A refrigerator liner thickness measuring device
By capturing images of the inner liner using a camera module to generate a thickness distribution cloud map and combining it with pressure sensor detection, the problem of inaccurate judgment of abnormal inner liner thickness in existing technologies is solved, realizing automated and accurate detection of refrigerator inner liner thickness.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HISENSE(SHANDONG)REFRIGERATOR CO LTD
- Filing Date
- 2024-12-09
- Publication Date
- 2026-06-09
AI Technical Summary
In existing technologies, when using vernier calipers or displacement sensors to measure the thickness of the refrigerator liner, it is easy to miss locations with abnormal thickness, leading to inaccurate judgment of thickness abnormalities.
The system uses a camera module to capture images of the inner liner under illumination, generating a thickness distribution cloud map. Combined with pressure sensors to detect the placement of the inner liner, it automatically identifies any abnormalities in the inner liner thickness.
It improves the accuracy and automation of judging abnormal thickness of the refrigerator liner, ensures comprehensive detection of the uniformity of the liner thickness, and reduces misjudgments.
Smart Images
Figure CN122170776A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of refrigerator liner thickness measurement technology, and in particular to a refrigerator liner thickness measurement device. Background Technology
[0002] A refrigerator is a refrigeration device that maintains a constant low temperature, and it's also a consumer product used to keep food or other items at a constant low temperature. People use refrigerators to store vegetables, meat, and cooked dishes to prolong their shelf life. In a refrigerator, the inner liner provides storage space and ensures the freshness and taste of food. A thinner inner liner reduces the refrigerator's insulation performance, thus affecting food storage. A thicker inner liner occupies more internal space, reducing the storage space available for food. Therefore, refrigerator manufacturers need to control the thickness of the inner liner. During the production process, factors such as the materials used, molding temperature, setting time, and operator errors can cause abnormalities in the thickness of the produced inner liner. Therefore, the thickness of the inner liner needs to be measured before the refrigerator leaves the factory.
[0003] In existing technologies, vernier calipers or displacement sensors are typically used to detect the thickness of the inner liner at the same set location. However, using vernier calipers or displacement sensors to measure the inner liner thickness may miss locations with abnormal thickness, easily leading to an inner liner with abnormal thickness being judged as having normal thickness, thus affecting the accuracy of thickness abnormality detection. Summary of the Invention
[0004] To address the aforementioned technical problems, embodiments of this application provide a refrigerator liner thickness measuring device.
[0005] The purpose of this application is to improve the accuracy of judging abnormal thickness of the refrigerator liner.
[0006] The problems addressed in this application are not limited to those mentioned above, and other unmentioned problems can be clearly understood by those skilled in the art from the following description.
[0007] According to one aspect of the embodiments of this application, an embodiment of this application provides a refrigerator liner thickness measuring device, comprising: a measuring platform for placing the refrigerator liner; the liner being inverted on the measuring platform; a light source module placed around the liner for illuminating the liner; a camera module placed on the measuring platform and inside the liner for capturing images of the liner under the light source illumination; and a processor electrically connected to the camera module and the light source module, the processor being configured to: upon detecting that the measuring platform contains the liner, activate the light source module and acquire an image of the liner under the light source illumination; generate a thickness distribution cloud map corresponding to the liner; obtain the thickness uniformity of the liner based on the thickness distribution cloud map; and determine any thickness anomalies in the liner based on the thickness uniformity.
[0008] In the above embodiments, a camera module installed inside the inner liner captures an image of the inner liner under illumination, generating a thickness distribution cloud map based on the image. The thickness distribution cloud map is then used to determine whether the inner liner thickness is abnormal. Compared to methods using calipers or displacement sensors to measure the inner liner thickness, the thickness distribution cloud map generated from the inner liner image comprehensively reflects the thickness of the inner liner. This allows for the identification of thickness anomalies based on the overall thickness of the inner liner, reducing the likelihood of missing any abnormalities and improving the accuracy of judging refrigerator inner liner thickness anomalies.
[0009] In one embodiment of this application, based on the foregoing scheme, a pressure sensor is placed below the measuring platform; the pressure sensor is used to detect the pressure value of the measuring platform and is electrically connected to the processor; the processor is further configured to detect that an inner liner is placed on the measuring platform, including:
[0010] Obtain the pressure value detected by the pressure sensor;
[0011] When the pressure value detected by the pressure sensor reaches the preset reference pressure value, it is determined that the measuring platform contains the inner liner.
[0012] In the above embodiment, the relationship between the pressure value detected by the pressure sensor and the preset reference pressure value determines whether an inner liner is placed on the measuring platform, so that when an inner liner is placed on the measuring platform, the detection of abnormal inner liner thickness will start automatically, thus realizing the automation of the detection of abnormal inner liner thickness.
[0013] In one embodiment of this application, based on the foregoing solution, the camera module includes a camera and a rotating component connected to the camera; the rotating component is used to control the rotation of the camera to adjust the shooting direction of the camera;
[0014] The light source module includes at least one light source; the light source is used to illuminate the surface to be detected.
[0015] The step of activating the light source module and acquiring an image of the inner liner under the illumination of the light source includes:
[0016] Control the light source corresponding to the surface to be tested of the inner liner to be turned on, and control the rotating component to rotate the camera to the shooting direction corresponding to the surface to be tested of the inner liner;
[0017] Control the camera to start shooting in order to obtain an image of the inner liner corresponding to the surface to be inspected.
[0018] In the above embodiment, when an inner liner is detected on the measuring platform, the light source corresponding to the surface to be tested of the inner liner is activated, so that the surface to be tested of the inner liner is illuminated by the light source. Then, the rotating component is controlled to rotate the camera to the shooting direction corresponding to the surface to be tested of the inner liner, so that the camera placed inside the inner liner can capture an image of the inner liner corresponding to the surface to be tested under the illumination of the light source. This allows the image of the inner liner to reflect the light transmittance of the detection point on the surface to be tested, thereby comprehensively and accurately reflecting the thickness of the surface to be tested.
[0019] In one embodiment of this application, based on the foregoing scheme, generating a thickness distribution cloud map corresponding to the inner liner based on the light transmittance of multiple detection points in the inner liner image includes:
[0020] The inner liner image is converted to grayscale to obtain a grayscale image corresponding to the inner liner;
[0021] Obtain the grayscale values of the detection points in the grayscale image, and obtain the wall thickness value mapped by the grayscale values of each detection point; the grayscale values are used to characterize the light transmittance of each detection point under the light source;
[0022] A thickness distribution cloud map corresponding to the inner liner is generated based on the wall thickness value corresponding to each detection point.
[0023] In the above embodiment, when light passes through the inner liner, the light is absorbed by the inner liner. The thicker the inner liner, the worse its light transmittance, resulting in more absorbed light. The worse the light transmittance, the lower the brightness of the light passing through the inner liner; conversely, the higher the light transmittance, the higher the brightness of the light passing through the inner liner. Therefore, the grayscale value of the detection point in the grayscale image not only characterizes the brightness of that detection point but also reflects the brightness of the light passing through the inner liner. This establishes a cascaded correlation between the light transmittance, grayscale value, and thickness of the inner liner, allowing the corresponding wall thickness value to be directly determined from each grayscale value. This generates a thickness distribution cloud map that accurately reflects the thickness of the inner liner, achieving accurate, comprehensive, and efficient measurement of the inner liner's wall thickness.
[0024] In one embodiment of this application, based on the foregoing scheme, the thickness distribution cloud map includes the wall thickness values of multiple detection points of the inner liner; determining the thickness anomaly of the inner liner based on the thickness distribution cloud map includes:
[0025] The thickness uniformity of the inner liner is calculated based on the wall thickness value contained in the thickness distribution cloud map.
[0026] The thickness anomaly of the inner liner is determined based on the thickness uniformity.
[0027] In the above embodiments, since the thickness distribution cloud map can comprehensively characterize the wall thickness of the inner liner, the thickness uniformity calculated by the thickness distribution cloud map can reflect the uniformity of the overall thickness of the inner liner, i.e. the fluctuation, so as to determine the thickness abnormality of the inner liner based on the thickness uniformity, so as to accurately determine whether the thickness fluctuation of the inner liner is abnormal, and thus determine whether the thickness of the inner liner is abnormal, thereby improving the accuracy of judging the thickness abnormality of the refrigerator inner liner.
[0028] In one embodiment of this application, based on the foregoing scheme, the processor is further configured as follows:
[0029] Based on the thickness uniformity of multiple inner liners from the same production line, a detection curve corresponding to the production line is generated.
[0030] The abnormal conditions of the production line are determined based on the detection curve.
[0031] In the above embodiments, the thickness uniformity of the inner liner can reflect the uniformity of its thickness. By using the thickness uniformity of multiple inner liners from the same production line, a detection curve reflecting the wall thickness fluctuation of the inner liners produced by that production line can be generated. This detection curve can directly reflect abnormal conditions on the production line, allowing staff to repair the production line in a timely manner and improving the efficiency of judging abnormal conditions on the refrigerator's inner liner production line.
[0032] In one embodiment of this application, based on the foregoing scheme, determining the abnormal condition of the production line according to the detection curve includes:
[0033] Determine the first difference between the thickness uniformity of each component in the detection curve and a preset standard reference line;
[0034] If the total number of first differences that exceed the preset second difference reaches or exceeds the preset reference number, it is determined that the production line is abnormal.
[0035] Otherwise, the wall thickness limit value of each inner liner is obtained based on the wall thickness values corresponding to multiple detection points of each inner liner.
[0036] If the wall thickness limit values are evenly distributed on both sides of the detection curve, it is determined that there is an abnormality in the production line.
[0037] In the above embodiments, since the thickness uniformity of each component in the detection curve can reflect the thickness uniformity of each inner liner, the first difference between the thickness uniformity of each component in the detection curve and the preset standard reference line can reflect whether the thickness fluctuation of each inner liner is drastic. The total number of first differences exceeding the preset second difference can determine the number of inner liners with drastic wall thickness fluctuations produced by the production line, thereby accurately reflecting whether there is an abnormality in the production line and improving the accuracy of judging abnormalities in the refrigerator inner liner production line. Simultaneously, when the number of inner liners with abnormal thickness does not reach the preset reference number, the distribution of the wall thickness limit values of each inner liner is combined to determine whether the production line is abnormal, further improving the accuracy of judging production line thickness abnormalities.
[0038] In one embodiment of this application, based on the foregoing scheme, after determining whether the thickness of the inner liner is abnormal according to the thickness uniformity, the method further includes:
[0039] Information to be displayed is generated based on the detection curve corresponding to the inner liner, the abnormal conditions of the production line, and the abnormal thickness of the inner liner.
[0040] The information to be displayed is sent to a preset display device.
[0041] In the above embodiments, the detection curve corresponding to the inner liner, the abnormal conditions of the production line, and the abnormal thickness of the inner liner can be displayed through the display device, so that the staff can intuitively determine the abnormal conditions of the inner liner and the abnormal conditions of the production line, so that the staff can remove the abnormal inner liner or repair the abnormal production line in a timely manner.
[0042] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this application. Attached Figure Description
[0043] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:
[0044] Figure 1 This is a schematic diagram of the structure of a refrigerator liner thickness measuring device, as shown in an exemplary embodiment of this application;
[0045] Figure 2This is a schematic diagram of the structure of a refrigerator liner thickness measuring device with an inner liner placed, as shown in an exemplary embodiment of this application;
[0046] Figure 3 This is a schematic diagram illustrating the circuit connection principle of a refrigerator liner thickness measuring device, as shown in an exemplary embodiment of this application.
[0047] Figure 4 This is a flowchart illustrating whether an inner liner is placed on the measuring stage in the refrigerator inner liner thickness measuring device provided in an exemplary embodiment of this application.
[0048] Figure 5 This is a flowchart illustrating how a processor in a refrigerator liner thickness measuring device determines whether the refrigerator liner is abnormal, provided in an exemplary embodiment of this application.
[0049] Figure 6 This is an example diagram of the detection curve corresponding to the surface to be detected, a provided in an exemplary embodiment of this application;
[0050] Figure 7 This is a flowchart illustrating how a processor in a refrigerator liner thickness measuring device determines whether an anomaly exists in the production line, as provided in an exemplary embodiment of this application. Detailed Implementation
[0051] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.
[0052] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.
[0053] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and processor devices and microcontroller devices.
[0054] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.
[0055] It should be noted that "multiple" in this article refers to two or more. The "and" characters describe the relationship between related objects, indicating that there can be three relationships. For example, A and B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.
[0056] Currently, vernier calipers or displacement sensors are commonly used to measure the thickness of the inner liner at the same set location. However, using vernier calipers or displacement sensors to measure the inner liner thickness may miss locations with abnormal thickness, thus affecting the accuracy of thickness anomaly detection.
[0057] To solve the above-mentioned technical problems, this application proposes a refrigerator liner thickness measuring device.
[0058] Please see Figure 1 , Figure 2 and Figure 3 , Figure 1 This is a schematic diagram of the structure of a refrigerator liner thickness measuring device provided in an exemplary embodiment of this application. Figure 2 A schematic diagram of a refrigerator liner thickness measuring device. Figure 3 This is a schematic diagram of the circuit connection principle of a refrigerator liner thickness measuring device provided in an exemplary embodiment of this application.
[0059] like Figure 1 , Figure 2 and Figure 3 As shown, the refrigerator liner thickness measuring device 10 may include a measuring platform 11. The measuring platform 11 is used to place the refrigerator liner 21. The refrigerator liner 21 is placed upside down on the measuring platform 11.
[0060] The refrigerator's inner liner 21 is the frame and outer shell inside the refrigerator, typically made of metal, such as steel plate or aluminum alloy, or other synthetic materials, such as plastic, glass, or composite materials. The main function of the refrigerator's inner liner 21 is to protect the internal mechanical and electronic components from external environmental factors such as temperature, humidity, and dust, while also providing storage space.
[0061] The refrigerator liner thickness measuring device 10 may include a camera module 13. The camera module 13 is placed on the measuring platform 11.
[0062] It should be noted that when the refrigerator liner 21 is inverted on the measuring platform 11, the camera module 13 is located inside the liner 21 and is used to photograph the liner 21 under the illumination of the light source. The light source here refers to the light source module 12.
[0063] The camera module 13 may include a camera 14 and a rotating component 15 connected to the camera. The rotating component 15 is used to control the rotation of the camera 14 to adjust the shooting direction of the camera 14.
[0064] It should be noted that the rotating component 15 can control the camera 14 to rotate clockwise or counterclockwise, so that the camera 14 can capture images of the front, left, right, and rear of the inner liner 21. The rotating component can also control the tilt angle of the camera 14, so that the camera 14 can capture images of the top surface of the inner liner 21.
[0065] The refrigerator liner thickness measuring device 10 may include a light source module 12. The light source module 12 is placed around the liner 21 and is used to illuminate the liner 21.
[0066] It should be noted that the light source module 12 includes at least one light source. This light source is used to illuminate the surface of the inner liner to be inspected. The surface to be inspected is the surface whose thickness needs to be measured. The light source can be positioned on the top surface of the inner liner 21. The light source can be positioned on the front side of the inner liner. The light source can be positioned on the left side of the inner liner. The light source can be positioned on the right side of the inner liner. The light source can be positioned on the rear side of the inner liner.
[0067] In some optional embodiments, the light source module 12 may include multiple light curtains, such as at least two of a first light curtain 12-1, a second light curtain 12-2, a third light curtain 12-3, a fourth light curtain 12-4, and a fifth light curtain 12-5. The first light curtain 12-1 is disposed on the top of the inner liner 21 to illuminate its top surface. The second light curtain 12-2, the third light curtain 12-3, the fourth light curtain 12-4, and the fifth light curtain 12-5 are respectively disposed around the perimeter of the inner liner 21 to illuminate the corresponding surfaces of the inner liner 21. For example, the second light curtain 12-2 illuminates the front side of the inner liner 21; the third light curtain 12-3 illuminates the left side of the inner liner 21; the fourth light curtain 12-4 illuminates the right side of the inner liner 21; and the fifth light curtain 12-5 illuminates the rear side of the inner liner 21.
[0068] In some alternative embodiments, the light source module 12 can be an arc-shaped ring light source that can cover the inner liner 21, thereby illuminating at least one of the top, front, left, right and rear surfaces of the inner liner 21 to be tested.
[0069] In some alternative embodiments, the light source module 12 includes a light source. This light source is disposed on the surface of the camera 14. The illumination direction of the light source is consistent with the shooting direction of the camera.
[0070] When the rotating component controls the camera to rotate clockwise or counterclockwise, the light source also rotates accordingly.
[0071] For example, when the rotating component controls the camera to take a picture of the front of the inner liner 21, the light source shines on the left side of the inner liner 21 so as to obtain an image of the left side of the inner liner.
[0072] When the rotating component controls the camera to take a picture of the left side of the inner liner 21, the light source shines on the left side of the inner liner 21 so as to obtain an image of the left side of the inner liner.
[0073] When the rotating component controls the camera to take a picture of the right side of the inner liner 21, the light source shines on the right side of the inner liner 21 so as to obtain an image of the right side of the inner liner.
[0074] When the rotating component controls the camera to take a picture of the back of the inner liner 21, the light source shines on the back of the inner liner 21 so as to obtain an image of the back of the inner liner.
[0075] When the rotating component controls the camera to take a picture of the top surface of the inner liner 21, the light source shines on the top surface of the inner liner 21 so as to obtain an image of the top surface of the inner liner.
[0076] The refrigerator liner thickness measuring device 10 may include a pressure sensor 16. The pressure sensor 16 is placed below the measuring platform 11. The pressure sensor 16 is used to detect the pressure value of the measuring platform 11.
[0077] It should be noted that when the inner liner 21 is not placed on the measuring platform 11, the pressure value detected by the pressure sensor 16 represents the pressure value of the measuring platform 11 itself. When the inner liner 21 is placed on the measuring platform 11, the pressure value detected by the pressure sensor 16 represents the sum of the pressure value of the measuring platform 11 and the pressure value of the inner liner 21.
[0078] like Figure 1 , Figure 2 and Figure 3 As shown, the refrigerator liner thickness measuring device 10 may include a processor 17.
[0079] In the refrigerator liner thickness measuring device 10, the processor 17 is electrically connected to the camera module 13, the light source module 12 and the pressure sensor 16 respectively.
[0080] The processor 17 can control the light source module 12 to turn on, so as to irradiate the inner liner 21 placed on the measuring stage 11 using the light source module 12.
[0081] The processor 17 can control the rotation of the rotating component of the camera module 13 to rotate the camera.
[0082] The processor 17 can control the camera of the camera module 13 to take pictures of the inner liner 21 placed on the measuring table 11.
[0083] The processor 17 can acquire the pressure value detected by the pressure sensor 16 to determine whether the measuring stage 11 is equipped with the inner liner 21.
[0084] In an optional embodiment, processor 17 may be configured to:
[0085] When the measuring platform is found to have an inner liner, the light source module is turned on and an image of the inner liner under the illumination of the light source is acquired.
[0086] Generate a thickness distribution cloud map corresponding to the inner liner;
[0087] Determine whether the thickness of the inner liner is abnormal based on the thickness distribution cloud map.
[0088] In this embodiment, a camera module installed inside the inner liner captures an image of the inner liner under illumination, generating a thickness distribution cloud map based on the image. This cloud map is then used to determine if the inner liner's thickness is abnormal. Compared to methods using calipers or displacement sensors to measure inner liner thickness, the thickness distribution cloud map generated from the inner liner image comprehensively reflects the liner's thickness. This allows for the identification of thickness anomalies based on the overall thickness of the inner liner, reducing the likelihood of overlooking abnormalities and improving the accuracy of inner liner thickness assessment.
[0089] Furthermore, the processor is configured to detect whether the measuring stage has an inner liner, including: acquiring the pressure value detected by the pressure sensor; and determining that the measuring stage has an inner liner when the pressure value detected by the pressure sensor reaches a preset reference pressure value. If the pressure value detected by the pressure sensor does not reach the preset reference pressure value, that is, the pressure value detected by the pressure sensor is less than the preset reference pressure value, then it is determined that the measuring stage does not have an inner liner.
[0090] In this way, the relationship between the pressure value detected by the pressure sensor and the preset reference pressure value determines whether an inner liner is placed on the measuring platform. This allows for the automatic detection of abnormal inner liner thickness when an inner liner is placed on the measuring platform, thus automating the detection of abnormal inner liner thickness.
[0091] It should be noted that a pressure value detected by the pressure sensor reaching a preset reference pressure value indicates that the pressure value detected by the pressure sensor is greater than the preset reference pressure value. The preset reference pressure value is greater than the pressure value of the measuring stage.
[0092] If the preset reference pressure value is less than or equal to the pressure value of the measuring platform, the pressure sensor will detect a pressure value higher than the preset reference pressure value even when no inner liner is placed on the measuring platform, leading to an increased error rate in detecting the inner liner. Therefore, the preset reference pressure value needs to be greater than the pressure value of the measuring platform to reduce the error rate in detecting the inner liner.
[0093] It should be noted that the preset reference pressure value is less than the sum of the pressure value of the measuring platform and the pressure value of the inner liner.
[0094] Since the pressure values of the inner liner are not exactly the same, the preset reference pressure value needs to be less than the sum of the pressure values of the measuring platform and the inner liner, and cannot be greater than or equal to the sum of the pressure values of the measuring platform and the inner liner, so as to accurately determine whether the inner liner is placed on the measuring platform.
[0095] In some optional embodiments, it is determined that the measuring stage has an inner liner if the pressure value detected by the pressure sensor reaches or exceeds a preset reference pressure value. This indicates that the measuring stage has an inner liner if the pressure value detected by the pressure sensor is greater than the preset reference pressure value. Otherwise, it is determined that the measuring stage does not have an inner liner, that is, it indicates that the measuring stage does not have an inner liner if the pressure value detected by the pressure sensor is less than or equal to the preset reference pressure value.
[0096] In some alternative embodiments, it is determined that the measuring stage has an inner liner if the pressure value detected by the pressure sensor reaches or exceeds a preset reference pressure value. This indicates that the measuring stage has an inner liner if the pressure value detected by the pressure sensor is greater than or equal to the preset reference pressure value. Otherwise, it is determined that the measuring stage does not have an inner liner, i.e., it indicates that the measuring stage does not have an inner liner if the pressure value detected by the pressure sensor is less than the preset reference pressure value.
[0097] For details, please refer to Figure 4 , Figure 4 This is a schematic diagram of the processor in the refrigerator liner thickness measuring device detecting whether the liner is placed on the measuring platform.
[0098] like Figure 4 As shown, the processor is configured to perform the following method to detect whether the measuring stage contains an inner liner:
[0099] Step S401: Obtain the pressure value detected by the pressure sensor.
[0100] Step S402: Determine whether the pressure value detected by the pressure sensor reaches or exceeds the preset reference pressure value. If yes, proceed to step S403. If no, proceed to step S404.
[0101] Step S403: Confirm that the measuring stage has an inner liner.
[0102] Step S404: Confirm that the measuring stage is not fitted with an inner liner.
[0103] In this embodiment, by acquiring the pressure value detected by the pressure sensor, if the pressure value reaches or exceeds a preset reference pressure value, it is determined that the measuring platform is equipped with an inner liner. If the pressure value does not reach or exceed the preset reference pressure value, it is determined that the measuring platform is not equipped with an inner liner. This allows for the automatic start of detection of abnormal inner liner thickness when an inner liner is equipped on the measuring platform, thus automating the detection of abnormal inner liner thickness.
[0104] Furthermore, the light source module is turned on, and an image of the inner liner under the illumination of the light source is obtained, including: controlling the light source corresponding to the surface to be tested of the inner liner, and controlling the rotating component to rotate the camera to the shooting direction corresponding to the surface to be tested of the inner liner; controlling the camera to start shooting to obtain an image of the inner liner corresponding to the surface to be tested.
[0105] In this way, by controlling the light source corresponding to the surface to be tested of the inner liner when the inner liner is detected on the measuring platform, the surface to be tested of the inner liner is illuminated by the light source. Then, the rotating component is controlled to rotate the camera to the shooting direction corresponding to the surface to be tested of the inner liner, so that the camera placed inside the inner liner can capture an image of the inner liner corresponding to the surface to be tested under the illumination of the light source. This image of the inner liner can reflect the light transmittance of the detection point on the surface to be tested, thereby comprehensively and accurately reflecting the thickness of the surface to be tested.
[0106] In some optional embodiments, the surface to be tested, i.e. the surface in the inner liner whose thickness needs to be tested, can be one or more of the top surface, front side, left side, right side, and rear side of the inner liner.
[0107] It should be noted that when the light source module is an arc-shaped ring light source, this light source module forms a one-to-many correspondence with each surface of the inner liner. Controlling the light source corresponding to the surface to be tested within the inner liner activates the light source module directly. When the light source module includes multiple light curtains, the light curtains form a one-to-one correspondence with each surface of the inner liner. Controlling the light source corresponding to the surface to be tested within the inner liner activates only the light curtain on the surface to be tested.
[0108] It should be noted that the camera can be rotated by rotating the component so that the camera faces the surface to be inspected, thereby facilitating the camera to take pictures of the surface to be inspected and to obtain an image of the inner liner corresponding to the surface to be inspected under the illumination of the light source.
[0109] If there is only one surface to be inspected, the processor can directly control the rotating component to rotate the camera to the shooting direction corresponding to the surface to be inspected on the inner liner, and then control the camera to start shooting to obtain the inner liner image corresponding to the surface to be inspected.
[0110] If there are multiple surfaces to be inspected, the processor needs to control the rotating component in sequence to rotate the camera to the shooting direction corresponding to each surface to be inspected in the inner liner. Each time the camera rotates to the shooting direction corresponding to the surface to be inspected in the inner liner, the processor controls the camera to start shooting so as to obtain the image of the inner liner corresponding to the surface to be inspected that the camera is facing.
[0111] Further, generating a thickness distribution cloud map corresponding to the inner liner includes: performing grayscale processing on the inner liner image to obtain a grayscale image corresponding to the inner liner; obtaining the grayscale values of the detection points in the grayscale image, and obtaining the wall thickness value mapped by the grayscale value of each detection point; the grayscale value is used to characterize the light transmittance of each detection point under the light source; and generating a thickness distribution cloud map corresponding to the inner liner based on the wall thickness value corresponding to each detection point.
[0112] In this way, as light passes through the inner liner, it is absorbed. The thicker the inner liner, the lower its light transmittance, resulting in more absorbed light. Lower light transmittance means lower brightness of the light passing through the inner liner, and vice versa. Therefore, the grayscale value of the detection point in the grayscale image not only characterizes the brightness of that point but also reflects the brightness of the light passing through the inner liner. This establishes a cascaded correlation between the light transmittance, grayscale value, and thickness of the inner liner, allowing the wall thickness value to be directly determined from each grayscale value. This generates a thickness distribution cloud map that accurately reflects the thickness of the inner liner, achieving accurate, comprehensive, and efficient measurement of the inner liner's wall thickness.
[0113] Further, the inner liner image is converted to grayscale to obtain a corresponding grayscale image, including: performing a grayscale transformation on the inner liner image to convert it from a color image to a grayscale image; and performing denoising processing on the converted grayscale image to obtain the corresponding grayscale image. In some embodiments, the grayscale image is an image that contains only brightness information and not color information, typically represented as different grayscale levels from black (lowest brightness) to white (highest brightness). Under illumination, this brightness information can characterize the degree of light transmission.
[0114] It should be noted that the detection points are manually preset measurement points for thickness measurement. The more detection points there are, the greater the density, the more grayscale values of the detection points, and the more wall thickness values are obtained. This results in a more complete and comprehensive thickness distribution cloud map that reflects the thickness of the inner liner surface to be measured.
[0115] Furthermore, obtaining the wall thickness value mapped to the grayscale value of each detection point includes: performing a lookup operation on the grayscale value of each detection point using a preset grayscale wall thickness database to obtain the wall thickness value mapped to the grayscale value. The preset grayscale wall thickness database stores the correspondence between grayscale values and wall thickness values.
[0116] Furthermore, a thickness distribution cloud map of the inner liner is generated based on the wall thickness values corresponding to each detection point. This includes inputting the wall thickness values corresponding to each detection point into preset visualization software to obtain the thickness distribution cloud map of the inner liner. The thickness distribution cloud map is usually in two-dimensional or three-dimensional form, which can intuitively reflect the wall thickness values at different locations on the surface to be tested of the inner liner.
[0117] It should be noted that when there are multiple surfaces to be inspected, multiple thickness distribution cloud maps are also generated. For any surface to be inspected, the inner liner image corresponding to the surface to be inspected is converted to grayscale to obtain a grayscale image of the inner liner corresponding to the surface to be inspected; then, the grayscale values of the detection points in the grayscale image are obtained, and the wall thickness value mapped by the grayscale value of each detection point is obtained; finally, the thickness distribution cloud map corresponding to the surface to be inspected of the inner liner is generated based on the wall thickness value corresponding to each detection point.
[0118] Optionally, the thickness anomaly of the inner liner can be determined based on the thickness distribution cloud map, including: for the same surface to be inspected, obtaining the maximum and minimum values of each wall thickness value in the thickness distribution cloud map. If the maximum value is greater than a preset upper limit of wall thickness or the minimum value is less than a preset lower limit of wall thickness, the thickness anomaly of the surface to be inspected is determined to be a thickness anomaly. Otherwise, the thickness anomaly of the surface to be inspected is determined to be a thickness anomaly.
[0119] The upper limit for wall thickness is the maximum value set for the thickness of the inner liner. If the inner liner thickness exceeds this upper limit, the refrigerator inner liner will be too thick, resulting in an excessively large overall size and weight, which will increase power consumption.
[0120] The minimum wall thickness is the set minimum value for the thickness of the inner liner. If the inner liner thickness is less than this minimum value, the refrigerator inner liner is too thin, resulting in poor insulation performance.
[0121] Optionally, the thickness anomaly of the inner liner can be determined based on the thickness distribution cloud map, including: calculating the thickness uniformity of the inner liner based on the wall thickness value contained in the thickness distribution cloud map; and determining the thickness anomaly of the inner liner based on the thickness uniformity.
[0122] In this way, since the thickness distribution cloud map can comprehensively represent the wall thickness of the inner liner, the thickness uniformity calculated by the thickness distribution cloud map can reflect the uniformity of the overall thickness of the inner liner, i.e. the fluctuation. This allows for the determination of thickness anomalies in the inner liner based on the thickness uniformity, and thus accurately determines whether the thickness fluctuation of the inner liner is abnormal, thereby improving the accuracy of judging abnormal thickness of the refrigerator inner liner.
[0123] Furthermore, the thickness uniformity of the inner liner is obtained based on the thickness distribution cloud map, including: calculating the standard deviation of the wall thickness corresponding to the inner liner based on each wall thickness value contained in the thickness distribution cloud map; and determining the standard deviation of the wall thickness as the thickness uniformity corresponding to the inner liner.
[0124] In this way, since the standard deviation of wall thickness can reflect the dispersion of each wall thickness value contained in the thickness distribution cloud map, it can also reflect the fluctuation of each wall thickness value around the average value. The standard deviation of the wall thickness corresponding to the inner liner is calculated using each wall thickness value contained in the thickness distribution cloud map. Then, this standard deviation is determined as the thickness uniformity of the inner liner. This thickness uniformity reflects the overall fluctuation of the wall thickness value at each detection point of the inner liner, allowing for accurate determination of whether the inner liner thickness is abnormal, thereby improving the accuracy of judging abnormal refrigerator inner liner thickness.
[0125] Furthermore, the standard deviation of the inner liner's wall thickness is calculated based on the wall thickness values contained in the thickness distribution cloud map. This includes: for the same surface to be inspected, calculating the average based on each wall thickness value in the thickness distribution cloud map to obtain the average wall thickness value. This is achieved through calculation... Obtain the standard deviation of the wall thickness corresponding to the inner liner. Where σ is the standard deviation of the wall thickness corresponding to the inner liner; N is the number of test points; t i is the gray value of the i-th detection point; Mean is the average wall thickness value.
[0126] It should be noted that when there are multiple surfaces to be inspected, the thickness distribution cloud map corresponding to each surface to be inspected is used to calculate the standard deviation of the wall thickness, that is, the standard deviation of the wall thickness corresponds one-to-one with the surface to be inspected. The standard deviation of the wall thickness is determined as the thickness uniformity of the inner liner, that is, the standard deviation of the wall thickness corresponding to each surface to be inspected is determined as the thickness uniformity of each surface to be inspected in the inner liner.
[0127] Furthermore, determining whether the thickness of the inner liner is abnormal based on the thickness uniformity includes: determining the thickness anomaly of the same surface under test based on the thickness uniformity corresponding to that surface; and determining whether the thickness of the inner liner is abnormal based on the thickness anomaly of the surface under test.
[0128] Furthermore, based on the same surface to be inspected, the thickness anomaly of the surface to be inspected is determined according to the thickness uniformity corresponding to the surface to be inspected, including: based on the same surface to be inspected, if the difference between the thickness uniformity and the preset reference uniformity is less than a preset second difference, the thickness anomaly of the surface to be inspected is determined to be normal. Otherwise, the thickness anomaly of the surface to be inspected is determined to be abnormal.
[0129] It should be noted that if the difference between the thickness uniformity and the preset reference uniformity is less than the preset second difference, it can be determined that the thickness of the surface to be tested is relatively uniform, and thus the thickness of the inner liner is normal.
[0130] If the difference between the thickness uniformity and the preset reference uniformity is greater than or equal to the preset second difference, it can be determined that the thickness of the surface to be tested is not uniform, and thus the thickness of the inner liner is abnormal.
[0131] Furthermore, the thickness of the inner liner is determined to be abnormal based on the thickness anomalies of the surface to be inspected. This includes: if there is only one surface to be inspected, and the thickness anomaly of that surface is normal, then the thickness of the inner liner is determined to be normal; otherwise, the thickness of the inner liner is determined to be abnormal. If there are multiple surfaces to be inspected, and the thickness anomalies of each surface are normal, then the thickness of the inner liner is determined to be normal; otherwise, the thickness of the inner liner is determined to be abnormal.
[0132] For details, please refer to Figure 5 , Figure 5 A schematic diagram illustrating how the processor in a refrigerator liner thickness measuring device determines whether the refrigerator liner is abnormal.
[0133] In cases with multiple surfaces to be inspected, such as Figure 5 As shown, the processor is configured to determine whether the refrigerator's inner liner is abnormal by performing the following steps:
[0134] Step S501: When it is detected that the measuring stage has an inner liner, the light source corresponding to the surface to be tested of the inner liner is turned on, and the rotating component is controlled to rotate the camera to the shooting direction corresponding to the surface to be tested of the inner liner.
[0135] Step S502: Control the camera to start shooting to obtain the inner liner image corresponding to each surface to be inspected.
[0136] Step S503: Perform grayscale processing on each inner liner image to obtain the grayscale image corresponding to each surface to be detected.
[0137] Step S504: Obtain the grayscale value of the detection point in each grayscale image, and obtain the wall thickness value mapped by the grayscale value of each detection point.
[0138] Step S505: Generate a thickness distribution cloud map for each surface to be inspected based on the wall thickness value corresponding to each detection point.
[0139] Step S506: Calculate the standard deviation of the wall thickness corresponding to each surface to be tested based on the wall thickness values contained in each thickness distribution cloud map.
[0140] Step S507: Determine the standard deviation of the wall thickness of each surface to be tested as the thickness uniformity of each surface to be tested.
[0141] Step S508: Determine the thickness anomalies of each surface to be tested based on the thickness uniformity of each surface to be tested.
[0142] Step S509: Determine whether the thickness of each surface to be inspected is normal based on the thickness anomalies. If yes, proceed to step S510. If no, proceed to step S511.
[0143] Step S510: Confirm that the thickness of the inner liner is normal.
[0144] Step S511: Determine if the inner liner thickness is abnormal.
[0145] In this embodiment, when the inner liner is detected on the measuring platform, the light source corresponding to the surface to be tested of the inner liner is activated, so that the surface to be tested is illuminated by the light source. Then, the rotating component is controlled to rotate the camera to the shooting direction corresponding to the surface to be tested of the inner liner. This allows the camera, placed inside the inner liner, to capture an image of the inner liner corresponding to the surface to be tested under the illumination of the light source. The image of the inner liner reflects the light transmittance of the detection points on the surface to be tested, and thus the thickness of each detection point can be characterized by the light transmittance of multiple detection points in the inner liner image. A thickness distribution cloud map corresponding to the inner liner is then generated, enabling accurate and comprehensive measurement of the inner liner thickness. Based on this thickness distribution cloud map, a thickness uniformity that can more accurately characterize the uniformity of the inner liner thickness is obtained. This allows for accurate determination of whether the inner liner thickness is abnormal, making it less likely to miss the location of thickness abnormalities, thereby improving the accuracy of judging the thickness abnormality of the refrigerator inner liner. At the same time, this method eliminates the need to cut open the inner liner to measure its thickness, ensuring the integrity of the inner liner and allowing it to be put on the market, reducing cost consumption.
[0146] Furthermore, the processor is also configured to: generate a detection curve corresponding to the production line based on the thickness uniformity of multiple inner liners from the same production line; and determine the abnormal conditions of the production line based on the detection curve.
[0147] In this way, the thickness uniformity of the inner liner can reflect the uniformity of its thickness. By using the thickness uniformity data from multiple inner liners from the same production line, a detection curve reflecting the wall thickness fluctuation of the inner liners produced by that production line can be generated. This detection curve can directly indicate abnormalities in the production line, allowing staff to promptly repair the line and improving the efficiency of identifying abnormalities in the refrigerator's inner liner production line.
[0148] Optionally, based on the thickness uniformity of multiple inner liners from the same production line, a test curve corresponding to the inner liner is generated, including: when there is only one surface to be tested, generating a test curve corresponding to that surface based on the thickness uniformity of the surfaces to be tested from multiple inner liners from the same production line. The test curve corresponding to that surface is then determined as the test curve corresponding to the inner liner.
[0149] Optionally, based on the thickness uniformity of multiple inner liners from the same production line, a detection curve corresponding to the inner liner is generated, including: when there are multiple surfaces to be inspected, generating a detection curve corresponding to each surface to be inspected based on the thickness uniformity of the surfaces to be inspected from multiple inner liners from the same production line. The detection curve corresponding to each surface to be inspected is then determined as the detection curve corresponding to the inner liner.
[0150] It should be noted that the refrigerator liner thickness measuring device stores n thickness uniformity values corresponding to the inner liner, including: the thickness uniformity value corresponding to the first measured thickness, the second measured thickness, the third measured thickness, ..., the nth measured thickness. These n inner liners are all produced on the same production line. After measuring the thickness uniformity value corresponding to the (n+1)th inner liner, the thickness uniformity value corresponding to the first measured thickness (i.e., the first measured thickness) can be deleted. Then, a detection curve is generated based on the thickness uniformity values corresponding to the second, third, ..., nth, and (n+1)th measured thicknesses.
[0151] For the detection curve corresponding to the surface to be detected, please refer to Figure 6 , Figure 6 This is an example diagram of the detection curve corresponding to the surface to be inspected, a. The detection curve is in a Cartesian coordinate system, where the x-axis represents the number of inner linings, and the y-axis represents the thickness uniformity of the surface to be inspected, a. M is 4. Figure 6 The test curves shown are plotted as the thickness uniformity of the five inner liner surfaces (a) to be tested. The first to fourth thickness uniformity curves represent the thickness uniformity of the first four tested inner liner surfaces (a) relative to the tested surface (a). The five thickness uniformity curves represent the thickness uniformity of the currently tested inner liner surface (a) relative to the tested surface (a).
[0152] To save storage space, when acquiring and storing the thickness uniformity of the inner liner relative to the surface a to be tested, the earliest stored thickness uniformity for the surface a to be tested can be deleted, so as to ensure that the refrigerator inner liner thickness measuring device always stores M+1 thickness uniformities of the inner liner relative to the surface a to be tested.
[0153] Furthermore, the abnormal conditions of the production line are determined based on the detection curve, including: determining the first difference between the thickness uniformity of each component in the detection curve and the preset standard reference line; if the total number of the first differences that reach or exceed the preset second difference reaches or exceeds the preset reference number, it is determined that there is an abnormality in the production line; otherwise, the wall thickness limit value of each inner liner is obtained based on the wall thickness values corresponding to multiple detection points of each inner liner; if the wall thickness limit values are evenly distributed on both sides of the detection curve, it is determined that there is an abnormality in the production line.
[0154] In this way, since the thickness uniformity of each component in the detection curve reflects the uniformity of the thickness of each inner liner, the first difference between the thickness uniformity of each component in the detection curve and the preset standard reference line can reflect whether the thickness fluctuation of each inner liner is drastic. The total number of first differences exceeding the preset second difference can determine the number of inner liners with drastic wall thickness fluctuations produced on the production line, thus accurately indicating whether there is an abnormality in the production line and improving the accuracy of judging abnormalities in the refrigerator inner liner production line. Simultaneously, when the number of inner liners with abnormal thickness does not reach the preset reference number, the distribution of the wall thickness limit values of each inner liner can be combined to determine whether the production line is abnormal, further improving the accuracy of judging production line thickness abnormalities.
[0155] It should be noted that the first difference between each thickness uniformity in the detection curve and the preset standard reference line is the perpendicular distance between each thickness uniformity and the preset standard reference line. The preset standard reference line is a straight line parallel to the x-axis corresponding to a preset reference uniformity. A perpendicular segment is drawn from the thickness uniformity corresponding to the detection point in the Cartesian coordinate system to the standard reference line. The length of this perpendicular segment is the perpendicular distance between the thickness uniformity corresponding to the detection point and the preset standard reference line.
[0156] It should be noted that when there are multiple surfaces to be inspected, the total number of first differences exceeding a preset second difference value needs to be counted in the inspection curve corresponding to each surface to obtain the number of inner liners with thickness abnormalities for each surface to be inspected. If the number of inner liners with thickness abnormalities corresponding to any surface to be inspected exceeds a preset reference number, an abnormality is determined to exist in the production line. Otherwise, the wall thickness limit value of each inner liner is obtained based on the wall thickness values corresponding to multiple inspection points of each inner liner; the existence of an abnormality in the production line is determined based on the wall thickness limit value of each inner liner.
[0157] Furthermore, the wall thickness limit value of each inner liner is obtained based on the wall thickness values corresponding to multiple detection points of each inner liner, including: when there is only one surface to be inspected, obtaining the maximum and minimum wall thickness values corresponding to the surface to be inspected based on the wall thickness values corresponding to each detection point of the surface to be inspected of each inner liner. The maximum and minimum wall thickness values are then used to determine the wall thickness limit value of the inner liner. Alternatively, when there are multiple surfaces to be inspected, for the same surface to be inspected, the wall thickness limit value corresponding to the surface to be inspected is obtained based on the wall thickness values corresponding to each detection point of the surface to be inspected of each inner liner. The wall thickness limit value corresponding to the surface to be inspected is then used to determine the wall thickness limit value of the inner liner. The wall thickness limit value corresponding to the surface to be inspected includes the maximum and minimum wall thickness values corresponding to the surface to be inspected.
[0158] Optionally, the presence of an anomaly in the production line can be determined based on the wall thickness limits of each inner liner. This includes: if, when there is only one surface to be inspected, the wall thickness limits are evenly distributed on both sides of a preset standard reference line, the production line for producing the inner liner is deemed to have an anomaly. Otherwise, the production line for producing the inner liner is deemed to have no anomaly.
[0159] Optionally, the presence of an anomaly in the production line can be determined based on the wall thickness limits of each inner liner. This includes: in the case of multiple surfaces to be inspected, if the wall thickness limits for any surface to be inspected are evenly distributed on both sides of a preset standard reference line, then the production line for producing the inner liner is determined to be an anomaly. Otherwise, the production line for producing the inner liner is determined to be normal.
[0160] In this way, if the wall thickness limit values are evenly distributed on both sides of the preset standard reference line, it indicates that there is a non-uniformity in the thickness of the inner liner produced by the production line, which can reflect the abnormality of the inner liner production line.
[0161] For details, please refer to Figure 7 , Figure 7 A schematic diagram illustrating how the processor in a refrigerator liner thickness measuring device determines if there are any abnormalities in the production line.
[0162] In cases with multiple surfaces to be inspected, such as Figure 7 As shown, the processor in the refrigerator liner thickness measuring device is configured to determine whether there is an anomaly in the production line by performing the following method:
[0163] Step S701: Based on the thickness uniformity of each surface to be tested from multiple inner liners from the same production line, generate a test curve corresponding to the surface to be tested.
[0164] Step S702: In each detection curve, determine the first difference between each thickness uniformity and the preset standard reference line.
[0165] Step S703: In each detection curve, count the total number of first differences that reach or exceed the preset second difference value.
[0166] Step S704: Determine the total quantity as the number of inner liners with abnormal thickness corresponding to the surface to be inspected.
[0167] Step S705: Determine whether the number of inner liners with abnormal thickness corresponding to each surface to be inspected has not reached the preset reference number. If yes, proceed to step S707. If no, proceed to step S706.
[0168] Step S706: Determine if there is an abnormality in the production line.
[0169] Step S707: Obtain the wall thickness limit value of each surface to be tested based on the wall thickness values corresponding to multiple detection points of each inner liner. Then, proceed to step S708.
[0170] Step S708: Determine whether any wall thickness limit values corresponding to any surface to be tested are uniformly distributed on both sides of a preset standard reference line. If yes, proceed to step S706. If no, proceed to step S709.
[0171] Step S709: Confirm that there are no abnormalities in the production line.
[0172] In this embodiment, the thickness uniformity of the inner liner reflects the uniformity of its thickness. By analyzing the thickness uniformity of multiple inner liners from the same production line, a detection curve reflecting the wall thickness fluctuation of the inner liners produced by that production line can be generated. Then, the first difference between each thickness uniformity in the detection curve and a preset standard reference line can reflect whether the thickness fluctuation of each inner liner is severe. Combined with the inner liner wall thickness limit value, it can be determined whether the production line is abnormal, thus improving the accuracy and efficiency of judging production line thickness anomalies.
[0173] Furthermore, after determining whether the thickness of the inner liner is abnormal based on the thickness uniformity, the process also includes: generating information to be displayed based on the detection curve corresponding to the inner liner, the abnormality of the production line, and the abnormality of the inner liner thickness; and sending the information to be displayed to a preset display device.
[0174] In this way, the display device can show the detection curve corresponding to the inner liner, the abnormal conditions of the production line, and the abnormal thickness of the inner liner, so that the staff can intuitively determine the abnormal conditions of the inner liner and the production line, so that the staff can remove the abnormal inner liner or repair the abnormal production line in a timely manner.
[0175] Furthermore, after sending the information to be displayed to the preset display device, the system also includes illuminating an indicator light on the measuring platform to prompt the operator to remove the inner liner. This allows the pressure sensor to accurately determine that the measuring platform is not occupied by the inner liner, facilitating the next inner liner thickness measurement.
[0176] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.
[0177] It should be understood that the above content is only a preferred exemplary embodiment of this application and is not intended to limit the implementation of this application. Those skilled in the art can easily make corresponding modifications or alterations based on the main concept and spirit of this application. Therefore, the scope of protection of this application should be determined by the scope of protection claimed in the claims.
Claims
1. A refrigerator liner thickness measuring device, characterized in that, include: A measuring platform for placing the inner liner of a refrigerator; the inner liner is placed upside down on the measuring platform. A light source module is placed around the inner liner to illuminate the inner liner; A camera module is placed on the measuring platform and inside the inner liner, and is used to photograph the inner liner under light source illumination; A processor, electrically connected to the camera module and the light source module, is configured to: When it is detected that the measuring platform has an inner liner, the light source module is turned on and an image of the inner liner under the illumination of the light source is acquired. Generate a thickness distribution cloud map corresponding to the inner liner; The thickness anomaly of the inner liner is determined based on the thickness distribution cloud map.
2. The refrigerator liner thickness measuring device according to claim 1, characterized in that, A pressure sensor is placed below the measuring platform; the pressure sensor is used to detect the pressure value of the measuring platform and is electrically connected to the processor. The processor is configured to detect that the measuring stage contains an inner liner, including: Obtain the pressure value detected by the pressure sensor; When the pressure value detected by the pressure sensor reaches the preset reference pressure value, it is determined that the measuring platform contains the inner liner.
3. The refrigerator liner thickness measuring device according to claim 1 or 2, characterized in that, The camera module includes a camera and a rotating component connected to the camera; the rotating component is used to control the rotation of the camera to adjust the shooting direction of the camera. The light source module includes at least one light source; the light source is used to illuminate the surface of the inner liner to be tested; The step of activating the light source module and acquiring an image of the inner liner under the illumination of the light source includes: Control the light source corresponding to the surface to be tested of the inner liner to be turned on, and control the rotating component to rotate the camera to the shooting direction corresponding to the surface to be tested of the inner liner; Control the camera to start shooting in order to obtain an image of the inner liner corresponding to the surface to be inspected.
4. The refrigerator liner thickness measuring device according to claim 1, characterized in that, Generating the thickness distribution cloud map corresponding to the inner liner includes: The inner liner image is converted to grayscale to obtain a grayscale image corresponding to the inner liner; Obtain the grayscale values of the detection points in the grayscale image, and obtain the wall thickness value mapped by the grayscale values of each detection point; the grayscale values are used to characterize the light transmittance of each detection point under the light source; A thickness distribution cloud map corresponding to the inner liner is generated based on the wall thickness value corresponding to each detection point.
5. The refrigerator liner thickness measuring device according to claim 4, characterized in that, The thickness distribution cloud map includes the wall thickness values of multiple detection points of the inner liner; determining the thickness anomaly of the inner liner based on the thickness distribution cloud map includes: The thickness uniformity of the inner liner is calculated based on the wall thickness value contained in the thickness distribution cloud map. The thickness anomaly of the inner liner is determined based on the thickness uniformity.
6. The refrigerator liner thickness measuring device according to claim 1, characterized in that, The processor is also configured to: Based on the thickness uniformity of multiple inner liners from the same production line, a detection curve corresponding to the production line is generated. The abnormal conditions of the production line are determined based on the detection curve.
7. The refrigerator liner thickness measuring device according to claim 6, characterized in that, The step of determining the abnormality of the production line based on the detection curve includes: Determine the first difference between the thickness uniformity of each component in the detection curve and a preset standard reference line; If the total number of first differences that exceed the preset second difference reaches or exceeds the preset reference number, it is determined that the production line is abnormal. Otherwise, the wall thickness limit value of each inner liner is obtained based on the wall thickness values corresponding to multiple detection points of each inner liner. If the wall thickness limit values are evenly distributed on both sides of the detection curve, it is determined that there is an abnormality in the production line.
8. The refrigerator liner thickness measuring device according to claim 6 or 7, characterized in that, After determining whether the thickness of the inner liner is abnormal based on the thickness uniformity, the method further includes: Information to be displayed is generated based on the detection curve corresponding to the inner liner, the abnormal conditions of the production line, and the abnormal thickness of the inner liner. The information to be displayed is sent to a preset display device.