Quality detection method and quality detection device for air conditioner control panel

Abstract

The application provides a quality detection method and device for an air conditioner control panel. The method comprises the following steps: receiving an overhead view image and a front view image of the air conditioner control panel to be detected, the air conditioner control panel to be detected being the air conditioner control panel after springs are installed at the groove positions of each buckle; performing binarization processing on the overhead view image and the front view image respectively to obtain the binarized overhead view image and the binarized front view image; detecting the number of springs on the air conditioner control panel to be detected based on the binarized overhead view image, and detecting whether the springs on the air conditioner control panel to be detected are wound and the installation quality of the springs based on at least the binarized front view image, thereby solving the problems that the accuracy and efficiency of detecting the springs on the air conditioner control panel by using the manual method are low in the prior art.

Description

Technical Field

[0001] This application relates to the field of vertical air conditioners, and more specifically, to a quality inspection method, a quality inspection device, and a computer-readable storage medium for an air conditioner control panel. Background Technology

[0002] In practical applications, users typically use the air conditioner control panel (i.e., the air conditioner display box) to control the air conditioner, including turning it on and off, adjusting the temperature, and setting the operating mode. Therefore, the installation quality of the buttons on the air conditioner control panel significantly impacts the user experience.

[0003] If the support springs on the air conditioner control panel are poorly installed after assembly—for example, if springs are missing, wrapped, or not properly secured in their corresponding clips—it can lead to problems such as malfunctioning buttons, poor button operation, and / or complete unusability. Therefore, inspecting the springs on the air conditioner control panel during assembly is crucial.

[0004] Existing inspection methods are based on manual inspection, where workers shake or invert the air conditioner control panel to determine if any springs are missing, wrapped, or not properly secured in their corresponding clips. However, manual work over extended periods can lead to missed detections and inaccurate spring checks. Furthermore, manually inspecting for missing, wrapped, or improperly secured springs on the control panel is not only time-consuming and labor-intensive but also inefficient. Summary of the Invention

[0005] The main objective of this application is to provide a quality inspection method, a quality inspection device, and a computer-readable storage medium for air conditioner control panels, so as to at least solve the problem that the accuracy and efficiency of manually inspecting the springs on air conditioner control panels are both low in the prior art.

[0006] To achieve the above objectives, according to one aspect of this application, a quality inspection method for an air conditioner control panel is provided, comprising: receiving a top view image and a front view image of the air conditioner control panel to be inspected, wherein the air conditioner control panel to be inspected is an air conditioner control panel with springs installed at the groove positions of each clip; performing binarization processing on the top view image and the front view image respectively to obtain binarized top view image and front view image; detecting the number of springs on the air conditioner control panel to be inspected based on the binarized top view image; and detecting, at least based on the binarized front view image, whether the springs on the air conditioner control panel to be inspected are wound and the installation quality of the springs.

[0007] Optionally, binarizing the top-view image to obtain a binarized top-view image includes: converting the top-view image to grayscale to obtain a grayscale-converted top-view image; determining a segmentation threshold based on the local Otsu algorithm and the grayscale-converted top-view image; and using the segmentation threshold to perform binarized segmentation on the grayscale-converted top-view image to obtain the binarized top-view image.

[0008] Optionally, based on the binarized top-view image, the number of springs on the air conditioner control panel to be detected is detected, including: constructing a grayscale histogram of the binarized top-view image, where the horizontal axis of the grayscale histogram is the pixel value of a pixel in the binarized top-view image, and the vertical axis of the grayscale histogram is the total number of pixels in the binarized top-view image corresponding to each pixel value; counting the total number of pixels in the binarized top-view image corresponding to a target pixel value to obtain the target pixel count, where the target pixel value is the pixel value of the pixels occupied by the outline of the spring; and obtaining the number of springs on the air conditioner control panel to be detected based on the ratio of the target pixel count to the total number of pixels occupied by the outline of one spring.

[0009] Optionally, after obtaining the number of springs on the air conditioner control panel to be tested based on the ratio of the number of target pixels to the total number of pixels occupied by the outline of one spring, the quality detection method further includes: if the number of springs on the air conditioner control panel to be tested meets a preset value, determining that the air conditioner control panel to be tested has no missing springs, where the preset value is the total number of springs installed on the air conditioner control panel to be tested; if the number of springs on the air conditioner control panel to be tested does not meet the preset value, determining that the air conditioner control panel to be tested has missing springs.

[0010] Optionally, binarizing the front view image to obtain the binarized front view image includes: converting the front view image to grayscale to obtain the grayscale-processed front view image; smoothing the grayscale-processed front view image using a median filter to obtain the smoothed front view image; and segmenting the smoothed front view image using a threshold-based image segmentation method to obtain the binarized front view image.

[0011] Optionally, at least based on the binarized front view image, the detection of whether the spring on the air conditioner control panel to be detected is entangled includes: constructing an image matrix of the binarized front view image; based on the image matrix, determining the vertical distance between any two adjacent parallel lines to obtain multiple spring gap distances, wherein the pixels occupied by any two adjacent parallel lines are pixels in the image matrix and belonging to the same spring; determining the absolute values ​​of the differences between the multiple spring gap distances and a predetermined distance to obtain multiple absolute values ​​of spring gap distances, wherein the predetermined distance is the standard distance of the spring gap when the spring is not entangled and has not undergone tensile deformation; if there is a case where the absolute value of one spring gap distance is not within the preset range, it is determined that the spring is entangled on the air conditioner control panel to be detected.

[0012] Optionally, the installation quality of the spring on the air conditioner control panel to be inspected is detected based at least on the binarized front view image, including: performing line detection on the projected image of the binarized front view image using Hough transform to obtain multiple sets of spring contour pixel points, each set of spring contour pixel points corresponding to one spring; performing a space transformation with invariant moments on each set of spring contour pixel points to obtain spatially transformed sets of spring contour pixel points; filtering the spatially transformed sets of spring contour pixel points using a scanning matrix to obtain multiple sets of target pixel points, each set of target pixel points corresponding to one spring, and each set of target pixel points can form the straight line occupied by the corresponding spring; and detecting the installation quality of the spring at the corresponding buckle position based on the multiple sets of target pixel points.

[0013] Optionally, based on multiple sets of target pixels, the installation quality of the spring at the corresponding buckle position is detected, including: using the least squares method to fit each set of target pixels to obtain multiple spring lines; determining whether the endpoints of the multiple spring lines are on the same straight line, wherein the endpoint of each spring line is the endpoint on the corresponding spring line that is far from the buckle; if the endpoints of the multiple spring lines are on the same straight line, determining that each spring is installed in the groove of the corresponding buckle; if the endpoints of the multiple spring lines are not on the same straight line, determining that there is a spring that is not installed in the groove of the corresponding buckle.

[0014] According to another aspect of this application, a quality inspection device for an air conditioner control panel is provided, comprising: a receiving unit for receiving a top view image and a front view image of the air conditioner control panel to be inspected, wherein the air conditioner control panel to be inspected is an air conditioner control panel with springs installed at the groove positions of each clip; a processing unit for performing binarization processing on the top view image and the front view image respectively to obtain binarized top view image and front view image; and a detection unit for detecting the number of springs on the air conditioner control panel to be inspected based on the binarized top view image, and detecting whether the springs on the air conditioner control panel to be inspected are wound and the installation quality of the springs based at least on the binarized front view image.

[0015] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform any of the described quality inspection methods.

[0016] This application utilizes a technical solution where the received top-view image of the air conditioner control panel to be tested is binarized to obtain a binarized top-view image. This binarized top-view image is then used to detect the number of springs on the control panel. Similarly, the received front-view image of the air conditioner control panel to be tested is binarized to obtain a binarized front-view image. This binarized front-view image is then used to detect whether the springs on the control panel are coiled and the quality of their installation. Compared to existing technologies that rely on manual methods to detect missing springs, coiled springs, and / or springs not properly secured to their corresponding clip grooves, this solution binarizes both the top-view and front-view images of the air conditioner control panel, then uses the binarized top-view image to detect the number of springs on the control panel, and uses the binarized front-view image to detect whether the springs are coiled and the quality of their installation. This automates the inspection of the installation quality of springs on the control panel, such as checking the number of springs, spring gaps, and spring end facets. This solves the problem of low accuracy and efficiency in the existing manual inspection of springs on air conditioner control panels. Furthermore, this quality inspection method reduces worker workload, alleviates worker stress, and lowers operating costs, significantly saving time and labor costs during assembly while improving efficiency and productivity and ensuring a high product pass rate. Attached Figure Description

[0017] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0018] Figure 1 A hardware structure block diagram of a mobile terminal for performing a quality detection method for an air conditioning control panel, according to an embodiment of this application, is shown.

[0019] Figure 2 A flowchart illustrating a quality inspection method for an air conditioner control panel according to an embodiment of this application is shown.

[0020] Figure 3 A top view of an air conditioner control panel to be tested, provided according to an embodiment of this application, is shown.

[0021] Figure 4A predetermined distance L1 is shown for a spring that is not wound, according to an embodiment of this application;

[0022] Figure 5 The spring gap distance L2 in a spring winding case provided by an embodiment of this application is shown;

[0023] Figure 6 A schematic diagram is shown showing an embodiment of the present application in which the endpoints of each spring are on the same straight line;

[0024] Figure 7 A flowchart illustrating another quality inspection method for an air conditioning control panel provided according to an embodiment of this application is shown.

[0025] Figure 8 A schematic diagram of the structure of a quality testing device for an air conditioning control panel provided according to an embodiment of this application is shown.

[0026] The above figures include the following reference numerals:

[0027] 102. Processor; 104. Memory; 106. Transmission device; 108. Input / output device. Detailed Implementation

[0028] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0029] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] As described in the background section, the accuracy and efficiency of manually inspecting the springs on the air conditioner control panel in the prior art are both low. To solve the above problems, the embodiments of this application provide a quality inspection method, a quality inspection device, and a computer-readable storage medium for air conditioner control panels.

[0032] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0033] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a quality inspection method of an air conditioner control panel according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0034] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the air conditioner control panel quality detection method in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0035] This embodiment provides a quality detection method for an air conditioner control panel that runs on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0036] Figure 2 This is a flowchart of a quality inspection method for an air conditioner control panel according to an embodiment of this application. Figure 2 As shown, the quality inspection method includes the following steps:

[0037] Step S201: Receive the top view image and front view image of the air conditioner control panel to be tested. The air conditioner control panel to be tested is an air conditioner control panel with springs installed at the groove positions of each clip.

[0038] Specifically, at the manufacturing stage of the air conditioner control panel (i.e., the air conditioner display box), after installing each spring into the corresponding recessed position of the clip, it is generally necessary to inspect the installation quality of each spring to avoid affecting the normal use of the buttons after they are installed in the corresponding spring positions. Therefore, the air conditioner control panel to be tested in this application is an air conditioner control panel in which springs are installed in the recessed positions of each clip and no buttons are installed.

[0039] In one specific embodiment of this application, an image capturing device (e.g., a camera) can be installed at the assembly location of the control panel. The image capturing device is then used to capture images of the air conditioning control panel with the spring installed (i.e., the air conditioning control panel to be tested), thereby obtaining top-view and front-view images of the air conditioning control panel to be tested.

[0040] Step S202: Binarize the top view image and the front view image respectively to obtain the top view image and the front view image after binarization.

[0041] In step S202 above, the received top view image is binarized to obtain a binarized top view image, and the received front view image is binarized to obtain a binarized front view image. By binarizing the top view image and the front view image respectively, the corresponding images are simplified, thereby reducing image storage space and storage processing.

[0042] Step S203: Based on the binarized top view image, the number of springs on the air conditioner control panel to be tested is detected, and at least based on the binarized front view image, the springs on the air conditioner control panel to be tested are detected as to whether they are wrapped and the installation quality of the springs.

[0043] In step S203 above, the number of springs on the air conditioner control panel to be tested is detected based on the binarized top view image. Since the top view image is a binarized image at this time, this not only ensures that the computational workload for detecting the number of springs on the air conditioner control panel to be tested is small, but also ensures that the overall detection efficiency is high.

[0044] In step S203 above, at least based on the binarized front view image, the detection of whether the springs on the air conditioner control panel are wound and the installation quality of the springs are performed. Since the front view image is a binarized image at this time, this not only ensures that the calculation of detecting whether the springs on the air conditioner control panel are wound and the installation quality of the springs is small, but also ensures high overall detection efficiency.

[0045] In one specific embodiment of this application, the left view image and / or top view image of the air conditioner control panel to be tested, after binarization processing, can also be used to detect whether the spring on the air conditioner control panel to be tested is wound and the installation quality of the spring.

[0046] In the aforementioned quality inspection method for air conditioner control panels, the received top-view image of the air conditioner control panel to be inspected is binarized to obtain a binarized top-view image. This binarized top-view image is then used to detect the number of springs on the control panel. Similarly, the received front-view image of the air conditioner control panel to be inspected is binarized to obtain a binarized front-view image. This binarized front-view image is then used to detect whether the springs on the control panel are coiled and the quality of their installation. Compared to existing technologies that manually detect missing springs, coiled springs, and / or springs not properly fixed to the corresponding clip grooves, this method binarizes both the top-view and front-view images of the air conditioner control panel to be inspected. Based on the binarized top-view image, the number of springs on the control panel is detected. Furthermore, the binarized front-view image is used to detect whether the springs are coiled and the quality of their installation. This automates the inspection of the installation quality of springs on the control panel, such as checking the number of springs, spring gaps, and spring end facets. This solves the problem of low accuracy and efficiency in the existing manual inspection of springs on air conditioner control panels. Furthermore, this quality inspection method reduces worker workload, alleviates worker stress, and lowers operating costs, significantly saving time and labor costs during assembly while improving efficiency and productivity and ensuring a high product pass rate.

[0047] In one specific embodiment, after the air conditioning control panel of this application undergoes spring detection and button assembly, the finished air conditioning control panel can be used in a floor-standing air conditioner. Of course, it is not limited to floor-standing air conditioners; it can also be used in vehicle air conditioners, etc.

[0048] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0049] In specific implementation, step S202 can be achieved through steps S2021, S2022, and S2023.

[0050] Step S2021: Perform grayscale processing on the above top view image to obtain the grayscale processed top view image.

[0051] Specifically, any suitable method in the prior art can be used to perform grayscale processing on the top view image. For example, the maximum value method, weighted average method, or average value method can be used to perform grayscale processing on the top view image to obtain the grayscale processed top view image.

[0052] Step S2022: Determine the segmentation threshold based on the local Otsu algorithm and the grayscaled top view image above;

[0053] In step S2022 above, the segmentation threshold is not limited to being determined by the local Otsu algorithm to segment the top view image. Threshold-based image segmentation methods, histogram thresholding, region growing, or image-based random field model methods can also be used.

[0054] Step S2023: Using the above-mentioned segmentation threshold, the grayscale-processed top view image is binarized to obtain the binarized top view image.

[0055] In this embodiment, the local Otsu method is used to determine the segmentation threshold for segmenting the grayscale-processed top-view image. This ensures that the determined segmentation threshold is relatively accurate. Based on the segmentation threshold, the grayscale-processed top-view image is binarized, resulting in a more accurate binarized top-view image. This further ensures the accuracy of subsequent detection of whether the spring on the air conditioner control panel is missing.

[0056] The above-described step S203 of this application can also be implemented through steps S2031, S2032, and S2033. Among them,

[0057] Step S2031: Construct a grayscale histogram of the binarized top view image. The horizontal axis of the grayscale histogram is the pixel value of the pixel in the binarized top view image, and the vertical axis of the grayscale histogram is the total number of pixels in the binarized top view image corresponding to each pixel value.

[0058] Specifically, a gray-level histogram is a function of the gray-level distribution, representing a statistical representation of the gray-level distribution in an image. It calculates the frequency of occurrence of each pixel in a digital image based on its gray-level value (pixel value). As a function of gray levels, a gray-level histogram can be used to represent the number of pixels in an image with a specific gray level, reflecting the frequency of that particular gray-level value (pixel value) in the image.

[0059] Step S2032: Count the total number of pixels in the binarized top view image corresponding to the target pixel value to obtain the target pixel value. The target pixel value is the pixel value of the pixels occupied by the outline of the spring.

[0060] In practical applications, the binarized top-view image can be represented by the numbers 0 or 1. For example, the pixel value corresponding to the outline of the spring is 1, and the pixel value of other parts is 0. Therefore, the total number of pixels with a pixel value of 1 (i.e., the target pixel value) can be counted by using a grayscale histogram, which gives the target pixel count.

[0061] Step S2033: Based on the ratio of the target pixel count to the total number of pixels occupied by the outline of one spring, the number of springs on the air conditioner control panel to be detected is obtained.

[0062] In the above embodiments, the number of target pixels corresponding to the target pixel value is counted by using a grayscale histogram, and then the number of springs on the air conditioner control panel to be detected is determined based on the ratio of the number of target pixels to the total number of pixel values ​​occupied by the outline of a spring. This achieves a relatively simple way to count the number of springs on the air conditioner control panel.

[0063] To more easily determine whether a spring is missing from the air conditioner control panel to be tested, the quality inspection method of this application further includes step S204: after obtaining the number of springs on the air conditioner control panel to be tested based on the ratio of the number of target pixels to the total number of pixels occupied by the outline of one spring, if the number of springs on the air conditioner control panel to be tested meets a preset value, it is determined that the air conditioner control panel to be tested does not have any missing springs, where the preset value is the total number of springs installed on the air conditioner control panel to be tested; if the number of springs on the air conditioner control panel to be tested does not meet the preset value, it is determined that the air conditioner control panel to be tested has missing springs.

[0064] In one specific embodiment, such as Figure 3 As shown, under normal circumstances, an air conditioner control panel is equipped with five springs: spring A, spring B, spring C, spring D, and spring E. If the number of spring outlines on the air conditioner control panel under test is also five, it can be determined that the air conditioner control panel under test is not missing any springs. If the number of spring outlines on the air conditioner control panel under test is less than five, it can be determined that the air conditioner control panel under test is missing some springs.

[0065] In some embodiments, step S202 can also be implemented via steps 2024 and S2025.

[0066] Step S2024: The above front view image is grayscaled to obtain the grayscaled front view image, and a median filter is used to smooth the grayscaled front view image to obtain the smoothed front view image.

[0067] Specifically, any suitable method in the prior art can be used to perform grayscale processing on the front view image. For example, the maximum value method, weighted average method, or average value method can be used to perform grayscale processing on the front view image to obtain the grayscale processed front view image.

[0068] In practical applications, the median filter replaces each pixel with the median or "median pixel" within a rectangular neighborhood surrounding that pixel. Simple blurring through averaging is highly sensitive to noisy images, especially large, isolated outliers. Even a small number of points with significant deviations can severely impact the effectiveness of mean filtering. Median filtering can eliminate outliers by taking the median point. This results in less noise in the smoothed front view image. Furthermore, it ensures higher accuracy when using threshold-based image segmentation methods to segment the smoothed front view image, leading to a binarized front view image.

[0069] Step S2025: Using a threshold-based image segmentation method, the smoothed front view image is segmented to obtain the binarized front view image.

[0070] In step S2025 above, the method is not limited to using a threshold-based image segmentation method to segment the smoothed front view image and obtain a binarized front view image. Histogram thresholding, region growing, or image-based random field model methods can also be used to segment the smoothed front view image and obtain a binarized front view image.

[0071] In the above embodiments, a threshold-based image segmentation method is used to segment the smoothed front view image, resulting in a binarized front view image. This effectively segments the springs in the binarized front view image, allowing for the identification of the blank areas between the springs, i.e., spring gaps, further ensuring that subsequent detection of whether spring winding occurs on the air conditioner control panel can be performed with greater accuracy.

[0072] In practical applications, the spring gap is fixed when the spring has not deformed or undergone tensile deformation. If the spring becomes entangled or undergoes tensile deformation, the spring gap will increase or decrease. Therefore, in specific implementations, the spring gap can be used to determine whether the spring has become entangled or undergone tensile deformation. Step S203 can also be implemented through steps S2034, S203, S2036, and S2037. Wherein:

[0073] Step S2034: Construct the image matrix of the above-mentioned front view image after binarization;

[0074] Specifically, constructing the image matrix of the binarized front view image means converting the binarized front view image into a numerical form that can be processed by a computer, thereby facilitating the subsequent acquisition and calculation of information such as spring gaps in the binarized front view image through image processing, analysis, and calculation.

[0075] Step S2035: Based on the above image matrix, determine any two adjacent parallel lines ( Figure 4 The two parallel lines corresponding to L1 in the middle, or Figure 5 The perpendicular distance between the two parallel straight lines constituting L2 in the figure is used to obtain multiple spring gap distances (such as...). Figure 5 As shown in L2), any two adjacent parallel lines occupy pixels in the above image matrix and on the same spring; that is, any two adjacent parallel lines are parallel lines on the same spring.

[0076] Step S2036: Determine the absolute value of the difference between the multiple spring gap distances and the predetermined distance, and obtain multiple absolute values ​​of spring gap distances. The predetermined distance is the standard distance of the spring gap when the spring has not been wrapped or stretched.

[0077] Step S2037: If there is a case where the absolute value of the spring gap distance is not within the preset range, it is determined that the spring is wrapped around the air conditioner control panel to be tested.

[0078] In one specific embodiment, such as Figure 5 As shown, this represents the predetermined distance (i.e., spring gap) L1, also known as the normal distance, when the spring has not undergone tensile deformation or spring winding. Figure 6The figure shows the spring gap distance L2, also known as the abnormal distance, when the spring is entangled. If the spring is not entangled or stretched, the absolute value of the difference between the spring gap distance L2 and the predetermined distance L1 is within the preset range; if the spring is entangled or stretched, the absolute value of the difference between the spring gap distance L2 and the predetermined distance L1 will not be within the preset range. Therefore, this method can relatively easily determine whether the air conditioner control panel to be tested has spring entanglement.

[0079] In practical applications, if each spring is installed into the corresponding groove of the snap fastener, the other ends of each spring will be aligned in a straight line. Therefore, in some embodiments, step S203 can also be implemented via steps S2038, S2039, S2040, and S2041. Wherein:

[0080] Step S2038: The Hough transform is used to perform line detection on the projection image of the above-mentioned front view image after binarization to obtain multiple sets of spring contour pixel points, and one set of the above-mentioned spring contour pixel points corresponds to one spring.

[0081] In step S2038 above, the binarized front view image is projected to obtain a projected image of the binarized front view image. For example, the binarized front view image can be projected onto the same plane to obtain a projected image of the binarized front view image.

[0082] In step S2038 above, after obtaining multiple sets of spring contour pixel points, the least squares method can be used to perform initial fitting optimization on each set of spring contour pixel points, which further ensures that the target pixel point set selected later is more accurate.

[0083] Step S2039: Perform a spatial transformation with invariant moments on each of the above spring contour pixel sets to obtain the spatially transformed set of each of the above spring contour pixel sets.

[0084] In practical applications, applying spatial transformations of invariant moments can reduce the impact of image noise or shape deformation on invariant moment calculations, improving the accuracy of contour set processing. Contour set matching and recognition can also be performed by comparing differences between invariant moments. Since invariant moments remain unchanged after spatial transformation, shape matching and object recognition tasks can be effectively performed. Invariant moments provide a compact and descriptive way to represent the shape information of contour sets. These features can capture the geometric characteristics of contours, such as area, central moment, and direction, and can be used for shape analysis, classification, and retrieval. By applying spatial transformations of invariant moments, features related to the relative positions and layouts of individual contours in a spring contour pixel set can be obtained. These features can be used to detect spatial relationships between springs, such as intersection and encirclement, aiding in further analysis and understanding of the spring contour pixel set.

[0085] Step S2040: The scanning matrix is ​​used to filter the set of pixels of each spring contour line after spatial transformation to obtain multiple sets of target pixels. Each set of target pixels corresponds to one spring, and each set of target pixels can form the straight line occupied by the corresponding spring.

[0086] In step 2040 above, when using a scanning matrix for target detection, it can be likened to a virtual magnifying glass, examining each pixel in the set of pixels along the spring contour line one by one, and filtering out target pixels that meet the requirements according to the set conditions. By continuously moving and scanning, a set of target pixels that meet the conditions is obtained.

[0087] Step S2041: Based on the multiple sets of the above-mentioned target pixels, the installation quality of the above-mentioned spring at the corresponding position of the above-mentioned buckle is detected.

[0088] To further and more accurately and simply inspect the installation quality of the spring at the corresponding snap-fit ​​position, step S2041 can also be implemented through the following steps: Using the least squares method, fit each set of target pixels to obtain multiple spring lines; determine whether the endpoints of the multiple spring lines are on the same straight line, where the endpoint of each spring line is the endpoint on the corresponding spring line furthest from the snap-fit ​​end; if the endpoints of the multiple spring lines are on the same straight line, determine that each spring is installed in the groove of the corresponding snap-fit; if the endpoints of the multiple spring lines are not on the same straight line, determine that there are springs that are not installed in the groove of the corresponding snap-fit. This achieves a relatively simple determination of whether the spring is installed in the groove of the corresponding snap-fit, whether it is without deformation, and whether it is securely installed.

[0089] In one specific embodiment, such as Figure 6 As shown, if the endpoints of springs A, B, C, D, and E are all on the same straight line L, it indicates that springs A, B, C, D, and E are all installed in the corresponding buckle grooves. If the endpoint of any spring is not on the same straight line L, it indicates that the corresponding spring is not installed in the corresponding buckle groove.

[0090] In practical applications, it is not limited to using linear fitting to determine whether the endpoints of each spring are on the same straight line, thereby detecting the installation quality of the spring at the corresponding buckle position. Any feasible method in the existing technology can be used to fit the endpoints to the same straight line or the same plane to detect the installation quality of the spring at the corresponding buckle position.

[0091] To enable those skilled in the art to better understand the technical solution of this application, the implementation process of the quality inspection method for the air conditioning control panel of this application will be described in detail below with reference to specific embodiments.

[0092] This embodiment relates to a specific method for quality testing of an air conditioner control panel, such as... Figure 7 As shown, it includes the following steps:

[0093] Step S1: Receive images, that is, receive the top view image and the front view image of the air conditioner control panel to be tested.

[0094] Step S2: Image processing to detect the number of springs on the air conditioner control panel to be inspected. Specifically:

[0095] The top-view image is converted to grayscale to obtain a grayscale-processed top-view image. Based on the local Otsu algorithm and the grayscale-processed top-view image, a segmentation threshold is determined. Using the segmentation threshold, the grayscale-processed top-view image is binarized to obtain a binarized top-view image.

[0096] Construct a grayscale histogram of the binarized top-view image; count the total number of pixels in the binarized top-view image corresponding to the target pixel value to obtain the target pixel count; based on the ratio of the target pixel count to the total number of pixels occupied by the outline of a spring, obtain the number of springs on the air conditioner control panel to be detected.

[0097] If the number of springs on the air conditioner control panel to be tested meets the preset value, it is determined that the air conditioner control panel to be tested has no missing springs. The preset value is the total number of springs installed on the air conditioner control panel to be tested. If the number of springs on the air conditioner control panel to be tested does not meet the preset value, it is determined that the air conditioner control panel to be tested has missing springs.

[0098] Step S3: Image processing to detect spring winding on the air conditioner control panel to be inspected. Specifically:

[0099] The front view image is converted to grayscale to obtain a grayscale front view image. Then, a median filter is used to smooth the grayscale front view image to obtain a smoothed front view image.

[0100] A threshold-based image segmentation method is used to segment the smoothed front view image to obtain a binarized front view image. An image matrix of the binarized front view image is constructed. Based on the image matrix, the vertical distance between any two adjacent parallel lines is determined to obtain multiple spring gap distances.

[0101] Determine the absolute values ​​of the differences between multiple spring gap distances and predetermined distances to obtain multiple absolute values ​​of spring gap distances; if one absolute value of a spring gap distance is not within the preset range, it is determined that a spring is wrapped around the control panel of the air conditioner to be tested.

[0102] Step S4: Image processing to inspect the snap-fit ​​installation quality of the air conditioner control panel to be inspected, specifically:

[0103] Hough transform is used to detect straight lines in the projected image of the binarized front view image, resulting in multiple sets of spring contour pixel points. Invariant moment spatial transformation is performed on each set of spring contour pixel points to obtain spatially transformed sets of spring contour pixel points. A scanning matrix is ​​used to filter the spatially transformed sets of spring contour pixel points to obtain multiple sets of target pixel points.

[0104] The least squares method is used to fit the set of target pixels to obtain multiple spring lines; it is then determined whether the endpoints of the multiple spring lines are on the same straight line; if the endpoints of the multiple spring lines are on the same straight line, it is determined that each spring is installed in the groove of the corresponding snap fastener; if the endpoints of the multiple spring lines are not on the same straight line, it is determined that there are springs that are not installed in the groove of the corresponding snap fastener.

[0105] This application also provides a quality inspection device for an air conditioner control panel. It should be noted that this quality inspection device can be used to execute the quality inspection method for an air conditioner control panel provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0106] The following describes the quality testing device for the air conditioning control panel provided in the embodiments of this application.

[0107] Figure 8 This is a structural schematic diagram of a quality inspection device for an air conditioner control panel according to an embodiment of this application. Figure 8 As shown, the quality inspection device includes:

[0108] The receiving unit 10 is used to receive a top view image and a front view image of the air conditioner control panel to be tested, wherein the air conditioner control panel to be tested is an air conditioner control panel in which springs are installed at the groove positions of each clip.

[0109] Specifically, at the manufacturing stage of the air conditioner control panel (i.e., the air conditioner display box), after installing each spring into the corresponding recessed position of the clip, it is generally necessary to inspect the installation quality of each spring to avoid affecting the normal use of the buttons after they are installed in the corresponding spring positions. Therefore, the air conditioner control panel to be tested in this application is an air conditioner control panel in which springs are installed in the recessed positions of each clip and no buttons are installed.

[0110] In one specific embodiment of this application, an image capturing device (e.g., a camera) can be installed at the assembly location of the control panel. The image capturing device is then used to capture images of the air conditioning control panel with the spring installed (i.e., the air conditioning control panel to be tested), thereby obtaining top-view and front-view images of the air conditioning control panel to be tested.

[0111] Processing unit 20 is used to perform binarization processing on the top view image and the front view image respectively to obtain the top view image and the front view image after binarization processing.

[0112] In the aforementioned processing unit, the received top-view image is binarized to obtain a binarized top-view image, and the received front-view image is binarized to obtain a binarized front-view image. By binarizing the top-view and front-view images separately, the corresponding images are simplified, thereby reducing image storage space and processing.

[0113] The detection unit 30 is used to detect the number of springs on the air conditioner control panel to be tested based on the binarized top view image, and to detect whether the springs on the air conditioner control panel to be tested are wound and the installation quality of the springs based at least on the binarized front view image.

[0114] In the aforementioned detection unit, the number of springs on the air conditioner control panel to be tested is detected based on the binarized top-view image. Since the top-view image is binarized at this time, this not only ensures that the computational workload for detecting the number of springs on the air conditioner control panel to be tested is small, but also ensures high overall detection efficiency.

[0115] In the aforementioned detection unit, at least based on the binarized front view image, the detection of whether the springs on the air conditioner control panel are wound and the quality of their installation are performed. Since the front view image is now a binarized image, this not only ensures a small computational load for detecting whether the springs on the air conditioner control panel are wound and the quality of their installation, but also ensures high overall detection efficiency.

[0116] In one specific embodiment of this application, the left view image and / or top view image of the air conditioner control panel to be tested, after binarization processing, can also be used to detect whether the spring on the air conditioner control panel to be tested is wound and the installation quality of the spring.

[0117] In the aforementioned air conditioner control panel quality inspection device, the processing unit performs binarization processing on the top view image of the air conditioner control panel to be inspected received by the receiving unit to obtain a binarized top view image. The inspection unit uses the binarized top view image to detect the number of springs on the air conditioner control panel to be inspected. The processing unit also performs binarization processing on the front view image of the air conditioner control panel to be inspected received by the receiving unit to obtain a binarized front view image. The inspection unit also uses the binarized front view image to detect whether the springs on the air conditioner control panel to be inspected are wound and the installation quality of the springs. Compared to existing technologies that rely on manual methods to detect missing, tangled, or improperly secured springs in their corresponding clip grooves, this solution binarizes the top and front view images of the air conditioner control panel. Based on the binarized top view image, it detects the number of springs on the control panel, and based on the binarized front view image, it detects whether the springs are tangled and the installation quality. This automates the inspection of spring installation quality on the control panel, such as detecting the number of springs, spring gaps, and spring end facets, thus solving the problem of low accuracy and efficiency in manual spring inspection of air conditioner control panels in existing technologies. Furthermore, this quality inspection method reduces worker workload, alleviates worker stress, and lowers operating costs, significantly saving time and labor costs during assembly, while also improving efficiency and productivity and ensuring a high product pass rate.

[0118] In one specific embodiment, after the air conditioning control panel of this application undergoes spring detection and button assembly, the finished air conditioning control panel can be used in a floor-standing air conditioner. Of course, it is not limited to floor-standing air conditioners; it can also be used in vehicle air conditioners, etc.

[0119] In the specific implementation process, the above-mentioned processing unit further includes a first processing module, a first determining module, and a first segmentation module. The first processing module is used to perform grayscale processing on the top-view image to obtain a grayscale-processed top-view image. The first determining module is used to determine a segmentation threshold based on the local Otsu algorithm and the grayscale-processed top-view image. The first segmentation module is used to perform binarization segmentation on the grayscale-processed top-view image using the segmentation threshold to obtain a binarized top-view image. In this embodiment, the local Otsu algorithm is used to determine the segmentation threshold for segmenting the grayscale-processed top-view image, ensuring that the determined segmentation threshold is relatively accurate. Therefore, based on the segmentation threshold, the grayscale-processed top-view image is binarized, resulting in a relatively accurate binarized top-view image. This further ensures that the subsequent detection of whether the spring on the air conditioner control panel is missing is relatively accurate.

[0120] Specifically, any suitable method in the prior art can be used to perform grayscale processing on the top view image. For example, the maximum value method, weighted average method, or average value method can be used to perform grayscale processing on the top view image to obtain the grayscale processed top view image.

[0121] In the above embodiments, the segmentation threshold is not limited to being determined by the local Otsu algorithm to segment the top view image. Threshold-based image segmentation methods, histogram thresholding, region growing, or image-based random field model methods can also be used.

[0122] The detection unit of this application further includes a first construction module, a statistics module, and a second determination module. The first construction module constructs a grayscale histogram of the binarized top-view image, where the horizontal axis of the grayscale histogram represents the pixel value of each pixel in the binarized top-view image, and the vertical axis represents the total number of pixels in the binarized top-view image corresponding to each pixel value. The statistics module counts the total number of pixels in the binarized top-view image corresponding to a target pixel value, obtaining the target pixel count, where the target pixel value is the pixel value of the pixels occupied by the outline of the spring. The second determination module determines the number of springs on the air conditioner control panel to be detected based on the ratio of the target pixel count to the total number of pixels occupied by the outline of one spring. In this embodiment, the number of target pixels corresponding to the target pixel value is counted by using a grayscale histogram. Then, based on the ratio of the number of target pixels to the total number of pixel values ​​occupied by the outline of a spring, the number of springs on the air conditioner control panel to be detected is determined. This achieves a relatively simple way to count the number of springs on the air conditioner control panel.

[0123] Specifically, a gray-level histogram is a function of the gray-level distribution, representing a statistical representation of the gray-level distribution in an image. It calculates the frequency of occurrence of each pixel in a digital image based on its gray-level value (pixel value). As a function of gray levels, a gray-level histogram can be used to represent the number of pixels in an image with a specific gray level, reflecting the frequency of that particular gray-level value (pixel value) in the image.

[0124] In practical applications, the binarized top-view image can be represented by the numbers 0 or 1. For example, the pixel value corresponding to the outline of the spring is 1, and the pixel value of other parts is 0. Therefore, the total number of pixels with a pixel value of 1 (i.e., the target pixel value) can be counted by using a grayscale histogram, which gives the target pixel count.

[0125] To more easily determine whether a spring is missing from an air conditioner control panel under test, the quality inspection device of this application further includes a first determining unit and a second determining unit. The first determining unit, after determining the number of springs on the air conditioner control panel under test based on the ratio of the target pixel count to the total number of pixels occupied by the outline of a spring, determines that the air conditioner control panel under test does not have any missing springs if the number of springs on the air conditioner control panel under test meets a preset value, where the preset value is the total number of springs installed on the air conditioner control panel under test. The second determining unit is used to determine that the air conditioner control panel under test has missing springs if the number of springs on the air conditioner control panel under test does not meet the preset value.

[0126] In one specific embodiment, such as Figure 3 As shown, under normal circumstances, an air conditioner control panel is equipped with five springs: spring A, spring B, spring C, spring D, and spring E. If the number of spring outlines on the air conditioner control panel under test is also five, it can be determined that the air conditioner control panel under test is not missing any springs. If the number of spring outlines on the air conditioner control panel under test is less than five, it can be determined that the air conditioner control panel under test is missing some springs.

[0127] In some embodiments, the processing unit further includes a second processing module and a second segmentation module. The second processing module performs grayscale processing on the front view image to obtain a grayscale-processed front view image, and then smooths the grayscale-processed front view image using a median filter to obtain a smoothed front view image. The second segmentation module performs image segmentation on the smoothed front view image using a threshold-based image segmentation method to obtain a binarized front view image. In this embodiment, a threshold-based image segmentation method is used to segment the smoothed front view image to obtain a binarized front view image. This achieves effective segmentation of the springs in the binarized front view image, thereby identifying the blank areas between the springs, i.e., spring gaps, further ensuring that subsequent detection of whether spring winding occurs on the air conditioner control panel can be made more accurately.

[0128] Specifically, any suitable method in the prior art can be used to perform grayscale processing on the front view image. For example, the maximum value method, weighted average method, or average value method can be used to perform grayscale processing on the front view image to obtain the grayscale processed front view image.

[0129] In practical applications, the median filter replaces each pixel with the median or "median pixel" within a rectangular neighborhood surrounding that pixel. Simple blurring through averaging is highly sensitive to noisy images, especially large, isolated outliers. Even a small number of points with significant deviations can severely impact the effectiveness of mean filtering. Median filtering can eliminate outliers by taking the median point. This results in less noise in the smoothed front view image. Furthermore, it ensures higher accuracy when using threshold-based image segmentation methods to segment the smoothed front view image, leading to a binarized front view image.

[0130] In the second segmentation module described above, the method is not limited to using a threshold-based image segmentation method to segment the smoothed front view image and obtain a binarized front view image. Histogram thresholding, region growing, or image-based random field model methods can also be used to segment the smoothed front view image and obtain a binarized front view image.

[0131] In practical applications, the spring gap is fixed when the spring has not deformed or undergone tensile deformation. If the spring becomes entangled or undergoes tensile deformation, the spring gap will increase or decrease. Therefore, in specific implementations, the spring gap can be used to determine whether the spring has become entangled or undergone tensile deformation. The aforementioned detection unit also includes a second construction module, a third determination module, a fourth determination module, and a fifth determination module. The second construction module is used to construct an image matrix of the binarized front view image; the third determination module is used to determine any two adjacent parallel lines based on the image matrix. Figure 4 The two parallel lines corresponding to L1 in the middle, or Figure 5 The perpendicular distance between the two parallel straight lines constituting L2 in the figure is used to obtain multiple spring gap distances (such as...). Figure 5 As shown in L2), the pixels occupied by any two adjacent parallel lines are pixels in the image matrix and on the same spring; that is, any two adjacent parallel lines are parallel lines on the same spring; the fourth determining module is used to determine the absolute value of the difference between the multiple spring gap distances and a predetermined distance, and obtain multiple absolute values ​​of spring gap distances, wherein the predetermined distance is the standard distance of the spring gap when the spring has not been wrapped or stretched; the fifth determining module is used to determine that the spring wrapping occurs on the air conditioner control panel to be detected when there is a case where the absolute value of one spring gap distance is not within the preset range.

[0132] Specifically, constructing the image matrix of the binarized front view image means converting the binarized front view image into a numerical form that can be processed by a computer, thereby facilitating the subsequent acquisition and calculation of information such as spring gaps in the binarized front view image through image processing, analysis, and calculation.

[0133] In one specific embodiment, such as Figure 5 As shown, this represents the predetermined distance (i.e., spring gap) L1, also known as the normal distance, when the spring has not undergone tensile deformation or spring winding. Figure 6 The figure shows the spring gap distance L2, also known as the abnormal distance, when the spring is entangled. If the spring is not entangled or stretched, the absolute value of the difference between the spring gap distance L2 and the predetermined distance L1 is within the preset range; if the spring is entangled or stretched, the absolute value of the difference between the spring gap distance L2 and the predetermined distance L1 will not be within the preset range. Therefore, this method can relatively easily determine whether the air conditioner control panel to be tested has spring entanglement.

[0134] In practical applications, if each spring is installed in the corresponding groove of the buckle, the other ends of each spring will be on the same straight line. Therefore, in some embodiments, the detection unit may further include a first detection module, a conversion module, a filtering module, and a second detection module. The first detection module uses Hough transform to perform straight line detection on the projected image of the binarized front view image, obtaining multiple sets of spring contour pixel points, each set corresponding to one spring. The conversion module performs a space transformation on each set of spring contour pixel points using invariant moments, obtaining spatially transformed sets of spring contour pixel points. The filtering module uses a scanning matrix to filter the spatially transformed sets of spring contour pixel points, obtaining multiple target pixel sets, each target pixel set corresponding to one spring, and each target pixel set can form the straight line occupied by the corresponding spring. The second detection module detects the installation quality of the spring at the corresponding buckle position based on the multiple target pixel sets.

[0135] In the above embodiments, the binarized front view image is projected to obtain a projected image of the binarized front view image. For example, the binarized front view image can be projected onto the same plane to obtain a projected image of the binarized front view image.

[0136] In the above embodiments, after obtaining multiple sets of spring contour pixel points, the least squares method can be used to perform initial fitting optimization on each set of spring contour pixel points, which further ensures that the target pixel point set selected subsequently is more accurate.

[0137] In the above embodiments, when using a scanning matrix for target detection, it can be likened to a virtual magnifying glass, examining each pixel in the set of pixels along the spring contour line one by one, and filtering out target pixels that meet the requirements according to set conditions. By continuously moving and scanning, a set of target pixels that meet the conditions is obtained.

[0138] In practical applications, applying spatial transformations of invariant moments can reduce the impact of image noise or shape deformation on invariant moment calculations, improving the accuracy of contour set processing. Contour set matching and recognition can also be performed by comparing differences between invariant moments. Since invariant moments remain unchanged after spatial transformation, shape matching and object recognition tasks can be effectively performed. Invariant moments provide a compact and descriptive way to represent the shape information of contour sets. These features can capture the geometric characteristics of contours, such as area, central moment, and direction, and can be used for shape analysis, classification, and retrieval. By applying spatial transformations of invariant moments, features related to the relative positions and layouts of individual contours in a spring contour pixel set can be obtained. These features can be used to detect spatial relationships between springs, such as intersection and encirclement, aiding in further analysis and understanding of the spring contour pixel set.

[0139] To further and more accurately and simply detect the installation quality of the springs at their corresponding snap-fit ​​positions, the second detection module includes a fitting submodule, a first determining submodule, a second determining submodule, and a third determining submodule. The fitting submodule uses the least squares method to fit the target pixel set to obtain multiple spring lines. The first determining submodule determines whether the endpoints of the multiple spring lines lie on the same straight line, with each spring line's endpoint being the endpoint on its corresponding spring line furthest from the snap-fit ​​end. The second determining submodule determines that if the endpoints of the multiple spring lines lie on the same straight line, each spring is installed in the corresponding snap-fit ​​groove. The third determining submodule determines that if the endpoints of the multiple spring lines do not lie on the same straight line, there are springs that are not installed in the corresponding snap-fit ​​groove. This achieves a relatively simple determination of whether the spring is installed in the corresponding snap-fit ​​groove, whether it is without deformation, and whether it is securely installed.

[0140] In one specific embodiment, such as Figure 6 As shown, if the endpoints of springs A, B, C, D, and E are all on the same straight line L, it indicates that springs A, B, C, D, and E are all installed in the corresponding buckle grooves. If the endpoint of any spring is not on the same straight line L, it indicates that the corresponding spring is not installed in the corresponding buckle groove.

[0141] In practical applications, it is not limited to using linear fitting to determine whether the endpoints of each spring are on the same straight line, thereby detecting the installation quality of the spring at the corresponding buckle position. Any feasible method in the existing technology can be used to fit the endpoints to the same straight line or the same plane to detect the installation quality of the spring at the corresponding buckle position.

[0142] The aforementioned quality detection device for the air conditioning control panel includes a processor and a memory. The receiving unit, processing unit, and detection unit are all stored as program units in the memory, and the processor executes these program units to achieve the corresponding functions. All of the above modules are located in the same processor; alternatively, the modules may be located in different processors in any combination.

[0143] The processor contains a kernel, which retrieves the corresponding program unit from memory. One or more kernels can be configured, and adjusting kernel parameters can address the low accuracy and efficiency issues of manual detection of springs on air conditioner control panels in existing technologies.

[0144] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0145] This invention provides a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the quality detection method for the air conditioning control panel.

[0146] Specifically, the quality testing methods for air conditioner control panels include:

[0147] This invention provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor is configured to execute the aforementioned quality detection method for an air conditioning control panel through the computer program.

[0148] This invention provides a device including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs at least the following steps:

[0149] Step S201: Receive the top view image and front view image of the air conditioner control panel to be tested. The air conditioner control panel to be tested is an air conditioner control panel with springs installed at the groove positions of each clip.

[0150] Step S202: Binarize the top view image and the front view image respectively to obtain the top view image and the front view image after binarization.

[0151] Step S203: Based on the binarized top view image, the number of springs on the air conditioner control panel to be tested is detected, and at least based on the binarized front view image, whether the springs on the air conditioner control panel to be tested are wound and the installation quality of the springs are detected.

[0152] The devices mentioned in this article can be servers, PCs, tablets, mobile phones, etc.

[0153] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having at least the following method steps:

[0154] Step S201: Receive the top view image and front view image of the air conditioner control panel to be tested. The air conditioner control panel to be tested is an air conditioner control panel with springs installed at the groove positions of each clip.

[0155] Step S202: Binarize the top view image and the front view image respectively to obtain the top view image and the front view image after binarization.

[0156] Step S203: Based on the binarized top view image, the number of springs on the air conditioner control panel to be tested is detected, and at least based on the binarized front view image, whether the springs on the air conditioner control panel to be tested are wound and the installation quality of the springs are detected.

[0157] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0158] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0159] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0160] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0161] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0162] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0163] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0164] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0165] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0166] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A quality inspection method for an air conditioner control panel, characterized in that, include: Receive top view image and front view image of the air conditioner control panel to be tested, wherein the air conditioner control panel to be tested is an air conditioner control panel with springs installed at the groove positions of each clip; The top view image and the front view image are binarized respectively to obtain the binarized top view image and the front view image; Based on the binarized top view image, the number of springs on the air conditioner control panel to be tested is detected, and at least based on the binarized front view image, whether the springs on the air conditioner control panel to be tested are wound and the installation quality of the springs are detected. Based at least on the binarized front view image, the detection of whether the spring on the air conditioner control panel to be detected is wound includes: Construct the image matrix of the front view image after binarization; Based on the image matrix, the vertical distance between any two adjacent parallel lines is determined to obtain multiple spring gap distances. The pixel points occupied by any two adjacent parallel lines are the pixel points in the image matrix and in the same spring. The absolute values ​​of the differences between the multiple spring gap distances and the predetermined distances are determined to obtain multiple absolute values ​​of spring gap distances. The predetermined distance is the standard distance of the spring gap when the spring has not been wrapped or stretched. If the absolute value of the spring gap distance is not within the preset range, it is determined that the spring is wrapped around the control panel of the air conditioner to be tested.

2. The quality inspection method according to claim 1, characterized in that, The top-view image is binarized to obtain the binarized top-view image, including: The top view image is converted to grayscale to obtain the grayscale-processed top view image; Based on the local Otsu algorithm and the grayscale processed top view image, the segmentation threshold is determined; Using the aforementioned segmentation threshold, the grayscale-processed top-view image is binarized to obtain the binarized top-view image.

3. The quality inspection method according to claim 1, characterized in that, Based on the binarized top-view image, the number of springs on the air conditioner control panel to be detected is determined, including: Construct a grayscale histogram of the top view image after binarization, wherein the horizontal axis of the grayscale histogram is the pixel value of the pixel in the top view image after binarization, and the vertical axis of the grayscale histogram is the total number of pixels in the top view image after binarization corresponding to each pixel value. The total number of pixels in the binarized top view image corresponding to the target pixel value is counted to obtain the target pixel value, where the target pixel value is the pixel value of the pixels occupied by the outline of the spring. The number of springs on the air conditioner control panel to be detected is obtained based on the ratio of the number of target pixels to the total number of pixels occupied by the outline of a spring.

4. The quality inspection method according to claim 3, characterized in that, After determining the number of springs on the air conditioner control panel to be inspected based on the ratio of the target pixel count to the total number of pixels occupied by the outline of one spring, the quality inspection method further includes: If the number of springs on the air conditioner control panel to be tested meets the preset value, it is determined that the air conditioner control panel to be tested has no missing springs. The preset value is the total number of springs installed on the air conditioner control panel to be tested. If the number of springs on the air conditioner control panel to be tested does not meet the preset value, it is determined that the air conditioner control panel to be tested is missing a spring.

5. The quality inspection method according to claim 1, characterized in that, The front view image is binarized to obtain the binarized front view image, including: The front view image is converted to grayscale to obtain the grayscale-processed front view image, and a median filter is used to smooth the grayscale-processed front view image to obtain the smoothed front view image. A threshold-based image segmentation method is used to segment the smoothed front view image to obtain the binarized front view image.

6. The quality inspection method according to any one of claims 1 to 4, characterized in that, Based at least on the binarized front view image, the installation quality of the spring on the air conditioner control panel to be inspected is detected, including: The Hough transform is used to perform line detection on the projected image of the binarized front view image to obtain multiple sets of spring contour pixel points, and one set of spring contour pixel points corresponds to one spring. A spatial transformation with invariant moments is performed on each set of spring contour pixel points to obtain the spatially transformed set of each set of spring contour pixel points. A scanning matrix is ​​used to filter the set of pixels of each spring contour line after spatial transformation to obtain multiple sets of target pixels. Each set of target pixels corresponds to one spring, and each set of target pixels can form the straight line occupied by the corresponding spring. Based on multiple sets of target pixels, the installation quality of the spring at the corresponding buckle position is detected.

7. The quality inspection method according to claim 6, characterized in that, Based on multiple sets of target pixels, the installation quality of the spring at the corresponding buckle position is detected, including: The least squares method is used to fit the target pixel set to obtain multiple spring lines; Determine whether the endpoints of the multiple spring lines are on the same straight line, wherein the endpoint of each spring line is the endpoint on the corresponding spring line that is furthest from the end of the latch; When the endpoints of multiple spring lines are on the same straight line, it is determined that each spring is installed into the groove of the corresponding snap fastener; If the endpoints of multiple spring lines are not on the same straight line, it is determined that there is a spring that is not installed into the groove of the corresponding snap fastener.

8. A quality inspection device for an air conditioner control panel, characterized in that, include: The receiving unit is used to receive a top view image and a front view image of the air conditioner control panel to be tested, wherein the air conditioner control panel to be tested is an air conditioner control panel with springs installed at the groove positions of each clip. The processing unit is used to perform binarization processing on the top view image and the front view image respectively, to obtain the top view image and the front view image after binarization processing; The detection unit is used to detect the number of springs on the air conditioner control panel to be detected based on the binarized top view image, and to detect whether the springs on the air conditioner control panel to be detected are wound and the installation quality of the springs based at least on the binarized front view image. The detection unit further includes a second construction module, a third determination module, a fourth determination module, and a fifth determination module. The second construction module is used to construct an image matrix of the front view image after binarization. The third determining module is used to determine the vertical distance between any two adjacent parallel lines based on the image matrix, thereby obtaining multiple spring gap distances. The pixels occupied by any two adjacent parallel lines are the pixels in the image matrix and within the same spring. The fourth determining module is used to determine the absolute value of the difference between the multiple spring gap distances and the predetermined distance, thereby obtaining multiple absolute values ​​of spring gap distances. The predetermined distance is the standard distance of the spring gap when the spring has not been wrapped or stretched. The fifth determining module is used to determine if the spring is wrapped around the control panel of the air conditioner to be tested when the absolute value of the spring gap distance is not within the preset range.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the quality inspection method according to any one of claims 1 to 7.

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