Multi-scale integrated design optimization method and system

Abstract

The invention relates to the technical field of structural engineering, and particularly discloses a multi-scale integrated design optimization method and system, and the method comprises the steps: defining a design target, and collecting first performance data; performing multi-scale hierarchical division on the first performance data; carrying out first scale hierarchy attribute modeling on the multi-scale hierarchy, and carrying out second scale hierarchy modeling by adopting an analysis method; topological structure form optimization is carried out based on a design target, and parallel processing is carried out through multi-target optimization. According to the method provided by the invention, the performance of the whole system is improved through cross-scale optimization, and design deviation caused by inconsistency of optimization targets at all levels is avoided. The lightweight level of the design is remarkably improved, and meanwhile the durability and stability of a mechanical system are guaranteed. The method greatly accelerates the simulation and calculation process, effectively shortens the design period, supports the rapid evaluation and comparison of a plurality of optimization schemes, and improves the design efficiency and optimization precision.

Description

Technical Field

[0001] The present invention relates to the field of structural engineering technology, and in particular to a multi-scale integrated design optimization method and system. Background Art

[0002] With the rapid development of computer-aided design (CAD) and computer-aided engineering (CAE) technologies, complex system design and structural optimization have gradually developed towards multi-scale analysis. Traditional mechanical and automotive design usually adopts single-scale, single-objective optimization methods, which limits the global optimization effect of the system from microscopic material properties to macroscopic structural behavior. Multi-scale design methods introduce multi-level analysis from microscopic material properties to macroscopic structural optimization, allowing designers to balance between multiple levels and multiple objectives to achieve more comprehensive performance improvement. In this process, simulation methods such as topology optimization, morphology optimization, and finite element analysis (FEA) have gradually been widely used to improve the strength, stiffness, weight reduction, and durability of components. At the same time, the application of artificial intelligence and multi-objective optimization algorithms has further enhanced the efficiency and effect of the optimization process. However, the common problem of existing multi-scale optimization methods is that they cannot achieve full-scale integrated modeling and optimization from materials to structures, lack the ability to effectively integrate and process cross-scale data, which makes it difficult to unify design goals at different scales, and there are significant limitations in the analysis and feedback of interactions between levels.

[0003] A major challenge facing the current multi-scale integrated design system is the lack of a top-down systematic optimization process in complex mechanical systems. In particular, it is difficult to effectively balance the performance indicators at all levels when facing multi-objective design requirements. Conventional optimization methods rely on hierarchical modeling at a single scale, while ignoring the mutual influence between microscopic material properties and macroscopic structural requirements, resulting in the design results easily deviating from the optimal performance of the overall system. In addition, the existing topology optimization and morphology optimization methods lack integration in multi-scale hierarchical division, material distribution optimization, and topological structure morphology optimization, and fail to fully utilize emerging technologies such as parallel computing to accelerate the optimization process. Therefore, how to achieve data sharing and parallel solving at different levels of multi-scale design and obtain the overall optimal solution through integrated multi-objective optimization is still a technical problem in the current field. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that the existing multi-scale optimization technology has limitations in multi-objective parallel processing, hierarchical modeling and data integration.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a multi-scale integrated design optimization method, comprising:

[0007] Define design goals and collect first performance data;

[0008] Performing multi-scale hierarchical division on the first performance data;

[0009] The first scale level attributes are modeled for the multi-scale levels, and the second scale level is modeled using analytical methods;

[0010] The topological structure is optimized based on the design objectives and parallel processing is performed through multi-objective optimization.

[0011] As a preferred solution of the multi-scale integrated design optimization method described in the present invention, the collection of the first performance data includes, after defining the design goals, clarifying the core functional requirements of the system and collecting the operating data of the system.

[0012] As a preferred solution of the multi-scale integrated design optimization method described in the present invention, the multi-scale hierarchical division includes dividing the multi-level structure according to the complexity of the system, including micro, meso and macro scale levels.

[0013] As a preferred solution of the multi-scale integrated design optimization method described in the present invention, the first-scale hierarchical property modeling includes analyzing material unit characteristics, simulating mechanical properties, and performing property modeling at a microscopic scale.

[0014] As a preferred scheme of the multi-scale integrated design optimization method described in the present invention, the second-scale hierarchical modeling includes analyzing the performance of parts and using multi-scale finite element analysis to model mechanical components and systems at the meso- and macro-scales.

[0015] As a preferred solution of the multi-scale integrated design optimization method described in the present invention, the topological structure morphology optimization includes performing multi-scale shape optimization, optimizing material distribution according to design requirements based on the result of first-scale hierarchical attribute modeling using topological optimization technology, and preliminarily determining the structural shape design of parts;

[0016] On the basis of topology optimization, the results of the second-scale hierarchical modeling are further optimized for shape, and the boundary shape and structural thickness parameters are fine-tuned to ensure the best performance of the optimized structure under various load conditions.

[0017] As a preferred solution of the multi-scale integrated design optimization method described in the present invention, wherein: the multi-objective optimization and parallel processing include applying genetic algorithms and multi-objective optimization methods to perform optimal solutions for multi-scale shape optimization at multi-scale levels;

[0018] Use parallel computing platforms to accelerate simulation and calculation in the multi-objective optimization process, and perform multi-level simulation of complex systems at multiple scales;

[0019] Through multi-objective optimization result analysis, the advantages and disadvantages of each design scheme are evaluated and the optimal design scheme is selected.

[0020] In a second aspect, the present invention also provides a multi-scale integrated design optimization system, including a demand calculation module, a simulation analysis module, an intelligent optimization module, and a verification and feedback module;

[0021] Demand calculation module, which collects and manages all data and demand information required for design, defines and records design goals, functional requirements, performance indicators, and user requirements;

[0022] Simulation and analysis module, which is responsible for building models at different scales and performing simulation and analysis;

[0023] Intelligent optimization module, which performs multi-objective optimization analysis and generates the best design solution using machine learning and optimization algorithms;

[0024] The verification and feedback module is used to verify the final optimized design solution and collect feedback information to support the continuous improvement of the system.

[0025] In a third aspect, the present invention further provides a computing device, comprising: a memory and a processor;

[0026] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the multi-scale integrated design optimization method are implemented.

[0027] In a fourth aspect, the present invention further provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the multi-scale integrated design optimization method.

[0028] Beneficial effects of the invention: The multi-scale integrated design optimization method provided by the invention improves the performance of the overall system and avoids design deviations caused by inconsistent optimization targets at all levels. It significantly improves the lightweight level of the design while ensuring the durability and stability of the mechanical system. It greatly accelerates the simulation and calculation process, effectively shortens the design cycle, and supports rapid evaluation and comparison of multiple optimization schemes, improving design efficiency and optimization accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing 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. Among them:

[0030] Figure 1 An overall flow chart of a multi-scale integrated design optimization method provided for one embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0032] Example 1, reference Figure 1 , is an embodiment of the present invention, and provides a multi-scale integrated design optimization method, comprising:

[0033] S1: Define design goals and collect first performance data.

[0034] After collecting the first performance data and defining the design goals, clarify the core functional requirements of the system and collect the system's operating data.

[0035] The first performance data may be functional requirement data, structural and material performance data, geometric and structural constraint data, space constraint and installation position, and performance index data.

[0036] Functional requirements data include: clarifying the core functional requirements of the system, such as the safety and fuel efficiency of the car, the output power and precision of mechanical equipment, etc. The operating requirements of the system under various conditions, including factors such as temperature, humidity, vibration, corrosion, etc. For example, the different performance requirements of the car at high and low temperatures. Understanding the end-user's usage needs and preferences, including comfort, noise level, ease of operation and other ergonomics-related factors.

[0037] Structural and material performance data, collect physical and chemical properties of materials such as strength, hardness, density, thermal conductivity, electrical conductivity, corrosion resistance, fatigue life and durability data. Fatigue life and durability of different materials and structures under repeated loads are key data.

[0038] In an optional embodiment, the functional requirement data may also be to collect cost data, availability, and supply chain conditions of materials and manufacturing processes to ensure that the design is economically feasible.

[0039] Geometric and structural constraint data, collecting the maximum size and weight limits of a system or part, is crucial in topology and shape optimization. For example, the weight limit of a car body design has a significant impact on fuel efficiency and driving performance.

[0040] It should be noted that the key parameter ranges and limits are determined: the range of design variables, including material thickness, structural geometric parameters, etc., is determined so that reasonable parameter exploration can be carried out in multi-level optimization. Workload data, usage frequency and load conditions: load data, operating frequency and fatigue cycles of the system or product under normal working conditions are collected.

[0041] S2: Perform multi-scale hierarchical division on the first performance data.

[0042] In the embodiment of the present application, the multi-scale hierarchical division may be to divide the multi-level structure into micro, meso and macro scale levels according to the complexity of the system.

[0043] The micro-scale includes the identification and definition of material properties: selecting materials suitable for the design objectives, and determining the basic physical and chemical properties of the materials, such as density, elastic modulus, tensile strength, thermal conductivity, etc. By establishing a microstructural model of the material (such as crystal structure, micro-porosity, fiber arrangement, etc.), the influence of its internal characteristics on the overall performance is defined.

[0044] The mesoscale includes component division and feature identification: determining the functional requirements and structural characteristics of each major component in the system, such as the strength requirements of gears, the wear resistance of bearings, etc. Component geometry modeling establishes a three-dimensional geometric model based on the shape, size and material properties of the component, and defines key structures and boundary conditions in the model.

[0045] The macro scale includes system decomposition and hierarchical division: functional decomposition of the entire system, determination of the relationships between modules and components in the system and the overall structural characteristics, and clarification of the main tasks and mutual influences of each level of the system.

[0046] In an optional embodiment, the multi-scale hierarchical division can also be a time domain scale division, specifically including: transient scale: focusing on fast-changing dynamic processes, such as the transient response of materials under stress impact or the instantaneous change of electrical signals in the system. Short-term scale: focusing on the change process within a medium time range, such as the change in part size caused by thermal expansion or the fatigue damage of components under cyclic stress. Long-term scale: focusing on slow changes over a long period of time, such as material aging, corrosion process or structural fatigue failure caused by long-term vibration.

[0047] S3: Model the attributes of the first scale level of the multi-scale hierarchy and use analytical methods to model the second scale level.

[0048] The first-scale hierarchical property modeling includes analyzing material unit characteristics at a microscopic scale, simulating mechanical properties, and performing property modeling.

[0049] In an optional embodiment, the first scale level property modeling can be a micromechanical performance simulation, which applies molecular dynamics (MD) and other micromechanical simulation methods to simulate the response of material units at the microscale, such as stress-strain relationship.

[0050] The mathematical formula of micromechanical properties is expressed as:

[0051] σ ij =C ijkl ∈ kl

[0052] Among them, σ ij represents the microscopic stress tensor, ∈ kl represents the strain tensor, C ijkl It represents the elastic modulus tensor of the material, that is, it represents the mechanical response characteristics of the material.

[0053] Conduct microscopic property modeling and obtain the mechanical properties of material units, such as elastic modulus, strength, thermal conductivity, etc., through microscopic numerical simulation, to provide a basis for mesoscopic and macroscopic scale modeling. Establish an equivalent material property model to convert microscopic properties into isotropic or anisotropic properties:

[0054]

[0055] Among them, E eff represents the equivalent elastic modulus, and V represents the unit volume of the material.

[0056] The second-scale hierarchical modeling includes analyzing the performance of parts and using multi-scale finite element analysis to model mechanical components and systems at the meso- and macro-scales.

[0057] Part performance analysis: According to design requirements, determine the key performance indicators of parts, such as strength, stiffness, fatigue life, etc. Set boundary conditions and load conditions according to different part characteristics.

[0058] In an optional embodiment, multi-scale finite element analysis can also be to establish a meso-scale model: for major parts (such as gears, bearings), a finite element model is constructed at the meso-scale. The model contains the shape, material properties and geometric details of the parts, and can capture the local characteristics of the parts. Establish a macro-scale model: At the macro-scale, a system-level finite element model is established to analyze the performance of the entire system. The macro-model simplifies the parts into units with equivalent material properties to reduce the complexity of the calculation. The mathematical formula is expressed as:

[0059] Ku=F

[0060] Among them, K represents the stiffness matrix, u represents the node displacement vector, and F represents the external force vector.

[0061] S4: Optimize the topological structure based on the design objectives and perform parallel processing through multi-objective optimization.

[0062] In an embodiment of the present application, the topological structure morphology optimization may be to perform multi-scale shape optimization, optimize the material distribution according to the design requirements based on the result of first-scale level attribute modeling, and preliminarily determine the structural shape design of the parts using topological optimization technology.

[0063] A multi-scale hierarchical model is established for the design object, where the first scale is a macro-scale model (such as an overall component) and the second scale is a micro-scale model (such as a local microscopic feature). For the first scale model, topology optimization technology is applied to design a preliminary material distribution plan with the goal of minimizing material usage or increasing structural stiffness.

[0064] Multi-scale model hierarchical modeling builds corresponding models for different scale levels. For example, in macro-scale modeling, the material is assumed to be uniformly distributed; in micro-scale modeling, local details and features (such as porosity, pore size, etc.) are refined.

[0065] The structural shape design is based on topological optimization. By modifying the boundary shape and adjusting parameters such as structural thickness, the optimized structural performance is ensured to be optimal under various load conditions. The mathematical formula is expressed as:

[0066]

[0067] Where ρ(x) represents the material distribution, c(ρ(x)) represents the synthetic performance index function of the structure, and Ω represents the design area.

[0068] Set the objective function of topology optimization, usually minimizing material usage or maximizing structural stiffness, and determine constraint strip constraints, stress constraints, etc.

[0069] The performance response of the structure is calculated by the finite element method, and the design variables are updated based on the sensitivity analysis. The density method (SIMP method) is used for topology optimization. The update formula of the SIMP method is:

[0070]

[0071] in, is the material density variable, α is the step size, is the sensitivity of the objective function to density.

[0072] After several iterations, the optimal material distribution solution is obtained, and the preliminary structural shape design is realized. On the basis of topology optimization, the shape of the result of the second-scale hierarchical modeling is further optimized, and the boundary shape and structural thickness parameters are fine-tuned to ensure the best performance of the optimized structure under various load conditions.

[0073] The multi-objective optimization and parallel processing include applying genetic algorithms and multi-objective optimization methods to perform optimal solution of multi-scale shape optimization at multi-scale levels.

[0074] In the multi-objective optimization process, the parallel computing platform is used to accelerate simulation and calculation, and multi-level simulation is performed on multi-scale complex systems. Through the analysis of multi-objective optimization results, the advantages and disadvantages of each design scheme are evaluated and the optimal design scheme is selected.

[0075] The genetic algorithm (GA) optimization method is used to solve the shape optimization problem with multiple objectives at multiple scales.

[0076] Set optimization goals, such as minimizing weight, maximizing stiffness, etc., and introduce fitness functions to adapt

[0077] Degree function:

[0078]

[0079] Among them, f i (x) is each objective function, w i is the weight.

[0080] Parallel computing, using parallel computing platforms to accelerate the simulation of multi-scale complex systems. On the parallel platform, simulate and solve various load conditions, and achieve linkage optimization of multi-scale models through collaborative solving and data exchange.

[0081] Evaluation and selection of design solutions, using multi-objective optimization results to analyze the advantages and disadvantages of each solution and select the optimal design solution. Mathematical expression is Pareto optimization, that is, solving the optimal solution set that meets the following conditions:

[0082]

[0083] Among them, x * represents the Pareto optimal solution, and x represents the design variable vector.

[0084] It should be noted that the result set of multi-objective optimization is analyzed, and the final optimal design scheme is selected using the weight evaluation method of performance indicators or the method based on manual decision-making. The final decision can be made by combining the advantages and disadvantages of each design scheme and its corresponding design requirements. The stability and robustness of the results are analyzed to ensure that the selected design scheme can meet the optimization objectives under different working conditions.

[0085] This embodiment also provides a multi-scale integrated design optimization system using the method of the present invention, characterized in that it includes a demand calculation module, a simulation analysis module, an intelligent optimization module, and a verification and feedback module.

[0086] The demand calculation module collects and manages all data and demand information required for design, defines and records design goals, functional requirements, performance indicators, and user needs.

[0087] Simulation and analysis module, which is responsible for building models at different scales and performing simulation and analysis.

[0088] Intelligent optimization module performs multi-objective optimization analysis and generates the best design solution using machine learning and optimization algorithms.

[0089] The verification and feedback module is used to verify the final optimized design solution and collect feedback information to support the continuous improvement of the system.

[0090] This embodiment also provides a computing device, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the multi-scale integrated design optimization method proposed in the above embodiment.

[0091] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the multi-scale integrated design optimization method proposed in the above embodiment is implemented.

[0092] The storage medium proposed in this embodiment and the multi-scale integrated design optimization method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0093] Through the above description of the implementation method, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccessMemory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0094] Example 2: The following is an embodiment of the present invention, which provides a multi-scale integrated design optimization method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0095] In order to verify the advantages of the multi-scale integrated design optimization method in actual engineering design, an experiment was set up to optimize the structural performance of a lightweight load-bearing component. The experiment took the strength, stiffness, material distribution and weight of the component as the design goals, simulated the operating data of the system, and collected the first performance data. The bearing capacity of the component under normal conditions and impact loads is the main indicator. The experiment adopted a multi-scale modeling and optimization method based on topological structure morphology optimization.

[0096] According to the goal, the strength and stiffness of the component were determined as the core functional requirements. The first performance data collected included material density, elastic modulus, stress-strain relationship, thermal conductivity, etc. In order to obtain the authenticity and representativeness of the data, the experiment recorded the real-time response data of the component under different loads through sensors to obtain the subtle characteristics of material performance changing with environmental factors.

[0097] According to the complexity of the system, the components are divided into microscopic (material properties), mesoscopic (geometric characteristics of components) and macroscopic (system assembly as a whole) scales. The microscopic scale includes details such as the porosity and crystal orientation of the material unit structure; the mesoscopic scale covers the local structure of the component (such as stress concentration areas), and the macroscopic scale covers the load-bearing capacity and structural layout of the entire component.

[0098] Based on multi-scale modeling, the topology optimization method is used to optimize the material distribution and determine the preliminary shape design of the component. After the preliminary optimization, the boundary shape and thickness are fine-tuned based on the results of the first and second scale modeling to ensure that the optimized component achieves the best structural performance under multiple loads. Some experimental results are shown in the figure below.

[0099] Table 1 Experimental data record table

[0100]

[0101]

[0102] The experimental data show that the optimized design is significantly better than the original design in many key performance indicators. First, the material density is increased from the original 7.85g / cm 3 Reduced to 7.05g / cm2 for the final design 3 , indicating that the optimization process reduces the amount of material used while maintaining strength, which helps reduce costs and system weight. Compared with the existing technology, this optimization method avoids excessive use of high-density materials through subtle adjustments to material distribution, ensuring component performance while reducing weight.

[0103] In terms of elastic modulus, the optimized design gradually increased from the original 210GPa to 230GPa, which illustrates the role of multi-scale hierarchical modeling in enhancing mechanical properties. Through microstructure optimization and reasonable distribution of material properties, the material exhibits higher stiffness at the macro scale, which is conducive to improving the overall stability of the structure. The increase in yield strength also shows that microscale modeling plays a positive role in the effective utilization of local strength of materials, so that the yield strength of the final design reaches 390MPa, which is about 11.4% higher than the initial design of 350MPa.

[0104] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A multi-scale integrated design optimization method, characterized in that: include: One approach Define design goals and collect first performance data; Performing multi-scale hierarchical division on the first performance data; The first scale level attributes are modeled for the multi-scale levels, and the second scale level is modeled using analytical methods; The topological structure is optimized based on the design objectives and parallel processing is performed through multi-objective optimization.

2. The multi-scale integrated design optimization method according to claim 1, characterized in that: The collecting of the first performance data includes, after defining the design goal, clarifying the core functional requirements of the system and collecting the operating data of the system.

3. The multi-scale integrated design optimization method according to claim 2, characterized in that: The multi-scale hierarchical division includes dividing the multi-level structure according to the complexity of the system, including micro, meso and macro scale levels.

4. The multi-scale integrated design optimization method according to claim 3, characterized in that: The first-scale hierarchical property modeling includes analyzing material unit characteristics at a microscopic scale, simulating mechanical properties, and performing property modeling.

5. The multi-scale integrated design optimization method according to claim 4, characterized in that: The second-scale hierarchical modeling includes analyzing the performance of parts and using multi-scale finite element analysis to model mechanical components and systems at the meso- and macro-scales.

6. The multi-scale integrated design optimization method according to claim 5, characterized in that: The topological structure morphology optimization includes performing multi-scale shape optimization, optimizing material distribution according to design requirements using topological optimization technology based on the results of first-scale hierarchical attribute modeling, and preliminarily determining the structural shape design of the parts; On the basis of topology optimization, the results of the second-scale hierarchical modeling are further optimized for shape, and the boundary shape and structural thickness parameters are fine-tuned to ensure the best performance of the optimized structure under various load conditions.

7. The multi-scale integrated design optimization method according to claim 6, characterized in that: The multi-objective optimization and parallel processing include applying genetic algorithms and multi-objective optimization methods to perform optimal solutions for multi-scale shape optimization at multi-scale levels; Use parallel computing platforms to accelerate simulation and calculation in the multi-objective optimization process, and perform multi-level simulation of complex systems at multiple scales; Through multi-objective optimization result analysis, the advantages and disadvantages of each design scheme are evaluated and the optimal design scheme is selected.

8. A multi-scale integrated design optimization system using the method according to any one of claims 1 to 7, characterized in that: Including demand calculation module, simulation analysis module, intelligent optimization module, verification and feedback module; Demand calculation module, which collects and manages all data and demand information required for design, defines and records design goals, functional requirements, performance indicators, and user requirements; Simulation and analysis module, which is responsible for building models at different scales and performing simulation and analysis; Intelligent optimization module, which performs multi-objective optimization analysis and generates the best design solution using machine learning and optimization algorithms; The verification and feedback module is used to verify the final optimized design solution and collect feedback information to support the continuous improvement of the system.

9. A computing device comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, implement the steps of the method according to any one of claims 1 to 7.

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