Automobile product physical cost optimization method, device, equipment and medium

Abstract

The invention discloses an automobile product physical cost optimization method, device and equipment and a medium, and relates to the field of automobile product physical cost optimization, and the method comprises the steps: obtaining the structure information of an automobile product physical and the attributes of a plurality of target parts; according to the attribute of each target part, screening out a plurality of reference parts of each target part from a reference part library; calculating a plurality of cost adjustment coefficients of each target part according to the attribute difference between each target part and a plurality of corresponding reference parts so as to obtain a plurality of candidate costs of each target part; and determining the lowest cost of each target part from the plurality of candidate costs of each target part, and calculating the total cost of the automobile product entity according to the lowest cost and the structure information of all the target parts. According to the method, the subjective measurement and calculation process is converted into a standardized process based on a unified data source, and the problem that the difference of measurement and calculation results is large due to different cognition of personnel is solved.

Description

Technical Field

[0001] This application relates to the field of physical cost optimization for automotive products, specifically to a method, apparatus, equipment, and medium for physical cost optimization of automotive products. Background Technology

[0002] Currently, the physical cost of automotive products is analyzed and optimized at the component assembly level. However, since automotive products are assembled from various sub-parts through manufacturing methods such as assembly, welding, and riveting, the sub-parts, as the smallest manufacturing units, determine the material costs. The physical cost of the automotive product is a major component of the component cost, directly determining the overall physical cost of the component assembly. Because different calculation and analysis personnel have varying understandings of the manufacturing methods of the sub-parts, the cost values ​​calculated by different personnel for the same part differ significantly, and the optimized physical cost tends to be too high. Furthermore, existing cost optimization schemes typically only analyze the cost of new products theoretically, lacking comparisons with previous products, leading to overly idealized analysis results and significant deviations from actual costs. Summary of the Invention

[0003] This application provides a method, apparatus, equipment, and medium for optimizing the physical cost of automotive products, which can solve the technical problems of large deviations in the optimization of the physical cost of automotive products or the high cost after optimization in the prior art.

[0004] In a first aspect, embodiments of this application provide a method for optimizing the physical cost of an automotive product, wherein the automotive product is composed of multiple target parts, and the method for optimizing the physical cost of the automotive product includes: Obtain structural information of the actual automotive product and the attributes of multiple target parts; Based on the properties of each target part, multiple reference parts for each target part are selected from the reference parts library. Based on the differences in attributes between each target part and its corresponding multiple reference parts, multiple cost adjustment coefficients are calculated for each target part to obtain multiple candidate costs for each target part. The lowest cost of each target part is determined from multiple candidate costs. Based on the lowest costs of all target parts and the structural information, the total cost of the actual automotive product is calculated.

[0005] In conjunction with the first aspect, in one implementation, the step of selecting multiple reference parts for each target part from a reference parts library based on the attributes of each target part includes: Based on the attributes of each target part, the similarity between the target part and each part in the reference part library is calculated. Parts with a similarity greater than or equal to a preset threshold in the reference parts library are selected as reference parts; The attributes include material, process, size, and weight.

[0006] In conjunction with the first aspect, in one implementation, the step of calculating the similarity between the target part and each part in the reference part library based on the attributes of each target part includes: Retrieve the properties of each part in the reference parts library; Based on the material identifier and process identifier of the target part and the material identifier and process identifier of each part in the reference parts library, a preset mapping table is queried to obtain the material and process difference value. The preset mapping table includes the mapping relationship between the combination pairs composed of material identifier and process identifier and the material and process difference value. The size difference value is calculated based on the size of the target part and the size of each part in the reference parts library; The weight difference value is calculated based on the weight of the target part and the weight of each part in the reference parts library; Based on the material and process differences, size differences, and weight differences, a weighted fusion is performed to obtain the similarity between the target part and each part in the reference part library.

[0007] In conjunction with the first aspect, in one implementation, the step of calculating multiple cost adjustment coefficients for each target part based on the differences in attributes between each target part and its corresponding multiple reference parts, to obtain multiple candidate costs for each target part, wherein obtaining the multiple candidate costs for the current target part specifically includes: Obtain the actual cost of each reference part corresponding to the current target part, wherein the actual cost includes the actual material cost and the actual manufacturing cost; For the current reference part of the current target part, the material unit price coefficient is determined based on the ratio of the difference in material unit price between the current target part and the current reference part, the physical material cost coefficient is determined based on the ratio of the difference in material usage between the current target part and the current reference part, and the manufacturing cost coefficient is determined based on the ratio of the difference in manufacturing parameters between the current target part and the current reference part. For the current reference part of the current target part, the actual material cost of the current reference part is multiplied by the material unit price coefficient and the physical material cost coefficient corresponding to the current reference part to obtain the material cost of the current target part. The actual manufacturing cost of the current reference part is multiplied by the manufacturing cost coefficient corresponding to the current reference part to obtain the manufacturing cost of the current target part. The material cost and manufacturing cost of the current target part, calculated based on the current reference part, are added together to obtain a candidate cost of the current target part, thereby obtaining multiple candidate costs of the current target part.

[0008] In conjunction with the first aspect, in one implementation, the step of filtering parts in the reference parts library with a similarity greater than or equal to a preset threshold as reference parts further includes: When there are no parts in the reference parts library with a similarity greater than or equal to the preset threshold, the part with the highest similarity is selected as the reference part.

[0009] In conjunction with the first aspect, in one implementation, it further includes: When a part with a similarity greater than or equal to a preset recommendation threshold exists in the reference parts library, that part is selected as a recommended reuse part.

[0010] In conjunction with the first aspect, in one implementation, determining the lowest cost of each target part from multiple candidate costs, and calculating the total cost of the actual automotive product based on the lowest costs of all target parts and the structural information, includes: The lowest cost of each target part is determined from multiple candidate costs, and the lowest costs of all target parts are summed to obtain the actual manufacturing cost of the automobile product. Based on the structural information, calculate the logistics cost and the connection process cost between multiple target parts; The total cost of the vehicle product is obtained by summing the manufacturing cost of the vehicle itself, the logistics cost, and the connection process cost between multiple target parts. The structural information includes the volume of the actual automotive product and the connection methods and assembly relationships between multiple target parts.

[0011] Secondly, embodiments of this application provide a device for optimizing the physical cost of automotive products, the device comprising: The acquisition module is used to acquire the structural information of the actual automotive product and the attributes of multiple target parts; The filtering module is used to filter multiple reference parts for each target part from the reference parts library based on the attributes of each target part. The first calculation module is used to calculate multiple cost adjustment coefficients for each target part based on the differences in attributes between each target part and its corresponding multiple reference parts, so as to obtain multiple candidate costs for each target part. The second calculation module is used to determine the lowest cost of each target part from multiple candidate costs, and calculate the total cost of the actual automobile product based on the lowest cost of all target parts and the structural information.

[0012] Thirdly, this application provides an automotive product physical cost optimization device, which includes a processor, a memory, and an automotive product physical cost optimization program stored in the memory and executable by the processor. When the automotive product physical cost optimization program is executed by the processor, it implements the steps of the automotive product physical cost optimization method as described in any of the above embodiments.

[0013] Fourthly, embodiments of this application provide a computer-readable storage medium storing a program for optimizing the physical cost of an automotive product, wherein when the program is executed by a processor, it implements the steps of the method for optimizing the physical cost of an automotive product as described in any of the above embodiments.

[0014] The beneficial effects of the technical solutions provided in this application include: This application's embodiments automatically select reference parts from a reference parts library based on the target part's attributes and calculate costs based on attribute differences. This transforms the subjective calculation process, which relies on personal experience and understanding, into a standardized process based on a unified data source, solving the problem of large discrepancies in calculation results caused by varying personnel perceptions. This application's embodiments generate multiple candidate costs for each target part and automatically determine its lowest cost. This solution provides each part with multiple optional cost paths based on historical data and automatically identifies lower-cost options, helping to suppress the problem of still high costs after optimization. This application's embodiments extrapolate new costs based on actual data in the reference parts library through quantified attribute differences, making cost analysis based on comparison with practical cases. This avoids idealization biases caused by purely theoretical analysis and makes the results closer to reality. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the method for optimizing the physical cost of automotive products in this application. Figure 2 This is a schematic diagram of the functional modules of the vehicle product physical cost optimization device of this application; Figure 3 This is a schematic diagram of the hardware structure of the vehicle product physical cost optimization device involved in the embodiments of this application. Detailed Implementation

[0016] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0018] This application provides a method, apparatus, equipment, and medium for optimizing the physical cost of automotive products, which can solve the technical problems of large deviations in the optimization of the physical cost of automotive products or the high cost after optimization in the prior art.

[0019] In a first aspect, embodiments of this application provide a method for optimizing the physical cost of automotive products.

[0020] In one embodiment, reference is made to Figure 1 , Figure 1 This is a flowchart illustrating the method for optimizing the physical cost of automotive products in this application. Figure 1 As shown, the method for optimizing the physical cost of automotive products specifically includes the following steps: Step S1: Obtain the structural information of the actual automotive product and the attributes of multiple target parts.

[0021] Specifically, the structural information of the actual automotive product includes its volume and the connection methods and assembly relationships between multiple target parts. The attributes of each target part include its material, manufacturing process, dimensions, and weight. The volume of the actual automotive product specifically refers to the volume of its bounding box. The connection methods and assembly relationships between multiple target parts specifically refer to the connection types such as welding, riveting, and bolting. The material of the target part specifically refers to the type and grade of the main material used to manufacture the part; the material used directly affects material costs. The manufacturing process of the target part specifically refers to the main manufacturing methods used in the final part, such as stamping, injection molding, and die casting. The dimensions of the target part specifically refer to its maximum external dimensions, thickness, and hole diameter, etc., used to calculate material usage and compare dimensional differences with reference parts. The weight of the target part specifically refers to its design net weight or estimated weight, used to calculate material costs and assess part lightweighting.

[0022] Step S2: Based on the attributes of each target part, select multiple reference parts for each target part from the reference part library.

[0023] In this embodiment of the application, step S2 specifically includes the following steps: Step S21: Calculate the similarity between the target part and each part in the reference part library based on the attributes of each target part.

[0024] In this embodiment of the application, step S21 specifically includes the following steps: Step S211: Obtain the properties of each part in the reference parts library.

[0025] Specifically, the reference parts library is a pre-generated historical parts database. It includes a collection of parts data that has been verified through actual production, including the attributes of each part, as well as data such as the actual material cost and actual manufacturing cost of each part.

[0026] Step S212: Based on the material identifier and process identifier of the target part and the material identifier and process identifier of each part in the reference part library, query the preset mapping table to obtain the material and process difference value. The preset mapping table includes the mapping relationship between the combination pairs composed of material identifier and process identifier and the material and process difference value.

[0027] Specifically, based on the materials and processes of the target part or each part in the reference part library, and according to the preset material standard coding rules, the specific materials and processes used are mapped to material identifiers and process identifiers.

[0028] The material and process identifiers of the target part are combined into one query condition, and the material and process identifiers of each part in the reference parts library are combined into another query condition, forming a combined pair. This combined pair is then input into a preset mapping table to query the material-process difference value. The preset mapping table stores the mapping relationship between the combined pairs of material and process identifiers and the material-process difference value. For example, inputting "Material A + Process X" and "Material A + Process Y" will retrieve a difference value of 0.3 between the two. The preset mapping table is based on historical cost data and engineering experience and is used to standardize the comparability between different combinations.

[0029] Step S213: Calculate the size difference value based on the size of the target part and the size of each part in the reference parts library.

[0030] Specifically, one or more dimensions of the target part, such as the length or thickness of the target part, are selected and compared with the corresponding dimensions of the candidate parts. In this embodiment, a normalized deviation formula can be used for calculation, specifically:

[0031] This transforms absolute differences in dimensions into relative proportions, allowing for comparisons between different dimensions.

[0032] Step S214: Calculate the weight difference value based on the weight of the target part and the weights of each part in the reference parts library.

[0033] Specifically, the calculation of the weight difference value is as follows:

[0034] This transforms the absolute difference in weight into a relative proportion, allowing for comparisons between different weights.

[0035] Step S215: Based on the material and process difference values, size difference values, and weight difference values, perform weighted fusion to obtain the similarity between the target part and each part in the reference part library.

[0036] Specifically, the similarity is calculated as follows:

[0037] in, , , All are weighting coefficients, and the sum of the weighting coefficients is 1. The closer the similarity result is to 1, the higher the similarity. The weighting coefficients can be allocated according to the design requirements of the specific target part. In a specific embodiment, It can be 0.5. It can be 0.3. It can be 0.2.

[0038] Step S22: Select parts from the reference parts library whose similarity is greater than or equal to a preset threshold as reference parts.

[0039] Specifically, after completing step S21 of traversing and calculating all parts in the reference parts library, a filtering process is performed based on a preset threshold. All parts with a similarity score greater than or equal to the preset threshold are filtered as reference parts for the current target part. In a specific embodiment, the preset threshold can be 0.75.

[0040] This application's embodiments, through the aforementioned screening method, transform the subjective judgment of similarity into an objective calculation process based on unified rules and data. This ensures that the set of reference parts selected for the same target part is consistent when different analysts or at different times perform analyses, fundamentally eliminating differences in matching benchmarks caused by individual experience or different perceptions. Furthermore, by separately calculating and weighting the differences in material processing, dimensions, and weight, key factors affecting the technical and cost characteristics of the part can be considered in a balanced manner, avoiding the one-sidedness of a single-dimensional evaluation and making the selected reference parts more accurate.

[0041] In this embodiment of the application, when there are no parts in the reference parts library with a similarity greater than or equal to a preset threshold, the part with the highest similarity is selected as the reference part.

[0042] Specifically, after completing step S21, which iterates through and calculates the similarity of all parts in the reference parts library, if no part in the reference parts library has a similarity greater than or equal to a preset threshold, then the part with the highest similarity is selected from all parts in the reference parts library as the reference part for the current target part. This ensures that even in the special case of insufficient coverage of the reference parts library, the cost analysis and optimization process can still be carried out based on the data of the closest reference part.

[0043] In this embodiment of the application, when there is a part in the reference parts library with a similarity greater than or equal to a preset recommendation threshold, the part is selected as a recommended reuse part.

[0044] Specifically, the preset recommended threshold is usually higher than the target threshold, meaning that parts with a value greater than or equal to the preset recommended threshold have a very high similarity to the current target part, and may be directly replaceable without redesign or with only minimal design modifications. Setting recommended reusable parts can provide designers with suggestions for part reuse, directly reducing the cost and time of designing new parts, and achieving cost savings and efficiency improvements from the source.

[0045] Step S3: Based on the differences in attributes between each target part and its corresponding multiple reference parts, calculate multiple cost adjustment coefficients for each target part to obtain multiple candidate costs for each target part.

[0046] In this embodiment of the application, step S3, obtaining multiple candidate costs for the current target part, specifically includes the following steps: Step S31: Obtain the actual cost of each reference part corresponding to the current target part. The actual cost includes the actual material cost and the actual manufacturing cost.

[0047] Specifically, the actual material cost of the reference part refers to the cost of raw materials consumed in producing the reference part, and the actual manufacturing cost of the reference part refers to the processing costs incurred in processing the part from raw materials to finished products.

[0048] Step S32: For the current reference part of the current target part, determine the material unit price coefficient based on the ratio of the difference in material unit price between the current target part and the current reference part, determine the physical material cost coefficient based on the ratio of the difference in material usage between the current target part and the current reference part, and determine the manufacturing cost coefficient based on the ratio of the difference in manufacturing parameters between the current target part and the current reference part.

[0049] Specifically, the determination of the material unit price coefficient is as follows:

[0050] Among them, the material unit price coefficient can reflect the difference in unit material price between the current target part and the current reference part due to the use of different specific materials.

[0051] The specific steps for determining the cost coefficient of physical materials are as follows:

[0052] Among them, the physical material cost coefficient can reflect the difference in material consumption between the current target part and the current reference part due to differences in size and structure.

[0053] The determination of the manufacturing cost coefficient is as follows:

[0054] The manufacturing parameters of a part can be set based on the time ratio of its machining processes, the equipment complexity coefficient ratio, or the overall process complexity. In a specific embodiment, if the target part requires five-axis machining, the manufacturing parameters of the target part can be set to 1.4, while the reference part only requires three-axis machining, and the manufacturing parameters of the reference part can be set to 1.0.

[0055] Step S33: For the current reference part of the current target part, multiply the actual material cost of the current reference part by the material unit price coefficient and the physical material cost coefficient corresponding to the current reference part to obtain the material cost of the current target part. Multiply the actual manufacturing cost of the current reference part by the manufacturing cost coefficient corresponding to the current reference part to obtain the manufacturing cost of the current target part.

[0056] Specifically, based on the actual material cost of the current reference part, the material unit price and material usage are adjusted using material unit price coefficients and physical material cost coefficients to obtain the material cost of the current target part. Based on the actual manufacturing cost of the current reference part, the manufacturing cost is adjusted using manufacturing cost coefficients to obtain the manufacturing cost of the current target part.

[0057] Step S34: Add the material cost and manufacturing cost of the current target part calculated based on the current reference part to obtain a candidate cost of the current target part, thereby obtaining multiple candidate costs of the current target part.

[0058] Specifically, for each selected reference part, the operations of steps S32 to S34 are repeated to generate multiple candidate costs for the target part based on different reference parts.

[0059] Step S4: Determine the lowest cost of each target part from multiple candidate costs, and calculate the total cost of the actual vehicle product based on the lowest cost and structural information of all target parts.

[0060] In this embodiment of the application, step S4 specifically includes the following steps: Step S41: Determine the lowest cost of each target part from multiple candidate costs, sum the lowest costs of all target parts, and obtain the manufacturing cost of the actual automobile product.

[0061] Specifically, for each target part constituting the physical automobile product, the lowest value among the multiple candidate costs generated in step S3 is selected and determined as the minimum cost of that target part. The minimum costs of all the target parts are arithmetically summed, and the resulting sum is defined as the manufacturing cost of the physical automobile product. The manufacturing cost of the physical automobile product represents the total cost required to process and manufacture all the raw materials constituting the product into the final form of each individual part.

[0062] Step S42: Based on the structural information, calculate the logistics cost and the connection process cost between multiple target parts.

[0063] Specifically, based on the total volume and weight of the actual vehicle as defined in the structural information, and combined with the logistics transportation rate standards of the logistics industry, the estimated transportation cost, i.e., the logistics cost, is calculated for transporting the actual vehicle from the production site to the usage site or the next stage. Based on the connection methods and quantities between multiple target parts as defined in the structural information, and combined with the standard operating hours and rates for each connection method, the assembly cost, i.e., the connection process cost, is calculated for assembling all the parts into the final product.

[0064] Step S43: Sum the manufacturing cost of the actual vehicle product with the logistics cost and the connection process cost between multiple target parts to obtain the total cost of the actual vehicle product.

[0065] Specifically, the embodiments of this application not only summarize the manufacturing costs of parts, but also combine the logistics costs of the actual automotive products and the connection process costs between multiple target parts, making the cost analysis results more comprehensive and closer to actual business scenarios.

[0066] This application's embodiments automatically select reference parts from a reference parts library based on the target part's attributes and calculate costs based on attribute differences. This transforms the subjective calculation process, which relies on personal experience and understanding, into a standardized process based on a unified data source, solving the problem of large discrepancies in calculation results caused by varying personnel perceptions. This application's embodiments generate multiple candidate costs for each target part and automatically determine its lowest cost. This solution provides each part with multiple optional cost paths based on historical data and automatically identifies lower-cost options, helping to suppress the problem of still high costs after optimization. This application's embodiments extrapolate new costs based on actual data in the reference parts library through quantified attribute differences, making cost analysis based on comparison with practical cases. This avoids idealization biases caused by purely theoretical analysis and makes the results closer to reality.

[0067] Secondly, embodiments of this application also provide a device for optimizing the physical cost of automotive products.

[0068] In one embodiment, reference is made to Figure 2 , Figure 2 This is a schematic diagram of the functional modules of the vehicle product physical cost optimization device of this application. For example... Figure 2 As shown, the vehicle product physical cost optimization device includes: The acquisition module is used to acquire the structural information of the actual automotive product and the attributes of multiple target parts; The filtering module is used to filter multiple reference parts for each target part from the reference parts library based on the attributes of each target part. The first calculation module is used to calculate multiple cost adjustment coefficients for each target part based on the differences in attributes between each target part and its corresponding multiple reference parts, so as to obtain multiple candidate costs for each target part. The second calculation module is used to determine the lowest cost of each target part from multiple candidate costs, and calculate the total cost of the actual automobile product based on the lowest cost of all target parts and structural information.

[0069] The functions of each module in the above-mentioned vehicle product physical cost optimization device correspond to the steps in the above-mentioned vehicle product physical cost optimization method embodiment, and their functions and implementation processes will not be described in detail here.

[0070] Thirdly, this application provides an automotive product physical cost optimization device, which includes a processor, a memory, and an automotive product physical cost optimization program stored in the memory and executable by the processor. When the automotive product physical cost optimization program is executed by the processor, it implements the steps of the automotive product physical cost optimization method as described in any of the above embodiments.

[0071] Equipment for optimizing the physical cost of automotive products can be devices with data processing capabilities, such as personal computers (PCs), laptops, and servers.

[0072] Reference Figure 3 , Figure 3 This is a schematic diagram of the hardware structure of the automotive product physical cost optimization device involved in the embodiments of this application. In this embodiment, the automotive product physical cost optimization device may include a processor, a memory, a communication interface, and a communication bus.

[0073] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0074] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting components within the automotive product cost optimization equipment, as well as interfaces used for interconnecting the automotive product cost optimization equipment with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0075] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0076] The processor can be a general-purpose processor, which can call the automotive product physical cost optimization program stored in the memory and execute the automotive product physical cost optimization method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the automotive product physical cost optimization program is called can refer to the various embodiments of the automotive product physical cost optimization method of this application, and will not be repeated here.

[0077] Those skilled in the art will understand that Figure 3The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0078] Fourthly, embodiments of this application provide a computer-readable storage medium storing a program for optimizing the physical cost of an automobile product, wherein when the program is executed by a processor, it implements the steps of the method for optimizing the physical cost of an automobile product as described in any of the above embodiments.

[0079] The present application stores a physical cost optimization program for automobile products on a computer-readable storage medium, wherein when the physical cost optimization program for automobile products is executed by a processor, it implements the steps of the physical cost optimization method for automobile products as described above.

[0080] The method implemented when the physical cost optimization procedure for automobile products is executed can be referred to in the various embodiments of the physical cost optimization method for automobile products in this application, and will not be repeated here.

[0081] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0082] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0083] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0084] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0085] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0086] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0087] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for optimizing the physical cost of an automotive product, wherein the automotive product is composed of multiple target parts, characterized in that, The method for optimizing the physical cost of automotive products includes: Obtain structural information of the actual automotive product and the attributes of multiple target parts; Based on the properties of each target part, multiple reference parts for each target part are selected from the reference parts library. Based on the differences in attributes between each target part and its corresponding multiple reference parts, multiple cost adjustment coefficients are calculated for each target part to obtain multiple candidate costs for each target part. The lowest cost of each target part is determined from multiple candidate costs. Based on the lowest costs of all target parts and the structural information, the total cost of the actual automotive product is calculated.

2. The method for optimizing the physical cost of automobile products according to claim 1, characterized in that, The step of selecting multiple reference parts for each target part from the reference parts library based on the attributes of each target part includes: Based on the attributes of each target part, the similarity between the target part and each part in the reference part library is calculated. Parts with a similarity greater than or equal to a preset threshold in the reference parts library are selected as reference parts; The attributes include material, process, size, and weight.

3. The method for optimizing the physical cost of automobile products according to claim 2, characterized in that, The step of calculating the similarity between the target part and each part in the reference part library based on the attributes of each target part includes: Retrieve the properties of each part in the reference parts library; Based on the material identifier and process identifier of the target part and the material identifier and process identifier of each part in the reference parts library, a preset mapping table is queried to obtain the material and process difference value. The preset mapping table includes the mapping relationship between the combination pairs composed of material identifier and process identifier and the material and process difference value. The size difference value is calculated based on the size of the target part and the size of each part in the reference parts library; The weight difference value is calculated based on the weight of the target part and the weight of each part in the reference parts library; Based on the material and process differences, size differences, and weight differences, a weighted fusion is performed to obtain the similarity between the target part and each part in the reference part library.

4. The method for optimizing the physical cost of automobile products according to claim 3, characterized in that, The step involves calculating multiple cost adjustment coefficients for each target part based on the differences in attributes between each target part and its corresponding multiple reference parts, thereby obtaining multiple candidate costs for each target part. Specifically, obtaining the multiple candidate costs for the current target part includes: Obtain the actual cost of each reference part corresponding to the current target part, wherein the actual cost includes the actual material cost and the actual manufacturing cost; For the current reference part of the current target part, the material unit price coefficient is determined based on the ratio of the difference in material unit price between the current target part and the current reference part, the physical material cost coefficient is determined based on the ratio of the difference in material usage between the current target part and the current reference part, and the manufacturing cost coefficient is determined based on the ratio of the difference in manufacturing parameters between the current target part and the current reference part. For the current reference part of the current target part, the actual material cost of the current reference part is multiplied by the material unit price coefficient and the physical material cost coefficient corresponding to the current reference part to obtain the material cost of the current target part. The actual manufacturing cost of the current reference part is multiplied by the manufacturing cost coefficient corresponding to the current reference part to obtain the manufacturing cost of the current target part. The material cost and manufacturing cost of the current target part, calculated based on the current reference part, are added together to obtain a candidate cost of the current target part, thereby obtaining multiple candidate costs of the current target part.

5. The method for optimizing the physical cost of automobile products according to claim 2, characterized in that, The step of filtering parts in the reference parts library with a similarity greater than or equal to a preset threshold as reference parts further includes: When there are no parts in the reference parts library with a similarity greater than or equal to the preset threshold, the part with the highest similarity is selected as the reference part.

6. The method for optimizing the physical cost of automobile products according to claim 2, characterized in that, Also includes: When a part with a similarity greater than or equal to a preset recommendation threshold exists in the reference parts library, that part is selected as a recommended reuse part.

7. The method for optimizing the physical cost of automobile products according to claim 1, characterized in that, The process of determining the lowest cost of each target part from multiple candidate costs, and calculating the total cost of the actual automotive product based on the lowest costs of all target parts and the structural information, includes: The lowest cost of each target part is determined from multiple candidate costs, and the lowest costs of all target parts are summed to obtain the actual manufacturing cost of the automobile product. Based on the structural information, calculate the logistics cost and the connection process cost between multiple target parts; The total cost of the vehicle product is obtained by summing the manufacturing cost of the vehicle itself, the logistics cost, and the connection process cost between multiple target parts. The structural information includes the volume of the actual automotive product and the connection methods and assembly relationships between multiple target parts.

8. A device for optimizing the physical cost of automotive products, characterized in that, The vehicle product physical cost optimization device includes: The acquisition module is used to acquire the structural information of the actual automotive product and the attributes of multiple target parts; The filtering module is used to filter multiple reference parts for each target part from the reference parts library based on the attributes of each target part. The first calculation module is used to calculate multiple cost adjustment coefficients for each target part based on the differences in attributes between each target part and its corresponding multiple reference parts, so as to obtain multiple candidate costs for each target part. The second calculation module is used to determine the lowest cost of each target part from multiple candidate costs, and calculate the total cost of the actual automobile product based on the lowest cost of all target parts and the structural information.

9. A device for optimizing the physical cost of automotive products, characterized in that, The vehicle product physical cost optimization device includes a processor, a memory, and a vehicle product physical cost optimization program stored in the memory and executable by the processor, wherein when the vehicle product physical cost optimization program is executed by the processor, it implements the steps of the vehicle product physical cost optimization method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a physical cost optimization program for automobile products, wherein when the physical cost optimization program for automobile products is executed by a processor, it implements the steps of the physical cost optimization method for automobile products as described in any one of claims 1 to 7.

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