A method for optimizing production design of automobile parts based on big data

Abstract

The present invention relates to a method for optimizing production design of automobile parts based on big data, which comprises constructing an initialization model of automobile parts to be produced, a working condition test model of automobile parts to be produced, and a car virtualization model of a target car type using the automobile parts to be produced, and constructing a test driver virtualization model, obtaining a virtual driving state model after the fusion of a person and a car model, and continuously adjusting automobile parts design parameters according to the test situation of the virtual driving state model under the working condition test model, until all automobile parts design parameters in the latest design model of automobile parts finally meet the working condition test requirements, using the latest design model of automobile parts as an automobile parts optimization model adapted to the target car type, and producing automobile parts with all automobile parts design parameters corresponding to the automobile parts optimization model. In this way, the optimized design of automobile parts in a virtualized environment is realized, the design efficiency is improved, and the design cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of automobile parts production, and in particular to a big data optimization production design method for automobile parts. Background Art

[0002] In traditional automobile parts production and design, technicians often design automobile parts based on their previous design experience, obtain automobile parts samples, and then conduct working condition tests (such as driving environment tests) on the automobile parts samples to detect whether the designed automobile parts samples meet the required processing design requirements and working condition requirements. If the produced automobile parts do not meet the processing design requirements, it is necessary to find the problem, readjust the processing design parameters of the automobile parts, and conduct sample tests again until the processed automobile parts samples finally meet the processing design requirements, and then the processing design work for the automobile parts will be completed.

[0003] However, there are some problems with the traditional production design method of automobile parts: the design stage of automobile parts relies too much on physical hardware equipment, which wastes the production cost of the design stage to a certain extent and also delays the time of the design stage; in addition, it is impossible to conduct timely and real-time detection of the working environment status of the designed automobile parts samples, and it is difficult to obtain comprehensive detection data. When the designed and processed automobile parts test samples do not meet the design requirements, it is impossible to grasp the specific data that causes them to not meet the design requirements, which reduces the production design efficiency of automobile parts. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method for optimizing production design of automobile parts with big data based on the above-mentioned prior art.

[0005] The technical solution adopted by the present invention to solve the above technical problems is: a method for optimizing production design of automobile parts based on big data, characterized in that it comprises the following steps:

[0006] Step 1: pre-build an automobile component initialization model of the automobile component to be produced, and use the automobile component initialization model as the latest design model of the automobile component; wherein the automobile component initialization model has an automobile component initial design parameter set corresponding to the automobile component to be produced, and the automobile component initial design parameter set includes at least one initial design size parameter when producing the automobile component to be produced;

[0007] Step 2: pre-build a working condition test model for the automobile component to be produced; wherein the working condition test model has a working condition test parameter set for the working condition test of the automobile component to be produced, and the working condition test parameter set has at least one test environment parameter required for testing the working condition of the automobile component to be produced;

[0008] Step 3, obtaining a target automobile type using the automobile component to be produced, and constructing an automobile virtualization model for the target automobile type;

[0009] Step 4, constructing a virtual model of a test driver who tests the target car type; wherein the virtual model of the test driver has a set of virtual body parameters of the test driver corresponding to the body shape of the test driver, and the set of virtual body parameters of the test driver at least includes a height parameter, a weight parameter, a leg length parameter, and an arm length parameter of the test driver;

[0010] Step 5, loading the latest design model of the automobile parts into the automobile virtualization model corresponding to the target automobile type, and then loading the test driver virtualization model into the driver's seat position of the automobile virtualization model to obtain a virtual driving state model;

[0011] Step 6, performing a virtual driving condition test on the virtual driving state model under the condition test model, and obtaining a subset of qualified design parameters of automobile parts that meet the condition test requirements and a subset of unqualified design parameters of automobile parts that do not meet the condition test requirements in the latest design model of automobile parts;

[0012] Step 7, adjusting the parameters of the unqualified design of the automobile parts corresponding to the latest design model of the automobile parts, and using the latest design model of the automobile parts after the parameter adjustment as the current latest design model of the automobile parts;

[0013] Step 8, the current latest design model of the automobile parts is used again as the latest design model of the automobile parts, and step 5 is executed again in a loop until all the design parameters of the automobile parts in the latest design model of the automobile parts obtained after the last adjustment meet the working condition test requirements;

[0014] Step 9, taking the latest design model of automobile parts corresponding to which all automobile parts design parameters meet the working condition test requirements as the preferred model of automobile parts adapted to the target automobile type, and producing automobile parts with all automobile parts design parameters corresponding to the preferred model of automobile parts.

[0015] Improved, in the automobile parts big data optimization production design method, the construction process of the automobile parts initialization model includes the following steps:

[0016] Step 11, dividing the automobile component to be produced into regions to obtain a plurality of sub-component regions that form the automobile component to be produced; wherein the plurality of sub-component regions together form the automobile component to be produced;

[0017] Step 12, obtaining the assembly relationship between each sub-component area, and forming a sub-component assembly relationship list;

[0018] Step 13, obtaining the material properties corresponding to each sub-component region, and generating a sub-component model for each sub-component region;

[0019] Step 14, according to the material properties corresponding to each sub-component region, the corresponding material properties are loaded into each sub-component model to obtain sub-component models with material properties respectively;

[0020] Step 15, assembling the sub-component models with material properties according to the formed sub-component assembly relationship list to obtain an automobile component assembly model for the automobile component to be produced, and using the automobile component assembly model as the automobile component initialization model.

[0021] Further improved, the automobile parts big data optimization production design method in the invention also includes:

[0022] Step a1, performing rasterization processing on each sub-component area to obtain a rasterized sub-component for each sub-component area;

[0023] Step a2, respectively obtaining the center of gravity of each rasterized subcomponent, and forming a center of gravity set with all the obtained centers of gravity;

[0024] Step a3, connecting all the centers of gravity in the center of gravity set to obtain a skeleton contour model of the automobile parts formed based on the center of gravity set, and marking each center of gravity forming the skeleton contour model of the automobile parts as a stress monitoring point;

[0025] Step a4, selecting the buried points of the assembly contact pressure detection device in the assembly contact area of ​​each rasterized sub-component;

[0026] Step a5, embedding a stress monitoring device at a stress monitoring point of a physical sub-component sample corresponding to each sub-component model having material properties that forms the automobile component assembly model;

[0027] Step a6, burying the assembly contact pressure detection device at the assembly contact pressure detection device burying points corresponding to all physical samples of the sub-components of the stress monitoring device that have been buried;

[0028] Step a7, assembling all embedded stress monitoring devices and contact pressure detection device sub-component physical samples into automobile parts assembly physical samples according to the sub-component assembly relationship list;

[0029] Step a8, performing a working condition simulation test on the physical sample of the automobile component assembly, and storing stress data detected by each stress monitoring point embedded in the physical sample of the automobile component assembly and pressure data detected by each assembly contact pressure detection device, to obtain a working condition simulation test stress data set and a working condition simulation test assembly contact pressure data set corresponding to the physical sample of the automobile component assembly;

[0030] Step a9, using the stored working condition simulation test stress data set and the working condition simulation test assembly contact pressure data set as an optimization parameter set for optimizing automobile parts, and performing data processing and structural optimization in sequence based on the optimization parameter set to obtain an optimized automobile parts assembly model;

[0031] Step a10, using the optimized automobile parts assembly model as an automobile parts optimization design model based on big data optimization.

[0032] Further improvement, in the automobile parts big data optimization production design method, in step a9, based on the optimization parameter set, data processing and structure optimization are performed in sequence to obtain the optimized automobile parts assembly model, which includes the following steps:

[0033] Step a91, obtaining the stress fluctuation index of the physical sample of the automobile component assembly according to the working condition simulation test stress data set; wherein the stress fluctuation index of the physical sample of the automobile component assembly is marked as η F The working condition simulation test stress data set is marked as X, X = {F m}, 1≤m≤M, F m is the stress data monitored by the stress monitoring device of the physical sample of the sub-component corresponding to the m-th sub-component model of the automobile component assembly model, and M is the total number of sub-component models of the automobile component assembly model;

[0034]

[0035] Max F is the maximum stress data value in the stress data set X of the working condition simulation test, Min F is the minimum stress data value in the working condition simulation test stress data set X; wherein each stress data in the working condition simulation test stress data set X is numbered according to the sequential assembly order of the corresponding sub-component physical samples;

[0036] Step a92, obtaining the assembly contact pressure fluctuation index of the physical sample of the automobile parts assembly according to the working condition simulation test assembly contact pressure data set; wherein the assembly contact pressure fluctuation index of the physical sample of the automobile parts assembly is marked as The contact pressure data set of the working condition simulation test assembly is marked as Y, Y = {f m}, 1≤m≤M, f m The assembly contact pressure data detected by the assembly contact pressure detection device of the physical sample of the sub-component corresponding to the m-th sub-component model of the automobile component assembly model;

[0037]

[0038] α f β is the maximum value of the assembly contact pressure data detected by the assembly contact pressure detection device on all the secondary sub-component physical samples in the secondary sub-component physical sample set formed by all the sub-component physical samples that have an assembly contact relationship with at least one sub-component physical sample; f The minimum value of the assembly contact pressure data detected by the assembly contact pressure detection device on all the second-level sub-component physical samples in the second-level sub-component physical sample set formed by all the sub-component physical samples that have an assembly contact relationship with at least one sub-component physical sample; the second-level sub-component physical sample is located in the set of all sub-component physical samples and has an assembly contact relationship with at least one sub-component physical sample;

[0039] Step a93, according to the obtained assembly contact pressure fluctuation index of the physical sample of the automobile component assembly and the assembly contact pressure data corresponding to each physical sample of the subcomponent, respectively obtain the assembly bearing coefficient that characterizes the assembly contact pressure bearing capacity of each physical sample of the subcomponent; wherein the assembly bearing coefficient of the physical sample of the subcomponent corresponding to the mth subcomponent model of the automobile component assembly model is marked as ε m :

[0040]

[0041] Step a94, selecting, from among all the obtained assembly bearing coefficients, the physical sample of the sub-component corresponding to the assembly bearing coefficient whose value is less than the preset coefficient value as the physical sample of the sub-component whose design parameters are to be adjusted;

[0042] Step a95, adjusting the design parameters of the physical sample of the subcomponent whose design parameters are to be adjusted until the corresponding assembly bearing coefficient is greater than or equal to the preset coefficient value;

[0043] Step a96, assembling the subcomponent models corresponding to all the physical samples of subcomponents whose assembly bearing coefficient is greater than or equal to the preset coefficient value into an automobile parts assembly model, and taking the newly assembled automobile parts assembly model as the optimized automobile parts assembly model.

[0044] Compared with the prior art, the advantages of the present invention are:

[0045] First, the invented method for optimizing the production design of automobile parts is to construct an initialization model of the automobile parts to be produced and a working condition test model corresponding to the automobile parts to be produced, and then construct a virtualized model of the automobile of the target automobile type using the automobile parts to be produced. After constructing the virtualized model of the test driver for the target automobile type, the latest design model of the automobile parts is loaded into the virtualized model of the automobile, and a virtual driving state model after the fusion of the human and vehicle models is obtained. Then, the virtual driving state model is used to perform a working condition test of virtual driving under the working condition test model. According to the virtualized working condition test results, the design parameters of the automobile parts are continuously adjusted until all the design parameters of the automobile parts in the latest design model of the automobile parts finally obtained meet the working condition test requirements, that is, the latest design model of the automobile parts is used as the preferred model of automobile parts adapted to the target automobile type, and the automobile parts are produced with all the design parameters of the automobile parts corresponding to the preferred model of the automobile parts. In this way, the optimized design of automobile parts in a virtualized environment is realized, without relying too much on the testing of physical products, improving the design efficiency and reducing the design cost;

[0046] Secondly, the present invention also divides the initialization model of the automobile parts to be produced into regions, performs material loading of the sub-components corresponding to each region, and obtains the assembly contact relationship list between multiple sub-components of the automobile parts, and performs assembly operations between all sub-components loaded with materials based on the obtained sub-component assembly contact relationship list to obtain an automobile parts assembly model, and uses the automobile parts assembly model as the automobile parts initialization model, so as to be closer to the real assembly state of all sub-components in the automobile parts;

[0047] Finally, the acquisition process of the automobile parts assembly model is optimized. Specifically, the stress monitoring device and the assembly contact pressure detection device are pre-embedded for each sub-component, and then the stress fluctuation index and the assembly contact pressure fluctuation index of the assembled automobile parts assembly physical sample are obtained, and then the assembly bearing coefficient that characterizes the assembly contact pressure bearing capacity of each sub-component physical sample is obtained. Then, the design parameters of the physical samples of each sub-component are adjusted until the corresponding assembly bearing coefficient is greater than or equal to the preset coefficient value. In this way, the newly assembled automobile parts assembly model of all sub-components at that time is used as the optimized automobile parts assembly model. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 The figure is a schematic diagram of the process of optimizing production design of automobile parts based on big data in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The present invention is further described in detail below with reference to the accompanying drawings.

[0050] This embodiment provides a big data optimization production design method for automobile parts, specifically a big data optimization production design method for engines in automobile powertrains. Figure 1 As shown, the automobile parts big data optimization production design method of this embodiment includes the following steps S1 to S9:

[0051] Step S1, pre-constructing an engine initialization model of the engine to be produced, and using the engine initialization model as the latest design model of the engine; wherein the engine initialization model has an engine initial design parameter set corresponding to the engine to be produced, and the engine initial design parameter set includes at least one initial design dimension parameter when producing the engine to be produced; specifically, in this embodiment, the construction process of the engine initialization model here includes the following steps 11 to 15:

[0052] Step 11, divide the engine to be produced into regions to obtain multiple sub-component regions that form the engine to be produced; wherein the multiple sub-component regions together form the engine to be produced; wherein, assuming that after the region division, the engine to be produced is divided into C sub-component regions, the cth sub-component region is marked as Q c , sub-component area Q c The corresponding sub-component of the engine to be produced is marked as DQ c , 1≤c≤C;

[0053] Step 12, obtain the assembly relationship between each sub-component area, and form a sub-component assembly relationship list; for example, the total number C of sub-components of the engine is 5, that is, the 5 sub-components of the engine are DQ 1 , DQ 2 , DQ 3 , DQ 4 and DQ 5 ; After the assembly relationship is obtained, a sub-component assembly relationship list List is formed. In the sub-component assembly relationship list List, the assembly relationship is as follows:

[0054] Subcomponent DQ 1 →DQ 3 and DQ 4 ,

[0055] Subcomponent DQ 2 →DQ 3 and DQ 5 ,

[0056] Subcomponent DQ 3 →DQ1 and DQ 2 ,

[0057] Subcomponent DQ 4 →DQ 1 and DQ 5 ,

[0058] Subcomponent DQ 5 →DQ 4 ;

[0059] It should be noted that two sub-components that have an assembly relationship with each other are in contact with each other, and the two sub-components in contact with each other will respectively bear the contact pressure (referred to as contact force) applied by the other sub-component;

[0060] Step 13, obtaining the material properties corresponding to each sub-component region, and generating a sub-component model for each sub-component region;

[0061] Step 14, according to the material properties corresponding to each sub-component region, the corresponding material properties are loaded into each sub-component model to obtain sub-component models with material properties respectively;

[0062] Step 15, assembling the sub-component models with material attributes according to the formed sub-component assembly relationship list to obtain an engine assembly model for the engine to be produced, and using the engine assembly model as the engine initialization model;

[0063] In the specific implementation process, the engine assembly model for the engine to be produced obtained here is preferably an optimized engine assembly model obtained after optimization processing. Specifically, in this embodiment, the process of processing to obtain the optimized engine assembly model includes the following steps a1 to a10:

[0064] Step a1, rasterizing each sub-component region to obtain a rasterized sub-component for each sub-component region; wherein the rasterization process here adopts a conventional rasterization method, which will not be described in detail here; assuming that for the sub-component region Q c After rasterization, the resulting rasterized subcomponent is marked as D'Q c ;

[0065] Step a2, respectively obtain the centroid of each rasterized subcomponent, and form a centroid set with all the obtained centroids; wherein, assuming that the obtained rasterized subcomponent D'Q c The center of gravity is marked as The resulting centroid set is labeled G.

[0066] Step a3, connecting all the centers of gravity in the center of gravity set to obtain an engine skeleton contour model formed based on the center of gravity set, and marking each center of gravity forming the engine skeleton contour model as a stress monitoring point;

[0067] Step a4, selecting the buried points of the assembly contact pressure detection device in the assembly contact area of ​​each gridded sub-component; assuming that the gridded sub-component D'Q c The assembly contact area is marked as The assembly contact area That is, according to the above-obtained sub-component assembly relationship list List, the sub-component D'Q is rasterized. c contact areas with other subcomponents);

[0068] Step a5, embedding a stress monitoring device at a stress monitoring point of a physical sub-component sample corresponding to each sub-component model having material properties that forms the engine assembly model;

[0069] Step a6, burying the assembly contact pressure detection device at the assembly contact pressure detection device burying points corresponding to all physical samples of the sub-components of the stress monitoring device that have been buried;

[0070] Step a7, assembling all embedded stress monitoring devices and assembly contact pressure detection device sub-component physical samples into an engine assembly physical sample according to the sub-component assembly relationship list;

[0071] Step a8, performing a working condition simulation test on the physical sample of the engine assembly, and storing stress data detected by each stress monitoring point embedded in the physical sample of the engine assembly and pressure data detected by each assembly contact pressure detection device, to obtain a working condition simulation test stress data set and a working condition simulation test assembly contact pressure data set corresponding to the physical sample of the engine assembly;

[0072] Step a9, taking the stored working condition simulation test stress data set and the working condition simulation test assembly contact pressure data set as an optimization parameter set for optimizing the engine, and performing data processing and structural optimization in sequence based on the optimization parameter set to obtain an optimized engine assembly model; wherein the process of obtaining the optimized engine assembly model here includes the following steps a91 to a96:

[0073] Step a91, obtaining the stress fluctuation index of the physical sample of the engine assembly according to the working condition simulation test stress data set; wherein the stress fluctuation index of the physical sample of the engine assembly is marked as η F , the stress data set of the working condition simulation test is marked as X, X = {F m}, 1≤m≤M, F mis the stress data monitored by the stress monitoring device of the physical sample of the sub-component corresponding to the m-th sub-component model of the engine assembly model, and M is the total number of sub-component models of the engine assembly model;

[0074]

[0075] Max F is the maximum stress data value in the stress data set X of the working condition simulation test, Min F is the minimum stress data value in the working condition simulation test stress data set X; wherein each stress data in the working condition simulation test stress data set X is numbered according to the sequential assembly order of the corresponding sub-component physical samples;

[0076] Step a92, obtaining the assembly contact pressure fluctuation index of the physical sample of the engine assembly according to the working condition simulation test assembly contact pressure data set; wherein the assembly contact pressure fluctuation index of the physical sample of the engine assembly is marked as The contact pressure data set of the working condition simulation test assembly is marked as Y, Y = {f m}, 1≤m≤M, f m The assembly contact pressure data detected by the assembly contact pressure detection device of the physical sample of the sub-component corresponding to the m-th sub-component model of the engine assembly model;

[0077]

[0078] α f β is the maximum value of the assembly contact pressure data detected by the assembly contact pressure detection device on all the secondary sub-component physical samples in the secondary sub-component physical sample set formed by all the sub-component physical samples that have an assembly contact relationship with at least one sub-component physical sample; f The minimum value of the assembly contact pressure data detected by the assembly contact pressure detection device on all the second-level sub-component physical samples in the second-level sub-component physical sample set formed by all the sub-component physical samples that have an assembly contact relationship with at least one sub-component physical sample; the second-level sub-component physical sample is located in the set of all sub-component physical samples and has an assembly contact relationship with at least one sub-component physical sample;

[0079] Step a93, according to the obtained assembly contact pressure fluctuation index of the physical sample of the engine assembly and the assembly contact pressure data corresponding to each physical sample of the sub-component, respectively obtain the assembly bearing coefficient that characterizes the assembly contact pressure bearing capacity of each physical sample of the sub-component; wherein, the assembly bearing coefficient of the physical sample of the sub-component corresponding to the mth sub-component model of the engine assembly model is marked as ε m :

[0080]

[0081] Step a94, selecting, from among all the obtained assembly bearing coefficients, the physical sample of the sub-component corresponding to the assembly bearing coefficient whose value is less than the preset coefficient value as the physical sample of the sub-component whose design parameters are to be adjusted;

[0082] Step a95, adjusting the design parameters of the physical sample of the subcomponent whose design parameters are to be adjusted until the corresponding assembly bearing coefficient is greater than or equal to the preset coefficient value;

[0083] Step a96, assembling the sub-component models corresponding to all the physical samples of the sub-components whose assembly bearing coefficient is greater than or equal to the preset coefficient value into an engine assembly model, and using the newly assembled engine assembly model as the optimized engine assembly model;

[0084] Step a10, using the optimized engine assembly model as an engine optimization design model based on big data optimization;

[0085] Step S2, pre-constructing a working condition test model for the engine to be produced; wherein the working condition test model has a working condition test parameter set for the working condition test of the engine to be produced, and the working condition test parameter set has at least one test environment parameter required for testing the working condition of the engine to be produced;

[0086] Step S3, obtaining a target vehicle type using the engine to be produced, and constructing a vehicle virtualization model for the target vehicle type;

[0087] Step S4, constructing a virtual model of a test driver who tests the target car type; wherein the virtual model of the test driver has a set of virtual body parameters of the test driver corresponding to the body shape of the test driver, and the set of virtual body parameters of the test driver at least includes a height parameter, a weight parameter, a leg length parameter, and an arm length parameter of the test driver;

[0088] Step S5, loading the latest design model of the engine into the virtual model of the car corresponding to the target car type, and then loading the virtual model of the test driver into the driver's seat position of the virtual model of the car to obtain a virtual driving state model;

[0089] Step S6, performing a virtual driving condition test on the virtual driving state model under the condition test model, and obtaining a subset of qualified engine design parameters that meet the condition test requirements and a subset of unqualified engine design parameters that do not meet the condition test requirements in the latest engine design model;

[0090] Step S7, adjusting the unqualified engine design parameters corresponding to the latest engine design model, and using the latest engine design model after parameter adjustment as the current latest engine design model;

[0091] Step S8, taking the current latest engine design model as the latest engine design model again, and looping through step 5 until all engine design parameters in the latest engine design model obtained after the last adjustment meet the operating condition test requirements;

[0092] Step S9, taking the latest engine design model corresponding to which all engine design parameters satisfy the working condition test requirements as the engine preferred model adapted to the target vehicle type, and producing the engine with all engine design parameters corresponding to the engine preferred model.

[0093] In order to make the obtained engine optimal model meet the vibration requirements in the actual driving environment, the embodiment further includes:

[0094] Step c1, virtual simulation of the engine optimization model based on the overall vibration intensity of the engine; wherein the overall vibration intensity of the engine is marked as Γ 1 :

[0095]

[0096] Among them, v x,u represents the vibration velocity value detected by the u-th vibration velocity detection point of the engine optimization model in the X-axis direction in the spatial coordinate system, where U is the total number of vibration velocity detection points arranged on the engine optimization model along the X-axis direction in the spatial coordinate system; v y,w represents the vibration velocity value detected by the w-th vibration velocity detection point of the engine optimization model in the Y-axis direction in the spatial coordinate system, where W is the total number of vibration velocity detection points arranged on the engine optimization model along the Y-axis direction in the spatial coordinate system; z,n Characterizes the vibration velocity value detected by the nth vibration velocity detection point of the engine preferred model in the Z-axis direction in the spatial coordinate system, where N is the total number of vibration velocity detection points along the Z-axis direction in the spatial coordinate system and arranged on the engine preferred model; the specific values ​​of U, W and N are set according to actual design requirements, and preferably, each of U, W and N has a value greater than 100 and is an odd value;

[0097] Step c2, performing virtual simulation on the engine optimization model based on the overall vibration impact strength of the engine; wherein the overall vibration impact strength of the engine is marked as Γ 2 :

[0098]

[0099] Among them, a x,u Characterizes the vibration acceleration value detected by the u-th vibration acceleration detection point in the X-axis direction of the engine preferred model in the spatial coordinate system, a y,w Characterizes the vibration acceleration value detected by the wth vibration acceleration detection point in the Y-axis direction of the engine preferred model in the spatial coordinate system, a z,n Characterizes the vibration acceleration value detected by the nth vibration acceleration detection point in the Z-axis direction of the engine preferred model in the spatial coordinate system;

[0100] Step c3, according to the overall vibration intensity of the engine and the overall vibration impact intensity of the engine obtained by virtual simulation, the design parameters of each sub-component in the engine preferred model are adjusted until the overall vibration intensity of the engine obtained by virtual simulation finally meets the preset vibration intensity requirement and the overall vibration impact intensity of the engine also meets the preset vibration impact intensity requirement, that is, the adjusted engine preferred model that meets both the preset vibration intensity requirement and the preset vibration impact intensity requirement is used as the final engine preferred model to be produced. Wherein:

[0101] The preset vibration intensity requirements are as follows:

[0102]

[0103] The preset vibration shock intensity requirements are set as follows:

[0104]

[0105] This embodiment adjusts the design parameters of each sub-component in the engine preferred model by making simulation adjustments to the engine preferred model based on preset vibration intensity requirements and preset vibration impact intensity requirements, thereby obtaining an engine model that meets the vibration requirements of the engine under actual driving conditions and is more in line with the vibration requirements of the engine under actual driving conditions.

[0106] Of course, according to actual production design conditions, the engine in this embodiment can be replaced by other automobile parts.

[0107] Although the preferred embodiments of the present invention are described in detail above, it should be clearly understood that various modifications and variations are possible for those skilled in the art. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for optimizing production design of automobile parts based on big data, characterized in that: The steps include: Step 1: pre-build an automobile component initialization model of the automobile component to be produced, and use the automobile component initialization model as the latest design model of the automobile component; wherein the automobile component initialization model has an automobile component initial design parameter set corresponding to the automobile component to be produced, and the automobile component initial design parameter set includes at least one initial design size parameter when producing the automobile component to be produced; Step 2: pre-build a working condition test model for the automobile component to be produced; wherein the working condition test model has a working condition test parameter set for the working condition test of the automobile component to be produced, and the working condition test parameter set has at least one test environment parameter required for testing the working condition of the automobile component to be produced; Step 3, obtaining a target automobile type using the automobile component to be produced, and constructing an automobile virtualization model for the target automobile type; Step 4, constructing a virtual model of a test driver who tests the target car type; wherein the virtual model of the test driver has a set of virtual body parameters of the test driver corresponding to the body shape of the test driver, and the set of virtual body parameters of the test driver at least includes a height parameter, a weight parameter, a leg length parameter, and an arm length parameter of the test driver; Step 5, loading the latest design model of the automobile parts into the automobile virtualization model corresponding to the target automobile type, and then loading the test driver virtualization model into the driver's seat position of the automobile virtualization model to obtain a virtual driving state model; Step 6, performing a virtual driving condition test on the virtual driving state model under the condition test model, and obtaining a subset of qualified design parameters of automobile parts that meet the condition test requirements and a subset of unqualified design parameters of automobile parts that do not meet the condition test requirements in the latest design model of automobile parts; Step 7, adjusting the parameters of the unqualified design of the automobile parts corresponding to the latest design model of the automobile parts, and using the latest design model of the automobile parts after the parameter adjustment as the current latest design model of the automobile parts; Step 8, the current latest design model of the automobile parts is used again as the latest design model of the automobile parts, and step 5 is executed again in a loop until all the design parameters of the automobile parts in the latest design model of the automobile parts obtained after the last adjustment meet the working condition test requirements; Step 9, taking the latest design model of automobile parts corresponding to which all automobile parts design parameters meet the working condition test requirements as the preferred model of automobile parts adapted to the target automobile type, and producing automobile parts with all automobile parts design parameters corresponding to the preferred model of automobile parts.

2. The method for optimizing production design of automobile parts based on big data according to claim 1, characterized in that: The construction process of the automobile parts initialization model includes the following steps: Step 11, dividing the automobile component to be produced into regions to obtain a plurality of sub-component regions that form the automobile component to be produced; wherein the plurality of sub-component regions together form the automobile component to be produced; Step 12, obtaining the assembly relationship between each sub-component area, and forming a sub-component assembly relationship list; Step 13, obtaining the material properties corresponding to each sub-component region, and generating a sub-component model for each sub-component region; Step 14, according to the material properties corresponding to each sub-component region, the corresponding material properties are loaded into each sub-component model to obtain sub-component models with material properties respectively; Step 15, assembling the sub-component models with material properties according to the formed sub-component assembly relationship list to obtain an automobile component assembly model for the automobile component to be produced, and using the automobile component assembly model as the automobile component initialization model.

3. The method for optimizing production design of automobile parts based on big data according to claim 2, characterized in that: Also includes: Step a1, performing rasterization processing on each sub-component area to obtain a rasterized sub-component for each sub-component area; Step a2, respectively obtaining the center of gravity of each rasterized subcomponent, and forming a center of gravity set with all the obtained centers of gravity; Step a3, connecting all the centers of gravity in the center of gravity set to obtain a skeleton contour model of the automobile parts formed based on the center of gravity set, and marking each center of gravity forming the skeleton contour model of the automobile parts as a stress monitoring point; Step a4, selecting the buried points of the assembly contact pressure detection device in the assembly contact area of ​​each rasterized sub-component; Step a5, embedding a stress monitoring device at a stress monitoring point of a physical sub-component sample corresponding to each sub-component model having material properties that forms the automobile component assembly model; Step a6, burying the assembly contact pressure detection device at the assembly contact pressure detection device burying points corresponding to all physical samples of the sub-components of the stress monitoring device that have been buried; Step a7, assembling all embedded stress monitoring devices and contact pressure detection device sub-component physical samples into automobile parts assembly physical samples according to the sub-component assembly relationship list; Step a8, performing a working condition simulation test on the physical sample of the automobile component assembly, and storing stress data detected by each stress monitoring point embedded in the physical sample of the automobile component assembly and pressure data detected by each assembly contact pressure detection device, to obtain a working condition simulation test stress data set and a working condition simulation test assembly contact pressure data set corresponding to the physical sample of the automobile component assembly; Step a9, using the stored working condition simulation test stress data set and the working condition simulation test assembly contact pressure data set as an optimization parameter set for optimizing automobile parts, and performing data processing and structural optimization in sequence based on the optimization parameter set to obtain an optimized automobile parts assembly model; Step a10, using the optimized automobile parts assembly model as an automobile parts optimization design model based on big data optimization.

4. The method for optimizing production design of automobile parts based on big data according to claim 3 is characterized in that: In step a9, data processing and structural optimization are performed in sequence based on the optimization parameter set to obtain the optimized automobile parts assembly model, which includes the following steps: Step a91, obtaining the stress fluctuation index of the physical sample of the automobile component assembly according to the working condition simulation test stress data set; wherein the stress fluctuation index of the physical sample of the automobile component assembly is marked as η F The working condition simulation test stress data set is marked as X, X = {F m }, 1≤m≤M, F m is the stress data monitored by the stress monitoring device of the physical sample of the sub-component corresponding to the m-th sub-component model of the automobile component assembly model, and M is the total number of sub-component models of the automobile component assembly model; Max F is the maximum stress data value in the stress data set X of the working condition simulation test, Min F is the minimum stress data value in the working condition simulation test stress data set X; wherein each stress data in the working condition simulation test stress data set X is numbered according to the sequential assembly order of the corresponding sub-component physical samples; Step a92, obtaining the assembly contact pressure fluctuation index of the physical sample of the automobile parts assembly according to the working condition simulation test assembly contact pressure data set; wherein the assembly contact pressure fluctuation index of the physical sample of the automobile parts assembly is marked as The contact pressure data set of the working condition simulation test assembly is marked as Y, Y = {f m }, 1≤m≤M, f m The assembly contact pressure data detected by the assembly contact pressure detection device of the sub-component physical sample corresponding to the m-th sub-component model of the automobile component assembly model; α f β is the maximum value of the assembly contact pressure data detected by the assembly contact pressure detection device on all the secondary sub-component physical samples in the secondary sub-component physical sample set formed by all the sub-component physical samples that have an assembly contact relationship with at least one sub-component physical sample; f The minimum value of the assembly contact pressure data detected by the assembly contact pressure detection device on all the second-level sub-component physical samples in the second-level sub-component physical sample set formed by all the sub-component physical samples that have an assembly contact relationship with at least one sub-component physical sample; the second-level sub-component physical sample is located in the set of all sub-component physical samples and has an assembly contact relationship with at least one sub-component physical sample; Step a93, according to the obtained assembly contact pressure fluctuation index of the physical sample of the automobile component assembly and the assembly contact pressure data corresponding to each physical sample of the subcomponent, respectively obtain the assembly bearing coefficient that characterizes the assembly contact pressure bearing capacity of each physical sample of the subcomponent; wherein the assembly bearing coefficient of the physical sample of the subcomponent corresponding to the mth subcomponent model of the automobile component assembly model is marked as ε m : Step a94, selecting, from among all the obtained assembly bearing coefficients, the physical sample of the sub-component corresponding to the assembly bearing coefficient whose value is less than the preset coefficient value as the physical sample of the sub-component whose design parameters are to be adjusted; Step a95, adjusting the design parameters of the physical sample of the subcomponent whose design parameters are to be adjusted until the corresponding assembly bearing coefficient is greater than or equal to the preset coefficient value; Step a96, assembling the subcomponent models corresponding to all the physical samples of subcomponents whose assembly bearing coefficient is greater than or equal to the preset coefficient value into an automobile parts assembly model, and using the newly assembled automobile parts assembly model as the optimized automobile parts assembly model.

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