Omnibearing inspection system for appearance of automobile pipeline finished product

Abstract

The invention, which belongs to the technical field of product appearance inspection, discloses an all-dimensional inspection system for the appearance of an automobile pipeline finished product, and the system comprises four core modules, namely a full-view image acquisition work station, an image processing and interpretation engine, a result feedback and execution mechanism and a data management and tracing interface. Wherein the image acquisition work station realizes multi-angle dead-corner-free image acquisition of a pipeline through cooperative work of a multi-axis positioning clamp and a fixed camera array; the image processing engine adopts a white-box algorithm library based on physical imaging characteristics and combines a graphical configuration interface to realize efficient and reliable defect detection; and the data management interface is used for binding and tracing the whole process of the inspection data, and reversely guiding the quality information to the previous process through the closed-loop feedback module. According to the invention, manual inspection is converted into automatic and intelligent inspection, the detection efficiency and accuracy are obviously improved, the labor cost is reduced, and reliable quality guarantee is provided for automobile pipeline manufacturing.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of product appearance inspection, in particular to a kind of automobile pipeline finished product appearance all-around inspection system. BACKGROUND

[0002] In the manufacturing process of automobile pipeline product, finished product appearance inspection is the key link to ensure product quality. At present, this field mainly relies on the combination of manual inspection and semi-automatic equipment. Specifically, a single industrial camera or manual tool in fixed position is generally used for visual inspection, and different features or parts are inspected through multiple dispersed inspection stations (such as quick plug inspection table, assembly inspection tool, etc.).

[0003] This prior art solution has several significant defects: first, due to fixed and limited viewing angle, it is difficult to achieve full coverage without dead angle for pipeline with complex three-dimensional structure, resulting in missed detection of some surface defects. Especially in the station using manual inspection, the operator needs to manually turn over the pipeline to check different angles. This repetitive physical labor is very easy to cause misjudgment or missed detection due to fatigue, inattention or experience difference. Secondly, the inspection process is artificially divided into different stations, causing isolated inspection data and incoherent information, which cannot form a unified quality view, and the overall efficiency is low. Thirdly, the existing automatic detection equipment mostly uses "black box" type intelligent algorithm, whose judgment logic is not transparent, and the detection strategy is fixed. When the product model is changed, it needs to be re-adjusted by professional technicians, and ordinary production line personnel cannot quickly adapt, with poor flexibility. In addition, there is no effective closed-loop feedback mechanism between the inspection results and the previous assembly process, which cannot realize preventive quality control.

[0004] Therefore, there is an urgent need in the field for an appearance inspection system that can achieve full coverage, high efficiency and deep integration with production process to overcome the above technical bottlenecks. SUMMARY

[0005] The main purpose of the present application is to provide an automobile pipeline finished product appearance all-around inspection system, which can effectively solve the problems mentioned in the background art.

[0006] To achieve the above purpose, the technical solution adopted by the present application is as follows: An automobile pipeline finished product appearance all-around inspection system, which is composed of four function modules working cooperatively: full-view image acquisition station, image processing and interpretation engine, result feedback and execution mechanism, and data management and traceability interface; The full-view image acquisition station acquires multi-angle image data of the outer surface of the pipeline finished product through the cooperation of fixed optical sensor and mechanical positioning device; The image processing and interpretation engine is connected to the full-view image acquisition station, used for processing image data and automatic interpretation based on a special defect detection algorithm library; The result feedback and execution mechanism is connected to the image processing and interpretation engine, used for converting the interpretation result into on-site instructions and performing sorting operations; The data management and traceability interface is connected to all modules of the system, used for realizing the structured storage of inspection data, process monitoring, and integration with the upper-layer manufacturing management system.

[0007] The above four core modules realize efficient cooperation through standardized data interfaces, forming a complete detection closed loop from image acquisition to quality traceability. This modular design not only facilitates the maintenance and upgrading of the system, but also allows flexible adjustment of module configurations according to different production needs, effectively solving the problem of disconnection between various links in the traditional inspection process.

[0008] Preferably, the full-view image acquisition station includes a multi-axis rapid positioning fixture, a static multi-light source vision box, and a fixed industrial camera array; the cooperative working process is as follows: Positioning-shooting cycle: the multi-axis rapid positioning fixture rotates and precisely positions the pipeline to multiple specific observation angles according to the preset program; at each angle, the corresponding light source group (including low-angle light sources for highlighting three-dimensional defects and backlight sources for outlining the profile) in the static multi-light source vision box is triggered, and the corresponding camera in the fixed industrial camera array is synchronously exposed, thereby obtaining a high-definition image sequence of the entire outer surface of the pipeline step by step without missing any part.

[0009] This step-by-step positioning and synchronous triggering acquisition method can realize full-coverage shooting of the pipeline surface with a limited number of cameras, significantly reducing system costs while ensuring detection effectiveness. The multi-light source switching mechanism can provide optimal lighting conditions for different types of defects, such as using low-angle light sources to highlight scratches and backlight sources to detect profile integrity, ensuring that all types of defects can be effectively captured.

[0010] Preferably, the image processing and interpretation engine includes a standardized preprocessing unit; the preprocessing unit performs the following processes: Illumination compensation: by calculating the background illumination model of the image and performing image decomposition based on the Retinex theory, the original image is corrected for uneven illumination, ensuring that subsequent analysis is not affected by on-site illumination fluctuations; Image stitching: for pipeline images collected from different angles with overlapping areas, registration and fusion are performed based on SIFT or ORB feature points to generate a continuous development map or panoramic map of the pipeline for global review.

[0011] The preprocessing procedure effectively eliminates the influence of on-site light fluctuation on the detection result, and provides a complete pipeline surface view for subsequent analysis through image stitching technology. The feature point registration algorithm used can automatically identify the feature points in the overlapping area, achieving high-precision alignment of images at different angles and providing a reliable image basis for subsequent defect detection.

[0012] Preferably, the image processing and interpretation engine includes a dedicated defect detection algorithm library; the algorithm library integrates various white-box algorithms based on the physical imaging characteristics of defects, specifically including: Scratch detection algorithm: based on the histogram of oriented gradients (HOG) feature, the image is scanned in multiple directions to identify scratches with linear features; Pit detection algorithm: using local binary pattern (LBP) combined with Gaussian Laplacian (LoG) operator to capture local texture and brightness changes caused by pits; Part missing / misplacement algorithm: using template matching technology based on edge features, the edge information of the area to be detected is matched with the standard template to quickly judge the correctness of the assembly state.

[0013] The white-box algorithm based on explicit physical characteristics not only has high detection efficiency, but also has transparent and controllable judgment logic, greatly facilitating the verification and optimization process of the algorithm. Each algorithm is specifically optimized for a particular type of defect, for example, the scratch algorithm can effectively distinguish between real scratches and texture interference through multi-directional gradient analysis, and the pit algorithm can accurately identify small surface depressions through local texture analysis.

[0014] Preferably, the image processing and interpretation engine includes a decision logic configurator; the configurator realizes a user-friendly inspection scheme configuration in the following ways: Provide a graphical programming interface, encapsulate different defect detection algorithms as visual function blocks; Allow users to intuitively combine inspection processes for specific product models by dragging and connecting function blocks and data streams; For each function block, provide a parameter configuration panel to allow users to set key parameters such as gradient threshold, matching score, tolerance range, etc. without understanding the underlying code.

[0015] The graphical configuration method significantly reduces the technical threshold, allowing ordinary production line personnel to quickly adjust and optimize the detection process without programming knowledge. The system also provides real-time preview functionality, allowing users to immediately see the impact of parameter adjustments on detection results during the configuration process, greatly improving the accuracy and efficiency of configuration.

[0016] Preferably, the decision logic configurator also has a built-in inspection process template; the template predefines standard inspection steps and algorithm combinations for common types of pipelines (such as brake pipes, fuel pipes), and the user only needs to select the corresponding template and fine-tune the parameters to quickly deploy. The pre-installed inspection template greatly shortens the deployment time of new products, ensures the consistency of inspection standards between different products, and improves the practicality and ease of use of the system. The template library supports continuous expansion, and users can save verified inspection schemes as new templates to form an enterprise-specific knowledge base, promoting the accumulation and inheritance of detection experience.

[0017] Preferably, the data management and traceability interface implements a whole-process quality traceability process including: When the product is loaded, its unique identity (barcode or RFID) is obtained through a scanning device; The system automatically associates and binds the identity with all original images, processing intermediate results, final determination conclusions, and corresponding equipment and process parameters generated during the inspection process of this batch, and stores them; Through a data dashboard, the complete inspection data file of the product can be queried based on the identity, and forward and reverse traceability from the finished product to the original data can be achieved.

[0018] The whole-process data binding mechanism realizes complete traceability from the product to the original data, provides sufficient data support for quality analysis, and significantly improves the quality control capability. The system uses a distributed storage architecture to efficiently manage massive images and detection data, and supports fast retrieval and statistical analysis, providing data support for production process optimization.

[0019] Preferably, the system also includes a closed-loop feedback module; this module continuously monitors the frequency of occurrence of specific defect types (such as single type of buckle missing), and when the frequency exceeds a pre-set threshold, automatically sends a warning message to the designated front assembly station, prompting equipment adjustment or manual intervention. The closed-loop feedback mechanism realizes reverse quality control from inspection to production, can timely discover and block the occurrence of systemic quality problems, and realizes preventive quality control. The system also provides trend analysis functions, can predict quality risks based on historical data, and provide forward-looking suggestions for production decisions, thereby realizing the transition from passive inspection to active prevention.

[0020] Compared with the prior art, the present application has the following beneficial effects: The application adopts a modular collaborative architecture, realizes no dead angle image acquisition through multi-axis positioning and fixed camera array, realizes accurate and efficient automatic interpretation based on a configurable white box defect algorithm library and a graphical process configurator, and reversely guides the previous process with the help of a data closed loop feedback mechanism. The application systematically solves the pain points such as visual angle limitation, strong artificial dependence and process fragmentation in traditional inspection, and significantly improves the comprehensiveness, consistency and overall quality control level of the inspection. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 It is a system workflow schematic diagram of the application. Figure 2 It is a system composition architecture schematic diagram of the application. DETAILED DESCRIPTION

[0022] In order to make the technical means, creative features, purposes and effects realized by the application easy to understand, the application is further described below in combination with specific embodiments.

[0023] Reference Figure 1 、 Figure 2 The system architecture and process shown in the drawings, the embodiment takes a certain type of automobile brake pipe (specification: length 500mm, pipe diameter 15mm, containing 3 90° bending places, 2 standard buckle mounting positions and 1 quick plug connector mounting surface) as the inspection object, and details the specific implementation process, equipment parameters, operation details and technical principles of the automobile pipe product appearance all-around inspection system, which fully reflects the modular collaborative advantages of the system.

[0024] I. System deployment and early configuration 1. Hardware deployment parameters Full-view image acquisition workstation: configure a 4-axis rapid positioning clamp, carry 8 fixed industrial cameras, and evenly distribute them in a static multi-light source vision box; the vision box is built-in with 3 groups of light sources: low-angle ring light source (illumination angle 15°, brightness adjustable range 0-1000 lux), back light source (area light source size 300x200mm, color temperature 5500K), and coaxial light source (brightness adjustable range 0-800 lux), which are respectively adapted to different defect detection requirements.

[0025] Result feedback and execution mechanism: equipped with an audible and visual alarm, a pneumatic automatic sorting device, and a 10.1 inch touch screen human-computer interaction interface.

[0026] Data storage and communication equipment: adopt a distributed server, support Ethernet (TCP / IP protocol) and upper layer manufacturing management system (MES system) communication, and are equipped with an RFID scanning module and a bar code scanner (support Code128 code, QR code).

[0027] 2. Inspection scheme configuration (based on decision logic configurator) Template selection and function block combination: After the operator logs in to the man-machine interface, selects the pre-installed "brake pipe appearance inspection template" in the "inspection template library", and the system automatically loads the basic inspection process. Through the graphical programming interface, four function blocks of "scratch detection", "pit detection", "buckle missing / misassembly detection", and "quick plug mounting surface defect detection" are dragged and connected in the order of "image input → scratch detection → pit detection → assembly state detection → comprehensive judgment".

[0028] Parameter fine setting: Scratch detection function block: Set the gradient threshold value to 0.8, the scanning direction to 0°, 45°, 90°, and 135° in four directions, the minimum scratch recognition length to 0.5 mm, and the width to 0.02 mm.

[0029] Pit detection function block: LBP operator sampling radius 1.5 mm, LoG operator σ value 1.2, minimum pit diameter 0.3 mm, depth 0.05 mm.

[0030] Buckle missing / misassembly detection: Template matching score threshold ≥ 95 points, tolerance range ± 0.2 mm, supporting identification of the difference between buckle model A (standard part) and model B (misassembly part).

[0031] Quick plug mounting surface detection: Edge feature matching threshold 0.9, maximum contour deviation allowed 0.1 mm.

[0032] Real-time preview and debugging: After configuration is completed, put in one piece of known qualified brake pipe sample for preview detection, the system generates a panoramic development diagram, the operator checks the detection marks of each algorithm (scratch is marked with red dashed line box, pit is marked with blue solid circle) through the interface, adjusts the buckle matching score to 96 points, excludes the false judgment caused by slight surface stains, confirms that the detection result is consistent with the actual state of the sample, and saves the configuration scheme.

[0033] II. Full-view image acquisition process 1. Product loading and identity binding The operator places the brake pipe to be inspected into the V-shaped clamping groove of the multi-axis quick positioning clamp, and the clamp automatically clamps (clamping pressure 0.3 MPa, to avoid damaging the pipe surface). At the same time, the RFID scanning module automatically reads the pre-installed unique identifier on the product surface, which is pre-associated with the production batch, previous assembly station and other information of the product. The system takes this identifier as the core index to start the subsequent data binding process.

[0034] 2. Step-by-step positioning and multi-light source cooperative shooting Multi-axis fast positioning fixture starts positioning-shooting cycle according to preset program, 12 observation angles (0°, 30°, 60°, …, 330°) are set to realize 360° dead angle coverage.

[0035] When the fixture rotates the pipeline to 0° angle (the joint mounting surface faces the front), the system triggers the backlight and 2 cameras opposite the mounting surface to work synchronously: the backlight is turned on to outline the profile boundary of the joint mounting surface; the camera exposure time is set to 50 ms to shoot 2 high-definition images (focus on the edge and center area of the mounting surface respectively) for detecting profile integrity and surface depression.

[0036] When rotated to 30° angle (the pipeline side faces the camera), the low-angle ring light and 3 side-view cameras are triggered to expose synchronously: the low-angle light shines on the pipeline surface at an angle of 15° to form obvious light and dark contrast for scratches above 0.5 mm, and the camera shoots 3 images to cover the bending and straight sections of the pipeline side.

[0037] Each subsequent angle triggers the corresponding light source group and camera according to the above logic, and the coaxial light source is additionally turned on for the buckle mounting position (120° and 240° angles) to enhance the contrast between the buckle and the pipeline and ensure that the assembly status is clear. A total of 28 high-definition image sequences are obtained during the entire acquisition process.

[0038] III. Image processing and defect interpretation process 1. Image preprocessing Illumination compensation: the system calls the standardized preprocessing unit to process each original image based on the Retinex theory. Taking the side-view image at 30° angle as an example, first calculate the background lighting model of the image (by analyzing the gray scale distribution of the defect-free area), then decompose the image into reflected light component (reflecting the true features of the pipeline surface) and incident light component (reflecting the light interference), remove the uneven part of the incident light component, and correct the gray scale standard deviation of the image from the original 45 to 12, ensuring that the contrast between the scratch and the background is not affected by the on-site light fluctuation.

[0039] Image stitching: for the 28 images at 12 angles, use the SIFT feature point registration algorithm for fusion. The algorithm automatically identifies the overlapping areas of adjacent angle images, extracts the key feature points of each overlapping area, and maps the coordinates of the feature points to stitch the scattered images into one complete pipeline panoramic development image. The bending and buckle positions of the pipeline surface in the panoramic image are not stretched and deformed, and the stitching error is ≤0.1 mm.

[0040] 2. Defect automatic detection (based on special defect detection algorithm library) Scratch detection: HOG feature algorithm is used to scan the panoramic image in multiple directions. The algorithm divides the image into 16x16 pixel cells and calculates the gradient histogram of each cell in 4 scanning directions. By comparing the gradient distribution with the preset threshold (0.8), the algorithm identifies the area with linear features. For example, in the area corresponding to the 150° angle of the pipeline, the algorithm detects a linear gradient abnormal area with a length of 0.8 mm and a width of 0.03 mm, which is determined as a scratch defect and its coordinates are recorded.

[0041] Pit detection: Local texture features of the image are extracted by LBP operator, and LoG operator (σ=1.2) is used to detect local brightness changes. In the flat section of the pipeline at an angle of 270°, the algorithm captures a circular texture abnormal area with a diameter of 0.4 mm, whose LoG response value exceeds the threshold of 1.5, which is determined as a pit defect. The gray value of this area is 30 levels lower than the surrounding area, which is consistent with the difference in light reflection caused by the actual pit.

[0042] Missing / wrong buckle detection: Template matching technology based on edge features is used to compare the buckle installation position in the image to be detected with the standard template (containing edge profile data of 2 model A buckles). If the edge feature matching score of one buckle installation position of a product to be detected is 82 (lower than the threshold of 95), the system further calls the template of model B buckle for secondary matching, and the matching score reaches 98, which is determined as a wrong buckle installation. If there is no effective edge feature matching in the installation position (matching score <60), it is determined as a missing buckle.

[0043] Quick connector mounting surface detection: The edge feature extraction algorithm is used to obtain the contour curve of the mounting surface, which is compared with the standard contour curve to calculate the maximum deviation value. If the deviation value is ≤0.1 mm, it is determined to be qualified, and if the deviation value is >0.1 mm (such as the contour is shifted by 0.15 mm due to installation inclination), it is determined as a mounting surface defect.

[0044] 3. Comprehensive judgment The judgment logic configurator receives the detection results of each algorithm and makes a comprehensive judgment based on the "one vote veto" principle: if there is any serious defect (such as a scratch with a length of ≥1 mm, a pit with a diameter of ≥0.5 mm, a missing / wrong buckle), the product is determined to be unqualified; if there are only minor defects (such as scratches with a length of <0.5 mm) and the number of defects is ≤2, the product is determined to be qualified; and no defect product is directly determined to be qualified.

[0045] Four, result feedback and execution process 1. On-site instruction output The image processing and interpretation engine sends the judgment result of "unqualified (wrong buckle installation)" to the result feedback and execution mechanism: The audible and visual alarm starts flashing red light and emits a buzzing sound, which lasts for 3 seconds and then turns into a constant red light.

[0046] The human-computer interaction interface displays the determination result, defect type (snap buckle misassembly), defect coordinates and corresponding local enlarged image in real time, facilitating intuitive viewing by the operator.

[0047] 2. Automatic sorting operation After receiving the instruction, the pneumatic push rod extends within 0.3 s, pushing the unqualified product from the main conveying line to the special repair conveying line (conveying line B), and pasting a hidden positioning mark on the surface of the product (corresponding to the defect coordinate position), facilitating the quick positioning of defects by the repair personnel. Qualified products are conveyed from the main conveying line (conveying line A) to the next production process.

[0048] Five, data management and traceability process 1. Data association and storage The system automatically associates and binds the product unique identifier with the full data, and the storage content includes: Raw data: 28 raw images of 12 angles, RFID scan records.

[0049] Processed data: single image after illumination compensation, panoramic unfolded image after splicing, intermediate results of each algorithm (such as HOG feature histogram, LBP texture matrix).

[0050] Result data: comprehensive determination conclusion (unqualified), defect details (snap buckle misassembly, matching score 98 points), sorting record.

[0051] Equipment and process parameters: camera exposure time, light source brightness, clamp clamping pressure, inspection template version.

[0052] 2. Whole-process traceability query Forward traceability: management personnel can view the inspection result statistics and defect type distribution of all products in the batch by inputting the production batch through the data board.

[0053] Reverse traceability: the customer feedback that a brake pipe has assembly problems, by scanning the product RFID tag, the complete inspection file can be quickly retrieved, including the intuitive evidence of snap buckle misassembly in the original image, the equipment parameters during detection, and the traceability to the previous assembly station, providing data support for quality problem analysis.

[0054] Six, closed-loop feedback and production optimization process 1. Defect frequency monitoring The closed-loop feedback module continuously calculates the frequency of a specific defect type, and accumulates the calculation according to the production batch. Taking the "snap buckle misassembly" defect as an example, the system sets the early warning threshold to 3% (single batch misassembly rate > 3% triggers early warning).

[0055] 2. Early warning information sending and intervention The system automatically sends early warning information to the PLC control system of the previous assembly station through the standardized communication interface. The information content includes: "Batch XXXX-XX, buckle misassembly rate 7%, please check the buckle supply device and the installation machine pressure parameters". At the same time, an early warning work order is generated in the MES system to notify production management personnel to intervene.

[0056] 3. Trend analysis and prevention The system performs trend analysis based on historical data. By fitting the "buckle misassembly" defect frequency curve of the last 10 batches, for example, it is found that the misassembly rate has gradually risen from 1% to 7%. If no intervention is taken, the next batch may have a misassembly rate of 9%. Production management personnel adjust the sorting parameters of the buckle supply device (such as improving the model recognition accuracy from 95% to 99%) and calibrate the pressure of the installation machine based on the early warning information, achieving preventive quality control.

[0057] The above shows and describes the basic principles and main features of the present application and the advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above embodiments, and the above embodiments and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A comprehensive inspection system for the appearance of finished automotive piping products, characterized in that: The system consists of four collaborative functional modules: a full-view image acquisition station, an image processing and interpretation engine, a result feedback and execution mechanism, and a data management and traceability interface; The all-view image acquisition station acquires multi-angle image data of the outer surface of the finished pipeline through the cooperation of a fixed optical sensor and a mechanical positioning device. The image processing and interpretation engine is connected to the full-view image acquisition station and is used to process image data and perform automated interpretation based on a dedicated defect detection algorithm library. The result feedback and execution mechanism is connected to the image processing and interpretation engine, which is used to convert the interpretation results into on-site instructions and execute sorting operations; The data management and traceability interface is connected to all modules of the system to realize the structured storage of inspection data, process monitoring, and integration with the upper-level manufacturing management system.

2. The comprehensive inspection system for the appearance of finished automotive piping products according to claim 1, characterized in that: The full-view image acquisition station includes a multi-axis rapid positioning fixture, a static multi-light source vision box, and a fixed industrial camera array. Its collaborative workflow is as follows: Positioning-shooting cycle: The multi-axis rapid positioning fixture rotates the pipeline sequentially and precisely positions it to multiple specific observation angles according to a preset program; at each angle, the corresponding preset light source group in the static multi-light source vision box is triggered, and at the same time, the corresponding camera in the fixed industrial camera array is exposed synchronously to obtain a high-definition image sequence of the entire outer surface of the pipeline.

3. The comprehensive inspection system for the appearance of finished automotive piping products according to claim 1, characterized in that: The image processing and interpretation engine includes a standardized preprocessing unit; the preprocessing unit performs the following process: Illumination compensation: By calculating the background illumination model of the image and performing image decomposition based on Retinex theory, the uneven illumination of the original image is corrected. Image stitching: For pipeline images acquired from different angles with overlapping areas, registration and fusion are performed based on SIFT or ORB feature points to generate continuous unfolded or panoramic views of the pipeline for global review.

4. The comprehensive inspection system for the appearance of finished automotive piping products according to claim 1, characterized in that: The image processing and interpretation engine includes a dedicated defect detection algorithm library; this library integrates various white-box algorithms based on the physical imaging features of defects, specifically including: Scratch detection algorithm: Based on histogram of oriented gradients (HOG) features, the image is scanned in multiple directions to identify scratches with linear features; Pockmark detection algorithm: It uses Local Binary Pattern (LBP) combined with the Laplacian of Gaussian (LoG) operator to capture local texture and brightness changes caused by pits; Part missing / incorrect assembly algorithm: It adopts template matching technology based on edge features to match the edge information of the area to be inspected with the standard template to determine the assembly status.

5. The comprehensive inspection system for the appearance of finished automotive piping products according to claim 1, characterized in that: The image processing and interpretation engine includes a decision logic configurator; the configurator: It provides a graphical programming interface that encapsulates different defect detection algorithms into visual functional blocks; Allows users to combine inspection processes for specific product models by dragging and dropping function blocks and connecting data streams; For each function block, a parameter configuration panel is provided, allowing users to set parameters.

6. The comprehensive inspection system for the appearance of finished automotive piping products according to claim 5, characterized in that: The decision logic configurator also has a built-in inspection process template; the template predefines standard inspection steps and algorithm combinations for common types of pipelines.

7. The comprehensive inspection system for the appearance of finished automotive piping products according to claim 1, characterized in that: The data management and traceability interface enables full-process quality traceability, including the following steps: When loading products, their unique identification is obtained through scanning equipment; The system automatically associates and stores the identifier with all original images, intermediate processing results, final judgments, and corresponding equipment and process parameters generated during the batch inspection process.

8. The comprehensive inspection system for the appearance of finished automotive piping products according to claim 1, characterized in that: The system also includes a closed-loop feedback module; this module continuously monitors the frequency of occurrence of specific defect types, and when the frequency exceeds a preset threshold, it automatically sends an early warning message to the designated upstream assembly station, prompting equipment adjustment or manual intervention.

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