A method and system for detecting the wear degree of the grinding roller of a Raymond mill

Abstract

The present application relates to the field of image processing, and particularly to a method and system for detecting the wear degree of the grinding roller of a Raymond mill. The method includes the steps of: performing edge detection on the obtained grayscale image of the grinding roller to obtain the edges of multiple grinding rollers; calculating the defect features and the mean value of the defect features of the edges; calculating the Euclidean distance and the mean value of the Euclidean distances between the centroid of any edge and the centroid of its adjacent edge; calculating the absolute difference between each Euclidean distance and the mean value of the Euclidean distances to obtain the deviation of the edge, and performing negative correlation normalization on the mean value of all edge deviations to obtain the distribution regularity of the edges; calculating the wear degree of the grinding roller according to the mean value of the defect features and the distribution regularity, and the wear degree of the grinding roller is positively correlated with the mean value of the defect features and negatively correlated with the edge distribution regularity. The present application has the effect of improving the accuracy of wear degree detection.

Description

Technical Field

[0001] This application relates to the field of image processing, and particularly to a method and system for detecting the wear degree of the grinding rollers of a Raymond mill. Background Art

[0002] A Raymond mill is a new type of environmental protection grinding equipment that replaces the ball mill for processing ore powder. This equipment is suitable for grinding ore materials such as limestone, granite, quartzite, and marble. The grinding roller is the core grinding component of the Raymond mill and is usually made of high wear-resistant materials (such as high manganese steel and high chromium steel). It realizes revolution and rotation through a device suspended on the plum blossom frame of the main machine, and cooperates with the grinding ring to roll and crush the material. After the material enters the Raymond mill from the feed inlet, the grinding roller clings to the surface of the grinding ring under the action of centrifugal force. At the same time, the scraper sends the material between the grinding roller and the grinding ring. The rotational movement (revolution and rotation) of the grinding roller generates a rolling pressure to crush the material.

[0003] If the grinding roller is worn, the contact area between the grinding roller and the grinding ring decreases, resulting in a decline in the grinding capacity. At the same time, the fineness distribution of the powder particles becomes wider and the quality significantly decreases. Therefore, it is necessary to detect the wear degree of the grinding roller. For example, the application document with the publication number CN117540331A discloses a monitoring and analysis system for the wear degree of the grinding roller of a roller medium-speed coal mill. A static image of the grinding roller of the roller medium-speed coal mill is obtained through a camera and recorded as the grinding roller information image. The edge detection algorithm is used to detect the crack edge contour in the grinding roller information image, the crack part in the grinding roller information image is segmented according to the crack edge contour, and the characteristics of the segmented crack part are extracted. The prior art provides a method for analyzing the wear degree through the edge detection algorithm. The change in the edge strength can reflect the wear degree of the grinding roller surface, and the edge strength in the severely worn area will decrease. Or compare the edge positions of the new and old grinding rollers. Wear will cause the edge position to contract inward.

[0004] Traditional edge detection algorithms can only identify the edges of the grinding rollers. When manufacturing the grinding rollers of a Raymond mill, in order to fully grind the ore, sandblasting and sand pressing treatments are carried out on the surface of the grinding rollers to increase the friction force. However, there are also edges on the sand grains on the surface of the grinding rollers. Traditional edge detection algorithms cannot effectively identify the edges of the sand grains and the worn edges, resulting in inaccurate wear degree detection results. Summary of the Invention

[0005] In order to solve the technical problem of low accuracy in detecting the wear degree, this application provides a method and system for detecting the wear degree of the grinding rollers of a Raymond mill.

[0006] In the first aspect, this application provides a method for detecting the wear degree of the grinding rollers of a Raymond mill, adopting the following technical solution:

[0007] A method for detecting the wear of a grinding roller of a Raymond mill comprises the following steps: performing edge detection on a grayscale image of a grinding roller to obtain edges of multiple grinding rollers; calculating defect features of the edges and defect feature means; wherein the calculation of defect features comprises: calculating the similarity between any edge and other edges and the similarity mean; calculating the standard deviation of the similarity and the similarity mean; performing negative correlation normalization on the ratio of the similarity mean to the standard deviation to obtain the defect features of the edges; calculating the Euclidean distance and the mean of the Euclidean distance between the centroid of any edge and the centroid of its adjacent edges; calculating the absolute difference between each Euclidean distance and the mean of the Euclidean distance to obtain the deviation of the edge, performing negative correlation normalization on the means of all edge deviations to obtain the distribution regularity of the edge; and calculating the wear degree of the grinding roller according to the defect feature mean and the distribution regularity, wherein the wear degree of the grinding roller is positively correlated with the defect feature mean and negatively correlated with the edge distribution regularity.

[0008] For the grinding roller, the surface texture is regular and rough before wear, and irregular and smooth after wear. By analyzing the edge feature changes before and after wear of the grinding roller, calculating the mean value of the defect feature and the regularity of the edge distribution of the grinding roller, and combining these two factors to quantify the wear degree of the grinding roller, the actual wear condition of the grinding roller can be more accurately evaluated to avoid equipment failures caused by misjudgment or omission. At the same time, this method can provide a scientific basis for the maintenance and replacement of the grinding roller, help to reasonably arrange maintenance plans, and improve production efficiency.

[0009] Optionally, the expression for the degree of roller wear is: ; In the formula, Indicates the degree of roller wear; Indicates Defect characteristics of the edge; represents the total number of edges; Indicates the regularity of the distribution of all edges; Indicates An exponential function with base .

[0010] Provides a way to quantify the degree of roller wear. The exponential function maps the value of roller wear to In the range of The larger the size (i.e. the more defects), the more regular the distribution The smaller the value (i.e. the more irregular the distribution), the greater the wear degree, and vice versa. The curve obtained by this method is smoother and more sensitive to initial wear.

[0011] Optionally, the expression for the degree of roller wear is: ; In the formula, Indicates the degree of roller wear; Indicates Defect characteristics of the edge; represents the total number of edges; Represents the distribution regularity of all edges; Represents the hyperbolic tangent function.

[0012] Provides another way to quantify the wear degree of the grinding roller, which is more non-linear and can better reflect the result of increased wear in the later stage.

[0013] Optionally, the similarity calculation method is as follows: Calculate the distance between each pixel point on the edge and the centroid of the edge and the average distance, and use the standard deviation of the distance and the average distance as the distance change between all pixel points on the edge and the centroid; Use the normalized result of the difference in distance changes between any two edges as the similarity between the two edges, and the difference in distance changes is negatively correlated with the similarity.

[0014] Perform edge detection on the surface of the grinding roller. If the grinding roller has no wear or little wear, the edges on its surface will be highly similar; otherwise, the similarity of the edges will be low.

[0015] Optionally, the similarity expression is: ; In the formula, Represents the th edge and the th edge similarity; Represents the th edge all pixel points and the th edge centroid distance change difference, Represents the th edge all pixel points and the th edge centroid distance change difference, Represents function.

[0016] Provides a similarity calculation method. When is larger, that is, the difference in distance changes between the two edges is significantly different, the output is closer to 0, indicating that the similarity of the two edges is low; when is smaller, that is, the difference in distance changes between the two edges is very close, the output is closer to 0.5, indicating that the similarity of the two edges is high.

[0017] Optionally, the similarity expression is: ; In the formula, Represents the th edge and the th edge similarity; Represents the th edge all pixel points and the th edge centroid distance change difference, Represents the th edge all pixel points and the The distance change difference between the centroids of the strip edges.

[0018] Another method for calculating similarity is provided. When is larger, that is, the distance change differences between the two edges are significantly different, the output is closer to 0, indicating that the similarity of these two edges is lower; when is smaller, that is, the distance change differences between the two edges are very close, the output is closer to 1, indicating that the similarity of these two edges is higher.

[0019] Optionally, the calculation formula for the distribution regularity is: ; In the formula, represents the distribution regularity of all edges; represents the total number of edges; represents the total number of adjacent edges; represents the th edge and the th adjacent edge, the Euclidean distance between the centroids; represents the th edge and the mean Euclidean distance of all adjacent edges; represents the exponential function with as the base.

[0020] represents the mean Euclidean distance between the centroids of all edges and their adjacent edges. The smaller its value, the more uniform the distribution, that is, the more regular the distribution of all edges. At this time, the distribution regularity is larger. On the contrary, the distribution regularity is smaller.

[0021] Optionally, the method for determining adjacent edges is: calculate the Euclidean distance between any edge and the centroids of other edges and construct a Euclidean distance sequence in ascending order, and take the edges corresponding to the first Euclidean distances in the Euclidean distance sequence as the adjacent edges of any edge;

[0022] Or calculate the Euclidean distance between any edge and the centroids of other edges and construct a Euclidean distance sequence in descending order, and take the edges corresponding to the last Euclidean distances in the Euclidean distance sequence as the adjacent edges of any edge.

[0023] Optionally, the canny edge detection algorithm is used for edge detection on the obtained grayscale image of the grinding roller to obtain the edges of multiple grinding rollers.

[0024] In a second aspect, the present application provides a Raymond mill grinding roller wear degree detection system, adopting the following technical solutions:

[0025] A Raymond mill roller wear detection system comprises: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the Raymond mill roller wear detection method described above is implemented.

[0026] The above-mentioned Raymond mill roller wear detection method is generated into a computer program and stored in a memory so as to be loaded and executed by a processor. Thus, a system is made based on the memory and the processor for easy use.

[0027] This application has the following technical effects:

[0028] For the grinding roller, the surface texture is regular and rough before wear, and irregular and smooth after wear. By analyzing the edge feature changes before and after wear of the grinding roller, calculating the mean value of the defect feature and the regularity of the edge distribution of the grinding roller, and combining these two factors to quantify the wear degree of the grinding roller, the actual wear condition of the grinding roller can be more accurately evaluated to avoid equipment failures caused by misjudgment or omission. At the same time, this method can provide a scientific basis for the maintenance and replacement of the grinding roller, help to reasonably arrange maintenance plans, and improve production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a flow chart of a method for detecting wear of a Raymond mill roller according to an embodiment of the present application.

[0030] Figure 2 This is a method flow chart of step S2 of a method for detecting wear of a Raymond mill roller in an embodiment of the present application. DETAILED DESCRIPTION

[0031] The present application discloses a method for detecting the wear of a Raymond mill roller. Figure 1 , including steps S1 to S4, which are specifically as follows:

[0032] S1: Perform edge detection on the acquired grayscale image of the grinding roller to obtain edges of multiple grinding rollers.

[0033] The video stream of the idling grinding roller is collected by a camera, and multiple frames of RGB (Red, Green, Blue) images are obtained by extracting the video stream. The grinding roller area in the RGB image is extracted and converted into a grayscale image. The Canny operator is used to perform edge detection on the grayscale image to obtain the edge detection result.

[0034] Collect a video stream containing the roller area from the operation port of the Raymond mill housing when the roller rotates idly (rotates without processing materials). The frame rate is 30 fps (frames per second) - 60 fps, and an appropriate frame rate can be selected according to the actual situation. If the roller rotates relatively fast, a higher frame rate is required to ensure clear images of the roller at different positions can be captured, such as 60 fps.

[0035] Determine the frame extraction frequency according to the characteristics of the roller's idle rotation and the frame rate of the video. If the movement of the roller changes relatively slowly, the frame extraction frequency can be appropriately reduced. For example, for a 30-fps video, one frame can be extracted every 3 frames, so an image sequence of approximately 10 frames per second can be obtained, which can basically reflect the movement of the roller. If the movement of the roller changes relatively fast, a higher frame extraction frequency is required. For example, for a 60-fps video, one frame can be extracted every 2 frames to obtain an image sequence of 30 frames per second, which can more accurately capture the movement details of the roller.

[0036] The method for extracting the roller area in the RGB image can be: a color segmentation method or an object detection method based on deep learning. The color segmentation method is: the color segmentation method can be used to extract the roller area. The roller is black and the background is white. The roller area can be extracted by setting color thresholds. In the RGB color space, threshold segmentation is performed on the pixel values of the R, G, and B channels respectively. For example, set the threshold of the R channel to be less than a certain value (such as 50), and set the thresholds of the G and B channels similarly, so a binary image can be obtained, with the roller area being white and the background being black. Then, through image morphological operations (such as dilation, erosion, etc.), noise is removed and holes in the roller area are filled to obtain the complete roller area. The prior art will not be elaborated further.

[0037] Object detection method based on deep learning: For example, use the YOLO (You Only Look Once) algorithm. First, a large number of roller images need to be collected as training data, and these images are labeled to mark the position and category of the roller. Then, these labeled images are used to train the YOLO model. After training, the frame-extracted images are input into the trained model, and the model can automatically detect the position of the roller and extract the roller area. This method has good adaptability to situations where the roller shape changes greatly and the background is complex, but requires a large amount of training data and computing resources. The prior art will not be elaborated further.

[0038] After extracting the roller area, convert the RGB image into a grayscale image, perform edge detection on the grayscale image using the Canny edge detection algorithm to obtain the edge detection result, and the edge detection result is the edges of multiple rollers; the conversion of the edge detection and the grayscale image to the prior art will not be elaborated further.

[0039] S2: Calculate the defect features and the mean value of defect features of the edge.

[0040] Refer to Figure 2 , the calculation of defect features includes steps S20 - S21, specifically as follows:

[0041] S20: Calculate the similarity between any edge and other edges and the mean value of similarity.

[0042] When crushing ore, since the ore grinding relies on the friction between the grinding roller and the material to achieve crushing, therefore, the surface of the grinding roller of the Raymond mill will be subjected to controllable roughening treatment by sandblasting and sand pressing. When sandblasting or sand pressing the grinding roller, the process parameters will be strictly controlled to make the texture distribution on the surface of the grinding roller uniform. If the sandblasting pressure is uneven, the sand grains will be randomly distributed, resulting in uneven surface roughness distribution of the grinding roller, reducing the friction stability, and causing the material to slip or accumulate in some areas. Therefore, edge detection is performed on the surface of the grinding roller. If the grinding roller has no wear or less wear, the edges on its surface will be highly similar. On the contrary, the similarity of the edges is low.

[0043] The calculation method of similarity is: calculate the distance between each pixel point on the edge and the centroid of the edge and the mean value of the distance, and take the standard deviation of the distance and the mean value of the distance as the distance change between all pixel points on the edge and the centroid; take the normalized result of the difference in distance change between any two edges as the similarity between the two edges, and the difference in distance change is negatively correlated with the similarity.

[0044] Specifically, for any edge, take the mean value of the index positions of all pixel points of the edge (the mean value of the abscissa, the mean value of the ordinate) as the centroid of the edge, and obtain the Euclidean distance between any pixel point of the edge and the centroid.

[0045] Calculate the difference in distance change between pixel points and the centroid in any two edges. Specifically, the mathematical formula for the difference in distance change is: ; in the formula, represents the distance change situation between all pixel points and the centroid in the th edge; represents the total number of pixel points in the th edge; represents the pixel point ordinal number in the th edge; represents the Euclidean distance between the th pixel point in the th edge and the centroid of this edge; represents the mean value of the Euclidean distances between all pixel points and the centroid of the th edge.

[0046] The expression for similarity is as follows: ; where represents the similarity between the th edge and the th edge; represents the difference in the distance variation between all the pixel points in the th edge and the centroid of the th edge, represents the difference in the distance variation between all the pixel points in the th edge and the centroid of the th edge, represents function.

[0047] When is larger, that is, the difference in the distance variation between the two edges is significantly different, the output is closer to 0, indicating that the similarity between these two edges is lower; when is smaller, that is, the difference in the distance variation between the two edges is very close, the output is closer to 0.5, indicating that the similarity between these two edges is higher.

[0048] The expression for similarity can also be: ; where represents the similarity between the th edge and the th edge; represents the difference in the distance variation between all the pixel points in the th edge and the centroid of the th edge, represents the difference in the distance variation between all the pixel points in the th edge and the centroid of the th edge.

[0049] When is larger, that is, the difference in the distance variation between the two edges is significantly different, the output is closer to 0, indicating that the similarity between these two edges is lower; when is smaller, that is, the difference in the distance variation between the two edges is very close, the output is closer to 1, indicating that the similarity between these two edges is higher.

[0050] S21: Calculate the standard deviation of the similarity and the mean similarity; perform negative correlation normalization on the ratio of the mean similarity to the standard deviation to obtain the defect feature of the edge.

[0051] The calculation of the defect feature can be expressed by the following mathematical formula:

[0052] ; where represents the defect feature of the th edge; represents the total number of edges; represents the edge ordinal number, ; Indicates Edge and similarity of strip edges; Indicates The mean similarity between an edge and all other edges.

[0053] in, It represents the mean similarity between this edge and other edges. The larger the value, the more edges are similar to this edge. Usually, the defective edge is partially detached or worn, that is, the similarity between the defective edge and most edges is low. Therefore, the larger the value, the smaller the defect feature of the edge.

[0054] is the standard deviation of the similarity and the mean of similarity, indicating the difference in similarity between the edge and other edges. The smaller the value, the smaller the difference, which means that the similarity difference between the edge and other edges is more stable.

[0055] S3: Calculate the Euclidean distance and the mean of the Euclidean distances between the centroid of any edge and the centroid of its adjacent edges; calculate the absolute difference between each Euclidean distance and the mean of the Euclidean distance to obtain the edge deviation, and negatively normalize the means of all edge deviations to obtain the distribution regularity of the edge.

[0056] The formula for calculating the distribution regularity is: ; In the formula, Indicates the regularity of the distribution of all edges; represents the total number of edges; represents the total number of adjacent edges; Indicates Edge and The Euclidean distance between the centroids of adjacent edges; Indicates The mean Euclidean distance between an edge and all adjacent edges; Indicates An exponential function with base .

[0057] The method for determining the adjacent edges is to calculate the Euclidean distance between any edge and the centroid of other edges and construct a Euclidean distance sequence in ascending order. The edges corresponding to the Euclidean distance are taken as the neighboring edges of any edge; for example, .

[0058] Or calculate the Euclidean distance between any edge and the centroid of other edges and construct a Euclidean distance sequence in descending order. The edge corresponding to the Euclidean distance is used as the adjacent edge of any edge.

[0059] It represents the average value of the Euclidean distances between all edges and the centroid of their adjacent edges. The smaller its value, the more uniform the distribution, that is, the more regular the distribution of all edges. At this time, the distribution regularity is greater. On the contrary, the distribution regularity is smaller.

[0060] S4: Calculate the wear degree of the grinding roller according to the defect feature mean value and the distribution regularity. The wear degree of the grinding roller is positively correlated with the defect feature mean value and negatively correlated with the edge distribution regularity.

[0061] In one embodiment, the expression of the wear degree of the grinding roller can be:

[0062] ; In the formula, represents the wear degree of the grinding roller; represents the th defect feature of the edge; represents the total number of edges; represents the distribution regularity of all edges; represents the exponential function with the base of

[0063] The exponential function maps the value of the wear degree of the grinding roller to the range of . When the defect feature mean value is larger (i.e., more defects) and the distribution regularity is smaller (i.e., the distribution is more irregular), the wear degree is larger. On the contrary, the wear degree is smaller. The curve obtained in this embodiment is smoother and more sensitive to initial wear.

[0064] In one embodiment, the expression of the wear degree of the grinding roller can also be:

[0065] ; In the formula, represents the wear degree of the grinding roller; represents the th defect feature of the edge; represents the total number of edges; represents the distribution regularity of all edges; represents the hyperbolic tangent function. This embodiment has stronger nonlinearity and can better reflect the result of increased wear in the later stage. The calculation formula of the wear degree of the grinding roller can be selected according to the actual application scenario.

[0066] The embodiment of the present application also discloses a Raymond mill grinding roller wear degree detection system, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the Raymond mill grinding roller wear degree detection method according to the present application is implemented.

[0067] The above system further includes other components well-known to those skilled in the art, such as a communication bus and a communication interface, and their settings and functions are known in the art, so they will not be described herein again.

[0068] In this application, the aforementioned memory can be any tangible medium that contains or stores a program, and this program can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory, a dynamic random access memory, a static random access memory, etc., or any other medium that can be used to store the required information and can be accessed by an application program, a module, or both. Any such computer storage medium can be part of the device or accessible or connectable to the device.

[0069] The above are all the preferred embodiments of this application, and do not limit the protection scope of this application accordingly. Therefore, any equivalent changes made according to the structure, shape, and principle of this application shall be covered within the protection scope of this application.

Claims

1. A method for detecting the wear degree of the grinding roller of a Raymond mill, characterized in that, Including the steps: Performing edge detection on the obtained grayscale image of the grinding roller to obtain the edges of multiple grinding rollers; Calculate the defect features and the mean value of defect features of the edges; among which, the calculation of the defect features includes: calculating the similarity between any edge and other edges and the mean value of the similarity; calculating the standard deviation of the similarity and the mean value of the similarity; performing negative correlation normalization on the ratio of the mean value of the similarity to the standard deviation to obtain the defect features of the edge; the calculation formula of the defect features is: , represents the defect feature of the -th edge, represents the total number of edges; represents the edge ordinal number, ; represents the -th edge and the -th edge similarity, represents the similarity mean value of the -th edge and all other edges; Calculate the Euclidean distance and the average Euclidean distance between the centroid of any edge and the centroid of its adjacent edges; calculate the absolute difference between each Euclidean distance and the average Euclidean distance to obtain the edge deviation, and perform negative correlation normalization on the average of all edge deviations to obtain the distribution regularity of the edges. The calculation formula is: , represents the distribution regularity of all edges, represents the total number of adjacent edges, represents the th edge and the th adjacent edge's Euclidean distance between the centroids, represents the th edge's average Euclidean distance from all adjacent edges, represents the exponential function with as the base; Calculating the wear degree of the grinding roller according to the defect feature mean value and the distribution regularity, where the wear degree of the grinding roller is positively correlated with the defect feature mean value and negatively correlated with the edge distribution regularity.

2. The method for detecting the wear degree of the roller of a Raymond mill according to claim 1, characterized in that, The expression for the wear degree of the grinding roll is: ; In the formula, represents the wear degree of the grinding roll; represents the defect feature of the th edge; represents the total number of edges; represents the distribution regularity of all edges; represents the exponential function with as the base.

3. The method for detecting the wear degree of the roller of a Raymond mill according to claim 1, characterized in that, The expression for the wear degree of the grinding roll is: ; In the formula, represents the wear degree of the grinding roll; represents the defect feature of the th edge; represents the total number of edges; represents the distribution regularity of all edges; represents the hyperbolic tangent function.

4. The method for detecting the wear degree of the roller of a Raymond mill according to claim 1, wherein The calculation method of similarity is as follows: Calculating the distance between each pixel point on the edge and the edge centroid and the mean value of the distances, and taking the standard deviation of the distance and the mean value of the distances as the distance change between all pixel points on the edge and the centroid; Taking the normalized result of the difference in distance changes between any two edges as the similarity between the two edges, and the difference in distance changes is negatively correlated with the similarity.

5. The method for detecting the wear degree of the roller of a Raymond mill according to claim 4, wherein, The expression for similarity is as follows: ; in the formula, represents the similarity between the -th edge and the -th edge; represents the difference in the distance changes between all the pixel points in the -th edge and the centroid of the -th edge, represents the difference in the distance changes between all the pixel points in the -th edge and the centroid of the -th edge, represents function.

6. The method for detecting the wear degree of the roller of a Raymond mill according to claim 4, characterized in that, The expression for similarity is as follows: ; where represents the similarity between the th edge and the th edge; represents the difference in the distance changes between all the pixel points in the th edge and the centroid of the th edge, represents the difference in the distance changes between all the pixel points in the th edge and the centroid of the th edge.

7. The method for detecting the wear degree of the roller of a Raymond mill according to claim 1, characterized in that, Wherein, The method for determining adjacent edges is as follows: calculate the Euclidean distance between any edge and the centroid of other edges, construct a Euclidean distance sequence in ascending order, and take the edges corresponding to the first Euclidean distances in the Euclidean distance sequence as the adjacent edges of any edge; Or calculate the Euclidean distance between any edge and the centroid of other edges, construct a Euclidean distance sequence in descending order, and take the edges corresponding to the last Euclidean distances in the Euclidean distance sequence as the neighboring edges of any edge.

8. The method for detecting the wear degree of the roller of a Raymond mill according to claim 1, characterized in that, The canny edge detection algorithm is used in performing edge detection on the obtained grayscale image of the grinding roller to obtain the edges of multiple grinding rollers.

9. A Raymond mill roller wear detection system, characterized in that, Including: A processor and a memory, the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the method for detecting the wear degree of the Raymond mill grinding roller according to any one of claims 1-8 is implemented.

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