A method and system for the quantitative analysis of surface defects on a body outer covering

Abstract

A kind of quantitative analysis method and system for the surface defects of vehicle body outer covering, belong to vehicle body outer covering technical field.It includes the following steps: step S1, using optical detection equipment to scan the outer surface of vehicle body outer covering, to generate the actual point cloud data of vehicle body outer covering;Step S2, the actual point cloud data of vehicle body outer covering generated in step S1 and the theoretical surface number model of the vehicle body outer covering are imported into computer aided design software, the actual zebra line stripe of vehicle body outer covering is compared with the zebra line stripe of the theoretical surface number model of the vehicle body outer covering;Step S3, the area that the actual zebra line stripe of vehicle body outer covering and the zebra line stripe of the theoretical surface number model of the vehicle body outer covering are not consistent is defined as the potential surface defect area of vehicle body outer covering;Step S4, the potential surface defect area of vehicle body outer covering is quantitatively analyzed, and the surface defect distribution nephogram of vehicle body outer covering is created.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle body outer covering parts, and in particular to a quantitative analysis method and system for surface defects of vehicle body outer covering parts. Background Art

[0002] Against the backdrop of fierce competition in the current automotive industry, automakers are placing increasingly stringent demands on the appearance quality of vehicle bodies, and the appearance quality of vehicle bodies is primarily determined by the surface quality of exterior coverings. When there are surface defects such as local depressions or protrusions on the surface of exterior coverings that destroy the original curvature distribution characteristics of the vehicle body, the light and shadow stripes on the paint surface will be distorted at these surface defects, thereby affecting the aesthetics of the vehicle body. To this end, these surface defects must be eliminated or minimized during the manufacturing phase of the exterior coverings of the vehicle body. Currently, exterior coverings of vehicle bodies are primarily manufactured by stamping metal sheets. During the stamping process, some surface defects are inevitably produced due to uneven plastic deformation, rebound release of bending deformation at fillets, or material accumulation.

[0003] There are three main evaluation methods for noodle defects in production: the first is the oil stone detection method, such as Figure 1 As shown in the figure, the surface of the outer cover is polished along a specific direction with an oil stone. The places where the oil stone cannot polish indicate the presence of local pits. The second method is the light and shadow detection method, such as Figure 2 As shown in the figure, the outer covering surface is painted or partially coated with a paint film and placed in a light chamber to evaluate surface defects by observing the characteristics of light and shadow stripes; the third method is the three-point gauge detection method, such as Figure 3 As shown, the surface defects are quantitatively detected by relying on the numerical value of the gauge scale.

[0004] Among the three existing detection methods, the oilstone detection method and the light and shadow detection method are both qualitative detection methods. They rely entirely on the personal experience of the inspectors to assess the defect level. They are greatly influenced by subjective factors, and oilstone detection cannot be implemented in the concave areas of the vehicle body exterior cover. Although the three-point gauge detection method is a quantitative detection method, the measurement results are greatly affected by the span of the gauge. It can only achieve fixed-point detection, and cannot obtain the regional distribution characteristics of surface defects, and cannot detect surface defects near the edge of the exterior cover. In addition, the analysis results of the above three detection methods cannot be stored in a digital form, which is not conducive to the accumulation of technical experience. Due to the limitations of existing detection methods, the current rectification of surface defects is still mainly based on trial and error. The purpose of eliminating or reducing surface defects is finally achieved through multiple rounds of repeated adjustments to the rectification area and the measurement value. The rectification cycle is long and the workload of rectification is large.

[0005] In summary, the existing detection methods lack digital detection methods, resulting in the inability to realize digital storage of analysis results, a long rectification cycle, and a large workload for rectification. Summary of the Invention

[0006] The present invention solves the problems in existing detection methods that, due to the lack of a digital detection method, analysis results cannot be stored digitally, the rectification cycle is long, and the rectification workload is large.

[0007] The present invention provides a quantitative analysis method for surface defects of vehicle body exterior panels, comprising the following steps:

[0008] Step S1, scanning the outer surface of the vehicle body outer covering by optical detection equipment, thereby generating actual point cloud data of the vehicle body outer covering;

[0009] Step S2, importing the actual point cloud data of the vehicle body outer cover generated in step S1 and the theoretical surface digital model of the vehicle body outer cover into computer-aided design software, and comparing the actual zebra stripes of the vehicle body outer cover with the zebra stripes of the theoretical surface digital model of the vehicle body outer cover;

[0010] Step S3, defining an area where the zebra stripes of the actual vehicle body outer cover are inconsistent with the zebra stripes of the theoretical curved surface digital model of the vehicle body outer cover as a potential surface defect area of ​​the vehicle body outer cover;

[0011] Step S4: quantitatively analyze the potential surface defect areas of the vehicle body outer covering parts and create a surface defect distribution cloud map of the vehicle body outer covering parts.

[0012] Furthermore, in one embodiment of the present invention, the vehicle body outer covering includes an engine hood outer panel, fenders, door outer panels, side panel outer panels, trunk outer panel, tailgate outer panel and roof cover outer panel.

[0013] Furthermore, in one embodiment of the present invention, the point cloud data includes direct data without any processing and indirect data obtained by processing the direct data.

[0014] Furthermore, in one embodiment of the present invention, the processing of direct data is repairing or smoothing.

[0015] Furthermore, in one embodiment of the present invention, the actual point cloud data of the vehicle body outer covering and the theoretical surface digital model of the vehicle body outer covering generated in step S1 are imported into computer-aided design software having a light and shadow analysis function.

[0016] Furthermore, in one embodiment of the present invention, the comparison between the actual zebra stripes of the vehicle body outer covering and the zebra stripes of the theoretical curved surface digital model of the vehicle body outer covering is performed using a light and shadow analysis tool.

[0017] Furthermore, in one embodiment of the present invention, the potential surface defect areas of the vehicle body outer covering include pit-type defects, convex-type defects and wave-type defects.

[0018] Furthermore, in one embodiment of the present invention, the quantitative analysis of the potential surface defect areas of the vehicle body outer covering parts is performed using Gom Inspect software.

[0019] Furthermore, in one embodiment of the present invention, the quantitative analysis of potential surface defect areas of the vehicle body outer covering and the creation of a surface defect distribution cloud map of the vehicle body outer covering include the following steps:

[0020] Step S401: Importing actual point cloud data of the vehicle body outer panel and a theoretical surface model of the vehicle body outer panel into the software, fitting the imported actual point cloud data of the vehicle body outer panel and the theoretical surface model of the vehicle body outer panel, and extracting surface patches and point cloud patches of the vehicle body outer panel where potential surface defects are located;

[0021] Step S402 , using a mesh defect repair tool to repair and smooth the actual point cloud data of the vehicle body exterior panel;

[0022] Step S403 , respectively calculating the surface defect value of the actual point cloud data of the vehicle body outer cover and the surface defect value of the theoretical surface numerical model of the vehicle body outer cover;

[0023] Step S404 , performing a subtraction calculation on the surface defect values ​​of the actual point cloud data of the vehicle body outer panel and the vector set of surface defect values ​​of the theoretical surface model of the vehicle body outer panel to generate a surface defect distribution cloud map of the vehicle body outer panel.

[0024] The present invention provides a quantitative analysis system for surface defects of vehicle body exterior panels, comprising the following modules:

[0025] The point cloud data module uses optical inspection equipment to scan the outer surface of the vehicle body outer cover to generate actual point cloud data of the vehicle body outer cover;

[0026] a comparison module for importing the actual point cloud data of the vehicle body outer covering generated by the point cloud data module and the theoretical surface digital model of the vehicle body outer covering into the computer-aided design software to compare the actual zebra stripes of the vehicle body outer covering with the zebra stripes of the theoretical surface digital model of the vehicle body outer covering;

[0027] a definition module, defining an area where the zebra stripes of the vehicle body outer covering are inconsistent when comparing the actual zebra stripes of the vehicle body outer covering with the theoretical surface digital model of the vehicle body outer covering as a potential surface defect area of ​​the vehicle body outer covering;

[0028] The analysis module quantitatively analyzes the potential surface defect areas of the vehicle body exterior panels and creates a surface defect distribution cloud map of the vehicle body exterior panels.

[0029] The present invention solves the problem that the existing detection methods in the prior art lack digital detection methods, resulting in the inability to store analysis results in data, a long rectification cycle, and a large workload for rectification. Specific beneficial effects include:

[0030] 1. The quantitative analysis method for surface defects of vehicle body exterior panels described in the present invention is based on point cloud data of real exterior panels and product surface digital models, uses digital light and shadow technology to quickly determine potential defect locations, and conducts quantitative analysis of the defects. It ultimately outputs the defect type (pit or protrusion) and its distribution characteristics, as well as the defect value, providing effective data support for the rectification of surface defects, thereby providing a digital detection and analysis method that assists in shortening the surface defect rectification cycle.

[0031] 2. The present invention describes a quantitative analysis method for surface defects of vehicle body exterior panels. This method imports point cloud data of an actual part and its theoretical surface model into Gom Inspect software, fits the imported point cloud data to the surface data to ensure that they are spatially comparable. The software then extracts surface patches and point cloud patches containing potential surface defects, deleting the remaining areas to simplify analysis.

[0032] 3. The quantitative analysis method for surface defects of vehicle body exterior panels described in the present invention repairs mesh defects in point cloud data and then smoothes the repaired point cloud data to improve the accuracy of surface defect calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0034] Figure 1 This is a diagram of the oilstone detection method for noodle defects described in the background art.

[0035] Figure 2 This is a diagram of the light and shadow detection method for surface defects described in the background art.

[0036] Figure 3 This is a diagram of the three-point gauge detection method for noodle defects described in the background art.

[0037] Figure 4 It is a flow chart of quantitative evaluation of noodle defects described in a specific implementation method.

[0038] Figure 5is the light and shadow analysis result diagram of the door outer panel point cloud data and the theoretical curved surface numerical model according to the embodiment.

[0039] Figure 6 is the surface defect distribution nephogram of the door outer panel according to the embodiment.

[0040] Figure 7 is the light and shadow analysis result diagram of the roof outer panel point cloud data and the theoretical curved surface numerical model according to the embodiment.

[0041] Figure 8 is the surface defect distribution nephogram of the roof outer panel according to the embodiment. EMBODIMENT

[0042] The various embodiments of the present application will be described clearly and completely below in conjunction with the accompanying drawings. The embodiments described by reference to the accompanying drawings are exemplary and are intended to explain the present application, and cannot be understood as a limitation of the present application.

[0043] The quantitative analysis method for the surface defects of the vehicle body outer covering according to the embodiment includes the following steps:

[0044] Step S1, using an optical detection device to scan the outer surface of the vehicle body outer covering, thereby generating the actual point cloud data of the vehicle body outer covering;

[0045] Step S2, importing the actual point cloud data of the vehicle body outer covering generated in step S1 and the theoretical curved surface numerical model of the vehicle body outer covering into a computer-aided design software, and comparing the actual zebra line stripes of the vehicle body outer covering with the zebra line stripes of the theoretical curved surface numerical model of the vehicle body outer covering;

[0046] Step S3, defining the area where the actual zebra line stripes of the vehicle body outer covering and the zebra line stripes of the theoretical curved surface numerical model of the vehicle body outer covering are inconsistent as the potential surface defect area of the vehicle body outer covering;

[0047] Step S4, quantitatively analyzing the potential surface defect area of the vehicle body outer covering, and creating the surface defect distribution nephogram of the vehicle body outer covering.

[0048] In the embodiment, the vehicle body outer covering includes the engine cover outer panel, the fender, the door outer panel, the side wall outer panel, the trunk outer panel, the back door outer panel and the roof outer panel.

[0049] In the embodiment, the point cloud data includes direct data without any processing and indirect data obtained by processing the direct data.

[0050] In the embodiment, the processing of the direct data is repairing or fairing.

[0051] In this embodiment, the actual point cloud data of the vehicle body outer covering part generated in step S1 and the theoretical surface digital model of the vehicle body outer covering part are imported into the computer-aided design software, which is any computer-aided design software with light and shadow analysis function.

[0052] In this embodiment, the comparison between the actual zebra stripes of the vehicle body outer covering and the zebra stripes of the theoretical curved surface digital model of the vehicle body outer covering is performed by using a light and shadow analysis tool.

[0053] In this embodiment, the potential surface defect areas of the vehicle body outer covering include pit-type defects, convex-type defects and wave-type defects.

[0054] In this embodiment, the quantitative analysis of the potential surface defect areas of the vehicle body outer covering parts is performed using Gom Inspect software.

[0055] In this embodiment, the quantitative analysis of potential surface defect areas of the vehicle body outer covering and the creation of a surface defect distribution cloud map of the vehicle body outer covering include the following steps:

[0056] Step S401: Importing actual point cloud data of the vehicle body outer panel and a theoretical surface model of the vehicle body outer panel into the software, fitting the imported actual point cloud data of the vehicle body outer panel and the theoretical surface model of the vehicle body outer panel, and extracting surface patches and point cloud patches of the vehicle body outer panel where potential surface defects are located;

[0057] Step S402 , using a mesh defect repair tool to repair and smooth the actual point cloud data of the vehicle body exterior panel;

[0058] Step S403 , respectively calculating the surface defect value of the actual point cloud data of the vehicle body outer cover and the surface defect value of the theoretical surface numerical model of the vehicle body outer cover;

[0059] Step S404 , performing a subtraction calculation on the surface defect values ​​of the actual point cloud data of the vehicle body outer panel and the vector set of surface defect values ​​of the theoretical surface model of the vehicle body outer panel to generate a surface defect distribution cloud map of the vehicle body outer panel.

[0060] The quantitative analysis system for surface defects of vehicle body exterior panels described in this embodiment includes the following modules:

[0061] The point cloud data module uses optical inspection equipment to scan the outer surface of the vehicle body outer cover to generate actual point cloud data of the vehicle body outer cover;

[0062] a comparison module for importing the actual point cloud data of the vehicle body outer covering generated by the point cloud data module and the theoretical surface digital model of the vehicle body outer covering into the computer-aided design software to compare the actual zebra stripes of the vehicle body outer covering with the zebra stripes of the theoretical surface digital model of the vehicle body outer covering;

[0063] a definition module, defining an area where the zebra stripes of the vehicle body outer covering are inconsistent when comparing the actual zebra stripes of the vehicle body outer covering with the theoretical surface digital model of the vehicle body outer covering as a potential surface defect area of ​​the vehicle body outer covering;

[0064] The analysis module quantitatively analyzes the potential surface defect areas of the vehicle body exterior panels and creates a surface defect distribution cloud map of the vehicle body exterior panels.

[0065] This embodiment is based on the quantitative analysis of surface defects of vehicle body exterior panels described in the present invention, combined with Figure 4 To better understand this implementation, a practical implementation is provided:

[0066] Step 1: Obtaining point cloud data of actual parts: Using optical inspection equipment to scan the outer surface of the vehicle body outer cover to generate actual point cloud data of the vehicle body outer cover;

[0067] Step 2: Identify surface defects: Import the actual point cloud data of the body panel and the theoretical surface model of the body panel into the CAD software. Use light and shadow analysis tools to compare and analyze the actual zebra stripes of the body panel with the zebra stripes of the theoretical surface model. Identify areas where the actual zebra stripe characteristics of the body panel are inconsistent with the theoretical surface model and define these areas as potential surface defects.

[0068] Step 3: Quantitatively evaluate noodle defects: Quantitatively analyze potential noodle defect areas in the Gom Inspect software, including the following sub-steps:

[0069] Sub-step 1: Data import, fitting, and cropping: Import the actual point cloud data of the exterior body panel and its theoretical surface model into the Gom Inspect software. Fit the imported point cloud data and surface data to ensure that they are spatially comparable. Surface patches and point cloud patches containing potential surface defects are then extracted, and the remaining areas are deleted to simplify analysis.

[0070] Sub-step 2: Repair and smooth the point cloud data: Repair the mesh defects in the point cloud data, and then smooth the repaired point cloud data to improve the accuracy of surface defect calculation;

[0071] Sub-step 3: Calculating surface defects of the actual point cloud data and theoretical surface data of the vehicle body outer panel: Calculating surface defects of the actual point cloud data and theoretical surface data of the vehicle body outer panel in GomInspect software;

[0072] Sub-step 4: Create a surface defect distribution cloud map: In the Gom Inspect software, perform a subtraction calculation on the vector set of surface defects in the actual point cloud data of the vehicle body exterior panel and the surface defects in the theoretical surface data to generate a surface defect distribution cloud map.

[0073] The exterior body covering refers to any part that can reflect the exterior styling characteristics of the vehicle body; the exterior body covering may be the engine hood outer panel; the exterior body covering may be the fender; the exterior body covering may be the door outer panel; the exterior body covering may be the side outer panel; the exterior body covering may be the trunk outer panel; the exterior body covering may be the tailgate outer panel; the exterior body covering may be the roof cover outer panel.

[0074] The point cloud data refers to the vector set data of the vehicle body exterior cover in the three-dimensional coordinate system obtained by any optical detection equipment; the point cloud data can be direct data obtained by the optical detection equipment without any processing; the point cloud data can be indirect data obtained after repairing or smoothing the direct data.

[0075] The surface defect refers to any surface defect that can cause the zebra stripes on the exterior cover of the vehicle body to distort and deform under light; the surface defect can be a pit-type defect; the surface defect can be a convex-type defect; the surface defect can be a wave-type defect.

[0076] The CAD software in step 2 refers to any computer-aided design software with light and shadow analysis function.

[0077] The present invention can accurately obtain the types and distribution characteristics of surface defects of vehicle body exterior covering parts, as well as the magnitude of the surface defects, thereby providing intuitive, real and effective data support for the rectification of surface defects.

[0078] This embodiment provides a practical implementation method based on the quantitative analysis of surface defects of vehicle body exterior panels described in the present invention:

[0079] Taking the car body door outer panel as an example, the specific implementation process is as follows:

[0080] Step 1: Obtain point cloud data of the body door outer panel: Use the ATOS blue light scanning device to obtain actual point cloud data of the body door outer panel;

[0081] Step 2: Identify surface defects of the body door outer panel: Import the actual point cloud data of the body door outer panel and the theoretical surface model of the body door outer panel into CATIA software, and use the light and shadow analysis tool to compare and analyze the zebra stripes of the actual point cloud data of the body door outer panel and the theoretical surface model of the body door outer panel, such as Figure 5 As shown in the figure, the zebra stripes of the actual point cloud data of the body door outer panel in the circled area are distorted compared with the theoretical surface digital model of the body door outer panel. Therefore, these areas are defined as potential surface defect areas of the body door outer panel.

[0082] Step 3: Quantitatively evaluate surface defects of the body door outer panel: Quantitatively analyze the potential surface defect areas of the body door outer panel identified in Step 2 using the Gom Inspect software. This includes the following sub-steps:

[0083] Sub-step 1: Data import, fitting, and cropping: Import the actual point cloud data of the body door outer panel and the theoretical surface digital model of the body door outer panel into the Gom Inspect software. Initially fit the imported actual point cloud data of the body door outer panel with the spatial position of the surface data of the body door outer panel. Surface patches and point cloud patches are extracted for the potential surface defect areas of the body door outer panel, and the remaining areas are deleted.

[0084] Sub-step 2: Repair and smooth the door outer panel point cloud data: Use the mesh defect repair tool in the Gom Inspect software to repair the actual point cloud data of the body door outer panel. Then use the mesh smoothing tool in the Gom Inspect software to smooth the actual point cloud data of the repaired body door outer panel.

[0085] Sub-step 3, calculating surface defects of the actual point cloud data of the vehicle body door outer panel and the theoretical surface data of the vehicle body door outer panel: Calculating surface defects of the actual point cloud data of the vehicle body door outer panel and the theoretical surface data of the vehicle body door outer panel in Gom Inspect software;

[0086] Sub-step 4: Create a surface defect distribution cloud map of the body door outer panel: In the Gom Inspect software, perform a subtraction calculation on the surface defects of the actual point cloud data of the body door outer panel and the vector set of surface defects of the theoretical surface data of the body door outer panel to generate a surface defect distribution cloud map of the body door outer panel, such as Figure 6 As shown in the figure, positive values ​​represent convex hull defects and negative values ​​represent pit defects.

[0087] This embodiment provides a practical implementation method based on the quantitative analysis of surface defects of vehicle body exterior panels described in the present invention:

[0088] Taking the car body roof outer panel as an example, the specific implementation process is as follows:

[0089] Step 1: Obtain point cloud data of the vehicle body roof outer panel: Use the ATOS blue light scanning device to obtain point cloud data of the vehicle body roof outer panel;

[0090] Step 2: Identify surface defects of the body roof outer panel: Import the actual point cloud data of the body roof outer panel and the theoretical surface digital model of the body roof outer panel into CATIA software, and use the light and shadow analysis tool to compare and analyze the zebra stripes of the actual point cloud data of the body roof outer panel and the theoretical surface digital model of the body roof outer panel, such as Figure 7 As shown in the figure, the zebra stripes of the point cloud data of the vehicle body roof outer panel in the area circled around the shark fin antenna installation nest are distorted compared to the theoretical surface digital model of the vehicle body roof outer panel. Therefore, these areas are defined as potential surface defect areas of the vehicle body roof outer panel.

[0091] Step 3: Quantitatively evaluate the surface defects of the body roof outer panel: Quantitatively analyze the potential surface defect areas of the body roof outer panel identified in Step 2 using the Gom Inspect software. This includes the following sub-steps:

[0092] Sub-step 1: Data import, fitting, and cropping: Import the actual point cloud data of the roof panel and the theoretical surface model of the roof panel into the Gom Inspect software. Perform an initial fit between the imported actual point cloud data and the spatial position of the surface data of the roof panel. Extract the surface patch and point cloud patch of the area around the shark fin antenna mounting socket, which contains potential surface defects of the roof panel, and delete the remaining areas.

[0093] Sub-step 2: Repair and smooth the actual point cloud data of the vehicle roof panel: Use the mesh defect repair tool in the Gom Inspect software to repair the actual point cloud data of the vehicle roof panel, and then use the mesh smoothing tool in the Gom Inspect software to smooth the repaired actual point cloud data of the vehicle roof panel;

[0094] Sub-step 3: Calculate surface defects of the actual point cloud data and theoretical surface data of the vehicle body roof outer panel: Calculate surface defects of the actual point cloud data and theoretical surface data of the vehicle body roof outer panel corresponding to the area surrounding the shark fin antenna mounting hole in the vehicle body roof outer panel using the Gom Inspect software.

[0095] Sub-step 4: Create a surface defect distribution cloud map of the vehicle body roof outer panel: In the Gom Inspect software, perform a subtraction calculation on the surface defects of the actual point cloud data of the vehicle body roof outer panel and the vector set of surface defects of the theoretical surface data of the vehicle body roof outer panel to generate a surface defect distribution cloud map of the vehicle body roof outer panel, such as Figure 8 As shown in the figure, positive values ​​represent convex hull defects and negative values ​​represent pit defects.

[0096] Based on point cloud data of real exterior panels and a digital model of the product's curved surface, digital light and shadow technology is used to quickly identify potential defects and quantitatively analyze them. Ultimately, the system outputs the defect type (pit or bump), its distribution characteristics, and the defect magnitude, providing effective data support for correcting surface defects. This invention is a digital detection and analysis method that helps shorten the defect rectification cycle.

[0097] The above is a detailed introduction to the quantitative analysis method and system for surface defects of vehicle body exterior covering parts proposed in the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for general technical personnel in this field, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A quantitative analysis method for surface defects of vehicle body exterior panels, characterized in that: The method comprises the following steps: Step S1, scanning the outer surface of the vehicle body outer covering by optical detection equipment, thereby generating actual point cloud data of the vehicle body outer covering; Step S2, importing the actual point cloud data of the vehicle body outer cover generated in step S1 and the theoretical surface digital model of the vehicle body outer cover into computer-aided design software, and comparing the actual zebra stripes of the vehicle body outer cover with the zebra stripes of the theoretical surface digital model of the vehicle body outer cover; Step S3, defining an area where the zebra stripes of the actual vehicle body outer cover are inconsistent with the zebra stripes of the theoretical curved surface digital model of the vehicle body outer cover as a potential surface defect area of ​​the vehicle body outer cover; Step S4, quantitatively analyzing potential surface defect areas of the vehicle body outer covering parts and creating a surface defect distribution cloud map of the vehicle body outer covering parts; The quantitative analysis of potential surface defect areas of the vehicle body outer covering parts and the creation of a surface defect distribution cloud map of the vehicle body outer covering parts include the following steps: Step S401: Importing actual point cloud data of the vehicle body outer panel and a theoretical surface model of the vehicle body outer panel into the software, fitting the imported actual point cloud data of the vehicle body outer panel and the theoretical surface model of the vehicle body outer panel, and extracting surface patches and point cloud patches of the vehicle body outer panel where potential surface defects are located; Step S402 , using a mesh defect repair tool to repair and smooth the actual point cloud data of the vehicle body exterior panel; Step S403 , respectively calculating the surface defect value of the actual point cloud data of the vehicle body outer cover and the surface defect value of the theoretical surface numerical model of the vehicle body outer cover; Step S404 , performing a subtraction calculation on the surface defect values ​​of the actual point cloud data of the vehicle body outer panel and the vector set of surface defect values ​​of the theoretical surface model of the vehicle body outer panel to generate a surface defect distribution cloud map of the vehicle body outer panel.

2. A quantitative analysis method for surface defects of vehicle body exterior panels according to claim 1, characterized in that: The vehicle body outer covering parts include an engine hood outer panel, fenders, door outer panels, side panel outer panels, trunk outer panels, back door outer panels and roof cover outer panels.

3. The quantitative analysis method for surface defects of vehicle body exterior panels according to claim 1, characterized in that: The point cloud data includes direct data without any processing and indirect data obtained by processing the direct data.

4. A quantitative analysis method for surface defects of vehicle body exterior panels according to claim 3, characterized in that: The processing of direct data is repairing or smoothing.

5. The quantitative analysis method for surface defects of vehicle body exterior panels according to claim 1, characterized in that: The method of importing the actual point cloud data of the vehicle body outer covering part generated in step S1 and the theoretical surface digital model of the vehicle body outer covering part into the computer-aided design software is any computer-aided design software having a light and shadow analysis function.

6. The quantitative analysis method for surface defects of vehicle body exterior panels according to claim 1, characterized in that: The comparison between the actual zebra stripes of the vehicle body outer covering and the zebra stripes of the theoretical curved surface digital model of the vehicle body outer covering is performed by using a light and shadow analysis tool.

7. The quantitative analysis method for surface defects of vehicle body exterior panels according to claim 1, characterized in that: The potential surface defect areas of the vehicle body outer covering include pit-type defects, convex-type defects and wave-type defects.

8. The quantitative analysis method for surface defects of vehicle body exterior panels according to claim 1, characterized in that: The quantitative analysis of potential surface defect areas of the vehicle body outer covering parts is performed using Gom Inspect software.

9. A quantitative analysis system for surface defects of vehicle body exterior panels, characterized in that: The system includes the following modules: The point cloud data module uses optical inspection equipment to scan the outer surface of the vehicle body outer cover to generate actual point cloud data of the vehicle body outer cover; a comparison module for importing the actual point cloud data of the vehicle body outer covering generated by the point cloud data module and the theoretical surface digital model of the vehicle body outer covering into the computer-aided design software to compare the actual zebra stripes of the vehicle body outer covering with the zebra stripes of the theoretical surface digital model of the vehicle body outer covering; a definition module, defining an area where the zebra stripes of the vehicle body outer covering are inconsistent when comparing the actual zebra stripes of the vehicle body outer covering with the theoretical surface digital model of the vehicle body outer covering as a potential surface defect area of ​​the vehicle body outer covering; The analysis module quantitatively analyzes potential surface defect areas of vehicle body exterior panels and creates a surface defect distribution cloud map of vehicle body exterior panels; The quantitative analysis of potential surface defect areas of vehicle body exterior panels and the creation of a surface defect distribution cloud map of vehicle body exterior panels include the following modules: Module S401: Importing actual point cloud data of a vehicle body exterior panel and a theoretical surface model of the vehicle body exterior panel into the software, fitting the imported actual point cloud data and the theoretical surface model of the vehicle body exterior panel, and extracting surface patches and point cloud patches of potential surface defect areas of the vehicle body exterior panel; Module S402, using a mesh defect repair tool to repair and smooth the actual point cloud data of the vehicle body exterior panel; Module S403, respectively calculating the surface defect value of the actual point cloud data of the vehicle body outer cover and the surface defect value of the theoretical surface digital model of the vehicle body outer cover; Module S404 performs a subtraction calculation on the surface defect values ​​of the actual point cloud data of the vehicle body outer cover and the vector set of surface defect values ​​of the theoretical surface numerical model of the vehicle body outer cover to generate a surface defect distribution cloud map of the vehicle body outer cover.

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