A fitness detection method and system for the selection of automotive metal fittings

Abstract

The present invention discloses a fitness detection method and system for the selection of automotive metal fittings, which relates to the field of data processing. Among them, the method includes: inputting the demand parameters of the metal parts to be selected into the fitting group screening database to generate the first fitting group of parts, the second fitting group of parts until the Nth fitting group of parts; and encoding them to generate the first fitting group coding result, the second fitting group coding result until the Nth fitting group coding result; optimizing and screening the nth fitting group coding result according to the fitting screening fitness function to generate the nth fitting screening result, and judging whether the nth fitting screening result meets the preset fitness; if it meets, adding the nth fitting screening result to the automotive metal fitting selection result. It solves the technical problems of low selection efficiency and low quality in the selection of automotive metal fittings in the prior art. It achieves technical effects such as improving the efficiency and quality of the selection of automotive metal fittings.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular, to a fitness detection method and system for selecting automotive metal fittings. Background Art

[0002] Automotive metal fittings are an important part of automobiles and have an important impact on various performances of automobiles, such as safety, comfort, driving stability, and operation stability. With the improvement of people's living standards, the automotive sales market has gradually expanded, and people have put forward higher-level requirements for various performances of automobiles. The demand for automotive metal fittings also develops in the direction of diversification. How to effectively select automotive metal fittings and improve the fitness of automotive metal fittings has attracted wide attention.

[0003] In the prior art, there are technical problems such as low accuracy and low fitness in the selection of automotive metal fittings, resulting in low efficiency and low quality in the selection of automotive metal fittings. Summary of the Invention

[0004] This application provides a fitness detection method and system for selecting automotive metal fittings. It solves the technical problems in the prior art that the accuracy of selecting automotive metal fittings is low and the fitness is not high, resulting in low efficiency and low quality in the selection of automotive metal fittings.

[0005] In view of the above problems, this application provides a fitness detection method and system for selecting automotive metal fittings.

[0006] In a first aspect, this application provides a fitness detection method for selecting automotive metal fittings. The method is applied to a fitness detection system for selecting automotive metal fittings, and the method includes: matching the required parameters of the metal parts to be selected according to the information of the automobile model to be assembled, where the required parameters of the metal parts to be selected include the required parameters of part performance and the required parameters of part dimensions; inputting the required parameters of part performance and the required parameters of part dimensions into the fitting group screening database to generate the first part fitting group, the second part fitting group until the Nth part fitting group; traversing the first part fitting group, the second part fitting group until the Nth part fitting group for coding to generate the first fitting group coding result, the second fitting group coding result until the Nth fitting group coding result; constructing a fitting screening fitness function; according to the fitting screening fitness function, optimizing and screening the nth fitting group coding result to generate the nth fitting screening result, where n ∈ N; judging whether the nth fitting screening result meets the preset fitness; if it meets, adding the nth fitting screening result to the result of selecting automotive metal fittings.

[0007] Second aspect, the present application also provides a fitness detection system for the selection of automotive metal fittings. Among them, the system includes: a demand parameter matching module, which is used to match the demand parameters of the metal parts to be selected according to the information of the vehicle model to be assembled. Among them, the demand parameters of the metal parts to be selected include part performance demand parameters and part size demand parameters; a part fitting group generation module, which is used to input the part performance demand parameters and the part size demand parameters into the fitting group screening database to generate the first part fitting group, the second part fitting group until the Nth part fitting group; a coding result generation module, which is used to traverse the first part fitting group, the second part fitting group until the Nth part fitting group for coding to generate the first fitting group coding result, the second fitting group coding result until the Nth fitting group coding result; a function construction module, which is used to construct a fitting screening fitness function; a fitting screening result generation module, which is used to optimize and screen the coding result of the nth fitting group according to the fitting screening fitness function to generate the nth fitting screening result, where n ∈ N; a judgment module, which is used to judge whether the nth fitting screening result meets the preset fitness; an addition module, which is used to add the nth fitting screening result to the selection result of the automotive metal fittings if it meets the requirements.

[0008] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0009] By matching the part performance demand parameters and part size demand parameters through the information of the vehicle model to be assembled, the demand parameters of the metal parts to be selected are obtained; the demand parameters of the metal parts to be selected are input into the fitting group screening database to generate the first part fitting group, the second part fitting group... the Nth part fitting group; traverse the first part fitting group, the second part fitting group... the Nth part fitting group for coding to generate the first fitting group coding result, the second fitting group coding result... the Nth fitting group coding result; construct a fitting screening fitness function; optimize and screen the coding result of the nth fitting group through the fitting screening fitness function to generate the nth fitting screening result, n ∈ N; judge whether the nth fitting screening result meets the preset fitness; if it meets the requirements, add the nth fitting screening result to the selection result of the automotive metal fittings. It achieves the technical effect of improving the accuracy and fitness of the selection of automotive metal fittings, as well as the efficiency and quality of the selection of automotive metal fittings through intelligent and efficient multiple optimization and screening of automotive metal fittings; at the same time, it improves the automation and scientific nature of the selection of automotive metal fittings and minimizes the waste of resources such as manpower and material resources caused by the selection of automotive metal fittings. Description of the Drawings

[0010] Figure 1Schematic flowchart of a fitness detection method for selecting automotive metal fittings in this application;

[0011] Figure 2 Schematic flowchart of generating the Nth part fitting group in a fitness detection method for selecting automotive metal fittings in this application;

[0012] Figure 3 Schematic flowchart of generating the coding result of the nth fitting group in a fitness detection method for selecting automotive metal fittings in this application;

[0013] Figure 4 Schematic structural diagram of a fitness detection system for selecting automotive metal fittings in this application.

[0014] Explanation of reference numerals: Requirement parameter matching module 11, part fitting group generation module 12, coding result generation module 13, function construction module 14, fitting screening result generation module 15, judgment module 16, addition module 17. Detailed implementation mode

[0015] This application provides a fitness detection method and system for selecting automotive metal fittings, which solves the technical problems in the prior art that the accuracy of selecting automotive metal fittings is low and the fitness is not high, resulting in low efficiency and poor quality of selecting automotive metal fittings. It achieves the technical effects of improving the accuracy and fitness of selecting automotive metal fittings through intelligent and efficient multiple optimization and screening of automotive metal fittings, improving the efficiency and quality of selecting automotive metal fittings; at the same time, improving the automation degree and scientific nature of selecting automotive metal fittings, and minimizing the waste of resources such as manpower and material resources caused by selecting automotive metal fittings.

[0016] Embodiment 1

[0017] Please refer to the attached Figure 1 , this application provides a fitness detection method for selecting automotive metal fittings. Among them, the method is applied to a fitness detection system for selecting automotive metal fittings, and the method specifically includes the following steps:

[0018] Step S100: Match the demand parameters of the metal parts to be selected according to the information of the vehicle model to be assembled. Among them, the demand parameters of the metal parts to be selected include part performance demand parameters and part size demand parameters;

[0019] Furthermore, step S100 of this application further includes:

[0020] Step S110: Obtain the original design drawing of the vehicle to be assembled according to the information of the vehicle model to be assembled;

[0021] Step S120: Disassemble the original design drawing of the to-be-assembled vehicle to generate the original design drawing of the nth part;

[0022] Step S130: Extract the dimensional requirement parameters of the nth part according to the original design drawing of the nth part;

[0023] Step S140: Send the dimensional requirement parameters of the nth part to the user interface to obtain the nth feedback information, where the nth feedback information includes the performance requirement parameters of the nth part;

[0024] Step S150: Add the dimensional requirement parameters of the nth part to the dimensional requirement parameters of the part;

[0025] Step S160: Add the performance requirement parameters of the nth part to the performance requirement parameters of the part.

[0026] Specifically, query the original design drawing of the vehicle based on the model information of the to-be-assembled vehicle to obtain the original design drawing of the to-be-assembled vehicle, and decompose the original design drawing of the to-be-assembled vehicle to obtain the original design drawing of the nth part. Further, extract the dimensional requirement parameters of the nth part based on the original design drawing of the nth part to obtain the dimensional requirement parameters of the nth part, and send the dimensional requirement parameters of the nth part to the user interface to obtain the nth feedback information. Furthermore, add the dimensional requirement parameters of the nth part to the dimensional requirement parameters of the part, and add the performance requirement parameters of the nth part to the performance requirement parameters of the part. Based on this, obtain the requirement parameters of the metal parts to be selected.

[0027] Among them, the information of the vehicle model to be assembled includes the vehicle type information of the vehicle to be assembled. The vehicle to be assembled is any vehicle that uses the fitness detection system for intelligent selection of automotive metal fittings to select automotive metal fittings. For example, the information of the vehicle model to be assembled includes vehicle models such as compact models, medium models, luxury models, CDV models, MPV models, and SUVs. The original design drawing of the vehicle to be assembled includes the vehicle design drawing information corresponding to the information of the vehicle model to be assembled. The original design drawing of the nth part includes various automotive part design drawings such as the engine part design drawing, automotive bearing design drawing, brake pad design drawing, and handbrake design drawing in the original design drawing of the vehicle to be assembled. The dimensional requirement parameters of the nth part include dimensional data information such as nominal dimension parameters, dimensional deviation parameters, linear dimension parameters, and radius dimension parameters in the original design drawing of the nth part. The user interaction interface has the function of collecting and matching the part performance requirements for the dimensional requirement parameters of the nth part. The nth feedback information includes the nth part performance requirement parameters. The nth part performance requirement parameters include performance requirement information such as wear resistance requirement, corrosion resistance parameter requirement, elasticity requirement, and fatigue strength requirement corresponding to the dimensional requirement parameters of the nth part. The requirement parameters of the metal part to be selected include part performance requirement parameters and part dimensional requirement parameters. The part dimensional requirement parameters include the dimensional requirement parameters of the nth part. The part performance requirement parameters include the nth part performance requirement parameters. It achieves the technical effect of matching and analyzing the part performance requirement parameters and part dimensional requirement parameters through the information of the vehicle model to be assembled, obtaining accurate and reliable requirement parameters of the metal part to be selected, thereby improving the accuracy of automotive metal fitting selection.

[0028] Step S200: Input the part performance requirement parameters and the part dimensional requirement parameters into the accessory group screening database to generate the first part accessory group, the second part accessory group, and up to the Nth part accessory group;

[0029] Further, as shown in the Figure 2 appendix, step S200 of the present application further includes:

[0030] Step S210: Load multiple groups of accessory pairing data sets from multiple automotive assembly manufacturers. Among them, any one of the multiple groups of accessory pairing data sets includes part performance requirement record data, part dimensional requirement record data, and accessory group pairing results;

[0031] Step S220: Perform cluster analysis on the multiple groups of accessory pairing data sets according to the part performance requirement record data and the part dimensional requirement record data to generate a clustering result of the accessory pairing data sets;

[0032] Step S230: Update the accessory group screening database according to the clustering result of the accessory pairing data sets;

[0033] Step S240: Input the part performance requirement parameters and the part dimension requirement parameters into the updated accessory group screening database in sequence to generate the first part accessory group, the second part accessory group, up to the Nth part accessory group.

[0034] Specifically, based on multiple automobile assembly manufacturers, collect the part performance requirement record data, part dimension requirement record data, and accessory group pairing results to obtain multiple groups of accessory pairing data sets. Furthermore, perform clustering analysis on the multiple groups of accessory pairing data sets based on the part performance requirement record data and the part dimension requirement record data to obtain the clustering result of the accessory pairing data sets. Further, input the clustering result of the accessory pairing data sets into the accessory group screening database. The accessory group screening database includes a large number of historical clustering results of accessory pairing data sets. Add the clustering result of the accessory pairing data sets to the accessory group screening database, and update the accessory group screening database through the clustering result of the accessory pairing data sets to obtain the updated accessory group screening database. Then, sequentially use the obtained part performance requirement parameters and part dimension requirement parameters as input information and input them into the updated accessory group screening database. Through the updated accessory group screening database, match the part performance requirement parameters and part dimension requirement parameters with automobile accessories to obtain the first part accessory group, the second part accessory group... the Nth part accessory group.

[0035] Among them, the multi-party automobile assembly manufacturers include multiple automobile parts manufacturers and multiple automobile parts assembly manufacturers. Any one of the multiple groups of parts pairing data sets includes part performance requirement record data, part size requirement record data, and parts group pairing results. The part performance requirement record data includes the performance requirement information of multiple automobile parts of multi-party automobile assembly manufacturers. The part size requirement record data includes the size requirement information of multiple automobile parts of multi-party automobile assembly manufacturers. The parts group pairing results include multiple automobile parts corresponding to the part performance requirement record data and multiple automobile parts corresponding to the part size requirement record data. The clustering analysis refers to classifying the same type of automobile parts that simultaneously meet the part performance requirement record data and the part size requirement record data in multiple groups of parts pairing data sets, so that the automobile parts in the clustering results of the same parts pairing data set have the same part performance requirement record data and part size requirement record data. Exemplarily, in the multiple groups of parts pairing data sets obtained, the part performance requirement record data A corresponds to automobile parts a, b, and c, and the part size requirement record data B corresponds to automobile parts b, c, and d. And, automobile parts a, b, c, and d are of the same type of automobile parts. Then, in the clustering results of the parts pairing data sets obtained, the clustering results of a certain parts pairing data set include automobile parts b and c, and automobile parts b and c simultaneously meet the part performance requirement record data A and the part size requirement record data B. Each of the first parts group, the second parts group... the Nth parts group includes multiple same type of automobile parts that simultaneously meet the part performance requirement parameters and the part size requirement parameters. It achieves the technical effect of updating the parts group screening database through the clustering results of the parts pairing data sets, and efficiently matching automobile parts for the part performance requirement parameters and the part size requirement parameters through the updated parts group screening database, obtaining the first parts group, the second parts group... the Nth parts group, and improving the efficiency of selecting automobile metal parts.

[0036] Step S300: Traverse the first parts group, the second parts group until the Nth parts group for encoding, generating the first parts group encoding result, the second parts group encoding result until the Nth parts group encoding result;

[0037] Further, step S300 of the present application further includes:

[0038] Step S310: Extract the nth parts group according to the first parts group, the second parts group until the Nth parts group, where n ∈ N;

[0039] Step S320: Obtain the first part, the second part until the Mth part according to the nth parts group;

[0040] Step S330: Traverse the first accessory, the second accessory until the Mth accessory, and extract the model information and service duration information;

[0041] Specifically, extract from the first group of part accessories, the second group of part accessories... the Nth group of part accessories to obtain the nth group of part accessories, where n ∈ N. Furthermore, extract automotive accessories from the nth group of part accessories to obtain the first accessory, the second accessory... the Mth accessory, and collect the model parameters and service duration parameters of the first accessory, the second accessory... the Mth accessory to obtain the model information and service duration information. The model information includes the style information, vehicle model information, specification information, and model parameters corresponding to the first accessory, the second accessory... the Mth accessory. The service duration information includes the service life information corresponding to the first accessory, the second accessory... the Mth accessory. It achieves the technical effect of providing data support for encoding the first group of part accessories, the second group of part accessories... the Nth group of part accessories by extracting the first accessory, the second accessory... the Mth accessory through multiple extractions from the obtained first group of part accessories, the second group of part accessories... the Nth group of part accessories, and collecting the model information and service duration information of the first accessory, the second accessory... the Mth accessory.

[0042] Step S340: According to the model information and the service duration information, traverse the first accessory, the second accessory until the Mth accessory for encoding to generate the encoding result of the nth group of accessories;

[0043] Further, as shown in the appendix Figure 3 This application's step S340 further includes:

[0044] Step S341: According to the model information, match the accessory material information and accessory structure information;

[0045] Step S342: Traverse the first accessory, the second accessory until the Mth accessory, and encode according to the service duration information to generate the front - end encoding result;

[0046] Step S343: Traverse the first accessory, the second accessory until the Mth accessory, and encode according to the accessory material information to generate the middle - part encoding result;

[0047] Step S344: Traverse the first accessory, the second accessory until the Mth accessory, and encode according to the accessory structure information to generate the tail - end encoding result;

[0048] Step S345: Combine the front - end encoding result, the middle - part encoding result and the tail - end encoding result to generate the encoding result of the nth group of accessories.

[0049] Step S350: Add the encoding result of the nth accessory group into the encoding results of the first accessory group, the second accessory group, up to the Nth accessory group.

[0050] Specifically, based on the model information, collect the material parameters and structural parameters of the first accessory, the second accessory,..., the Mth accessory to obtain the accessory material information and the accessory structural information. Further, based on the service duration information, use data encoding technology to encode the first accessory, the second accessory,..., the Mth accessory to obtain the front encoding result. Based on the accessory material information, use data encoding technology to encode the first accessory, the second accessory,..., the Mth accessory to obtain the middle encoding result. Based on the accessory structural information, use data encoding technology to encode the first accessory, the second accessory,..., the Mth accessory to obtain the tail encoding result. Then, merge the obtained front encoding result, middle encoding result, and tail encoding result to obtain the encoding result of the nth accessory group, and add the encoding result of the nth accessory group to the encoding results of the first accessory group, the second accessory group,..., the Nth accessory group.

[0051] Among them, the accessory material information includes material parameter information such as the material type, material composition, and material component content of the first accessory, the second accessory,..., the Mth accessory corresponding to the model information. The accessory structural information includes the structural composition and dimensional parameter information of the first accessory, the second accessory,..., the Mth accessory corresponding to the model information. The data encoding technology is a technology in the prior art that encodes data to establish internal connections between data, facilitating the identification, storage, and management of data. The front encoding result includes the encoding information of the first accessory, the second accessory,..., the Mth accessory corresponding to the service duration information. The middle encoding result includes the encoding information of the first accessory, the second accessory,..., the Mth accessory corresponding to the accessory material information. The tail encoding result includes the encoding information of the first accessory, the second accessory,..., the Mth accessory corresponding to the accessory structural information. The encoding result of the nth accessory group includes the front encoding result, the middle encoding result, and the tail encoding result. The encoding result of the nth accessory group corresponds to the encoding results of the first accessory group, the second accessory group,..., the Nth accessory group. It achieves the technical effect of encoding the first accessory, the second accessory,..., the Mth accessory multiple times through the service duration information, the accessory material information, and the accessory structural information to obtain reliable encoding results of the first accessory group, the second accessory group,..., the Nth accessory group, reducing the data storage pressure; at the same time, facilitating the identification and query of the service duration information, the accessory material information, and the accessory structural information for the encoding results of the first accessory group, the second accessory group,..., the Nth accessory group, thereby improving the efficiency of subsequent fitness calculation and optimization screening for the encoding results of the first accessory group, the second accessory group,..., the Nth accessory group.

[0052] Step S400: Construct a fitness function for fitting part screening;

[0053] Further, the construction of the fitness function for fitting part screening is as follows:

[0054]

[0055] where represents the fitness of the m-th fitting in the coding result of the n-th fitting group, represents the service duration, represents the fitting material information, represents the fitting structure information, represents the force level on the fitting, represents the corrosion resistance level of the fitting.

[0056] Step S500: Optimize and screen the coding result of the n-th fitting group according to the fitness function for fitting part screening, and generate the screening result of the n-th fitting, where n ∈ N;

[0057] Further, step S500 of this application further includes:

[0058] Step S510: Obtain the coding result of the m-th fitting according to the coding result of the n-th fitting group;

[0059] Step S520: Input the coding result of the m-th fitting into the fitness function for fitting part screening to generate the screening fitness of the m-th fitting;

[0060] Specifically, randomly select from the obtained coding results of the first fitting group, the second fitting group... the N-th fitting group to obtain the coding result of the m-th fitting, and use the coding result of the m-th fitting as input information to input into the fitness function for fitting part screening to obtain the screening fitness of the m-th fitting. Among them, the coding result of the m-th fitting can be the coding result of the m-th fitting corresponding to any one of the coding results of the first fitting group, the second fitting group... the N-th fitting group. In the fitness function for fitting part screening, represents the fitness of the m-th fitting in the coding result of the n-th fitting group, that is, is the screening fitness of the m-th fitting corresponding to the input coding result of the m-th fitting. is the service duration information of the m-th fitting corresponding to the coding result of the m-th fitting. is the fitting material information of the m-th fitting corresponding to the coding result of the m-th fitting. is the fitting structure information of the m-th fitting corresponding to the coding result of the m-th fitting. , can be obtained by querying the coding result of the m-th fitting. Take , Taking the input information and inputting it into the accessory evaluation model, the force level of the m-th accessory corresponding to the m-th accessory coding result can be obtained. and the corrosion resistance level of the accessory The accessory evaluation model is trained by a large amount of data information related to service life information, accessory material information, and accessory structure information, and has the function of intelligently evaluating the force level of accessories and the corrosion resistance level of accessories for the input service life information, accessory material information, and accessory structure information.

[0061] Step S530: Determine whether the screening fitness of the m-th accessory is greater than or equal to the screening fitness of the (m - 1)-th accessory;

[0062] Step S540: If the screening fitness of the m-th accessory is greater than or equal to the screening fitness of the (m - 1)-th accessory, add the (m - 1)-th accessory to the elimination data group; determine whether m meets the preset number of iterations. If it meets, set the m-th accessory as the screening result of the n-th accessory;

[0063] Step S550: If the screening fitness of the m-th accessory is less than the screening fitness of the (m - 1)-th accessory, add the m-th accessory to the elimination data group; determine whether m meets the preset number of iterations. If it meets, set the (m - 1)-th accessory as the screening result of the n-th accessory.

[0064] Specifically, randomly select the coding results of the first accessory group, the second accessory group... the N-th accessory group that have been obtained again to obtain the coding result of the (m - 1)-th accessory. Take the coding result of the (m - 1)-th accessory as the input information and input it into the accessory screening fitness function to obtain the screening fitness of the (m - 1)-th accessory. The method for obtaining the screening fitness of the (m - 1)-th accessory is the same as that of the m-th accessory. For the sake of simplicity of the specification, it will not be elaborated here. Further, determine whether the screening fitness of the m-th accessory is greater than or equal to the screening fitness of the (m - 1)-th accessory. If the screening fitness of the m-th accessory is greater than or equal to the screening fitness of the (m - 1)-th accessory, add the (m - 1)-th accessory to the elimination data group. Furthermore, determine whether m meets the preset number of iterations. If m meets the preset number of iterations, set the m-th accessory as the screening result of the n-th accessory. If m does not meet the preset number of iterations, perform iterative optimization based on the m-th accessory until m meets the preset number of iterations.

[0065] In addition, when determining whether the screening fitness of the m-th component is greater than or equal to the screening fitness of the (m - 1)-th component, if the screening fitness of the m-th component is less than the screening fitness of the (m - 1)-th component, the m-th component is added to the elimination data group. Furthermore, it is determined whether m meets the preset number of iterations. If m meets the preset number of iterations, the (m - 1)-th component is set as the screening result of the n-th component. If m does not meet the preset number of iterations, iterative optimization is performed based on the (m - 1)-th component until m meets the preset number of iterations.

[0066] Among them, the coding result of the (m - 1)-th component is the coding result of the (m - 1)-th component corresponding to any of the first component group coding result, the second component group coding result... the N-th component group coding result. And, the (m - 1)-th component is different from the m-th component. The elimination data group includes the (m - 1)-th component corresponding to the screening fitness of the (m - 1)-th component when the screening fitness of the m-th component is greater than or equal to the screening fitness of the (m - 1)-th component; the m-th component corresponding to the screening fitness of the m-th component when the screening fitness of the m-th component is less than the screening fitness of the (m - 1)-th component. The preset number of iterations includes the threshold value of the iterative optimization number set in advance. It achieves the technical effect of obtaining a screening result of the n-th component with higher fitness through iterative optimization of the coding result of the n-th component group for a preset number of iterations, and improving the accuracy of selecting automotive metal components.

[0067] Step S600: Determine whether the screening result of the n-th component meets the preset fitness;

[0068] Step S700: If it meets, add the screening result of the n-th component to the selection result of automotive metal components.

[0069] Specifically, the screening fitness corresponding to the screening result of the n-th component is compared with the preset fitness to determine whether the screening fitness corresponding to the screening result of the n-th component meets the preset fitness. If the screening fitness corresponding to the screening result of the n-th component meets the preset fitness, the screening result of the n-th component is added to the selection result of automotive metal components. If the screening fitness corresponding to the screening result of the n-th component does not meet the preset fitness, the coding result of the n-th component group is returned to continue the optimization screening until the screening fitness corresponding to the screening result of the n-th component meets the preset fitness. Among them, the preset fitness includes the fitness threshold value and fitness requirement parameters set in advance. The selection result of automotive metal components includes the screening result of the n-th component that meets the preset fitness. It achieves the technical effect of further optimizing and screening the screening result of the n-th component through the preset fitness, and improving the fitness and accuracy of the obtained selection result of automotive metal components.

[0070] In summary, the fitness detection method for selecting automotive metal components provided by the present application has the following technical effects:

[0071] 1. Match the component performance requirement parameters and component dimension requirement parameters through the information of the vehicle model to be assembled, and obtain the requirement parameters of the metal components to be selected; input the requirement parameters of the metal components to be selected into the accessory group screening database to generate the first component accessory group, the second component accessory group... the Nth component accessory group; traverse the first component accessory group, the second component accessory group... the Nth component accessory group for coding to generate the first accessory group coding result, the second accessory group coding result... the Nth accessory group coding result; construct an accessory screening fitness function; optimize and screen the nth accessory group coding result through the accessory screening fitness function to generate the nth accessory screening result, where n ∈ N; determine whether the nth accessory screening result meets the preset fitness; if it meets, add the nth accessory screening result to the vehicle metal accessory selection result. It achieves the technical effects of improving the accuracy and fitness of vehicle metal accessory selection, as well as the efficiency and quality of vehicle metal accessory selection by performing intelligent and efficient multiple optimization screenings on vehicle metal accessories; at the same time, it improves the automation and scientific nature of vehicle metal accessory selection and minimizes the waste of resources such as manpower and material resources caused by vehicle metal accessory selection.

[0072] 2. Conduct a matching analysis on the component performance requirement parameters and component dimension requirement parameters through the information of the vehicle model to be assembled to obtain accurate and reliable requirement parameters of the metal components to be selected, thereby improving the accuracy of vehicle metal accessory selection.

[0073] 3. Update the accessory group screening database based on the clustering results of accessory pairing data, and perform efficient matching of vehicle accessories on the component performance requirement parameters and component dimension requirement parameters through the updated accessory group screening database to obtain the first component accessory group, the second component accessory group... the Nth component accessory group, improving the efficiency of vehicle metal accessory selection.

[0074] 4. Conduct multiple encodings on the first accessory, the second accessory... the Mth accessory through the service duration information, accessory material information, and accessory structure information to obtain reliable first accessory group coding results, second accessory group coding results... Nth accessory group coding results, reducing the data storage pressure; at the same time, it is convenient to identify and query the service duration information, accessory material information, and accessory structure information for the first accessory group coding results, second accessory group coding results... Nth accessory group coding results, thereby improving the efficiency of fitness calculation and optimization screening for the first accessory group coding results, second accessory group coding results... Nth accessory group coding results.

[0075] Example Two

[0076] Based on a fitness detection method for the selection of automotive metal fittings in the foregoing embodiments, with the same inventive concept, the present invention also provides a fitness detection system for the selection of automotive metal fittings. Please refer to the attached Figure 4 , the system includes:

[0077] A demand parameter matching module 11, which is used to match the demand parameters of the metal parts to be selected according to the information of the vehicle model to be assembled. Among them, the demand parameters of the metal parts to be selected include part performance demand parameters and part size demand parameters;

[0078] A part fitting group generation module 12, which is used to input the part performance demand parameters and the part size demand parameters into the fitting group screening database to generate the first part fitting group, the second part fitting group until the Nth part fitting group;

[0079] A coding result generation module 13, which is used to traverse the first part fitting group, the second part fitting group until the Nth part fitting group for coding, and generate the first fitting group coding result, the second fitting group coding result until the Nth fitting group coding result;

[0080] A function construction module 14, which is used to construct a fitting screening fitness function;

[0081] A fitting screening result generation module 15, which is used to optimize and screen the coding result of the nth fitting group according to the fitting screening fitness function to generate the nth fitting screening result, where n ∈ N;

[0082] A judgment module 16, which is used to judge whether the nth fitting screening result meets the preset fitness;

[0083] An addition module 17, which is used to add the nth fitting screening result to the automotive metal fitting selection result if it meets the requirements.

[0084] Further, the system further includes:

[0085] A to-be-assembled vehicle design original drawing acquisition module, which is used to acquire the to-be-assembled vehicle design original drawing according to the information of the to-be-assembled vehicle model;

[0086] A part design original drawing generation module, which is used to disassemble the to-be-assembled vehicle design original drawing to generate the nth part design original drawing;

[0087] Part dimension requirement parameter acquisition module, which is used to extract the nth part dimension requirement parameters according to the nth part design original drawing;

[0088] Feedback information acquisition module, which is used to send the nth part dimension requirement parameters to the user interaction interface and obtain the nth feedback information, where the nth feedback information includes the nth part performance requirement parameters;

[0089] First execution module, which is used to add the nth part dimension requirement parameters into the part dimension requirement parameters;

[0090] Second execution module, which is used to add the nth part performance requirement parameters into the part performance requirement parameters.

[0091] Furthermore, the system further includes:

[0092] Dataset loading module, which is used to load multiple groups of accessory pairing datasets from multiple automobile assembly manufacturers, where any one of the multiple groups of accessory pairing datasets includes part performance requirement record data, part dimension requirement record data, and accessory group pairing results;

[0093] Clustering analysis module, which is used to perform clustering analysis on multiple groups of accessory pairing datasets according to the part performance requirement record data and the part dimension requirement record data, and generate an accessory pairing dataset clustering result;

[0094] Database update module, which is used to update the accessory group screening database according to the accessory pairing dataset clustering result;

[0095] Part accessory group determination module, which is used to sequentially input the part performance requirement parameters and the part dimension requirement parameters into the updated accessory group screening database to generate the first part accessory group, the second part accessory group until the Nth part accessory group.

[0096] Furthermore, the system further includes:

[0097] Part accessory group extraction module, which is used to extract the nth part accessory group according to the first part accessory group, the second part accessory group until the Nth part accessory group, n ∈ N;

[0098] Accessory acquisition module, which is used to obtain the first accessory, the second accessory until the Mth accessory according to the nth part accessory group;

[0099] An accessory information acquisition module, which is used to traverse the first accessory, the second accessory until the Mth accessory, and extract model information and service duration information;

[0100] An accessory group coding result determination module, which is used to code by traversing the first accessory, the second accessory until the Mth accessory according to the model information and the service duration information, and generate the nth accessory group coding result;

[0101] A third execution module, which is used to add the nth accessory group coding result into the first accessory group coding result, the second accessory group coding result until the Nth accessory group coding result.

[0102] Furthermore, the system further includes:

[0103] An accessory information matching module, which is used to match accessory material information and accessory structure information according to the model information;

[0104] A front-end coding result generation module, which is used to traverse the first accessory, the second accessory until the Mth accessory, and code according to the service duration information to generate a front-end coding result;

[0105] A middle coding result generation module, which is used to traverse the first accessory, the second accessory until the Mth accessory, and code according to the accessory material information to generate a middle coding result;

[0106] A tail coding result generation module, which is used to traverse the first accessory, the second accessory until the Mth accessory, and code according to the accessory structure information to generate a tail coding result;

[0107] A coding result merging module, which is used to merge the front-end coding result, the middle coding result and the tail coding result to generate the nth accessory group coding result.

[0108] Furthermore, the system further includes:

[0109] A function characterization module, which is used to construct an accessory screening fitness function as:

[0110]

[0111] Wherein, Characterizes the fitness of the mth accessory in the nth accessory group coding result, Represents the service duration, Characterize the information of the accessory material, Characterize the information of the accessory structure, Characterize the force level on the accessory, Characterize the corrosion resistance level of the accessory.

[0112] Further, the system further includes:

[0113] A fourth execution module, which is used to obtain the m-th accessory coding result according to the n-th accessory group coding result;

[0114] An accessory screening fitness generation module, which is used to input the m-th accessory coding result into the accessory screening fitness function to generate the m-th accessory screening fitness;

[0115] A fitness judgment module, which is used to judge whether the m-th accessory screening fitness is greater than or equal to the m-1-th accessory screening fitness;

[0116] A fifth execution module, which is used to add the m-1-th accessory to the elimination data group if the m-th accessory screening fitness is greater than or equal to the m-1-th accessory screening fitness; judge whether m meets the preset number of iterations, and if so, set the m-th accessory as the n-th accessory screening result;

[0117] A sixth execution module, which is used to add the m-th accessory to the elimination data group if the m-th accessory screening fitness is less than the m-1-th accessory screening fitness; judge whether m meets the preset number of iterations, and if so, set the m-1-th accessory as the n-th accessory screening result.

[0118] The present application provides a fitness detection method for the selection of automotive metal fittings. Among them, the method is applied to a fitness detection system for the selection of automotive metal fittings, and the method includes: matching the performance requirement parameters and part size requirement parameters of parts through the information of the vehicle model to be assembled, and obtaining the requirement parameters of the metal parts to be selected; inputting the requirement parameters of the metal parts to be selected into the accessory group screening database to generate the first part accessory group, the second part accessory group... the Nth part accessory group; traversing the first part accessory group, the second part accessory group... the Nth part accessory group for coding to generate the first accessory group coding result, the second accessory group coding result... the Nth accessory group coding result; constructing an accessory screening fitness function; optimizing and screening the nth accessory group coding result through the accessory screening fitness function to generate the nth accessory screening result, where n ∈ N; judging whether the nth accessory screening result meets the preset fitness; if it meets, adding the nth accessory screening result to the selection result of automotive metal fittings. This solves the technical problems in the prior art that the accuracy of the selection of automotive metal fittings is low and the fitness is not high, resulting in low efficiency and poor quality in the selection of automotive metal fittings. It achieves the technical effects of improving the accuracy and fitness of the selection of automotive metal fittings, as well as the efficiency and quality of the selection of automotive metal fittings through intelligent and efficient multiple optimization and screening of automotive metal fittings; at the same time, improving the automation degree and scientificity of the selection of automotive metal fittings, and minimizing the waste of resources such as manpower and material resources caused by the selection of automotive metal fittings.

[0119] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, 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, it should be considered as the scope described in this specification.

[0120] This specification and the drawings are only exemplary descriptions of the present application. If the modifications and variations of the present invention fall within the scope of the present invention and its equivalent technologies, the present invention also intends to include these modifications and variations.

Claims

1. A fitness detection method for selecting automotive metal fittings, characterized in that, it is implemented based on a fitness detection system for selecting automotive metal fittings, including: According to the information of the vehicle model to be assembled, match the required parameters of the metal parts to be selected. Among them, the required parameters of the metal parts to be selected include the required parameters of part performance and the required parameters of part dimensions; Input the required parameters of part performance and the required parameters of part dimensions into the fitting group screening database to generate the first part fitting group, the second part fitting group until the Nth part fitting group; Traverse the first part fitting group, the second part fitting group until the Nth part fitting group for coding to generate the first fitting group coding result, the second fitting group coding result until the Nth fitting group coding result, including: According to the first part fitting group, the second part fitting group until the Nth part fitting group, extract the nth part fitting group, n ∈ N; According to the nth part fitting group, obtain the first fitting, the second fitting until the Mth fitting; Traverse the first fitting, the second fitting until the Mth fitting, and extract the model information and service life information; According to the model information and the service life information, traverse the first fitting, the second fitting until the Mth fitting for coding to generate the coding result of the nth fitting group; Add the coding result of the nth fitting group into the first fitting group coding result, the second fitting group coding result until the Nth fitting group coding result; Construct a fitting screening fitness function, and the constructed fitting screening fitness function is: Among them, characterizes the fitness of the m-th part in the coding result of the n-th part group, represents the length of service, characterizes the part material information, characterizes the part structure information, characterizes the force level on the part, characterizes the corrosion resistance level of the part; According to the fitting screening fitness function, optimize and screen the coding result of the nth fitting group to generate the screening result of the nth fitting; judge whether the screening result of the nth fitting meets the preset fitness; If it meets the requirement, add the screening result of the nth fitting into the selection result of automotive metal fittings.

2. The method according to claim 1, characterized in that, The step of matching the required parameters of the metal parts to be selected according to the information of the vehicle model to be assembled, where the required parameters of the metal parts to be selected include the required parameters of part performance and the required parameters of part dimensions, includes: According to the information of the vehicle model to be assembled, obtain the original design drawing of the vehicle to be assembled; Disassemble the original design drawing of the vehicle to be assembled to generate the original design drawing of the nth part; According to the original design drawing of the nth part, extract the required parameters of the nth part dimensions; Send the required parameters of the nth part dimensions to the user interaction interface to obtain the nth feedback information, where the nth feedback information includes the required parameters of the nth part performance; Add the required parameters of the nth part dimensions into the required parameters of part dimensions; Add the required parameters of the nth part performance into the required parameters of part performance.

3. The method according to claim 1, characterized in that, Inputting the required parameters of part performance and the required parameters of part dimensions into the fitting group screening database to generate the first part fitting group, the second part fitting group until the Nth part fitting group, includes: Load multiple groups of accessory pairing datasets from multiple automotive assembly manufacturers. Any one of the multiple groups of accessory pairing datasets includes part performance requirement record data, part size requirement record data, and accessory group pairing results; Perform clustering analysis on the multiple groups of accessory pairing datasets according to the part performance requirement record data and the part size requirement record data to generate an accessory pairing dataset clustering result; Update the accessory group screening database according to the accessory pairing dataset clustering result; Input the part performance requirement parameters and the part size requirement parameters into the updated accessory group screening database in sequence to generate the first part accessory group, the second part accessory group, and up to the Nth part accessory group.

4. The method according to claim 1, wherein, The process of traversing the first accessory, the second accessory, and up to the Mth accessory according to the model information and the service duration information for encoding to generate the nth accessory group encoding result includes: Match accessory material information and accessory structure information according to the model information; Traverse the first accessory, the second accessory, and up to the Mth accessory, and perform encoding according to the service duration information to generate a front-end encoding result; Traverse the first accessory, the second accessory, and up to the Mth accessory, and perform encoding according to the accessory material information to generate a middle encoding result; Traverse the first accessory, the second accessory, and up to the Mth accessory, and perform encoding according to the accessory structure information to generate a tail encoding result; Merge the front-end encoding result, the middle encoding result, and the tail encoding result to generate the nth accessory group encoding result.

5. The method according to claim 1, wherein, The process of optimizing and screening the nth accessory group encoding result according to the accessory screening fitness function to generate the nth accessory screening result includes: Obtain the mth accessory encoding result according to the nth accessory group encoding result; Input the mth accessory encoding result into the accessory screening fitness function to generate the mth accessory screening fitness; Judge whether the mth accessory screening fitness is greater than or equal to the m-1th accessory screening fitness; If the mth accessory screening fitness is greater than or equal to the m-1th accessory screening fitness, add the m-1th accessory to the elimination data group; judge whether m meets the preset iteration times, if so, set the mth accessory as the nth accessory screening result; If the mth accessory screening fitness is less than the m-1th accessory screening fitness, add the mth accessory to the elimination data group; judge whether m meets the preset iteration times, if so, set the m-1th accessory as the nth accessory screening result.

6. A fitness detection system for selecting automotive metal accessories, wherein, The system includes: A requirement parameter matching module, which is used to match the demand parameters of the metal parts to be selected according to the model information of the vehicle to be assembled. The demand parameters of the metal parts to be selected include part performance requirement parameters and part size requirement parameters; A spare part group generation module, which is used to input the part performance requirement parameters and the part dimension requirement parameters into a spare part group screening database to generate a first spare part group, a second spare part group until an Nth spare part group; An encoding result generation module, which is used to traverse the first spare part group, the second spare part group until the Nth spare part group for encoding to generate a first spare part group encoding result, a second spare part group encoding result until an Nth spare part group encoding result; A function construction module, which is used to construct a spare part screening fitness function; A spare part screening result generation module, which is used to optimize and screen the nth spare part group encoding result according to the spare part screening fitness function to generate an nth spare part screening result; A judgment module, which is used to judge whether the nth spare part screening result meets a preset fitness; An addition module, which is used to add the nth spare part screening result to the automotive metal spare part selection result if it meets the requirement; The system further includes: A spare part group extraction module, which is used to extract an nth spare part group according to the first spare part group, the second spare part group until the Nth spare part group, where n ∈ N; A spare part acquisition module, which is used to acquire a first spare part, a second spare part until an Mth spare part according to the nth spare part group; A spare part information acquisition module, which is used to traverse the first spare part, the second spare part until the Mth spare part to extract model information and service duration information; A spare part group encoding result determination module, which is used to traverse the first spare part, the second spare part until the Mth spare part for encoding according to the model information and the service duration information to generate the nth spare part group encoding result; A third execution module, which is used to add the nth spare part group encoding result to the first spare part group encoding result, the second spare part group encoding result until the Nth spare part group encoding result; A function representation module, which is used to represent the constructed spare part screening fitness function as: ; among which, represents the fitness of the m-th component in the coding result of the n-th component group, represents the service duration, represents the component material information, represents the component structure information, represents the force level on the component, represents the corrosion resistance level of the component.

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