Automatic measurement method and system for automobile white body parts

Abstract

The invention discloses an automatic measurement method and system for automobile white body parts, and relates to the technical field of automobile detection, and the method comprises the steps: positioning and clamping an automobile white body in a clamping tool, and forming a rough positioning and clamping state based on a first clamping mechanism; identifying and outputting coarse positioning coordinates through a machine vision system arranged on the detection station; based on the coarse positioning coordinates, an automobile body-in-white target posture used for optimization detection is planned, and according to the target posture, a plurality of second clamping mechanisms are controlled to carry out height adjustment and cooperate with the first clamping mechanism to form a fine positioning clamping state; constructing a detection path according to the actual posture of the automobile white body; and a flexible execution mechanism carrying a high-precision sensor is controlled to accurately detect and analyze the body-in-white of the automobile along the detection path, and detection data are output. Through coarse positioning and clamping, image acquisition and preliminary analysis, attitude planning and conversion, detection path construction and accurate detection analysis, the problems of detection blind areas and low efficiency caused by a fixed attitude are solved.

Description

Technical Field

[0001] This invention relates to the field of automotive testing technology, specifically to an automated testing system for automotive body parts. Background Technology

[0002] In the automobile manufacturing process, the quality inspection of the vehicle body is a core step in ensuring the structural integrity and safety performance of the entire vehicle. Currently, the industry commonly uses a combination of fixed clamping fixtures, photoelectric sensors, and cameras to inspect the connection points of components and surface defects of the overall structure of the vehicle body. This type of fixed fixture rigidly locks the vehicle body in a single posture, limiting the inspection operation to a preset path and preventing dynamic adjustment of the body posture based on the actual defect distribution. This limitation results in significant blind spots in the inspection of complex curved areas, internal structures, or hidden parts, making comprehensive coverage difficult and easily leading to missed or misjudged surface defects. Simultaneously, the fixed posture severely restricts the sensor's observation angle, reducing the accuracy and consistency of defect identification. Furthermore, when inspecting different areas, operators need to repeatedly disassemble and reposition the fixtures or body, not only extending the inspection cycle but also introducing additional human error, resulting in low overall inspection efficiency and failing to meet the urgent needs of modern automotive production lines for high-precision, high-efficiency inspection. These problems directly restrict the level of automation and quality control capabilities of the inspection process. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art. This invention provides an automated measurement method and system for automotive body parts. Through coarse positioning clamping, image acquisition and preliminary analysis, posture planning and conversion, detection path construction and precise detection analysis, it solves the problems of detection blind spots and low efficiency caused by fixed posture. It has the advantages of realizing dynamic posture adjustment of automotive body parts, reducing detection blind spots, and improving detection accuracy and efficiency.

[0004] This invention provides an automated measurement method for automotive body parts. The method is applicable to automotive body clamping fixtures, which include: a worktable, two sets of first clamping mechanisms symmetrically distributed on both sides of the worktable, and a second clamping mechanism disposed between the two sets of first clamping mechanisms. The measurement method includes: The car body is positioned and clamped in the clamping fixture. The car body is initially positioned and clamped based on the first clamping mechanism to form a coarse positioning and clamping state. The machine vision system deployed at the inspection station performs image acquisition and preliminary analysis on the overall structural surface and connection position of the car body in the coarse positioning clamping state, and identifies and outputs the coarse positioning coordinates of one or more defective areas to be confirmed. Based on the coarse positioning coordinates, a target posture of the car body-in-white for optimized detection is planned. According to the target posture, multiple second clamping mechanisms are controlled to adjust their height and work in conjunction with the first clamping mechanism to convert the car body-in-white from the coarse positioning clamping state to the fine positioning clamping state corresponding to the target posture. A detection path is constructed based on the coarse positioning coordinates and the actual posture of the car body-in-white under the fine positioning clamping state. The system controls a flexible actuator equipped with high-precision sensors to perform precise detection and analysis of the vehicle body-in-white along the detection path and outputs detection data.

[0005] Furthermore, the step of using a machine vision system positioned at the inspection station to acquire and perform preliminary analysis of images of the overall structural surface and connection positions of the vehicle body in the coarse positioning clamping state, and to identify and output the coarse positioning coordinates of one or more defect areas to be confirmed, includes: A panoramic image of the vehicle body is acquired by a global scanning camera, and the panoramic image of the vehicle body is matched with a preset CAD model of the vehicle body. By projecting the CAD model onto the matched panoramic image of the vehicle body, the edge detection algorithm is used to find the actual edges in the panoramic image of the vehicle body that correspond to the projected contour and feature points, thereby obtaining the vehicle body contour and key feature points. Based on the identification of the vehicle body outline and key feature points, a preliminary defect detection is performed on the surface of the vehicle body to screen out areas that may have defects, and the three-dimensional coarse positioning coordinates of the defect areas to be confirmed in the initial coordinate system are output.

[0006] Furthermore, the step of planning the target pose of the vehicle body-in-white for optimized detection based on the coarse positioning coordinates includes: The coarse positioning coordinates are grouped based on the K-means clustering algorithm, and the distribution data of the coarse positioning coordinates are divided on the car body. The concentrated distribution location of the defect area to be detected is extracted from the distribution data of the coarse positioning coordinates. Based on the concentrated distribution location, the corresponding target posture is selected from the preset posture library. The target posture includes one of the forward tilt posture, backward tilt posture, side tilt posture and composite tilt posture.

[0007] Furthermore, the step of controlling multiple second clamping mechanisms to adjust their height according to the target posture, and coordinating with the first clamping mechanism to convert the car body-in-white from the coarse positioning clamping state to the fine positioning clamping state corresponding to the target posture, includes: Based on the selected target posture and the CAD model of the car body in white, calculate the target height required for each second clamping mechanism; The servo lifting column of the plurality of second clamping mechanisms is controlled to move to the target height, and during the adjustment process, the clamping force of each second clamping mechanism is monitored and adjusted in real time by the force sensor on each clamping mechanism. The car body-in-white is clamped and fixed by several second clamping mechanisms to form a precise positioning clamping state.

[0008] Furthermore, the step of constructing the detection path based on the coarse positioning coordinates and the actual posture of the car body in the fine positioning clamping state includes: Based on the height values ​​of each second clamping mechanism under the precise positioning and clamping state, the spatial transformation parameters of the actual posture of the vehicle body are calculated. The coarse positioning coordinates are transformed to the current robot coordinate system using the spatial transformation parameters. Accessibility analysis and collision detection simulation are performed on the transformed coordinate point set to plan the optimal motion sequence and smooth trajectory of the flexible actuator.

[0009] Furthermore, the calculation of the spatial transformation parameters of the vehicle body's actual posture based on the height values ​​of each of the second clamping mechanisms in the precise positioning and clamping state includes: When performing precise positioning and clamping on the car body, acquire the body posture change data of the car body, and obtain the theoretical height value of each second clamping mechanism based on the body posture change data; A sensitivity matrix is ​​constructed based on the mapping relationship between vehicle posture change data and the theoretical height value; The actual height values ​​of each second clamping mechanism are obtained, and the spatial transformation parameters of the actual attitude of the vehicle body are calculated by combining the theoretical height values ​​and the sensitivity matrix.

[0010] Furthermore, the step of transforming the coarse positioning coordinates to the current robot coordinate system using the spatial transformation parameters includes: Based on the spatial transformation parameters, the coarse positioning coordinates are corrected to obtain several corresponding coordinate points of the coarse positioning coordinates on the digital model of the vehicle body. Based on the installation method of the robot's end-effector, obtain the relative positional relationship between the end-effector and the digital model of the vehicle body in the robot coordinate system; The corresponding coordinate points are converted into motion node coordinates in the robot coordinate system based on the relative positional relationship.

[0011] Furthermore, the control mechanism equipped with a high-precision sensor performs precise detection and analysis of the vehicle body-in-white along the detection path, and outputs detection data including: Based on the actual posture and defect location, at least two fixed cameras with optimal viewing angles are dynamically selected to synchronously acquire images and obtain fixed detection data. Simultaneously, the end sensor carried by the flexible actuator is controlled to move to a supplementary observation position to collect data and obtain dynamic detection data; The fixed detection data and the dynamic detection data are mapped to the surface features of the same three-dimensional vehicle body model and fused for analysis to output the detection data of the vehicle body.

[0012] Furthermore, the automated measurement method also includes: Collect and record the key dimensional measurements, defect statistics, and corresponding process parameters for each automotive body-in-white; A continuous production data control chart is drawn based on the key dimension measurements, the defect statistics, and the corresponding process parameters. Based on the connected production data control chart, the detection change trend of the automated measurement system is obtained. If the detection change trend shows an abnormality, real-time early warning information is generated.

[0013] The present invention also provides an automated measurement system for automotive body parts, the measurement system being used to execute the automated measurement method, the measurement system comprising: Coarse positioning clamping assembly: used to position and clamp the car body in the clamping fixture, and to perform initial positioning and clamping of the car body based on the first clamping mechanism to form a coarse positioning clamping state; Coarse positioning analysis component: Used to acquire and perform preliminary analysis of the overall structural surface and connection position of the car body in the coarse positioning clamping state by a machine vision system arranged on the inspection station, and to identify and output the coarse positioning coordinates of one or more defect areas to be confirmed. Fine positioning clamping component: used to plan the target posture of the vehicle body for optimized detection based on the coarse positioning coordinates, control multiple second clamping mechanisms to adjust the height according to the target posture, and coordinate with the first clamping mechanism to convert the vehicle body from the coarse positioning clamping state to the fine positioning clamping state corresponding to the target posture; Detection path planning component: used to construct a detection path based on the coarse positioning coordinates and the actual posture of the vehicle body under the fine positioning clamping state; Detection component: Used to control a flexible actuator equipped with a high-precision sensor to perform precise detection and analysis of the vehicle body along the detection path, and output detection data.

[0014] This invention provides an automated measurement method and system for automotive body parts. By using coarse positioning clamping, preliminary identification, and fine positioning clamping detection, it solves the problems of blind spots and low efficiency caused by the fixed posture of existing automotive body parts inspection. It has the advantages of realizing dynamic posture adjustment of automotive body parts, reducing blind spots, and improving detection accuracy and efficiency. Attached Figure Description

[0015] Figure 1 This is a flowchart of the automated measurement method for automotive body parts in this embodiment of the invention; Figure 2 This is a schematic diagram of the clamping fixture structure for automated inspection of automotive body parts in an embodiment of the present invention; Figure 3 This is a side view of the clamping fixture for automated inspection of automotive body parts in an embodiment of the present invention; Figure 4 This is a schematic diagram of an automated measurement system for automotive body parts in an embodiment of the present invention. Detailed Implementation

[0016] 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1: Figure 1 A flowchart of an automated measurement method for automotive body parts according to an embodiment of the present invention is shown. Figure 2 This diagram illustrates a clamping fixture structure for automated inspection of automotive body parts in an embodiment of the present invention. Figure 3 This diagram shows a side view of the clamping fixture for automated inspection of automotive body parts in an embodiment of the present invention. The measurement method is applicable to the automotive body clamping fixture, which includes: a worktable 100, two sets of first clamping mechanisms 200 symmetrically distributed on both sides of the worktable 100, and a second clamping mechanism 300 disposed between the two sets of first clamping mechanisms 200. The automotive body clamping fixture is a special device used to fix the automotive body during the inspection process. The automotive body refers to the structural state of the car after the main automotive components, including the frame, front beam, and chassis, have been welded and installed, but before painting and sheet metal installation. By inspecting the automotive body, the structural stability of the connection positions between the various main automotive components is obtained, and the surface defects of the main automotive components are detected to ensure the reliability of the automotive manufacturing inspection.

[0018] The automated measurement method includes: S11: Position the car body and clamp it in the clamping fixture. Based on the first clamping mechanism 200, perform initial positioning and clamping of the car body to form a coarse positioning and clamping state.

[0019] First, the car body is positioned and clamped in the clamping fixture. The first clamping mechanism 200 performs initial positioning and clamping of the car body to form a coarse positioning and clamping state, providing a stable initial platform for subsequent fine inspection.

[0020] Furthermore, a multi-axis robot can be used to place the car body on the workbench 100, and then the robotic arm or pneumatic clamp on the first clamping mechanism 200 can be operated to fix the car body and achieve initial positioning. The clamping force applied by the first clamping mechanism 200 can ensure that the initial clamping state of the first clamping mechanism 200 can maintain the car body in a roughly stable clamping state at the inspection station, avoiding large displacements in subsequent operations.

[0021] Specifically, step S11 includes: The first repeatability positioning accuracy is achieved by using a short-stroke cylinder-driven area positioner to cooperate with the process positioning hole of the body. The short-stroke cylinder-driven area positioner is a device that uses compressed air as a power source and drives the positioning element by the reciprocating motion of the piston within a short distance. Its function is to provide accurate and fast linear motion, drive the positioning element to cooperate with the body, and achieve rapid positioning.

[0022] Furthermore, the process positioning holes of the blank refer to the standardized holes pre-drilled during the manufacturing process for positioning and assembly. These holes typically possess high machining and positional accuracy. Their function is to serve as positioning references, working in conjunction with the positioner to ensure the accuracy and repeatability of the blank's initial position in the clamping fixture.

[0023] Furthermore, a pneumatic floating clamp is used to flexibly hold the workpiece in non-critical areas. The pneumatic floating clamp is a clamping device driven by compressed air, possessing a certain degree of floating or flexibility. The floating characteristic allows the clamp to adaptively adjust to minor positional deviations or shape irregularities of the workpiece during clamping, avoiding excessive stress.

[0024] Once the car body is inserted into the clamping fixture, the area positioner driven by the short-stroke cylinder extends rapidly, and its positioning element precisely inserts into the pre-set process positioning hole on the car body. Because the process positioning hole has standardized dimensions and positions, and the short-stroke cylinder provides highly repeatable driving force, this engagement ensures that the car body achieves initial repeatability in different directions, thus initially fixing the car body in a relatively accurate position.

[0025] Furthermore, once the vehicle body is initially positioned within the first clamping mechanism 200, the pneumatic floating clamp is activated, applying a flexible clamping force to non-critical areas of the vehicle body. The floating characteristic of the pneumatic floating clamp allows it to adapt to minor irregularities or positional deviations on the vehicle body surface, avoiding stress concentration or vehicle body deformation that could result from traditional rigid clamping. This flexible clamping not only protects the vehicle body but also provides sufficient clamping stability to prevent displacement during subsequent inspections.

[0026] S12: The machine vision system deployed at the inspection station performs image acquisition and preliminary analysis on the overall structural surface and connection position of the car body in the coarse positioning clamping state, identifies and outputs the coarse positioning coordinates of one or more defect areas to be confirmed.

[0027] Specifically, the machine vision system deployed at the inspection station acquires and performs preliminary analysis on the overall structural surface and connection positions of the car body in a coarse positioning clamping state, identifies and outputs the coarse positioning coordinates of one or more defect areas to be confirmed, and uses this to quickly identify potential defect areas, providing targets for subsequent fine inspection.

[0028] Specifically, step S12 includes: A panoramic image of the vehicle body is acquired using a global scanning camera, and then matched with a preset CAD model of the vehicle body. A global scanning camera is a device capable of acquiring a large-area, high-resolution image in a single scan or with a few scans, providing comprehensive visual information about the overall structural surface and connection positions of the vehicle body. For example, a high-resolution industrial line scan camera, along with a linear guide rail, can be used to perform a single scan above the vehicle body, and software can then stitch the continuously acquired image frames into a complete panoramic image of the vehicle body.

[0029] Furthermore, matching the panoramic image of the vehicle body with a pre-set CAD model of the vehicle body refers to comparing the actually acquired image with the theoretical design model (CAD model) to determine the correspondence between the two, providing an accurate geometric reference for subsequent feature recognition and defect localization. Specifically, feature-point-based matching algorithms, such as SIFT, SURF, or ORB, can be used to extract key feature points from the image and CAD model, and then matching and pose estimation can be performed using methods such as RANSAC.

[0030] Based on the matching of images and CAD models, the geometric boundaries (contours) of the car body and important structural feature points, such as holes, edges, and connection points, are identified to accurately locate the actual posture and key parts of the car body, providing an accurate reference for the preliminary identification of defects.

[0031] Based on matching and recognizing the vehicle body contour and key feature points, and preliminarily identifying surface defects, the system outputs the three-dimensional coarse positioning coordinates of the defect area to be confirmed in the initial coordinate system. This can be achieved by projecting the CAD model onto the matched image and then using edge detection algorithms (such as Canny and Sobel) to find the actual edges in the image corresponding to the projected contour and feature points. Alternatively, image processing techniques, such as morphological operations and threshold segmentation, can be used to extract the vehicle body contour from the image and then verify and correct it in conjunction with the CAD model. Preliminary identification of surface defects refers to performing preliminary defect detection on the surface of the vehicle body based on the recognition of the vehicle body contour and key feature points, in order to quickly screen out areas that may contain defects and narrow down the scope of subsequent high-precision detection.

[0032] A machine vision system deployed at the inspection station acquires and performs preliminary image analysis on the overall structural surface and connection positions of the vehicle body under coarse positioning and clamping conditions. This allows for the identification and output of coarse positioning coordinates for one or more defect areas to be confirmed. Specifically, firstly, a panoramic image of the vehicle body is acquired using a global scanning camera, ensuring comprehensive coverage of the entire vehicle surface and avoiding omissions that might occur with partial acquisition. Then, the acquired panoramic image of the vehicle body is matched with a pre-set, high-precision CAD model of the vehicle body. This matching process utilizes the precise geometric information of the CAD model to calibrate and position the actual image, thereby accurately identifying the vehicle body contour and key feature points. Based on this precise matching, the system can preliminarily identify potential defects on the vehicle body surface. Finally, the location information of these preliminarily identified defect areas is calculated through geometric transformation and output as three-dimensional coarse positioning coordinates in the initial coordinate system.

[0033] Furthermore, by using a machine vision system to perform preliminary analysis of the surface defect locations in the coarse positioning and clamping state of the vehicle, the spatial information and defect locations of the vehicle body can be obtained comprehensively and accurately. This provides reliable and accurate initial data for subsequent fine positioning and clamping state transitions and high-precision inspection, effectively improving the accuracy and efficiency of the entire measurement method.

[0034] S13: Based on the coarse positioning coordinates, plan the target posture of the car body for optimized detection. According to the target posture, control multiple second clamping mechanisms 300 to adjust their height and coordinate with the first clamping mechanism 200 to convert the car body from the coarse positioning clamping state to the fine positioning clamping state corresponding to the target posture.

[0035] Specifically, based on coarse positioning coordinates, a target posture for the vehicle body is planned for optimized detection. According to the target posture, multiple second clamping mechanisms 300 are controlled to adjust their height, and in coordination with the first clamping mechanism 200, the vehicle body is converted from a coarse positioning clamping state to a fine positioning clamping state corresponding to the target posture. The vehicle body posture is dynamically adjusted according to the defect distribution to facilitate observation by high-precision sensors. By operating the drive motor assembly of the second clamping mechanism 300, the height of the support point of the second clamping mechanism 300 changes, thereby changing the overall posture of the vehicle body. During the adjustment process, the first clamping mechanism 200 maintains its clamping function to ensure the stability of the vehicle body during the posture transition.

[0036] Furthermore, the clamping end of the first clamping mechanism 200 can be configured to be in a hinged state, so that when the second clamping mechanism 300 is adjusting its height, the first clamping mechanism 200 can meet the positional adjustment of the vehicle body.

[0037] Specifically, step S13 includes: Cluster analysis is performed on the coarse positioning coordinates to determine the concentrated distribution of the defect areas to be detected. The coarse positioning coordinates initially identified by the machine vision system are grouped to discover clustering patterns or concentrated areas of defects. This cluster analysis can be implemented using various algorithms. For example, the K-means clustering algorithm can be used, which iteratively calculates the centroid of each cluster and assigns each data point to the cluster containing the nearest centroid until the cluster assignment no longer changes or a preset number of iterations is reached.

[0038] Based on the concentrated distribution locations, a corresponding target posture is selected from a preset posture library. The target posture includes one of the following: forward tilt, backward tilt, side tilt, and combined tilt posture. After clustering analysis of the coarse positioning coordinates, the concentrated distribution locations of the defect areas to be detected are determined. The purpose is to identify areas with high defect point density and a large number of defect points from the clustering results, i.e., the concentrated distribution locations of defects. These locations are the focus of subsequent posture planning. This step can determine the most representative concentrated distribution locations by calculating the center point (e.g., centroid or average coordinates) of each cluster and combining it with the number or density of defect points within the cluster. For example, the center point of the cluster containing the largest number of defect points can be selected as the concentrated distribution location. Alternatively, further statistical analysis of the clustering results can be performed. For example, a threshold can be set, and areas exceeding the threshold density or number of points can be marked as concentrated distribution locations, and the bounding boxes or geometric centers of these areas can be calculated as their representative locations.

[0039] Based on the concentrated distribution locations, a corresponding target posture is selected from a preset posture library. This step intelligently selects the most suitable vehicle body posture for fine inspection from a pre-established posture set based on the determined concentrated distribution locations of defects. The purpose is to ensure that, under precise positioning and clamping conditions, the high-precision sensor can detect the defect area with optimal viewing angle and accessibility. The posture library can store the mapping relationship between different defect locations and recommended postures, and the system can match and select postures according to the concentrated distribution locations and preset rules in the posture library.

[0040] Furthermore, by selecting the corresponding target pose, most of the coarse positioning coordinates of several coarse positioning coordinates are directly exposed within the detection area, so as to construct the detection path and improve detection efficiency.

[0041] Step S13 further includes: Based on the selected target posture and the CAD model of the vehicle body, the target height required for each second clamping mechanism 300 is calculated. When calculating the target height required for each second clamping mechanism 300, three-dimensional posture planning software can be used to set the basic parameters of the target posture state of the vehicle body based on several coarse positioning coordinate data. By automatically inputting the basic parameters (e.g., tilt angle, rotation angle, etc.) into the three-dimensional posture planning software, the precise coordinate points that each second clamping mechanism 300 should support in three-dimensional space can be automatically calculated, and then the corresponding vertical height value can be calculated.

[0042] Based on the acquired vertical height, the servo lifting columns of the multiple second clamping mechanisms 300 are controlled to move to the target height. During the adjustment process, the clamping force of each second clamping mechanism 300 is monitored and adjusted in real time by force sensors on each clamping mechanism. Piezoelectric, strain gauge, or capacitive force sensors are installed at the clamping end of each second clamping mechanism 300 to collect clamping force data in real time and feed the data back to the control system. The control system dynamically adjusts the output torque of the servo motor or the pressure of the pneumatic / hydraulic clamps according to a preset safety force threshold and target clamping force range to prevent excessive clamping force from causing vehicle body deformation or damage, while also avoiding insufficient clamping force from causing vehicle body loosening, ensuring the stability and safety of the clamping process.

[0043] The vehicle body is clamped and fixed by several second clamping mechanisms 300, forming a precise positioning clamping state. When all the second clamping mechanisms 300 reach their respective target heights, the clamping force of the second clamping mechanisms 300 is precisely controlled within a safe and stable range, thereby firmly fixing the vehicle body in a position that precisely corresponds to the target posture, providing a stable benchmark for subsequent accurate testing.

[0044] Furthermore, by precisely calculating the target height of each of the second clamping mechanisms 300 and utilizing a servo lifting column to achieve high-precision height adjustment, the vehicle body is ensured to accurately transition to the preset target posture. Simultaneously, the clamping force is monitored and dynamically adjusted in real time during the adjustment process, effectively avoiding potential damage to the vehicle body due to improper clamping force and ensuring the stability and safety of the clamping. This precise positioning and clamping state provides a reliable foundation for subsequent accurate detection and analysis along the detection path by a flexible actuator equipped with high-precision sensors, resulting in more accurate detection data and significantly improving the accuracy and reliability of the entire automated measurement method.

[0045] S14: Construct the detection path based on the coarse positioning coordinates and the actual posture of the car body under the fine positioning clamping state.

[0046] Specifically, by constructing the detection path, precise trajectory guidance is provided for the movement of the flexible actuator. Based on the coarse positioning coordinates and the current posture of the vehicle body in the fine positioning clamping state, the coarse positioning coordinates are mapped onto the robot's workspace through simple geometric transformations. Then, by connecting these mapped coordinate points and considering the size and range of motion of the robot's end effector, a motion trajectory consisting of a series of straight line segments and circular arc segments is generated. This trajectory ensures that the flexible actuator can sequentially pass through all the defect areas to be detected.

[0047] Specifically, step S14 includes: Based on the height values ​​of each of the second clamping mechanisms 300 under the precise positioning and clamping state, the spatial transformation parameters of the vehicle body's actual posture are calculated; the actual spatial position and posture of the vehicle body under the precise positioning and clamping state are accurately obtained, providing a precise basis for subsequent coordinate transformation. A mathematical model between the height values ​​of the second clamping mechanisms 300 and the vehicle body posture is pre-calibrated, and combined with the real-time acquired actual height values ​​of each of the second clamping mechanisms 300, the six-degree-of-freedom spatial transformation parameters of the vehicle body's actual posture are calculated using interpolation or regression algorithms.

[0048] Specifically, the calculation of the spatial transformation coefficient of the actual posture of the vehicle body based on the height values ​​of each of the second clamping mechanisms 300 in the precise positioning and clamping state includes: When performing precise positioning and clamping on the car body, acquire the body posture change data of the car body, and obtain the theoretical height value of each second clamping mechanism based on the body posture change data; A sensitivity matrix is ​​constructed based on the mapping relationship between vehicle posture change data and the theoretical height value; The actual height values ​​of each second clamping mechanism are obtained, and the spatial transformation parameters of the actual attitude of the vehicle body are calculated by combining the theoretical height values ​​and the sensitivity matrix.

[0049] During the precise positioning and clamping process, the attitude change of the vehicle body is adjusted based on the cooperation between the first clamping mechanism 200 and the second clamping mechanism 300. The attitude change of the vehicle body includes 6 parameters: ΔX, ΔY, ΔZ, ΔRx, ΔRy, and ΔRz, which are the attitude changes of translation and deflection in various directions.

[0050] Based on the clamping and adjustment operations of several second clamping mechanisms 300 during precise positioning and clamping of the vehicle body, the height change data of all second clamping mechanisms 300 at the corresponding clamping points on the vehicle body are recorded. A linear relationship can be fitted through multiple sets of data: ,in, This is the sensitivity matrix. For highly variable data, This is the attitude change vector of the vehicle body. Each column of the sensitivity matrix J represents the change in height of each clamping point when the corresponding degree of freedom changes by one unit.

[0051] Least squares solution: .

[0052] The obtained ΔPose is the current attitude change of the vehicle body.

[0053] For example: If we have 4 gripping points (N=4), then J is a 4x6 matrix.

[0054] Four height change values ​​(Δh1, Δh2, Δh3, Δh4) were obtained from online measurements, forming a 4×1 vector ΔH.

[0055] The formula ΔPose=(J^T) J)^(-1) J^T ΔH is calculated to obtain a 6×1 vector, namely [ΔX, ΔY, ΔZ, ΔRx, ΔRy, ΔRz]^T.

[0056] Specifically, the step of transforming the coarse positioning coordinates to the current robot coordinate system using the spatial transformation parameters includes: Based on the spatial transformation parameters, the coarse positioning coordinates are corrected to obtain several corresponding coordinate points of the coarse positioning coordinates on the digital model of the vehicle body. Based on the installation method of the robot's end-effector, obtain the relative positional relationship between the end-effector and the digital model of the vehicle body in the robot coordinate system; The corresponding coordinate points are converted into motion node coordinates in the robot coordinate system based on the relative positional relationship.

[0057] By using the spatial transformation parameters to transform the coarse positioning coordinates to the current robot coordinate system, coordinate deviations caused by vehicle posture adjustments can be eliminated, ensuring that the coarse positioning coordinates are consistent with the robot's operating space, thereby ensuring the accuracy of the detection path.

[0058] Furthermore, the coarse positioning coordinates (usually relative to the vehicle's own coordinate system or the initial clamping coordinate system) can be calculated using matrix multiplication with spatial transformation parameters (rotation matrix R and translation vector T), i.e. This allows us to obtain the precise coordinates in the robot's coordinate system.

[0059] Accessibility analysis and collision detection simulation are performed on the transformed coordinate point set to plan the optimal motion sequence and smooth trajectory of the flexible actuator. This ensures that the flexible actuator can safely and efficiently reach all points to be detected during the detection process, avoiding collisions with tooling, vehicle body, or other equipment, and optimizing motion efficiency.

[0060] S15: Controls a flexible actuator equipped with high-precision sensors to perform precise detection and analysis of the vehicle body along the detection path and outputs detection data.

[0061] Specifically, by controlling a flexible actuator equipped with high-precision sensors to accurately detect and analyze the vehicle body along the detection path and output detection data, the laser scanner or high-resolution camera carried by the flexible actuator can be controlled to move according to a preset detection path. During the movement, the sensors continuously collect three-dimensional point cloud data or high-resolution images of the vehicle body surface. This data is transmitted in real time to the data processing unit for further analysis, such as comparing deviations with a standard CAD model or performing surface texture analysis, thereby identifying and quantifying the type, size, and location of defects, and outputting these detection results as detection data.

[0062] Specifically, step S15 includes: Based on the actual posture and defect location, at least two fixed cameras with optimal viewing angles are dynamically selected for synchronous image acquisition to obtain fixed detection data. The system can intelligently activate and use fixed cameras at different locations for image acquisition according to current detection needs, such as the identified defect location and the actual posture of the vehicle body. At least two optimal viewing angles ensure multi-angle coverage of the target area, effectively avoiding occlusion or information loss that may occur with a single viewing angle.

[0063] Synchronous acquisition ensures temporal consistency of images from different perspectives, facilitating subsequent fusion processing. This can be achieved by pre-calibrating the positions and fields of view of multiple fixed cameras and establishing a camera selection strategy library. When defect location and vehicle body attitude information are received, the system calculates the optimal camera combination that best covers the defect area and provides the best observation angle based on the strategy library, and then sends commands to activate these cameras for image acquisition.

[0064] Simultaneously, the end effector carried by the flexible actuator is moved to a supplementary observation position to collect data and acquire dynamic detection data. The end effector carried by the flexible actuator is used to collect supplementary, close-range data in areas that are difficult for a fixed camera to cover or that require more detailed information. Moving to a supplementary observation position means that the actuator can flexibly adjust its position and attitude as needed to obtain optimal supplementary data.

[0065] Furthermore, the flexible actuator can be a multi-axis industrial robot with a high-precision sensor mounted at its end effector, such as a laser scanner, structured light sensor, high-resolution macro camera, or ultrasonic probe. Based on the preliminary image analysis results acquired by the fixed camera, the system determines the areas requiring supplementary observation and plans the motion path of the flexible actuator to ensure its end effector sensors accurately reach these supplementary observation locations for data acquisition.

[0066] The fixed and dynamic detection data are mapped onto the surface features of the same 3D vehicle body model for fusion analysis, outputting the detection data for the vehicle body. Images and sensor data from different perspectives are mapped onto the same 3D vehicle body model for feature fusion and defect determination, thereby outputting detection data for surface defects on the vehicle body. By integrating multi-source heterogeneous data (fixed camera images, flexible actuator sensor data) into a unified 3D space, comprehensive analysis and defect determination can be performed. Mapping onto the same 3D vehicle body model can eliminate spatial differences between different data sources, achieving data alignment and fusion. Feature fusion refers to integrating feature information (such as color, texture, depth, and geometry) provided by different data sources to form a more comprehensive and robust defect description.

[0067] Furthermore, the defect determination is based on the fused feature information to determine the existence, type, and severity of defects. As one implementation method, firstly, the intrinsic and extrinsic parameters of all data acquisition devices are acquired through camera and sensor calibration. Then, using these parameters and the CAD model of the vehicle body, all acquired 2D images and 3D point cloud data are converted and projected onto a unified 3D coordinate system and registered with a pre-set 3D vehicle body model. On the registered 3D model, features from different data sources can be fused; for example, texture information from high-resolution images can be overlaid onto the geometric model acquired by a laser scanner. Finally, the fused features are analyzed using image processing algorithms, pattern recognition, or deep learning models to automatically identify and determine defects.

[0068] Specifically, a shooting and detection network consisting of fixed cameras is pre-deployed on the clamping fixture. After the fine positioning and clamping are completed, the pre-deployed fixed camera network is used to perform fixed visual shooting and detection operations on the car body. The shooting data of the fixed position camera with the best angle is selected in the shooting and detection network by combining the coarse positioning coordinates, thereby improving the accuracy of visual detection of the location of defects on the surface of the car body.

[0069] The flexible actuator is driven to inspect the vehicle body along the inspection path. The end-effector of the flexible actuator is used for close-range inspection. The shooting angle is adjusted so that the dynamic inspection data acquired by the flexible actuator can be adapted to the inspection requirements of the defect location of the vehicle body.

[0070] By controlling flexible actuators for supplementary observation, the blind spot problem of fixed cameras can be solved, and the accuracy requirements of detection for complex vehicle body structures (such as the inside of door frames, deep cavities, and the bottom of bolt holes) can be met.

[0071] Based on the fusion analysis of multi-source data on the surface of the 3D model, that is, by combining the fixed detection data and the dynamic detection data, the surface structure feature state of the corresponding defect location is reconstructed in the digital model of the car body. The fixed detection data and the dynamic detection data are processed in a unified coordinate system and transformed into the same global coordinate system through their respective calibration parameters. In this embodiment, the fixed detection data and the dynamic detection data are uniformly converted into the robot coordinate system for feature fusion analysis.

[0072] Furthermore, the surface defect analysis of the vehicle body includes: analysis of the dimensions and geometric tolerances of the mating positions of various components. By integrating multi-view data, data parameters such as hole spacing, edge straightness, and assembly clearance surface difference can be calculated more accurately.

[0073] 3D Reconstruction and Comparison: For complex regions, by fusing all observation data (especially dense point clouds collected by mobile sensors), a high-precision actual 3D surface of the region can be reconstructed and compared with the design CAD model using a 3D Delta Analysis.

[0074] Results Generation: The final output is a structured inspection report, including: pass / fail determination, specific defect type, location (visually marked on the 3D model), dimensions, and related measurement data. Simultaneously, this data can be uploaded to the MES (Manufacturing Execution System) for quality traceability and process analysis.

[0075] Specifically, step S15 further includes: Before data fusion, spatial registration is performed on images from different perspectives based on the intrinsic and extrinsic parameters of each camera and the actual posture of the vehicle body. Spatial alignment is then performed to ensure that all data to be fused are in the same spatial reference frame, eliminating spatial misalignment caused by differences in viewpoint, position, and posture. This can be achieved by executing an image registration algorithm after data acquisition but before any feature extraction or fusion algorithm runs, or by performing it during the data preprocessing stage as part of data cleaning and standardization before inputting the data into the fusion module.

[0076] Feature-level fusion is performed on the registered images to comprehensively evaluate the texture, geometry, and depth information of defects, thereby improving the accuracy of defect classification and quantification.

[0077] Specifically, the automated measurement method for automotive body parts also includes: Collect and record the key dimensional measurements, defect statistics, and corresponding process parameters for each automotive body piece; Based on the measured values ​​of the key dimensions, the statistical data of defects, and the corresponding process parameters, a continuous production data control chart is drawn; based on the continuous production data, the control chart is drawn, the process capability index is calculated, and real-time warnings are given for abnormal trends. During the automated inspection of multiple automotive body parts in the measurement system, disputes are comprehensively analyzed.

[0078] Based on the connected production data control chart, the detection change trend of the automated measurement system is obtained. If the detection change trend shows an abnormality, real-time early warning information is generated.

[0079] Furthermore, the process parameters include at least the clamping force, the height value of the second clamping mechanism 300, the vehicle body posture type, and the ambient temperature and humidity.

[0080] By systematically collecting and recording key dimensional measurements, defect statistics, and corresponding process parameters for each automotive body piece, a comprehensive data foundation is constructed. This data not only includes quality information about the automotive body itself but also covers key operational and environmental conditions affecting the stability of the inspection process and the accuracy of results. For example, clamping force, the 300° height value of the second clamping mechanism, and the vehicle body posture type are directly related to the stability of the automotive body in the precise positioning clamping state and the accuracy of the inspection posture, while environmental temperature and humidity may affect the accuracy of the measuring equipment and material properties. Statistical analysis of this continuous production data, such as drawing control charts and calculating process capability indices, allows for intuitive monitoring of the inspection process's stability and quantitative assessment of its ability to meet quality requirements. When abnormal fluctuations or trends occur in the inspection process or product quality, the system can issue real-time warnings based on preset statistical rules or models, thereby promptly identifying and correcting potential problems. This mechanism elevates the entire automated measurement method from simple defect identification to a system with self-monitoring and quality management capabilities. By closely integrating these process parameters with the coarse positioning clamping, fine positioning clamping, and precise inspection analysis steps in the aforementioned automated measurement method, the causal relationship between inspection results and process conditions can be analyzed in depth. For example, when the defect rate is found to be rising, the system can trace back to whether the corresponding clamping force or the 300 height value of the second clamping mechanism is abnormal, and then guide the adjustment of the clamping fixture or detection strategy. This not only detects defects, but also prevents the generation of defects from the source, significantly improving the reliability and quality control level of the entire automated measurement method.

[0081] This invention, through dynamically adjusting the posture of the vehicle body, combined with machine vision for preliminary identification, precise positioning and clamping, and optimized detection path planning, forms a collaborative and efficient automated inspection system. This system effectively addresses the limitations of traditional fixed clamping fixtures in inspecting complex structures and hidden defects, significantly improving the accuracy, coverage, and efficiency of automated inspection of automotive body parts.

[0082] Example 2: Figure 4 A schematic diagram of an automated measurement system for automotive body parts according to an embodiment of the present invention is shown. The measurement system is used to execute the automated measurement method and includes: Coarse positioning clamping assembly 10: used to position and clamp the car body in the clamping fixture, and to perform initial positioning clamping of the car body based on the first clamping mechanism to form a coarse positioning clamping state; first, the car body is positioned and clamped in the clamping fixture, and the car body is initially positioned and clamped based on the first clamping mechanism to form a coarse positioning clamping state, providing a stable initial platform for subsequent fine inspection.

[0083] Furthermore, a multi-axis robot can be used to place the car body on the worktable, and then the robotic arm or pneumatic clamp on the first clamping mechanism can be operated to fix the car body and achieve initial positioning. The clamping force is applied by the first clamping mechanism so that the initial clamping state of the first clamping mechanism can meet the requirement of maintaining the car body in a roughly stable clamping state at the inspection station, avoiding large displacements in subsequent operations.

[0084] Coarse positioning analysis component 20: It is used to acquire and perform preliminary analysis of the overall structural surface and connection position of the car body in the coarse positioning clamping state through a machine vision system arranged on the inspection station, identify and output coarse positioning coordinates of one or more defect areas to be confirmed; it can quickly identify potential defect areas and provide guidance for subsequent fine inspection.

[0085] Furthermore, the machine vision system can consist of multiple high-resolution industrial cameras, which are fixed above and to the side of the inspection station to capture images of the vehicle body from multiple angles and transmit the images to an image processing unit for analysis.

[0086] The fine-positioning clamping component 30 is used to plan the target posture of the vehicle body for optimized detection based on the coarse positioning coordinates. According to the target posture, it controls multiple second clamping mechanisms to adjust their height and, in coordination with the first clamping mechanism, transforms the vehicle body from the coarse-positioning clamping state to a fine-positioning clamping state corresponding to the target posture. The fine-positioning clamping component 30 enables flexible adjustment of the vehicle body posture to adapt to the optimal detection angle for different defect areas. The second clamping mechanism can consist of multiple independent servo-electric lifting columns, each with a flexible clamp and force sensor at its top, capable of precisely adjusting height and clamping force according to commands.

[0087] Detection path planning component 40: used to construct a detection path based on the coarse positioning coordinates and the actual posture of the vehicle body under the fine positioning clamping state; using the CAD model of the vehicle body, the coarse positioning coordinates provided by the coarse positioning analysis component, and the actual posture data fed back by the fine positioning clamping component, the robot motion trajectory is generated through inverse kinematics and collision detection algorithms, so as to minimize the detection time and maximize the detection coverage.

[0088] Detection component 50: Used to control a flexible actuator equipped with a high-precision sensor to perform precise detection and analysis of the vehicle body along the detection path, and output detection data.

[0089] The flexible actuator can be a multi-axis industrial robot with sensors such as a high-precision 3D laser scanner, a high-resolution industrial camera, or an ultrasonic probe at its end. The robot controller precisely controls the robot's movement and collects data synchronously based on the path instructions generated by the detection path planning component. Alternatively, a gantry or cantilever high-precision motion platform can be used, equipped with replaceable sensor modules to adapt to different types of detection needs.

[0090] The automated measurement system of this application organically integrates a coarse positioning clamping component 10, a coarse positioning analysis component 20, a fine positioning clamping component 30, a detection path planning component 40, and a detection component 50 into a collaborative whole. First, the coarse positioning clamping component 10 provides a stable initial positioning for the vehicle body, ensuring consistency of the starting point for subsequent inspections. Next, the coarse positioning analysis component 20 uses a machine vision system to quickly identify potential defect areas and outputs coarse positioning coordinates, providing a preliminary target for fine inspection. Based on this, the fine positioning clamping component 30 intelligently adjusts the posture of the vehicle body according to these coarse positioning coordinates, placing it at the optimal angle for high-precision sensor detection, thus overcoming the limitations of traditional fixed clamping methods. Subsequently, the detection path planning component 40 generates an optimal detection path for the flexible actuator based on the adjusted actual posture of the vehicle body and the coarse positioning coordinates, ensuring comprehensive and efficient inspection. Finally, the detection component 50 controls the flexible actuator equipped with a high-precision sensor to perform precise inspection along the planned path and outputs detailed inspection data. Through close cooperation and information flow between its components, the entire system achieves seamless integration from coarse positioning to fine positioning, and from preliminary analysis to precise detection, greatly improving the efficiency and accuracy of automated inspection of automotive body parts.

[0091] Through the above technical solutions, the automated measurement system of this application effectively solves the problems of poor detection results and low efficiency caused by the fixed clamping fixtures in traditional measurement methods. By introducing a clamping strategy combining coarse and fine positioning, the system allows the vehicle body to dynamically adjust its posture according to detection requirements, thus providing the optimal detection perspective for high-precision sensors and significantly improving the accuracy and comprehensiveness of defect identification. Simultaneously, the integrated coarse positioning analysis, detection path planning, and precision detection components automate and intelligentize the detection process, reducing manual intervention and improving detection efficiency and data reliability. Furthermore, the collaborative work between the system components ensures the smoothness and accuracy of the detection process, providing strong technical support for the quality control of automotive body parts.

[0092] This invention provides an automated measurement system for automotive body parts. By using coarse positioning clamping, preliminary identification, and fine positioning clamping detection, it solves the problems of blind spots and low efficiency caused by the fixed posture of existing automotive body parts inspection. It has the advantages of realizing dynamic posture adjustment of automotive body parts, reducing blind spots, and improving detection accuracy and efficiency.

[0093] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc.

[0094] Furthermore, the embodiments of the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. An automated measurement method for automotive body parts, characterized in that, The measurement method is applicable to automotive body clamping fixtures, which include: a worktable, two sets of first clamping mechanisms symmetrically distributed on both sides of the worktable, and a second clamping mechanism disposed between the two sets of first clamping mechanisms. The measurement method includes: The car body is positioned and clamped in the clamping fixture. The car body is initially positioned and clamped based on the first clamping mechanism to form a coarse positioning and clamping state. The machine vision system deployed at the inspection station performs image acquisition and preliminary analysis on the overall structural surface and connection position of the car body in the coarse positioning clamping state, and identifies and outputs the coarse positioning coordinates of one or more defective areas to be confirmed. Based on the coarse positioning coordinates, a target posture of the car body-in-white for optimized detection is planned. According to the target posture, multiple second clamping mechanisms are controlled to adjust their height and work in conjunction with the first clamping mechanism to convert the car body-in-white from the coarse positioning clamping state to the fine positioning clamping state corresponding to the target posture. A detection path is constructed based on the coarse positioning coordinates and the actual posture of the car body-in-white under the fine positioning clamping state. The system controls a flexible actuator equipped with high-precision sensors to perform precise detection and analysis of the vehicle body-in-white along the detection path and outputs detection data.

2. The automated measurement method for automotive body parts as described in claim 1, characterized in that, The process of acquiring and performing preliminary image analysis of the overall structural surface and connection positions of the car body under the coarse positioning clamping state using a machine vision system deployed at the inspection station, and identifying and outputting the coarse positioning coordinates of one or more defective areas to be confirmed, includes: A panoramic image of the vehicle body is acquired by a global scanning camera, and the panoramic image of the vehicle body is matched with a preset CAD model of the vehicle body. By projecting the CAD model onto the matched panoramic image of the vehicle body, the edge detection algorithm is used to find the actual edges in the panoramic image of the vehicle body that correspond to the projected contour and feature points, thereby obtaining the vehicle body contour and key feature points. Based on the identification of the vehicle body outline and key feature points, a preliminary defect detection is performed on the surface of the vehicle body to screen out areas that may have defects, and the three-dimensional coarse positioning coordinates of the defect areas to be confirmed in the initial coordinate system are output.

3. The automated measurement method for automotive body parts as described in claim 1, characterized in that, The step of planning the target pose of the car body-in-white for optimized detection based on the coarse positioning coordinates includes: The coarse positioning coordinates are grouped based on the K-means clustering algorithm, and the distribution data of the coarse positioning coordinates are divided on the car body. The concentrated distribution location of the defect area to be detected is extracted from the distribution data of the coarse positioning coordinates. Based on the concentrated distribution location, the corresponding target posture is selected from the preset posture library. The target posture includes one of the forward tilt posture, backward tilt posture, side tilt posture and composite tilt posture.

4. The automated measurement method for automotive body parts as described in claim 1, characterized in that, The step of controlling multiple second clamping mechanisms to adjust their height according to the target posture, and coordinating with the first clamping mechanism to convert the car body-in-white from the coarse positioning clamping state to the fine positioning clamping state corresponding to the target posture, includes: Based on the selected target posture and the CAD model of the car body in white, calculate the target height required for each second clamping mechanism; The servo lifting column of the plurality of second clamping mechanisms is controlled to move to the target height, and during the adjustment process, the clamping force of each second clamping mechanism is monitored and adjusted in real time by the force sensor on each clamping mechanism. The car body-in-white is clamped and fixed by several second clamping mechanisms to form a precise positioning clamping state.

5. The automated measurement method for automotive body parts as described in claim 1, characterized in that, The step of constructing the detection path based on the coarse positioning coordinates and the actual posture of the car body in the fine positioning clamping state includes: Based on the height values ​​of each second clamping mechanism under the precise positioning and clamping state, the spatial transformation parameters of the actual posture of the vehicle body are calculated. The coarse positioning coordinates are transformed to the current robot coordinate system using the spatial transformation parameters. Accessibility analysis and collision detection simulation are performed on the transformed coordinate point set to plan the optimal motion sequence and smooth trajectory of the flexible actuator.

6. The automated measurement method for automotive body parts as described in claim 5, characterized in that, The calculation of the spatial transformation parameters of the actual vehicle body posture based on the height values ​​of each of the second clamping mechanisms in the precise positioning and clamping state includes: When performing precise positioning and clamping on the car body, acquire the body posture change data of the car body, and obtain the theoretical height value of each second clamping mechanism based on the body posture change data; A sensitivity matrix is ​​constructed based on the mapping relationship between vehicle posture change data and the theoretical height value; The actual height values ​​of each second clamping mechanism are obtained, and the spatial transformation parameters of the actual attitude of the vehicle body are calculated by combining the theoretical height values ​​and the sensitivity matrix.

7. The automated measurement method for automotive body parts as described in claim 6, characterized in that, The step of transforming the coarse positioning coordinates to the current robot coordinate system using the spatial transformation parameters includes: Based on the spatial transformation parameters, the coarse positioning coordinates are corrected to obtain several corresponding coordinate points of the coarse positioning coordinates on the digital model of the vehicle body. Based on the installation method of the robot's end-effector, obtain the relative positional relationship between the end-effector and the digital model of the vehicle body in the robot coordinate system; The corresponding coordinate points are converted into motion node coordinates in the robot coordinate system based on the relative positional relationship.

8. The automated measurement method for automotive body parts as described in claim 1, characterized in that, The control mechanism, equipped with a high-precision sensor, performs precise detection and analysis of the vehicle body-in-white along the detection path, and outputs detection data including: Based on the actual posture and defect location, at least two fixed cameras with optimal viewing angles are dynamically selected to synchronously acquire images and obtain fixed detection data. Simultaneously, the end sensor carried by the flexible actuator is controlled to move to a supplementary observation position to collect data and obtain dynamic detection data; The fixed detection data and the dynamic detection data are mapped to the surface features of the same three-dimensional vehicle body model and fused for analysis to output the detection data of the vehicle body.

9. The automated measurement method for automotive body parts as described in claim 1, characterized in that, The automated measurement method also includes: Collect and record the key dimensional measurements, defect statistics, and corresponding process parameters for each automotive body-in-white; A continuous production data control chart is drawn based on the key dimension measurements, the defect statistics, and the corresponding process parameters. Based on the connected production data control chart, the detection change trend of the automated measurement system is obtained. If the detection change trend shows an abnormality, real-time early warning information is generated.

10. An automated measurement system for automotive body parts, characterized in that, The measurement system is used to perform the automated measurement method as described in any one of claims 1 to 9, and the measurement system includes: Coarse positioning clamping assembly: used to position and clamp the car body in the clamping fixture, and to perform initial positioning and clamping of the car body based on the first clamping mechanism to form a coarse positioning clamping state; Coarse positioning analysis component: Used to acquire and perform preliminary analysis of the overall structural surface and connection position of the car body in the coarse positioning clamping state by a machine vision system arranged on the inspection station, and to identify and output the coarse positioning coordinates of one or more defect areas to be confirmed. Fine positioning clamping component: used to plan the target posture of the car body-in-white for optimized detection based on the coarse positioning coordinates, control multiple second clamping mechanisms to adjust the height according to the target posture, and coordinate with the first clamping mechanism to convert the car body-in-white from the coarse positioning clamping state to the fine positioning clamping state corresponding to the target posture; Detection path planning component: used to construct a detection path based on the coarse positioning coordinates and the actual posture of the car body in the fine positioning clamping state; Detection component: Used to control a flexible actuator equipped with high-precision sensors to perform precise detection and analysis of the vehicle body-in-white along the detection path, and output detection data.

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