Ointment coating quality detection method, system and device

Abstract

The invention relates to the technical field of detection, and particularly discloses an ointment coating quality detection method, system and device, and the method comprises the steps: collecting the image data of a coating area in real time in the ointment coating process, and reading the real-time operation parameter data of a coating device; analyzing and processing the image data based on an edge detection method and an image segmentation method to obtain multi-dimensional coating features; analyzing defect positioning data of an ointment coating layer based on the multi-dimensional coating characteristics and the real-time operation parameter data of the smearing device; generating a coating quality detection report based on the defect positioning data of the ointment coating layer; according to the method, real-time and accurate detection and evaluation of the ointment coating quality are achieved, problems can be found in time, improvement measures can be taken, and the product quality and the coating process efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the field of detection technology, and in particular to an ointment coating quality detection method, system and device. Background Art

[0002] At present, ointment coating operations are widely used in the medical field. For example, during the treatment process, the ointment needs to be evenly coated on the patient's skin surface. Traditional ointment coating quality inspection methods mostly rely on manual visual inspection. Inspectors use their naked eyes to directly observe the coating surface to determine whether the ointment is evenly coated and whether there are defects such as missing coating. With the improvement of the degree of automation, the medical industry has begun to introduce machine vision technology for inspection, by collecting images of the coating area and using simple image processing algorithms to analyze the coating status. In addition, some inspection systems have begun to pay attention to the operating parameters of the coating device, such as coating speed, pressure, etc., and try to infer the coating quality by monitoring these parameters.

[0003] However, even with the introduction of machine vision technology, most existing methods perform inspections after coating is completed, and cannot detect problems in real time during the coating process. Once a coating quality problem occurs, the rework time of the entire coating process will increase, greatly reducing coating efficiency. Moreover, current machine vision-based inspection methods are mostly limited to simple analysis of images. At the same time, the correlation analysis between the real-time operating parameters of the coating device and the coating quality is not in-depth enough, and the operating parameters are not fully utilized to comprehensively evaluate the coating quality. Moreover, after discovering coating quality problems, existing inspection technologies are difficult to accurately locate the defective position. This makes it difficult to accurately carry out subsequent re-coating work, affecting the overall quality of the product and the efficiency of the coating process.

[0004] Therefore, the present invention provides an ointment coating quality detection method, system and device. Summary of the invention

[0005] The present invention provides an ointment coating quality detection method, system and device. The method overcomes the problem of insufficient real-time detection in the prior art by collecting image data of the coating area and real-time operating parameter data of the coating device in real time during the ointment coating process, and can timely discover coating quality problems during the coating process. The image data is analyzed and processed based on the edge detection method and the image segmentation method to obtain multi-dimensional coating features, which makes up for the single and incomplete defects of the existing analysis method and extracts the coating layer features more comprehensively and deeply. The defect location data of the ointment coating layer is analyzed based on the multi-dimensional coating features and the real-time operating parameter data of the coating device, and the operating parameters are fully utilized to comprehensively evaluate the coating quality, thereby achieving accurate defect location and facilitating subsequent precise repair and adjustment. A coating quality detection report is generated based on the defect location data of the ointment coating layer, which meets the demand for generating a comprehensive and detailed report, provides comprehensive and accurate information for medical staff, and helps to improve coating quality and coating efficiency.

[0006] The present invention provides an ointment coating quality detection method, comprising: S1: During the ointment coating process, real-time image data of the coating area is collected, and real-time operating parameter data of the coating device is read; S2: Analyze and process the image data based on edge detection method and image segmentation method to obtain multi-dimensional coating features; S3: Analyze the defect location data of the ointment coating layer based on the multi-dimensional coating characteristics and the real-time operating parameter data of the coating device; S4: Generate a coating quality inspection report based on the defect location data of the ointment coating layer.

[0007] Preferably, the ointment coating quality detection method, S1: real-time acquisition of image data of the coating area during the ointment coating process, and reading real-time operating parameter data of the coating device, including: Based on a high-resolution camera, real-time image data of the coating area is collected during the ointment coating process; The real-time ointment extrusion amount and the real-time application speed of the application device are read as the real-time operation parameter data of the application device.

[0008] Preferably, the ointment coating quality detection method, S2: analyzing and processing the image data based on the edge detection method and the image segmentation method to obtain multi-dimensional coating features, including: Analyze and process the image data based on edge detection method and image segmentation method to obtain the boundary morphological features of the target coating area and the boundary morphological features of the actual coating area; Analyzing the thickness distribution characteristics of the actual coating area based on the color change characteristics of the actual coating area contained in the image data; The multi-dimensional coating features include the boundary morphological features of the target coating area and the boundary morphological features and thickness distribution features of the actual coating area.

[0009] Preferably, the ointment coating quality detection method analyzes the thickness distribution characteristics of the actual coating area based on the color change characteristics of the actual coating area contained in the image data, including: Based on the boundary morphological features of the actual coated area contained in the image data, an actual coated area image is divided, and the actual coated area image is converted from the RGB color model to the CIELAB color space to obtain all color components of each pixel in the actual coated area image in the CIELAB color space; Calculate the color change of each pixel in the actual coated area image in the CIELAB color space based on all color components of each pixel in the actual coated area image in the CIELAB color space; Based on the color change of each pixel in the actual coating area image in the CIELAB color space, the thickness distribution characteristics of the actual coating area are analyzed.

[0010] Preferably, the ointment coating quality detection method analyzes the thickness distribution characteristics of the actual coating area based on the color change of each pixel in the actual coating area image in the CIELAB color space, including: The relationship model between the color change and absorbance of each pixel in the CIELAB color space in the actual coating area image is established as follows: ,in, is the color change of a single pixel in the CIELAB color space and is dimensionless. is the dimensionless proportionality coefficient, is the absorbance at the corresponding pixel in the actual coating area image; Based on the color change of each pixel in the actual coating area image in the CIELAB color space, the relationship model, and the extinction coefficient of the ointment, the thickness value of each pixel in the actual coating area image is calculated: , in, is the thickness value of each pixel in the actual coating area image and the unit is meter, is the extinction coefficient of the ointment and its unit is , is the concentration of the substance and its unit is ; The thickness distribution characteristics of the actual coating area are obtained based on the thickness values ​​of all pixels in the actual coating area image.

[0011] Preferably, the ointment coating quality detection method, S3: analyzing defect location data of the ointment coating layer based on multi-dimensional coating characteristics and real-time operating parameter data of the coating device, comprises: Determine all boundary pixel coordinates in the target coating area image based on the boundary morphological features of the target coating area in the multi-dimensional coating feature, and determine all boundary pixel coordinates in the actual coating area image based on the boundary morphological features of the actual coating area in the multi-dimensional coating feature; quantizing all boundary pixel coordinates in the target coating area image and all boundary pixel coordinates in the actual coating area image using Fourier descriptors to obtain all Fourier coefficients of discrete Fourier transform of all boundary pixel coordinates in the target coating area image and all Fourier coefficients of discrete Fourier transform of boundary pixel coordinates in the actual coating area image; A defect judgment function is constructed based on all Fourier coefficients of the discrete Fourier transform of all boundary pixel coordinates in the target coating area image, all Fourier coefficients of the discrete Fourier transform of the boundary pixel coordinates in the actual coating area image, and the thickness distribution characteristics in the multi-dimensional coating characteristics. : , In the formula, is the pixel coordinate in the actual coating area image, is the weight coefficient of the boundary morphological feature, is the total number of Fourier coefficients, is the discrete Fourier transform of all boundary pixel coordinates in the target coating area image. Fourier coefficients, is the discrete Fourier transform of the boundary pixel coordinates in the actual coating area image. Fourier coefficients, is the weight coefficient of thickness distribution characteristics, The coordinates of the actual coating area in the image are The set of pixels in the neighborhood of the pixel, The coordinates of the actual coating area in the image are The first pixel in the neighborhood of the pixel The thickness value at pixels, The coordinates of the actual coating area in the image are The mean thickness value of all pixels in the pixel set in the neighborhood of the pixel; Determine a defect judgment value at each pixel in the actual coating area image based on the defect judgment function; The defect location data of the ointment coating layer is analyzed based on the defect judgment value at each pixel in the actual coating area image and the real-time operation parameter data of the coating device.

[0012] Preferably, the ointment coating quality detection method analyzes the defect location data of the ointment coating layer based on the defect judgment value at each pixel in the actual coating area image and the real-time operation parameter data of the coating device, including: Analyze the defect possibility score value of each pixel in the actual coating area image based on the real-time operating parameter data of the coating device and the defect possibility assessment model; Calculating a final defect judgment value at each pixel in the actual coating area image based on the defect judgment value and the defect possibility score value at each pixel in the actual coating area image; The defect location data of the paste coating layer is obtained based on the final defect judgment values ​​at all pixels in the actual coating area image.

[0013] Preferably, the ointment coating quality detection method, S4: generating a coating quality detection report based on the defect location data of the ointment coating layer, comprises: generating suggested improved operating parameters of the coating device based on the defect location data of the paste coating layer; Based on the defect location data of the ointment coating layer, multiple defect parameters are analyzed, and a coating quality inspection report is generated based on all defect parameters and the recommended improved operating parameters of the coating device.

[0014] The present invention provides an ointment coating quality detection system, which is used to perform any of the above ointment coating quality detection methods, comprising: An image and operation parameter acquisition module is used to collect image data of the coating area in real time during the ointment coating process and read real-time operation parameter data of the coating device; A multi-dimensional coating feature acquisition module is used to analyze and process image data based on edge detection methods and image segmentation methods to obtain multi-dimensional coating features; A coating defect location module is used to analyze defect location data of the ointment coating layer based on multi-dimensional coating characteristics and real-time operating parameter data of the coating device; The coating quality detection output module is used to generate a coating quality detection report based on the defect location data of the ointment coating layer.

[0015] The present invention provides an ointment coating quality detection device, comprising: Processors and storage devices; The storage device is used to store instructions; When the processor executes the instruction, any one of the above ointment coating quality detection methods is implemented.

[0016] The beneficial effects of the present invention compared to the prior art are as follows: by collecting the image data of the coating area and the real-time operating parameter data of the coating device in real time during the ointment coating process, the problem of insufficient real-time detection in the prior art is overcome, and coating quality problems can be discovered in time during the coating process. The image data is analyzed and processed based on the edge detection method and the image segmentation method to obtain multi-dimensional coating features, which makes up for the single and incomplete defects of the existing analysis method and extracts the coating layer features more comprehensively and deeply. The defect location data of the ointment coating layer is analyzed based on the multi-dimensional coating features and the real-time operating parameter data of the coating device, and the operating parameters are fully utilized to comprehensively evaluate the coating quality, thereby achieving accurate defect location and facilitating subsequent precise repair and adjustment. A coating quality inspection report is generated based on the defect location data of the ointment coating layer, which meets the needs of generating a comprehensive and detailed report, provides comprehensive and accurate information for medical staff, and helps to improve coating quality and coating efficiency.

[0017] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures specifically pointed out in this application document.

[0018] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 is a flow chart of an ointment coating quality detection method in an embodiment of the present invention; Figure 2 Schematic diagram of an ointment coating quality detection system in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention. Example 1

[0021] refer to Figure 1 The present invention provides an ointment coating quality detection method, comprising: S1: During the ointment coating process, real-time image data of the coating area is collected, and real-time operating parameter data of the coating device is read; S2: Analyze and process the image data based on edge detection method and image segmentation method to obtain multi-dimensional coating features; S3: Analyze the defect location data of the ointment coating layer based on the multi-dimensional coating characteristics and the real-time operating parameter data of the coating device; S4: Generate a coating quality inspection report based on the defect location data of the ointment coating layer.

[0022] In this embodiment, the image data of the coating area refers to the image information of the coating area collected by a specific device (such as a high-resolution camera) during the ointment coating process, including visual features such as shape, color, and texture of the coating area.

[0023] In this embodiment, the application device is a device used to apply the ointment to a designated area, and has components for controlling the amount of ointment extruded, the application speed, and other functions.

[0024] In this embodiment, the defect location data of the ointment coating layer refers to relevant data for determining the specific location of the defect in the ointment coating layer by analyzing the coating characteristics and the operating parameters of the coating device.

[0025] In this embodiment, the coating quality inspection report is a document generated based on the inspection and analysis results of the ointment coating quality, which includes the operation status of the coating device, defect information of the coating layer, multiple defect parameters, improvement suggestions, etc., providing a reference for evaluating the coating quality and improving the coating process.

[0026] The beneficial effects of the above technology are as follows: by collecting the image data of the coating area and the real-time operating parameter data of the coating device in real time during the ointment coating process (S1), the problem of insufficient real-time detection in the existing technology is overcome, and coating quality problems can be discovered in time during the coating process. The image data is analyzed and processed based on the edge detection method and the image segmentation method to obtain multi-dimensional coating features (S2), which makes up for the single and incomplete defects of the existing analysis method and extracts the coating layer features more comprehensively and deeply. The defect location data of the ointment coating layer is analyzed based on the multi-dimensional coating features and the real-time operating parameter data of the coating device (S3), and the operating parameters are fully utilized to comprehensively evaluate the coating quality, achieving accurate defect location, and facilitating subsequent precise repair and adjustment. A coating quality inspection report (S4) is generated based on the defect location data of the ointment coating layer, which meets the needs of generating a comprehensive and detailed report, provides comprehensive and accurate information for medical staff, and helps to improve coating quality and coating efficiency. Example 2

[0027] Based on Example 1, the ointment coating quality detection method, S1: real-time acquisition of image data of the coating area during the ointment coating process, and reading real-time operating parameter data of the coating device, including: Based on a high-resolution camera, real-time image data of the coating area is collected during the ointment coating process; The real-time ointment extrusion amount and the real-time application speed of the application device are read as the real-time operation parameter data of the application device.

[0028] In this embodiment, the real-time ointment extrusion amount and the real-time application speed of the coating device refer to the amount of ointment extruded by the coating device at each moment and the speed of the application operation at each moment when the ointment coating is being performed.

[0029] The beneficial effects of the above technical solution are: using a high-resolution camera to collect image data in real time, it is possible to obtain clear and detailed images of the coating area and improve the accuracy of detection. Reading the two key operating parameter data of the real-time ointment extrusion volume and coating speed of the coating device provides an important reference for accurately analyzing the coating quality. The acquisition of high-resolution image data and key operating parameters makes the evaluation of coating quality more comprehensive and accurate. It helps to promptly discover problems in the coating process, such as quality defects caused by insufficient extrusion or improper coating speed. It can provide better quality and more valuable data support for the detection and analysis of ointment coating quality. Example 3

[0030] Based on Example 1, the ointment coating quality detection method, S2: analyzing and processing the image data based on the edge detection method and the image segmentation method to obtain multi-dimensional coating features, including: Analyze and process the image data based on edge detection method and image segmentation method to obtain the boundary morphological features of the target coating area and the boundary morphological features of the actual coating area; Analyzing the thickness distribution characteristics of the actual coating area based on the color change characteristics of the actual coating area contained in the image data; The multi-dimensional coating features include the boundary morphological features of the target coating area and the boundary morphological features and thickness distribution features of the actual coating area.

[0031] In this embodiment, the image data is analyzed and processed based on the edge detection method and the image segmentation method to obtain the boundary morphological features of the target coating area and the boundary morphological features of the actual coating area. This means using the technical means of edge detection and image segmentation to analyze the image data of the ointment coating, so as to obtain the boundary shape, line direction and other characteristics of the pre-set area to be coated (i.e., the target coating area), as well as the corresponding boundary morphological characteristics of the area actually coated (i.e., the actual coating area).

[0032] The beneficial effects of the above technical solution are: the boundary morphological features of the target coating area and the actual coating area are obtained by edge detection and image segmentation methods, which helps to accurately evaluate the accuracy and consistency of the coating. The color change characteristics of the actual coating area are analyzed to obtain the thickness distribution characteristics, which can more comprehensively understand the uniformity and quality of the coating layer. Comprehensive consideration of multi-dimensional coating characteristics such as boundary morphological characteristics and thickness distribution characteristics makes the evaluation of coating quality more comprehensive and in-depth. It helps to discover subtle defects and unevenness in the coating process and improve the control level of ointment coating quality. It can provide multi-dimensional and detailed feature information for the accurate evaluation of ointment coating quality. Example 4

[0033] On the basis of Example 3, the ointment coating quality detection method analyzes the thickness distribution characteristics of the actual coating area based on the color change characteristics of the actual coating area contained in the image data, including: Based on the boundary morphological features of the actual coated area contained in the image data, an actual coated area image is divided, and the actual coated area image is converted from the RGB color model to the CIELAB color space to obtain all color components of each pixel in the actual coated area image in the CIELAB color space; Calculate the color change of each pixel in the actual coated area image in the CIELAB color space based on all color components of each pixel in the actual coated area image in the CIELAB color space; Based on the color change of each pixel in the actual coating area image in the CIELAB color space, the thickness distribution characteristics of the actual coating area are analyzed.

[0034] In this embodiment, dividing the actual coating area image based on the boundary morphological features of the actual coating area contained in the image data means distinguishing this part of the area from the entire image separately according to the edge shape and other characteristics of the actual coating area presented in the image data to form a specific actual coating area image.

[0035] In this embodiment, the actual coated area image is converted from the RGB color model to the CIELAB color space, and all color components of each pixel in the actual coated area image in the CIELAB color space are obtained, that is, the image of the actual coated area is converted from the common RGB color model to the CIELAB color space, thereby obtaining all color components corresponding to each pixel point in this area image in the CIELAB color space.

[0036] First, convert the acquired image from the common RGB color model to a color model that is more suitable for analysis, such as the CIELAB color space. Convert the acquired RGB image to the CIELAB color space because the CIELAB color space is more consistent with human visual perception and has more advantages in analyzing color differences. In the CIELAB color space, color is represented by three components: Indicates brightness, Represents the color components from green to red, Represents the color components from blue to yellow; for the RGB color space (value range 0-255), the conversion steps are as follows: 1. First normalize the RGB values ​​to the 0-1 range, that is: , , .

[0037] 2. Perform gamma correction, if ,but ;like ,but , similarly and Perform similar operations to obtain and .

[0038] 3. Convert to XYZ color space: , , .

[0039] 4. Finally convert to CIELAB color space: , , , in, , , It is the tristimulus value under standard lighting.

[0040] In this embodiment, the color change amount of each pixel in the actual coated area image in the CIELAB color space is calculated based on all color components of each pixel in the actual coated area image in the CIELAB color space, which means that the color change value of each pixel in the CIELAB color space is further calculated based on all color component data of each pixel in the CIELAB color space. , , The value obtained by taking the square root of the sum of the squares is used as the numerical value of the color change of each pixel in this color space.

[0041] The beneficial effects of the above technical solution are: converting the actual coating area image from the RGB color model to the CIELAB color space can more accurately analyze the color change. Calculating the color change of each pixel in the CIELAB color space provides a quantitative data basis for analyzing the thickness distribution. Analyzing the thickness distribution characteristics based on the color change improves the scientificity and accuracy of the analysis. It can more accurately detect the changes in the coating thickness and discover potential quality problems. It provides a more accurate and reliable thickness distribution characteristic analysis method for the evaluation of the ointment coating quality.

[0042] Embodiment 5: On the basis of Example 4, the ointment coating quality detection method analyzes the thickness distribution characteristics of the actual coating area based on the color change of each pixel in the actual coating area image in the CIELAB color space, including: The relationship model between the color change and absorbance of each pixel in the CIELAB color space in the actual coating area image is established as follows: ,in, is the color change of a single pixel in the CIELAB color space and is dimensionless. is the dimensionless proportionality coefficient, is the absorbance at the corresponding pixel in the actual coating area image; based on the color change of each pixel in the actual coating area image in the CIELAB color space, the relationship model, and the extinction coefficient of the ointment, the thickness value of each pixel in the actual coating area image is calculated: ,in, is the thickness value of each pixel in the actual coating area image and the unit is meter, is the extinction coefficient of the ointment and its unit is , is the concentration of the substance and its unit is ; The thickness distribution characteristics of the actual coating area are obtained based on the thickness values ​​of all pixels in the actual coating area image.

[0043] In this embodiment, the absorbance at the corresponding pixel in the actual coating area image refers to a measure of the degree of absorption of light by a substance at a specific pixel position in the actual coating area image.

[0044] In this embodiment, the extinction coefficient of the ointment refers to a quantitative indicator of the ability of the ointment to absorb light of a specific wavelength, which indicates the proportion of light absorbed when passing through the ointment of unit concentration and unit thickness.

[0045] The beneficial effects of the above technical solution are: establishing a relationship model between color change and absorbance, providing a theoretical basis for the calculation of thickness values. By calculating the thickness value of each pixel through the model and related parameters, the coating thickness can be accurately quantified. The thickness distribution characteristics are obtained based on the thickness values ​​of all pixels, which can fully and accurately reflect the thickness of the actual coating area. It helps to more accurately evaluate the uniformity and quality of ointment coating and discover local thickness anomalies. It provides effective methods and data support for the fine analysis and evaluation of ointment coating quality.

[0046] Embodiment 6: Based on Example 1, the ointment coating quality detection method, S3: analyzing the defect location data of the ointment coating layer based on the multi-dimensional coating characteristics and the real-time operation parameter data of the coating device, including: Determine all boundary pixel coordinates in the target coating area image based on the boundary morphological features of the target coating area in the multi-dimensional coating feature, and determine all boundary pixel coordinates in the actual coating area image based on the boundary morphological features of the actual coating area in the multi-dimensional coating feature; quantizing all boundary pixel coordinates in the target coating area image and all boundary pixel coordinates in the actual coating area image using Fourier descriptors to obtain all Fourier coefficients of discrete Fourier transform of all boundary pixel coordinates in the target coating area image and all Fourier coefficients of discrete Fourier transform of boundary pixel coordinates in the actual coating area image; A defect judgment function is constructed based on all Fourier coefficients of the discrete Fourier transform of all boundary pixel coordinates in the target coating area image, all Fourier coefficients of the discrete Fourier transform of the boundary pixel coordinates in the actual coating area image, and the thickness distribution characteristics in the multi-dimensional coating characteristics. : , where is the pixel coordinate in the actual coating area image, is the weight coefficient of the boundary morphological feature, is the total number of Fourier coefficients, is the discrete Fourier transform of all boundary pixel coordinates in the target coating area image. Fourier coefficients, is the discrete Fourier transform of the boundary pixel coordinates in the actual coating area image. Fourier coefficients, is the weight coefficient of thickness distribution characteristics, The coordinates of the actual coating area in the image are The set of pixels in the neighborhood of the pixel, The coordinates of the actual coating area in the image are The first pixel in the neighborhood of the pixel The thickness value at pixels, The coordinates of the actual coating area in the image are The mean thickness value of all pixels in the pixel set in the neighborhood of the pixel, and and The sum is 1; Determine a defect judgment value at each pixel in the actual coating area image based on the defect judgment function; The defect location data of the ointment coating layer is analyzed based on the defect judgment value at each pixel in the actual coating area image and the real-time operation parameter data of the coating device.

[0047] In this embodiment, all boundary pixel coordinates in the target coating area image are determined based on the boundary morphological features of the target coating area in the multi-dimensional coating feature, and all boundary pixel coordinates in the actual coating area image are determined based on the boundary morphological features of the actual coating area in the multi-dimensional coating feature: this means that based on the boundary shape characteristics of the target coating area and the actual coating area in the multi-dimensional coating feature, the position coordinates of all pixel points constituting the boundary in the two area images are clarified.

[0048] In this embodiment, Fourier descriptors are used to quantize all boundary pixel coordinates in the target coating area image and all boundary pixel coordinates in the actual coating area image, and all Fourier coefficients of the discrete Fourier transform of all boundary pixel coordinates in the target coating area image and all Fourier coefficients of the discrete Fourier transform of the boundary pixel coordinates in the actual coating area image are obtained. The boundary pixel coordinates of the target coating area and the actual coating area are numerically processed using the mathematical tool of Fourier descriptors, thereby obtaining all Fourier coefficients corresponding to their discrete Fourier transforms.

[0049] The Fourier descriptor is used to quantify the boundary morphology between the target coating area and the actual coating area. For a boundary curve, its Fourier descriptor can be obtained by performing discrete Fourier transform on the coordinates of the boundary points. Assume that the coordinate sequence of the boundary points is , then the discrete Fourier transform is: ,in, It is Fourier coefficients, These coefficients constitute the boundary morphological feature vector, which is used to describe the shape of the boundary. The value of is 2.71828, The value of is 3.14.

[0050] In this embodiment, the weight coefficient of the boundary morphological feature is a value of relative importance given to the boundary morphological feature during the comprehensive analysis and calculation process.

[0051] In this embodiment, the weight coefficient of the thickness distribution feature refers to a value set for the thickness distribution feature to indicate its importance in relevant calculations and evaluations.

[0052] In this embodiment, the pixel set in the neighborhood of a pixel refers to a set consisting of other pixel points within a certain range around a specific pixel point.

[0053] The beneficial effects of the above technical solution are as follows: by determining the boundary pixel coordinates of the target and actual coating areas and performing Fourier descriptor quantification, accurate quantification and comparison of the boundary morphology are achieved. The constructed defect judgment function comprehensively considers the boundary morphology, Fourier coefficients and thickness distribution characteristics, making the defect judgment more comprehensive and accurate. By calculating the defect judgment value at each pixel, the quality status of the coating area can be carefully evaluated. Combined with the real-time operating parameter data of the coating device, the accuracy and reliability of the defect location data are further improved. The ability to accurately analyze and locate the defects of the ointment coating layer provides a targeted basis for improving the coating quality.

[0054] Embodiment 7: On the basis of Example 6, the ointment coating quality detection method analyzes the defect location data of the ointment coating layer based on the defect judgment value at each pixel in the actual coating area image and the real-time operation parameter data of the coating device, including: Analyze the defect possibility score value of each pixel in the actual coating area image based on the real-time operating parameter data of the coating device and the defect possibility assessment model; Calculating a final defect judgment value at each pixel in the actual coating area image based on the defect judgment value and the defect possibility score value at each pixel in the actual coating area image; The defect location data of the paste coating layer is obtained based on the final defect judgment values ​​at all pixels in the actual coating area image.

[0055] In this embodiment, the defect possibility assessment model is a mathematical model or algorithm for assessing the possibility of defects occurring at each pixel in the actual coating area based on input data (such as real-time operating parameters of the coating device, etc.).

[0056] In this embodiment, analyzing the defect possibility score value at each pixel in the actual coating area image based on the real-time operating parameter data of the coating device and the defect possibility assessment model means inputting the real-time operating parameter data of the coating device into the defect possibility assessment model, and calculating the model to obtain a quantitative score of the possibility of defects occurring at each pixel position in the actual coating area image.

[0057] In this embodiment, the final defect judgment value at each pixel in the actual coating area image is calculated based on the defect judgment value and the defect possibility score value at each pixel in the actual coating area image. This means that the preliminary defect judgment value of each pixel and the defect possibility score value obtained by the model are comprehensively considered, and the final defect judgment value of each pixel is obtained by a specific calculation method. For example, the average of the defect judgment value and the defect possibility score value at each pixel in the actual coating area image is used as the final defect judgment value at each pixel in the actual coating area image.

[0058] In this embodiment, the defect location data of the ointment coating layer is obtained based on the final defect judgment values ​​at all pixels in the actual coating area image, that is, by summarizing the final defect judgment values ​​of all pixels in the actual coating area image, thereby determining the specific location of the defect in the ointment coating layer and other related data.

[0059] The beneficial effects of the above technical solution are: using the defect possibility assessment model combined with the real-time operating parameter data of the coating device to obtain the defect possibility score value at each pixel, which increases the dimension and accuracy of the defect assessment. The final defect judgment value is calculated by the defect judgment value and the possibility score value, making the evaluation result more comprehensive and reliable. Obtaining defect location data based on the final defect judgment value of all pixels can more accurately determine the location of defects in the ointment coating layer. It helps to timely discover and locate tiny defects and improve the accuracy of ointment coating quality control. It further improves the detection and positioning method of ointment coating defects, providing strong support for coating quality assurance.

[0060] Embodiment 8: Based on Example 1, the ointment coating quality detection method, S4: generating a coating quality detection report based on the defect location data of the ointment coating layer, comprising: generating suggested improved operating parameters of the coating device based on the defect location data of the paste coating layer; Based on the defect location data of the ointment coating layer, multiple defect parameters are analyzed, and a coating quality inspection report is generated based on all defect parameters and the recommended improved operating parameters of the coating device.

[0061] In this embodiment, generating suggested improved operating parameters of the coating device based on the defect location data of the ointment coating layer means proposing suggestions for adjusting the operating parameters of the coating device that can improve the coating effect based on relevant data such as the location of defects in the ointment coating layer.

[0062] In this embodiment, the suggested improved operating parameters of the coating device refer to the optimized and adjusted parameter values ​​such as coating speed, ointment extrusion amount, etc. given for the coating device in order to improve the ointment coating quality.

[0063] In this embodiment, analyzing multiple defect parameters based on the defect location data of the paste coating layer means calculating and summarizing multiple quantitative indicators that can describe the degree, type, distribution, etc. of the defects based on the location information of the defects of the paste coating layer.

[0064] In this embodiment, generating a coating quality inspection report based on all defect parameters and the suggested improvement of the operating parameters of the coating device is to comprehensively consider the quantitative indicators of various defects and the suggestions for adjusting the operating parameters of the coating device to form a detailed inspection report on the ointment coating quality.

[0065] The beneficial effects of the above technical solution are: generating suggested improved operating parameters for the coating device based on the defect location data can provide direct guidance for optimizing the coating process. By analyzing the defect location data, multiple defect parameters are obtained, making the report content richer and more comprehensive. Generating a test report based on defect parameters and suggested improved operating parameters provides a detailed and targeted basis for coating process adjustment and quality improvement. It helps medical staff to quickly understand the key to coating quality problems and take effective improvement measures. It can generate a coating quality test report with practical value to promote the continuous optimization of the ointment coating process and the improvement of coating quality.

[0066] Embodiment 9: The present invention provides an ointment coating quality detection system for executing any one of the ointment coating quality detection methods in embodiments 1 to 8, referring to Figure 2 ,include: An image and operation parameter acquisition module is used to collect image data of the coating area in real time during the ointment coating process and read real-time operation parameter data of the coating device; A multi-dimensional coating feature acquisition module is used to analyze and process image data based on edge detection methods and image segmentation methods to obtain multi-dimensional coating features; A coating defect location module is used to analyze defect location data of the ointment coating layer based on multi-dimensional coating characteristics and real-time operating parameter data of the coating device; The coating quality detection output module is used to generate a coating quality detection report based on the defect location data of the ointment coating layer.

[0067] The beneficial effects of the above technology are as follows: by collecting the image data of the coating area and the real-time operating parameter data of the coating device in real time during the ointment coating process, the problem of insufficient real-time detection in the prior art is overcome, and coating quality problems can be discovered in time during the coating process. The image data is analyzed and processed based on the edge detection method and the image segmentation method to obtain multi-dimensional coating features, which makes up for the single and incomplete defects of the existing analysis method and extracts the coating layer features more comprehensively and deeply. The defect location data of the ointment coating layer is analyzed based on the multi-dimensional coating features and the real-time operating parameter data of the coating device, and the operating parameters are fully utilized to comprehensively evaluate the coating quality, achieving accurate defect location, and facilitating subsequent precise repair and adjustment. The coating quality inspection report is generated based on the defect location data of the ointment coating layer, which meets the needs of generating a comprehensive and detailed report, provides comprehensive and accurate information for medical staff, and helps to improve coating quality and coating efficiency.

[0068] Embodiment 10: The present invention provides an ointment coating quality detection device, comprising: Processors and storage devices; The storage device is used to store instructions; When the processor executes the instruction, any one of the above ointment coating quality detection methods is implemented.

[0069] The beneficial effects of the above technical solution are: through the cooperation of the processor and the storage device, the ointment coating quality detection method can be efficiently executed to ensure the timeliness and accuracy of the detection. The above series of accurate and comprehensive detection steps can be implemented to ensure the effective evaluation of the ointment coating quality. Real-time online detection is achieved to improve the coating efficiency and coating quality control level. The integrated design of the device reduces the compatibility issues between devices and improves the stability and reliability of the system. It provides an efficient, stable and easy-to-use solution for ointment coating quality detection.

[0070] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for detecting the quality of ointment coating, characterized in that: include: S1: During the ointment coating process, real-time image data of the coating area is collected, and real-time operating parameter data of the coating device is read; S2: Analyze and process the image data based on edge detection method and image segmentation method to obtain multi-dimensional coating features; S3: Analyze the defect location data of the ointment coating layer based on the multi-dimensional coating characteristics and the real-time operating parameter data of the coating device; S4: Generate a coating quality inspection report based on the defect location data of the ointment coating layer.

2. The ointment coating quality detection method according to claim 1, characterized in that: S1: During the ointment coating process, real-time image data of the coating area is collected and real-time operating parameter data of the coating device is read, including: Based on a high-resolution camera, real-time image data of the coating area is collected during the ointment coating process; The real-time ointment extrusion amount and the real-time application speed of the application device are read as the real-time operation parameter data of the application device.

3. The ointment coating quality detection method according to claim 1, characterized in that: S2: Analyze and process the image data based on edge detection method and image segmentation method to obtain multi-dimensional coating features, including: Analyze and process the image data based on edge detection method and image segmentation method to obtain the boundary morphological features of the target coating area and the boundary morphological features of the actual coating area; Analyzing the thickness distribution characteristics of the actual coating area based on the color change characteristics of the actual coating area contained in the image data; The multi-dimensional coating features include the boundary morphological features of the target coating area and the boundary morphological features and thickness distribution features of the actual coating area.

4. The ointment coating quality detection method according to claim 3, characterized in that: The thickness distribution characteristics of the actual coating area are analyzed based on the color change characteristics of the actual coating area contained in the image data, including: Based on the boundary morphological features of the actual coated area contained in the image data, an actual coated area image is divided, and the actual coated area image is converted from the RGB color model to the CIELAB color space to obtain all color components of each pixel in the actual coated area image in the CIELAB color space; Calculate the color change of each pixel in the actual coated area image in the CIELAB color space based on all color components of each pixel in the actual coated area image in the CIELAB color space; Based on the color change of each pixel in the actual coating area image in the CIELAB color space, the thickness distribution characteristics of the actual coating area are analyzed.

5. The ointment coating quality detection method according to claim 4, characterized in that: Based on the color change of each pixel in the actual coating area image in the CIELAB color space, the thickness distribution characteristics of the actual coating area are analyzed, including: The relationship model between the color change and absorbance of each pixel in the CIELAB color space in the actual coating area image is established as follows: ,in, is the color change of a single pixel in the CIELAB color space and is dimensionless. is the dimensionless proportionality coefficient, is the absorbance at the corresponding pixel in the actual coating area image; Based on the color change of each pixel in the actual coating area image in the CIELAB color space, the relationship model, and the extinction coefficient of the ointment, the thickness value of each pixel in the actual coating area image is calculated: ,in, is the thickness value of each pixel in the actual coating area image and the unit is meter, is the extinction coefficient of the ointment and its unit is , is the concentration of the substance and its unit is ; The thickness distribution characteristics of the actual coating area are obtained based on the thickness values ​​of all pixels in the actual coating area image.

6. The ointment coating quality detection method according to claim 1, characterized in that: S3: Analyze the defect location data of the ointment coating layer based on the multi-dimensional coating characteristics and the real-time operating parameter data of the coating device, including: Determine all boundary pixel coordinates in the target coating area image based on the boundary morphological features of the target coating area in the multi-dimensional coating feature, and determine all boundary pixel coordinates in the actual coating area image based on the boundary morphological features of the actual coating area in the multi-dimensional coating feature; quantizing all boundary pixel coordinates in the target coating area image and all boundary pixel coordinates in the actual coating area image using Fourier descriptors to obtain all Fourier coefficients of discrete Fourier transform of all boundary pixel coordinates in the target coating area image and all Fourier coefficients of discrete Fourier transform of boundary pixel coordinates in the actual coating area image; A defect judgment function is constructed based on all Fourier coefficients of the discrete Fourier transform of all boundary pixel coordinates in the target coating area image, all Fourier coefficients of the discrete Fourier transform of the boundary pixel coordinates in the actual coating area image, and the thickness distribution characteristics in the multi-dimensional coating characteristics. : , where is the pixel coordinate in the actual coating area image, is the weight coefficient of the boundary morphological feature, is the total number of Fourier coefficients, is the discrete Fourier transform of all boundary pixel coordinates in the target coating area image. Fourier coefficients, is the discrete Fourier transform of the boundary pixel coordinates in the actual coating area image. Fourier coefficients, is the weight coefficient of thickness distribution characteristics, The coordinates of the actual coating area in the image are The set of pixels in the neighborhood of the pixel, The coordinates of the actual coating area in the image are The first pixel in the neighborhood of the pixel The thickness value at pixels, The coordinates of the actual coating area in the image are The mean thickness value of all pixels in the pixel set in the neighborhood of the pixel; Determine a defect judgment value at each pixel in the actual coating area image based on the defect judgment function; The defect location data of the ointment coating layer is analyzed based on the defect judgment value at each pixel in the actual coating area image and the real-time operation parameter data of the coating device.

7. The ointment coating quality detection method according to claim 6, characterized in that: Based on the defect judgment value at each pixel in the actual coating area image and the real-time operating parameter data of the coating device, the defect location data of the ointment coating layer is analyzed, including: Analyze the defect possibility score value of each pixel in the actual coating area image based on the real-time operating parameter data of the coating device and the defect possibility assessment model; Calculating a final defect judgment value at each pixel in the actual coating area image based on the defect judgment value and the defect possibility score value at each pixel in the actual coating area image; The defect location data of the paste coating layer is obtained based on the final defect judgment values ​​at all pixels in the actual coating area image.

8. The ointment coating quality detection method according to claim 1, characterized in that: S4: Generate a coating quality inspection report based on the defect location data of the ointment coating layer, including: generating suggested improved operating parameters of the coating device based on the defect location data of the paste coating layer; Based on the defect location data of the ointment coating layer, multiple defect parameters are analyzed, and a coating quality inspection report is generated based on all defect parameters and the recommended improved operating parameters of the coating device.

9. An ointment coating quality detection system, characterized in that: Used to perform any one of the ointment coating quality detection methods described in claims 1 to 8, comprising: An image and operation parameter acquisition module is used to collect image data of the coating area in real time during the ointment coating process and read real-time operation parameter data of the coating device; A multi-dimensional coating feature acquisition module is used to analyze and process image data based on edge detection methods and image segmentation methods to obtain multi-dimensional coating features; A coating defect location module is used to analyze defect location data of the ointment coating layer based on multi-dimensional coating characteristics and real-time operating parameter data of the coating device; The coating quality detection output module is used to generate a coating quality detection report based on the defect location data of the ointment coating layer.

10. An ointment coating quality detection device, characterized in that: include: Processors and storage devices; The storage device is used to store instructions; When the processor executes the instructions, the ointment coating quality detection method as described in any one of claims 1 to 8 is implemented.

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