Vehicle DTS intelligent design method and system

Abstract

The invention discloses a vehicle DTS intelligent design method and system, and relates to the technical field of vehicle design, and the method comprises the steps: determining a section picture of a target DTS and the name of an opponent part based on the vehicle preliminary modeling surface data of a target vehicle; screening a plurality of historical vehicle type DTSs consistent with the names of the opponent parts of the target DTS in a historical vehicle type DTS database to obtain a first DTS set; performing similarity matching on the section picture of the target DTS and the section picture of each historical vehicle type DTS in the first DTS set to obtain a similarity matching result; based on a similarity matching result, screening out a second DTS set from the first DTS set; screening out a third DTS set from the second DTS set based on the vehicle model feature information of the target vehicle; and selecting the design parameters of the target DTS in the third DTS set based on a preset selection rule. The technical problems of low accuracy and large workload in the prior art are relieved.

Description

Technical Field

[0001] This invention relates to the field of vehicle design technology, specifically to a vehicle DTS intelligent design method and system. Background Technology

[0002] The Dimensional Technical Specification (DTS) refers to the design requirements for gaps and surface differences between various components of the vehicle's interior and exterior. It is an important indicator of the manufacturing quality of the vehicle's interior and exterior, and reflects the overall dimensional quality of the vehicle, influencing customers' subjective evaluation of product quality.

[0003] Major automakers are placing increasing emphasis on DTS design and development, gradually establishing relevant standards and specifications, and accumulating a large amount of historical DTS design data. However, DTS design is usually still done manually by referring to historical data or based on experience, and the accumulated DTS data of a large number of historical models has not been fully considered. Therefore, the objectivity, rationality, and accuracy of the design are difficult to guarantee. At the same time, manually designing hundreds of DTS models for a whole vehicle is a large workload and a time-consuming and labor-intensive process. Summary of the Invention

[0004] The purpose of this invention is to provide a vehicle DTS intelligent design method and system to solve at least one of the above-mentioned technical problems.

[0005] In a first aspect, embodiments of the present invention provide a vehicle DTS intelligent design method, the method comprising: determining cross-sectional images of a target DTS and names of rival parts based on preliminary styling surface data of a target vehicle; filtering multiple historical model DTSs that match the names of rival parts of the target DTS in a historical model DTS database to obtain a first DTS set; the historical model DTS database includes multiple historical model DTSs and corresponding rival part names and cross-sectional images; performing similarity matching on the cross-sectional images of the target DTS with the cross-sectional images of each historical model DTS in the first DTS set to obtain similarity matching results; filtering a second DTS set from the first DTS set based on the similarity matching results; the second DTS set being a collection of multiple historical model DTSs whose similarity matching results satisfy a preset threshold condition; filtering a third DTS set from the second DTS set based on the vehicle model feature information of the target vehicle; the third DTS set being a collection of multiple historical model DTSs that match the vehicle model feature information of the target vehicle; and selecting design parameters of the target DTS from the third DTS set based on preset selection rules.

[0006] Optionally, the cross-sectional image of the target DTS is matched with the cross-sectional image of each historical model DTS in the first DTS set, including: based on an artificial intelligence algorithm, the cross-sectional image of the target DTS is matched with the cross-sectional image of each historical model DTS in the first DTS set.

[0007] Optionally, the vehicle feature information includes vehicle type, vehicle class, and vehicle price.

[0008] Optionally, the design parameters of the target DTS include nominal values ​​of gap surface difference and tolerances.

[0009] Optionally, the preset selection rule includes: selecting the DTS design parameter with the smallest nominal value of gap surface difference.

[0010] Optionally, the method further includes: performing DTS design on the target vehicle based on the design parameters of the target DTS.

[0011] Secondly, embodiments of the present invention also provide a vehicle DTS intelligent design system, including: a determination module, a first filtering module, a similarity matching module, a second filtering module, a third filtering module, and a selection module; wherein, the determination module is used to determine the cross-sectional image of the target DTS and the name of the competitor part based on the preliminary styling surface data of the target vehicle; the first filtering module is used to filter multiple historical model DTSs that have the same competitor part name as the target DTS in a historical model DTS database, to obtain a first DTS set; the historical model DTS database includes multiple historical model DTSs and corresponding competitor part names and cross-sectional images; the similarity matching module is used to match the cross-sectional image of the target DTS with the names of the competitors and the corresponding competitor parts. The first DTS module performs similarity matching on cross-sectional images of each historical model DTS in the first DTS set to obtain similarity matching results; the second filtering module is used to filter out a second DTS set from the first DTS set based on the similarity matching results; the second DTS set is a collection of multiple historical model DTSs whose similarity matching results meet a preset threshold condition; the third filtering module is used to filter out a third DTS set from the second DTS set based on the model feature information of the target vehicle; the third DTS set is a collection of multiple historical model DTSs that match the model feature information of the target vehicle; the selection module is used to select the design parameters of the target DTS from the third DTS set based on preset selection rules.

[0012] Optionally, it also includes a design module for performing DTS design on the target vehicle based on the design parameters of the target DTS.

[0013] Thirdly, embodiments of the present invention also provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method provided in the embodiments of the present invention.

[0014] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the method provided in the embodiments of the present invention.

[0015] This invention provides a vehicle DTS intelligent design method and system. By analyzing and comparing the feature information of new vehicle models and a large amount of historical vehicle data, it realizes intelligent design of vehicle DTS, including nominal values ​​and tolerances of gaps and surface differences. It can replace a lot of manual work, improve the efficiency and accuracy of DTS design, and alleviate the technical problems of low accuracy and large workload in the existing technology. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 A flowchart of a vehicle DTS intelligent design method provided in an embodiment of the present invention; Figure 2 A framework diagram of a vehicle DTS intelligent design method provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of a vehicle DTS intelligent design system provided in an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1 Figure 1 This is a flowchart of a vehicle DTS intelligent design method according to an embodiment of the present invention. Figure 2 This is a framework diagram of a vehicle DTS intelligent design method provided according to an embodiment of the present invention. Figure 1 and Figure 2 As shown, the method specifically includes the following steps: Step S102: Based on the preliminary concept a surface (CAS) data of the target vehicle, determine the cross-sectional images of the target DTS and the names of the competitor parts. For example, competitor part names include headlights, front bumper, etc.

[0020] Step S104: Filter multiple historical model DTSs with the same competitor part name as the target DTS in the historical model DTS database to obtain the first DTS set; the historical model DTS database includes multiple historical model DTSs and their corresponding competitor part names and cross-sectional images.

[0021] Specifically, firstly, a historical vehicle model DTS database is established to store historical vehicle model information, including the historical vehicle model DTS, the names of the opposing parts of the DTS, the design values ​​of clearance and surface difference, and cross-sectional images. Then, the first DTS set is selected from the historical vehicle model DTS database.

[0022] Step S106: Perform similarity matching between the cross-sectional image of the target DTS and the cross-sectional image of each historical model DTS in the first DTS set to obtain the similarity matching result.

[0023] Preferably, based on artificial intelligence algorithms, the cross-sectional images of the target DTS are matched with the cross-sectional images of each historical model DTS in the first DTS set for similarity.

[0024] Step S108: Based on the similarity matching results, a second DTS set is selected from the first DTS set; the second DTS set is a collection of multiple historical model DTSs whose similarity matching results meet the preset threshold conditions.

[0025] Step S110: Based on the vehicle model feature information of the target vehicle, a third DTS set is selected from the second DTS set; the third DTS set is a collection of multiple historical vehicle model DTSs that match the vehicle model feature information of the target vehicle.

[0026] Preferably, vehicle characteristic information includes vehicle type, vehicle class, and vehicle price range. For example, SUV, mid-size, priced between 150,000 and 200,000 yuan.

[0027] Step S112: Based on the preset selection rules, select the design parameters of the target DTS from the third DTS set.

[0028] In one optional embodiment of the present invention, the preset selection rule includes: selecting the DTS design parameter with the smallest nominal value of gap surface difference.

[0029] Optionally, the design parameters of the target DTS include nominal values ​​and tolerances for gap surface differences.

[0030] Specifically, after step S112, the method provided in this embodiment of the invention further includes: performing DTS design on the target vehicle based on the design parameters of the target DTS.

[0031] In one specific embodiment of the present invention, for example, intelligent design is performed on a certain DTS, such as from the headlights to the front bumper: First, we filtered out the competitor parts from the historical model DTS database, specifically the headlights and front bumper corresponding to the DTS.

[0032] Then, the corresponding DTS cross-sectional screenshot is retrieved and compared with the cross-sectional screenshot to be designed. An artificial intelligence image matching algorithm is used to obtain the image similarity score.

[0033] Then, filter out DTS cross-sectional images with similarity scores higher than a threshold, for example, those with similarity scores greater than 0.9.

[0034] Then, based on the vehicle model characteristics information, further filtering was performed to select DTS models that meet the criteria of being SUVs, mid-size, and priced between 150,000 and 200,000 yuan.

[0035] Finally, among the selected DTSs, the design value with the smallest nominal gap difference is chosen. The selected DTS design value is then used as the design value for the DTS to be designed. After completing the design of all DTSs, the result is output to the user.

[0036] As described above, the embodiments of the present invention provide a vehicle DTS intelligent design method. By analyzing and comparing the feature information of new vehicle models and a large amount of historical vehicle data, intelligent design of vehicle DTS is achieved, including nominal values ​​and tolerances of gaps and surface differences. This method can replace a large amount of manual work, improve the efficiency and accuracy of DTS design, and alleviate the technical problems of low accuracy and large workload in the prior art.

[0037] Example 2 Figure 3 This is a schematic diagram of a vehicle DTS intelligent design system provided according to an embodiment of the present invention. Figure 3 As shown, the system includes: a determination module 10, a first filtering module 20, a similarity matching module 30, a second filtering module 40, a third filtering module 50, and a selection module 60.

[0038] Specifically, module 10 is used to determine the cross-sectional images of the target DTS and the names of the opposing parts based on the preliminary automotive styling surface data of the target vehicle. The first filtering module 20 is used to filter multiple historical model DTSs that have the same competitor part name as the target DTS in the historical model DTS database, and obtain the first DTS set; the historical model DTS database includes multiple historical model DTSs and corresponding competitor part names and cross-sectional images. The similarity matching module 30 is used to perform similarity matching between the cross-sectional image of the target DTS and the cross-sectional image of each historical model DTS in the first DTS set, and obtain the similarity matching result. The second filtering module 40 is used to filter out a second DTS set from the first DTS set based on the similarity matching results; the second DTS set is a collection of multiple historical model DTSs whose similarity matching results meet the preset threshold conditions. The third filtering module 50 is used to filter out the third DTS set from the second DTS set based on the vehicle model feature information of the target vehicle; the third DTS set is a collection of multiple historical vehicle model DTS that match the vehicle model feature information of the target vehicle. The selection module 60 is used to select the design parameters of the target DTS from the third DTS set based on preset selection rules.

[0039] Specifically, such as Figure 3 As shown, it also includes a design module 70, which is used to perform DTS design on the target vehicle based on the design parameters of the target DTS.

[0040] The present invention also provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method provided in the embodiments of the present invention.

[0041] The present invention also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the method provided in the embodiments of the present invention.

[0042] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A vehicle DTS intelligent design method, characterized in that, The method includes: Based on the preliminary styling surface data of the target vehicle, determine the cross-sectional images of the target DTS and the names of the opposing parts; Multiple historical model DTSs with the same competitor part names as the target DTS are selected from the historical model DTS database to obtain the first DTS set; the historical model DTS database includes multiple historical model DTSs and corresponding competitor part names and cross-sectional images. The cross-sectional images of the target DTS are matched with the cross-sectional images of each historical model DTS in the first DTS set to obtain similarity matching results. Based on the similarity matching results, a second DTS set is selected from the first DTS set; the second DTS set is a collection of multiple historical model DTSs whose similarity matching results meet a preset threshold condition; Based on the vehicle model feature information of the target vehicle, a third DTS set is selected from the second DTS set; the third DTS set is a collection of multiple historical vehicle model DTSs that match the vehicle model feature information of the target vehicle. Based on preset selection rules, the design parameters of the target DTS are selected from the third DTS set.

2. The method according to claim 1, characterized in that: The cross-sectional image of the target DTS is matched with the cross-sectional image of each historical model DTS in the first DTS set, including: based on an artificial intelligence algorithm, the cross-sectional image of the target DTS is matched with the cross-sectional image of each historical model DTS in the first DTS set.

3. The method according to claim 1, characterized in that: The vehicle model characteristics information includes vehicle type, vehicle class, and vehicle price.

4. The method according to claim 1, characterized in that: The design parameters of the target DTS include the nominal value of the gap surface difference and the tolerance.

5. The method according to claim 4, characterized in that: The preset selection rules include: selecting the DTS design parameters with the smallest nominal value of gap surface difference.

6. The method according to claim 1, characterized in that: The method further includes: performing DTS design on the target vehicle based on the design parameters of the target DTS.

7. A vehicle DTS intelligent design system, characterized in that, include: The system comprises a determination module, a first filtering module, a similarity matching module, a second filtering module, a third filtering module, and a selection module; among which, The determining module is used to determine the cross-sectional image of the target DTS and the name of the opponent's part based on the preliminary styling surface data of the target vehicle. The first filtering module is used to filter multiple historical model DTSs that have the same competitor part name as the target DTS in the historical model DTS database, to obtain a first DTS set; the historical model DTS database includes multiple historical model DTSs and corresponding competitor part names and cross-sectional images; The similarity matching module is used to perform similarity matching between the cross-sectional image of the target DTS and the cross-sectional image of each historical model DTS in the first DTS set, and obtain similarity matching results. The second filtering module is used to filter out a second DTS set from the first DTS set based on the similarity matching results; the second DTS set is a collection of multiple historical model DTSs whose similarity matching results meet a preset threshold condition; The third filtering module is used to filter out a third DTS set from the second DTS set based on the vehicle model feature information of the target vehicle; the third DTS set is a collection of multiple historical vehicle model DTSs that match the vehicle model feature information of the target vehicle. The selection module is used to select the design parameters of the target DTS from the third DTS set based on preset selection rules.

8. The system according to claim 7, characterized in that: It also includes a design module for performing DTS design on the target vehicle based on the design parameters of the target DTS.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as claimed in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method as described in any one of claims 1-6.

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