A Cross-Scale Reverse Design Method and Device for Porous Structures

Abstract

This specification discloses a cross-scale reverse design method and device for porous structures. The method includes obtaining the mechanical properties of the porous structure; performing distribution optimization and continuous encoding on the mechanical properties to obtain the mechanical property vector of the porous structure; converting the mechanical property vector into a conditional control vector; generating random Gaussian noise, and inputting the random Gaussian noise and the conditional control vector into a trained reverse generation model to reversely generate a porous structure. This method improves the accuracy of the cross-scale reverse design method.

Description

Technical Field

[0001] This specification relates to the field of material structure design, and particularly to a cross-scale reverse design method and device for porous structures. Background Art

[0002] Porous structures are material structures applicable to fields such as aerospace and medicine. Through artificial design, porous materials can meet specific requirements, such as retaining the required stiffness and strength while minimizing the mass of the workpiece as much as possible. Current porous materials are usually realized by repeating porous units (generally cube units). Researchers design the geometric structure within the unit to meet the requirements of mechanical properties. Some researchers have adjusted the dimensions of the workpiece at the stress concentration site by analyzing the stress distribution to accurately improve the mechanical properties of the workpiece. However, such optimization methods rely on finite element analysis to obtain the stress distribution, resulting in a large amount of calculation.

[0003] In recent years, with the development of machine learning and neural network technologies, data-driven reverse design methods have emerged. The data-driven neural network model establishes a reverse mapping that outputs a porous structure based on the target properties through a large amount of "porous structure - mechanical property vector" data, enabling the customized generation of porous structures. Specifically, the target properties input into the neural network serve as the control conditions for the generation network to generate a porous structure that meets the target properties. Neural network frameworks based on generative adversarial models, autoencoders, or diffusion models can all achieve this function. However, the current neural network-based reverse design solutions for porous structures still have problems with poor effectiveness, especially for scenarios where the magnitude distribution of the target properties is relatively broad (cross-scale, such as 10 -5 ~10 -1 ), and it is difficult for the reverse generation model to achieve accurate customized design for the target properties across the entire range. Summary of the Invention

[0004] The purpose of the present invention is to provide a cross-scale reverse design method for porous structures to improve the problem of poor effectiveness of reverse design in the cross-scale target property domain in the prior art.

[0005] This specification adopts the following technical solutions:

[0006] This specification provides a cross-scale reverse design method for porous structures, including:

[0007] Obtain the mechanical properties of the porous structure;

[0008] Optimize the distribution of the mechanical properties to obtain optimized mechanical properties;

[0009] Perform continuous encoding on the optimized mechanical properties to obtain a mechanical property vector;

[0010] Convert the mechanical property vector into a conditional control vector; and

[0011] Generate random Gaussian noise of the porous structure, and input the random Gaussian noise and the conditional control vector into the trained inverse generation model to inversely generate a porous structure.

[0012] Optionally, the optimizing the distribution of the mechanical properties to obtain optimized mechanical properties includes:[[]]

[0013] Perform a linear function - logarithmic function transformation on the mechanical properties to obtain optimized mechanical properties, where the linear function - logarithmic function transformation formula is , where the coefficients and are preset, represents the mechanical property, represents the optimized mechanical property.

[0014] Optionally, the continuously encoding the optimized mechanical properties to obtain a mechanical property vector includes:[[]]

[0015] Perform Gaussian encoding and discrete sampling on the optimized mechanical properties to obtain a mechanical property vector.

[0016] Optionally, training the inverse generation model includes:[[]]

[0017] Construct a data set composed of porous structures and corresponding mechanical property vectors;

[0018] Convert the mechanical property vector into a conditional control vector;

[0019] Generate random Gaussian noise of the porous structure, input the random Gaussian noise and the conditional control vector into the inverse generation model, and output the generated porous structure;

[0020] Calculate the difference between the generated porous structure and the porous structure in the data set as the loss function;

[0021] Based on the loss function, implement the training of the inverse generation model.

[0022] Optionally, the constructing a data set composed of porous structures and corresponding mechanical property vectors includes:[[]]

[0023] Perform a linear function - logarithmic function transformation on the mechanical properties of the porous structure to obtain optimized mechanical properties, where the linear function - logarithmic function transformation formula is , where the coefficients and are preset, represents the mechanical property, Indicates optimized mechanical properties;

[0024] Continuously encode the optimized mechanical properties to obtain a mechanical property vector;

[0025] Obtain a data set composed of the porous structure and the corresponding mechanical property vector.

[0026] Optionally, the continuously encoding the optimized mechanical properties to obtain a mechanical property vector includes:

[0027] Perform Gaussian encoding and discrete sampling on the optimized mechanical properties to obtain a mechanical property vector.

[0028] Optionally, the trained inverse generation model obtains a predicted noise value according to the random Gaussian noise and the conditional control vector, and according to the formula Inverse generate a porous structure, where represents random Gaussian noise, represents the conditional control vector, represents the predicted noise value, represents the inversely generated porous structure, and represents the weight value.

[0029] This specification provides a porous structure cross-scale inverse design device, including:

[0030] A data acquisition module for acquiring the mechanical properties of the porous structure;

[0031] A property distribution optimization module for optimizing the distribution of the mechanical properties to obtain optimized mechanical properties;

[0032] A property continuous encoding module for continuously encoding the optimized mechanical properties to obtain a mechanical property vector;

[0033] A property vector mapping module for converting the mechanical property vector into a conditional control vector; and

[0034] An inverse generation module for generating random Gaussian noise of the porous structure and inputting the random Gaussian noise and the conditional control vector into the trained inverse generation model to inversely generate the porous structure.

[0035] This specification provides a computer-readable storage medium, including one or more programs for execution by one or more processors of an electronic device, and the one or more programs include instructions for executing the porous structure cross-scale inverse design method described above.

[0036] This specification provides an electronic device, including: one or more processors and a memory. The memory stores one or more programs, and the one or more programs include instructions for executing the cross-scale reverse design method of the porous structure.

[0037] The above at least one technical solution adopted in this specification can achieve the following beneficial effects:

[0038] Through distribution optimization, the difference in the numerical distribution at the microscale can be enhanced, thereby improving the distinguishability of control conditions at the microscale and facilitating accurate generation; through continuous coding optimization, simple numerical conditions are converted into vector conditions, thereby increasing the complexity and diversity of control conditions. These two optimizations can obtain high-quality conditional control vectors, thus significantly improving the accuracy of cross-scale reverse design;

[0039] Since high-quality conditional control vectors are obtained through the above two optimizations, it does not occupy the training cost and inference cost of the reverse generation model, and does not generate additional consumption of computing resources and time costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The drawings described herein are used to provide a further understanding of this specification, and constitute a part of this specification. The schematic embodiments of this specification and their descriptions are used to explain this specification and do not constitute an improper limitation of this specification.

[0041] In the drawings:

[0042] Figure 1 A flowchart showing the cross-scale reverse design method of the porous structure in the embodiment is shown;

[0043] Figure 2 An example diagram showing the reverse design effect of the cross-scale reverse design method of the porous structure in the embodiment is shown;

[0044] Figure 3 An effect diagram showing the distribution optimization of the mechanical properties in the cross-scale reverse design method of the porous structure in the embodiment is shown;

[0045] Figure 4 An effect diagram showing the Gaussian coding and discrete sampling of the optimized mechanical properties in the cross-scale reverse design method of the porous structure in the embodiment is shown;

[0046] Figure 5 A flowchart showing the process of training the reverse generation model in the cross-scale reverse design method of the porous structure in the embodiment is shown;

[0047] Figure 6 A schematic diagram showing the structure of the cross-scale reverse design device of the porous structure in the embodiment is shown;

[0048] Figure 7 The structural schematic diagram of the electronic device in the embodiment is shown. Detailed implementation manners

[0049] To make the objectives, technical solutions and advantages of this specification clearer, the technical solutions of this specification will be clearly and completely described below in conjunction with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only a part rather than all of the embodiments of this specification. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this specification without creative efforts shall fall within the scope of protection of this specification.

[0050] The technical solutions provided in the embodiments of this specification will be described in detail below in conjunction with the drawings.

[0051] Figure 1 It is a flowchart of a multi-scale reverse design method for a porous structure provided in an embodiment of the present invention. As Figure 1 shown, the multi-scale reverse design method for a porous structure in the embodiment of the present invention may include the following steps:

[0052] S100: Obtain the mechanical properties of the porous structure.

[0053] Obtain the mechanical properties of a preset target (porous structure). The porous structure is expressed in the form of voxels. A porous structure is represented as an array with a shape of , and each element value is 0 or 1. 0 indicates no filling at that position, and 1 indicates filling. The mechanical properties of the porous structure , that is, the stiffness matrix, can be represented as , and includes three elements: , , .

[0054] S200: Optimize the distribution of the mechanical properties to obtain optimized mechanical properties.

[0055] Since the numerical values of the mechanical properties , such as , exist in different scales, for the numerical values at a very small scale, extremely small differences will also bring relatively large relative errors. Therefore, the mechanical properties , such as , are transformed by a linear function-logarithmic function to widen the numerical differences of the properties at a very small scale. In an embodiment of this specification, optimizing the distribution of the mechanical properties to obtain optimized mechanical properties includes: performing a linear function-logarithmic function transformation on the mechanical properties to obtain optimized mechanical properties, where the linear function-logarithmic function transformation formula is Among them, the coefficients and are preset artificially, represent mechanical properties. For example, , or , represent the optimized mechanical properties. The optimized mechanical properties , for example basically conform to the standard Gaussian distribution.

[0056] S300: Continuously encode the optimized mechanical properties to obtain a mechanical property vector.

[0057] By continuous encoding, the optimized mechanical properties , for example are transformed into an encoded vector such as . In an embodiment of the present specification, continuously encoding the optimized mechanical properties to obtain a mechanical property vector includes performing Gaussian encoding and discretized sampling on the optimized mechanical properties to obtain a mechanical property vector. Specifically, as Figure 4 shown, first take the optimized mechanical properties , for example as the central value of the Gaussian function to obtain Gaussian functions with different central values and the same variance. Next, perform discretized sampling on the Gaussian function to obtain the corresponding mechanical property vector.

[0058] S400: Transform the mechanical property vector into a conditional control vector.

[0059] Perform a concatenation operation on the mechanical property vector such as to transform a vector with the original shape into a shape vector. Next, input it into a property vector mapping module composed of a multi-layer fully connected network to be transformed into a conditional control vector with a dimension of .

[0060] S500: Generate random Gaussian noise of a porous structure, and input the random Gaussian noise and the conditional control vector into a trained inverse generation model to inversely generate a porous structure.

[0061] Specifically, generate random Gaussian noise of a porous structure with a shape of , and input the random noise and the conditional control vector into an inverse generation model composed of a conditional diffusion model to output a generated structure ​​。This step is a general technique used in conditional diffusion models. The specific operations of the conditional diffusion model are divided into a diffusion process and a denoising process. In the diffusion process, random noise is weighted and summed with the porous structure to obtain the input noise signal , and represent weight values. In the denoising process, the conditional diffusion model predicts the original random noise based on the noise signal and the conditional control vector , and the predicted value is . Then, the generated porous structure is calculated.

[0062] In an embodiment of the present specification, as Figure 5 shown, training the reverse generation model includes the following steps:

[0063] S501: Construct a data set consisting of porous structures and corresponding mechanical property vectors.

[0064] For one training step, prepare a batch (with a quantity of n) of porous structures and their corresponding mechanical property vectors, such as . The shapes of the porous structure and the mechanical property are and , respectively.

[0065] S502: Convert the mechanical property vector into a conditional control vector.

[0066] Perform a concatenation operation on the mechanical property vector, such as . Convert the vector with the original shape into a vector with the shape. Next, input it into the property vector mapping module composed of a multi-layer fully connected network and convert it into a conditional control vector with a dimension of .

[0067] S503: Generate random Gaussian noise for the porous structure, input the random Gaussian noise and the conditional control vector into the reverse generation model, and output the generated porous structure.

[0068] Specifically, generate random Gaussian noise for the porous structure with the shape of , input the random noise and the conditional control vector into the reverse generation model composed of the conditional diffusion model, and output the generated structure .

[0069] S504: Calculate the difference between the generated porous structure and the porous structures in the dataset as the loss function.

[0070] For the generated structure and the porous structures in the dataset calculate the difference as the loss function.

[0071] S505: Based on the loss function, implement the training of the inverse generation model.

[0072] Update the network parameters in the mapping network composed of multi-layer fully connected networks and the inverse generation model through the gradient descent method; use different batches of datasets composed of porous structures and corresponding mechanical property vectors for repeated training until the network converges.

[0073] The above is the implementation of the cross-scale inverse design method for porous structures in this specification. Based on the same idea, this specification also provides a corresponding cross-scale inverse design device for porous structures, as Figure 6 shown. The cross-scale inverse design device for porous structures in the embodiments of the present invention may include:

[0074] A data acquisition module 601 for acquiring the mechanical properties of porous structures;

[0075] A property distribution optimization module 602 for optimizing the distribution of mechanical properties to obtain optimized mechanical properties ( Figure 3 )

[0076] A property continuous encoding module 603 for continuously encoding the optimized mechanical properties to obtain mechanical property vectors;

[0077] A property vector mapping module 604 for converting the mechanical property vectors into conditional control vectors; and

[0078] An inverse generation module 605 for generating random Gaussian noise of porous structures and inputting the random Gaussian noise and conditional control vectors into the trained inverse generation model to inversely generate porous structures.

[0079] Figure 2 Shows an example diagram of the inverse design effect of the cross-scale inverse design method for porous structures in the embodiments.

[0080] This specification also provides a computer-readable storage medium storing a computer program that can be used to execute the above Figure 1 provided cross-scale inverse design method for porous structures.

[0081] This specification also provides Figure 7 shown a schematic structural diagram of an electronic device corresponding to Figure 1 AsFigure 7 As shown, at the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include other hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the above Figure 1 porous structure cross-scale reverse design method.

[0082] In the 1990s, it was quite obvious to distinguish whether an improvement to a technology was a hardware improvement (e.g., improvement to circuit structures such as diodes, transistors, switches, etc.) or a software improvement (improvement to method flows). However, with the development of technology, many improvements to method flows today can be regarded as direct improvements to hardware circuit structures. Almost all designers obtain the corresponding hardware circuit structures by programming the improved method flows into the hardware circuits. Therefore, it cannot be said that an improvement to a method flow cannot be implemented with a hardware entity module. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by a user's programming of the device. Designers can program by themselves to "integrate" a digital system on a piece of PLD without having to ask a chip manufacturer to design and fabricate a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly implemented using "logic compiler" software, which is similar to the software compiler used in program development and writing. The original code before compilation also has to be written in a specific programming language, which is called a Hardware Description Language (HDL). And there is not only one kind of HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones currently are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also be aware that by simply making a little logical programming of the method flow with the above-mentioned several hardware description languages and programming it into an integrated circuit, it is easy to obtain a hardware circuit that implements the logical method flow.

[0083] The controller can be implemented in any suitable manner. For example, the controller can take the form of, for example, a microprocessor or a processor and a computer-readable medium storing computer-readable program code (such as software or firmware) executable by the (micro)processor, logic gates, switches, an application specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of the controller include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in the form of pure computer-readable program code, it is entirely possible to logically program the method steps to enable the controller to be implemented in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. to achieve the same function. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be regarded as the structures within the hardware component. Or even, the devices for implementing various functions can be regarded as either software modules for implementing the method or structures within the hardware component.

[0084] The systems, devices, modules, or units illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0085] For the convenience of description, when describing the above devices, they are described separately as various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0086] Those skilled in the art should understand that the embodiments of this specification can be provided as a method, a system, or a computer program product. Therefore, this specification can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0087] This specification is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the specification. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device generate means for implementing the specified functions in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the specified functions in one or more of the blocks.

[0088] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means for implementing the specified functions in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the specified functions in one or more of the blocks.

[0089] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the specified functions in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the specified functions in one or more of the blocks.

[0090] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0091] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0092] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0093] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0094] It should be understood by those skilled in the art that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0095] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0096] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and reference can be made to the corresponding part of the method embodiment for related content.

[0097] The above are only the embodiments of this specification and are not intended to limit this specification. For those skilled in the art, various changes and modifications can be made to this specification. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this specification shall be included within the scope of the claims of this specification.

Claims

1. A cross-scale reverse design method for a porous structure, characterized in that, including: obtaining the mechanical properties of a porous structure; performing distribution optimization on the mechanical properties to obtain optimized mechanical properties; performing continuous encoding on the optimized mechanical properties to obtain a mechanical property vector; converting the mechanical property vector into a conditional control vector; and generating random Gaussian noise of the porous structure, and inputting the random Gaussian noise and the conditional control vector into a trained inverse generation model to inversely generate a porous structure; wherein, the performing distribution optimization on the mechanical properties to obtain optimized mechanical properties includes: Perform a linear function - logarithmic function transformation on the mechanical properties to obtain optimized mechanical properties, where the linear function - logarithmic function transformation formula is , where the coefficients and are preset, represents the mechanical properties, represents the optimized mechanical properties; the performing continuous encoding on the optimized mechanical properties to obtain a mechanical property vector includes: performing Gaussian encoding and discretized sampling on the optimized mechanical properties to obtain a mechanical property vector.

2. The method for cross-scale inverse design of a porous structure according to claim 1, characterized in that Training the inverse generation model includes: constructing a data set composed of porous structures and corresponding mechanical property vectors; converting the mechanical property vector into a conditional control vector; generating random Gaussian noise of the porous structure, inputting the random Gaussian noise and the conditional control vector into the inverse generation model, and outputting the generated porous structure; calculating the difference between the generated porous structure and the porous structure in the data set as a loss function; realizing the training of the inverse generation model based on the loss function.

3. The porous structure cross-scale reverse design method according to claim 2, characterized in that The constructing a data set composed of porous structures and corresponding mechanical property vectors includes: Perform a linear function-logarithmic function transformation on the mechanical properties of the porous structure to obtain optimized mechanical properties, where the linear function-logarithmic function transformation formula is , where the coefficients and are preset, represents the mechanical properties, represents the optimized mechanical properties; performing continuous encoding on the optimized mechanical properties to obtain a mechanical property vector; obtaining a data set composed of porous structures and corresponding mechanical property vectors.

4. The multi-scale inverse design method of the porous structure according to claim 3, wherein The performing continuous encoding on the optimized mechanical properties to obtain a mechanical property vector includes: performing Gaussian encoding and discretized sampling on the optimized mechanical properties to obtain a mechanical property vector.

5. The method for cross-scale inverse design of a porous structure according to claim 1, wherein The trained inverse generation model obtains a predicted noise value according to the random Gaussian noise and the conditional control vector, according to the formula inversely generate a porous structure, where represents random Gaussian noise, represents the conditional control vector, represents the predicted noise value, represents the inversely generated porous structure, and represents the weight value.

6. A porous structure cross-scale reverse design device, characterized in that including: a data acquisition module for obtaining the mechanical properties of a porous structure; a property distribution optimization module for performing distribution optimization on the mechanical properties to obtain optimized mechanical properties; a property continuous encoding module for performing continuous encoding on the optimized mechanical properties to obtain a mechanical property vector; a property vector mapping module for converting the mechanical property vector into a conditional control vector; and an inverse generation module for generating random Gaussian noise of the porous structure and inputting the random Gaussian noise and the conditional control vector into a trained inverse generation model to inversely generate a porous structure; wherein, the performing distribution optimization on the mechanical properties to obtain optimized mechanical properties includes: Perform a linear function - logarithmic function transformation on the mechanical properties to obtain optimized mechanical properties, where the linear function - logarithmic function transformation formula is , where the coefficient and are preset, represents the mechanical properties, represents the optimized mechanical properties; the performing continuous encoding on the optimized mechanical properties to obtain a mechanical property vector includes: performing Gaussian encoding and discretized sampling on the optimized mechanical properties to obtain a mechanical property vector.

7. An electronic device, characterized in that, including: one or more processors and a memory, wherein the memory stores one or more programs, and the one or more programs include instructions for executing the cross-scale inverse design method of a porous structure according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, including one or more programs for execution by one or more processors of an electronic device, the one or more programs including instructions for executing the cross-scale inverse design method of a porous structure according to any one of claims 1-5.

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