; calculating a combined design value of the external load standard of the steel skeleton light plate according to the load sub-coefficient, the load standard value and the load index in the initial load parameters; calculating a basic combined design value of the external load of the steel skeleton light plate according to the self-weight coefficient, the load standard value coefficient and the load combination coefficient in the initial load parameters; determining the self-weight standard value, the combined design value of the external load standard and the basic combined design value of the external load as target load parameters, and determining the geometric parameters, the target load parameters and the performance parameters as target parameter data.

2. The method of claim 1, wherein, The method for establishing a macro model and a micro model for the steel skeleton light plate through a finite element network according to the target parameter data comprises: constructing a macro model for the steel skeleton light plate according to the target parameter data, wherein the macro model is composed of multiple finite element network modules; extracting regional parameters of a target region from the macro model, and establishing a micro model for the steel skeleton light plate based on the regional parameters, wherein the micro model is composed of multiple finite element network units.

3. The method of claim 2, wherein, The method for constructing a macro model for the steel skeleton light plate according to the target parameter data comprises: establishing a first geometric model of the steel skeleton light plate according to the geometric parameters in the target parameter data; determining a first grid density corresponding to each type of region, performing grid division on the first geometric model based on the first grid density to form multiple finite element network modules; applying load to each finite element network module according to the target load parameters in the target parameter data, and determining the boundary conditions of the macro model, and constraining each finite element network module based on the boundary conditions.

4. The method of claim 2, wherein, The method for extracting regional parameters of a target region from the macro model, and establishing a micro model for the steel skeleton light plate based on the regional parameters comprises: extracting the geometric size and shape of the target region from the macro model, and establishing a second geometric model according to the geometric size and shape; determining a second grid density corresponding to each type of region, and performing grid division on the second geometric model based on the second grid density to form multiple finite element network units; determining first load data of a target region in the macro model, applying load to each of the network finite element units based on the first load data, and determining boundary conditions of the micro model, and constraining each of the network finite element units based on the boundary conditions.

5. The method of claim 1, wherein, The coupling iterative processing of the macro model and the micro model is performed, and when a coupling end condition is met, target load data is obtained based on the coupled macro model and the micro model, including: determining second load data of the micro model, feeding back and regulating the macro model based on the second load data to obtain updated first load data of the macro model; feedback regulation of the micro model based on the updated first load data to obtain updated second load data of the micro model; returning to feedback regulation of the macro model based on the updated second load data, and iteratively updating the macro model and the micro model until the coupling end condition is met, to determine a corresponding target macro model and a target micro model; extracting plastic strain data based on the target macro model and the target micro model, and determining target load data of the steel skeleton light plate according to the plastic strain data.

6. The method of claim 1, wherein, Further comprising: determining the difference between the target load data and the ultimate bearing capacity of the steel skeleton light plate; taking the ratio of the difference to the target load data, comparing the ratio with a set consistency threshold to obtain a numerical simulation consistency result.

7. A load data determination apparatus characterized by comprising: including: a parameter data determination module configured to obtain initial parameter data of a steel skeleton light plate, and perform data processing on the initial parameter data to obtain target parameter data; a model establishment module configured to establish a macro model and a micro model for the steel skeleton light plate through a network finite element based on the target parameter data; a load data determination module configured to perform coupling iterative processing on the macro model and the micro model, and obtain target load data based on the coupled macro model and the micro model when a coupling end condition is met. The parameter data determination module is specifically configured to: obtain initial parameter data of a steel skeleton light plate, the initial parameter data including geometric parameters, initial load parameters, and performance parameters; calculate a self-weight standard value of the steel skeleton light plate based on material density and steel plate volume in the initial load parameters; calculate a combined design value of external load standard of the steel skeleton light plate based on load sub-coefficients, load standard values, and load indexes in the initial load parameters; calculate a combined design value of external load basic of the steel skeleton light plate based on self-weight coefficients, load standard value coefficients, and load combination coefficients in the initial load parameters; determine the self-weight standard value, the combined design value of external load standard, and the combined design value of external load basic as target load parameters, and determine the geometric parameters, the target load parameters, and the performance parameters as target parameter data.

8. An electronic device, comprising: including: at least one processor; and a memory in communication connection with the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the load data determination method in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to implement the load data determination method in any one of claims 1-6 when executed.

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