Vehicle body-in-white detection method and device, vehicle and storage medium

Abstract

The application discloses a kind of detection method, device, vehicle and storage medium of vehicle body-in-white, wherein the detection method of vehicle body-in-white includes: obtaining the first detection area and the second detection area of vehicle, wherein the first detection area includes frame main body, and the second detection area includes vehicle deep cavity and cab corner;Control double cantilever to detect the first detection area, obtain the first detection data;Control joint arm to detect the second detection area, obtain the second detection data;First detection data and second detection data are fused, and body-in-white detection data is obtained;Detection report is generated based on body-in-white detection data, wherein the detection report is used to determine whether vehicle body-in-white meets specification size requirements.The present application solves the technical problem that the detection accuracy is low due to the fact that only one of double cantilever or joint arm is used to detect body-in-white in the prior art.

Description

Technical Field

[0001] This invention relates to the field of automation control technology, and more specifically, to a method, apparatus, vehicle, and storage medium for detecting the body-in-white of a vehicle. Background Technology

[0002] The body-in-white of a commercial vehicle, as the core load-bearing structure, is large in size (up to 8.5m or more in length), with complex surfaces such as longitudinal beams, side panels, and roof, as well as multiple deep cavity structures. Its dimensional accuracy directly affects the assembly quality and driving safety of the entire vehicle. In mass production scenarios, high-precision dimensional inspection of the entire vehicle must be completed in a short time, requiring inspection methods with full-area coverage, low cumulative error, and high efficiency.

[0003] Currently, the industry generally uses a single measuring device for inspection, either relying on a dual-cantilever coordinate measuring machine (CMM), which has a large measurement range and high accuracy, but is limited by the degree of freedom of the robotic arm and cannot reach obstructed areas such as the inner side of the chassis longitudinal beam and the corner of the cab, forming a significant blind spot; or using an articulated arm CMM, which has good accessibility and can enter complex spaces, but due to the short measurement stroke and frequent changes in the device's posture, the coordinate system is converted multiple times, resulting in significant cumulative errors, especially in ultra-long vehicle scenarios where the error can exceed 0.1mm, making it difficult to meet the stringent accuracy requirements.

[0004] Furthermore, existing technologies have not yet achieved effective collaboration and fusion of measurement data from different devices. The independent operation of a single device cannot meet the dual requirements of high precision over a large area and high local accessibility, resulting in missing or insufficient data in key areas, which restricts the integrity and reliability of the test results. Summary of the Invention

[0005] This invention provides a method, apparatus, vehicle, and storage medium for detecting vehicle body-in-white, thereby at least solving the technical problem of low detection accuracy caused by the prior art using only one of double cantilever or articulated arm to detect body-in-white.

[0006] According to one embodiment of the present invention, a method for inspecting a vehicle body-in-white is provided, comprising: acquiring a first inspection area and a second inspection area of ​​the vehicle, wherein the first inspection area includes the main body of the vehicle frame, and the second inspection area includes the deep cavity of the vehicle and the corner of the cab; controlling a double cantilever to inspect the first inspection area to obtain first inspection data; controlling a joint arm to inspect the second inspection area to obtain second inspection data; fusing the first inspection data and the second inspection data to obtain body-in-white inspection data; and generating an inspection report based on the body-in-white inspection data, wherein the inspection report is used to determine whether the vehicle body-in-white meets the specified dimensional requirements.

[0007] Optionally, the method for detecting the vehicle body-in-white further includes: determining a first coordinate origin based on the position information of the body reference holes, and constructing a double cantilever coordinate system based on the first coordinate origin; obtaining multiple first preset detection points and a first detection path in the first detection area; controlling the double cantilever to detect the multiple first preset detection points according to the first detection path to obtain multiple first detection point coordinates, wherein each first preset detection point corresponds to one first detection point coordinate, and the multiple first detection point coordinates are based on the double cantilever coordinate system; and determining first detection data based on the multiple first detection point coordinates.

[0008] Optionally, the detection method for the vehicle body-in-white further includes: determining an initial detection path for the double cantilever based on a first detection area; and correcting the initial detection path using multiple collision devices in the first detection area to obtain a first detection path.

[0009] Optionally, the method for detecting the vehicle body-in-white further includes: acquiring position information of multiple test balls and center point position information of multiple test balls; determining a second coordinate origin based on the center point position information, and constructing an articulated arm coordinate system based on the second coordinate origin; transforming the articulated arm coordinate system to a double cantilever coordinate system based on the position information of multiple test balls; acquiring multiple second preset detection points and a second detection path in the second detection area; controlling the articulated arm to detect multiple second preset detection points according to the second detection path to obtain multiple second detection point coordinates, wherein each second preset detection point corresponds to one second detection point coordinate, and the multiple second detection point coordinates are based on the double cantilever coordinate system; and determining second detection data based on the multiple second detection point coordinates.

[0010] Optionally, the method for detecting the vehicle body-in-white further includes: acquiring multiple cantilever coordinates and multiple articulated arm coordinates of multiple test spheres, wherein each test sphere corresponds to one cantilever coordinate and one articulated arm coordinate, the multiple cantilever coordinates are based on the cantilever coordinate system, and the multiple articulated arm coordinates are based on the articulated arm coordinate system; determining the mapping relationship between the cantilever coordinates of each test sphere and the articulated arm coordinates of each test sphere; and transforming the articulated arm coordinate system to the cantilever coordinate system based on the mapping relationship.

[0011] Optionally, the method for inspecting the vehicle body-in-white further includes: calculating the difference between the coordinates of multiple measuring points and the coordinates of multiple standard measuring points in the body-in-white inspection data; recording at least one deviation measuring point coordinate, wherein the deviation measuring point coordinate is the measuring point coordinate among the multiple measuring point coordinates whose difference from the corresponding standard measuring point coordinate is greater than a preset difference; and generating an inspection report based on the at least one deviation measuring point coordinate.

[0012] According to one embodiment of the present invention, a vehicle body-in-white inspection device is also provided, comprising: an acquisition module for acquiring a first inspection area and a second inspection area of ​​the vehicle, wherein the first inspection area includes the vehicle frame body and the second inspection area includes the vehicle deep cavity and the cab corner; a first control module for controlling a double cantilever arm to inspect the first inspection area and obtain first inspection data; a second control module for controlling a joint arm to inspect the second inspection area and obtain second inspection data; a data fusion module for fusing the first inspection data and the second inspection data to obtain body-in-white inspection data; and a generation module for generating an inspection report based on the body-in-white inspection data, wherein the inspection report is used to determine whether the vehicle body-in-white meets the specified dimensional requirements.

[0013] Optionally, the first control module includes: a first construction unit, configured to determine a first coordinate origin based on the position information of the vehicle body reference hole, and construct a double cantilever coordinate system based on the first coordinate origin; a first acquisition unit, configured to acquire multiple first preset detection points and a first detection path in the first detection area; a first control unit, configured to control the double cantilever to detect the multiple first preset detection points according to the first detection path, and obtain multiple first detection point coordinates, wherein each first preset detection point corresponds to one first detection point coordinate, and the multiple first detection point coordinates are based on the double cantilever coordinate system; and a first determination unit, configured to determine first detection data based on the multiple first detection point coordinates.

[0014] Optionally, the first acquisition unit includes: a first determination subunit, used to determine the initial detection path of the double cantilever based on the first detection area; and a correction subunit, used to correct the initial detection path using multiple collision devices in the first detection area to obtain the first detection path.

[0015] Optionally, the second control module includes: a second acquisition unit for acquiring position information of multiple test balls and center point position information of multiple test balls; a second construction unit for determining a second coordinate origin based on the center point position information and constructing an articulated arm coordinate system based on the second coordinate origin; a transformation unit for transforming the articulated arm coordinate system to a double cantilever coordinate system based on the position information of multiple test balls; a third acquisition unit for acquiring multiple second preset detection points and a second detection path in the second detection area; a second control unit for controlling the articulated arm to detect multiple second preset detection points according to the second detection path to obtain multiple second detection point coordinates, wherein each second preset detection point corresponds to one second detection point coordinate, and the multiple second detection point coordinates are based on the double cantilever coordinate system; and a second determination unit for determining second detection data based on the multiple second detection point coordinates.

[0016] Optionally, the conversion unit includes: an acquisition subunit for acquiring multiple cantilever coordinates and multiple articulated arm coordinates of multiple test balls, wherein each test ball corresponds to one cantilever coordinate and one articulated arm coordinate, the multiple cantilever coordinates are based on the cantilever coordinate system, and the multiple articulated arm coordinates are based on the articulated arm coordinate system; a second determination subunit for determining the mapping relationship between the cantilever coordinates of each test ball and the articulated arm coordinates of each test ball; and a conversion subunit for converting the articulated arm coordinate system to the cantilever coordinate system based on the mapping relationship.

[0017] Optionally, the generation module includes: a calculation unit for calculating the difference between the coordinates of multiple measuring points and the coordinates of multiple standard measuring points in the body-in-white inspection data; a recording unit for recording at least one deviation measuring point coordinate, wherein the deviation measuring point coordinate is the measuring point coordinate among the multiple measuring point coordinates whose difference with the corresponding standard measuring point coordinate is greater than a preset difference; and a generation unit for generating an inspection report based on at least one deviation measuring point coordinate.

[0018] According to one embodiment of the present invention, a vehicle is also provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the vehicle body-in-white detection method of any of the above claims.

[0019] According to one embodiment of the present invention, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the vehicle body-in-white detection method of any of the above claims.

[0020] According to one embodiment of the present invention, a non-volatile storage medium is also provided, wherein a computer program is stored in the non-volatile storage medium, wherein the computer program is configured to execute the vehicle body-in-white detection method described in any of the above claims when running.

[0021] According to one embodiment of the present invention, a computer program product is also provided, which stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the vehicle body-in-white detection method described above.

[0022] In this embodiment of the invention, a first detection area and a second detection area of ​​the vehicle are obtained. The first detection area includes the main body of the vehicle frame, and the second detection area includes the deep cavity of the vehicle and the corner of the cab. The first detection area is then controlled by a double cantilever to detect the vehicle, and first detection data is obtained. At the same time, the second detection area is controlled by a joint arm to detect the vehicle, and second detection data is obtained. The first and second detection data are then fused to obtain body-in-white detection data. This achieves the purpose of generating a detection report based on the body-in-white detection data. The detection report is used to determine whether the vehicle body-in-white meets the specified dimensional requirements. This solves the technical problem in the prior art where only one of the double cantilever or joint arm is used to detect the body-in-white, resulting in low detection accuracy. Attached Figure Description

[0023] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0024] Figure 1 This is a flowchart of a method for detecting the body-in-white of a vehicle according to one embodiment of the present invention;

[0025] Figure 2 This is a structural block diagram of a vehicle body-in-white detection device according to one embodiment of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] According to an embodiment of the present invention, an embodiment of a method for detecting a vehicle body-in-white is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system containing at least a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] This method embodiment can also be executed in an electronic device, similar control device, or vehicle-mounted terminal that includes a memory and a processor. Taking a vehicle-mounted terminal as an example, the vehicle-mounted terminal may include one or more processors and a memory for storing data. Optionally, the vehicle-mounted terminal may also include a communication device for communication functions and a display device. Those skilled in the art will understand that the above structural description is merely illustrative and does not limit the structure of the vehicle-mounted terminal. For example, the vehicle-mounted terminal may include more or fewer components than those described above, or have a different configuration than those described above.

[0030] A processor may include one or more processing units. For example, a processor may include a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processing (DSP) chip, a microprocessor, a field-programmable gate array (FPGA), a neural network processing unit (NPU), a tensor processing unit (TPU), or an artificial intelligence (AI) type processor. Different processing units may be independent components or integrated into one or more processors. In some instances, electronic devices may also include one or more processors.

[0031] The memory can be used to store computer programs, such as the computer program corresponding to the vehicle body-in-white detection method in this embodiment of the invention. The processor implements the vehicle body-in-white detection method by running the computer program stored in the memory. The memory may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to electronic devices via a grid. Examples of such grids include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0032] The communication device is used to receive or transmit data via a grid. Specific examples of the aforementioned grid may include a wireless grid provided by the mobile terminal's communication provider. In one example, the communication device includes a network interface controller (NIC), which can connect to other grid devices via a base station to communicate with the Internet. In another example, the communication device may be a radio frequency (RF) module used for wireless communication with the Internet. In some embodiments of this solution, the communication device is used to connect to mobile devices such as mobile phones and tablets, enabling the mobile device to send commands to the vehicle-mounted terminal.

[0033] The display device can be a touchscreen liquid crystal display (LCD) or a touch display (also referred to as a "touchscreen" or "touch display screen"). This LCD allows the user to interact with the user interface of the in-vehicle terminal. In some embodiments, the in-vehicle terminal has a graphical user interface (GUI), allowing the user to interact with the GUI through finger contact and / or gestures on a touch-sensitive surface. The human-machine interaction function may include a vehicle gear shifting function, and executable instructions for performing these functions are configured / stored in one or more processor-executable computer program products or readable storage media.

[0034] Figure 1 This is a flowchart of a method for detecting the body-in-white of a vehicle according to one embodiment of the present invention, as follows: Figure 1 As shown, the method includes the following steps:

[0035] Step S101: Obtain the first detection area and the second detection area of ​​the vehicle, wherein the first detection area includes the main body of the vehicle frame, and the second detection area includes the deep cavity of the vehicle and the corner of the cab.

[0036] Optionally, the execution subject in this embodiment is the vehicle body detection system. It should be noted that other electronic devices and processors can also be used as the execution subject, and no further limitations are made here.

[0037] In the technical solution provided in step S101 of the present invention, the three-dimensional model of the vehicle body-in-white can be imported into path planning software. Using Boolean operations and spatial segmentation algorithms, based on structural rigidity, visibility, and processing characteristics, the outer contour surface of the main frame (including the upper plane of the longitudinal beams and the transverse beams) is automatically extracted as the first detection area. Simultaneously, the recessed areas at the junction of the inner wall of the longitudinal beams, the side panels, and the floor, as well as the inner angle area formed by the front and side panels of the cab, are identified as the second detection area. The above segmentation results can be output in the form of three-dimensional voxels or patches for subsequent use by the path planning module.

[0038] The aforementioned frame body is a rigid load-bearing frame composed of longitudinal beams, transverse beams, and connecting structures in the body-in-white of a commercial vehicle. It is usually located in the lower part of the vehicle body, with a large area, regular structure, and no obstruction, making it the area with the highest dimensional stability.

[0039] The aforementioned deep cavities in vehicles refer to enclosed or semi-enclosed spaces that are obscured by other parts of the vehicle body and are difficult for probes to access directly, such as the inner side of the chassis longitudinal beams and the subframe connection area. Their depth is often greater than 300mm, and measurements require flexible probes.

[0040] The aforementioned cab corner refers to the multi-faceted angle area formed by the intersection of the cab, the front wall, and the side wall. It has a complex geometry, with multiple curved surfaces and spatial obstructions. The typical angle is 90°±5°, and multiple posture probes are required to collect complete data.

[0041] As another optional implementation method, in the CAD environment, the operator can also manually select candidate areas according to preset detection area determination rules (e.g., a deep cavity is defined as a depth > 0.5m and an included angle < 120°, and a cab corner is defined as a straight-line distance between two points at a corner < 0.8m and a normal change > 60°). The system then automatically verifies and classifies the candidate areas based on the structural topology database, generating a set of boundary coordinates between the first and second detection areas.

[0042] It is worth noting that by identifying structural features and dividing space, the main body of the frame and the deep cavity / corner area can be clearly defined, providing a geometric basis for the subsequent allocation of dual-device measurement tasks. This ensures that large-scale high-rigidity areas and complex occluded areas are independently identified in space, establishing a basic topology for subsequent equipment division of labor and path planning.

[0043] Step S102: Control the double cantilever to detect the first detection area and obtain the first detection data.

[0044] In the technical solution provided in step S102 of the present invention, when the body-in-white is equipped with a special fixture, the double cantilever first contacts three reference circular holes on the fixture with known coordinate values ​​of the body-in-white through the probe, and then establishes a coordinate system by least squares fitting. Subsequently, the system automatically positions each measuring point of the chassis body according to the established double cantilever coordinate system, executes the point sampling process, and ensures that all first detection data are strictly aligned with the body design reference, avoiding coordinate offset caused by fixture installation deviation.

[0045] The aforementioned dual-cantilever coordinate measuring machine is a high-precision measuring device with two independent measuring arms. It typically adopts a bridge or gantry structure and features a large measuring range (e.g., 1200×1000×700mm), high repeatability, and rigid support. It is suitable for measuring stable, large-area, and unobstructed structures.

[0046] Specifically, the first detection data refers to the set of discrete point coordinates collected by a double cantilever coordinate measuring machine in the main body area of ​​the vehicle frame, based on the body-in-white coordinate system, including the spatial position information of geometric elements such as hole centers, planes, and line segments.

[0047] As an optional implementation, a pre-planned measurement path can be loaded into the measurement software. The path is generated based on the CAD model of the vehicle frame and includes multiple key measurement points such as the front reference hole of the frame, the height point of the top crossbeam, and the rear mounting surface. For example, after starting the measurement program, the dual-cantilever coordinate measuring machine automatically moves the probe to each measurement point, making vertical contact with the surface with a contact force of 0.2N and a speed of 8mm / s. The coordinates of each point are collected three times, and the software automatically calculates and stores the average value to form a structured first inspection dataset.

[0048] It is worth noting that through the above steps, high-precision and highly repeatable coordinate data acquisition of the main body area of ​​the frame was achieved, and complete geometric feature information of the structurally stable and unobstructed area was obtained, providing a reliable basic data source for subsequent dimensional analysis.

[0049] Step S103: Control the articulated arm to detect the second detection area and obtain the second detection data.

[0050] In the technical solution provided by step S103 of the present invention, during the testing of prototype or non-standard vehicle models without complete path planning, the operator controls the articulated arm to guide the probe to sequentially contact multiple curved points on the inner wall of the longitudinal beam and the corner of the cab, based on the structural characteristics of the body-in-white. Real-time visual feedback ensures vertical contact of the probe, and three samples are taken at each point. Specifically, the collected data is automatically denoised and interpolated by software to generate second test data that meets quality requirements.

[0051] The aforementioned articulated arm coordinate measuring machine is a portable measuring device composed of multiple rotary joints, with 6 to 7 degrees of freedom. It can move flexibly around obstacles and is suitable for measurement scenarios with limited space and complex structures. The typical measurement range is generally 1.5m.

[0052] Specifically, the second detection data refers to the set of three-dimensional point coordinates collected by the articulated arm coordinate measuring machine in the deep cavity of the vehicle (such as the inner side of the longitudinal beam) and the corner area of ​​the cab, with the articulated arm's own coordinate system as the initial reference. It includes key geometric elements such as the aperture center, surface normal, and included angle vertex, and the data accuracy can reach ±0.05mm.

[0053] As an optional implementation, for example, a measurement path optimized by a path planning algorithm can be loaded into the measurement software. The path covers three aperture points on the inner side of the longitudinal beam and four angle measurement points at the corner of the cab. After the operator starts the program, the articulated arm automatically drives the probe to move along the planned trajectory, contacting the target surface with a contact force of 0.2N and a speed of 6mm / s. The coordinates of each point are collected three times, and the system automatically calculates and records the average value to form the original second detection dataset. The data includes the measurement point position, measurement time, and equipment attitude information.

[0054] It is worth noting that the above steps enable the acquisition of complete geometric data for obstructed areas such as deep cavities and cab corners. By obtaining dimensional information on parts that cannot be reached by a single device, the blind spots of large-scale measuring equipment are compensated, providing direct measurement basis for the integrity assessment of complex structures.

[0055] Step S104: The first detection data and the second detection data are fused to obtain the white body detection data.

[0056] In the technical solution provided in step S104 of the present invention, the first detection data and the second detection data are imported into the simulation platform, and the fusion value is calculated for overlapping measurement points (such as the junction of the frame and the longitudinal beam), that is, the fusion coordinate = first detection data × 0.65 + second detection data × 0.35. For non-overlapping areas, the corresponding equipment data is directly retained. Specifically, the weight can be set according to the equipment calibration accuracy (double cantilever is better than articulated arm), thereby ensuring that high-precision data dominates the results.

[0057] Specifically, the first test data is discrete coordinate data of the main body area of ​​the vehicle frame collected by a dual-cantilever coordinate measuring machine, which has high precision (±0.03mm) and low uncertainty.

[0058] Specifically, the second detection data is discrete coordinate data of the deep cavity and the corner area of ​​the cab collected by the articulated arm coordinate measuring machine. It has the characteristics of complete spatial coverage but relatively low accuracy (±0.05mm).

[0059] The aforementioned data fusion refers to the process of weighting the coordinate values ​​of the same or related measurement points based on the reliability weights of different data sources to form comprehensive evaluation data, the output of which is white body inspection data under a unified coordinate system.

[0060] It is worth noting that through the above technical steps, the collaborative integration of high-precision regional data and high-coverage regional data is achieved, generating a unified detection dataset that covers all detection areas of the entire vehicle, has a reasonable accuracy distribution, and has no data gaps, thereby improving the completeness and reliability of the overall measurement results.

[0061] Step S105: Generate an inspection report based on the body-in-white inspection data. The inspection report is used to determine whether the vehicle body-in-white meets the dimensional requirements of the specifications.

[0062] In the technical solution provided in step S105 of the present invention, the body-in-white inspection data is imported into the analysis module. The system automatically matches the design coordinates and tolerance range corresponding to each measuring point and calculates the measured deviation (measured value – design value). For measuring points with deviations exceeding ±0.01mm, the system automatically marks them as "out of tolerance" and generates a tabular report containing the measuring point number, location description, measured value, deviation value, and judgment result (qualified / unqualified), along with a deviation distribution bar chart.

[0063] The aforementioned body-in-white inspection data is a set of discrete point coordinates across the entire area, generated by the fusion of multi-source data and unified under the body-in-white coordinate system. It covers all inspection points, including the main body of the frame, the inner side of the longitudinal beams, and the corners of the cab, and possesses spatial consistency and precision weighting characteristics.

[0064] Specifically, the standard dimensional requirements refer to the theoretical dimensional tolerance range defined in the body-in-white CAD design model, such as frame length tolerance ±0.01mm and critical hole diameter tolerance ±0.02mm, which serve as the benchmark for qualification judgment.

[0065] The aforementioned test report refers to a structured output file that includes measured values, design values, deviation values, out-of-tolerance indicators, trend charts, and comprehensive judgment conclusions for each measuring point, used to guide subsequent process adjustments or release decisions.

[0066] As an optional implementation, the data analysis platform performs linear trend fitting and spatial clustering analysis on the body-in-white inspection data to identify systematic deviation patterns such as "the left side of the frame is 0.008mm larger than normal" or "the flatness deviation of the rear axle mounting surface is concentrated at the rear end." In addition to single-point judgments, the report includes "trend analysis conclusions," "regional defect distribution heatmaps," and "comprehensive compliance conclusions" (e.g., "overall qualified, but with a tendency for localized welding deformation"), forming a complete assessment report containing diagnostic information.

[0067] It is worth noting that through the above steps, the process of converting raw measurement data into quality judgment conclusions is automated and structured, resulting in a traceable, reproducible, and clearly criterion-based inspection report that directly supports an objective judgment on whether the body-in-white meets the dimensional requirements of the design specifications.

[0068] Steps S101 to S105 above show that in this invention, by acquiring a first detection area and a second detection area of ​​the vehicle, wherein the first detection area includes the main body of the vehicle frame and the second detection area includes the deep cavity of the vehicle and the corner of the cab, and then controlling the double cantilever to detect the first detection area to obtain first detection data, and simultaneously controlling the articulated arm to detect the second detection area to obtain second detection data, and further fusing the first and second detection data to obtain body-in-white detection data, the purpose of generating a detection report based on the body-in-white detection data is achieved. The detection report is used to determine whether the vehicle body-in-white meets the specified dimensional requirements, thereby solving the technical problem of low detection accuracy caused by using only one of the double cantilever or articulated arm to detect the body-in-white in the prior art.

[0069] The method described in this embodiment will now be described in further detail.

[0070] Step S121: Determine the first coordinate origin based on the position information of the vehicle body reference hole, and construct a double cantilever coordinate system based on the first coordinate origin;

[0071] Step S122: Obtain multiple first preset detection points and a first detection path in the first detection area;

[0072] Step S123: Control the double cantilever to detect multiple first preset detection points according to the first detection path to obtain multiple first detection point coordinates, wherein each first preset detection point corresponds to a first detection point coordinate, and the multiple first detection point coordinates are based on the double cantilever coordinate system.

[0073] Step S124: Determine the first detection data based on the coordinates of multiple first detection points.

[0074] In this embodiment, the dual-cantilever coordinate measuring machine first contacts at least three reference holes at known design locations in the body-in-white structure. For each hole, 3–5 contact points are collected in the circumferential direction, forming a point cloud. These points are recorded as the original spatial coordinates in the dual-cantilever coordinate system. Subsequently, the software uses the least squares method to fit the center of the point cloud for each hole, obtaining the theoretical center coordinates of each hole. Then, based on the spatial distribution of these center points (e.g., four holes arranged linearly), their geometric center is calculated as the first coordinate origin. The X-axis is defined by the direction of the hole array, the Z-axis is defined perpendicular to the longitudinal beam plane, and the Y-axis is determined by the right-hand rule, ultimately constructing a dual-cantilever coordinate system that completely coincides with the body-in-white design coordinate system. This process does not rely on fixtures, but only uses the body's own structure as a reference, ensuring that the coordinate system originates from actual manufacturing features.

[0075] Furthermore, based on the CAD model of the vehicle body, the system pre-defines 15-20 key geometric feature points within the first detection area (such as the main frame), including the center of the crossbeam, the center of the mounting surface, and edge positioning holes, as "first pre-determined detection points." Each point is associated with its theoretical spatial coordinates and measurement priority. The path planning software calls a path planning algorithm, combined with equipment kinematic constraints and a collision model, to automatically generate a trajectory sequence from the starting point to the ending point, covering all pre-determined points, with optimal path length, and no equipment interference—the "first detection path." This path includes instructions such as the probe movement trajectory, stopping point sequence, and probe attitude angle, providing an executable instruction set for subsequent control.

[0076] After the dual-cantilever coordinate system is constructed, the system loads the preset first detection path and automatically starts the measurement program. The dual-cantilever coordinate system moves the probe to each preset detection point according to the path instructions, performing three contact samplings with a set contact force (0.2N) and measurement speed (8mm / s). Each sampling represents precise contact between the probe's center and the measured surface. After mean filtering of the three sampling data, a final coordinate representing the spatial position of that point is output. All coordinates are based on the dual-cantilever coordinate system constructed in the above steps; that is, the coordinate values ​​directly correspond to the body-in-white design datum, requiring no subsequent coordinate transformation.

[0077] Finally, the coordinates of all the first detection points obtained in the above steps (such as 15 sets of X / Y / Z coordinates) are written into a dedicated data acquisition system according to a preset format (including measurement point number, coordinate value, number of samplings, acquisition time, and device ID) to form a structured dataset. This dataset does not contain the original sampling points, but only retains the final coordinate values ​​after filtering and averaging, and all points belong to the same coordinate system (double cantilever coordinate system). This set is the "first detection data," which can be used for subsequent comparison with the standard model, multi-device data fusion, or report generation.

[0078] The aforementioned body reference holes refer to pre-designed positioning holes in the body-in-white structure with a positional accuracy of ≤0.02mm, such as process holes on longitudinal beams. Their theoretical coordinate values ​​are derived from the CAD model and serve as a physical reference for establishing the coordinate system.

[0079] The aforementioned double cantilever coordinate system is a three-dimensional rectangular coordinate system that coincides with the body-in-white design coordinate system, constructed by fitting the body reference holes using the least squares method. Its origin and X / Z axis directions are determined by the spatial distribution of the reference holes, serving as a reference frame for unifying all subsequent measurement data.

[0080] Specifically, the first preset detection point is a key geometric feature point located in the main body area of ​​the frame, which is predefined according to the white body CAD model, such as the center of the crossbeam, the center of the mounting surface, etc., and the number is no less than 15, which is used to characterize the key dimensions.

[0081] Specifically, the first detection path is an optimized measurement trajectory generated based on the path planning algorithm and the collision detection model, ensuring that the dual cantilever probes visit all the first preset detection points in sequence without interference.

[0082] It is worth noting that through the above technical steps, the first test data and the body-in-white design benchmark were precisely aligned, ensuring that the collected geometric data of the main body of the chassis are all unified in the same coordinate system, eliminating measurement offset caused by inconsistent benchmarks, and improving the comparability of the data.

[0083] Step S1221: Determine the initial detection path of the double cantilever based on the first detection area;

[0084] Step S1222: Correct the initial detection path using multiple collision devices in the first detection area to obtain the first detection path.

[0085] In this embodiment, within the body-in-white CAD model, the system identifies the geometric boundaries of the first detection area (such as the main frame, top crossbeam, etc.) and the defined first preset detection points (such as 15 key dimension points). The path planning software invokes a path planning algorithm, using the shortest path, fewest turns, and continuous reachability as optimization objectives, to search for the optimal sequence connecting all preset detection points in three-dimensional space. This sequence is generated solely based on point cloud geometric relationships, without considering any external physical obstacles, and the resulting trajectory is the "initial detection path." This path includes the probe's movement direction in space, velocity nodes, attitude angles, and straight-line or circular interpolation trajectories between adjacent detection points.

[0086] Furthermore, the system defines all structures or equipment around the first detection area that may physically come into contact with the double cantilever as "collision devices," including but not limited to: fuel tank brackets, wiring harness bundles, suspension mounting points, body reinforcements, detection platform columns, pneumatic gripper arms, etc. The three-dimensional geometric models of these obstacles (in the form of bounding boxes, point clouds, or solid models) are imported into the path simulation environment for spatial overlap detection with the initial detection path.

[0087] When the system detects that the probe or stylus interferes with any colliding device within a safety threshold (e.g., 0.5 mm) in the initial path, the path optimization module automatically triggers a correction mechanism:

[0088] 1) Local avoidance: Insert new intermediate transition points before and after the interference point to change the direction of probe movement and bypass the obstacle;

[0089] 2) Attitude adjustment: Adjust the incident angle of the probe at a specific measuring point to make the measuring arm avoid obstacles;

[0090] 3) Measurement point rearrangement: Without changing the detection target, fine-tune the access order of measurement points to avoid areas with path conflicts;

[0091] 4) Smoothing the trajectory: B-spline interpolation is performed on the corrected polyline trajectory to ensure motion continuity and avoid vibration errors caused by sudden stops and turns.

[0092] The above correction process needs to be iterated repeatedly until all collision risks are eliminated, and finally a "first detection path" that is interference-free, executable, and retains the efficiency of the initial path to the maximum extent is generated.

[0093] As an optional implementation, for example, in dedicated path planning software, a STEP format CAD model of the first detection area is imported, and three-dimensional boundary bodies of all potential collision devices are overlaid and annotated (e.g., the fuel tank bracket is a cuboid bounding box, and the wiring harness is a collection of cylinders). The software generates an initial detection path using a path planning algorithm, connecting the front end of the vehicle frame, five crossbeam measuring points, and the rear reference hole. Subsequently, the system performs virtual collision detection, identifying a 0.8mm interference between the probe arm and the fuel tank bracket in the path. The path optimization module automatically inserts two intermediate inflection points before and after the interference point, adjusts the probe movement direction, forms a bypass trajectory, and finally generates an interference-free first detection path.

[0094] It is worth noting that by generating a physically feasible measurement path, the risk of collision between the double cantilever and the vehicle body or surrounding equipment during the movement can be eliminated, ensuring stable and continuous execution of measurement actions and improving the reliability of path execution and equipment safety.

[0095] Step S131: Obtain the position information of multiple test balls and the center point position information of multiple test balls;

[0096] Step S132: Determine the second coordinate origin based on the center point position information, and construct the articulated arm coordinate system based on the second coordinate origin;

[0097] Step S133: Based on the position information of multiple test balls, transform the articulated arm coordinate system to the double cantilever coordinate system;

[0098] Step S134: Obtain multiple second preset detection points and a second detection path in the second detection area;

[0099] Step S135: Control the articulated arm to detect multiple second preset detection points according to the second detection path to obtain multiple second detection point coordinates. Each second preset detection point corresponds to a second detection point coordinate, and the multiple second detection point coordinates are based on the double cantilever coordinate system.

[0100] Step S136: Determine the second detection data based on the coordinates of multiple second detection points.

[0101] In this embodiment, multiple sets of high-precision ruby ​​spheres (test spheres) are fixed around the detection platform. Each set consists of three spheres, distributed within the overlapping measurement area accessible by both the cantilever and articulated arms. First, the cantilever coordinate system performs three contact samplings on each set of test spheres according to the established cantilever coordinate system. The center coordinates of each ruby ​​sphere are calculated using a spatial fitting algorithm, resulting in a set of "cantilever coordinates" based on the cantilever coordinate system. Subsequently, the articulated arm coordinate system, without moving its own reference, uses the same type of probe to perform three contact samplings on the same set of test spheres, obtaining the original spatial coordinates of each sphere in the articulated arm's own coordinate system, forming "articulated arm coordinates." Each test sphere corresponds to one cantilever coordinate and one articulated arm coordinate, totaling at least three sets (nine points), used to establish coordinate mapping relationships.

[0102] Based on the position information of the test sphere center points acquired by the articulated arm, the system selects the coordinates of three non-collinear sphere centers and uses the least squares method to fit a local three-dimensional coordinate system. The origin of this system is the geometric center of the three spheres. The X-axis is defined by the direction of the line connecting two spheres, the Z-axis is determined by the normal vector, and the Y-axis is completed according to the right-hand rule, forming the "articulated arm coordinate system." This coordinate system is entirely generated internally by the articulated arm sensor and has no direct geometric relationship with the vehicle body; it only reflects the current spatial attitude of the articulated arm measurement chain.

[0103] The "double cantilever coordinates" and "articular arm coordinates" of each test ball obtained in the above steps are used as a pair of spatial corresponding points and input into the coordinate fitting software. The system uses an iterative nearest-point algorithm to calculate an optimal rigid body transformation matrix, including translation vectors and rotation matrices, by minimizing the Euclidean distance error between all test ball point pairs in the two coordinate systems. This matrix represents the spatial transformation relationship from the articular arm coordinate system to the double cantilever coordinate system. After applying this transformation matrix, the origin and axis of the articular arm coordinate system are reoriented to completely coincide with the double cantilever coordinate system, with a fitting deviation ≤0.004mm. After the transformation is completed, all subsequent measurement data of the articular arm can be automatically mapped to the double cantilever coordinate system, achieving seamless transfer of coordinate reference.

[0104] Furthermore, based on the body-in-white CAD model, the system identifies key geometric feature points in obscured or deep cavity structures within the second detection area (such as the inner side of the chassis longitudinal beams, the corner of the cab, etc.), defining them as "second preset detection points," such as the center of the longitudinal beam inner hole, the normal corner point of the corner surface, etc., with no fewer than 9 points. The path planning software automatically generates an optimal measurement trajectory, i.e., the "second detection path," that covers all second preset detection points, is free from self-interference, and has no collision risk, based on the kinematic constraints of the articulated arm (such as degrees of freedom and swing angle range) and the reachability of the probe. This detection path includes control parameters such as probe movement sequence, attitude adjustment commands, and sampling point dwell time, providing executable instructions for automated measurement.

[0105] After the articulated arm coordinate system is transformed to the double cantilever coordinate system, the system loads the second detection path and starts the articulated arm's automatic measurement program. The articulated arm moves according to the path instructions, sequentially visiting each second preset detection point, and performing three contact measurements using a set contact force (0.2N) and sampling speed (6mm / s). Each measurement outputs a raw coordinate. The system automatically transforms each raw coordinate to the double cantilever coordinate system in real time using the rigid body transformation matrix obtained in step three, and performs mean filtering on the three sampled values ​​to generate a final coordinate system based on the double cantilever coordinate system. In particular, each second preset detection point corresponds to a coordinate system of this type, and all coordinates have a unified spatial reference, requiring no secondary calibration.

[0106] Finally, the coordinates of all the second detection points obtained in the above steps (such as 9 sets of X / Y / Z coordinates) are written into the data acquisition system according to a preset data format (including measurement point number, coordinate value, number of samplings, acquisition time, and device ID). This dataset only contains the final values ​​after coordinate transformation and averaging. All points belong to the double cantilever coordinate system and have a completely consistent spatial reference with the first detection data. This set is the "second detection data," used for comparison with the design model, fusion with the first detection data, or generation of a comprehensive detection report. It is the final output form of the articulated arm's measurement results for complex areas.

[0107] Specifically, the test ball refers to a high-precision ruby ​​ball (precision level ≤ 0.001mm) fixed on the testing platform. Each group consists of 3 balls arranged in an isosceles triangle, serving as a common coordinate reference accessible to both the double cantilever and the articulated arm, with the center of the ball being a precise geometric reference point.

[0108] Specifically, the aforementioned center point location information refers to the three-dimensional center coordinates of each ruby ​​ball calculated by spatial fitting after three contact measurements of each group of test balls using a double cantilever coordinate system. The data accuracy is ≤0.0001mm, with the double cantilever coordinate system as the reference.

[0109] As an optional implementation, for example, seven sets of ruby ​​balls (three in each set) are pre-fixed on the testing platform, with three sets located in the overlapping area of ​​the double cantilever and articulated arm measurement ranges. First, the center point coordinates of these nine test balls (three sets) are collected by the double cantilever coordinate system according to preset parameters, and the data is stored in the double cantilever coordinate system. Then, the articulated arm (equipped with the same type of probe) performs three contact measurements on the same nine test balls without moving its position, obtaining their original coordinates in the articulated arm coordinate system. The two sets of data are input into the measurement software, and an iterative nearest-point algorithm is used for optimal fitting, ensuring that the articulated arm coordinate system coincides with the double cantilever coordinate system, with a fitting deviation ≤0.004mm. After the conversion, the system calls a preset second detection path (covering three aperture points and four angle points on the inner side of the longitudinal beam), controlling the articulated arm to collect the second preset detection points along the path. Each point is sampled three times, and the average value is taken. The resulting coordinates are automatically mapped to the double cantilever coordinate system, forming the second detection data.

[0110] It is worth noting that through the above technical steps, high-precision, one-time synchronous conversion between articulated arm measurement data and the double cantilever coordinate system can be achieved, so that the coordinates of the second detection point collected by the articulated arm directly belong to the body-in-white design datum, avoiding the accumulation of errors caused by multiple coordinate system switching, thereby ensuring that the second detection data has a spatial reference datum that is completely consistent with the first detection data.

[0111] Step S1331: Obtain multiple cantilever coordinates and multiple articulated arm coordinates of multiple test balls. Each test ball corresponds to one cantilever coordinate and one articulated arm coordinate. The multiple cantilever coordinates are based on the cantilever coordinate system, and the multiple articulated arm coordinates are based on the articulated arm coordinate system.

[0112] Step S1332: Determine the mapping relationship between the double cantilever coordinates of each test ball and the articulated arm coordinates of each test ball;

[0113] Step S1333: Based on the mapping relationship, transform the articulated arm coordinate system to the double cantilever coordinate system.

[0114] In this embodiment, at least three sets of high-precision ruby ​​balls (test balls) are fixed on the testing platform. Each set consists of three balls and is distributed in the measurement overlap area that can be reached by both the cantilever and the articulated arm.

[0115] First, using the established baseline double cantilever coordinate system as a reference, the center of each test ball is sampled three times. The geometric center coordinates of each ball are calculated using a spatial fitting algorithm, forming a set of three-dimensional coordinate sequences based on the double cantilever coordinate system, denoted as "double cantilever coordinate".

[0116] Subsequently, without any external calibration, the articulated arm coordinate measuring machine independently measured the same group of test balls using the same type of probe, obtaining the original three-dimensional spatial coordinates of each ball in the articulated arm's own sensor calculation system, forming a set of three-dimensional coordinate sequences based on the articulated arm coordinate system, denoted as "articulated arm coordinates".

[0117] Specifically, each test ball corresponds to a unique pair of coordinates, one from the double cantilever system and one from the articulated arm system, forming n sets (n≥3) of spatial point pairs. All point pairs are not transformed and retain their original measurement state, forming the input data source for the transformation algorithm, with a data accuracy ≤0.0001mm.

[0118] The n sets of points (double cantilever coordinates, articulated arm coordinates) obtained in the above steps are then fitted to the input coordinates using software. The system employs an iterative nearest-point algorithm for nonlinear optimization. The specific implementation steps are as follows:

[0119] In the initial stage, a rigid body transformation (containing a 3D translation vector and a 3D rotation matrix) is assumed to exist between the articulated arm coordinate system and the double cantilever coordinate system. Through repeated iterations, the system adjusts the transformation parameters to minimize the Euclidean distance between each set of articulated arm coordinates and its corresponding double cantilever coordinates after the transformation. In each iteration, the system finds the closest double cantilever coordinate point ("closest point") under the current transformation for each articulated arm coordinate point and calculates the error residual. When the error converges to a threshold (≤0.004mm) or the number of iterations reaches the upper limit, the algorithm terminates and outputs the optimal rigid body transformation matrix.

[0120] In particular, this matrix is ​​the "mapping relationship", which is essentially a mathematical expression describing "how to accurately map any point in the articulated arm coordinate system to the double cantilever coordinate system through translation and rotation". It does not rely on geometric assumptions, but is based solely on the spatial distribution of measured point pairs.

[0121] Finally, the optimal rigid body transformation matrix (including translations ΔX, ΔY, ΔZ and rotation matrix R) obtained in the above steps is loaded into the articulated arm control system. The system performs real-time coordinate transformations on all subsequent measurement data within the articulated arm's internal coordinate solution layer; that is, when the articulated arm acquires a new point, the system automatically performs the transformation operation. After the transformation, all points in the articulated arm's measurement space are repositioned to a spatial frame completely consistent with the double cantilever coordinate system, with their origin, axis, and dimensions aligned with the body-in-white design datum. The accuracy is determined by the fitting residual of the mapping relationship. Therefore, every measurement output after the articulated arm can be considered a direct measurement result in the double cantilever coordinate system.

[0122] The above mapping relationship refers to the mathematical function relationship established through spatial geometric transformations (translation and rotation) that transforms the coordinates of any point in the articulated arm coordinate system to the double cantilever coordinate system. It is usually represented by a 6-DOF rigid body transformation matrix (3 translations + 3 rotations), and its parameters are solved by multiple sets of corresponding point coordinate pairs through optimization algorithms.

[0123] It is worth noting that through the above steps, a high-precision, one-time rigid body transformation between the articulated arm coordinate system and the double cantilever coordinate system is achieved, so that all subsequent measurement data collected by the articulated arm can be directly expressed in a unified body-in-white reference coordinate system, eliminating the coordinate system inconsistency problem caused by independent measurement of equipment, and ensuring that multi-source data have spatial comparability.

[0124] Step S151: Calculate the difference between the coordinates of multiple measuring points and the coordinates of multiple standard measuring points in the white body inspection data;

[0125] Step S152: Record at least one deviation measurement point coordinate, wherein the deviation measurement point coordinate is the coordinate of the measurement point among multiple measurement point coordinates whose difference from the corresponding standard measurement point coordinate is greater than a preset difference.

[0126] Step S153: Generate a detection report based on the coordinates of at least one deviation measurement point.

[0127] In this embodiment, the coordinates of all measured points in the body-in-white inspection data (including the coordinates of the first inspection point acquired by the double cantilever and the coordinates of the second inspection point acquired by the articulated arm) are matched one-to-one with the corresponding standard measurement point coordinates in the body-in-white CAD model. The standard measurement point coordinates are the theoretical ideal positions under the design benchmark, and their data comes from the STEP format digital model, which has higher accuracy than the measurement system. The system performs three-dimensional spatial coordinate difference calculations on each pair of measured points and standard points, and calculates the Euclidean deviation value of each point. All differences are in millimeters and retained to five decimal places to ensure that the calculation accuracy meets the judgment requirement of ±0.01mm. This process is a full data comparison, covering all measurement points in the first and second inspection areas.

[0128] A preset difference value of ±0.01mm (based on the body-in-white assembly tolerance standard) can be set, and the system automatically iterates through all calculated Euclidean deviations. When the Euclidean deviation of a measuring point is greater than 0.01mm, the measuring point is identified as a "deviation measuring point". The system records the following information for the measuring point: measured coordinates, corresponding standard coordinates, three-dimensional deviation components, Euclidean deviation value, measuring point number and its corresponding inspection area (e.g., "3rd point of the crossbeam in area A"), and the identification of the acquisition equipment (double cantilever or articulated arm). Specifically, all deviation measuring points that meet the above conditions are centrally stored as a "deviation sample set", and measuring points that do not exceed the tolerance are not included in report generation.

[0129] It is worth noting that the above steps enable automated judgment of the dimensional conformity of the body-in-white, outputting only information on abnormal measurement points that exceed the allowable tolerance, avoiding redundant data interference, and improving the focus and decision-making efficiency of the inspection report.

[0130] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or grid device, etc.) to execute the methods of the various embodiments of the present invention.

[0131] This embodiment also provides a vehicle body-in-white detection device, which is used to implement the above embodiments and preferred embodiments, and will not be repeated as described previously. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0132] Figure 2 This is a structural block diagram of a vehicle body-in-white detection device 200 according to one embodiment of the present invention, as shown below. Figure 2 As shown, the device includes: an acquisition module 201, a first control module 202, a second control module 203, a data fusion module 204, and a generation module 205.

[0133] The acquisition module 201 is used to acquire the first detection area and the second detection area of ​​the vehicle, wherein the first detection area includes the main body of the vehicle frame, and the second detection area includes the deep cavity of the vehicle and the corner of the cab.

[0134] The first control module 202 is used to control the double cantilever to detect the first detection area and obtain the first detection data;

[0135] The second control module 203 is used to control the articulated arm to detect the second detection area and obtain the second detection data;

[0136] Data fusion module 204 is used to fuse the first detection data and the second detection data to obtain white body detection data;

[0137] The generation module 205 is used to generate an inspection report based on the white body inspection data. The inspection report is used to determine whether the vehicle white body meets the specification dimensional requirements.

[0138] Optionally, the first control module 202 includes: a first construction unit, configured to determine a first coordinate origin based on the position information of the vehicle body reference hole, and construct a double cantilever coordinate system based on the first coordinate origin; a first acquisition unit, configured to acquire multiple first preset detection points and a first detection path in the first detection area; a first control unit, configured to control the double cantilever to detect the multiple first preset detection points according to the first detection path, and obtain multiple first detection point coordinates, wherein each first preset detection point corresponds to a first detection point coordinate, and the multiple first detection point coordinates are based on the double cantilever coordinate system; and a first determination unit, configured to determine first detection data based on the multiple first detection point coordinates.

[0139] Optionally, the first acquisition unit includes: a first determination subunit, used to determine the initial detection path of the double cantilever based on the first detection area; and a correction subunit, used to correct the initial detection path using multiple collision devices in the first detection area to obtain the first detection path.

[0140] Optionally, the second control module 203 includes: a second acquisition unit, used to acquire position information of multiple test balls and center point position information of multiple test balls; a second construction unit, used to determine a second coordinate origin based on the center point position information and construct an articulated arm coordinate system based on the second coordinate origin; a transformation unit, used to transform the articulated arm coordinate system to a double cantilever coordinate system based on the position information of multiple test balls; a third acquisition unit, used to acquire multiple second preset detection points and a second detection path in the second detection area; a second control unit, used to control the articulated arm to detect multiple second preset detection points according to the second detection path to obtain multiple second detection point coordinates, wherein each second preset detection point corresponds to one second detection point coordinate, and the multiple second detection point coordinates are based on the double cantilever coordinate system; and a second determination unit, used to determine second detection data based on the multiple second detection point coordinates.

[0141] Optionally, the conversion unit includes: an acquisition subunit for acquiring multiple cantilever coordinates and multiple articulated arm coordinates of multiple test balls, wherein each test ball corresponds to one cantilever coordinate and one articulated arm coordinate, the multiple cantilever coordinates are based on the cantilever coordinate system, and the multiple articulated arm coordinates are based on the articulated arm coordinate system; a second determination subunit for determining the mapping relationship between the cantilever coordinates of each test ball and the articulated arm coordinates of each test ball; and a conversion subunit for converting the articulated arm coordinate system to the cantilever coordinate system based on the mapping relationship.

[0142] Optionally, the generation module 205 includes: a calculation unit for calculating the difference between the coordinates of multiple measuring points and the coordinates of multiple standard measuring points in the body-in-white inspection data; a recording unit for recording at least one deviation measuring point coordinate, wherein the deviation measuring point coordinate is the measuring point coordinate among the multiple measuring point coordinates whose difference with the corresponding standard measuring point coordinate is greater than a preset difference; and a generation unit for generating an inspection report based on at least one deviation measuring point coordinate.

[0143] Embodiments of the present invention also provide a vehicle, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the above-described vehicle body-in-white detection method.

[0144] Optionally, in this embodiment, the vehicle may be configured to store a computer program for performing the following steps:

[0145] Step S101: Obtain the first detection area and the second detection area of ​​the vehicle, wherein the first detection area includes the main body of the vehicle frame, and the second detection area includes the deep cavity of the vehicle and the corner of the cab.

[0146] Step S102: Control the double cantilever to detect the first detection area and obtain the first detection data;

[0147] Step S103: Control the articulated arm to detect the second detection area and obtain the second detection data;

[0148] Step S104: The first detection data and the second detection data are fused to obtain the white body detection data;

[0149] Step S105: Generate an inspection report based on the body-in-white inspection data. The inspection report is used to determine whether the vehicle body-in-white meets the dimensional requirements of the specifications.

[0150] Optionally, the processor may further implement the following steps when executing the program: determining the first coordinate origin based on the position information of the vehicle body reference hole, and constructing a double cantilever coordinate system based on the first coordinate origin; acquiring multiple first preset detection points and a first detection path in the first detection area; controlling the double cantilever to detect the multiple first preset detection points according to the first detection path to obtain multiple first detection point coordinates, wherein each first preset detection point corresponds to one first detection point coordinate, and the multiple first detection point coordinates are based on the double cantilever coordinate system; and determining first detection data based on the multiple first detection point coordinates.

[0151] Optionally, the processor may also perform the following steps when executing the program: determining the initial detection path of the double cantilever based on the first detection area; and correcting the initial detection path using multiple collision devices in the first detection area to obtain the first detection path.

[0152] Optionally, the processor, when executing the program, also implements the following steps: acquiring the position information of multiple test balls and the center point position information of multiple test balls; determining the second coordinate origin based on the center point position information, and constructing an articulated arm coordinate system based on the second coordinate origin; transforming the articulated arm coordinate system to a double cantilever coordinate system based on the position information of multiple test balls; acquiring multiple second preset detection points and a second detection path in the second detection area; controlling the articulated arm to detect the multiple second preset detection points according to the second detection path to obtain the coordinates of multiple second detection points, wherein each second preset detection point corresponds to one second detection point coordinate, and the coordinates of multiple second detection points are based on the double cantilever coordinate system; and determining the second detection data based on the coordinates of multiple second detection points.

[0153] Optionally, when the processor executes the program, it also performs the following steps: obtaining multiple cantilever coordinates and multiple articulated arm coordinates of multiple test balls, wherein each test ball corresponds to one cantilever coordinate and one articulated arm coordinate, the multiple cantilever coordinates are based on the cantilever coordinate system, and the multiple articulated arm coordinates are based on the articulated arm coordinate system; determining the mapping relationship between the cantilever coordinates of each test ball and the articulated arm coordinates of each test ball; and transforming the articulated arm coordinate system to the cantilever coordinate system based on the mapping relationship.

[0154] Optionally, when the processor executes the program, it also performs the following steps: calculating the difference between the coordinates of multiple measuring points and the coordinates of multiple standard measuring points in the white body inspection data; recording at least one deviation measuring point coordinate, wherein the deviation measuring point coordinate is the measuring point coordinate among the multiple measuring point coordinates whose difference with the corresponding standard measuring point coordinate is greater than a preset difference; and generating an inspection report based on at least one deviation measuring point coordinate.

[0155] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0156] Embodiments of the present invention also provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the above-described vehicle body-in-white detection method.

[0157] Optionally, in this embodiment, the electronic device may be configured to store a computer program for performing the following steps:

[0158] Step S101: Obtain the first detection area and the second detection area of ​​the vehicle, wherein the first detection area includes the main body of the vehicle frame, and the second detection area includes the deep cavity of the vehicle and the corner of the cab.

[0159] Step S102: Control the double cantilever to detect the first detection area and obtain the first detection data;

[0160] Step S103: Control the articulated arm to detect the second detection area and obtain the second detection data;

[0161] Step S104: The first detection data and the second detection data are fused to obtain the white body detection data;

[0162] Step S105: Generate an inspection report based on the body-in-white inspection data. The inspection report is used to determine whether the vehicle body-in-white meets the dimensional requirements of the specifications.

[0163] Optionally, the processor may further implement the following steps when executing the program: determining the first coordinate origin based on the position information of the vehicle body reference hole, and constructing a double cantilever coordinate system based on the first coordinate origin; acquiring multiple first preset detection points and a first detection path in the first detection area; controlling the double cantilever to detect the multiple first preset detection points according to the first detection path to obtain multiple first detection point coordinates, wherein each first preset detection point corresponds to one first detection point coordinate, and the multiple first detection point coordinates are based on the double cantilever coordinate system; and determining first detection data based on the multiple first detection point coordinates.

[0164] Optionally, the processor may also perform the following steps when executing the program: determining the initial detection path of the double cantilever based on the first detection area; and correcting the initial detection path using multiple collision devices in the first detection area to obtain the first detection path.

[0165] Optionally, the processor, when executing the program, also implements the following steps: acquiring the position information of multiple test balls and the center point position information of multiple test balls; determining the second coordinate origin based on the center point position information, and constructing an articulated arm coordinate system based on the second coordinate origin; transforming the articulated arm coordinate system to a double cantilever coordinate system based on the position information of multiple test balls; acquiring multiple second preset detection points and a second detection path in the second detection area; controlling the articulated arm to detect the multiple second preset detection points according to the second detection path to obtain the coordinates of multiple second detection points, wherein each second preset detection point corresponds to one second detection point coordinate, and the coordinates of multiple second detection points are based on the double cantilever coordinate system; and determining the second detection data based on the coordinates of multiple second detection points.

[0166] Optionally, when the processor executes the program, it also performs the following steps: obtaining multiple cantilever coordinates and multiple articulated arm coordinates of multiple test balls, wherein each test ball corresponds to one cantilever coordinate and one articulated arm coordinate, the multiple cantilever coordinates are based on the cantilever coordinate system, and the multiple articulated arm coordinates are based on the articulated arm coordinate system; determining the mapping relationship between the cantilever coordinates of each test ball and the articulated arm coordinates of each test ball; and transforming the articulated arm coordinate system to the cantilever coordinate system based on the mapping relationship.

[0167] Optionally, when the processor executes the program, it also performs the following steps: calculating the difference between the coordinates of multiple measuring points and the coordinates of multiple standard measuring points in the white body inspection data; recording at least one deviation measuring point coordinate, wherein the deviation measuring point coordinate is the measuring point coordinate among the multiple measuring point coordinates whose difference with the corresponding standard measuring point coordinate is greater than a preset difference; and generating an inspection report based on at least one deviation measuring point coordinate.

[0168] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0169] Embodiments of the present invention also provide a computer-readable storage medium storing a computer program configured to perform the above-described vehicle body-in-white detection method when run on a computer or processor.

[0170] Optionally, in this embodiment, the computer-readable storage medium may be configured to store a computer program for performing the following steps:

[0171] Step S101: Obtain the first detection area and the second detection area of ​​the vehicle, wherein the first detection area includes the main body of the vehicle frame, and the second detection area includes the deep cavity of the vehicle and the corner of the cab.

[0172] Step S102: Control the double cantilever to detect the first detection area and obtain the first detection data;

[0173] Step S103: Control the articulated arm to detect the second detection area and obtain the second detection data;

[0174] Step S104: The first detection data and the second detection data are fused to obtain the white body detection data;

[0175] Step S105: Generate an inspection report based on the body-in-white inspection data. The inspection report is used to determine whether the vehicle body-in-white meets the dimensional requirements of the specifications.

[0176] Optionally, the storage medium is configured to store program code for performing the following steps: determining a first coordinate origin based on the position information of the vehicle body reference hole, and constructing a double cantilever coordinate system based on the first coordinate origin; acquiring multiple first preset detection points and a first detection path in the first detection area; controlling the double cantilever to detect the multiple first preset detection points according to the first detection path to obtain multiple first detection point coordinates, wherein each first preset detection point corresponds to one first detection point coordinate, and the multiple first detection point coordinates are based on the double cantilever coordinate system; and determining first detection data based on the multiple first detection point coordinates.

[0177] Optionally, the storage medium is configured to store program code for performing the following steps: determining an initial detection path for the double cantilever based on a first detection area; and correcting the initial detection path using multiple collision devices in the first detection area to obtain a first detection path.

[0178] Optionally, the storage medium is configured to store program code for performing the following steps: acquiring position information of multiple test balls and center point position information of multiple test balls; determining a second coordinate origin based on the center point position information, and constructing an articulated arm coordinate system based on the second coordinate origin; transforming the articulated arm coordinate system to a double cantilever coordinate system based on the position information of multiple test balls; acquiring multiple second preset detection points and a second detection path in the second detection area; controlling the articulated arm to detect multiple second preset detection points according to the second detection path to obtain multiple second detection point coordinates, wherein each second preset detection point corresponds to one second detection point coordinate, and the multiple second detection point coordinates are based on the double cantilever coordinate system; and determining second detection data based on the multiple second detection point coordinates.

[0179] Optionally, the storage medium is configured to store program code for performing the following steps: obtaining multiple cantilever coordinates and multiple articulated arm coordinates of multiple test balls, wherein each test ball corresponds to one cantilever coordinate and one articulated arm coordinate, the multiple cantilever coordinates are based on the cantilever coordinate system, and the multiple articulated arm coordinates are based on the articulated arm coordinate system; determining the mapping relationship between the cantilever coordinates of each test ball and the articulated arm coordinates of each test ball; and transforming the articulated arm coordinate system to the cantilever coordinate system based on the mapping relationship.

[0180] Optionally, the storage medium is configured to store program code for performing the following steps: calculating the difference between the coordinates of multiple measuring points and the coordinates of multiple standard measuring points in the white body inspection data; recording at least one deviation measuring point coordinate, wherein the deviation measuring point coordinate is the measuring point coordinate among the multiple measuring point coordinates whose difference from the corresponding standard measuring point coordinate is greater than a preset difference; and generating an inspection report based on the at least one deviation measuring point coordinate.

[0181] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0182] Embodiments of the present invention also provide a computer program product, including a computer program, wherein the computer program, when executed by a processor, implements the steps of the above-described vehicle body-in-white detection method.

[0183] Optionally, in this embodiment, the computer program product described above may be configured to store a computer program for performing the following steps:

[0184] Step S101: Obtain the first detection area and the second detection area of ​​the vehicle, wherein the first detection area includes the main body of the vehicle frame, and the second detection area includes the deep cavity of the vehicle and the corner of the cab.

[0185] Step S102: Control the double cantilever to detect the first detection area and obtain the first detection data;

[0186] Step S103: Control the articulated arm to detect the second detection area and obtain the second detection data;

[0187] Step S104: The first detection data and the second detection data are fused to obtain the white body detection data;

[0188] Step S105: Generate an inspection report based on the body-in-white inspection data. The inspection report is used to determine whether the vehicle body-in-white meets the dimensional requirements of the specifications.

[0189] Optionally, the computer program may further implement the following steps when executing the program: determining a first coordinate origin based on the position information of the vehicle body reference hole, and constructing a double cantilever coordinate system based on the first coordinate origin; acquiring multiple first preset detection points and a first detection path in the first detection area; controlling the double cantilever to detect the multiple first preset detection points according to the first detection path to obtain multiple first detection point coordinates, wherein each first preset detection point corresponds to one first detection point coordinate, and the multiple first detection point coordinates are based on the double cantilever coordinate system; and determining first detection data based on the multiple first detection point coordinates.

[0190] Optionally, the computer program may further perform the following steps when executing the program: determining the initial detection path of the double cantilever based on the first detection area; and correcting the initial detection path using multiple collision devices in the first detection area to obtain the first detection path.

[0191] Optionally, the computer program may further implement the following steps when executing the program: acquiring the position information of multiple test balls and the position information of the center points of multiple test balls; determining the second coordinate origin based on the center point position information, and constructing an articulated arm coordinate system based on the second coordinate origin; transforming the articulated arm coordinate system to a double cantilever coordinate system based on the position information of multiple test balls; acquiring multiple second preset detection points and a second detection path in the second detection area; controlling the articulated arm to detect the multiple second preset detection points according to the second detection path to obtain the coordinates of multiple second detection points, wherein each second preset detection point corresponds to one second detection point coordinate, and the coordinates of multiple second detection points are based on the double cantilever coordinate system; and determining the second detection data based on the coordinates of multiple second detection points.

[0192] Optionally, when the computer program executes the program, it also performs the following steps: obtaining multiple cantilever coordinates and multiple articulated arm coordinates of multiple test balls, wherein each test ball corresponds to one cantilever coordinate and one articulated arm coordinate, the multiple cantilever coordinates are based on the cantilever coordinate system, and the multiple articulated arm coordinates are based on the articulated arm coordinate system; determining the mapping relationship between the cantilever coordinates of each test ball and the articulated arm coordinates of each test ball; and transforming the articulated arm coordinate system to the cantilever coordinate system based on the mapping relationship.

[0193] Optionally, when the computer program executes the program, it also performs the following steps: calculating the difference between the coordinates of multiple measuring points and the coordinates of multiple standard measuring points in the white body inspection data; recording at least one deviation measuring point coordinate, wherein the deviation measuring point coordinate is the measuring point coordinate among the multiple measuring point coordinates whose difference with the corresponding standard measuring point coordinate is greater than a preset difference; and generating an inspection report based on at least one deviation measuring point coordinate.

[0194] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0195] In the embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0196] The units described as separate components may or may not be physically separate. Similarly, the components shown as units may or may not be physical units; they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0197] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0198] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or grid device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0199] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for detecting the body-in-white of a vehicle, characterized in that, include: The vehicle's first detection area and second detection area are obtained, wherein the first detection area includes the vehicle frame body and the second detection area includes the vehicle's deep cavity and the cab corner; Control the dual cantilever arms to detect the first detection area and obtain the first detection data; The articulated arm is controlled to detect the second detection area, thereby obtaining second detection data; The first and second detection data are fused to obtain the white body detection data. An inspection report is generated based on the white body inspection data, wherein the inspection report is used to determine whether the vehicle white body meets the specification dimensional requirements.

2. The method for detecting the vehicle body-in-white according to claim 1, characterized in that, Controlling the dual cantilever arms to detect the first detection area and obtaining the first detection data includes: The first coordinate origin is determined based on the position information of the vehicle body reference hole, and a double cantilever coordinate system is constructed based on the first coordinate origin; Obtain multiple first preset detection points and a first detection path in the first detection area; The double cantilever is controlled to detect the plurality of first preset detection points according to the first detection path to obtain the coordinates of the plurality of first detection points, wherein each first preset detection point corresponds to a first detection point coordinate, and the coordinates of the plurality of first detection points are based on the double cantilever coordinate system. The first detection data is determined based on the coordinates of the plurality of first detection points.

3. The method for detecting the vehicle body-in-white according to claim 2, characterized in that, Obtaining the first detection path includes: The initial detection path of the double cantilever is determined based on the first detection area; The initial detection path is corrected by using multiple collision devices in the first detection area to obtain the first detection path.

4. The method for detecting the vehicle body-in-white according to claim 2, characterized in that, Controlling the articulated arm to detect the second detection area and obtaining the second detection data includes: Obtain the position information of multiple test balls and the position information of the center point of the multiple test balls; The second coordinate origin is determined based on the center point position information, and the articulated arm coordinate system is constructed based on the second coordinate origin; Based on the position information of the multiple test balls, the articulated arm coordinate system is transformed to the double cantilever coordinate system; Obtain multiple second preset detection points and a second detection path in the second detection area; The articulated arm is controlled to detect the plurality of second preset detection points according to the second detection path to obtain the coordinates of the plurality of second detection points. Each second preset detection point corresponds to a second detection point coordinate, and the coordinates of the plurality of second detection points are based on the double cantilever coordinate system. The second detection data is determined based on the coordinates of the plurality of second detection points.

5. The method for detecting the vehicle body-in-white according to claim 4, characterized in that, Transforming the articulated arm coordinate system to the double cantilever coordinate system based on the position information of the multiple test balls includes: Obtain multiple cantilever coordinates and multiple articulated arm coordinates of the multiple test balls, wherein each test ball corresponds to one cantilever coordinate and one articulated arm coordinate, the multiple cantilever coordinates are based on the cantilever coordinate system, and the multiple articulated arm coordinates are based on the articulated arm coordinate system. Determine the mapping relationship between the cantilever coordinates of each test ball and the articulated arm coordinates of each test ball; Based on the mapping relationship, the articulated arm coordinate system is transformed to the double cantilever coordinate system.

6. The method for detecting the vehicle body-in-white according to claim 1, characterized in that, Generating the inspection report based on the white body inspection data includes: Calculate the difference between the coordinates of multiple measuring points and the coordinates of multiple standard measuring points in the white body inspection data; Record at least one deviation measurement point coordinate, wherein the deviation measurement point coordinate is the coordinate of the measurement point among the plurality of measurement point coordinates whose difference from the corresponding standard measurement point coordinate is greater than a preset difference; The detection report is generated based on the coordinates of at least one deviation measurement point.

7. A device for detecting the body-in-white of a vehicle, characterized in that, include: The acquisition module is used to acquire a first detection area and a second detection area of ​​the vehicle, wherein the first detection area includes the main body of the vehicle frame, and the second detection area includes the deep cavity of the vehicle and the corner of the cab; The first control module is used to control the double cantilever to detect the first detection area and obtain the first detection data; The second control module is used to control the articulated arm to detect the second detection area and obtain the second detection data; The data fusion module is used to fuse the first detection data and the second detection data to obtain the white body detection data; The generation module is used to generate an inspection report based on the white body inspection data, wherein the inspection report is used to determine whether the vehicle white body meets the specification dimensional requirements.

8. A vehicle comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the vehicle body-in-white detection method as described in any one of claims 1 to 6.

9. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the vehicle body-in-white detection method as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is configured to execute the vehicle body-in-white detection method as described in any one of claims 1 to 6 when run on a computer or processor.

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