Butterfly Valve Sealing Surface Identification Method

Abstract

The present invention provides a method for identifying the sealing surface of a butterfly valve. The present invention performs a first denoising on the overall point cloud data of the sealing surface of the butterfly valve to be identified, removes relatively discrete noise points, and makes it reliable to use the average value of RD as the dividing line of the second denoising; the X 2 +Y 2 The values are traversed from small to large, and the proportion is used to further determine the butterfly valve sealing surface, which improves the accuracy of the method. Subsequently, based on the identified butterfly valve sealing surface, the cone angle of the butterfly valve sealing surface can be calculated, and the cone angle of the butterfly valve sealing surface can be used to verify the processing accuracy of the butterfly valve.

Description

Technical Field

[0001] The invention relates to a method for identifying a sealing surface of a butterfly valve. Background Art

[0002] Butterfly valves are widely used in the industry because of their simple structure and small torque to open. Since leakage prevention is the main characteristic of butterfly valves, it is particularly important to study their sealing surface parameters. The traditional way to measure the sealing surface of butterfly valves is to use a manual vernier caliper, which has poor processing accuracy. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for identifying the sealing surface of a butterfly valve.

[0004] To solve the above problems, the present invention provides a butterfly valve sealing surface identification method, comprising:

[0005] Step S1: Obtain the overall point cloud data of the butterfly valve sealing surface to be identified, and calculate the average longitudinal spacing based on the overall point cloud data of the butterfly valve sealing surface to be identified. and horizontal spacing Based on the average vertical spacing and horizontal spacing Obtain a normalized parameter S, and normalize the overall point cloud data of the butterfly valve sealing surface to be identified based on the normalized parameter S to obtain point cloud data with uniform horizontal and vertical spacing;

[0006] Step S2: traverse each point in the uniformly spaced point cloud data, and calculate the number J of point clouds in a space cube with a side length of 2×b centered at the point. If J is less than a threshold N, the point is considered a noise point and deleted from the uniformly spaced point cloud data to obtain the point cloud data after initial denoising.

[0007] Laser displacement sensors are highly accurate and can generate massive point clouds. Therefore, laser scanning is increasingly used in reverse engineering to identify surface features. However, due to external factors such as lighting and vibration, the resulting scanned data can contain noise. Therefore, denoising point cloud data is beneficial for obtaining point cloud data that better reflects surface features.

[0008] Step S3, processing the point cloud data after the initial denoising based on the optics algorithm, generating optics parameters Esp and Minpts according to b and N, and obtaining a decision graph based on the optics algorithm and the parameters Esp and Minpts;

[0009] Step S4, clustering the point cloud data after the initial denoising using the average value of the vertical coordinate of the decision graph to obtain potential sealing surface point cloud data; calculating the coordinate square value of each point in the potential sealing surface point cloud data = X 2+Y 2 , where X is the horizontal coordinate of a point and Y is the coordinate of a point; traverse each point in the sealing surface point cloud data in order from small to large according to the square value of the coordinates, find the cluster where the currently traversed point is located, and judge whether it is a butterfly valve sealing surface based on the ratio of the number of point clouds in the cluster where the currently traversed point is located to the number of point clouds in the point cloud data after the initial denoising and the preset threshold F.

[0010] Furthermore, in the above method, step S1 includes:

[0011] S11, obtain the overall point cloud data of the butterfly valve sealing surface to be identified, find the layer where each point in the overall point cloud data of the butterfly valve sealing surface to be identified is located, where the longitudinal Z coordinate values of the points on the same layer are consistent; take the distance to the nearest point in the adjacent layer outside the layer where the point is located as the nearest longitudinal distance h of the point, and calculate the average longitudinal distance of all points Find the distance of the point closest to the point in the XY plane of the layer where the point is located as the nearest horizontal distance b of the point, and calculate the average horizontal distance of all points

[0012] S12, using the average longitudinal spacing and horizontal spacing A normalization parameter S is obtained, and the overall point cloud data of the butterfly valve sealing surface to be identified is normalized based on the normalization parameter S to obtain point cloud data with uniform horizontal and vertical spacing, wherein the X-axis coordinate and Y-axis coordinate of each point in the point cloud data with uniform horizontal and vertical spacing are the same as the X-axis and Y-axis coordinates of the corresponding point in the overall point cloud data of the butterfly valve sealing surface to be identified, and the Z-axis coordinate of each point in the point cloud data with uniform horizontal and vertical spacing is equal to the Z-axis coordinate of the corresponding point in the overall point cloud data of the butterfly valve sealing surface to be identified multiplied by the normalization parameter S.

[0013] Furthermore, in the above method, the normalization parameter S is obtained according to the following formula:

[0014]

[0015] Furthermore, in the above method, step S2 includes:

[0016] Step S21: For each point in the point cloud data with uniform horizontal and vertical spacing, divide the point into a space cube with a side length of 2×b according to the following formula;

[0017] In step S22, the number of points J in each cube is calculated. If the number is less than the threshold N, the point with coordinates X, Y, and Z is treated as a noise point and removed from the point cloud data with uniform horizontal and vertical spacing to obtain the point cloud data after initial denoising.

[0018] Furthermore, in the above method, step S21 includes:

[0019] The coordinates of a point in the point cloud data with uniform horizontal and vertical spacing are X, Y, and Z. Then the X1, Y1, and Z1 coordinates of any point other than this point in the 2×b cubic space range of this point satisfy the following conditions:

[0020]

[0021] Furthermore, in the above method, the preset value of the threshold N is 4.

[0022] Furthermore, in the above method, in step S3, the optics parameters Esp and Minpts are obtained according to the following formulas, including:

[0023]

[0024] Furthermore, in the above method, a decision graph is obtained based on the optics algorithm and the parameters Esp and Minpts, including:

[0025] Based on the optics algorithm and parameters Esp and Minpts, each three-dimensional point in the point cloud data after the initial denoising is mapped one-to-one to a two-dimensional point in the decision graph.

[0026] Furthermore, in the above method, step S4 includes:

[0027] S41, using the decision diagram, calculating the ordinate of each point in the decision diagram, i.e., the RD average value C, and dividing the decision diagram using the RD average value C to complete clustering, wherein, based on the RD average value C, continuous two-dimensional points (optics algorithm) less than 1.2*C are regarded as a cluster, and each two-dimensional point greater than or equal to 1.2*C is regarded as a separate cluster;

[0028] S42, extracting clusters with RD values less than 1.2*C as potential sealing surface point cloud data;

[0029] S43, calculate the square value of the coordinate of each point in the potential sealing surface point cloud data = X 2 +Y 2 ;

[0030] S44, find the square value of the point coordinate in the potential sealing surface point cloud = X 2 +Y 2 The smallest point and its cluster are calculated by calculating the ratio of the number of point clouds in the cluster to the number of point clouds in the point cloud data after initial denoising. If the ratio is greater than the preset threshold F, the cluster is judged to be a sealing surface.

[0031] S45, if the ratio is less than or equal to the preset threshold F, find the next point in the potential sealing surface point cloud with a square value of coordinates = X 2 +Y 2 After finding the smallest point, go back to step S44 until a point is found whose cluster quantity ratio is greater than a preset threshold.

[0032] Furthermore, in the above method, the RD average value C is calculated according to the following formula:

[0033]

[0034] Where n is the number of two-dimensional points in the decision diagram, (RD) n is the reachable distance of each point in the decision graph in the optics algorithm.

[0035] Compared with the existing technology, the present invention obtains point cloud data with uniform horizontal and vertical spacing based on the overall point cloud data of the butterfly valve sealing surface to be identified; denoises the point cloud data with uniform horizontal and vertical spacing to obtain point cloud data after initial denoising; processes the point cloud data after initial denoising, generates optics parameters EsP and Minpts based on b and N, and obtains a decision graph based on the optics algorithm and parameters Esp and Minpts; clusters the point cloud data after initial denoising using the average value of the vertical coordinate of the decision graph to obtain potential sealing surface point cloud data; based on the clustering, determines whether it is a butterfly valve sealing surface based on the ratio of the number of point clouds in the cluster where the currently traversed point is located to the number of point clouds in the point cloud data after initial denoising and the preset threshold F. The present invention removes relatively discrete noise points through the first denoising, making it reliable to use the average value of RD as the dividing line for the second denoising; the X 2 +Y 2 The values are traversed from small to large, and the proportion is used to further determine the butterfly valve sealing surface, which improves the accuracy of the method. Subsequently, based on the identified butterfly valve sealing surface, the cone angle of the butterfly valve sealing surface can be calculated, and the cone angle of the butterfly valve sealing surface can be used to verify the processing accuracy of the butterfly valve. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a flow chart of a method for identifying a butterfly valve sealing surface according to an embodiment of the present invention;

[0037] Figure 2 The point cloud data of three sets of butterfly valves (a), (b), and (c) collected according to an embodiment of the present invention are respectively;

[0038] Figure 3 for Figure 2 The result of denoising and filtering the point cloud data of the three groups of butterfly valves (a), (b), and (c). DETAILED DESCRIPTION

[0039] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0040] like Figure 1 As shown, the present invention provides a method for identifying a butterfly valve sealing surface, comprising:

[0041] Step S1: Obtain the overall point cloud data of the butterfly valve sealing surface to be identified, and calculate the average longitudinal spacing based on the overall point cloud data of the butterfly valve sealing surface to be identified. and horizontal spacing Based on the average vertical spacing and horizontal spacing Obtain a normalized parameter S, and normalize the overall point cloud data of the butterfly valve sealing surface to be identified based on the normalized parameter S to obtain point cloud data with uniform horizontal and vertical spacing;

[0042] Step S2: traverse each point in the uniformly spaced point cloud data, and calculate the number J of point clouds in a space cube with a side length of 2×b centered at the point. If J is less than a threshold N, the point is considered a noise point and deleted from the uniformly spaced point cloud data to obtain the point cloud data after initial denoising.

[0043] Step S3, processing the point cloud data after the initial denoising based on the optics algorithm, generating optics parameters Esp and Minpts according to b and N, and obtaining a decision graph based on the optics algorithm and the parameters Esp and Minpts;

[0044] Step S4, clustering the point cloud data after the initial denoising using the average value of the vertical coordinate of the decision graph to obtain potential sealing surface point cloud data; calculating the coordinate square value of each point in the potential sealing surface point cloud data = X 2 +Y 2 , where X is the horizontal coordinate of a point and Y is the coordinate of a point; traverse each point in the sealing surface point cloud data in order from small to large according to the square value of the coordinates, find the cluster where the currently traversed point is located, and judge whether it is a butterfly valve sealing surface based on the ratio of the number of point clouds in the cluster where the currently traversed point is located to the number of point clouds in the point cloud data after the initial denoising and the preset threshold F.

[0045] Here, the present invention obtains point cloud data with uniform horizontal and vertical spacing based on the overall point cloud data of the butterfly valve sealing surface to be identified; denoises the point cloud data with uniform horizontal and vertical spacing to obtain point cloud data after initial denoising; processes the point cloud data after initial denoising, generates optics parameters Esp and Minpts based on b and N, and obtains a decision graph based on the optics algorithm and parameters Esp and Minpts; clusters the point cloud data after initial denoising using the average value of the vertical coordinate of the decision graph to obtain potential sealing surface point cloud data; based on the clustering, determines whether it is a butterfly valve sealing surface based on the ratio of the number of point clouds in the cluster where the currently traversed point is located to the number of point clouds in the point cloud data after initial denoising and the preset threshold F. The present invention removes relatively discrete noise points through the first denoising, making it reliable to use the average value of RD as the dividing line for the second denoising; the X value of the point cloud is calculated. 2 +Y 2 The values are traversed from small to large, and the proportion is used to further determine the butterfly valve sealing surface, which improves the accuracy of the method. Subsequently, based on the identified butterfly valve sealing surface, the cone angle of the butterfly valve sealing surface can be calculated, and the cone angle of the butterfly valve sealing surface can be used to verify the processing accuracy of the butterfly valve.

[0046] In one embodiment of the butterfly valve sealing surface identification method of the present invention, step S1 includes:

[0047] S11, obtain the overall point cloud data of the butterfly valve sealing surface to be identified, find the layer where each point in the overall point cloud data of the butterfly valve sealing surface to be identified is located, where the longitudinal Z coordinate values of the points on the same layer are consistent; take the distance to the nearest point in the adjacent layer outside the layer where the point is located as the nearest longitudinal distance h of the point, and calculate the average longitudinal distance of all points Find the distance of the point closest to the point in the XY plane of the layer where the point is located as the nearest horizontal distance b of the point, and calculate the average horizontal distance of all points

[0048] S12, using the average longitudinal spacing and horizontal spacing A normalization parameter S is obtained, and the overall point cloud data of the butterfly valve sealing surface to be identified is normalized based on the normalization parameter S to obtain point cloud data with uniform horizontal and vertical spacing, wherein the X-axis coordinate and Y-axis coordinate of each point in the point cloud data with uniform horizontal and vertical spacing are the same as the X-axis and Y-axis coordinates of the corresponding point in the overall point cloud data of the butterfly valve sealing surface to be identified, and the Z-axis coordinate of each point in the point cloud data with uniform horizontal and vertical spacing is equal to the Z-axis coordinate of the corresponding point in the overall point cloud data of the butterfly valve sealing surface to be identified multiplied by the normalization parameter S.

[0049]

[0050] In one embodiment of the butterfly valve sealing surface identification method of the present invention, step S2 includes:

[0051] Step S21: For each point in the point cloud data with uniform horizontal and vertical spacing, divide the space cube with a side length of 2×b with the point as the center according to the following formula:

[0052] Preferably, for each point in the point cloud data with uniform horizontal and vertical spacing, a space cube with a side length of 2×b is divided with the point as the center according to the following formula, including:

[0053] If the coordinates of a point in the point cloud data with uniform horizontal and vertical spacing are X, Y, and Z, then the X1, Y1, and Z1 coordinates of any point other than this point in the 2×b cubic space range of this point satisfy the following conditions;

[0054]

[0055] In step S22, the number of points J in each cube is calculated. If the number is less than a threshold N, the point with coordinates X, Y, and Z is treated as a noise point and removed from the point cloud data with uniform horizontal and vertical spacing to obtain the point cloud data after initial denoising. The preset value of the threshold N is 4.

[0056] In one embodiment of the butterfly valve sealing surface recognition method of the present invention, step S3 processes the point cloud data after the initial denoising based on the optics algorithm, and generates optics parameters Esp and Minpts according to b and N, including:

[0057]

[0058] In one embodiment of the butterfly valve sealing surface identification method of the present invention, in step S3, a decision diagram is obtained based on the optics algorithm and the parameters Esp and Minpts, including:

[0059] Based on the optics algorithm and parameters Esp and Minpts, each three-dimensional point in the point cloud data after the initial denoising is mapped one-to-one to a two-dimensional point in the decision graph.

[0060] In one embodiment of the butterfly valve sealing surface identification method of the present invention, step S4 includes:

[0061] S41, using the decision diagram, calculating the vertical coordinate of each point in the decision diagram, that is, the RD average value C, and dividing the decision diagram using the RD average value C to complete clustering;

[0062]

[0063] Where n is the number of two-dimensional points in the decision diagram, (RD) nis the reachable distance of each point in the decision graph in the optics algorithm;

[0064] Based on the RD average value C, the continuous two-dimensional points (optics algorithm) less than 1.2*C are regarded as a cluster, and each two-dimensional point greater than or equal to 1.2*C is regarded as a separate cluster;

[0065] S42, extracting clusters with RD values less than 1.2*C as potential sealing surface point cloud data;

[0066] S43, calculate the square value of the coordinate of each point in the potential sealing surface point cloud data = X 2 +Y 2 ;

[0067] S44, find the square value of the point coordinate in the potential sealing surface point cloud = X 2 +Y 2 The smallest point and its cluster are calculated by calculating the ratio of the number of point clouds in the cluster to the number of point clouds in the point cloud data after initial denoising. If the ratio is greater than the preset threshold F, the cluster is judged to be a sealing surface.

[0068] S45, if the ratio is less than or equal to the preset threshold F, find the next point in the potential sealing surface point cloud with a square value of coordinates = X 2 +Y 2 After finding the smallest point, go back to step S44 until a point is found whose cluster quantity ratio is greater than a preset threshold.

[0069] Specifically, in order to verify the effectiveness of the method provided in this embodiment, on-site measurements were conducted at an enterprise that produces butterfly valves. A laser displacement sensor was installed at the tool holder position and the machine tool turntable was used to drive the butterfly valve to rotate. After the turntable rotated one circle, the laser sensor was moved down a predetermined distance along the Z axis for the next collection work to obtain point cloud information of the butterfly valve. The obtained point cloud data has a layered feature, and the Z coordinate values of points on the same layer are consistent.

[0070] like Figure 2 As shown in the figure, the first set of measurement data (a) and the second set of measurement data (b) are data measured for the same three-eccentric butterfly valve at different turntable speeds. The third set of measurement data (c) is for a three-eccentric butterfly valve of another size. The first, second, and third sets of measurement data have 10791, 2656, and 22667 points respectively, all of which contain noise points other than the sealing surface point cloud.

[0071] The above clustering-based butterfly valve sealing surface recognition method is mainly divided into two stages. The first stage removes discrete noise points based on the idea of spatial density. The second stage obtains the decision graph based on the optics algorithm. The third stage performs clustering based on the average RD in the decision graph. The fourth stage realizes the judgment of the sealing surface cluster based on the characteristics of the cluster where the sealing surface is located.

[0072] Figure 3 for Figure 2 The three sets of experimental data in the figure are the result of denoising and filtering. Figure 3 The experimental result graph shows that the method provided by the present invention can effectively remove noise points other than the sealing surface without damaging the sealing surface point cloud cluster. When variables such as machine tool speed and butterfly valve size are added to the experimental data, ideal experimental results can still be obtained, which reflects the robustness of the method provided by the present invention.

[0073] In summary, the method provided by the present invention can effectively remove noise. The relevant parameters involved are automatically generated and preset to achieve automatic extraction of sealing surfaces. When the parameter settings are fixed, sealing surface extraction is performed on three different sets of point cloud data. From the results, it can be seen that the method has high robustness. By removing the more discrete noise points through the first denoising, it becomes reliable to use the average value of RD as the dividing line for the second denoising. 2 +Y 2 The values are traversed from small to large, and the sealing surface is further judged by the proportion, which improves the accuracy of the method.

[0074] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0075] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention. Obviously, those skilled in the art can make various changes and modifications to the 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 identifying the sealing surface of a butterfly valve, characterized in that: include: Step S1: Obtain the overall point cloud data of the butterfly valve sealing surface to be identified, and calculate the average longitudinal spacing based on the overall point cloud data of the butterfly valve sealing surface to be identified. and horizontal spacing Based on the average vertical spacing and horizontal spacing Obtain a normalized parameter S, and normalize the overall point cloud data of the butterfly valve sealing surface to be identified based on the normalized parameter S to obtain point cloud data with uniform horizontal and vertical spacing; Step S2: traverse each point in the uniformly spaced point cloud data, and calculate the number J of point clouds in a space cube with a side length of 2×b centered at the point. If J is less than a threshold N, the point is considered a noise point and deleted from the uniformly spaced point cloud data to obtain the point cloud data after initial denoising. Step S3, processing the point cloud data after the initial denoising based on the optics algorithm, generating optics parameters Esp and Minpts according to b and N, and obtaining a decision graph based on the optics algorithm and the parameters Esp and Minpts; Step S4, clustering the point cloud data after the initial denoising using the average value of the vertical coordinate of the decision graph to obtain potential sealing surface point cloud data; Calculate the square value of the coordinates of each point in the potential sealing surface point cloud data = X 2 +Y 2 , where X is the horizontal coordinate of a point and Y is the coordinate of a point; traverse each point in the sealing surface point cloud data in order from small to large according to the square value of the coordinates, find the cluster where the currently traversed point is located, and judge whether it is a butterfly valve sealing surface based on the ratio of the number of point clouds in the cluster where the currently traversed point is located to the number of point clouds in the point cloud data after the initial denoising and the preset threshold F.

2. The butterfly valve sealing surface identification method according to claim 1, characterized in that: Step S1 includes: S11, obtain the overall point cloud data of the butterfly valve sealing surface to be identified, find the layer where each point in the overall point cloud data of the butterfly valve sealing surface to be identified is located, where the longitudinal Z coordinate values of the points on the same layer are consistent; take the distance to the nearest point in the adjacent layer outside the layer where the point is located as the nearest longitudinal distance h of the point, and calculate the average longitudinal distance of all points Find the distance of the point closest to the point in the XY plane of the layer where the point is located as the nearest horizontal distance b of the point, and calculate the average horizontal distance of all points S12, using the average longitudinal spacing and horizontal spacing A normalization parameter S is obtained, and the overall point cloud data of the butterfly valve sealing surface to be identified is normalized based on the normalization parameter S to obtain point cloud data with uniform horizontal and vertical spacing, wherein the X-axis coordinate and Y-axis coordinate of each point in the point cloud data with uniform horizontal and vertical spacing are the same as the X-axis and Y-axis coordinates of the corresponding point in the overall point cloud data of the butterfly valve sealing surface to be identified, and the Z-axis coordinate of each point in the point cloud data with uniform horizontal and vertical spacing is equal to the Z-axis coordinate of the corresponding point in the overall point cloud data of the butterfly valve sealing surface to be identified multiplied by the normalization parameter S.

3. The butterfly valve sealing surface identification method according to claim 2, characterized in that: The normalization parameter S is obtained according to the following formula:

4. The butterfly valve sealing surface identification method according to claim 1, characterized in that: The step S2 includes: Step S21: For each point in the point cloud data with uniform horizontal and vertical spacing, divide the point into a space cube with a side length of 2×b according to the following formula; In step S22, the number of points J in each cube is calculated. If the number is less than the threshold N, the point with coordinates X, Y, and Z is treated as a noise point and removed from the point cloud data with uniform horizontal and vertical spacing to obtain the point cloud data after initial denoising.

5. The butterfly valve sealing surface identification method according to claim 4, characterized in that: The step S21 includes: The coordinates of a point in the point cloud data with uniform horizontal and vertical spacing are X, Y, and Z. Then the X1, Y1, and Z1 coordinates of any point other than this point in the 2×b cubic space range of this point satisfy the following conditions:

6. The butterfly valve sealing surface identification method according to claim 4, characterized in that: The preset value of the threshold N is 4.

7. The method for identifying the sealing surface of a butterfly valve according to claim 4, wherein: In step S3, the optics parameters Esp and Minpts are calculated according to the following formulas:

8. The butterfly valve sealing surface identification method according to claim 1, characterized in that: In step S3, a decision graph is obtained based on the optics algorithm and the parameters Esp and Minpts, including: Based on the optics algorithm and parameters Esp and Minpts, each three-dimensional point in the point cloud data after the initial denoising is mapped one-to-one to a two-dimensional point in the decision graph.

9. The method for identifying the sealing surface of a butterfly valve according to claim 1, wherein: The step S4 comprises: S41, using the decision diagram, calculating the ordinate of each point in the decision diagram, i.e., the RD average value C, and dividing the decision diagram using the RD average value C to complete clustering, wherein, based on the RD average value C, continuous two-dimensional points (optics algorithm) less than 1.2*C are regarded as a cluster, and each two-dimensional point greater than or equal to 1.2*C is regarded as a separate cluster; S42, extracting clusters with RD values less than 1.2*C as potential sealing surface point cloud data; S43, calculate the square value of the coordinate of each point in the potential sealing surface point cloud data = X 2 +Y 2 ; S44, find the square value of the point coordinate in the potential sealing surface point cloud = X 2 +Y 2 The smallest point and its cluster are calculated by calculating the ratio of the number of point clouds in the cluster to the number of point clouds in the point cloud data after initial denoising. If the ratio is greater than the preset threshold F, the cluster is judged to be a sealing surface. S45, if the ratio is less than or equal to the preset threshold F, find the next point in the potential sealing surface point cloud with a square value of coordinates = X 2 +Y 2 After finding the smallest point, go back to step S44 until a point is found whose cluster quantity ratio is greater than a preset threshold.

10. The butterfly valve sealing surface identification method according to claim 9, characterized in that: The RD average value C is calculated according to the following formula: Where n is the number of two-dimensional points in the decision diagram, (RD) n is the reachable distance of each point in the decision graph in the optics algorithm.

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