Cooling fin shape parameter determination method and device, equipment and medium

Abstract

The invention relates to the technical field of heat dissipation equipment, in particular to a heat dissipation fin shape parameter determination method and device, equipment and a medium. The method comprises the steps of determining fitness data of a corresponding group of shape parameters in a current iteration process according to the fin mass and the fin surface area of a cooling fin needing to be designed under each group of shape parameters in a fin design population corresponding to the current iteration process; taking at least one group of reference shape parameters and at least one group of derivative shape parameters as a fin design population in the next iteration process under the condition that each piece of fitness data in the current iteration process does not meet a preset iteration termination condition; and under the condition that each piece of fitness data in the current iteration process meets a preset iteration termination condition, according to each group of shape parameters in the current iteration process, determining a target shape parameter of the heat dissipation fin. And the heat dissipation performance and the cost effectiveness of the heat dissipation fins are maximized.

Description

Technical Field

[0001] The present application relates to the technical field of heat dissipation equipment, and in particular to a method, device, equipment and medium for determining shape parameters of heat dissipation fins. Background Art

[0002] With the rapid development of the automotive industry, the performance and reliability requirements of automotive controllers are increasing. Among them, heat dissipation is one of the key factors affecting the performance and life of automotive controllers. Automotive controllers are generally required to have high reliability. Controller heat dissipation generally adopts natural cooling. Usually, heat dissipation fins are designed on the controller housing to enhance heat dissipation efficiency.

[0003] The shape and size design of the heat sink fins plays a vital role in the heat dissipation effect of the controller. Traditional heat sink fin design is generally completed by structural design engineers and thermal simulation engineers based on personal design experience. However, the above-mentioned size design process relies too much on subjective experience, and it is often difficult to achieve the optimal balance between heat dissipation efficiency and weight, which limits the heat dissipation performance and cost-effectiveness of the heat sink fins. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, equipment and medium for determining the shape parameters of heat sink fins that can achieve an optimal balance between heat dissipation efficiency and weight in order to address the above technical problems.

[0005] In a first aspect, the present application provides a method for determining shape parameters of a heat sink fin, comprising:

[0006] For each iteration process, according to the fin mass and fin surface area of ​​the heat sink fins to be designed under each group of shape parameters in the fin design population corresponding to the current iteration process, determine the fitness data of the corresponding group of shape parameters under the current iteration process;

[0007] When the fitness data in the current iteration process do not meet the preset iteration termination condition, selecting at least one set of reference shape parameters from the sets of shape parameters in the current iteration process according to the fitness data of the sets of shape parameters in the current iteration process;

[0008] Based on reference constraints of shape parameters corresponding to the heat sink fins, generating at least one set of derived shape parameters according to at least one set of reference shape parameters;

[0009] Using the at least one set of reference shape parameters and the at least one set of derived shape parameters as a fin design population for a next iteration process;

[0010] When each fitness data in the current iteration process satisfies a preset iteration termination condition, determining target shape parameters of the heat sink fins according to each group of shape parameters in the current iteration process;

[0011] Among them, each group of shape parameters in the fin design population in the first iteration process is randomly generated based on the reference constraint condition.

[0012] In one embodiment, determining the fitness data of the group of shape parameters in the current iteration process according to the fin surface area and fin mass of the heat sink fin to be designed under the group of shape parameters includes:

[0013] Based on a preset fitness function, according to the fin surface area and fin mass of the heat sink fin to be designed under the group of shape parameters, determining the fitness data of the group of shape parameters under the current iteration process;

[0014] The preset fitness function is an increasing function of the fin surface area and a decreasing function of the fin mass.

[0015] In one embodiment, the step of determining the fitness data of the set of shape parameters in the current iteration process based on the preset fitness function and the fin surface area and fin mass of the heat sink fins to be designed under the set of shape parameters includes:

[0016] Based on the preset weights, the fin surface area and the fin mass of the heat sink fins to be designed under the group of shape parameters are weighted respectively to update the corresponding fin surface area and fin mass;

[0017] According to the difference between the updated fin surface area and the updated fin mass, the fitness data of the group of shape parameters in the current iteration process is determined.

[0018] In one embodiment, the method of determining the fitness data of the corresponding group of shape parameters in the current iteration process according to the fin mass and fin surface area of ​​the heat sink fins to be designed under each group of shape parameters in the fin design population corresponding to the current iteration process includes:

[0019] For each group of shape parameters in the fin design population corresponding to the current iteration process, the fin surface area and the fin mass of the group of shape parameters in the current iteration process are standardized respectively;

[0020] According to the standardized fin surface area and the standardized fin mass, fitness data of the group of shape parameters in the current iteration process is determined.

[0021] In one embodiment, satisfying a preset iteration termination condition includes:

[0022] The fluctuation range of each fitness data in the current iteration process is not greater than a preset fluctuation threshold.

[0023] In one embodiment, the fluctuation range of each fitness data in the current iteration process is the data difference between the maximum fitness data and the minimum fitness data;

[0024] Among them, the maximum fitness data is the maximum value among the fitness data of the corresponding group of shape parameters in the current iteration process; the minimum fitness data is the minimum value among the fitness data of the corresponding group of shape parameters in the current iteration process.

[0025] In one embodiment, the reference constraint condition based on the shape parameter corresponding to the heat sink fin generates at least one group of derived shape parameters according to at least one group of reference shape parameters, including:

[0026] Based on the reference constraint conditions of the shape parameters corresponding to the heat sink fins, adjusting the parameters of at least one set of the reference shape parameters according to a first preset probability to obtain corresponding derived shape parameters; and / or,

[0027] Based on the reference constraint conditions of the shape parameters corresponding to the heat sink fins, a parameter cross transformation is performed on at least two groups of the reference shape parameters according to a second preset probability to obtain corresponding derived shape parameters.

[0028] In a second aspect, the present application further provides a device for determining shape parameters of a heat sink fin, comprising:

[0029] A first determination module is used to determine, for each iteration process, the fitness data of the corresponding group of shape parameters in the current iteration process according to the fin mass and fin surface area of ​​the heat sink fins to be designed under each group of shape parameters in the fin design population corresponding to the current iteration process;

[0030] A selection module, configured to select at least one set of reference shape parameters from the groups of shape parameters in the current iteration process according to the fitness data of the groups of shape parameters in the current iteration process when the fitness data in the current iteration process do not meet the preset iteration termination condition;

[0031] A generating module, configured to generate at least one set of derived shape parameters based on reference constraints of shape parameters corresponding to the heat sink fins and according to at least one set of reference shape parameters;

[0032] A second determination module is used to use the at least one set of reference shape parameters and the at least one set of derived shape parameters as a fin design population in a next iteration process;

[0033] The third determination module is used to determine the target shape parameters of the heat sink fins according to the groups of shape parameters under the current iteration process when the fitness data under the current iteration process meets the preset iteration termination conditions; wherein the groups of shape parameters in the fin design population under the first iteration process are randomly generated based on the reference constraint conditions.

[0034] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0035] For each iteration process, according to the fin mass and fin surface area of ​​the heat sink fins to be designed under each group of shape parameters in the fin design population corresponding to the current iteration process, determine the fitness data of the corresponding group of shape parameters under the current iteration process;

[0036] When the fitness data in the current iteration process do not meet the preset iteration termination condition, selecting at least one set of reference shape parameters from the sets of shape parameters in the current iteration process according to the fitness data of the sets of shape parameters in the current iteration process;

[0037] Based on reference constraints of shape parameters corresponding to the heat sink fins, generating at least one set of derived shape parameters according to at least one set of reference shape parameters;

[0038] Using the at least one set of reference shape parameters and the at least one set of derived shape parameters as a fin design population for a next iteration process;

[0039] When each fitness data in the current iteration process satisfies a preset iteration termination condition, determining target shape parameters of the heat sink fins according to each group of shape parameters in the current iteration process;

[0040] Among them, each group of shape parameters in the fin design population in the first iteration process is randomly generated based on the reference constraint condition.

[0041] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0042] For each iteration process, according to the fin mass and fin surface area of ​​the heat sink fins to be designed under each group of shape parameters in the fin design population corresponding to the current iteration process, determine the fitness data of the corresponding group of shape parameters under the current iteration process;

[0043] When the fitness data in the current iteration process do not meet the preset iteration termination condition, selecting at least one set of reference shape parameters from the sets of shape parameters in the current iteration process according to the fitness data of the sets of shape parameters in the current iteration process;

[0044] Based on reference constraints of shape parameters corresponding to the heat sink fins, generating at least one set of derived shape parameters according to at least one set of reference shape parameters;

[0045] Using the at least one set of reference shape parameters and the at least one set of derived shape parameters as a fin design population for a next iteration process;

[0046] When each fitness data in the current iteration process satisfies a preset iteration termination condition, determining target shape parameters of the heat sink fins according to each group of shape parameters in the current iteration process;

[0047] Among them, each group of shape parameters in the fin design population in the first iteration process is randomly generated based on the reference constraint condition.

[0048] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0049] For each iteration process, according to the fin mass and fin surface area of ​​the heat sink fins to be designed under each group of shape parameters in the fin design population corresponding to the current iteration process, determine the fitness data of the corresponding group of shape parameters under the current iteration process;

[0050] When the fitness data in the current iteration process do not meet the preset iteration termination condition, selecting at least one set of reference shape parameters from the sets of shape parameters in the current iteration process according to the fitness data of the sets of shape parameters in the current iteration process;

[0051] Based on reference constraints of shape parameters corresponding to the heat sink fins, generating at least one set of derived shape parameters according to at least one set of reference shape parameters;

[0052] Using the at least one set of reference shape parameters and the at least one set of derived shape parameters as a fin design population for a next iteration process;

[0053] When each fitness data in the current iteration process satisfies a preset iteration termination condition, determining target shape parameters of the heat sink fins according to each group of shape parameters in the current iteration process;

[0054] Among them, each group of shape parameters in the fin design population in the first iteration process is randomly generated based on the reference constraint condition.

[0055] The above-mentioned method, device, equipment and medium for determining the shape parameters of the heat sink fins determine the fitness data of the shape parameters in the fin design population under the current iteration process, and verify whether each fitness data under the current iteration process meets the preset iteration termination condition; if not, at least one group of reference shape parameters are selected according to the fitness data of each group of shape parameters under the current iteration process, and at least one group of reference shape parameters and at least one group of derived shape parameters are used as the fin design population under the next iteration process; if satisfied, the target shape parameters of the heat sink fins are determined according to each group of shape parameters under the current iteration process. According to the above content, in the process of determining the target shape parameters of the heat sink fins, the present application determines the fitness data of the corresponding group of shape parameters in the current iteration process to realize the conversion of the heat sink fin mass and surface area of ​​each group of shape parameters into a single value (fitness data), thereby ensuring that the heat dissipation performance and cost-effectiveness of the heat sink fins corresponding to each group of shape parameters can be intuitively known through the fitness data in the subsequent process; and, by verifying whether each fitness data in the current iteration process satisfies the preset iteration termination condition, it is judged whether each group of shape parameters in the current iteration process meets the heat dissipation requirements of the heat sink fins, and then, each fitness data in the current iteration process is determined. When the preset iteration termination condition is not met, at least one group of derived shape parameters is generated to construct the fin design population for the next iteration process, thereby providing a data basis for the next round of iteration process; when the fitness data in the current iteration process meets the preset iteration termination condition, the target shape parameters of the heat sink fins are determined according to the groups of shape parameters in the current iteration process, thereby achieving the optimal balance between heat dissipation efficiency and weight, ensuring the maximization of the heat dissipation performance and cost-effectiveness of the heat sink fins, reducing the reliance on the subjective experience of engineers in the process of size design of the heat sink fins, and standardizing the shape and size design process for the heat sink fins. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0057] Figure 1 A diagram of an application environment of a method for determining shape parameters of heat sink fins provided in an embodiment of the present application;

[0058] Figure 2 A schematic diagram of a heat sink and a controller provided in an embodiment of the present application;

[0059] Figure 3 A schematic flow chart of a first method for determining shape parameters of heat sink fins provided in an embodiment of the present application;

[0060] Figure 4 A schematic diagram of the structure of a heat sink fin provided in an embodiment of the present application;

[0061] Figure 5 A schematic diagram of the dimensions of a heat sink fin provided in an embodiment of the present application;

[0062] Figure 6 A schematic flow chart of a second method for determining shape parameters of heat sink fins provided in an embodiment of the present application;

[0063] Figure 7 A structural block diagram of a device for determining shape parameters of heat sink fins provided in an embodiment of the present application;

[0064] Figure 8 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0066] It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. In the description of the present application, the description of the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms is not necessarily for the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples without contradicting each other.

[0067] The method for determining the shape parameters of the heat sink fins provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. By determining the fitness data of the shape parameters in the fin design population under the current iteration process, and verifying whether each fitness data under the current iteration process meets the preset iteration termination condition; if not, then according to the fitness data of each group of shape parameters under the current iteration process, select at least one group of reference shape parameters, and use at least one group of reference shape parameters and at least one group of derived shape parameters as the fin design population under the next iteration process; if satisfied, then according to each group of shape parameters under the current iteration process, determine the target shape parameters of the heat sink fin. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, projection devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The head mounted device may be a virtual reality (VR) device, an augmented reality (AR) device, smart glasses, etc. The server 104 may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0068] like Figure 2 As shown in the figure, the heat sink fins are integrated into the controller housing body, and the housing is connected to the heat generating components on the control board through heat dissipation adhesive. The heat of the heat generating components on the control board is dissipated to the environment through the controller structure housing. Under certain conditions, the heat dissipation efficiency of the controller is positively correlated with the heat dissipation area (the surface area of ​​the structure housing), and the controller mass is negatively correlated with the heat dissipation area.

[0069] The controller is generally designed to last for ten years or support 250,000 kilometers of mobile equipment, and is generally required to be IP67 dust and water resistant, with high reliability requirements. Therefore, the controller generally uses natural cooling to dissipate heat. Natural cooling method requirements: Under normal pressure, the maximum power consumption per unit area is between 0.024W / cm² (watts per square centimeter) and 0.04W / cm², and the volume power density is generally required not to exceed 0.009 W / cm³ (watts per cubic centimeter). Define Q as the total power consumption of the vehicle controller, S as the heat dissipation surface area of ​​the controller, V as the volume of the controller, and q s is the surface heat flux, q v is the volume power density, then:

[0070] q s =Q / S;

[0071] q v =Q / V;

[0072] The controller is divided into two parts, the controller body and the heat sink fins, using the bottom surface of the heat sink fin design as the dividing line.

[0073] S=S K +S S ;

[0074] V=V K +V S ;

[0075] Among them, S K is the surface area of ​​the controller body, S S The surface area of ​​the controller is increased based on the surface area of ​​the controller body after the heat sink feature is deployed; V K is the volume of the controller body, V S The volume of the controller is increased based on the volume of the controller body after the heat sink feature is deployed.

[0076] Among them, S S =2*L*h*n;V S =a*h*L*n; n=(Wa) / (a+b); further, a is the thickness of the heat sink fins; b is the gap between the heat sink fins; h is the height of the heat sink fins; L is the design boundary length of the heat sink fins; n is the number of heat sink fins; W is the design boundary width of the heat sink fins.

[0077] In an exemplary embodiment, Figure 3 As shown, a method for determining the shape parameters of a heat sink fin is provided. Figure 1 The server 104 in the example is used as an example to illustrate, including the following S301 to S305. Among them:

[0078] S301, for each iteration process, according to the fin mass and fin surface area of ​​the heat sink fins to be designed under each group of shape parameters in the fin design population corresponding to the current iteration process, determine the fitness data of the corresponding group of shape parameters in the current iteration process.

[0079] Among them, the shape parameters may include at least two size parameters, and the size parameters may include the heat sink fin height and the heat sink fin gap. Further, under normal circumstances, the heat sink fin thickness is generally the same as the controller housing design thickness, but in order to further optimize the heat dissipation effect of the heat sink fin, the heat sink fin thickness can also be used as a size parameter to be determined in this application, that is, the shape parameters may include the heat sink fin height, the heat sink fin gap and the heat sink fin thickness.

[0080] It should be noted that, since the fitness data can be used to characterize the contribution degree of the fin mass and fin surface area of ​​the heat sink fins to be designed under each set of shape parameters to the heat dissipation demand of the heat sink fins, in the process of determining the fitness data of the corresponding group of shape parameters under the current iteration process, the first contribution sub-degree of the fin mass corresponding to each group of shape parameters to the heat dissipation demand of the heat sink fins, and the second contribution sub-degree of the fin surface area corresponding to each group of shape parameters to the heat dissipation demand of the heat sink fins are judged respectively, and then, based on the first contribution sub-degree and the second contribution sub-degree, the fitness data of the corresponding group of shape parameters under the current iteration process is determined.

[0081] To further explain, when it is necessary to determine the fitness data of the corresponding group of shape parameters under the current iteration process, in order to prevent the change in the value of one of the fin mass and fin surface area parameters from having a smaller impact on the determination of the fitness data due to a larger parameter value of the other parameter, the function value of the fin mass and the function value of the fin surface area can be normalized respectively before determining the fitness data of the corresponding group of shape parameters under the current iteration process.

[0082] In one embodiment of the present application, when it is necessary to determine the fitness data of the corresponding group of shape parameters under the current iteration process based on the first contribution sub-degree and the second contribution sub-degree, a first weight value for the first contribution sub-degree and a second weight value for the second contribution sub-degree can be determined in advance, and then, the first contribution sub-degree and the second contribution sub-degree are weighted according to the first weight value and the second weight value, respectively, and the obtained operation result is the fitness data of the corresponding group of shape parameters under the current iteration process.

[0083] In another embodiment of the present application, when it is necessary to determine the fitness data of the corresponding group of shape parameters under the current iteration process based on the first contribution sub-degree and the second contribution sub-degree, the fitness compensation amount for the corresponding group of shape parameters under the current iteration process is determined, and then, after performing a weighted operation on the first weight value and the second weight value, the weighted operation result and the fitness compensation amount are summed, and the obtained operation result is the fitness data of the corresponding group of shape parameters under the current iteration process.

[0084] S302, when the fitness data in the current iteration process does not meet the preset iteration termination condition, select at least one set of reference shape parameters from the groups of shape parameters in the current iteration process according to the fitness data of the groups of shape parameters in the current iteration process.

[0085] It should be noted that, based on the preset iteration termination condition, it can be determined whether each fitness data in the current iteration process meets the preset iteration termination condition, so as to implement the subsequent step of determining the target shape parameters of the heat sink fins.

[0086] The specific content of the iteration termination condition may be set or adjusted according to the actual situation and the specific requirements of the heat sink fins, and the specific content of the iteration termination condition is not limited here.

[0087] To further illustrate, when it is necessary to select at least one group of reference shape parameters from each group of shape parameters in the current iteration process, the target number of required reference shape parameters can be determined. Then, when selecting the reference shape parameters, each group of shape parameters can be sorted in descending order according to the fitness data of each group of shape parameters, and according to the target number, the target number of shape parameters at the front of the sorting position can be used as reference shape parameters.

[0088] In the solution, the target number is smaller than the total number of shape parameters in the fin design population.

[0089] S303 , generating at least one group of derived shape parameters based on reference constraints of shape parameters corresponding to the heat sink fins and according to at least one group of reference shape parameters.

[0090] It should be noted that in order to ensure that the determined derived shape parameters can more effectively screen the shape parameters after constituting the fin design population of the next iterative process, so as to determine the target shape parameters of the heat sink fins, it is necessary to ensure that the derived shape parameters are derived and combined on the basis of the reference shape parameters, so that the derived shape parameters have a certain shape parameter diversity without having a large difference with the reference shape parameters.

[0091] In one embodiment of the present application, when it is necessary to generate at least one set of derived shape parameters, parameter adjustment can be performed for each size parameter included in each reference shape parameter; specifically, at least one parameter adjustment amount corresponding to each size parameter can be determined respectively, and then, the corresponding size parameters can be adjusted according to each parameter adjustment amount to obtain the reference shape parameters after the parameter adjustment. The at least one set of reference shape parameters after the parameter adjustment is the generated derived shape parameters.

[0092] In another embodiment of the present application, when at least one set of derived shape parameters needs to be generated, corresponding replacement parameters can also be generated for each size parameter contained in each reference shape parameter, and then, the corresponding size parameters in the reference shape parameters are replaced with the replacement parameters to obtain the reference shape parameters that have completed the parameter replacement, and the reference shape parameters that have completed the parameter replacement are the generated derived shape parameters.

[0093] Among them, the replacement parameter can be determined based on the value of the same size parameter in at least one set of reference shape parameters; as an example, the values ​​of the same size parameter in at least one set of reference shape parameters can be averaged, and the obtained average result can be used as the replacement parameter corresponding to the size parameter; as another example, the maximum value and the minimum value of the same size parameter in at least one set of reference shape parameters can be removed in advance, and then the median of the remaining size parameter values ​​can be used as the replacement parameter corresponding to the size parameter; from the above content, it can be seen that there are many methods to determine the replacement parameter, and the determination process of the replacement parameter is not limited here.

[0094] S304 , using at least one set of reference shape parameters and at least one set of derived shape parameters as a fin design population for a next iteration process.

[0095] In one embodiment of the present application, in the process of determining the fin design population in the next iterative process, at least one set of reference shape parameters and at least one set of derived shape parameters can be used as shape parameters included in the fin design population to achieve the determination of the fin design population in the next iterative process.

[0096] It should be noted that, in order to ensure the efficiency of determining the target shape parameters, the repetition of shape parameters in the fin design population can be reduced to prevent shape parameters that have appeared repeatedly in the historical iteration process from occupying computing resources.

[0097] Specifically, it can be verified whether there are shape parameters in at least one group of reference shape parameters and at least one group of derived shape parameters that are identical to the historical shape parameters included in the fin design population under the historical iteration process. If so, the shape parameters identical to the historical shape parameters will be deleted, and the step of generating at least one group of derived shape parameters will be returned to execute until at least one group of reference shape parameters and at least one group of derived shape parameters do not contain shape parameters identical to the historical shape parameters, and the sum of the number of parameters of at least one group of reference shape parameters and at least one group of derived shape parameters is the same as the sum of the number of parameters included in the fin design population under the current iteration process, and then at least one group of reference shape parameters and at least one group of derived shape parameters will be used as the fin design population under the next iteration process.

[0098] S305 , when each fitness data in the current iteration process satisfies a preset iteration termination condition, determining target shape parameters of the heat sink fins according to each group of shape parameters in the current iteration process.

[0099] It should be noted that in order to ensure that the target shape parameters of the heat sink fins can achieve a good balance between the mass of the heat sink fins and the surface area of ​​the heat sink fins, so as to provide a reference for designers and thus determine the optimal heat sink fin design scheme, the target shape parameters of the heat sink fins can be determined based on the fitness data corresponding to each group of shape parameters in the current iteration process.

[0100] In one embodiment of the present application, the shape parameter with the largest fitness data value in each group of shape parameters may be used as the target shape parameter of the heat sink fin.

[0101] In another embodiment of the present application, each group of shape parameters can be arranged in sequence according to the order of fitness data values ​​from large to small, a preset number of groups of shape parameters with a front arrangement position are selected, and the parameter values ​​of each size parameter included in the preset number of groups of shape parameters are respectively averaged, and the obtained calculation results are the target values ​​corresponding to each size parameter in the target shape parameters.

[0102] In another embodiment of the present application, the mode of values ​​that appear most frequently in each size parameter in each group of shape parameters can be determined respectively; and the mode of values ​​corresponding to each size parameter is used as the target value corresponding to the corresponding size parameter in the target shape parameter; if there is no mode of values ​​for a certain size parameter, the parameter values ​​of the size parameter are averaged, and the result of the operation is used as the target value of the size parameter in the target shape parameter.

[0103] Among them, each group of shape parameters in the fin design population in the first iteration process is randomly generated based on the reference constraints.

[0104] In one embodiment of the present application, when it is necessary to determine each group of shape parameters in the fin design population under the first iteration process, a mathematical model for the heat sink fin design and reference constraints corresponding to the mathematical model can be established, thereby achieving random generation of each group of shape parameters in the fin design population under the first iteration process based on the reference constraints.

[0105] Specifically, the objective function corresponding to the mathematical model is as follows:

[0106]

[0107] Among them, f1 is the mass added by the heat sink fin feature to the entire automobile controller; f2 is the area added by the heat sink fin feature to the entire automobile controller; a is the thickness of the heat sink fin; b is the gap between the heat sink fins; h is the height of the heat sink fin; L is the design boundary length of the heat sink fin; n is the number of heat sink fins; W is the design boundary width of the heat sink fin; ρ is the material density of the heat sink fin; V is the volume of the heat sink fin.

[0108] Among them, the simplified shape of the heat sink fin is as follows Figure 4 As shown; the heat sink fin thickness, heat sink fin gap, heat sink fin height, heat sink fin design boundary length, heat sink fin number and heat sink fin design boundary width of the heat sink fin are as shown Figure 5 shown.

[0109] The function expression corresponding to the reference constraint is as follows:

[0110]

[0111] Among them, h / b represents the height-to-spacing ratio of the heat sink fins. If this value is too large, it may affect the air flow and reduce the heat dissipation effect. h / a represents the height-to-thickness ratio of the heat sink fins. If this value is too large, it will affect the structural strength of the heat sink fins and will be unfavorable for processing and use.

[0112] Among them, a is the thickness of the heat sink fin; b is the gap between the heat sink fins; h is the height of the heat sink fins; ρ is the material density of the heat sink fins; V is the volume of the heat sink fins; Q is the total power consumption of the controller; S is the heat dissipation surface area of ​​the controller; V is the volume of the controller; the controller is a device that requires heat sink fins for heat dissipation.

[0113] The above-mentioned method for determining the shape parameters of the heat sink fins determines the fitness data of the shape parameters in the fin design population under the current iteration process, and verifies whether each fitness data under the current iteration process satisfies the preset iteration termination condition; if not, at least one group of reference shape parameters is selected based on the fitness data of each group of shape parameters under the current iteration process, and at least one group of reference shape parameters and at least one group of derived shape parameters are used as the fin design population under the next iteration process; if satisfied, the target shape parameters of the heat sink fins are determined based on each group of shape parameters under the current iteration process. According to the above content, in the process of determining the target shape parameters of the heat sink fins, the present application determines the fitness data of the corresponding group of shape parameters in the current iteration process to realize the conversion of the heat sink fin mass and surface area of ​​each group of shape parameters into a single value (fitness data), thereby ensuring that the heat dissipation performance and cost-effectiveness of the heat sink fins corresponding to each group of shape parameters can be intuitively known through the fitness data in the subsequent process; and, by verifying whether each fitness data in the current iteration process satisfies the preset iteration termination condition, it is judged whether each group of shape parameters in the current iteration process meets the heat dissipation requirements of the heat sink fins, and then, each fitness data in the current iteration process is determined. When the preset iteration termination condition is not met, at least one group of derived shape parameters is generated to construct the fin design population for the next iteration process, thereby providing a data basis for the next round of iteration process; when the fitness data in the current iteration process meets the preset iteration termination condition, the target shape parameters of the heat sink fins are determined according to the groups of shape parameters in the current iteration process, thereby achieving the optimal balance between heat dissipation efficiency and weight, ensuring the maximization of the heat dissipation performance and cost-effectiveness of the heat sink fins, reducing the reliance on the subjective experience of engineers in the process of size design of the heat sink fins, and standardizing the shape and size design process for the heat sink fins.

[0114] In one embodiment, when it is necessary to determine the fitness data of the group of shape parameters in the current iteration process according to the fin surface area and fin mass of the heat sink fin to be designed under the group of shape parameters, the following contents may be specifically included:

[0115] Based on the preset fitness function, the fitness data of the group of shape parameters in the current iteration process is determined according to the fin surface area and the fin mass of the heat sink fin to be designed under the group of shape parameters.

[0116] The preset fitness function is an increasing function of the fin surface area and a decreasing function of the fin mass.

[0117] Furthermore, since there are differences in the effects of the fin surface area and the fin mass on the heat dissipation effect of the heat sink fins, in order to ensure that the preset fitness function can more accurately characterize the fitness data corresponding to each set of size parameters, the preset weights of the fin surface area and the fin mass of the heat sink fins to be designed under the shape parameters can be determined respectively, and then, the heat dissipation effect of the heat sink fins can be corrected according to the preset weights.

[0118] Specifically, when it is necessary to determine the fitness data of the group of shape parameters in the current iteration process based on a preset fitness function and according to the fin surface area and fin mass of the heat sink fins that need to be designed under this group of shape parameters, the following contents may be included: based on preset weights, the fin surface area and fin mass of the heat sink fins that need to be designed under this group of shape parameters are weighted respectively to update the corresponding fin surface area and fin mass; based on the difference between the updated fin surface area and the updated fin mass, the fitness data of the group of shape parameters in the current iteration process is determined.

[0119] It is further explained that when the fin surface area and fin mass of the heat sink fins to be designed under this group of shape parameters are weighted respectively, in order to prevent the change in the value of one of the fin mass and fin surface area factors from having a smaller impact on the determination of the fitness data due to a larger parameter value, the fin surface area and fin mass of the group of shape parameters under the current iteration process can be standardized respectively, so as to determine the fitness data of the group of shape parameters under the current iteration process based on the standardized fin surface area and the standardized fin mass.

[0120] Specifically, for each group of shape parameters in the fin design population corresponding to the current iteration process, the fin surface area and fin mass of the group of shape parameters in the current iteration process are standardized respectively; based on the standardized fin surface area and the standardized fin mass, the fitness data of the group of shape parameters in the current iteration process is determined.

[0121] Among them, in the process of standardizing the fin surface area and fin mass of the group of shape parameters in the current iteration process, the fin surface area and fin mass of the group of shape parameters can be standardized according to the maximum and minimum values ​​under the fin design population, so as to obtain the standardized fin surface area and the standardized fin mass.

[0122] Specifically, according to the above description, the preset fitness function can be determined as follows:

[0123]

[0124] Among them, w 1 is the preset weight corresponding to the fin surface area of ​​the heat sink fin to be designed under this set of shape parameters; w2 is the preset weight corresponding to the fin mass of the heat sink fin to be designed under this set of shape parameters; J i is the fitness data of the i-th group of shape parameters in the fin design population; is the mass function value of the heat sink fin under the i-th group of shape parameters; is the surface area function value of the heat sink fin under the i-th group of shape parameters; is the maximum value of the heat sink fin quality function under the fin design population; is the minimum value of the heat sink fin quality function under the fin design population; is the maximum value of the heat sink fin surface area function under the fin design population; It is the minimum value of the heat sink fin surface area function under the fin design population.

[0125] The above-mentioned method for determining the shape parameters of the heat sink fins determines the fitness data of the corresponding group of shape parameters in the current iteration process, so as to convert the mass and surface area of ​​the heat sink fins of each group of shape parameters into a single value, thereby ensuring that the heat dissipation performance and cost-effectiveness of the heat sink fins corresponding to each group of shape parameters can be intuitively known through the fitness data in the future.

[0126] In one embodiment, to ensure that it is possible to smoothly determine whether the target shape parameters of the heat sink fins can be determined based on the fitness data in the current iteration process, it is possible to determine whether the fitness data in the current iteration process meets the preset iteration termination condition.

[0127] It should be noted that if the target shape parameters of the heat sink fins can be determined based on each group of shape parameters in the current iteration process, then each group of shape parameters in the current iteration process can achieve a good balance between the quality of the heat sink fins and the surface area of ​​the heat sink fins while ensuring the heat dissipation effect of the heat sink fins. Therefore, it is necessary to ensure that the data difference between the fitness data corresponding to each group of shape parameters is small, so that the iteration termination condition can be set to achieve the data difference between the fitness data corresponding to each group of shape parameters limited according to the iteration termination condition.

[0128] As an example, the iteration termination condition includes: the fluctuation range of each fitness data in the current iteration process is not greater than a preset fluctuation threshold.

[0129] Furthermore, the fluctuation range of each fitness data under the current iteration process is the data difference between the maximum fitness data and the minimum fitness data; wherein the maximum fitness data is the maximum value among the fitness data of the corresponding group of shape parameters under the current iteration process; and the minimum fitness data is the minimum value among the fitness data of the corresponding group of shape parameters under the current iteration process.

[0130] Specifically, the function expression of the iteration termination condition is as follows:

[0131] J max -J min ﹤M

[0132] Among them, J max is the maximum fitness data; J min is the minimum fitness data; M is the preset fluctuation threshold.

[0133] The above-mentioned method for determining the shape parameters of the heat sink fins takes the fluctuation range of each fitness data in the current iteration process as not greater than the preset fluctuation threshold as satisfying the preset iteration termination condition, thereby ensuring that when each fitness data in the current iteration process satisfies the preset iteration termination condition, the data difference between the fitness data corresponding to each group of shape parameters is small, so that each group of shape parameters in the current iteration process can achieve a good balance between the quality of the heat sink fins and the surface area of ​​the heat sink fins while ensuring the heat dissipation effect of the heat sink fins, thereby ensuring that the target shape parameters of the heat sink fins can be smoothly determined in the subsequent process according to each group of shape parameters in the current iteration process.

[0134] In one embodiment, when it is necessary to generate at least one set of derived shape parameters based on reference constraints of shape parameters corresponding to the heat sink fins and according to at least one set of reference shape parameters, the following may be specifically included:

[0135] Based on the reference constraint conditions of the shape parameters corresponding to the heat sink fins, adjusting the parameters of at least one set of reference shape parameters according to a first preset probability to obtain corresponding derived shape parameters; and / or,

[0136] Based on the reference constraint conditions of the shape parameters corresponding to the heat sink fins, a parameter cross transformation is performed on at least two groups of reference shape parameters according to a second preset probability to obtain corresponding derived shape parameters.

[0137] In one embodiment of the present application, when adjusting parameters of at least one set of reference shape parameters according to a first preset probability, the uniform variation method can be referred to to realize that at least two size parameters included in the reference shape parameters are used as parameters that will mutate, and the first preset probability of the size parameter mutation and the variation range corresponding to each size parameter are pre-specified; then, when it is determined that the size parameter mutates according to the first preset probability, a certain variation parameter is randomly selected from the variation range as the mutated size parameter to realize the process of adjusting parameters of at least one set of reference shape parameters.

[0138] As an example, if the heat sink fin gap b and the heat sink fin height h are set to mutate, the b mutation range is set to [b-0.5, b+0.5], the h gene mutation range is set to [h-1, h+01], and the first preset probability is set to 0.2, that is, there is an 80% probability that h and b will not mutate, and a 20% probability that they will mutate, where the reference shape parameter is n x and n y , n x and n y As shown below:

[0139]

[0140] Among them, n x and n y are the reference shape parameters x and y respectively; b x is the heat sink fin gap with reference shape parameter x; h x is the heat sink fin height with reference to shape parameter x; b y is the heat sink fin gap with reference shape parameter y; h y is the heat sink fin height with reference to shape parameter y.

[0141] Therefore, when it is determined that the size parameter mutates according to the first preset probability, the value of the reference shape parameter is as follows:

[0142] b x ∈[b j -0.5, b j +0.5], h x ∈[h j -1,h j +01];

[0143] b y ∈[b k -0.5, b k +0.5], h y ∈[h k -1,h k +01].

[0144] The above parameters satisfy at least one of the following four items: b x -b j ≠0, h x -h j ≠0, b y -b k ≠0 and h y -h k ≠0.

[0145] In another embodiment of the present application, for at least two sets of reference shape parameters, when performing parameter cross-transformation according to a second preset probability, a single-point crossover method can be used to set the intersection point between size parameters included in the reference shape parameters, and a second preset probability of the size parameter crossing is pre-determined. Then, when it is determined that the size parameters cross according to the second preset probability, the size parameters corresponding to the reference shape parameters are cross-changed to implement the process of parameter adjustment for at least one set of reference shape parameters.

[0146] As an example, if the intersection point is set between the heat sink fin gap b and the heat sink fin height h, and the second preset probability is predetermined to be 80%, that is, there is an 80% probability that h and b will cross-change, and a 20% probability that they will not cross-change, wherein the reference shape parameter is n x and n y , n x and n y As shown below:

[0147]

[0148] Among them, n x and n y are the reference shape parameters x and y respectively; b x is the heat sink fin gap with reference shape parameter x; h x is the heat sink fin height with reference to shape parameter x; b y is the heat sink fin gap with reference shape parameter y; h y is the heat sink fin height with reference to shape parameter y.

[0149] If it is determined according to the second preset probability that no crossover change occurs, it is determined that no derivative shape parameter generation occurs, as shown below:

[0150]

[0151] If a crossover change occurs according to the second preset probability, a derivative shape parameter is generated, which is as follows:

[0152]

[0153] Furthermore, if the reference shape parameters are adjusted during the cross-change process of the reference shape parameters, the values ​​of the reference shape parameters are as follows:

[0154] b x ∈[b j -0.5, b j +0.5], h x ∈[h k -1,h k+01];

[0155] b y ∈[b k -0.5, b k +0.5], h y ∈[h j -1,h j +01].

[0156] The above parameters satisfy at least one of the following four items: b x -b j ≠0, h x -h k ≠0, b y -b k ≠0 and h y -h j ≠0.

[0157] The above-mentioned method for determining the shape parameters of the heat sink fins generates derived shape parameters by adjusting parameters of at least one set of reference shape parameters and / or cross-transforming parameters, thereby ensuring that the fin design population in the next iterative process can be smoothly constructed, providing a data basis for the next round of iterative process and ensuring the diversity of shape parameters.

[0158] In one embodiment, if Figure 6 As shown, when it is necessary to determine the target shape parameters of the heat sink fins, the following may be specifically included:

[0159] S601 , for each iteration process, for each group of shape parameters in the fin design population corresponding to the current iteration process, respectively standardize the fin surface area and fin mass of the group of shape parameters in the current iteration process.

[0160] S602: Based on preset weights, weight the standardized fin surface area and the standardized fin mass respectively to update the corresponding fin surface area and fin mass.

[0161] S603, determining the fitness data of the group of shape parameters in the current iteration process according to the difference between the updated fin surface area and the updated fin mass. Determine whether each fitness data in the current iteration process meets the preset iteration termination condition, if not, execute step S604; if yes, execute step S607.

[0162] S604: Select at least one set of reference shape parameters from the sets of shape parameters in the current iteration process according to the fitness data of the sets of shape parameters in the current iteration process.

[0163] S605, based on the reference constraints of the shape parameters corresponding to the heat sink fins, perform parameter adjustment on at least one set of reference shape parameters according to a first preset probability to obtain corresponding derived shape parameters; and / or, based on the reference constraints of the shape parameters corresponding to the heat sink fins, perform parameter cross-transformation on at least two sets of reference shape parameters according to a second preset probability to obtain corresponding derived shape parameters.

[0164] S606 , using at least one set of reference shape parameters and at least one set of derived shape parameters as a fin design population for a next iteration process.

[0165] S607, determining target shape parameters of the heat sink fins according to each group of shape parameters in the current iteration process.

[0166] The above-mentioned method for determining the shape parameters of the heat sink fins determines the fitness data of the shape parameters in the fin design population under the current iteration process, and verifies whether each fitness data under the current iteration process satisfies the preset iteration termination condition; if not, at least one group of reference shape parameters is selected based on the fitness data of each group of shape parameters under the current iteration process, and at least one group of reference shape parameters and at least one group of derived shape parameters are used as the fin design population under the next iteration process; if satisfied, the target shape parameters of the heat sink fins are determined based on each group of shape parameters under the current iteration process. According to the above content, in the process of determining the target shape parameters of the heat sink fins, the present application determines the fitness data of the corresponding group of shape parameters in the current iteration process to realize the conversion of the heat sink fin mass and surface area of ​​each group of shape parameters into a single value (fitness data), thereby ensuring that the heat dissipation performance and cost-effectiveness of the heat sink fins corresponding to each group of shape parameters can be intuitively known through the fitness data in the subsequent process; and, by verifying whether each fitness data in the current iteration process satisfies the preset iteration termination condition, it is judged whether each group of shape parameters in the current iteration process meets the heat dissipation requirements of the heat sink fins, and then, each fitness data in the current iteration process is determined. When the preset iteration termination condition is not met, at least one group of derived shape parameters is generated to construct the fin design population for the next iteration process, thereby providing a data basis for the next round of iteration process; when the fitness data in the current iteration process meets the preset iteration termination condition, the target shape parameters of the heat sink fins are determined according to the groups of shape parameters in the current iteration process, thereby achieving the optimal balance between heat dissipation efficiency and weight, ensuring the maximization of the heat dissipation performance and cost-effectiveness of the heat sink fins, reducing the reliance on the subjective experience of engineers in the process of size design of the heat sink fins, and standardizing the shape and size design process for the heat sink fins.

[0167] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0168] Based on the same inventive concept, the embodiment of the present application also provides a device for determining the shape parameters of heat sink fins for implementing the above-mentioned method for determining the shape parameters of heat sink fins. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of one or more devices for determining the shape parameters of heat sink fins provided below can refer to the limitations of the method for determining the shape parameters of heat sink fins above, and will not be repeated here.

[0169] In an exemplary embodiment, Figure 7 As shown, a device for determining shape parameters of a heat sink fin is provided, comprising: a first determining module 10, a selecting module 20, a generating module 30, a second determining module 40 and a third determining module 50, wherein:

[0170] The first determination module 10 is used to determine the fitness data of the corresponding group of shape parameters in the current iteration process according to the fin mass and fin surface area of ​​the heat sink fins to be designed under each group of shape parameters in the fin design population corresponding to the current iteration process for each iteration process.

[0171] The selection module 20 is used to select at least one set of reference shape parameters from the groups of shape parameters in the current iteration process according to the fitness data of the groups of shape parameters in the current iteration process when the fitness data in the current iteration process does not meet the preset iteration termination condition.

[0172] The generating module 30 is used to generate at least one set of derived shape parameters based on the reference constraint conditions of the shape parameters corresponding to the heat sink fins and according to at least one set of reference shape parameters.

[0173] The second determination module 40 is used to use at least one set of reference shape parameters and at least one set of derived shape parameters as the fin design population in the next iteration process.

[0174] The third determination module 50 is used to determine the target shape parameters of the heat sink fins according to the various groups of shape parameters in the current iteration process when the various fitness data in the current iteration process meet the preset iteration termination conditions; wherein, the various groups of shape parameters in the fin design population in the first iteration process are randomly generated based on the reference constraint conditions.

[0175] In one embodiment, based on a preset fitness function, according to the fin surface area and fin mass of the heat sink fin to be designed under the group of shape parameters, the fitness data of the group of shape parameters under the current iteration process is determined;

[0176] The preset fitness function is an increasing function of the fin surface area and a decreasing function of the fin mass.

[0177] In one embodiment, based on preset weights, the fin surface area and fin mass of the heat sink fins to be designed under the set of shape parameters are weighted respectively to update the corresponding fin surface area and fin mass;

[0178] According to the difference between the updated fin surface area and the updated fin mass, the fitness data of the group of shape parameters in the current iteration process is determined.

[0179] In one embodiment, for each group of shape parameters in the fin design population corresponding to the current iteration process, the fin surface area and the fin mass of the group of shape parameters in the current iteration process are respectively standardized;

[0180] According to the standardized fin surface area and the standardized fin mass, the fitness data of the group of shape parameters in the current iteration process is determined.

[0181] In one embodiment, the fluctuation range of each fitness data in the current iteration process is not greater than a preset fluctuation threshold.

[0182] In one embodiment, the fluctuation range of each fitness data in the current iteration process is the data difference between the maximum fitness data and the minimum fitness data;

[0183] The maximum fitness data is the maximum value among the fitness data of the corresponding group of shape parameters in the current iteration process; the minimum fitness data is the minimum value among the fitness data of the corresponding group of shape parameters in the current iteration process.

[0184] In one embodiment, based on the reference constraint conditions of the shape parameters corresponding to the heat sink fins, at least one set of reference shape parameters is adjusted according to a first preset probability to obtain corresponding derived shape parameters; and / or,

[0185] Based on the reference constraint conditions of the shape parameters corresponding to the heat sink fins, a parameter cross transformation is performed on at least two groups of reference shape parameters according to a second preset probability to obtain corresponding derived shape parameters.

[0186] The above-mentioned heat sink fin shape parameter determination device determines the fitness data of the shape parameters in the fin design population under the current iteration process, and verifies whether each fitness data under the current iteration process satisfies the preset iteration termination condition; if not, selects at least one group of reference shape parameters based on the fitness data of each group of shape parameters under the current iteration process, and uses at least one group of reference shape parameters and at least one group of derived shape parameters as the fin design population under the next iteration process; if satisfied, determines the target shape parameters of the heat sink fins based on each group of shape parameters under the current iteration process. According to the above content, in the process of determining the target shape parameters of the heat sink fins, the present application determines the fitness data of the corresponding group of shape parameters in the current iteration process to realize the conversion of the heat sink fin mass and surface area of ​​each group of shape parameters into a single value (fitness data), thereby ensuring that the heat dissipation performance and cost-effectiveness of the heat sink fins corresponding to each group of shape parameters can be intuitively known through the fitness data in the subsequent process; and, by verifying whether each fitness data in the current iteration process satisfies the preset iteration termination condition, it is judged whether each group of shape parameters in the current iteration process meets the heat dissipation requirements of the heat sink fins, and then, each fitness data in the current iteration process is determined. When the preset iteration termination condition is not met, at least one group of derived shape parameters is generated to construct the fin design population for the next iteration process, thereby providing a data basis for the next round of iteration process; when the fitness data in the current iteration process meets the preset iteration termination condition, the target shape parameters of the heat sink fins are determined according to the groups of shape parameters in the current iteration process, thereby achieving the optimal balance between heat dissipation efficiency and weight, ensuring the maximization of the heat dissipation performance and cost-effectiveness of the heat sink fins, reducing the reliance on the subjective experience of engineers in the process of size design of the heat sink fins, and standardizing the shape and size design process for the heat sink fins.

[0187] Each module in the above-mentioned heat sink fin shape parameter determination device can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0188] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 8As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (Near Field Communication, NFC) or other technologies. When the computer program is executed by the processor, a method for determining the shape parameters of a heat sink fin is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0189] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0190] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0191] For each iteration process, according to the fin mass and fin surface area of ​​the heat sink fins to be designed under each group of shape parameters in the fin design population corresponding to the current iteration process, determine the fitness data of the corresponding group of shape parameters under the current iteration process;

[0192] When the fitness data in the current iteration process does not meet the preset iteration termination condition, at least one set of reference shape parameters is selected from the sets of shape parameters in the current iteration process according to the fitness data of the sets of shape parameters in the current iteration process;

[0193] Based on reference constraints of shape parameters corresponding to the heat sink fins, generating at least one set of derived shape parameters according to at least one set of reference shape parameters;

[0194] using at least one set of reference shape parameters and at least one set of derived shape parameters as a fin design population for a next iteration process;

[0195] When each fitness data in the current iteration process satisfies a preset iteration termination condition, a target shape parameter of the heat sink fin is determined according to each group of shape parameters in the current iteration process;

[0196] Among them, each group of shape parameters in the fin design population in the first iteration process is randomly generated based on the reference constraints.

[0197] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0198] Based on a preset fitness function, according to the fin surface area and fin mass of the heat sink fin to be designed under the group of shape parameters, the fitness data of the group of shape parameters under the current iteration process is determined;

[0199] The preset fitness function is an increasing function of the fin surface area and a decreasing function of the fin mass.

[0200] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0201] Based on the preset weights, the fin surface area and the fin mass of the heat sink fins to be designed under the group of shape parameters are weighted respectively to update the corresponding fin surface area and fin mass;

[0202] According to the difference between the updated fin surface area and the updated fin mass, the fitness data of the group of shape parameters in the current iteration process is determined.

[0203] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0204] For each group of shape parameters in the fin design population corresponding to the current iteration process, the fin surface area and the fin mass of the group of shape parameters in the current iteration process are standardized respectively;

[0205] According to the standardized fin surface area and the standardized fin mass, the fitness data of the group of shape parameters in the current iteration process is determined.

[0206] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0207] Meeting the preset iteration termination conditions includes:

[0208] The fluctuation range of each fitness data in the current iteration process is not greater than the preset fluctuation threshold.

[0209] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0210] The fluctuation range of each fitness data in the current iteration process is the data difference between the maximum fitness data and the minimum fitness data;

[0211] The maximum fitness data is the maximum value among the fitness data of the corresponding group of shape parameters in the current iteration process; the minimum fitness data is the minimum value among the fitness data of the corresponding group of shape parameters in the current iteration process.

[0212] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0213] Based on the reference constraint conditions of the shape parameters corresponding to the heat sink fins, adjusting the parameters of at least one set of reference shape parameters according to a first preset probability to obtain corresponding derived shape parameters; and / or,

[0214] Based on the reference constraint conditions of the shape parameters corresponding to the heat sink fins, a parameter cross transformation is performed on at least two groups of reference shape parameters according to a second preset probability to obtain corresponding derived shape parameters.

[0215] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0216] For each iteration process, according to the fin mass and fin surface area of ​​the heat sink fins to be designed under each group of shape parameters in the fin design population corresponding to the current iteration process, determine the fitness data of the corresponding group of shape parameters under the current iteration process;

[0217] When the fitness data in the current iteration process does not meet the preset iteration termination condition, at least one set of reference shape parameters is selected from the sets of shape parameters in the current iteration process according to the fitness data of the sets of shape parameters in the current iteration process;

[0218] Based on reference constraints of shape parameters corresponding to the heat sink fins, generating at least one set of derived shape parameters according to at least one set of reference shape parameters;

[0219] using at least one set of reference shape parameters and at least one set of derived shape parameters as a fin design population for a next iteration process;

[0220] When each fitness data in the current iteration process satisfies a preset iteration termination condition, a target shape parameter of the heat sink fin is determined according to each group of shape parameters in the current iteration process;

[0221] Among them, each group of shape parameters in the fin design population in the first iteration process is randomly generated based on the reference constraints.

[0222] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0223] Based on a preset fitness function, according to the fin surface area and fin mass of the heat sink fin to be designed under the group of shape parameters, the fitness data of the group of shape parameters under the current iteration process is determined;

[0224] The preset fitness function is an increasing function of the fin surface area and a decreasing function of the fin mass.

[0225] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0226] Based on the preset weights, the fin surface area and the fin mass of the heat sink fins to be designed under the group of shape parameters are weighted respectively to update the corresponding fin surface area and fin mass;

[0227] According to the difference between the updated fin surface area and the updated fin mass, the fitness data of the group of shape parameters in the current iteration process is determined.

[0228] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0229] For each group of shape parameters in the fin design population corresponding to the current iteration process, the fin surface area and the fin mass of the group of shape parameters in the current iteration process are standardized respectively;

[0230] According to the standardized fin surface area and the standardized fin mass, the fitness data of the group of shape parameters in the current iteration process is determined.

[0231] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0232] Meeting the preset iteration termination conditions includes:

[0233] The fluctuation range of each fitness data in the current iteration process is not greater than the preset fluctuation threshold.

[0234] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0235] The fluctuation range of each fitness data in the current iteration process is the data difference between the maximum fitness data and the minimum fitness data;

[0236] The maximum fitness data is the maximum value among the fitness data of the corresponding group of shape parameters in the current iteration process; the minimum fitness data is the minimum value among the fitness data of the corresponding group of shape parameters in the current iteration process.

[0237] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0238] Based on the reference constraint conditions of the shape parameters corresponding to the heat sink fins, adjusting the parameters of at least one set of reference shape parameters according to a first preset probability to obtain corresponding derived shape parameters; and / or,

[0239] Based on the reference constraint conditions of the shape parameters corresponding to the heat sink fins, a parameter cross transformation is performed on at least two groups of reference shape parameters according to a second preset probability to obtain corresponding derived shape parameters.

[0240] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0241] For each iteration process, according to the fin mass and fin surface area of ​​the heat sink fins to be designed under each group of shape parameters in the fin design population corresponding to the current iteration process, determine the fitness data of the corresponding group of shape parameters under the current iteration process;

[0242] When the fitness data in the current iteration process does not meet the preset iteration termination condition, at least one set of reference shape parameters is selected from the sets of shape parameters in the current iteration process according to the fitness data of the sets of shape parameters in the current iteration process;

[0243] Based on reference constraints of shape parameters corresponding to the heat sink fins, generating at least one set of derived shape parameters according to at least one set of reference shape parameters;

[0244] using at least one set of reference shape parameters and at least one set of derived shape parameters as a fin design population for a next iteration process;

[0245] When each fitness data in the current iteration process satisfies a preset iteration termination condition, a target shape parameter of the heat sink fin is determined according to each group of shape parameters in the current iteration process;

[0246] Among them, each group of shape parameters in the fin design population in the first iteration process is randomly generated based on the reference constraints.

[0247] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0248] Based on a preset fitness function, according to the fin surface area and fin mass of the heat sink fin to be designed under the group of shape parameters, the fitness data of the group of shape parameters under the current iteration process is determined;

[0249] The preset fitness function is an increasing function of the fin surface area and a decreasing function of the fin mass.

[0250] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0251] Based on the preset weights, the fin surface area and the fin mass of the heat sink fins to be designed under the group of shape parameters are weighted respectively to update the corresponding fin surface area and fin mass;

[0252] According to the difference between the updated fin surface area and the updated fin mass, the fitness data of the group of shape parameters in the current iteration process is determined.

[0253] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0254] For each group of shape parameters in the fin design population corresponding to the current iteration process, the fin surface area and the fin mass of the group of shape parameters in the current iteration process are standardized respectively;

[0255] According to the standardized fin surface area and the standardized fin mass, the fitness data of the group of shape parameters in the current iteration process is determined.

[0256] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0257] Meeting the preset iteration termination conditions includes:

[0258] The fluctuation range of each fitness data in the current iteration process is not greater than the preset fluctuation threshold.

[0259] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0260] The fluctuation range of each fitness data in the current iteration process is the data difference between the maximum fitness data and the minimum fitness data;

[0261] The maximum fitness data is the maximum value among the fitness data of the corresponding group of shape parameters in the current iteration process; the minimum fitness data is the minimum value among the fitness data of the corresponding group of shape parameters in the current iteration process.

[0262] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0263] Based on the reference constraint conditions of the shape parameters corresponding to the heat sink fins, adjusting the parameters of at least one set of reference shape parameters according to a first preset probability to obtain corresponding derived shape parameters; and / or,

[0264] Based on the reference constraint conditions of the shape parameters corresponding to the heat sink fins, a parameter cross transformation is performed on at least two groups of reference shape parameters according to a second preset probability to obtain corresponding derived shape parameters.

[0265] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0266] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0267] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0268] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for determining shape parameters of a heat sink fin, characterized in that: The method comprises: For each iteration process, according to the fin mass and fin surface area of ​​the heat sink fins to be designed under each group of shape parameters in the fin design population corresponding to the current iteration process, determine the fitness data of the corresponding group of shape parameters under the current iteration process; When the fitness data in the current iteration process do not meet the preset iteration termination condition, selecting at least one set of reference shape parameters from the sets of shape parameters in the current iteration process according to the fitness data of the sets of shape parameters in the current iteration process; Based on reference constraints of shape parameters corresponding to the heat sink fins, generating at least one set of derived shape parameters according to at least one set of reference shape parameters; Using the at least one set of reference shape parameters and the at least one set of derived shape parameters as a fin design population for a next iteration process; When each fitness data in the current iteration process satisfies a preset iteration termination condition, determining target shape parameters of the heat sink fins according to each group of shape parameters in the current iteration process; Among them, each group of shape parameters in the fin design population in the first iteration process is randomly generated based on the reference constraint condition.

2. The method according to claim 1, characterized in that The step of determining the fitness data of the group of shape parameters in the current iteration process according to the fin surface area and the fin mass of the heat sink fin to be designed under the group of shape parameters includes: Based on a preset fitness function, according to the fin surface area and fin mass of the heat sink fin to be designed under the group of shape parameters, determining the fitness data of the group of shape parameters under the current iteration process; The preset fitness function is an increasing function of the fin surface area and a decreasing function of the fin mass.

3. The method according to claim 2, characterized in that The step of determining the fitness data of the group of shape parameters in the current iteration process based on the preset fitness function and according to the fin surface area and fin mass of the heat sink fin to be designed under the group of shape parameters comprises: Based on the preset weights, the fin surface area and the fin mass of the heat sink fins to be designed under the group of shape parameters are weighted respectively to update the corresponding fin surface area and fin mass; According to the difference between the updated fin surface area and the updated fin mass, the fitness data of the group of shape parameters in the current iteration process is determined.

4. The method according to claim 1, characterized in that: According to the fin mass and fin surface area of ​​the heat sink fins to be designed under each group of shape parameters in the fin design population corresponding to the current iteration process, the fitness data of the corresponding group of shape parameters under the current iteration process is determined, including: For each group of shape parameters in the fin design population corresponding to the current iteration process, the fin surface area and the fin mass of the group of shape parameters in the current iteration process are standardized respectively; According to the standardized fin surface area and the standardized fin mass, fitness data of the group of shape parameters in the current iteration process is determined.

5. The method according to any one of claims 1 to 4, characterized in that: The predetermined iteration termination condition is met: The fluctuation range of each fitness data in the current iteration process is not greater than a preset fluctuation threshold.

6. The method according to claim 5, characterized in that The fluctuation range of each fitness data in the current iteration process is the data difference between the maximum fitness data and the minimum fitness data; The maximum fitness data is the maximum value among the fitness data of the corresponding group of shape parameters in the current iteration process; the minimum fitness data is the minimum value among the fitness data of the corresponding group of shape parameters in the current iteration process.

7. The method according to any one of claims 1 to 4, characterized in that: The step of generating at least one group of derived shape parameters based on the reference constraint conditions corresponding to the shape parameters of the heat sink fins according to at least one group of reference shape parameters comprises: Based on the reference constraint conditions of the shape parameters corresponding to the heat sink fins, adjusting the parameters of at least one set of the reference shape parameters according to a first preset probability to obtain corresponding derived shape parameters; and / or, Based on the reference constraint conditions of the shape parameters corresponding to the heat sink fins, a parameter cross transformation is performed on at least two groups of the reference shape parameters according to a second preset probability to obtain corresponding derived shape parameters.

8. A device for determining shape parameters of a heat sink fin, characterized in that: The device comprises: A first determination module is used to determine, for each iteration process, the fitness data of the corresponding group of shape parameters in the current iteration process according to the fin mass and fin surface area of ​​the heat sink fins to be designed under each group of shape parameters in the fin design population corresponding to the current iteration process; A selection module, configured to select at least one set of reference shape parameters from the groups of shape parameters in the current iteration process according to the fitness data of the groups of shape parameters in the current iteration process when the fitness data in the current iteration process do not meet the preset iteration termination condition; A generating module, configured to generate at least one set of derived shape parameters based on reference constraints of shape parameters corresponding to the heat sink fins and according to at least one set of reference shape parameters; A second determination module is used to use the at least one set of reference shape parameters and the at least one set of derived shape parameters as a fin design population in a next iteration process; The third determination module is used to determine the target shape parameters of the heat sink fins according to the groups of shape parameters under the current iteration process when the fitness data under the current iteration process meets the preset iteration termination conditions; wherein the groups of shape parameters in the fin design population under the first iteration process are randomly generated based on the reference constraint conditions.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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