A cell filling method, device, storage medium and equipment

Abstract

This specification discloses a cell filling method, device, storage medium, and equipment. In this method, not only can cell filling be performed according to the alternative cells after Pareto optimization included in the pre-constructed cell library, so that there is no need to select specific cell types based on expert experience to fill the cells, thereby improving the accuracy of the filled cell structure, but also the correspondence relationship between the performance parameters and relative density of the optimized alternative cells can be used for topology optimization, thus simplifying the complexity of cell optimization and improving the accuracy of the determined cell structure.

Description

Technical Field

[0001] The present invention relates to the field of additive manufacturing technology, and in particular to a cell filling method, device, storage medium and equipment. Background Art

[0002] Cell filling technology is a technology that can intelligently adjust the filling density and path according to design requirements, thereby finely controlling the internal structure of additively manufactured (i.e. 3D printed) objects. Through this technology, resources can be used effectively while ensuring that the final product meets the preset functional requirements. Cell filling technology not only improves manufacturing efficiency and economy through intelligent design and optimization of the microstructure of materials, but also promotes more environmentally friendly engineering design. Therefore, this technology is widely used in many fields, providing strong support for more efficient, economical and environmentally friendly design and manufacturing.

[0003] Normally, when designing a cell structure, an object can be divided into multiple unit design areas. The cell filling technology can intelligently select cells corresponding to each unit design area of ​​the object according to the functional requirements of the object. For example, for a Rubik's Cube, each small cube that makes up the Rubik's Cube can be understood as a unit design area. Through the cell filling technology, different materials can be selected to fill these small cubes. For example, some small cubes use lightweight materials, some small cubes use solid materials, and some small cubes can be empty. After filling, the small cubes are cells, and each cell together constitutes the corresponding cell structure of the object. Intelligent cell filling technology is a method of determining what material should be used for each small cube that makes up an object according to the functional requirements of the object.

[0004] However, since the cell filling technology has many errors in selecting cells corresponding to each position of the object when designing the cell structure of the object, the accuracy of the cell structure determined by the cell intelligent filling technology is poor and it is difficult to meet the needs of actual applications. Summary of the invention

[0005] This specification provides a cell filling method, device, storage medium and equipment to partially solve the above-mentioned problems existing in the prior art.

[0006] This manual adopts the following technical solutions:

[0007] This specification provides a cell filling method, including:

[0008] Determine a unit design region, wherein the unit design region is obtained by meshing the target design space, and the size of the unit design region is consistent with the size of the cell to be filled;

[0009] According to the correspondence between the relative density of each alternative cell and the value of the physical property parameter determined in advance, perform topology optimization on the divided target design space to obtain the relative density distribution of the divided target design space; each of the alternative cells is obtained through optimization and stored in a preset cell library.

[0010] According to the relative density distribution, select alternative cells that match each unit design area from the cell library to fill the target design space.

[0011] Optionally, according to the correspondence between the relative density of each alternative cell and the value of the physical property parameter determined in advance, perform topology optimization on the divided target design space to obtain the relative density distribution of the divided target design space, specifically including:

[0012] Obtain the target constraint conditions;

[0013] According to the initial relative density of each unit design area, the target constraint conditions, and the correspondence between the relative density of each alternative cell and the value of the physical property parameter determined in advance, determine the global index parameter corresponding to the target design space; the global index parameter refers to the physical property parameter of the target design space under the condition of satisfying the target constraint conditions.

[0014] Determine the target loss value according to the global index parameter, and take minimizing the target loss value as the optimization objective, and adjust the initial relative density value of each unit design area respectively to obtain the relative density distribution of the divided target design space.

[0015] Optionally, the target constraint conditions include: the volume fraction constraint condition of the target design space, the load constraint condition of the target design space, and the minimum relative density constraint condition of each unit design area.

[0016] Optionally, the method further includes optimizing each alternative cell, specifically including:

[0017] Obtain the relative density and the target physical property parameter value of each alternative cell;

[0018] According to the relative density and the target physical property parameter value of each alternative cell, select alternative cells that have no dominant alternative cells from the alternative cells; wherein, the relative density of the dominant alternative cell of each alternative cell is not greater than the relative density of this alternative cell and the target physical property parameter value of the dominant alternative cell of this alternative cell is greater than the target physical property parameter value of this alternative cell.

[0019] Optionally, adjusting the initial relative density value of each unit design area respectively specifically includes:

[0020] Obtain the weight coefficient of each unit design area; the weight coefficient of each unit design area is used to characterize the influence degree of the global index parameter corresponding to the target design space when the relative density value of the unit design area is adjusted.

[0021] Adjust the initial relative density value of each unit design area according to the weight coefficient.

[0022] Optionally, according to the relative density distribution, select alternative cells that match each unit design area from the cell library to fill the target design space, including at least one of the following:

[0023] For each unit design area, if it is determined that the adjusted relative density value of the unit design area is greater than the maximum value of the preset relative density value range, fill the unit design area with an entity.

[0024] For each unit design area, if it is determined that the adjusted relative density value of the unit design area is less than the minimum value of the relative density value range, determine that the unit design area is empty.

[0025] For each unit design area, if it is determined that the adjusted relative density value of the unit design area is within the relative density value range, select alternative cells that match each unit design area from the cell library to fill the target design space.

[0026] Optionally, the method further includes:

[0027] If it is determined that re-filling is required according to the deviation between the global index parameter of the filled target design space and the preset reference index parameter, adjust the relative density value range according to the deviation.

[0028] According to the adjusted relative density value range and the relative density distribution, re-select alternative cells that match each unit design area from the cell library to fill the target design space.

[0029] This specification provides a cell filling device, including:

[0030] A determination module, configured to determine a unit design area, where the unit design area is obtained by dividing a target design space into grids, and the size of the unit design area is the same as the size of the cell to be filled.

[0031] An optimization module, configured to perform topology optimization on the divided target design space according to the correspondence between the relative densities and the physical property parameter values of each pre-determined alternative cell, so as to obtain the relative density distribution of the divided target design space; each of the alternative cells is obtained through optimization and stored in a preset cell library;

[0032] A cell filling module, configured to fill the target design space with alternative cells selected from the cell library according to the relative density distribution and matching each unit design region.

[0033] This specification provides a computer-readable storage medium storing a computer program, which when executed by a processor implements the above cell filling method.

[0034] This specification provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the above cell filling method when executing the program.

[0035] At least one of the above technical solutions adopted in this specification can achieve the following beneficial effects:

[0036] In the cell filling method provided in this specification, first, a unit design region is determined, where the unit design region is obtained by performing grid division on the target design space, and the size of the unit design region is the same as the size of the cell to be filled. Then, according to the correspondence between the relative densities and the physical property parameter values of each pre-determined optimized alternative cell, topology optimization is performed on the divided target design space to obtain the relative density distribution of the divided target design space. According to the relative density distribution, alternative cells matching each unit design region are selected from the cell library to fill the target design space.

[0037] It can be seen from the above method that the server can not only fill cells with the alternative cells optimized by Pareto included in the pre-constructed cell library, so that it is not necessary to select a specific cell type by relying on expert experience to fill the cells, thereby improving the accuracy of the filled cell structure, but also use the correspondence between the performance parameters and the relative density of the optimized alternative cells to perform topology optimization, so as to improve the accuracy of the determined relative density distribution of the target design space, and further improve the accuracy of the determined cell structure. Description of the Drawings

[0038] The drawings described herein are used to provide a further understanding of this specification, and constitute a part of this specification. The illustrative embodiments and descriptions of this specification are used to explain this specification and do not constitute an improper limitation to this specification. In the drawings:

[0039] Figure 1 It is a schematic flowchart of a cell filling method provided in this specification;

[0040] Figure 2 It is a schematic diagram of an alternative cell provided in this specification;

[0041] Figure 3 It is a schematic diagram of the optimization process of the alternative cell provided in this specification;

[0042] Figure 4 It is a schematic diagram of the topology optimization process provided in this specification;

[0043] Figure 5 It is a schematic diagram of a cell filling device provided in this specification;

[0044] Figure 6 It is provided in this specification corresponding to Figure 1 Schematic diagram of an electronic device. Specific embodiments

[0045] To make the purpose, 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 of the embodiments of this specification, rather than all the embodiments. Based on the embodiments in this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this specification.

[0046] The following will detail the technical solutions provided in each embodiment of this specification in conjunction with the drawings.

[0047] Figure 1 It is a schematic flowchart of a cell filling method provided in this specification, including the following steps:

[0048] S101: Determine the unit design area, which is obtained by dividing the target design space into grids, and the size of the unit design area is the same as the size of the cell to be filled.

[0049] In this specification, the service platform can pre-build a cell library so that when the cell structure of the target object needs to be designed, the corresponding cells can be screened from the cell library to fill the target design space corresponding to the target object, thereby obtaining the cell structure of the target object.

[0050] Among them, the above-mentioned target object can be set according to actual needs, for example: aerospace parts, automotive components, etc.

[0051] The above-mentioned target design space can be determined according to the overall geometric shape and topological structure of the target object, or can be determined according to the geometric shape and topological structure of a local area in the target object.

[0052] Specifically, after determining the target design space corresponding to the target object, the service platform can perform grid division on the target design space to obtain each unit design area.

[0053] Among them, the size of each unit design area is the same as the size of the cell to be filled. Here, the cell to be filled can refer to each alternative cell stored in the above-mentioned cell library for filling the target design space.

[0054] Furthermore, the service platform can, for each unit design area, screen out the alternative cells that match the unit design area from each alternative cell to fill the unit design area. Finally, according to each filled unit design area, the cell structure corresponding to the target object can be obtained.

[0055] It should be noted that the method for the service platform to construct the cell library can be to obtain the cell skeleton and adjust each component that makes up the cell skeleton to obtain each alternative cell with different volume fractions but the same corresponding cell skeleton. Among them, each cell skeleton is composed of the interconnected components, and at each vertex of the cell skeleton, there is a connection point of each component, so that each alternative cell can be geometrically connected to the adjacent alternative cells when constructing the cell structure.

[0056] Among them, the above-mentioned component can be a material unit for forming the structure inside the cell. Here, the component can be a rod-shaped object, a plate-shaped object, etc., specifically as Figure 2 shown.

[0057] Figure 2 This is a schematic diagram of the alternative cells provided in this specification.

[0058] Combined with Figure 2 it can be seen from the cell A and cell B in that the above-mentioned rod-shaped object is a cylindrical structure. The service platform can adjust the radius of the bottom circle of the rod-shaped object (it can be understood as adjusting the thickness of each rod-shaped object that makes up the cell skeleton) to obtain each alternative cell with different volume fractions (that is, the ratio of the total volume of all rod-shaped objects to the overall volume of the cell skeleton) but the same corresponding cell skeleton.

[0059] Combined with Figure 2 it can be seen from the cell C in that the above-mentioned plate-shaped object is a cuboid structure. The service platform can adjust the thickness of the rod-shaped object to obtain each alternative cell with different volume fractions but the same corresponding cell skeleton.

[0060] Furthermore, the service platform can obtain alternative unit cells with different volume fractions by adjusting each component that makes up the unit cell skeleton. After sorting the alternative unit cells according to the volume fraction from small to large, the difference in volume fraction between every two adjacent alternative unit cells is the same. In other words, the service platform can sequentially generate unit cells with different volume fractions at equal intervals between 0 and 1 based on the unit cell skeleton.

[0061] For example: Based on the unit cell skeleton, the service platform can sequentially obtain unit cells with volume fractions of 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, and 0.9 by adjusting the radius of the bottom circle of each rod-shaped object that makes up the unit cell skeleton.

[0062] Among them, the shape of the above-mentioned unit cell skeleton can be various, such as a regular tetrahedron, a regular octahedron, etc. Among them, the number and / or connection method of the rod-shaped objects included in the unit cell skeletons with the same shape can also be different. On this basis, the service platform can obtain corresponding unit cells with different volume fractions for each unit cell skeleton as alternative unit cells, and construct the above-mentioned unit cell library based on the alternative unit cells.

[0063] In this specification, the execution entity for implementing the unit cell filling method can refer to a designated device such as a server set up in the service platform, or can also refer to a terminal device such as a desktop computer or a laptop computer. For the convenience of description, the following only takes the server as the execution entity as an example to illustrate the unit cell filling method provided in this specification.

[0064] S102: According to the correspondence relationship between the relative density and the physical property parameter values of each pre-determined alternative unit cell, perform topology optimization on the divided target design space to obtain the relative density distribution of the divided target design space; each of the alternative unit cells is obtained after optimization and stored in a preset unit cell library.

[0065] After the server determines each unit design area, it can perform topology optimization on the divided target design space according to the correspondence relationship between the relative density and the physical property parameter values of each pre-determined alternative unit cell to obtain the relative density distribution of the divided target design space. Based on this relative density distribution, the relative density of each unit design area that makes up the target design space can be determined.

[0066] Among them, there can be various corresponding relationships between the relative densities and physical property parameter values of the above-mentioned alternative cells. That is, the server can take each physical property parameter (such as Young's modulus, thermal conductivity, Poisson's ratio, shear modulus, etc.) of the alternative cells as the target physical property parameter, and then can fit the relative densities of the alternative cells saved in the cell library and the target physical property parameter values of the alternative cells through a polynomial to obtain the corresponding relationship between the relative density of each alternative cell and the target physical property parameter. The specific corresponding relationship here can refer to the following formula:

[0067]

[0068] In the above formula, is the target physical property parameter value of the alternative cell, is the relative density of the alternative cell, and a, b, c... z are constants. The value of n can be set according to actual needs. Here, n takes 3 to 6 so that the fitting accuracy R2≥0.95.

[0069] In the actual application scenario, the accuracy of the corresponding relationship between the relative density and physical property parameter value of the determined alternative cell is particularly important for the accuracy of the cell structure determined by using cell filling. However, the accuracy of the corresponding relationship between the relative density and target physical property parameter of the alternative cell directly determined according to the relative density and target physical property parameter value of each alternative cell in the cell library is often poor.

[0070] Based on this, before determining the corresponding relationship between the relative density and target physical property parameter of the alternative cell according to the relative density and target physical property parameter value of each alternative cell in the cell library, the server can also optimize each alternative cell saved in the cell library to obtain each optimized alternative cell, specifically as Figure 3 shown.

[0071] Figure 3 is a schematic diagram of the optimization process of the alternative cell provided in this specification.

[0072] Combined with Figure 3 it can be seen that the server can obtain the relative density and target physical property parameter value of each alternative cell, and then can select the alternative cells that do not have dominant alternative cells from each alternative cell according to the relative density and target physical property parameter value of each alternative cell. Furthermore, according to the alternative cells selected from the cell library, each optimized alternative cell corresponding to the target physical property parameter can be obtained.

[0073] Among them, for each alternative cell, if the relative density of the alternative cell is not greater than that of another alternative cell, and the value of the target physical property parameter of the alternative cell is greater than that of the alternative cell, then the alternative cell can be used as the supporting alternative cell of the other alternative cell.

[0074] Furthermore, the server can determine the correspondence between the relative density and the target physical property parameter of each alternative cell according to the relative density and the value of the target physical property parameter of each optimized alternative cell corresponding to the target physical property parameter.

[0075] It can be seen from the above that in this specification, there can be multiple correspondences between the relative density and the physical property parameter value of each alternative cell. In an actual application scenario, after the server determines the type of the global index parameter to be used in the topology optimization process, it can determine the correspondence between the relative density and the physical property parameter value of each alternative cell that matches the type of the global index parameter to be used in the topology optimization process from the correspondences between the relative density and different physical property parameters of each alternative cell as the target correspondence. Furthermore, according to the target correspondence, the server can perform topology optimization on the divided target design space to obtain the relative density distribution of the divided target design space.

[0076] Among them, the type of the global index parameter to be used in the topology optimization process can be determined according to actual needs. For example: structural compliance, stiffness, heat conduction performance, strength, etc.

[0077] For each correspondence between the relative density and the physical property parameter value of each alternative cell, if it is determined that the correspondence is the one required in the process of calculating the global index parameter of this type when the target design space meets the target constraint conditions, then it can be determined that the correspondence matches the type of the global index parameter to be used in the topology optimization process.

[0078] For example: when the global index parameter is structural compliance, the correspondence required in the process of calculating the structural compliance of the target design space when it meets the target constraint conditions can be the correspondence between the relative density of each alternative cell and the Young's modulus.

[0079] Another example: when the global index parameter is heat conduction performance, the correspondence required in the process of calculating the heat conduction performance of the target design space when it meets the target constraint conditions can be the correspondence between the relative density of each alternative cell and the thermal conductivity.

[0080] Among them, the above-mentioned target constraint conditions can be determined according to actual requirements, and the above-mentioned target constraint conditions can include at least one of the following: volume fraction constraint conditions of the target design space, load constraint conditions of the target design space, mass constraint conditions of the target design space, and minimum relative density constraint conditions of each unit design area.

[0081] Further, after the server determines the correspondence between the target design space, the target constraint conditions, and the relative density and physical property parameter values of each alternative cell, it can perform topology optimization on the divided target design space according to the pre-determined correspondence between the relative density and physical property parameter values of each alternative cell, specifically as Figure 4 shown.

[0082] Figure 4 This is a schematic diagram of the topology optimization process provided in this specification.

[0083] Combined with Figure 4 it can be seen that the server can determine the initial relative density of each unit design area included in the target design space, and then can determine the global index parameter of the target design space under the condition of meeting the target constraint conditions according to the correspondence between the relative density and physical property parameter values of each alternative cell, and determine the target loss value according to the global index parameter. Furthermore, taking minimizing the target loss value as the optimization goal, the initial relative density values of each unit design area can be adjusted respectively until the preset termination condition is met to obtain the relative density distribution of the divided target design space.

[0084] Among them, the global index parameter corresponding to the target design space is positively correlated with the target loss value.

[0085] In the above content, the global index parameter refers to the physical property parameter of the target design space under the condition of meeting the target constraint conditions.

[0086] In the above content, the preset termination condition can be set according to actual requirements. For example: when it is determined that the relative density values of each unit design area after adjustment converge, it can be regarded as meeting the above termination condition.

[0087] For another example: when it is determined that the preset target adjustment round is reached, it can be regarded as meeting the above termination condition.

[0088] For the sake of easy understanding, the following takes the above-mentioned target constraint conditions as the volume fraction constraint condition of the target design space, the load constraint condition of the target design space, and the minimum relative density constraint condition of each unit design area, and the global index parameter as the structural compliance as an example to elaborate on the above topology optimization process in detail. The mathematical model of this topology optimization process can specifically refer to the following formula:

[0089] Find:

[0090] Obj:min

[0091] s.t. ,n

[0092] F = KU

[0093] 0 <

[0094] In the above formula, C(x) is the structural flexibility of the target design space, is the relative density of the unit design area, and are the stiffness and displacement of the i-th unit design area respectively. U represents any allowable displacement field, K is the global stiffness matrix. n is the total number of unit design areas, V is the volume of the target design space. F is the load acting on the target design space determined according to the target constraint conditions, and f is the specified volume fraction of the target design space determined according to the target constraint conditions. is the minimum relative density determined according to the target constraint conditions.

[0095] It should be noted that the setting of the above minimum relative density is to avoid the singularity of the total stiffness in the finite element calculation. It is necessary to ensure that the stiffness matrix of the unit design area is not 0, so the lower limit of the relative density is set, usually 0.001.

[0096] It is worth noting that in the actual application scenario, in order to improve the accuracy of the relative density distribution of the determined target design space, the server can also obtain the weight coefficient of each unit design area during the topology optimization process, and adjust the initial relative density value of each unit design area according to the weight coefficient.

[0097] Among them, the weight coefficient of each unit design area is used to characterize the influence degree of the adjustment of the relative density value of the unit design area on the global index parameters corresponding to the target design space.

[0098] Specifically, the server can determine the partial derivatives of the global metric function with respect to each unit design region, and determine the weight coefficients of each unit design region based on the partial derivatives of the global metric function with respect to each unit design region. Furthermore, the server can determine the normalized weight coefficients of each unit design region, and determine the mean value of the normalized weight coefficients of each unit design region as the reference weight coefficient. For each unit design region, when the server determines that the normalized weight coefficient of the unit design region is greater than the reference weight coefficient, it can be determined that the impact on the global metric parameter corresponding to the target design space when the relative density of the unit design region is adjusted is relatively large. At this time, the server can increase the relative density of the unit design region by a specified amplitude. When the server determines that the normalized weight coefficient of the unit design region is less than the reference weight coefficient, it can be determined that the impact on the global metric parameter corresponding to the target design space when the relative density of the unit design region is adjusted is relatively small. At this time, the server can reduce the relative density of the unit design region by a specified amplitude.

[0099] S103: According to the relative density distribution, select alternative cells that match each unit design region from the cell library to fill the target design space.

[0100] After determining the relative density distribution of the target design space, the server can, for each unit design region included in the target design space, select alternative cells from the cell library whose difference from the relative density of the unit design region is within a preset range as the cells to be filled according to the relative density of the unit design region, and fill the cells to be filled into the unit design region, so as to obtain the cell structure corresponding to the target design space based on each filled unit design region.

[0101] In an actual application scenario, in order to improve the filling efficiency, the server can also, for each unit design region included in the target design space, if it is determined that the adjusted relative density value of the unit design region is greater than the maximum value of the preset relative density value range, fill the unit design region with an entity. If it is determined that the adjusted relative density value of the unit design region is less than the minimum value of the relative density value range, it is determined that the unit design region is empty. If it is determined that the adjusted relative density value of the unit design region is within the relative density value range, select alternative cells that match each unit design region from the cell library to fill the target design space.

[0102] Furthermore, after obtaining each filled unit design region, the server can also detect the entire target design space to be able to refill the target design space according to the relative density distribution of the target design space when an abnormality is found.

[0103] Specifically, the server can determine whether re-filling is required based on the deviation between the global metric parameter of the filled target design space and the preset reference metric parameter. If it is determined that the deviation between the global metric parameter of the filled target design space and the preset reference metric parameter exceeds the preset difference threshold, the relative density value range used in the filling process can be adjusted according to the above deviation. Thus, the target design space can be re-filled by selecting alternative cells that match each unit design area from the cell library according to the adjusted relative density value range and the relative density distribution.

[0104] For ease of understanding, the following takes the global metric parameter as the structural compliance as an example to elaborate in detail on the method of adjusting the relative density value range used in the filling process according to the deviation between the global metric parameter of the filled target design space and the preset reference metric parameter.

[0105] Among them, when the server determines that the difference between the structural compliance of the filled target design space and the preset reference metric parameter is greater than the preset difference threshold, it indicates that the structural compliance of the filled target design space is too large, that is, the structural stiffness is insufficient. At this time, the lower limit of the relative density value region can be further lowered to reduce the number of unit design areas set to be empty, so that the continuity of the filled target design space can be better and the structural stiffness can be higher.

[0106] In addition, when the server determines that the difference between the structural compliance of the filled target design space and the preset reference metric parameter is less than the preset difference threshold, it indicates that the structural compliance of the filled target design space is too small, that is, the structural stiffness is too strong. At this time, the upper limit of the relative density value region can be further raised to reduce the number of unit design areas set to be solid, so that the solid volume of the filled target design space can be smaller and the structural compliance can be stronger.

[0107] It can be seen from the above method that the server can not only perform cell filling based on the Pareto-optimized alternative cells included in the pre-constructed cell library, so that there is no need to select specific cell types based on expert experience to fill the cells, thereby improving the accuracy of the filled cell structure, but also utilize the correspondence between the performance parameters of the optimized alternative cells and the relative density to perform topology optimization, simplifying the complexity of cell optimization, avoiding the complex computational amount brought by double topology optimization (i.e., cell topology optimization and component topology optimization), and improving the accuracy of the determined cell structure.

[0108] The above is one or more embodiments of the cell filling method in this specification. Based on the same idea, this specification also provides a corresponding cell filling device, as Figure 5 shown.

[0109] Figure 5 A schematic diagram of a cell filling device provided for this specification, including:

[0110] A determination module 501, configured to determine a unit design area, where the unit design area is obtained by performing grid division on a target design space, and the size of the unit design area is the same as the size of the cell to be filled;

[0111] An optimization module 502, configured to perform topology optimization on the divided target design space according to the correspondence relationship between the relative density and physical property parameter values of each alternative cell determined in advance, so as to obtain the relative density distribution of the divided target design space; each alternative cell is obtained after optimization and stored in a preset cell library;

[0112] A cell filling module 503, configured to fill the target design space with alternative cells matching each unit design area from the cell library according to the relative density distribution.

[0113] Optionally, the optimization module 502 is specifically configured to obtain target constraint conditions; determine global index parameters corresponding to the target design space according to the initial relative density of each unit design area, the target constraint conditions, and the correspondence relationship between the relative density and physical property parameter values of each alternative cell determined in advance; the global index parameters refer to the physical property parameters of the target design space under the condition of meeting the target constraint conditions; determine a target loss value according to the global index parameters, and adjust the initial relative density values of each unit design area respectively with the goal of minimizing the target loss value, so as to obtain the relative density distribution of the divided target design space.

[0114] Optionally, the target constraint conditions include: the volume fraction constraint condition of the target design space, the load constraint condition of the target design space, and the minimum relative density constraint condition of each unit design area.

[0115] Optionally, the optimization module 502 is specifically configured to obtain the relative density and target physical property parameter values of each alternative cell; select alternative cells without dominated alternative cells from the alternative cells according to the relative density and target physical property parameter values of each alternative cell; where the relative density of the dominated alternative cell of each alternative cell is not greater than the relative density of this alternative cell and the target physical property parameter value of the dominated alternative cell of this alternative cell is greater than the target physical property parameter value of this alternative cell.

[0116] Optionally, the optimization module 502 is specifically configured to obtain the weight coefficient of each unit design area; the weight coefficient of each unit design area is used to characterize the influence degree of the global index parameter corresponding to the target design space when the relative density value of the unit design area is adjusted; and adjust the initial relative density value of each unit design area according to the weight coefficient.

[0117] Optionally, the cell filling module 503 is specifically configured to, for each unit design area, if it is determined that the adjusted relative density value of the unit design area is greater than the maximum value of the preset relative density value range, fill the unit design area with an entity; for each unit design area, if it is determined that the adjusted relative density value of the unit design area is less than the minimum value of the relative density value range, determine that the unit design area is empty; for each unit design area, if it is determined that the adjusted relative density value of the unit design area is within the relative density value range, select an alternative cell matching each unit design area from the cell library to fill the target design space.

[0118] Optionally, the cell filling module 503 is specifically configured to, if it is determined that re-filling is required according to the deviation between the global index parameter of the filled target design space and the preset reference index parameter, adjust the relative density value range according to the deviation; and re-select an alternative cell matching each unit design area from the cell library according to the adjusted relative density value range and the relative density distribution to fill the target design space.

[0119] This specification also provides a computer-readable storage medium storing a computer program, which can be used to execute the above Figure 1 provided cell filling method.

[0120] This specification also provides Figure 6 a schematic structural diagram of an electronic device corresponding to Figure 1 as shown. As Figure 6 described, 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 described cell filling method. Of course, in addition to the software implementation, this specification does not exclude other implementation methods, such as logic devices or a combination of software and hardware. That is to say, the execution subject of the following processing flow is not limited to each logic unit, and can also be hardware or logic devices.

[0121] The improvement of a technology can be clearly distinguished as either a hardware improvement (e.g., the improvement of circuit structures such as diodes, transistors, switches, etc.) or a software improvement (the improvement of method processes). However, with the development of technology, many improvements of method processes today can be regarded as direct improvements of hardware circuit structures. Almost all designers obtain the corresponding hardware circuit structure by programming the improved method process into the hardware circuit. Therefore, it cannot be said that an improvement of a method process 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 logical function is determined by the user programming the device. The designer can program by himself to "integrate" a digital system on a piece of PLD without asking the chip manufacturer to design and manufacture a dedicated integrated circuit chip. Moreover, nowadays, instead of manually manufacturing 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 process with the above-mentioned several hardware description languages and programming it into the integrated circuit, it is easy to obtain the hardware circuit that implements the logical method process.

[0122] 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, and embedded microcontrollers 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.

[0123] 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.

[0124] 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.

[0125] 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 memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.

[0126] 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 flowchart and / or block diagram, and combinations of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processors 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 functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0127] 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 specific manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0128] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational 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 functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0129] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0130] 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 RAM. The memory is an example of computer-readable media.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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, and the key point of each embodiment is to illustrate 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 for the relevant parts, reference can be made to the partial description of the method embodiment.

[0136] The above is only the embodiment of this specification and is not used to limit this specification. For those skilled in the art, various modifications and changes 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 cell filling method, characterized in that: include: Determine a unit design region, wherein the unit design region is obtained by meshing the target design space, and the size of the unit design region is consistent with the size of the cell to be filled; According to the predetermined correspondence between the relative density and the physical performance parameter value of each candidate cell, the divided target design space is topologically optimized to obtain the relative density distribution of the divided target design space; the candidate cells are obtained after optimization and stored in a preset cell library; wherein the correspondence is determined according to the relative density and the target physical performance parameter value of each optimized candidate cell, and the method for obtaining each optimized candidate cell is: obtaining the relative density and the target physical performance parameter value of each candidate cell; according to the relative density and the target physical performance parameter value of each candidate cell, selecting the candidate cells without supporting candidate cells from each candidate cell; wherein the relative density of the supporting candidate cell of each candidate cell is not greater than the relative density of the candidate cell and the target physical performance parameter value of the supporting candidate cell of the candidate cell is greater than the target physical performance parameter value of the candidate cell; According to the relative density distribution, candidate cells matching each unit design region are selected from the cell library to fill the target design space.

2. The method according to claim 1, characterized in that According to the predetermined correspondence between the relative density of each candidate cell and the physical performance parameter value, the divided target design space is topologically optimized to obtain the relative density distribution of the divided target design space, specifically including: Get target constraints; Determine the global index parameter corresponding to the target design space according to the initial relative density of each unit design area, the target constraint condition, and the predetermined correspondence between the relative density and the physical performance parameter value of each candidate cell; the global index parameter refers to the physical performance parameter of the target design space under the condition of satisfying the target constraint condition; The target loss value is determined according to the global indicator parameter, and the initial relative density value of each unit design area is adjusted respectively with minimizing the target loss value as the optimization goal to obtain the relative density distribution of the divided target design space.

3. The method according to claim 2, characterized in that The target constraint conditions include: a volume fraction constraint condition of the target design space, a load constraint condition of the target design space, and a minimum relative density constraint condition of each unit design area.

4. The method according to claim 2, characterized in that The initial relative density value of each unit design area is adjusted separately, including: Obtaining a weight coefficient for each unit design area; the weight coefficient for each unit design area is used to characterize the degree of influence on the global index parameter corresponding to the target design space when the relative density value of the unit design area is adjusted; According to the weight coefficient, the initial relative density value of each unit design area is adjusted respectively.

5. The method according to claim 1, characterized in that According to the relative density distribution, selecting candidate cells matching each unit design region from the cell library to fill the target design space includes at least one of: For each unit design area, if it is determined that the adjusted relative density value of the unit design area is greater than the maximum value of the preset relative density value interval, the unit design area is filled by the entity; For each unit design area, if it is determined that the adjusted relative density value of the unit design area is less than the minimum value of the relative density value interval, then the unit design area is determined to be empty; For each unit design region, if it is determined that the adjusted relative density value of the unit design region is within the relative density value interval, then an alternative cell matching each unit design region is selected from the cell library to fill the target design space.

6. The method according to claim 5, characterized in that The method further comprises: If it is determined that refilling is required according to the deviation between the global index parameter of the filled target design space and the preset reference index parameter, adjusting the relative density value interval according to the deviation; According to the adjusted relative density value interval and the relative density distribution, candidate cells matching each unit design area are reselected from the cell library to fill the target design space.

7. A cell filling device, characterized in that: include: A determination module, used for determining a unit design area, wherein the unit design area is obtained by meshing the target design space, and the size of the unit design area is consistent with the size of the cell to be filled; An optimization module, used for performing topological optimization on the divided target design space according to the predetermined correspondence between the relative density and the physical performance parameter value of each candidate cell, and obtaining the relative density distribution of the divided target design space; each candidate cell is obtained after optimization and stored in a preset cell library; wherein the correspondence is determined according to the relative density and the target physical performance parameter value of each optimized candidate cell, and the method for obtaining each optimized candidate cell is: obtaining the relative density and the target physical performance parameter value of each candidate cell; according to the relative density and the target physical performance parameter value of each candidate cell, selecting a candidate cell without supporting candidate cells from each candidate cell; wherein the relative density of the supporting candidate cell of each candidate cell is not greater than the relative density of the candidate cell and the target physical performance parameter value of the supporting candidate cell of the candidate cell is greater than the target physical performance parameter value of the candidate cell; A cell filling module is used to select candidate cells matching each unit design area from the cell library according to the relative density distribution to fill the target design space.

8. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of claims 1 to 6 is implemented.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method described in any one of claims 1 to 6 is implemented.

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