Load data determination method and device, equipment and storage medium

Abstract

The invention discloses a load data determination method and device, equipment and a storage medium, and the method comprises the steps: obtaining initial parameter data of a steel skeleton light plate, and carrying out the data processing of the initial parameter data, and obtaining target parameter data; according to the target parameter data, establishing a macroscopic model and a microscopic model for the steel skeleton light plate through a finite element network; and performing coupling iteration processing on the macroscopic model and the microscopic model, and when a coupling ending condition is met, obtaining target load data based on the macroscopic model and the microscopic model after coupling. According to the technical scheme, safe, reliable and accurate model construction analysis and numerical simulation are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of construction and civil engineering, and in particular to a method, device, equipment and storage medium for determining load data. Background Art

[0002] As a new type of lightweight, energy-saving and environmentally friendly material, steel frame light plate is widely used in industrial and civil construction fields such as coal transport trestle. Its unique structure and performance characteristics, such as light weight, load-bearing, energy-saving, fireproof, etc., make it an indispensable part of modern construction projects.

[0003] As the construction industry's requirements for material performance continue to increase, in-depth research and analysis of steel frame lightweight panels is particularly important. Summary of the invention

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

[0005] In a first aspect, an embodiment of the present disclosure provides a method for determining load data, comprising:

[0006] Acquiring initial parameter data of the 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 macro model and the micro model are coupled and iterated, and when a coupling end condition is met, target load data is obtained based on the coupled macro model and the micro model.

[0009] In a second aspect, an embodiment of the present disclosure provides a load data determination device, comprising:

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

[0011] A model building module, used to build a macro model and a micro model for the steel skeleton light plate through a finite element network according to the target parameter data;

[0012] The load data determination module is used to perform coupling iterative processing on the macro model and the micro model, and when the coupling end condition is met, obtain the target load data based on the coupled macro model and the micro model.

[0013] In a third aspect, an embodiment of the present disclosure provides an electronic device, including:

[0014] at least one processor; and

[0015] a memory communicatively connected to at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute a load data determination method provided in the above-mentioned first aspect embodiment.

[0017] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a processor to implement a load data determination method provided in the embodiment of the first aspect above when executing.

[0018] A load data determination method, device, equipment and storage medium of an embodiment of the present invention obtains initial parameter data of a steel skeleton light plate, and processes the initial parameter data to obtain target parameter data; based on 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 the coupling end condition is met, the target load data is obtained based on the coupled macro model and the micro model. The above technical solution realizes safe, reliable and accurate model construction analysis and numerical simulation.

[0019] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 is a flow chart of a load data determination method provided in Embodiment 1 of the present invention;

[0022] Figure 2 is a flow chart of a method for determining load data provided by Embodiment 2 of the present invention;

[0023] Figure 3 This is a stiffening rib stress cloud diagram provided by the second embodiment of the present invention;

[0024] Figure 4 is a surface load-plastic strain curve diagram provided in the second embodiment of the present invention;

[0025] Figure 5 is a structural schematic diagram of a load data determination device provided in Embodiment 3 of the present invention;

[0026] Figure 6 It is a structural schematic diagram of an electronic device provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

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

[0029] Steel skeleton light plate is widely used in the field of construction and civil engineering because of its advantages of light weight, high strength and convenient construction. However, when conducting structural design and bearing capacity analysis of steel skeleton light plate, finite element analysis (FEA) method is usually relied on. Traditional finite element analysis mostly adopts a single scale model, which cannot fully consider the multi-scale characteristics of steel skeleton light plate, such as the interaction between different structural parts such as steel skeleton, reinforcing ribs and core materials. 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, thereby affecting the reliability and safety of the structural design. In addition, the prior art is usually unable to accurately predict the response of the structure under different load conditions during the load test, which increases the uncertainty and potential risks of the design. Therefore, there is an urgent need for 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. Therefore, the present invention provides a multi-scale finite element simulation method of steel skeleton light plate for load test to solve the above technical problems.

[0030] Embodiment 1

[0031] Figure 1 It is a flow chart of a load data determination method provided in Example 1 of the present invention. This embodiment can be applied to multi-scale finite element simulation of steel frame light panels for load tests. The method can be executed by a load data determination device, which can be implemented in the form of hardware and / or software.

[0032] like Figure 1 As shown, the method includes:

[0033] S101, obtaining initial parameter data of a steel skeleton light plate, and performing data processing on the initial parameter data to obtain target parameter data.

[0034] In this embodiment, the initial parameter data can be understood as the 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 factor, load standard value, load index, deadweight coefficient, load standard value coefficient and load combination coefficient, and the performance parameters are the technical performance index parameters of the core material, including dry apparent density, cube strength, splitting tensile strength, elastic modulus, thermal conductivity and heat storage coefficient. The target parameter data can be understood as the parameter information used to construct the steel skeleton light plate model, which is determined by the initial parameter data. The target parameter data includes geometric parameters, target load parameters and performance parameters, wherein the geometric parameters and performance parameters are the same as those in the initial parameter data, and the target load parameters include the standard value of the deadweight of the plate, the design value of the standard combination value of the external load and the design value of the basic combination value of the external load, which are obtained by the initial load parameters.

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

[0036] S102. 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.

[0037] In this 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 macroscopic geometric model is established based on the geometric parameters in the target parameter data, the geometric model is meshed according to different mesh densities, smaller mesh units are divided in the stress concentration area, larger mesh units are divided in the unit with smaller stress change, and boundary conditions are applied to each mesh based on the target load parameters in the target parameter data, and the support type and support node of the model are determined, and corresponding constraints are applied to the selected process nodes to complete the establishment of the macroscopic model. After the establishment of the macroscopic model is completed, the load parameter information (stress, strain and displacement) of the macroscopic model is solved and the size information of the key area in the macroscopic model is extracted. Based on the size information, a microscopic geometric model is established, the geometric model is meshed according to different mesh densities, smaller mesh units are divided in the key area, and loads are applied to the microscopic model based on the load parameter information determined by the macroscopic model, and corresponding boundary conditions are determined to impose constraints on the microscopic model to complete the establishment of the microscopic model.

[0039] S103, performing coupling iterative processing on the macro model and the micro model, and when the coupling end condition is met, obtaining target load data based on the coupled macro model and the micro model.

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

[0041] Specifically, after the establishment of the micro model is completed, the load parameter information (stress, strain and displacement) of the micro model is solved, and the load parameter information is fed back to the macro model, the corresponding boundary conditions of the macro model are regulated, and the load parameter information of the macro model after the regulation is solved. Based on the new load parameter information of the macro model, the corresponding boundary conditions of the micro model are regulated, and the updated load parameter information of the 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 two adjacent rounds of iterations of the macro model is less than the set convergence threshold, and it is determined that the coupling end condition is met. At this time, the unit in which the plastic strain appears earliest is determined. According to the unit in which the plastic strain appears earliest, the bearing capacity limit state point of the model is determined, and the finite element network analysis is performed. The corresponding external surface load is read from the analysis results, and the external surface load at the bearing capacity limit state point is determined as the external surface load bearing capacity of the steel skeleton precast panel. Based on the external surface load bearing capacity, the basic combination design value of the uniformly distributed load of the numerical simulation is calculated to obtain the target load parameter.

[0042] A method for determining load data provided by an embodiment of the present invention includes 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, establishing a macro model and a micro model for the steel skeleton light plate through a finite element network; performing coupling and iterative processing on the macro model and the micro model, and obtaining target load data based on the coupled macro model and the micro model when the 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 this embodiment, the method further includes:

[0044] a. Determine the difference between the target load data and the ultimate bearing capacity of the steel frame lightweight panel.

[0045] In this embodiment, after simulating the target load parameters, the target load parameters are checked for consistency based on actual experimental data. First, the difference between the simulated target load parameters and the actual ultimate bearing capacity of the steel skeleton light panel is determined.

[0046] Specifically, according to the basic combination design value of the uniformly distributed load added by numerical simulation, the actual load test is carried out, the displacement, stress and strain data during the loading process are recorded, and the actual bearing capacity data is obtained based on the displacement, stress and strain data.

[0047] Specifically, stress sensors, strain gauges and displacement sensors are installed at 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. Each time a load value is added, the corresponding stress, strain and displacement are obtained, as shown in the following formula: in, For the The load value of the level, is the loading level, Δq is the load increment of each level; under each level of load, the corresponding displacement, stress and strain data are recorded Continue loading to the basic load combination design value q design , the corresponding displacement, stress and strain data are {δ design , σ design ,∈ design Generate a loading-stress curve and analyze the loading-stress curve to determine the ultimate bearing capacity of the specimen, that is, the uniformly distributed load when the specimen reaches the limit state, and obtain the ultimate bearing capacity q of the steel skeleton light plate actual According to q cz =q actual -q design Determine the difference q between the target load parameter and the ultimate bearing capacity of the steel skeleton light plate cz , where q design It is the basic combination design value of the external uniformly distributed 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, and obtain the numerical simulation consistency result.

[0049] In this embodiment, the setting of 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 used to characterize the accuracy of the simulation result of the steel skeleton light plate model.

[0050] Specifically, through Determine the consistency coefficient T of the steel frame light plate simulation, compare the consistency coefficient T of the steel frame light plate simulation with the set consistency threshold yz, and judge the consistency degree between the numerical simulation and the actual test results. When T>yz, the error exceeds the acceptable range, and the consistency result of the numerical simulation is determined to have a low consistency degree; when T≤yz, the error is within the acceptable range, and the consistency result of the numerical simulation is determined to have a 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, providing reliable technical support for actual engineering applications.

[0052] Embodiment 2

[0053] Figure 2 It is a flow chart of a load data determination method provided in the second embodiment of the present invention. This embodiment is a further optimization of any of the above embodiments and can be applied to multi-scale finite element simulation of steel frame light panels for load tests. The method can be executed by a load data determination device, which can be implemented in the form of hardware and / or software.

[0054] like Figure 2 As shown, the method includes:

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

[0056] In this 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 include at least material density, steel plate volume, load partial coefficient, load standard value, load index, deadweight 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, cube strength, splitting tensile strength, elastic modulus, thermal conductivity and heat storage coefficient.

[0057] S202. Calculate the standard value of the deadweight of the steel frame lightweight panel based on the material density and steel plate volume in the initial load parameters.

[0058] In this embodiment, the self-weight standard value represents the weight of the steel frame light panel itself.

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

[0060] S203. Calculate the standard combined design value of the additional load of the steel frame light plate according to the load partial factor, load standard value and load index in the initial load parameters.

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

[0062] Specifically, according to the load partial factor γ in the initial load parameters θ 、Standard values ​​of various loads Q θ With load index θ, through The standard combined design value Q of the external load on the steel frame light plate is calculated d Among them, the standard values ​​of various loads Q θ Including live load, wind load and snow load, the load index θ ranges from 1, 2, ..., N.

[0063] S204. The basic combined design value of the external load of the steel frame light plate is calculated based on the self-weight coefficient, load standard value coefficient and load combination coefficient in the initial load parameters.

[0064] In this embodiment, the design value of the basic combination value of the external load includes a combination of permanent load (such as deadweight) and variable load (such as live load and wind load). The deadweight coefficient is the coefficient of the standard value of the deadweight of the steel frame light plate, and the load standard value coefficient is the coefficient of the standard value of various loads.

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

[0066] S205. Determine the self-weight standard value, the standard combination design value of the applied load, and the basic combination design value of the applied load as target load parameters, and determine the geometric parameters, target load parameters, and performance parameters as target parameter data.

[0067] In this embodiment, after determining several target load parameters such as the self-weight standard value, the standard combination design value of the external load and the basic combination design value of the external load based on each initial load parameter, the target load parameters replace the initial load parameters, so that the geometric parameters, target load parameters and performance parameters are combined to form target parameter data.

[0068] S206. Construct 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.

[0069] In this 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 target load parameters in the target parameter data are processed, and a macro model of the steel skeleton light plate is constructed using a finite element network.

[0071] Optionally, a macro model for the steel skeleton lightweight plate is constructed based on the target parameter data, including:

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

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

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

[0075] S2062. Determine a first mesh density corresponding to each type of region, and mesh the first geometric model based on the first mesh density to form a plurality of finite element network modules.

[0076] In this embodiment, the first grid density can be understood as the grid density when the macro model is divided, which is associated with the position of the steel frame lightweight panel. When the divided type area is a key area such as a connection, the first grid density is large. When the divided type area is a non-critical area such as a large area plate, the first grid density is small.

[0077] Specifically, select the appropriate unit type according to the structural characteristics, such as solid unit; use the finite element network to automatically generate the mesh, according to The first geometric model is meshed to divide the geometric domain of the macro structure into several macro finite element network modules. i In the formula, Ω ma is the first macroscopic geometric model, 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] When dividing the grid, control the first grid density to ensure that finer grids are used in key areas (such as joints), that is, use smaller units in areas of stress concentration to improve calculation accuracy; use larger units in areas with smaller stress changes to reduce the amount of calculation. The first grid density is determined as follows: In the formula, s mesh is the first grid density, fine is a fine grid, which means more nodes and units, and is used for higher-precision calculations. It is usually used in critical areas (such as connections). Coarse is a coarse grid, which means fewer nodes and units, and is used to reduce calculation costs. It is usually used in non-critical areas.

[0079] S2063. Apply load to each finite element network module according to the target load parameters in the target parameter data, determine the boundary conditions of the macro model, and constrain each finite element network module based on the boundary conditions.

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

[0081] Specifically, according to the target load parameters in the target parameter data, the total load is applied to the finite element network module. The total load q total The calculation formula is as follows: total =G+Q d +Q c , where G is the deadweight load, Q d is the personnel load, Q c is the wind load; according to the actual working conditions (such as dead load or live load), the load is applied. Among them, F ma is the load vector applied to the macro model, F i is the force applied by the i-th macroscopic finite element unit.

[0082] Determine the support type and select nodes, set boundary conditions BC on the selected support nodes ma , apply corresponding constraints to simulate the real support conditions. Specifically, select the simply supported support as the support node. The translational degree of freedom of the simply supported support is constrained, but the rotational degree of freedom is not constrained; select the corresponding node in the finite element network. Usually, these nodes are located at the edge or specific position of the structure; for this 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 and y directions in the horizontal plane is restricted. z When =0, the displacement of the structure in the vertical direction is restricted.

[0083] S207, extracting regional parameters of the target area 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 a plurality of finite element network units.

[0084] In this embodiment, the target area can be understood as the key area of ​​the steel skeleton light plate, such as the connection area. The regional parameters can be understood as the parameter data such as the geometric size, shape, stress, strain and displacement of the key area. The finite element network unit can be understood as the grid divided in the microscopic model based on the finite element network.

[0085] In the finite element network, the technical performance index parameters of the core material are input into the macro model of the steel skeleton light plate, that is, the dry apparent density, cube strength, splitting tensile strength, elastic modulus, thermal conductivity, thermal storage coefficient and Poisson's ratio are assigned to the core material part in the macro model. The regional parameter data of the connection between the stiffening rib and the steel skeleton are extracted from the macro model of the steel skeleton light plate, and the micro model is established using the finite element network based on the regional parameter data of the connection between the stiffening rib and the steel skeleton.

[0086] Optionally, regional parameters of the target area are extracted from the macro model, and a micro model for the steel skeleton lightweight panel is established based on the regional parameters, including:

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

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

[0089] In this embodiment, before establishing the microscopic model, a finite element solution is run to calculate the ma =R∈ ma , and Obtain the stress vector σ of the steel skeleton light plate macro model ma , strain vector∈ ma and displacement vector u ma , where K ma is the stiffness matrix of the macro model, F ma is the load vector of the macro model, R is the constitutive matrix of the material, and the stress, strain and displacement data (σ key ,∈ key ,u key ).

[0090] The geometric size and shape of the target area are extracted from the macroscopic model, and a second geometric model, namely the microscopic model Ω, is established based on the geometric size and shape of the target area. mi .

[0091] S2072. Determine a second mesh density corresponding to each type of region, and mesh the second geometric model based on the second mesh density to form a plurality of finite element network units.

[0092] In this embodiment, the second grid density can be understood as the grid density when the micro model is divided, and is also related to the position of the steel frame light plate. When the divided type area is a key area such as a connection, the second grid density is large. When the divided type area is a non-critical area such as a large area plate, the first grid density is small.

[0093] Specifically, the second grid density of each area is determined, the second grid density of the key target area (such as the connection) is large, and the second grid density of the non-key area is small. The second geometric model is meshed with a second mesh density to ensure that the mesh density in the key area is high enough, and the second geometric model of the microstructure is divided into a plurality of micro finite element network units. j , where e j is the jth micro-finite element unit, j is the index of the micro-finite element, the value range of j is 1, 2, ..., m, and m is the number of micro-finite element units.

[0094] S2073. Determine the first load data of the target area in the macro model, apply a load to each finite element network unit based on the first load data, and determine the boundary conditions of the micro model, and constrain each finite element network unit based on the boundary conditions.

[0095] In this embodiment, the first load data can be understood as the stress, strain and displacement data of the target area solved by the macro model, that is, the stress, strain and displacement data (σ key , key ,u key ).

[0096] Specifically, according to the stress σ of the target area extracted from the macro model key ,based on Apply the corresponding load to the micro model, where F mi is the load vector applied to the microscopic model, V j is the e of the microscopic model j is the volume of , and K is the derivative matrix used to convert stress into nodal forces.

[0097] The displacement data u extracted from the macro model key, through B.C. mi，τ =u key，τ Form boundary conditions and apply them to the boundary nodes of the microscopic model, where BC mi，τ is the displacement boundary condition of the microscopic model boundary node τ, u key，τ is the displacement vector of the key node τ, the value range of τ is 1, 2, ..., Υ, Υ is the number of key nodes.

[0098] S208, determining second load data of the micro model, and performing feedback control on 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 microscopic model.

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

[0101] S209: performing feedback control on the microscopic model based on the updated first load data to obtain updated second load data of the microscopic model.

[0102] In this embodiment, for the updated macro model, new first load data σ is extracted. ma / σ key ,∈ ma / ∈ key and uma / u key ,based on and Update the boundary conditions and loads of the micromodel, where BC' mi (σ) is the boundary condition of the stress vector of the adjusted microscopic model, BC' mi (∈) is the boundary condition of the strain vector of the adjusted microscopic model, BC' mi (u) is the boundary condition of the displacement stress vector of the adjusted micro-model. After completing a round of adjustment of the micro-model, the finite element solution for the micro-model is run again to obtain the second load data of the updated micro-structure, that is, the 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, feedback control is performed on the macro model again, and the macro model and the micro model are updated through coupling iteration until the coupling end condition is met, and the corresponding target macro model and target micro model are determined.

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

[0105] Specifically, a macro model is established, the first load parameter of the macro model is determined, the micro model is created / updated based on the first load parameter, the second load parameter of the micro model is obtained by solving, the macro model is updated based on the second load parameter, and the first load parameter of the macro model is obtained by solving, and the coupling update of the macro model and the micro model is performed in a continuous cycle until the results of the macro and micro models meet the requirements. in, is the stress of the macro model obtained at the ξ+1th iteration, is the stress of the macro model obtained in the ξth iteration, ξ is the iteration index, and the value range of ξ is 1, 2, ..., α, where α is the number of iterations. is the convergence threshold.

[0106] S211. Extract plastic strain data based on the target macro model and the target micro model, and determine the 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 undergoes plastic deformation.

[0108] Specifically, for each dangerous unit in the target area, based on Determine its plastic strain at each loading (applying load) time point, where ∈ 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, and the value range of β is 1, 2, ..., B, where B is the number of dangerous units. Traverse all dangerous units and detect the plastic strain ∈ pl，β Whether it is greater than the preset plastic strain threshold, record the unit where plastic strain first appears. According to the unit where plastic strain first appears, determine the ultimate state point of the model, perform finite element network analysis, read the corresponding external surface load from the analysis results, determine the external surface load at the ultimate state point of the bearing capacity as the external surface load bearing capacity of the steel skeleton light plate, and determine the basic combination design value of the uniformly distributed load of the numerical simulation, that is, the target load data.

[0109] For example, a finite element network analysis is performed to record the strain data of each unit, especially the change of plastic strain, record the change curve of strain data over time, analyze the strain data, and find the unit with the earliest 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 invention, such as Figure 3 As shown by the arrow, the unit where plastic strain first appears is the connection between the stiffener and the steel skeleton (red unit). Continue loading until the plastic strain of the unit reaches the preset limit value, and record the time point at this time. When the plastic strain grows to a preset limit value (for example, the yield strain or failure strain of the material), it is considered that the model has reached the ultimate bearing capacity state. The loading time point at this time is the ultimate bearing capacity state point of the model, and the applied surface load at the ultimate bearing capacity state point is obtained. Figure 4 is a surface load-plastic strain curve diagram provided by the second embodiment of the present invention, wherein the ordinate is the surface load and the abscissa is the plastic strain. Figure 4 As shown in the figure, the surface load-plastic strain curve shows that the bearing capacity of the steel skeleton light plate is 23kN / m2. The applied surface load at the limit state point is converted into the basic combined design value of the uniformly distributed load. According to and q design =q limit xs determines the target load data for numerical simulation, i.e. the basic combined design value of uniformly distributed load q design , where q limit is the applied surface load per unit area, P limitis the applied surface load, A is the loaded area, xs is the load combination coefficient, xs∈[0.5,1.2].

[0110] A load data determination method provided by an embodiment of the present invention includes obtaining initial parameter data of a steel skeleton light plate, the initial parameter data including geometric parameters, initial load parameters and performance parameters; calculating the self-weight standard value of the steel skeleton light plate according to the material density and steel plate volume in the initial load parameters; calculating the standard combination design value of the external load of the steel skeleton light plate according to the load partial coefficient, load standard value and load index in the initial load parameters; calculating the basic combination design value of the external load of the steel skeleton light plate according to the self-weight coefficient, load standard value coefficient and load combination coefficient in the initial load parameters; determining the self-weight standard value, the standard combination design value of the external load and the basic combination design value of the external load as target load parameters, and determining the geometric parameters, target load parameters and performance parameters as target parameter data; constructing a macro model for the steel skeleton light plate according to the target parameter data, wherein In the method, the macro model is composed of multiple finite element network modules; the regional parameters of the target area are extracted from the macro model, and a micro model for the steel skeleton light plate is established based on the regional parameters, wherein the micro model is composed of multiple finite element network units; the second load data of the micro model is determined, and the macro model is feedback-regulated based on the second load data to obtain the first load data after the macro model is updated; the micro model is feedback-regulated based on the updated first load data to obtain the second load data after the micro model is updated; the macro model is feedback-regulated based on the updated second load data, and the macro model is coupled and iteratively updated, until the coupling end condition is met, and the corresponding target macro model and target micro model are determined; the plastic strain data is extracted based on the target macro model and the target micro model, and the target load data of the steel skeleton light plate is determined according to the plastic strain data. The above technical scheme generates geometric models and load distribution by collecting detailed geometric parameters, load parameters and core material technical performance index parameters, and uses finite element network for meshing and material property definition to construct macro and micro models of steel skeleton light panels. Through coupling analysis and iterative solution, the plastic strain-loading time curve of each dangerous unit is accurately extracted, and the unit and its position where plastic strain appears first are determined. According to the unit where plastic strain appears first, the bearing capacity limit state point of the model is determined, and finite element network analysis is performed to read the analysis results. Take the corresponding external surface load, determine the external surface load at the ultimate limit state point as the external surface load bearing capacity of the steel skeleton precast panel, obtain the external surface load at the ultimate limit state point, convert the external surface load at the ultimate limit state point into the basic combination design value of uniformly distributed load, obtain the basic combination design value of uniformly distributed load of numerical simulation, carry out actual load test according to the basic combination design value of external uniformly distributed load of numerical simulation, compare and analyze the basic combination design value of external uniformly distributed load of numerical simulation and the ultimate bearing capacity of the specimen, and judge the consistency of the simulation of steel skeleton light plate.Therefore, the present invention can effectively improve the accuracy of simulation results and significantly enhance the design reliability and safety of steel frame light panels.

[0111] Embodiment 3

[0112] Figure 5 Schematic diagram of a load data determination device provided by Embodiment 3 of the present invention. Figure 5 As shown, the device comprises:

[0113] The parameter data determination module 31 is used to obtain the initial parameter data of the steel skeleton light plate and perform data processing on the initial parameter data to obtain the target parameter data;

[0114] A model building module 32, used to build a macroscopic model and a microscopic model for the steel skeleton light plate through a finite element network according to the target parameter data;

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

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

[0117] Optionally, the parameter data determination module 31 is specifically used for:

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

[0119] The standard value of the deadweight of the steel skeleton light plate is calculated based on the material density and the steel plate volume in the initial load parameters;

[0120] The standard combined design value of the applied load of the steel skeleton light plate is calculated according to the load partial coefficient, the load standard value and the load index in the initial load parameters;

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

[0122] The self-weight standard value, the external load standard combination design value and the external load basic combination design value are determined as target load parameters, and the geometric parameters, the target load parameters and the performance parameters are determined as target parameter data.

[0123] Optionally, the model building module 32 includes:

[0124] A macro model building unit, used for building 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 building unit is used to extract regional parameters of the target area from the macro-model, and to build a micro-model for the steel skeleton lightweight panel based on the regional parameters, wherein the micro-model is composed of a plurality of finite element network units.

[0126] Optional macro model building unit, specifically used for:

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

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

[0129] A load is applied to each of the finite element network modules according to the target load parameters in the target parameter data, and a boundary condition of the macro model is determined, and each of the finite element network modules is constrained based on the boundary condition.

[0130] Optional, micro-model building unit, specifically used for:

[0131] Extracting geometric dimensions and shapes of the target area from the macro model, and establishing a second geometric model based on the geometric dimensions and shapes;

[0132] Determining a second mesh density corresponding to each type of region, and meshing the second geometric model based on the second mesh density to form a plurality of finite element network units;

[0133] Determine the first load data of the target area in the macro model, apply load to each of the finite element network units based on the first load data, and determine the 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 determination module 33 is specifically used to:

[0135] Determining second load data of the microscopic model, and performing feedback control on the macroscopic model based on the second load data to obtain updated first load data of the macroscopic model;

[0136] Performing feedback control on the microscopic model based on the updated first load data to obtain updated second load data of the microscopic model;

[0137] Based on the updated second load data, the macro model is feedback-controlled again, and the macro model and the micro model are coupled and iteratively updated until the coupling end condition is met, and the corresponding target macro model and target micro model are determined;

[0138] Plastic strain data are extracted based on the target macroscopic model and the target microscopic model, and target load data of the steel skeleton light panel are determined according to the plastic strain data.

[0139] Optionally, the device further includes a consistency checking module, configured to:

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

[0141] The ratio of the difference to the target load data is taken, and the ratio is compared with a set consistency threshold to obtain a numerical simulation consistency result.

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

[0143] Embodiment 4

[0144] Figure 6 : is a structural schematic diagram of an electronic device provided in Embodiment 4 of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, 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 implementation of the present invention described and / or required herein.

[0145] like Figure 6As shown, the electronic device 40 includes at least one processor 41, and a memory connected to the at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 to the random access memory (RAM) 43. 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] A number of 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, speakers, etc.; a storage unit 48, such as a 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 may be a variety of general and / or special processing components with 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 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 executes the various methods and processes described above, such as the load data determination method.

[0148] In some embodiments, the load data determination method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on 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 may be performed. Alternatively, in other embodiments, the processor 41 may be configured to perform the load data determination method in any other appropriate manner (e.g., by means of firmware).

[0149] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0150] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0151] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0152] To provide interaction with a user, the systems and techniques described herein may 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 trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0153] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may 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] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may 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 difficult management and weak business scalability in traditional physical hosts and VPS services.

[0155] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0156] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining load data, characterized in that: include: Acquiring initial parameter data of the 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 macroscopic model and a microscopic model for the steel skeleton light plate are established through a finite element network; The macro model and the micro model are coupled and iterated, and when a coupling end condition is met, target load data is obtained based on the coupled macro model and the micro model.

2. The method according to claim 1, characterized in that The obtaining of initial parameter data of the steel skeleton light plate and data processing of the initial parameter data to obtain target parameter data include: Acquiring initial parameter data of the steel skeleton light plate, wherein the initial parameter data includes geometric parameters, initial load parameters and performance parameters; The standard value of the deadweight of the steel skeleton light plate is calculated based on the material density and the steel plate volume in the initial load parameters; The standard combined design value of the applied load of the steel skeleton light plate is calculated according to the load partial coefficient, the load standard value and the load index in the initial load parameters; The basic combined design value of the external load of the steel skeleton light plate is calculated according to the self-weight coefficient, the load standard value coefficient and the load combination coefficient in the initial load parameters; The self-weight standard value, the external load standard combination design value and the external load basic combination design value are determined as target load parameters, and the geometric parameters, the target load parameters and the performance parameters are determined as target parameter data.

3. The method according to claim 1, characterized in that The method of establishing a macroscopic model and a microscopic model for the steel skeleton light plate through a finite element network according to the target parameter data includes: Constructing a macro model for the steel skeleton light panel according to the target parameter data, wherein the macro model is composed of a plurality of finite element network modules; The regional parameters of the target area are extracted from the macro model, and a micro model for the steel skeleton lightweight panel is established based on the regional parameters, wherein the micro model is composed of a plurality of finite element network units.

4. The method according to claim 3, characterized in that The step of constructing a macro model for the steel skeleton light plate according to the target parameter data includes: Establishing a first geometric model of the steel skeleton light plate according to the geometric parameters in the target parameter data; Determine a first mesh density corresponding to each type of region, and mesh the first geometric model based on the first mesh density to form a plurality of finite element network modules; A load is applied to each of the finite element network modules according to the target load parameters in the target parameter data, and a boundary condition of the macro model is determined, and each of the finite element network modules is constrained based on the boundary condition.

5. The method according to claim 3, characterized in that: The step of extracting the regional parameters of the target area from the macro model and establishing a micro model for the steel skeleton light plate based on the regional parameters includes: Extracting geometric dimensions and shapes of the target area from the macro model, and establishing a second geometric model based on the geometric dimensions and shapes; Determining a second mesh density corresponding to each type of region, and meshing the second geometric model based on the second mesh density to form a plurality of finite element network units; Determine the first load data of the target area in the macro model, apply load to each of the finite element network units based on the first load data, and determine the boundary conditions of the micro model, and constrain each of the finite element network units based on the boundary conditions.

6. The method according to claim 1, characterized in that The coupling iterative 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, including: Determining second load data of the microscopic model, and performing feedback control on the macroscopic model based on the second load data to obtain updated first load data of the macroscopic model; Performing feedback control on the microscopic model based on the updated first load data to obtain updated second load data of the microscopic model; Based on the updated second load data, the macro model is feedback-controlled again, and the macro model and the micro model are coupled and iteratively updated until the coupling end condition is met, and the corresponding target macro model and target micro model are determined; Plastic strain data are extracted based on the target macroscopic model and the target microscopic model, and target load data of the steel skeleton light panel are determined according to the plastic strain data.

7. The method according to claim 1, characterized in that Also includes: Determining the difference between the target load data and the ultimate bearing capacity of the steel skeleton light plate; The ratio of the difference to the target load data is taken, and the ratio is compared with a set consistency threshold to obtain a numerical simulation consistency result.

8. A load data determination device, characterized in that: include: A parameter data determination module is used to obtain initial parameter data of the steel skeleton light plate and perform data processing on the initial parameter data to obtain target parameter data; A model building module, used to build a macro model and a micro model for the steel skeleton light plate through a finite element network according to the target parameter data; The load data determination module is used to perform coupling iterative processing on the macro model and the micro model, and when the coupling end condition is met, obtain the target load data based on the coupled macro model and the micro model.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to 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 so that the at least one processor can execute a load data determination method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement a load data determination method according to any one of claims 1 to 7 when executed.

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