Hierarchical decoupling design method for data-driven complex structure system

Abstract

The invention discloses a hierarchical decoupling design method for a data-driven complex structure system, and the method comprises the steps: carrying out the performance demand analysis of the complex structure system, and determining a performance index; performing hierarchical decoupling analysis according to the structure and performance indexes of the complex structure system, and establishing a system hierarchical model; completing hierarchical parameter definition of the hierarchical complex structure system, and forming a hierarchical structure design system with mutually associated hierarchies through design variables, characterization performance parameters and transition variables; on the basis, identifying a specific association mode between the hierarchies through data driving in a top-down mode, and carrying out sequential design on each layer; and then carrying out design synthesis of each level, and carrying out system performance and reliability evaluation to complete the design of the system. According to the method, correlation with sub-hierarchy parameters is established through data learning of each hierarchy, the incidence relation of different hierarchy designs is formed, and the overall efficient design method from top to bottom is formed by taking guarantee of the overall performance as the target.

Description

Technical Field

[0001] The present invention belongs to the technical field of complex equipment system design, and in particular relates to a data-driven hierarchical decoupling design method for complex structural systems. Background Art

[0002] A complex structural system refers to a structural system composed of a large number of interrelated and interacting structural components, such as wind turbine structures, offshore oil and gas platform structures, automobile body structures, ship structures, aircraft structures, and shield machine structures. The design of a complex structural system is crucial to its final performance and reliability. Complex structural systems are often composed of a large number of components of different shapes through welding, bolts, bonding, and riveting. There is a strong coupling relationship between the components, which jointly determine the overall structural performance. After long-term accumulation of technology and experience, the design of complex structural systems has a specific process.

[0003] Taking the automobile body structure system as an example, the same part has different association modes and degrees of association with different functions of the subsystem (such as the front or side collision safety of the car). Here, the association mode refers to the mechanism by which different parts interact to determine the function of the subsystem. Thirdly, different design parameters of the same part, such as plate thickness and local curvature, have different association modes and degrees of association with the specific functions of the subsystem to which they belong. Here, the association mode refers to the mechanism by which different structural or geometric parameters jointly affect the specific performance parameters of the parts. Finally, after the component level, the next level can still be subdivided according to the actual situation. For example, after the macroscopic thickness and geometric parameter level of carbon fiber reinforced polyurethane plate, the next level can be subdivided: carbon fiber ply and content. The above different levels, from the bottom microscopic material design and performance, part geometric design and performance, subsystem design and performance, system design and performance, each level interacts with each other and jointly determines the performance of the entire system. In response to the different system performance requirements of complex structural systems, the interaction mechanisms between the various levels are different, including the association mode and degree of association between sub-levels, forming the design problem of complex hierarchical structural systems.

[0004] The traditional method analyzes the performance requirements of complex systems and conducts "manual iteration" trial and error on the design of subsystems and components to systems: engineers design components based on experience or specification requirements, including material selection (type), component design, integrated design and performance testing. Iterate the design based on the performance test results of components; evaluate the performance of subsystems to decide whether to adjust their component design solutions; evaluate the performance of the system to decide whether to adjust the subsystem design solutions. Repeat the above process until the system performance meets the design or specification requirements. However, this multi-level "bottom-up" "manual iteration" design process from components to the overall system is inefficient; and the sensitivity of the final design to part parameters, performance and post-manufacturing processes is unknown. Due to the uncertainty of various factors, the performance of the final product may not meet the established index requirements and have poor robustness. Summary of the invention

[0005] The main purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art and to provide a data-driven hierarchical decoupling design method for complex structural systems. Through data learning, the correlation between parameters at different levels is established, and the correlation relationship between designs at different levels is formed, with the goal of ensuring overall performance, thus forming a "top-down" overall design method.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a data-driven hierarchical decoupling design method for a complex structural system, comprising the following steps:

[0008] Perform performance demand analysis on complex structure systems and determine performance indicators; the performance indicators include performance evaluation indicators and labels, the performance evaluation indicators are used to evaluate system-level performance, and the labels refer to evaluation results of performance evaluation indicators judged by evaluation criteria, including feasible solution labels and infeasible solution labels;

[0009] According to the structure and performance indicators of the complex structural system, a hierarchical decoupling analysis is performed to establish a system hierarchical model, which includes multiple sub-levels and defines the basic parameters of each sub-level in the model; the basic parameters include design variables, performance characterization parameters and transition variables, the design variables are used to represent the basic design elements of this level, the performance characterization parameters are used to characterize the performance of each sub-level that affects the performance indicators of this level, and the transition variables are used to facilitate the current level to regulate the performance characterization parameters of the sub-level;

[0010] According to the design goal, the hierarchical elements of the non-lowest sub-level are digitally simulated given the design variables and transition variables, the performance evaluation index and the characterization performance parameters of the current level are obtained, the performance evaluation index is judged according to the given evaluation criteria, different design variables and characterization performance parameters are marked, and a data set is obtained. The data set includes different design variables and characterization performance parameters and corresponding labels, and the data label entropy reduction method is used to mine the key design variables and their value ranges that meet the design goals and the key characterization performance parameters and their value ranges. The key design variables and their value ranges are the current level design set; the key characterization performance parameters and their ranges are passed to the next level and used as one of the judgment constraints for the feasible solution or infeasible solution of the next level; the above process is repeated for each non-lowest sub-level to complete the design of each level; the lowest sub-level data is mined to obtain the design variables and their value ranges, and the design variables and their value ranges are the current level design set;

[0011] Integrate the design sets obtained at each level to obtain the system design set, build and connect components according to the design variables in the system design set, obtain the complex structure system design simulation model, and use performance indicators to verify the performance of the complex structure system simulation model.

[0012] As a preferred technical solution, the performance requirement analysis of the complex structural system includes the following steps:

[0013] Conduct qualitative and quantitative analysis of the performance requirements of complex structural systems based on prior knowledge, and determine various performance evaluation indicators and their feasible standards;

[0014] The labels are set based on an indication of whether the feasible criteria for the evaluation metric are met.

[0015] As a preferred technical solution, the hierarchical decoupling analysis is performed according to the structure and performance indicators of the complex structural system, including the following steps:

[0016] The complex structural system is structurally analyzed according to the performance indicators, and the hierarchy of the complex structural system is divided from the upper level to the lower level according to the structural analysis results to construct a system hierarchy model; the first level of the system hierarchy model represents the uppermost complex structural system as a whole, and the lowest level represents the lowest level of parts or material design, and its specific design scale is determined according to actual conditions.

[0017] As a preferred technical solution, the design objectives include:

[0018] For non-bottom-level levels, the hierarchical design goal is set to obtain the design subdomain The design subdomain needs to meet the probability of feasibility of performance indicators (p(ψ(′g′)|γ c )) and the robustness of performance indicators (Φ(ψ(′g′)|γc )) are maximized, where 'g' represents the feasible solution label; set the design variable x and transition variable x t The constraint interval is as follows:

[0019] x l <x<x u

[0020] x tl <x t <x tu

[0021]

[0022] Among them, x l represents the upper limit of the design variable x, u represents the lower limit of the design variable x, x tl Represents the transition variable x t The upper limit of x tu Represents the transition variable x t The lower limit of Represents a design constraint.

[0023] As a preferred technical solution, the marking method comprises the following steps:

[0024] According to the set design goal, the system response under the combination of the given design variables and the characterization performance parameters determined by the transition variables is judged. Only when the characterization performance parameter range transmitted from the previous level is met and other performance evaluations meet the design goal, the feasible solution label 'g' is output, that is, the design variable is a feasible solution. Otherwise, the infeasible solution label 'p' is output, that is, the design variable is an infeasible solution. The data set of the design variables, characterization performance parameters and their corresponding labels at this level is obtained.

[0025] As a preferred technical solution, the design objectives include:

[0026] For the lowest sub-level, the goal of hierarchical design is set to obtain the design subdomain <x> of this level. The probability (p(ψ(′g′)|x)) and the robustness (Φ(ψ(′g′)|x)) of the design subdomain to meet the performance index are maximized, where 'g′ represents the feasible solution label, and the constraint interval of the design variable x is set as follows:

[0027] x l <x<x u

[0028]

[0029] Among them, xl represents the upper limit of the design variable x, xu represents the lower limit of the design variable x, Indicates that the performance of the design components at this level meets the requirements of the previous level. Represents a design constraint.

[0030] As a preferred technical solution, the transfer of the performance characterization parameters and their ranges to the next level includes:

[0031] The subset of performance parameters that meet the set design conditions is passed to the next level, and the next level uses the subset of performance parameters as constraints. When the performance index of the hierarchical component meets the evaluation standard ψ(′g′), and its performance parameters meet the performance parameters passed down from the level When , it is marked as 'g' and it is a feasible solution.

[0032] As a preferred technical solution, the dataset label entropy reduction method identifies the design variables that meet the performance indicators and the respective subdomains that characterize the performance parameters, including the following steps:

[0033] The dataset S obtained by simulation is divided into subsets based on the entropy decreasing mode. The dataset S includes the characteristic parameters of all samples at the current level, that is, the design variables and the performance parameters, as well as the corresponding labels. The dataset is divided by selecting the characteristic parameter A with the maximum information gain GR(A) value, as shown in the following formula:

[0034]

[0035] Among them, G(A) is the information gain, that is, the entropy of the current data set S The entropy after dividing the data set using feature parameter A The difference between the two, SI(A) is the information increment of the data set partition, which means the amount of information generated by partitioning the data set by A, m is the number of labels, p i is the proportion of the number of designs of the i-th label to the total number, N is the number of data subsets generated after the data set is divided, S j is the subset generated after splitting the data set S, For the subset S j The information entropy of |·| represents the number of samples in the sample set;

[0036] By following the above indicators, the set S is recursively divided until the set conditions are met and the division is stopped to obtain the recursive division result;

[0037] The recursive partitioning results are analyzed to obtain the subset with the most labels 'g' and the highest purity, and all key design variables and their value ranges and key performance characterization parameters and their value ranges used to obtain the subset are recorded.

[0038] As a preferred technical solution, if the characteristic parameter A is a discrete variable, N is the number of discrete values; if the characteristic parameter A is a continuous variable, N is 2.

[0039] As a preferred technical solution, it is characterized in that, in the process of recursively partitioning the current set D, the subset S j The division stops when one of the following conditions (1) to (3) is met:

[0040] (1) Subset S j The label purity p i (S j ) reaches a preset value;

[0041] (2)|S j | Reached the preset minimum value;

[0042] (3) The current data set S is split a predefined number of times.

[0043] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0044] In view of the problem that the design of traditional complex structural systems does not fully consider the overall performance of the system due to the "bottom-up" design process from parts to systems, and the design inefficiency caused by the "trial and error" iteration method, a hierarchical design method for complex structural systems has been developed. First, the present invention adopts a "top-down" design approach to establish a systematic hierarchical design method. By characterizing performance parameters The correlation between different levels is realized, so that the lower-level design takes into account the performance requirements of the upper level, and a method system with clear system goal orientation is established. Secondly, a data set partitioning method with decreasing information entropy is proposed, with the goal of maximizing the purity of the sub-data set labels, to obtain the design variables x and the performance parameters Design subdomains to meet the hierarchical performance index ψ(′g ′ ) to maximize the high performance probability, obtain the sub-level key elements and the range of the key elements' performance parameters, and improve the design efficiency. Through the 'top-down' hierarchical system design method system and the selection of characterization parameters and design domain reduction, a systematic and efficient hierarchical decoupling design method for complex structural systems is established. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0046] Figure 1A flowchart of a data-driven hierarchical decoupling design method for a complex structure system according to an embodiment of the present invention;

[0047] Figure 2 A schematic diagram of a hierarchical design system for a complex structure system according to an embodiment of the present invention;

[0048] Figure 3 Data set construction, information entropy reduction data set division and data drive γ for the embodiment of the present invention c Schematic diagram of the parameter identification process. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present application.

[0050] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments.

[0051] See also Figure 1 This embodiment provides a data-driven hierarchical decoupling design method for a complex structure system, comprising the following steps:

[0052] S01. Conduct performance requirement analysis on complex structural systems.

[0053] According to the requirements of experience, test regulations and specifications, qualitative and quantitative analysis of performance indicators such as stiffness and strength is carried out to determine the main performance indicator system of the complex structural system. In this embodiment, the performance indicator is set as a performance evaluation indicator and its label. The performance evaluation indicator (ψ) is used to characterize the design performance of this level, and the label indicates the evaluation result of the performance evaluation indicator. Each performance evaluation indicator represents a sub-requirement of the target requirement for a complex structural system. For example, based on the need for collision safety, the frontal collision safety requirement for the vehicle body structure includes safety sub-requirements such as collision acceleration peak and floor intrusion. As for the label of the performance evaluation indicator, this embodiment can mark the specific design as 'g' (a feasible solution if satisfied) and 'p' (an infeasible solution if not satisfied) according to the indicator requirements.

[0054] S02. Conduct system-level decoupling analysis.

[0055] like Figure 2 As shown, in this embodiment, the complex structure system is structurally analyzed according to the performance index, and the hierarchy of the complex structure system is divided from the upper level to the lower level according to the structural analysis results to construct a system hierarchy model; the first level of the system hierarchy model represents the uppermost complex structure system as a whole, and the bottom level represents the lowest level. The uppermost complex structure system structure, such as the complex structure system itself, and the lowest level can be a part. According to actual design requirements, the part can also be further divided into material and process design sub-levels.

[0056] Next, a system hierarchical model is constructed according to the divided hierarchies, wherein each hierarchical level has its own design basic elements or basic attributes. In this embodiment, these design basic elements or basic attributes are defined as three types of parameters, namely, design variables (x), performance characterization parameters and transition variables (x t ).

[0057] Among them, (1) design variable x is a parameter directly designed at this level, and the relationship it maps may be the connection method, material, shape, thickness or other basic properties of the complex structural system. The design subdomain is subsequently obtained through simulation data drive, and the feasible subdomain of x is obtained through the characteristics of the design subdomain. For specific steps, please see steps S03 and S04. It is also necessary to explain that the design variable is not only a continuous variable, such as the thickness of the steel plate, but may also be discrete, such as the connection method between steel plates, and the values ​​may include: welding, riveting, bonding, and bolt connection. This embodiment needs to evaluate the effects or system performance under different values.

[0058] (2) Characterization performance parameters This parameter is used to characterize the performance parameters of each sub-level that affect the performance indicators of this level. It is set to solve the problem that it is difficult to change the basic elements of the underlying levels during the design of this level, and the cost of system simulation evaluation is high. For example, it is difficult to directly design the shape of the components in the frontal collision simulation model of the whole vehicle, and the evaluation cost is high. The average stiffness of the components has a significant impact on the performance of this level, so it can be used as a performance parameter to characterize the performance. By changing the thickness of the component sheet or the material stiffness, the average stiffness of the component can be easily changed, that is, changing the transition variable x t Through data mining, the average stiffness range requirements of components that meet the safety of the entire vehicle are obtained, which is used as the performance goal or constraint of component design, and the correlation between the system and the components is established, so as to realize the design guided by system performance indicators at the component level.

[0059] (3) Transition variable x t, is a basic transition parameter that is easily parameterized in order to change the value of the performance parameter, as described above. When the level of the part is reached, there is no next level, only the design variables (such as thickness, shape and material selection) and no transition variables.

[0060] Through the above decoupling analysis process, the hierarchical division of the complex structural system is completed, and the hierarchical structural design system with mutual correlation between the levels is formed through design variables, performance characterization parameters and transition variables, as shown in the figure. Figure 2 As shown on the left, it provides the basis for the decoupling design of S03.

[0061] S03. Carry out top-down decoupling design at different levels of the data-driven system.

[0062] exist Figure 1 The right frame diagram of this step shows a data-driven design method for a certain level. Data is obtained through hierarchical digital simulation, and the characterization performance requirements of the next level are obtained by data-driven, and the correlation between levels is established to design the different levels divided in step S02.

[0063] This embodiment uses a certain level in the system hierarchy model as a reference, which is recorded as this level, and the level below it is called the next level. For the convenience of description, this embodiment uses "i" to describe the position of a specific level. Figure 1 The data-driven hierarchical design method mentioned in is as follows:

[0064] S03-1 Clarify the parameters of this level, including design variables x and performance parameters Transition variable x t and the evaluation index ψ(′·′).

[0065] S03-2 Establish a mathematical model for hierarchical design and clarify the objectives of hierarchical design.

[0066] For non-bottom-level levels, the goal of hierarchical design is to obtain design subdomains The probability that the subdomain satisfies the hierarchical performance index ψ(′g′) (p(ψ(′g′)|γ c )) and robustness (Φ(ψ(′g′)|γ c )) is maximized. At the same time, the design satisfies the design variables, transition variables and other constraints The requirements are as follows:

[0067]

[0068] Among them, x l represents the lower limit of the design variable x, x u represents the upper limit of the design variable x, tl Represents the transition variable x tThe lower limit of x tu Represents the transition variable x t The upper limit of Represents a design constraint.

[0069] S03-3 Carry out simulation evaluation parametric modeling, and evaluate and calculate the performance of the representation and hierarchical indicators under different combinations of design variables and transition variables based on the parametric simulation model. Specifically, in this step, parametric modeling needs to establish a model calculation framework for automatic update definition and calculation evaluation of parameters based on different systems and design parameters. Further, random or Latin hypercube sampling is performed within the range of design variables and transition variables to obtain different combinations. The number of sampling combinations depends on the actual model evaluation cost and the scale of the problem; the design variables and transition variable values ​​of different combinations are assigned to the parametric model to obtain the corresponding simulation model, and the corresponding performance evaluation indicators and performance parameter responses are calculated.

[0070] In addition, the generation of the label is specifically as shown in step S03-4.

[0071] S03-4 generates hierarchical performance simulation data, including input attributes: design variables x and performance parameters and labels. Specifically, based on the hierarchical indicator performance evaluation results of step S03-3 above, labels are defined according to the evaluation criteria: if the performance requirements or the objectives of the hierarchical design are met, it is marked as 'g', otherwise it is marked as 'p'. For non-first-level 'g' labels, they must also meet the requirements passed from the previous level. The requirements are then used to obtain data sets with different combinations of design variables and performance parameters (input parameters) and their corresponding performance labels (output parameters).

[0072] S03-5 learns the simulation data and obtains the correlation between the performance of the first level and the next level, including:

[0073] a. Dataset division. Based on the entropy reduction model, the key parameter value A is selected to subset the data set S obtained by simulation. The data set S includes all the combinations of design variables and performance parameters at the current level, as well as their corresponding labels. The formula of the parameter selection index (GR(A): information gain rate) is as follows:

[0074]

[0075] Among them, G(A) is the information gain, that is, the entropy of the current data set S The entropy after dividing the data set by variable A The difference is the reduction in the entropy of the data set, so the larger G(A), the better. It needs to be explained that the above variable A includes the design variables and performance parameters of this level. and They can be calculated by formulas (3) and (4) respectively.

[0076]

[0077] Among them, SI(A) is the information increment of data set partition, which means the amount of information generated by partitioning the data set by A, m is the number of labels, and p is i is the proportion of the number of designs of the i-th label to the total number, N is the number of data subsets generated after the data set is divided, S j is the subset generated after splitting the data set S, For the subset S j The information entropy of , |·| represents the number of samples in the sample set.

[0078] It should be explained that, for the number of label types m, in this embodiment, it includes 'g' and 'p', that is, m=2. If there are other classification methods, the number of labels can be set to different numbers according to actual needs; for the number of data subsets N, if the variable A is a discrete variable, then N is the number of discrete values; if A is a continuous variable, all possible values ​​of A can be selected according to ">" and "≤" A to divide the data set into two parts, that is, N=2.

[0079] b. Recursive partitioning of data sets

[0080] In order to obtain a subset with a more pure label, follow step a to recursively partition the data set and record the attribute A or its corresponding value A and direction (greater than or less than or equal to) of each partition until a specific condition is met. Specifically, when the subset S j The division stops when one of the following conditions (1) to (3) is met:

[0081] (1) Subset S j The label purity pi(S j )( or ) reaches a preset value;

[0082] (2)|S j | Reached the preset minimum value;

[0083] (3) The dataset S is segmented a predefined number of times.

[0084] In particular, the subset S j The label purity pi(S j ) is generally selected in the interval [0.7, 1.0], and the label purity pi (S j ) value is larger, indicating that it is closer to the design target requirement. The value should be selected based on actual needs.

[0085] In order to balance the computational effort and design goal requirements, the label purity pi (S j ) is preferably set to 0.9.

[0086] S03-6 Obtain the hierarchical performance requirements by summarizing the data partitioning process Design subdomains.

[0087] In step S03-6, all subsets generated by completing the data set division are summarized and the probability of meeting the performance index (p(ψ(′g′)|γ c )) and the robustness of performance indicators (Φ(ψ(′g′)|γ c ))Maximize the dual objectives to select the subset, and at the same time extract the key A and the partition value from the original data set to the selected subset, and obtain the key variables and their value ranges.

[0088] At this point, all key partitioning variables are identified as key design variables or performance characterization parameters, and the value range of the key variables is the design subdomain, that is, in,<x(ψ(′g′))> As the value subdomain of the design variables at this level; As the performance constraint of the key design elements (components) of the next level, it ensures that the performance of this level satisfies the maximization of p(ψ(′g′)).

[0089] In order to better understand the present invention, a more specific example is given here. Figure 3 As shown in the example. Each sample of the sample original data contains two continuous attributes And the corresponding label (1abel), a total of 210 groups of data. Through data learning, the algorithm realizes three data set divisions (P1, P2, P3), forming four data subsets (R1, R2, R3, R4). It can be seen that: R2 satisfies the maximum p(ψ(′g′)), and the γ of R2 can be obtained. c Contains key parameters and its subdomains

[0090] By iterating the above S03-1 to S03-6 processes, the design of each key element (component) of the sub-level is completed. The design of each sub-level must fully consider the characterization performance requirements of ψ(′g′) and other related specification requirements. In this iteration, by evaluating the sub-levels x and x t The hierarchical performance of Hierarchical design of 〈x(ψ(′g′))> and the characterization performance requirements for the next sub-level

[0091] For the lowest level component design, as shown in formula (5), there is no next level, so there is no next level to characterize performance parameters and transition variables. Only the model performance evaluation ψ(′g′) designed by the elements of this level is used to meet the phenotypic performance parameter requirements passed from the previous level.

[0092]

[0093] Through the above process, the decoupling design of the complex hierarchical structure system is completed from top to bottom. The obtained design system with L levels is as follows Figure 2 As shown. Among them, x L and Represent the design variables and transition variables of the Lth level, respectively, <x L (ψ(′g′))> is the set of design variables at the Lth level that satisfies the maximization of p(ψ(′g′)), To obtain the set of performance parameters that maximize p(ψ('g')) at the Lth level, associate the element design of the next level as the key elements and performance requirements of the next level design. Finally, the design set that satisfies ψ('g') at each level is obtained, that is, the design variable subset (subdomain).

[0094] S04, matching different levels of design based on system performance, specifically including combining the design subdomains of different levels obtained in step S03 <x 1 (ψ(′g′)), x 2 (ψ(′g′)),…,x L (ψ(′g′))>, considering the system performance and other configuration requirements, such as the adjustment of the structure of the vehicle for the installation of individual accessories, design selection or matching is performed within the corresponding component subset (subdomain) to obtain different levels of design matching sets. Then, the hierarchical design matching set is completed, and a set containing several subsystem designs is integrated.

[0095] S05. Construct a system design simulation model. Use the underlying part design variable values ​​of the final system design set to construct components, combine the design of component connection method variables, consider system boundaries and load conditions, and build an integrated system simulation model based on specifications and test requirements.

[0096] S06. Verify the overall system performance of the design: The system design model obtained through the above systematic design method identifies high-performance sub-domains that characterize performance parameters, obtains sub-level key design elements and their performance requirements, and ensures the performance of the hierarchical design. The final system design set can basically guarantee the preset performance requirement ψ(′g′), thereby improving design efficiency.

[0097] It should be noted that, for the sake of convenience, the aforementioned method embodiments are all expressed as a series of action combinations, but those skilled in the art should know that the present invention is not limited to the described order of actions, because according to the present invention, certain steps can be performed in other orders or simultaneously.

[0098] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0099] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A data-driven hierarchical decoupling design method for complex structural systems, characterized in that: The steps include: Perform performance demand analysis on complex structure systems and determine performance indicators; the performance indicators include performance evaluation indicators and labels, the performance evaluation indicators are used to evaluate system-level performance, and the labels refer to evaluation results of performance evaluation indicators judged by evaluation criteria, including feasible solution labels and infeasible solution labels; According to the structure and performance indicators of the complex structural system, a hierarchical decoupling analysis is performed to establish a system hierarchical model, which includes multiple sub-levels and defines the basic parameters of each sub-level in the model; the basic parameters include design variables, performance characterization parameters and transition variables, the design variables are used to represent the basic design elements of this level, the performance characterization parameters are used to characterize the performance of each sub-level that affects the performance indicators of this level, and the transition variables are used to facilitate the current level to regulate the performance characterization parameters of the sub-level; According to the design goal, the hierarchical elements of the non-lowest sub-level are digitally simulated given the design variables and transition variables, the performance evaluation index and the characterization performance parameter of the current level are obtained, the performance evaluation index is judged according to the given evaluation standard, different design variables and characterization performance parameters are marked, and a data set is obtained. The data set includes different design variables and characterization performance parameters and corresponding labels, and the data label entropy reduction method is used to mine the key design variables and their value ranges that meet the design goals and the key characterization performance parameters and their value ranges. The key design variables and their value ranges are the current level design set; The key performance characterization parameters and their ranges are passed to the next level and used as one of the constraints for determining the feasible or infeasible solution of the next level; Repeat the above process for each non-lowest sub-level to complete the design of each level; mine the lowest sub-level data to obtain design variables and their value ranges, which are the current level design set; Integrate the design sets obtained at each level to obtain the system design set, build and connect components according to the design variables in the system design set, obtain the complex structure system design simulation model, and use performance indicators to verify the performance of the complex structure system simulation model.

2. The data-driven hierarchical decoupling design method for complex structural systems according to claim 1 is characterized in that: The performance requirement analysis of the complex structural system includes the following steps: Conduct qualitative and quantitative analysis of the performance requirements of complex structural systems based on prior knowledge, and determine various performance evaluation indicators and their feasible standards; The labels are set based on an indication of whether the feasible criteria for the evaluation metric are met.

3. The data-driven hierarchical decoupling design method for complex structural systems according to claim 1 is characterized in that: The hierarchical decoupling analysis based on the structure and performance indicators of the complex structural system includes the following steps: The complex structural system is structurally analyzed according to the performance indicators, and the hierarchy of the complex structural system is divided from the upper level to the lower level according to the structural analysis results to construct a system hierarchy model; the first level of the system hierarchy model represents the uppermost complex structural system as a whole, and the lowest level represents the lowest level of parts or material design, and its specific design scale is determined according to actual conditions.

4. The data-driven hierarchical decoupling design method for complex structural systems according to claim 1 is characterized in that: The design goals include: For non-bottom-level levels, the hierarchical design goal is set to obtain the design subdomain The design subdomain needs to meet the probability of feasibility of performance indicators (p(ψ(′g′)|γ c )) and the robustness of performance indicators (Φ(ψ(′g′)|γ c )) are maximized, where 'g' represents the feasible solution label; set the design variable x and transition variable x t The constraint interval is as follows: x l <x<x u x tl <x t <x tu Among them, x l represents the upper limit of the design variable x, u represents the lower limit of the design variable x, x tl Represents the transition variable x t The upper limit of x tu Represents the transition variable x t The lower limit of Represents a design constraint.

5. The data-driven hierarchical decoupling design method for complex structural systems according to claim 4 is characterized in that: The marking method comprises the following steps: According to the set design goal, the system response under the combination of the given design variables and the characterization performance parameters determined by the transition variables is judged. Only when the characterization performance parameter range passed from the previous level is met and other performance evaluations meet the design goal, the feasible solution label ′g is output ′ , that is, the design variable is a feasible solution, otherwise the infeasible solution label 'p' is output, that is, the design variable is an infeasible solution; obtain the data set of the design variables, performance parameters and their corresponding labels at this level.

6. The data-driven hierarchical decoupling design method for complex structural systems according to claim 1 is characterized in that: The design goals include: For the lowest sub-level, the goal of the hierarchical design is set to obtain the design sub-domain of this level. <x>, the probability (p(ψ(′g′)|x)) and robustness (Φ(ψ(′g′)|x)) of the design subdomain to meet the performance index are maximized, where ′g′ represents the feasible solution label; set the constraint interval of the design variable x as follows:< / x> x l <x<x u Among them, x l represents the upper limit of the design variable x, u represents the lower limit of the design variable x, Indicates that the performance of the design components at this level meets the requirements of the previous level. Represents a design constraint.

7. The data-driven hierarchical decoupling design method for complex structural systems according to claim 1 is characterized in that: The characterization of performance parameters and their ranges are passed to the next level, including: The subset of performance parameters that meet the set design conditions is passed to the next level, and the next level uses the subset of performance parameters as constraints. When the performance index of the hierarchical component meets the evaluation standard ψ(′g′), and its performance parameters meet the performance parameters passed down from the level When , it is marked as 'g' and it is a feasible solution.

8. The data-driven hierarchical decoupling design method for complex structural systems according to claim 1 is characterized in that: The dataset label entropy reduction method identifies the design variables that meet the performance indicators and the respective subdomains that characterize the performance parameters, including the following steps: The dataset S obtained by simulation is divided into subsets based on the entropy decreasing mode. The dataset S includes the characteristic parameters of all samples at the current level, that is, the design variables and the performance parameters, as well as the corresponding labels. The dataset is divided by selecting the characteristic parameter A with the maximum information gain GR(A) value, as shown in the following formula: Among them, G(A) is the information gain, that is, the entropy of the current data set S The entropy after dividing the data set using feature parameter A The difference between the two, SI(A) is the information increment of the data set partition, which means the amount of information generated by partitioning the data set with feature parameter A, m is the number of labels, p i is the proportion of the number of designs of the i-th label to the total number, N is the number of data subsets generated after the data set is divided, S j is the subset generated after splitting the data set S, For the subset S j The information entropy of |·| represents the number of samples in the sample set; By following the above indicators, the set S is recursively divided until the set conditions are met and the division is stopped to obtain the recursive division result; The recursive partitioning results are analyzed to obtain the subset with the most labels 'g' and the highest purity, and all key design variables and their value ranges and key performance characterization parameters and their value ranges used to obtain the subset are recorded.

9. The data-driven hierarchical decoupling design method for complex structural systems according to claim 8, characterized in that: If the characteristic parameter A is a discrete variable, then N is the number of discrete values; If the characteristic parameter A is a continuous variable, N is 2.

10. The data-driven hierarchical decoupling design method for complex structural systems according to claim 8, characterized in that: In the process of recursively partitioning the current set D, the subset S j The division stops when one of the following conditions (1) to (3) is met: (1) Subset S j The label purity p i (S j ) reaches a preset value; (2)|S j | Reached the preset minimum value; (3) The current data set S is split a predefined number of times.

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