A big data-based umbrella production quality inspection method and system

Abstract

The application relates to a big data-based umbrella production quality inspection method and system, and belongs to the field of data processing. In the method, a processing terminal acquires a video stream collected by a collection terminal for an umbrella structure, the umbrella structure contained in different frames of images in the video stream is obtained by the collection terminal based on different shooting angles; the processing terminal determines whether the shape of the umbrella structure in the video stream matches the shape of a preset umbrella structure in a database; if the shapes match, the processing terminal determines that the shape quality inspection of the umbrella structure is passed; or, if the shapes do not match, the processing terminal analyzes the illumination change of the umbrella structure in the video stream through digital twin technology, determines whether the shape of the umbrella structure in the video stream is tampered with by an attacker, and if the shape is tampered with by the attacker, the processing terminal sends an alarm to a control center; if the shape is not tampered with by the attacker, it is determined that the shape quality inspection of the umbrella structure is not passed.

Description

Technical Field

[0001] The present invention belongs to the field of data processing, and relates to a method and system for quality inspection of umbrella production based on big data. Background Art

[0002] With the development of image processing technology, automated quality inspection methods based on machine vision have gradually been applied to the field of umbrella production. By analyzing the product images collected on the production line, it is possible to quickly identify the appearance defects of umbrella structural components (such as umbrella handles, connectors, etc.), improving the efficiency and accuracy of quality inspection.

[0003] However, existing image quality inspection technologies have security risks. Since the quality inspection results completely depend on the authenticity of the image content, attackers may tamper with the collected product images using image editing software, disguising defective umbrella structural components as qualified products in the image, thereby avoiding detection. How to improve the security of image quality inspection technology while ensuring the detection efficiency has become a technical problem that needs to be solved in the current field of umbrella production. Summary of the Invention

[0004] In view of this, in order to solve the above problems, the present invention provides a method and system for quality inspection of umbrella production based on big data.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] In the first aspect, a method for quality inspection of umbrella production based on big data is provided. The method is applied to a processing terminal and includes: the processing terminal obtains a video stream collected by a collection terminal for an umbrella structural component, and the umbrella structural components included in the images of different frames in the video stream are obtained by the collection terminal based on different shooting perspectives; the processing terminal determines whether the shape of the umbrella structural component in the video stream matches the preset shape of the umbrella structural component in the database; if it matches, the processing terminal determines that the shape quality inspection of the umbrella structural component passes; or, if it does not match, the processing terminal analyzes the light change of the umbrella structural component in the video stream through digital twin technology to determine whether the shape of the umbrella structural component in the video stream has been tampered with by an attacker. If it has been tampered with by an attacker, the processing terminal sends an alarm to the control center. If it has not been tampered with by an attacker, it is determined that the shape quality inspection of the umbrella structural component fails.

[0007] Therefore, by acquiring the video stream of the umbrella structure moving on the conveyor belt, and utilizing the lighting differences caused by changes in viewing angle in different frames, combined with digital twin technology to physically model the lighting changes, it is possible to determine whether the shape mismatch is caused by a genuine defect or by attacker tampering. This method introduces the laws of light transmission in physical space into the image verification process, making the judgment result physically interpretable. Compared with traditional single-frame image detection, this scheme uses multi-frame temporal information to construct physical constraints, which can effectively identify defects forged by image editing software and prevent malicious circumvention of quality inspection. Furthermore, this method does not require additional hardware such as multiple light sources or multiple cameras, and has the advantages of low cost and ease of deployment.

[0008] Optionally, the processing terminal determines whether the shape of the umbrella structure in the video stream matches the preset shape of the umbrella structure in the database, including: the processing terminal extracts the pixels located at the edge of the umbrella structure in the first image to obtain the shape of the umbrella structure in the first image, wherein the first image is the first frame containing the umbrella structure in a series of multiple frames of the video stream; the processing terminal determines whether the shape of the umbrella structure in the first image matches the preset shape of the umbrella structure.

[0009] Therefore, by extracting the edge pixels of the umbrella structure in the first frame image to determine its shape, and using the first frame of a multi-frame video stream as the benchmark for shape matching, the computational redundancy of frame-by-frame processing is avoided. Utilizing edge pixels to represent the shape contour can effectively capture the macroscopic geometric features of the structure while reducing the influence of interference factors such as lighting and texture on shape comparison, thus providing accurate input data for subsequent shape matching.

[0010] Optionally, the processing terminal determines whether the shape of the umbrella structure in the first image matches the shape of a preset umbrella structure, including: the processing terminal projects the shape of the umbrella structure in the first image onto the shape of the preset umbrella structure to obtain a projection pattern; the processing terminal determines whether the shape of the preset umbrella structure completely covers the projection pattern; if it completely covers the projection pattern, it indicates that the shape of the umbrella structure in the first image matches the shape of the preset umbrella structure; otherwise, it indicates that the shape of the umbrella structure in the first image does not match the shape of the preset umbrella structure; the shape of the preset umbrella structure is larger than the shape of an umbrella structure without shape defects within the error range.

[0011] Therefore, by projecting the shape in the first image onto a preset shape and determining whether the preset shape completely covers the projected pattern, rapid matching of the umbrella structural component's shape is achieved. The preset shape is larger than the shape of a defect-free structural component within the error range; this inclusive design allows for normal manufacturing errors and avoids misjudging qualified products within a small tolerance range as defects. The method for determining projection coverage is intuitive and simple to calculate, enabling efficient screening of structural components with significant shape deviations.

[0012] Optionally, the processing terminal analyzes the illumination changes of the umbrella structure in the video stream using digital twin technology to determine whether the shape of the umbrella structure in the video stream has been tampered with by an attacker. This includes: the processing terminal analyzing the illumination of the umbrella structure in the first image using digital twin technology, determining the simulated illumination changes of the umbrella structure after the first image, and the processing terminal determining the illumination changes of the umbrella structure in K consecutive frames after the first image. The first image is the first frame in a series of consecutive frames of the video stream that contains the umbrella structure, and K is an integer greater than 1. The processing terminal determines whether the simulated illumination changes of the umbrella structure match the illumination changes of the umbrella structure in the K frames. If the simulated illumination changes of the umbrella structure match the illumination changes of the umbrella structure in the K frames, it indicates that it has not been tampered with by an attacker; otherwise, it indicates that it has been tampered with by an attacker.

[0013] Therefore, this method incorporates physical laws into the image verification process. By utilizing the continuity of illumination changes in the real physical world, it forces any attack attempting to forge defects to simultaneously simulate physically consistent illumination changes across multiple frames, significantly increasing the difficulty of the attack. If the prediction matches the observation, it indicates that the illumination change conforms to physical laws and the defect is real; otherwise, it suggests the possibility of tampering.

[0014] Optionally, the umbrella structure is mounted on a conveyor belt. The processing terminal analyzes the illumination of the umbrella structure in the first image using digital twin technology to determine the simulated illumination changes of the umbrella structure after the first image. This includes: the processing terminal acquiring the illumination of the umbrella structure in the first image; the processing terminal analyzing the illumination of the umbrella structure in the first image, the speed of the conveyor belt, the positional relationship between the umbrella structure and the shooting direction of the acquisition terminal, and the positional relationship between the indoor light source and the umbrella structure and the acquisition terminal, respectively, using digital twin technology to determine the simulated illumination changes of the umbrella structure after the first image.

[0015] Therefore, the above scheme clarifies the key physical parameters required for digital twin analysis. By incorporating these physical parameters into the illumination change prediction model, the simulation results can realistically reflect the illumination attenuation and angle changes during object movement, improving prediction accuracy. This feature transforms the inherent physical conditions of the production line into a natural constraint for verifying tampering, enabling high-precision physical modeling without additional hardware.

[0016] Optionally, the processing terminal uses digital twin technology to analyze the lighting conditions of the umbrella structure in the first image, the speed of the conveyor belt, the positional relationship between the umbrella structure and the shooting direction of the acquisition terminal, and the positional relationship between the indoor light source and the umbrella structure and the acquisition terminal, respectively, to determine the simulated lighting changes of the umbrella structure after the first image. This includes: the lighting conditions of the umbrella structure in the first image are the light intensity values ​​of each pixel belonging to the umbrella structure in the first image; for each pixel belonging to the umbrella structure in the first image: the processing terminal uses digital twin technology to analyze the light intensity value of the pixel, the speed of the conveyor belt, the positional relationship between the umbrella structure and the shooting direction of the acquisition terminal, and the positional relationship between the indoor light source and the umbrella structure and the acquisition terminal, respectively, to determine multiple simulated light intensities of the pixel after the first image. The processing terminal determines whether the simulated illumination changes of the umbrella structure match the illumination changes of the umbrella structure in the K-frame images. This includes: determining whether, among the multiple simulated illumination intensity values ​​of a pixel, there are more than a preset number of simulated illumination intensity values ​​that match the corresponding illumination intensity values ​​of the pixel in the K-frame images; if so, the pixel is determined to be a matching pixel; otherwise, the pixel is determined to be a non-matching pixel; and determining the proportion of matching pixels among the pixels belonging to the umbrella structure in the first image; and determining whether the proportion exceeds a preset ratio. If it exceeds the preset ratio, the simulated illumination changes of the umbrella structure match the illumination changes of the umbrella structure in the K-frame images; otherwise, the simulated illumination changes of the umbrella structure do not match the illumination changes of the umbrella structure in the K-frame images.

[0017] Therefore, by refining the analysis of illumination changes to the pixel level, independently calculating and matching the simulated illumination intensity value for each pixel belonging to the umbrella structural component, and judging the overall matching degree by statistically analyzing the proportion of matching pixels, this pixel-level processing method can capture the details of illumination changes in tiny defect areas, avoiding the masking of local anomalies by regional averaging. Simultaneously, by setting dual thresholds for a preset number and a preset proportion, the sensitivity and specificity of the detection are balanced, effectively distinguishing between genuine physical defects and local tampering.

[0018] Optionally, the processing terminal analyzes the following using digital twin technology:

[0019] The light source is a point light source, and its illumination direction is consistent with the shooting direction of the acquisition terminal. The light source is located at world coordinates (0,0,H), where H is the height of the acquisition terminal. The conveyor belt moves at a constant speed V along the positive X-axis, with V in millimeters per frame. The coordinates of the pixel in the first image are... The coordinates of a pixel in the next frame after the first image are estimated as follows: The depth Z corresponding to a pixel satisfies the following relationship:

[0020] , , ;

[0021] The physical world coordinates of a pixel in the first image are: , ;

[0022] in, and These are the equivalent focal lengths of the acquisition terminal in the X and Y directions, respectively. and These are the coordinates of the principal point, which is the intersection of the optical axis of the acquisition terminal and the image captured by the acquisition terminal.

[0023] The distance from the physical location of a pixel in the first image to the physical location of the light source. as follows:

[0024] ;

[0025] Assuming the conveyor belt movement changes only the X coordinate while the Y and Z coordinates remain unchanged, the distance from the physical location of a pixel to the physical location of the light source in the t-th frame image following the first image is... as follows:

[0026] ; ;

[0027] The grayscale value of a pixel in the first image is In the case of t, the estimated gray value of a pixel in the t-th frame of the image. for ; where the gray value of a pixel in the first image is the illumination intensity value of the pixel in the first image, and the estimated gray value of a pixel in the t-th frame image is one of the simulated illumination intensity values ​​among multiple simulated illumination intensity values.

[0028] Therefore, the above scheme provides a specific mathematical model for pixel-level illumination prediction. Under the simplified conditions of the light source and camera moving in the same direction and the conveyor belt moving horizontally, the depth information of the pixel is recovered using motion parallax. Then, the distance from that point to the light source in different frames is calculated, and the grayscale value is predicted using the inverse square law of distance. This model simplifies the complex three-dimensional illumination transmission problem into an analytically calculable mathematical expression. All parameters can be obtained through calibration or real-time acquisition, requiring no iterative optimization, making it suitable for the high-speed real-time detection needs of production lines.

[0029] Optionally, if the processing terminal determines that the external quality inspection of the umbrella structural component has passed, the method further includes: the processing terminal determining whether there is a structural defect in the umbrella structural component in the video stream; if there is no structural defect, the processing terminal determines that the structural quality inspection of the umbrella structural component has passed; or, if there is a structural defect, the processing terminal analyzes the illumination changes of the structural defect in the video stream using digital twin technology to determine whether the structural defect in the video stream is a fake defect or a real defect; if it is a fake defect, the processing terminal sends an alarm to the control center; if it is a real defect, the processing terminal determines that the structural quality inspection of the umbrella structural component has failed.

[0030] Therefore, by extending the tamper verification method from appearance inspection to structural defect detection, a complete quality inspection system for umbrella structural components has been formed. After passing the appearance quality inspection, further verification of the authenticity of structural defects can identify malicious tampering targeting minor defects (such as cracks, dents, and improper assembly). This layered detection strategy not only ensures detection efficiency but also improves the coverage of anti-counterfeiting measures, making it impossible for attackers to circumvent both appearance and structural quality inspections simultaneously by tampering with images.

[0031] Optionally, the processing terminal analyzes the illumination changes of structural defects in the video stream using digital twin technology to determine whether the structural defects in the video stream are fake defects or real defects. This includes: each pixel in the first image that belongs to the structural defect is denoted as a target pixel; the processing terminal determines whether, among the multiple simulated illumination intensity values ​​of the target pixel, there are more than a preset number of simulated illumination intensity values ​​that match the illumination intensity values ​​corresponding to the target pixel in K frames of the image. If so, the target pixel is determined to be a target matching pixel; otherwise, the target pixel is determined to be a target non-matching pixel; and the processing terminal determines the target proportion of the target matching pixel among the pixels in the first image that belong to the structural defect; the processing terminal determines whether the target proportion exceeds a preset ratio. If it exceeds the preset ratio, the structural defect is a real defect; otherwise, the structural defect is a fake defect.

[0032] Therefore, the above scheme applies the pixel-level illumination change verification method to structural defect areas, determining the authenticity of defects by statistically analyzing the proportion of matching pixels within the defect area. This feature is designed specifically for the characteristics of defect areas, effectively addressing the detection challenges such as small defect area and weak features. Through pixel-level fine analysis and statistical judgment, it achieves accurate identification of minute defects, preventing attackers from exploiting the small size of defects for targeted tampering.

[0033] Secondly, a quality inspection system for umbrella production is provided. The system includes a processing terminal, which is configured to: acquire video streams captured by a data acquisition terminal for umbrella structural components, wherein the umbrella structural components contained in different frames of the video stream are captured by the data acquisition terminal from different shooting angles; determine whether the shape of the umbrella structural component in the video stream matches the preset shape of the umbrella structural component in the database; if they match, the processing terminal determines that the shape quality inspection of the umbrella structural component has passed; or, if they do not match, the processing terminal analyzes the illumination changes of the umbrella structural component in the video stream using digital twin technology to determine whether the shape of the umbrella structural component in the video stream has been tampered with by an attacker; if it has been tampered with by an attacker, the processing terminal sends an alarm to the control center; if it has not been tampered with by an attacker, the shape quality inspection of the umbrella structural component has failed.

[0034] It should be understood that the specific implementation of the second aspect can also refer to the method described in the first aspect, and will not be repeated here.

[0035] The objectives and other advantages of this invention can be realized and obtained through the following description. Attached Figure Description

[0036] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0037] Figure 1 This invention provides a schematic diagram of the architecture of an umbrella production quality inspection system.

[0038] Figure 2 A flowchart of a big data-based umbrella production quality inspection method provided by the present invention;

[0039] Figure 3 This is a schematic diagram of the structure of a processing device provided by the present invention. Detailed Implementation

[0040] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. The accompanying drawings are for illustrative purposes only, representing only schematic diagrams and not actual physical objects, and should not be construed as limiting the present invention. To better illustrate the embodiments of the present invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent actual products.

[0041] like Figure 1 As shown in the figure, this application provides an umbrella production quality inspection system, which includes a processing terminal, a data acquisition terminal, and a control center.

[0042] The data acquisition terminal can be implemented as an industrial camera or a smart camera, installed above the conveyor belt of the umbrella production line. To ensure clear capture of the surface details of the umbrella structural components, the camera resolution should be at least 5 megapixels, and equipped with an appropriate optical lens. The acquisition terminal's shooting direction is vertically downward, aligned with the conveyor belt. Its installation height H is a known fixed value. The camera's intrinsic parameter matrix, including the equivalent focal length f in the X direction, can be obtained through offline calibration. x Equivalent focal length f in the Y direction y and principal point coordinates (c x , c y To meet the assumptions of the illumination prediction model, a point light source is used in this embodiment and is coaxially mounted with the camera, meaning the light source is located at the same position as the camera's optical center, at world coordinates (0,0,H). The acquisition terminal continuously acquires video streams of the moving umbrella structure on the conveyor belt at a fixed frame rate (e.g., 30 frames / second) and transmits the video streams to the processing terminal in real time via wired or wireless means.

[0043] The processing terminal can be implemented as an industrial control computer or an embedded image processing unit, possessing strong computing power and storage resources. Internally, the processing terminal deploys an image processing module, a digital twin analysis module, and a communication module. The image processing module is responsible for decoding the received video stream, extracting frames, and performing optical flow tracking, extracting edge features and pixel-level motion information of the umbrella structure. The digital twin analysis module has a built-in illumination prediction mathematical model, capable of calculating the simulated illumination intensity value of subsequent frames pixel-by-pixel based on parameters such as the grayscale value of each pixel in the first frame, the conveyor belt speed V, camera intrinsic parameters, and light source position. The communication module connects to the acquisition terminal and control center via Ethernet or fieldbus, used to receive video stream data and send alarm signals. The processing terminal can be deployed on the production line or on a cloud server, interacting with field equipment via a high-speed network.

[0044] The control center can be implemented as a monitoring computer or mobile management terminal in the production line's central control room, equipped with a display screen and alarm devices. The control center maintains real-time communication with the processing terminal via a local area network (LAN) or industrial Internet of Things (IIoT), receiving quality inspection results and alarm information uploaded by the processing terminal. When suspected tampering of an umbrella structural component is detected, the processing terminal sends an alarm signal to the control center. The control center displays an alarm window on its screen, showing the tampered image frame, the location of the defective area, and the tampering confidence score, while simultaneously triggering an audible and visual alarm to alert on-site quality inspectors. The control center can also link with the production line's actuators (such as rejection devices) to automatically remove suspicious products from the conveyor belt or pause the production line for manual review. Furthermore, the control center has data storage capabilities, storing historical quality inspection records and alarm logs in a database for subsequent quality traceability and algorithm optimization.

[0045] Therefore, in this system, the processing terminal acquires the video stream captured by the acquisition terminal for the umbrella structure. The umbrella structure contained in different frames of the video stream is captured by the acquisition terminal from different shooting angles. The processing terminal determines whether the shape of the umbrella structure in the video stream matches the preset shape of the umbrella structure in the database. If they match, the processing terminal determines that the shape quality inspection of the umbrella structure has passed. Alternatively, if they do not match, the processing terminal analyzes the illumination changes of the umbrella structure in the video stream using digital twin technology to determine whether the shape of the umbrella structure in the video stream has been tampered with by an attacker. If it has been tampered with, the processing terminal sends an alarm to the control center. If it has not been tampered with, the shape quality inspection of the umbrella structure has failed.

[0046] like Figure 2 As shown, a big data-based quality inspection method for umbrella production is provided. This method is applied to the aforementioned system, and the specific process is as follows:

[0047] S201, The processing terminal acquires the video stream captured by the acquisition terminal for the umbrella structure. The umbrella structure contained in the images of different frames in the video stream is captured by the acquisition terminal from different shooting angles.

[0048] For example, in the specific implementation of this application, the umbrella structural components can be various parts such as the umbrella handle, umbrella rib connectors, umbrella handle buttons, and center rod joints. Taking the umbrella handle as an example, it is usually injection molded from plastic, and the surface may have anti-slip textures or brand logos. During assembly, it is necessary to ensure that the button is properly engaged and the spring is functioning correctly. The conveyor belt moves at a constant speed V along the X-axis, and the acquisition terminal is installed vertically downwards, with the point light source coaxially arranged with the camera. When the umbrella handle enters the camera's field of view along the conveyor belt, the acquisition terminal begins recording a video stream and sends the first frame and subsequent frames to the processing terminal in real time.

[0049] S202, the processing terminal determines whether the shape of the umbrella structure in the video stream matches the preset umbrella structure shape in the database.

[0050] For example, the processing terminal can extract the pixels located at the edges of the umbrella structure in the first image to obtain the shape of the umbrella structure in the first image; that is, the shape formed by these edge pixels is the shape of the umbrella structure in the first image. The first image is the first frame containing the umbrella structure in a series of consecutive frames of a video stream. Therefore, the processing terminal determines whether the shape of the umbrella structure in the first image matches a preset umbrella structure shape. In other words, by extracting the edge pixels of the umbrella structure in the first frame, the shape is determined, and the first frame in a series of consecutive frames of video stream is used as the benchmark for shape matching, avoiding computational redundancy from frame-by-frame processing. Using edge pixels to represent the shape contour can effectively capture the macroscopic geometric features of the structure while reducing the influence of interference factors such as lighting and texture on shape comparison, providing accurate input data for subsequent shape matching.

[0051] In one possible implementation, the processing terminal can project the shape of the umbrella structure in the first image onto the shape of a preset umbrella structure to obtain a projected pattern. The processing terminal determines whether the preset umbrella structure shape completely covers the projected pattern. If it completely covers the pattern, it means that the shape of the umbrella structure in the first image matches the preset umbrella structure shape; otherwise, it means that the shape of the umbrella structure in the first image does not match the preset umbrella structure shape. The preset umbrella structure shape is larger than the shape of an umbrella structure without shape defects within the error range. Therefore, by projecting the shape in the first image onto the preset shape and determining whether the preset shape completely covers the projected pattern, rapid matching of the umbrella structure shape is achieved. The preset shape being larger than the shape of a defect-free structure within the error range allows for normal manufacturing errors and avoids misjudging qualified products within a small tolerance range as defects. The method of determining projection coverage is intuitive and simple to calculate, and can efficiently screen out structural components with significant shape deviations.

[0052] S203, if a match is found, the processing terminal determines that the shape quality inspection of the umbrella structural component has passed; or, if a match is not found, the processing terminal analyzes the lighting changes of the umbrella structural component in the video stream using digital twin technology to determine whether the shape of the umbrella structural component in the video stream has been tampered with by an attacker. If it has been tampered with by an attacker, the processing terminal sends an alarm to the control center; if it has not been tampered with by an attacker, it determines that the shape quality inspection of the umbrella structural component has failed.

[0053] For example, the processing terminal can analyze the lighting conditions of the umbrella structure in the first image using digital twin technology to determine the simulated lighting changes of the umbrella structure after the first image, and the lighting changes of the umbrella structure in K consecutive frames after the first image. The first image is the first frame containing the umbrella structure in a series of consecutive frames of a video stream, and K is an integer greater than 1. The processing terminal determines whether the simulated lighting changes of the umbrella structure match the lighting changes of the umbrella structure in the K frames. If the simulated lighting changes of the umbrella structure match the lighting changes of the umbrella structure in the K frames, it indicates that it has not been tampered with by an attacker; otherwise, it indicates that it has been tampered with by an attacker. Thus, this method introduces physical laws into the image verification process, utilizing the continuity of lighting changes in the real physical world. This forces any attack attempting to forge a defect to simultaneously simulate lighting changes that conform to physical laws across multiple frames, greatly increasing the difficulty of the attack. If the prediction matches the observation, it indicates that the lighting changes conform to physical laws, and the defect truly exists; otherwise, it suggests the possibility of tampering.

[0054] In one possible implementation, as described above, the umbrella structure is positioned on a conveyor belt. The processing terminal can acquire the lighting conditions of the umbrella structure in the first image, i.e., by analyzing the first image. Therefore, the processing terminal can use digital twin technology to analyze the lighting conditions of the umbrella structure in the first image, the speed of the conveyor belt, the positional relationship between the umbrella structure and the shooting direction of the acquisition terminal, and the positional relationship between indoor light sources and the umbrella structure and the acquisition terminal, respectively, to determine the simulated lighting changes of the umbrella structure after the first image. In other words, the above scheme clarifies the key physical parameters required for digital twin analysis. By incorporating these physical parameters into the lighting change prediction model, the simulation results can realistically reflect the light attenuation and angle changes during the object's movement, improving the accuracy of the prediction. This feature transforms the inherent physical conditions of the production line into a natural constraint for verifying tampering, achieving high-precision physical modeling without additional hardware.

[0055] Specifically, the lighting conditions of the umbrella structure in the first image are the light intensity values ​​of each pixel belonging to the umbrella structure in the first image. For each pixel belonging to the umbrella structure in the first image: the processing terminal analyzes the light intensity value of the pixel, the movement speed of the conveyor belt, the positional relationship between the umbrella structure and the shooting direction of the acquisition terminal, and the positional relationship between the indoor light source and the umbrella structure and the acquisition terminal respectively, to determine multiple simulated light intensity values ​​of the pixel after the first image. Therefore, the processing terminal can determine whether more than a preset number of simulated illumination intensity values ​​of a pixel match the corresponding illumination intensity values ​​of that pixel in the K-frame images. If so, the pixel is determined to be a matching pixel; otherwise, it is determined to be a non-matching pixel. Furthermore, the processing terminal determines the proportion of matching pixels among the pixels belonging to the umbrella structure in the first image. The processing terminal determines whether this proportion exceeds a preset ratio. If it does, the simulated illumination change of the umbrella structure matches the illumination change of the umbrella structure in the K-frame images; otherwise, the simulated illumination change of the umbrella structure does not match the illumination change of the umbrella structure in the K-frame images. Thus, by refining the illumination change analysis to the pixel level, calculating and matching the simulated illumination intensity value independently for each pixel belonging to the umbrella structure, and judging the overall matching degree by statistically analyzing the proportion of matching pixels, this pixel-level processing method can capture the details of illumination changes in small defect areas, avoiding the masking of local anomalies due to regional averaging. Meanwhile, by setting dual thresholds for the preset number and preset ratio, the sensitivity and specificity of the detection are balanced, which can effectively distinguish between real physical defects and local tampering.

[0056] More specifically, the analysis performed by the processing terminal using digital twin technology includes the following:

[0057] The light source is a point light source, and its illumination direction is consistent with the shooting direction of the acquisition terminal. The light source is located at world coordinates (0,0,H), where H is the height of the acquisition terminal. The conveyor belt moves at a constant speed V along the positive X-axis, with V in millimeters per frame. The coordinates of the pixel in the first image are... The coordinates of a pixel in the next frame after the first image are estimated as follows: The depth Z corresponding to a pixel satisfies the following relationship:

[0058] , , ;

[0059] The physical world coordinates of the pixel in the first image are derived from the camera model: , ;

[0060] in, and These are the equivalent focal lengths of the acquisition terminal in the X and Y directions, respectively. and These are the coordinates of the principal point, which is the intersection of the optical axis of the acquisition terminal and the image captured by the acquisition terminal. These parameters are all intrinsic parameters of the acquisition terminal (i.e., the camera).

[0061] Furthermore, the distance from the physical location of a pixel in the first image to the physical location of the light source can be calculated. as follows:

[0062] ;

[0063] Furthermore, if the conveyor belt movement only changes the X coordinate while the Y and Z coordinates remain unchanged, the distance from the physical location of a pixel to the physical location of the light source in the t-th frame image after the first image can be calculated. as follows:

[0064] ; ;

[0065] The grayscale value of a pixel in the first image is In the case of t, the estimated gray value of a pixel in the t-th frame of the image. for ; where the gray value of a pixel in the first image is the illumination intensity value of the pixel in the first image, and the estimated gray value of a pixel in the t-th frame image is one of the simulated illumination intensity values ​​among multiple simulated illumination intensity values.

[0066] Therefore, the above scheme provides a specific mathematical model for pixel-level illumination prediction. Under the simplified conditions of the light source and camera moving in the same direction and the conveyor belt moving horizontally, the depth information of the pixel is recovered using motion parallax. Then, the distance from that point to the light source in different frames is calculated, and the grayscale value is predicted using the inverse square law of distance. This model simplifies the complex three-dimensional illumination transmission problem into an analytically calculable mathematical expression. All parameters can be obtained through calibration or real-time acquisition, requiring no iterative optimization, making it suitable for the high-speed real-time detection needs of production lines.

[0067] Optionally, if the processing terminal determines that the external appearance quality inspection of the umbrella structural component has passed, the method further includes: the processing terminal determining whether there is a structural defect in the umbrella structural component in the video stream; if there is no structural defect, the processing terminal determines that the structural quality inspection of the umbrella structural component has passed; or, if there is a structural defect, the processing terminal analyzes the illumination changes of the structural defect in the video stream using digital twin technology to determine whether the structural defect in the video stream is a fake defect or a real defect. If it is a fake defect, the processing terminal sends an alarm to the control center; if it is a real defect, the structural quality inspection of the umbrella structural component has failed. Thus, by extending the tamper verification method from appearance detection to structural defect detection, a complete quality inspection system for umbrella structural components is formed. After passing the appearance quality inspection, further verification of the authenticity of structural defects can identify malicious tampering targeting minor defects (such as cracks, dents, and improper assembly). This layered detection strategy ensures both detection efficiency and improves the coverage of anti-counterfeiting measures, preventing attackers from simultaneously circumventing quality inspections in both appearance and structure dimensions by tampering with images.

[0068] Specifically, each pixel in the first image that represents a structural defect is designated as a target pixel. The processing terminal then determines whether more than a preset number of simulated illumination intensity values ​​for the target pixel match the corresponding illumination intensity values ​​of the target pixel in each of the K frames of the image. If so, the target pixel is determined to be a target matching pixel; otherwise, it is determined to be a target non-matching pixel. Furthermore, the processing terminal determines the target proportion of the target matching pixels among the pixels in the first image that represent structural defects. The processing terminal then determines whether this target proportion exceeds a preset ratio. If it does, the structural defect is considered a genuine defect; otherwise, it is considered a forged defect. Thus, the above scheme specifically applies pixel-level illumination change verification to the structural defect region, judging the authenticity of the defect by statistically analyzing the proportion of matching pixels within the defect region. This feature is designed specifically for the characteristics of defect regions, effectively addressing the detection difficulties of small defect areas and weak features. Through pixel-level fine analysis and statistical judgment, it achieves accurate identification of minute defects, preventing attackers from exploiting the small size of defects for targeted tampering.

[0069] The calculation method for the multiple simulated light intensity values ​​of the target pixel is the same as that for the multiple simulated light intensity values ​​of the aforementioned pixels, which can be used for reference and understanding, and will not be repeated here.

[0070] In summary, by acquiring video streams of the umbrella structure moving on a conveyor belt and utilizing the lighting differences caused by changes in viewing angle across different frames, combined with digital twin technology, this method physically models the lighting variations to determine whether the shape mismatch is caused by a genuine defect or by attacker tampering. This approach introduces the physical laws of light transmission into the image verification process, making the judgment physically interpretable. Compared to traditional single-frame image detection, this scheme utilizes multi-frame temporal information to construct physical constraints, effectively identifying defects forged using image editing software and preventing malicious circumvention of quality inspection. Furthermore, this method does not require additional hardware such as multiple light sources or cameras, offering advantages such as low cost and ease of deployment.

[0071] Figure 3 This is a schematic diagram of the structure of a processing device provided in an embodiment of this application. Exemplarily, the processing device may be a terminal, or a chip (system) or other component or assembly that can be disposed on the terminal. Figure 3 As shown, the processing device 200 may include a processor 201. Optionally, the processing device 200 may also include a memory 202 and / or a transceiver 203. The processor 201 is coupled to the memory 202 and the transceiver 203, for example, via a communication bus.

[0072] The following is combined with Figure 3 A detailed description of each component of the processing equipment 200 is provided below:

[0073] The processor 201 is the control center of the processing device 200. It can be a single processor or a collective term for multiple processing elements. For example, the processor 201 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement the embodiments of this application, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0074] Optionally, the processor 201 can perform various functions of the processing device 200 by running or executing software programs stored in the memory 202 and calling data stored in the memory 202, such as performing the above-mentioned functions. Figure 2 The method shown.

[0075] In a specific implementation, as one example, the processor 201 may include one or more CPUs, for example... Figure 3CPU0 and CPU1 are shown in the diagram.

[0076] In a specific implementation, as one example, the processing device 200 may also include multiple processors, for example... Figure 3 The processor 201 shown is an example. Each of the processors 201 can be a single-core processor or a multi-core processor. Here, "processor" can refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0077] The memory 202 is used to store the software program that executes the solution of this application, and is controlled by the processor 201 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0078] Optionally, the memory 202 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 202 may be integrated with the processor 201 or exist independently, and may be connected via the interface circuitry of the processing device 200. Figure 3 (Not shown in the image) is coupled to processor 201, but this embodiment does not specifically limit this.

[0079] Transceiver 203 is used for communication with other processing devices. For example, if processing device 200 is a terminal, transceiver 203 can be used to communicate with a network device or with another terminal device. As another example, if processing device 200 is a network device, transceiver 203 can be used to communicate with a terminal or with another network device.

[0080] Optionally, transceiver 203 may include a receiver and a transmitter. Figure 3 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function.

[0081] Optionally, the transceiver 203 can be integrated with the processor 201, or it can exist independently and be connected via the interface circuit of the processing device 200. Figure 3 (Not shown in the image) is coupled to processor 201, but this embodiment does not specifically limit this.

[0082] Understandable, Figure 3 The structure of the processing device 200 shown does not constitute a limitation on the processing device. Actual processing devices may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0083] Furthermore, the technical effects of the processing device 200 can be referred to the technical effects of the method described in the above method embodiments, and will not be repeated here.

[0084] It should be understood that the processor in the embodiments of this application can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0085] It should also be understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which serves as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0086] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0087] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0088] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0089] It should be understood that in the various embodiments of this application, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0090] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0091] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0092] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only 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 coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0093] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0094] In addition, the functional units in the various embodiments of this application 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.

[0095] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion 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 network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0096] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A big data-based quality inspection method for umbrella production, characterized in that, The method is applied to a processing terminal, and the method includes: The processing terminal acquires the video stream captured by the acquisition terminal for the umbrella structure component. The umbrella structure component contained in the images of different frames in the video stream is captured by the acquisition terminal from different shooting angles. The processing terminal determines whether the shape of the umbrella structure in the video stream matches the preset shape of the umbrella structure in the database; If a match is found, the processing terminal determines that the external quality inspection of the umbrella structural component has passed; or, If there is a mismatch, the processing terminal analyzes the illumination changes of the umbrella structure in the video stream using digital twin technology to determine whether the shape of the umbrella structure in the video stream has been tampered with by an attacker. If it has been tampered with by an attacker, the processing terminal sends an alarm to the control center. If it has not been tampered with by an attacker, it is determined that the shape quality inspection of the umbrella structure has failed.

2. The method according to claim 1, characterized in that, The processing terminal determines whether the shape of the umbrella structural component in the video stream matches a preset umbrella structural component shape in the database, including: The processing terminal extracts pixels located at the edge of the umbrella structure in the first image to obtain the shape of the umbrella structure in the first image. The first image is the first frame in a series of consecutive frames of the video stream that contains the umbrella structure. The processing terminal determines whether the shape of the umbrella structure in the first image matches the shape of the preset umbrella structure.

3. The method according to claim 2, characterized in that, The processing terminal determines whether the shape of the umbrella structure in the first image matches the preset shape of the umbrella structure, including: The processing terminal projects the shape of the umbrella structure in the first image onto the shape of the umbrella structure to obtain a projection pattern; The processing terminal determines whether the preset umbrella structure shape completely covers the projected pattern. If it completely covers the pattern, it means that the shape of the umbrella structure in the first image matches the preset umbrella structure shape. Otherwise, it means that the shape of the umbrella structure in the first image does not match the preset umbrella structure shape. The preset umbrella structure shape is larger than the shape of an umbrella structure without shape defects within the error range.

4. The method according to claim 1, characterized in that, The processing terminal analyzes the illumination changes of the umbrella structure in the video stream using digital twin technology to determine whether the shape of the umbrella structure in the video stream has been tampered with by an attacker, including: The processing terminal analyzes the lighting conditions of the umbrella structure in the first image using digital twin technology, determines the simulated lighting changes of the umbrella structure after the first image, and determines the lighting changes of the umbrella structure in K consecutive frames after the first image. The first image is the first frame in a series of consecutive frames of the video stream that contains the umbrella structure, and K is an integer greater than 1. The processing terminal determines whether the simulated illumination change of the umbrella structure matches the illumination change of the umbrella structure in the K-frame image. If the simulated illumination change of the umbrella structure matches the illumination change of the umbrella structure in the K-frame image, it indicates that it has not been tampered with by an attacker; otherwise, it indicates that it has been tampered with by an attacker.

5. The method according to claim 4, characterized in that, The umbrella structure is mounted on a conveyor belt. The processing terminal analyzes the illumination of the umbrella structure in the first image using digital twin technology to determine the simulated illumination changes of the umbrella structure after the first image, including: The processing terminal acquires the illumination of the umbrella structure in the first image; The processing terminal uses digital twin technology to analyze the lighting conditions of the umbrella structure in the first image, the speed of the conveyor belt, the positional relationship between the umbrella structure and the shooting direction of the acquisition terminal, and the positional relationship between the indoor light source and the umbrella structure and the acquisition terminal, respectively, to determine the simulated lighting changes of the umbrella structure after the first image.

6. The method according to claim 5, characterized in that, The processing terminal analyzes the lighting conditions of the umbrella structure in the first image, the speed of the conveyor belt, the positional relationship between the umbrella structure and the shooting direction of the acquisition terminal, and the positional relationship between the indoor light source and the umbrella structure and the acquisition terminal, respectively, using digital twin technology, to determine the simulated lighting changes of the umbrella structure after the first image, including: The illumination condition of the umbrella structure in the first image is the illumination intensity value of each pixel belonging to the umbrella structure in the first image. For each pixel belonging to the umbrella structure in the first image: the processing terminal analyzes the illumination intensity value of the pixel, the movement speed of the conveyor belt, the positional relationship between the umbrella structure and the shooting direction of the acquisition terminal, and the positional relationship between the indoor light source and the umbrella structure and the acquisition terminal, respectively, to determine multiple simulated illumination intensity values ​​of the pixel after the first image. The processing terminal determines whether the simulated illumination changes of the umbrella structure match the illumination changes of the umbrella structure in the K-frame images, including: The processing terminal determines whether, among the plurality of simulated light intensity values ​​of the pixel, there is more than a preset number of simulated light intensity values ​​that match the light intensity values ​​corresponding to the pixel in the K-frame images. If so, the pixel is determined to be a matching pixel; otherwise, the pixel is determined to be a non-matching pixel. The processing terminal also determines the proportion of the matching pixel in the pixels of the first image that belong to the umbrella structure. The processing terminal determines whether the proportion exceeds a preset ratio. If it exceeds the preset ratio, the simulated illumination change of the umbrella structure matches the illumination change of the umbrella structure in the K-frame image. Otherwise, the simulated illumination change of the umbrella structure does not match the illumination change of the umbrella structure in the K-frame image.

7. The method according to claim 6, characterized in that, The processing terminal analyzes the following using digital twin technology: The light source is a point light source, and its illumination direction is consistent with the shooting direction of the acquisition terminal. The light source is located at world coordinates (0,0,H), where H is the height of the acquisition terminal. The conveyor belt moves uniformly along the positive X-axis at a speed of V, where V is in millimeters per frame. The coordinates of the pixel in the first image are... The estimated coordinates of the pixel in the next frame image after the first image are: The depth Z corresponding to the pixel satisfies the following relationship: ， ， ； The physical world coordinates of the pixel in the first image are: , ; in, and These are the equivalent focal lengths of the acquisition terminal in the X and Y directions, respectively. and These are the coordinates of the principal point, which is the intersection of the optical axis of the acquisition terminal and the image captured by the acquisition terminal; The distance from the physical location of the pixel in the first image to the physical location of the light source. as follows: ； When the conveyor belt movement changes only the X coordinate while the Y and Z coordinates remain unchanged, the distance from the physical location of the pixel in the t-th frame image following the first image to the physical location of the light source is... as follows: ； ； The grayscale value of the pixel in the first image In the case of the pixel, the estimated gray value of the pixel in the t-th frame image. for Wherein, the grayscale value of the pixel in the first image is the illumination intensity value of the pixel in the first image, and the estimated grayscale value of the pixel in the t-th frame image is one of the simulated illumination intensity values ​​among the plurality of simulated illumination intensity values.

8. The method according to claim 6 or 7, characterized in that, If the processing terminal determines that the external quality inspection of the umbrella structural component has passed, the method further includes: The processing terminal determines whether the umbrella structural component in the video stream has structural defects; If no structural defects are found, the processing terminal determines that the structural quality inspection of the umbrella structural component has passed; or, If a structural defect exists, the processing terminal analyzes the illumination changes of the structural defect in the video stream using digital twin technology to determine whether the structural defect in the video stream is a fake defect or a real defect. If it is a fake defect, the processing terminal sends an alarm to the control center. If it is a real defect, it is determined that the structural quality inspection of the umbrella structural component has failed.

9. The method according to claim 8, characterized in that, The processing terminal analyzes the illumination changes of the structural defects in the video stream using digital twin technology to determine whether the structural defects in the video stream are fake or real, including: Each pixel in the first image that belongs to the structural defect is denoted as a target pixel: The processing terminal determines whether, among the plurality of simulated illumination intensity values ​​of the target pixel, there is more than a preset number of simulated illumination intensity values ​​that match the illumination intensity values ​​corresponding to the target pixel in the K-frame images. If so, the target pixel is determined to be a target matching pixel; otherwise, the target pixel is determined to be a target non-matching pixel. The processing terminal also determines the target proportion of the target matching pixel among the pixels in the first image that belong to the structural defect. The processing terminal determines whether the target proportion exceeds a preset proportion. If it exceeds the preset proportion, the structural defect is a real defect; otherwise, the structural defect is a fake defect.

10. A quality inspection system for umbrella production, characterized in that, The system includes a processing terminal, which is configured to: The processing terminal acquires the video stream captured by the acquisition terminal for the umbrella structure component. The umbrella structure component contained in the images of different frames in the video stream is captured by the acquisition terminal from different shooting angles. The processing terminal determines whether the shape of the umbrella structure in the video stream matches the preset shape of the umbrella structure in the database; If a match is found, the processing terminal determines that the external quality inspection of the umbrella structural component has passed. or, If there is a mismatch, the processing terminal analyzes the illumination changes of the umbrella structure in the video stream using digital twin technology to determine whether the shape of the umbrella structure in the video stream has been tampered with by an attacker. If it has been tampered with by an attacker, the processing terminal sends an alarm to the control center. If it has not been tampered with by an attacker, it is determined that the shape quality inspection of the umbrella structure has failed.

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