A quality inspection system based on industrial big data image analysis

Abstract

The present invention discloses a quality detection system based on industrial big data image analysis, belonging to the technical field of industrial quality detection; the present invention acquires a key plane area through an image acquisition module; acquires a horizontal scan image and a vertical scan image corresponding to the key plane area through a set flat scan model; performs image registration on the horizontal scan image and the vertical scan image to obtain a standard flat scan image; then performs multiple defect identifications on the standard flat scan image through a set defect type identification model to obtain multiple defect images; further uses an industrial defect analysis module to perform defect analysis on the multiple defect images to obtain a key defect image corresponding to the standard flat scan image; performs quality evaluation on the key defect image to obtain a quality anomaly value; and finally obtains available industrial objects and industrial objects to be recycled according to the quality anomaly value, effectively improving the accuracy of quality detection.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial quality inspection, and particularly to a quality inspection system based on industrial big data image analysis. Background Art

[0002] Industry is currently the largest downstream application field of the machine vision industry in China. The sales volume in the industrial field accounts for 81.2%. The industrial industry includes industries such as electronic manufacturing, display panels, automobiles, printing, semiconductors, food and beverage packaging, etc. Therefore, it is necessary to ensure the quality of industrial products sold.

[0003] Currently, the quality inspection of industrial products is usually carried out through manual inspection or roughly through image inspection. The current image inspection methods include obtaining images through traditional shooting, traditional image processing, threshold analysis detection, etc. Therefore, there are image quality problems in obtaining images through traditional shooting. For example, in a high-speed production line, if ordinary cameras are used to shoot industrial images, it may cause image blurring or loss of details, resulting in a decrease in the accuracy of industrial image quality inspection. Again, by setting one or more thresholds, the pixel points in the image are divided into different categories, so as to directly separate the target object from the background to obtain defect features, which will cause unclear contours, resulting in loss of details, and further resulting in a decrease in the accuracy of industrial image quality inspection. Therefore, in order to solve the above technical problems, the present invention provides a quality inspection system based on industrial big data image analysis. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a quality inspection system based on industrial big data image analysis.

[0005] The object of the present invention can be achieved by the following technical solutions: A quality inspection system based on industrial big data image analysis, the system includes an image acquisition module, an image processing module, an industrial defect analysis module, a quality evaluation module, and a classification module.

[0006] The image acquisition module is provided with a flat scan model for obtaining a three-dimensional industrial object, marking key areas of the three-dimensional industrial object to obtain key plane areas, and acquiring a horizontal flat scan image and a vertical flat scan image corresponding to the key plane areas according to the flat scan model.

[0007] The image processing module is provided with an image registration unit and a multiple recognition unit. The image registration unit is used to register the horizontal flat scan image and the vertical flat scan image to obtain a standard flat scan image. The multiple recognition unit is provided with a defect type recognition model for performing multiple defect recognition on the standard flat scan image according to the defect type recognition model to obtain multiple defect images.

[0008] The industrial defect analysis module is used to perform defect analysis on multiple defect images to obtain the key defect images corresponding to the standard flat scan images;

[0009] The quality assessment module is used to perform quality assessment on the key defect images to obtain quality outliers;

[0010] The classification module is used to obtain available industrial objects and industrial objects to be recycled according to the quality outliers.

[0011] Furthermore, the setting process of the flat scan model includes:

[0012] The flat scan model is provided with an automatic recognition unit, a linear flat scan camera, and a detection channel; the automatic recognition unit is used to recognize the key plane area corresponding to the industrial object to be detected; the linear flat scan camera includes a horizontal linear flat scan camera and a vertical linear flat scan camera, which are respectively used to scan the key plane area to obtain the horizontal flat scan image and the vertical flat scan image corresponding to the key plane area;

[0013] The detection channel is provided with a transmission interval time and a transmission speed; the transmission distance is obtained according to the transmission interval time and the transmission speed, and detection areas are set at the positions of several transmission distances in the detection channel for collecting the industrial object to be detected according to the transmission interval time; the linear flat scan camera is arranged directly above the detection area.

[0014] Furthermore, the process of the image acquisition module acquiring the horizontal flat scan image and the vertical flat scan image corresponding to the key plane area according to the flat scan model includes:

[0015] Obtain the three-dimensional industrial object image corresponding to the industrial object to be detected through three-dimensional modeling; perform key area marking on the plane area of the three-dimensional industrial object image to obtain the key plane area, and send it to the automatic recognition unit;

[0016] The industrial object to be detected is sequentially transmitted to the detection area through the detection channel according to the transmission interval time and the transmission speed; the automatic recognition unit sequentially recognizes the key plane areas of the industrial object to be detected corresponding to the detection area, and sequentially flips the key plane areas to directly above, and then sequentially performs flat scanning through the linear flat scan camera to obtain the corresponding horizontal flat scan image and vertical flat scan image.

[0017] Furthermore, the process of the image registration unit obtaining the standard flat scan image includes:

[0018] Obtain the center points, corner points, and edge points corresponding to the horizontal plain scan image and the vertical plain scan image; construct a unit two-dimensional coordinate system, translate the center points of the horizontal plain scan image and the vertical plain scan image corresponding to the key plane area to the origin of the unit two-dimensional coordinate system, and then obtain the coordinate points corresponding to the corner points and edge points. Furthermore, completely coincide the coordinate points corresponding to the horizontal plain scan image and the vertical plain scan image to obtain the standard plain scan image corresponding to the key plane area; and mark the corresponding codes, denoted as i-k; where i represents the number of the industrial object to be detected; k represents the number of the key plane area; and both i and k take positive integer values.

[0019] Furthermore, the process of the multiple recognition unit setting the defect type recognition model includes:

[0020] Based on big data, collect the defect types corresponding to different industrial object materials and the defect characteristics corresponding to the defect types; map the industrial object materials to the defect types, and map the defect types to the defect characteristics to obtain the industrial object defect type chain.

[0021] Collect a defect type sample image and several defect characteristic sample images corresponding to the industrial object defect type chain; extract the illumination parameter sets corresponding to each defect characteristic sample image, and integrate them to generate a defect characteristic illumination parameter sample library; perform illumination processing on the defect type sample image through the defect characteristic illumination parameter sample library to obtain the corresponding illumination sample image, and obtain the defect characteristic regions corresponding to the defect characteristics in each illumination sample image, and then obtain the clarity of the defect characteristics.

[0022] Mark the illumination parameters of the illumination sample image corresponding to the highest clarity as the best illumination parameter set corresponding to the industrial object defect type chain, and map it with the industrial object defect type chain to generate a defect type recognition chain; then connect all the defect type recognition chains to construct a defect type recognition model.

[0023] Furthermore, the process of obtaining multiple defect images includes:

[0024] Send the industrial object material corresponding to the industrial object to be detected in the standard plain scan image to the defect type recognition model; schedule the defect types corresponding to the industrial object material based on the defect type recognition model, and then schedule the best illumination parameter set corresponding to the defect type recognition chain according to the defect types. Furthermore, perform illumination processing on the standard plain scan image according to the best illumination parameter set to obtain the corresponding defect image, and integrate the defect images corresponding to the standard plain scan image to obtain multiple defect images.

[0025] Furthermore, the process of the industrial defect analysis module obtaining the key defect image corresponding to the standard plain scan image includes:

[0026] Determine whether each defective image among multiple defective images exhibits the corresponding defect feature; if so, mark the corresponding defective image as a defect feature image; otherwise, do not perform any processing;

[0027] Translate and extract the defect features corresponding to the defect feature images at the same position and integrate them to obtain the key defective images corresponding to the key plane regions.

[0028] Furthermore, the process by which the quality assessment module obtains the quality anomaly value includes:

[0029] Obtain the total number of defect types of the key defective images corresponding to the industrial object to be detected according to the defect features; and obtain the areas of each standard flat scan image of the industrial object to be detected, denoted as S i-k ; Set the unit defect border to obtain the number of unit defect borders corresponding to the defect features by covering the defect features of the key defective images with the fewest unit defect borders; obtain the defect feature areas corresponding to each defect feature through the number of unit defect borders, and then obtain the sum of the defect feature areas corresponding to the defect types, denoted as the total defect type area corresponding to the defect types; furthermore, obtain the quality anomaly value corresponding to the industrial object to be detected according to the total number of defect types and the total defect type area;

[0030] That is, the specific formula is:

[0031] ;

[0032] M i and Q i respectively represent the quality anomaly value and the total number of defect types corresponding to the i-th industrial object to be detected; S L represents the total defect type area corresponding to the defect type of the L-th category; S i-k represents the area of the standard flat scan image corresponding to the encoding i-k; a and b respectively represent the weight coefficients corresponding to the total number of defect types and the total defect type area, that is, a + b = 1; W L represents the quality anomaly weight coefficient corresponding to the defect type of the L-th category.

[0033] Furthermore, the process by which the classification module obtains the available industrial objects and the industrial objects to be recycled includes:

[0034] Set the threshold range (0, M1) of the available quality anomaly value corresponding to the industrial object to be detected;

[0035] If 0 < M i < M1, then mark the corresponding industrial object to be detected as an available industrial object;

[0036] If M i ≥ M1, then mark the corresponding industrial object to be detected as an industrial object to be recycled.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0038] 1. The present invention acquires the key plane area through the image acquisition module; collects the horizontal and vertical flat-swept images corresponding to the key plane area through the set flat-sweeping model; performs image registration on the horizontal and vertical flat-swept images to obtain the standard flat-swept image; the flat-sweeping model can improve the shooting clarity of industrial objects, thereby improving the accuracy of quality inspection;

[0039] 2. The present invention performs multiple defect identifications on the standard flat-swept image through the set defect type identification model to obtain multiple defect images; then uses the industrial defect analysis module to perform defect analysis on the multiple defect images to obtain the key defect image corresponding to the standard flat-swept image; effectively improves the contour clarity of defect features, thereby improving the detection ability of defect features to be detected, and further improving the accuracy of quality inspection. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0041] Figure 1 It is the system schematic diagram of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present invention.

[0043] As Figure 1 shown, a quality inspection system based on industrial big data image analysis, the system includes an image acquisition module, an image processing module, an industrial defect analysis module, a quality evaluation module, and a classification module;

[0044] The image acquisition module is provided with a flat-sweeping model for acquiring a three-dimensional industrial object, marking a key area of the three-dimensional industrial object to obtain a key plane area; collecting the horizontal and vertical flat-swept images corresponding to the key plane area according to the flat-sweeping model;

[0045] The image processing module is provided with an image registration unit and multiple recognition units; the image registration unit is used to register the horizontal plain scan image and the vertical plain scan image to obtain a standard plain scan image; the multiple recognition units are provided with a defect type recognition model, which is used to perform multiple defect recognitions on the standard plain scan image according to the defect type recognition model to obtain multiple defect images;

[0046] The industrial defect analysis module is used to analyze the multiple defect images to obtain the key defect images corresponding to the standard plain scan image;

[0047] The quality assessment module is used to perform quality assessment on the key defect images to obtain quality outliers;

[0048] The classification module is used to obtain available industrial objects and industrial objects to be recycled according to the quality outliers.

[0049] It should be further noted that the setting process of the plain scan model includes:

[0050] The plain scan model is provided with an automatic recognition unit, a linear plain scan camera and a detection channel. The automatic recognition unit is used to recognize the key plane area corresponding to the industrial object to be detected; the linear plain scan camera includes a horizontal linear plain scan camera and a vertical linear plain scan camera, which are respectively used to scan the key plane area to obtain the horizontal plain scan image and the vertical plain scan image corresponding to the key plane area;

[0051] The detection channel is provided with a transmission interval time and a transmission speed; the transmission distance is obtained according to the transmission interval time and the transmission speed, and detection areas are set at the positions of several transmission distances in the detection channel for collecting the industrial object to be detected according to the transmission interval time; the linear plain scan camera is arranged directly above the detection area;

[0052] In the above embodiment, it should be further noted that the transmission interval time is used to represent the time for the detection area to detect all plane areas of the industrial object to be detected;

[0053] It should be further noted that the process of the image acquisition module obtaining the three-dimensional industrial object image and performing key area marking on the three-dimensional industrial object image to obtain the key plane area includes:

[0054] The three-dimensional industrial object image corresponding to the industrial object to be detected is obtained through three-dimensional modeling; the key area of the plane area of the three-dimensional industrial object image is marked to obtain the key plane area, and it is sent to the automatic recognition unit;

[0055] In the above embodiments, it should be further noted that the key mark can mark the key plane area as a color area, for example, red, yellow, blue, etc.; the number corresponding to the key plane area includes but is not limited to one or more;

[0056] It should be further noted that the process of collecting the transverse plain scan image and the longitudinal plain scan image corresponding to the key plane area according to the plain scan model includes:

[0057] The industrial object to be detected is sequentially transmitted to the detection area through the detection channel according to the transmission interval time and the transmission speed; the automatic recognition unit sequentially recognizes the key plane areas of the industrial object to be detected corresponding to the detection area, and sequentially flips the key plane areas to directly above, and then sequentially performs plain scan through the linear plain scan camera to obtain the corresponding transverse plain scan image and longitudinal plain scan image;

[0058] In the above embodiments, it should be further noted that the transmission interval time is set to the time for detecting all the plane areas of the industrial object to be detected. The plain scan time of the linear plain scan camera for one plane is about 10 - 30 s. In the case of a complex structure of the industrial object to be detected, the transmission interval time is too long. Here, the maximum value of the key plane area corresponding to the industrial object to be detected with a complex structure can be obtained and marked as the transmission interval time; using the key plane area can not only avoid the detection of unnecessary areas, but also save the time for detecting all the complex-structured industrial objects to be detected; further, the transmission order and the detection order can be better controlled; after all the key plane areas of an industrial object to be detected are completely detected, wait for the next industrial object to be detected to reach the detection area for detection.

[0059] It should be further noted that the process of the image registration unit performing image registration processing on the transverse plain scan image and the longitudinal plain scan image to obtain the standard plain scan image includes:

[0060] Obtain the center point, corner points and edge points corresponding to the transverse plain scan image and the longitudinal plain scan image;

[0061] In the above embodiments, it should be further noted that the corner points are used to represent the intersection points of the edges in the image (such as the corners of an object); the edge points are used to represent the boundary points of the object contour;

[0062] Construct a unit two-dimensional coordinate system, which is used to represent a two-dimensional coordinate system with the origin at (0, 0) and the unit length of the horizontal and vertical coordinates being 1;

[0063] Translate the central points of the horizontal and vertical plain scan images corresponding to the key plane region to the origin of the unit two-dimensional coordinate system, and then obtain the coordinate points corresponding to the corner points and edge points. Furthermore, completely overlap the coordinate points corresponding to the horizontal and vertical plain scan images to obtain the standard plain scan image corresponding to the key plane region;

[0064] In the above embodiments, it should be further noted that the horizontal and vertical plain scan images obtained by the linear plain scan camera for horizontal and vertical plain scans are translated to the unit two-dimensional coordinate system without changing their positions to obtain the coordinate points of the corner points and edge points, and then the corresponding coordinate points are completely overlapped to obtain the standard plain scan image; in this process, not only the incompleteness of the overlap caused by directly overlapping the intersection points and edge points is avoided; but also the incompleteness of the obtained plain scan image caused by only horizontal or vertical plain scan is avoided; thus improving the integrity of the plain scan image and preventing the loss of details; furthermore, it may also include multi-angle direction plain scan, etc.; obtain the coordinate points of the corner points and edge points of the obtained plain scan image through the unit two-dimensional coordinate system, and then completely overlap them to obtain the standard plain scan image.

[0065] Number the industrial objects to be detected according to the transmission order, denoted as i, where i = 1, 2, 3,..., j, and j takes positive integer values; and number the key plane regions according to the plain scan order, denoted as k, where k = 1, 2, 3,..., g, and g takes positive integer values; then obtain the encoding corresponding to each standard plain scan image, denoted as i - k.

[0066] It should be further noted that the process of setting the defect type recognition model in the multiple recognition units includes:

[0067] Based on big data, collect the defect types corresponding to different industrial object materials and the defect characteristics corresponding to the defect types; map the industrial object materials to the defect types, and map the defect types to the defect characteristics to obtain the industrial object defect type chain;

[0068] Collect a defect type sample image and several defect characteristic sample images corresponding to the industrial object defect type chain; extract the illumination parameter sets corresponding to each defect characteristic sample image, and integrate them to generate a defect characteristic illumination parameter sample library; perform illumination processing on the defect type sample image through the defect characteristic illumination parameter sample library to obtain the corresponding illumination sample image, and obtain the defect characteristic regions corresponding to the defect characteristics in each illumination sample image, and then obtain the clarity of the defect characteristics;

[0069] In the above embodiments, it should be further noted that the curve feature region is extracted from the defect feature region through a binary image, and then the average extraction amplitude corresponding to the feature region is obtained through a gradient algorithm as the clarity of the defect feature corresponding to the defect feature region; this is not specifically described in this embodiment; further, the illumination parameter set includes, but is not limited to, illumination intensity, angle, wavelength, polarization direction, spectral range, illumination method, etc.;

[0070] Mark the illumination parameters of the illumination sample image corresponding to the highest clarity as the optimal illumination parameter set corresponding to the industrial object defect type chain, and map it with the industrial object defect type chain to generate a defect type recognition chain; then connect all the defect type recognition chains to construct a defect type recognition model.

[0071] In the above embodiments, it should be further noted that the defect types include, but are not limited to, scratches, cracks, depressions, protrusions, dirt, oil stains, rust, material impurities, etc.; for example, the defect feature corresponding to a scratch is a line.

[0072] It should be further noted that the process of obtaining multiple defect images by performing multiple defect recognitions on a standard flat scan image according to the defect type recognition model includes:

[0073] Obtain the industrial object material of the industrial object to be detected corresponding to the standard flat scan image and send it to the defect type recognition model; based on the defect type recognition model, schedule the defect types corresponding to the industrial object material, and then schedule the optimal illumination parameter set corresponding to the defect type recognition chain according to the defect types, and then perform illumination processing on the standard flat scan image according to the optimal illumination parameter set to obtain the corresponding defect image, and integrate the defect images corresponding to the standard flat scan image to obtain multiple defect images.

[0074] It should be further noted that the process of the industrial defect analysis module analyzing the defect types of multiple images to obtain the key defect image corresponding to the standard flat scan image includes:

[0075] Judge whether the defect features corresponding to each defect image of the multiple defect images appear; if they appear, mark the corresponding defect image as a defect feature image; otherwise, do not perform any processing;

[0076] Extract and integrate the defect features of the defect feature images by translation at the same position to obtain the key defect image corresponding to the key plane region.

[0077] It should be further noted that the process of the quality evaluation module evaluating the quality of the key defect image to obtain a quality anomaly value includes:

[0078] Obtain the total number of defect types of the key defect images corresponding to the industrial object to be detected according to the defect characteristics; and obtain the areas of the respective standard scan images of the industrial object to be detected, denoted as S i-k ; Set a unit defect border for covering the defect characteristics of the key defect image with the fewest unit defect borders to obtain the number of unit defect borders corresponding to the defect characteristics; obtain the defect characteristic areas corresponding to the respective defect characteristics through the number of unit defect borders, and further obtain the sum of the defect characteristic areas corresponding to the defect types, denoted as the total defect type area corresponding to the defect type; and then obtain the quality anomaly value corresponding to the industrial object to be detected according to the total number of defect types and the total defect type area;

[0079] That is, the specific formula is:

[0080] ;

[0081] Wherein, M i and Q i respectively represent the quality anomaly value and the total number of defect types corresponding to the i-th industrial object to be detected; S L represents the total defect type area corresponding to the defect type of the L-th category; S i-k represents the area of the standard scan image corresponding to the encoding i-k; a and b respectively represent the weight coefficients corresponding to the total number of defect types and the total defect type area, that is, a + b = 1; W L represents the quality anomaly weight coefficient corresponding to the defect type of the L-th category.

[0082] In the above embodiment, it should be further noted that the unit defect border represents a square of m×m; m of the unit defect border can be set according to the size of the industrial object to be detected; the defect characteristic area of the defect characteristic can be accurately obtained by using the area of the unit defect border; this process reflects the diversity of defect types; the more the number of defect types, the more complex the quality risk, and the accuracy of the quality anomaly value is improved.

[0083] It should be further noted that the process by which the classification module classifies the industrial object to be detected according to the quality anomaly value to obtain the available industrial object and the industrial object to be recycled includes:

[0084] Set the available quality anomaly value threshold range (0, M1) corresponding to the industrial object to be detected;

[0085] If 0 < M i < M1, then mark the corresponding industrial object to be detected as an available industrial object;

[0086] If M i ≥ M1, then mark the corresponding industrial object to be detected as an industrial object to be recycled.

[0087] In the above embodiments, it should be further noted that the quality outlier cannot be equal to 0 because the total number and total area of the defect types included in the key defect image are not 0.

[0088] The features and exemplary embodiments of various aspects of the present application will be described in detail below. For the purpose of making the objectives, technical solutions, and advantages of the present application more clear and understandable, the present application will be further described in detail with reference to the accompanying drawings and specific embodiments; it should be understood that the specific embodiments described herein are only intended to explain the present application, rather than limiting the present application; for those skilled in the art, the present application can be implemented without some of these specific details; the above description of the embodiments is only to provide a better understanding of the present application by showing examples of the present application.

[0089] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A quality inspection system based on industrial big data image analysis, characterized in that, The system includes an image acquisition module, an image processing module, an industrial defect analysis module, a quality assessment module, and a classification module; The image acquisition module is provided with a flat scanning model for obtaining a three-dimensional industrial object, marking key areas of the three-dimensional industrial object to obtain key plane areas; collecting a horizontal flat scanning image and a vertical flat scanning image corresponding to the key plane areas according to the flat scanning model; The image processing module is provided with an image registration unit and a multiple recognition unit; the image registration unit is used for registering the horizontal flat scanning image and the vertical flat scanning image to obtain a standard flat scanning image; the multiple recognition unit is provided with a defect type recognition model for performing multiple defect recognitions on the standard flat scanning image according to the defect type recognition model to obtain multiple defect images; The industrial defect analysis module is used for performing defect analysis on the multiple defect images to obtain key defect images corresponding to the standard flat scanning image; The quality assessment module is used for performing quality assessment on the key defect images to obtain quality anomaly values; The classification module is used for obtaining available industrial objects and industrial objects to be recycled according to the quality anomaly values.

2. The quality inspection system based on industrial big data image analysis according to claim 1, characterized in that, The setting process of the flat scanning model includes: The flat scanning model is provided with an automatic recognition unit, a linear flat scanning camera, and a detection channel; the automatic recognition unit is used for recognizing the key plane area corresponding to the industrial object to be detected; the linear flat scanning camera includes a horizontal linear flat scanning camera and a vertical linear flat scanning camera, which are respectively used for scanning the key plane area to obtain a horizontal flat scanning image and a vertical flat scanning image corresponding to the key plane area; The detection channel is provided with a transmission interval time and a transmission speed; the transmission distance is obtained according to the transmission interval time and the transmission speed, and detection areas are set at positions of several transmission distances in the detection channel for collecting the industrial object to be detected according to the transmission interval time; the linear flat scanning camera is arranged directly above the detection area.

3. The quality inspection system based on industrial big data image analysis according to claim 2, characterized in that, The process of the image acquisition module collecting the horizontal flat scanning image and the vertical flat scanning image corresponding to the key plane area according to the flat scanning model includes: Obtaining a three-dimensional industrial object image corresponding to the industrial object to be detected through three-dimensional modeling; marking key areas of the plane area of the three-dimensional industrial object image to obtain key plane areas, and sending them to the automatic recognition unit; The industrial object to be detected is sequentially transmitted to the detection area through the detection channel according to the transmission interval time and the transmission speed; the automatic recognition unit sequentially recognizes the key plane areas of the industrial object to be detected corresponding to the detection area, and sequentially flips the key plane areas to the direct above, and then performs flat scanning through the linear flat scanning camera in sequence to obtain corresponding horizontal flat scanning images and vertical flat scanning images.

4. The quality inspection system based on industrial big data image analysis according to claim 3, characterized in that, The process of the image registration unit obtaining the standard flat scanning image includes: Obtaining the center points, corner points, and edge points corresponding to the horizontal flat scanning image and the vertical flat scanning image; constructing a unit two-dimensional coordinate system, translating the center points of the horizontal flat scanning image and the vertical flat scanning image corresponding to the key plane area to the origin of the unit two-dimensional coordinate system, and then obtaining the coordinate points corresponding to the corner points and the edge points, and then making the coordinate points corresponding to the horizontal flat scanning image and the vertical flat scanning image completely coincide to obtain the standard flat scanning image corresponding to the key plane area.

5. A quality inspection system based on industrial big data image analysis according to claim 1, characterized in that, The process of setting the defect type recognition model for the multiple recognition units includes: Based on big data, collect the defect types corresponding to different industrial object materials and the defect features corresponding to the defect types; map the industrial object materials to the defect types, and map the defect types to the defect features to obtain the industrial object defect type chain; Collect a defect type sample image and several defect feature sample images corresponding to the industrial object defect type chain; extract the illumination parameter sets corresponding to each defect feature sample image, and integrate them to generate a defect feature illumination parameter sample library; perform illumination processing on the defect type sample image through the defect feature illumination parameter sample library to obtain the corresponding illumination sample image, and obtain the defect feature regions corresponding to the defect features in each illumination sample image, and then obtain the clarity of the defect features; Mark the illumination parameters of the illumination sample image corresponding to the highest clarity as the optimal illumination parameter set corresponding to the industrial object defect type chain, and map it to the industrial object defect type chain to generate a defect type recognition chain; then connect all the defect type recognition chains to construct a defect type recognition model.

6. The quality inspection system based on industrial big data image analysis according to claim 5, wherein, The process of obtaining multiple defect images includes: Send the industrial object material corresponding to the standard plain scan image of the industrial object to be detected to the defect type recognition model; schedule the defect types corresponding to the industrial object material based on the defect type recognition model, and then schedule the optimal illumination parameter set corresponding to the defect type recognition chain according to the defect type, and then perform illumination processing on the standard plain scan image according to the optimal illumination parameter set to obtain the corresponding defect image, and integrate the defect images corresponding to the standard plain scan image to obtain multiple defect images.

7. The quality inspection system based on industrial big data image analysis according to claim 6, characterized in that, The process by which the industrial defect analysis module obtains the key defect image corresponding to the standard plain scan image includes: Judge whether the corresponding defect features appear in each defect image of the multiple defect images; if they appear, mark the corresponding defect image as a defect feature image; otherwise, do nothing; Perform translation extraction of the defect features corresponding to the defect feature images at the same position and integrate them to obtain the key defect image corresponding to the key plane area.

8. A quality inspection system based on industrial big data image analysis according to claim 7, characterized in that, The process by which the quality evaluation module obtains the quality outlier value includes: Obtain the total number of defect types of the key defect image corresponding to the industrial object to be detected according to the defect features; and obtain the areas of each standard plain scan image of the industrial object to be detected; set a unit defect border, which is used to cover the defect features of the key defect image with the fewest unit defect borders to obtain the number of unit defect borders corresponding to the defect features; obtain the defect feature areas corresponding to each defect feature through the number of unit defect borders, and then obtain the sum of the defect feature areas corresponding to the defect types and mark it as the total defect type area corresponding to the defect type; then obtain the quality outlier value corresponding to the industrial object to be detected according to the total number of defect types and the total defect type area.

9. The quality inspection system based on industrial big data image analysis according to claim 8, characterized in that, The process by which the classification module obtains the available industrial objects and the industrial objects to be recycled includes: Set the available quality outlier value threshold range (0, M1) corresponding to the industrial object to be detected; If the quality outlier value is greater than 0 and less than M1, then mark the corresponding industrial object to be detected as an available industrial object; If the quality anomaly value is greater than or equal to M1, the corresponding industrial object to be detected is marked as an industrial object to be recycled.

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