Cell element filling method and device, storage medium and equipment

Abstract

The invention discloses a cell filling method and device, a storage medium and equipment. In the method, cell element filling can be carried out according to the alternative cell elements which are subjected to Pareto optimization and contained in the pre-constructed cell element library, so that a specific cell element type does not need to be selected by virtue of expert experience to fill the cell elements, and the accuracy of a cell element structure obtained by filling is improved; in addition, topological optimization can be carried out by utilizing the corresponding relation between the performance parameters of the optimized alternative cell elements and the relative density, so that the complexity of cell element optimization is simplified, and the accuracy of the determined cell element structure is improved.

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: This specification provides a cell filling method, including: 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 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; the candidate cells are obtained after optimization and stored in a preset cell library; 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.

[0007] Optionally, according to the predetermined correspondence between the relative density of each candidate cell and the physical performance parameter value, topological optimization is performed on the divided target design space 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.

[0008] Optionally, 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.

[0009] Optionally, the method further includes optimizing each candidate cell, specifically including: Obtaining the relative density and target physical performance parameter value of each candidate cell; According to the relative density and target physical performance parameter value of each candidate cell, a candidate cell without a supporting candidate cell is selected from the candidate cells; 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.

[0010] Optionally, 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.

[0011] Optionally, 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.

[0012] Optionally, 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.

[0013] This specification provides a cell filling device, comprising: 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 to perform topological optimization on the divided target design space according to the predetermined correspondence between the relative density of each candidate cell and the physical performance parameter value, so as 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; 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.

[0014] This specification provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned cell filling method is implemented.

[0015] This specification provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned cell filling method when executing the program.

[0016] At least one of the above technical solutions adopted in this specification can achieve the following beneficial effects: In the cell filling method provided in this specification, the unit design area is first determined, wherein the unit design area is obtained by gridding the target design space, and the size of the unit design area is consistent with the size of the cell to be filled. Then, according to the predetermined correspondence between the relative density and the physical performance parameter value of each optimized candidate cell, the divided target design space is topologically optimized to obtain the relative density distribution of the divided target design space. According to the relative density distribution, the candidate cells matching each unit design area are selected from the cell library to fill the target design space.

[0017] It can be seen from the above method that the server can not only fill cells according to the Pareto-optimized candidate cells contained in the pre-built cell library, so that there is no need to rely on expert experience to select specific cell types to fill cells, so as to improve the accuracy of the filled cell structure, but also use the correspondence between the performance parameters and relative density of the optimized candidate cells to perform topology optimization, thereby improving the accuracy of the relative density distribution of the determined target design space, and then improving the accuracy of the determined cell structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] 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 on this specification. In the drawings: Figure 1 A schematic diagram of a cell filling method provided in this specification; Figure 2 A schematic diagram of an alternative cell provided in this specification; Figure 3 A schematic diagram of the optimization process of the candidate cells provided in this specification; Figure 4 A schematic diagram of the topology optimization process provided in this specification; Figure 5 A schematic diagram of a cell filling device provided in this specification; Figure 6 A method corresponding to the Figure 1 Schematic diagram of electronic equipment. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of this specification more clear, the technical solutions of this specification will be clearly and completely described below in combination with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification.

[0020] The technical solutions provided by the embodiments of this specification are described in detail below in conjunction with the accompanying drawings.

[0021] Figure 1 A schematic diagram of a cell filling method provided in this specification includes the following steps: S101: Determine a unit design region, wherein the unit design region is obtained by gridding a target design space, and the size of the unit design region is consistent with the size of a cell to be filled.

[0022] In this specification, the business 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 selected from the cell library to fill the target design space corresponding to the target object, thereby obtaining the cell structure of the target object.

[0023] The above-mentioned target objects can be set according to actual needs, such as aerospace parts, automobile components, etc.

[0024] The above-mentioned target design space may be determined according to the overall geometric shape and topological structure of the target object, or may be determined according to the geometric shape and topological structure of a local area in the target object.

[0025] Specifically, after determining the target design space corresponding to the target object, the business platform may grid the target design space to obtain each unit design area.

[0026] The size of each unit design area is consistent with the size of the cell to be filled, and the cell to be filled here may refer to each candidate cell stored in the cell library for filling the target design space.

[0027] Furthermore, the business platform can select candidate cells matching each unit design area from the candidate cells to fill the unit design area, and finally obtain the cell structure corresponding to the target object according to the filled unit design areas.

[0028] It should be noted that the method for the business platform to construct a cell library is to obtain a cell skeleton and adjust each component that makes up the cell skeleton to obtain alternative cells with different volume fractions but the same corresponding cell skeleton, wherein each cell skeleton is composed of components connected to each other, and each vertex of the cell skeleton corresponds to a connection point of each component, so that when constructing the cell structure, each alternative cell can be geometrically connected to the adjacent alternative cells.

[0029] The above-mentioned components can be material units used to form the structure inside the cell. The components here can be rod-shaped objects, plate-shaped objects, etc. Figure 2 shown.

[0030] Figure 2 Schematic diagram of an alternative cell provided in this specification.

[0031] Combination 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 business platform can adjust the radius of the bottom circle of the rod-shaped object (which can be understood as adjusting the thickness of each rod-shaped object that constitutes the cell skeleton) to obtain alternative cells with different volume fractions (that is, the ratio of the total volume of all rod-shaped objects to the volume of the entire cell skeleton) but the same corresponding cell skeleton.

[0032] Combination Figure 2 It can be seen from the cell C in that the plate-like object is a rectangular parallelepiped structure. The business platform can adjust the thickness of the rod-like object to obtain alternative cells with different volume fractions but the same corresponding cell skeleton.

[0033] Furthermore, the business platform can adjust each component that makes up the cell skeleton to obtain alternative cells with different volume fractions, and after sorting the alternative cells according to the volume fraction, the difference in volume fractions between every two adjacent alternative cells is the same. In other words, the business platform can generate cells with equal spacing and different volume fractions in sequence between volume fractions of 0 and 1 based on the cell skeleton.

[0034] For example: Based on the cell skeleton, the business platform adjusts the radius of the bottom circle of each rod-shaped object that constitutes the cell skeleton to obtain cells with volume fractions of 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, and 0.9 respectively.

[0035] The cell skeleton may have a variety of shapes, such as a regular tetrahedron, a regular octahedron, etc., wherein the number and / or connection method of the rods contained in the cell skeleton of the same shape may also be different. On this basis, the business platform can obtain cells with different volume fractions corresponding to the cell skeleton for each cell skeleton as candidate cells, and construct the cell library based on the candidate cells.

[0036] In this specification, the execution entity used to implement the cell filling method may refer to a designated device such as a server set up on a business platform, or may refer to a terminal device such as a desktop computer or a laptop computer. For the sake of ease of description, the cell filling method provided in this specification is described below using the server as an example of the execution entity.

[0037] S102: 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; the candidate cells are obtained after optimization and stored in a preset cell library.

[0038] After determining each unit design area, the server can perform topological optimization on the divided target design space according to the predetermined correspondence between the relative density of each candidate cell and the physical performance parameter value, and obtain the relative density distribution of the divided target design space. Based on the relative density distribution here, the relative density of each unit design area constituting the target design space can be determined.

[0039] Among them, there can be multiple corresponding relationships between the relative density of each candidate cell and the physical performance parameter value. That is, the server can use each physical performance parameter of the candidate cell (such as Young's modulus, thermal conductivity, Poisson's ratio, shear modulus, etc.) as the target physical performance parameter, and then fit the relative density of each candidate cell stored in the cell library and the target physical performance parameter value of each candidate cell through a polynomial to obtain the corresponding relationship between the relative density of each candidate cell and the target physical performance parameter. The specific corresponding relationship here can refer to the following formula:

[0040] In the above formula, is the target physical performance parameter value of the candidate cell, is the relative density of the candidate cell, a, b, c…z are constants, and the value of n can be set according to actual needs. Here n is set to 3~6 to make the fitting accuracy R2≥0.95.

[0041] In actual application scenarios, the accuracy of the correspondence between the relative density of candidate cells and the physical performance parameter values ​​is particularly important for the accuracy of the cell structure determined using cell filling, while the accuracy of the correspondence between the relative density of candidate cells and the target physical performance parameters determined directly based on the relative density of each candidate cell in the cell library and the target physical performance parameter values ​​is often poor.

[0042] Based on this, before determining the correspondence between the relative density of the candidate cells and the target physical performance parameter according to the relative density of each candidate cell in the cell library and the target physical performance parameter value, the server can also optimize each candidate cell stored in the cell library to obtain the optimized candidate cells, as shown in the following example. Figure 3 shown.

[0043] Figure 3 A schematic diagram of the optimization process of the alternative cells provided in this specification.

[0044] Combination Figure 3 It can be seen that the server can obtain the relative density and target physical performance parameter value of each candidate cell, and then can select the candidate cells that do not have supporting candidate cells from the candidate cells according to the relative density and target physical performance parameter value of each candidate cell, and then can obtain the optimized candidate cells corresponding to the target physical performance parameters according to the candidate cells selected from the cell library.

[0045] Among them, for each alternative cell, if the relative density of the alternative cell is not greater than the relative density of another alternative cell, and the target physical performance parameter value of the alternative cell is greater than the target physical performance parameter value of the alternative cell, then the alternative cell can be used as a supporting alternative cell for the above-mentioned other alternative cell.

[0046] Furthermore, the server may determine the corresponding relationship between the relative density of each candidate cell and the target physical performance parameter according to the relative density of each optimized candidate cell corresponding to the target physical performance parameter and the target physical performance parameter value.

[0047] It can be seen from the above content that in this specification, there can be multiple correspondences between the relative density of each candidate cell and the value of the physical performance parameter. In actual application scenarios, after determining the type of global indicator parameter to be used in the topology optimization process, the server can determine the correspondence between the relative density of each candidate cell and the value of the physical performance parameter that matches the type of global indicator parameter to be used in the topology optimization process from the correspondence between the relative density of each candidate cell and different physical performance parameters as the target correspondence, and then the target design space after division can be topologically optimized according to the target correspondence to obtain the relative density distribution of the target design space after division.

[0048] Among them, the types of global indicator parameters required to be used in the topology optimization process can be determined according to actual needs, such as: structural flexibility, stiffness, thermal conductivity, strength, etc.

[0049] For the correspondence between the relative density and the physical performance parameter value of each candidate cell, if it is determined that the correspondence is the correspondence required in the process of calculating the target design space while satisfying the target constraint conditions for the global indicator parameter of this type, then it can be determined that the correspondence is a correspondence that matches the type of global indicator parameter that needs to be used in the topology optimization process.

[0050] For example, when the global index parameter is structural flexibility, the corresponding relationship required in the process of calculating the structural flexibility of the target design space while satisfying the target constraint condition may be the corresponding relationship between the relative density of each candidate cell and Young's modulus.

[0051] For another example, when the global indicator parameter is thermal conductivity, the corresponding relationship required in the process of calculating the thermal conductivity of the target design space under the target constraint condition may be the corresponding relationship between the relative density and thermal conductivity of each candidate cell.

[0052] Among them, the above-mentioned target constraints can be determined according to actual needs, and the above-mentioned target constraints may include at least one of: volume fraction constraints of the target design space, load constraints of the target design space, mass constraints of the target design space, and minimum relative density constraints of each unit design area.

[0053] Furthermore, after determining the target design space, the target constraint conditions, and the corresponding relationship between the relative density of each candidate cell and the physical performance parameter value, the server can perform topological optimization on the divided target design space according to the predetermined corresponding relationship between the relative density of each candidate cell and the physical performance parameter value, as shown in the following example: Figure 4 shown.

[0054] Figure 4 A schematic diagram of the topology optimization process provided in this specification.

[0055] Combination Figure 4 It can be seen that the server can determine the initial relative density of each unit design area contained in the target design space, and then determine the global indicator parameters of the target design space under the target constraint conditions based on the corresponding relationship between the relative density of each candidate cell and the physical performance parameter value, and determine the target loss value based on the global indicator parameters. Then, the initial relative density value of each unit design area can be adjusted separately with minimizing the target loss value as the optimization goal until the preset termination condition is met to obtain the relative density distribution of the divided target design space.

[0056] Among them, the global indicator parameters corresponding to the target design space are positively correlated with the target loss value.

[0057] In the above content, the global indicator parameters refer to the physical performance parameters of the target design space under the conditions of meeting the target constraints.

[0058] In the above content, the preset termination condition can be set according to actual needs. For example, when it is determined that the adjusted relative density value of each unit design area converges, it can be regarded as satisfying the above termination condition.

[0059] For another example: when it is determined that the preset target number of adjustment rounds has been reached, it can be regarded as satisfying the above-mentioned termination condition.

[0060] For ease of understanding, the following takes the above target constraints as the volume fraction constraint of the target design space, the load constraint of the target design space, and the minimum relative density constraint of each unit design area, and the global index parameter as structural flexibility as an example to explain the above topology optimization process in detail. The mathematical model of the topology optimization process here can be specifically referred to the following formula: Find:

[0061] Object: min

[0062] st , n

[0063] F=KU 0<

[0064] 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 region, respectively. U represents any allowed displacement field, and K is the global stiffness matrix. n is the total number of unit design regions, and V is the volume of the target design space. F is the load acting on the target design space determined according to the target constraints, and f is the volume fraction of the specified target design space determined according to the target constraints. is the minimum relative density determined according to the target constraints.

[0065] It should be noted that the above minimum relative density is set to avoid total stiffness singularity in finite element calculations. 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.

[0066] It is worth noting that in actual application scenarios, 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.

[0067] The weight coefficient of 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.

[0068] Specifically, the server can determine the partial derivative of the global index function with respect to each unit design area, so as to determine the weight coefficient of each unit design area according to the partial derivative of the global index function with respect to each unit design area, and then determine the normalized weight coefficient of each unit design area, and determine the average of the normalized weight coefficients of each unit design area as the reference weight coefficient. For each unit design area, when the server determines that the normalized weight coefficient of the unit design area is greater than the reference weight coefficient, it can be determined that the relative density of the unit design area has a greater impact on the global index parameter corresponding to the target design space when it is adjusted. At this time, the server can increase the relative density of the unit design area by a specified amplitude. When the server determines that the normalized weight coefficient of the unit design area is less than the reference weight coefficient, it can be determined that the relative density of the unit design area has a smaller impact on the global index parameter corresponding to the target design space when it is adjusted. At this time, the server can reduce the relative density of the unit design area by a specified amplitude.

[0069] S103: According to the relative density distribution, select candidate cells matching each unit design region from the cell library to fill the target design space.

[0070] After determining the relative density distribution of the target design space, the server can, for each unit design area contained in the target design space, select from the cell library, according to the relative density of the unit design area, an alternative cell whose relative density has a difference with the relative density of the unit design area within a preset range as a cell to be filled, and fill the cell to be filled into the unit design area to obtain a cell structure corresponding to the target design space according to each filled unit design area.

[0071] In actual application scenarios, in order to improve the filling efficiency, the server can also target each unit design area contained in the target design space. 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 can be filled through the entity. 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, the unit design area is determined to be empty. If it is determined that the adjusted relative density value of the unit design area is within the relative density value interval, the target design space can be filled with alternative cells that match each unit design area from the cell library.

[0072] Furthermore, after acquiring each filled unit design area, the server may also detect the target design space as a whole, so that when an abnormality is found, the target design space can be refilled according to the relative density distribution of the target design space.

[0073] Specifically, the server can determine whether refilling is needed based on the deviation between the global indicator parameters of the target design space after filling and the preset reference indicator parameters. If it is determined that the deviation between the global indicator parameters of the target design space after filling and the preset reference indicator parameters exceeds the preset difference threshold, the relative density value interval used in the filling process can be adjusted according to the above deviation, so that the target design space can be re-selected from the cell library according to the adjusted relative density value interval and relative density distribution. Alternative cells that match each unit design area are selected to fill the target design space.

[0074] For ease of understanding, the following takes the global indicator parameter as structural flexibility as an example to explain in detail the method of adjusting the relative density value interval used in the filling process according to the deviation between the global indicator parameter of the target design space after filling and the preset reference indicator parameter.

[0075] Among them, when the server determines that the difference between the structural flexibility of the filled target design space and the preset reference index parameter is greater than the preset difference threshold, it indicates that the structural flexibility 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 area can be further lowered to reduce the number of unit design areas set to empty, so that the continuity of the filled target design space can be better and the structural stiffness can be higher.

[0076] In addition, when the server determines that the difference between the structural flexibility of the target design space after filling and the preset reference index parameter is less than the preset difference threshold, it indicates that the structural flexibility of the target design space after filling is too small, that is, the structural stiffness is too strong. At this time, the upper limit of the relative density value area can be further adjusted to reduce the number of unit design areas set as entities, so that the entity volume of the target design space after filling can be smaller and the structural flexibility can be stronger.

[0077] It can be seen from the above method that the server can not only fill cells according to the Pareto-optimized candidate cells contained in the pre-built cell library, thereby eliminating the need to rely on expert experience to select specific cell types to fill cells, so as to improve the accuracy of the filled cell structure, but also use the corresponding relationship between the performance parameters and relative density of the optimized candidate cells for topology optimization, which simplifies the complexity of cell optimization, avoids the complex calculation amount brought by dual topology optimization (i.e., cell topology optimization and component topology optimization), and improves the accuracy of the determined cell structure.

[0078] The above are one or more cell filling methods implemented in this specification. Based on the same idea, this specification also provides a corresponding cell filling device, such as Figure 5 shown.

[0079] Figure 5 A schematic diagram of a cell filling device provided in this specification includes: A determination module 501 is used to 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; The optimization module 502 is used to perform 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, so as 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; The cell filling module 503 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.

[0080] Optionally, the optimization module 502 is specifically used to obtain target constraints; determine the global indicator parameters corresponding to the target design space according to the initial relative density of each unit design area, the target constraints, and the predetermined correspondence between the relative density and physical performance parameter values ​​of each candidate cell; the global indicator parameters refer to the physical performance parameters of the target design space under the target constraints; determine the target loss value according to the global indicator parameters, and take minimizing the target loss value as the optimization goal, 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.

[0081] Optionally, 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.

[0082] Optionally, the optimization module 502 is specifically used to obtain the relative density and target physical performance parameter value of each candidate cell; and select, from the candidate cells, the candidate cells in which there are no supporting candidate cells according to the relative density and target physical performance parameter value of 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.

[0083] Optionally, the optimization module 502 is specifically used to obtain 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 indicator parameter corresponding to the target design space when the relative density value of the unit design area is adjusted; and according to the weight coefficient, the initial relative density value of each unit design area is adjusted respectively.

[0084] Optionally, the cell filling module 503 is specifically used 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 a preset relative density value interval, fill the unit design area through 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 interval, 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 in the relative density value interval, select alternative cells matching each unit design area from the cell library to fill the target design space.

[0085] Optionally, the cell filling module 503 is specifically used to, if it is determined that refilling is required based on the deviation between the global index parameter of the target design space after filling and the preset reference index parameter, adjust the relative density value interval based on the deviation; based on the adjusted relative density value interval and the relative density distribution, re-select alternative cells matching each unit design area from the cell library to fill the target design space.

[0086] This specification also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 A cell filling method is provided.

[0087] This manual also provides Figure 6 The one shown corresponds to Figure 1 A schematic diagram of the electronic device. Figure 6 As mentioned above, at the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 Of course, in addition to the software implementation, this specification does not exclude other implementations, such as logic devices or a combination of software and hardware, etc., that is, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0088] For the improvement of a technology, it can be clearly distinguished whether it is a hardware improvement (for example, improvement of the circuit structure of diodes, transistors, switches, etc.) or a software improvement (improvement of the method flow). However, with the development of technology, many improvements of the method flow today can be regarded as direct improvements of the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that the improvement of 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 logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming themselves, without having to ask chip manufacturers to design and make dedicated integrated circuit chips. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). 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 are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0089] The controller may be implemented in any suitable manner, for example, the controller may take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (e.g., software or firmware) executable by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include but are not limited to the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, and the memory controller may also be implemented as part of the control logic of the memory. It is also known to those skilled in the art that, in addition to implementing the controller in a purely computer-readable program code manner, the controller may be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, such a controller may be considered as a hardware component, and the devices for implementing various functions included therein may also be considered as structures within the hardware component. Or even, the devices for implementing various functions may be considered as both software modules for implementing the method and structures within the hardware component.

[0090] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may 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 a combination of any of these devices.

[0091] For the convenience of description, the above device is described in 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.

[0092] Those skilled in the art will appreciate 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.

[0093] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes 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 a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0094] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0095] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0096] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0097] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and 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 the relevant parts can be referred to the partial description of the method embodiment.

[0103] The above description is only an embodiment of the present specification and is not intended to limit the present specification. For those skilled in the art, the present specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification shall be included in the scope of the claims of the present 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 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; the candidate cells are obtained after optimization and stored in a preset cell library; 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 1, characterized in that The method further includes optimizing each candidate cell, specifically including: Obtaining the relative density and target physical performance parameter value of each candidate cell; According to the relative density and target physical performance parameter value of each candidate cell, a candidate cell without a supporting candidate cell is selected from the candidate cells; 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.

5. 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.

6. 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.

7. The method according to claim 6, 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.

8. 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 to perform topological optimization on the divided target design space according to the predetermined correspondence between the relative density of each candidate cell and the physical performance parameter value, so as 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; 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.

9. 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 7 is implemented.

10. 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 7 is implemented.

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