Method and device for rapidly measuring cavitation erosion damage of water turbine rotating wheel and computer equipment

Abstract

The invention relates to a rapid measurement method and device for cavitation damage of a water turbine runner and computer equipment. The method comprises the following steps: acquiring point cloud data collected by a 3D laser scanner for the surface of the turbine runner; determining a cavitation erosion area according to geometric features of the point cloud data; according to the point cloud data corresponding to the cavitation erosion area, the cavitation erosion damage area, the cavitation erosion damage depth and the cavitation erosion damage volume are determined; and according to the cavitation erosion area, the cavitation erosion damage area, the cavitation erosion damage depth and the cavitation erosion damage volume, determining a cavitation erosion damage measurement result of the water turbine runner. According to the method, based on the characteristics of high precision and interference resistance of the 3D laser scanner, the area with cavitation erosion is determined by using the acquired point cloud data, the area, depth and volume of the cavitation erosion area are calculated, an accurate cavitation erosion damage measurement result of the water turbine runner is generated, the limitation of a detection environment and an operation condition on cavitation erosion damage detection is avoided, and the accuracy of the detection result is improved. And efficient cavitation damage detection is realized, so that the safety of the hydroelectric equipment is improved.

Description

Technical Field

[0001] The present application relates to the technical field of maintenance and detection of water turbine equipment, and in particular to a method, device, computer equipment, computer-readable storage medium and computer program product for rapid determination of cavitation damage of a water turbine runner. Background Art

[0002] The turbine is the core equipment of the hydropower station. Its performance and reliability directly affect the power generation efficiency and safety of the hydropower station. However, due to the impact of high-speed water flow during long-term operation, the turbine is prone to cavitation on the runner surface. Cavitation is a tiny impact force caused by the bursting of bubbles in the water flow. Long-term action will cause gradual damage to the material surface, manifested as surface pitting, pits, and even peeling, which seriously affects the life and performance of the turbine.

[0003] Traditional technology for turbine runner cavitation damage detection requires a long detection time and is limited by the detection environment and operating conditions. Especially for on-site detection of large turbines, the operation is inconvenient and the efficiency is low, which is not conducive to improving the safety of hydropower equipment. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for quickly determining cavitation damage of a turbine runner that can improve the safety of hydropower equipment in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for rapidly determining cavitation damage of a turbine runner, comprising:

[0006] Obtain point cloud data collected by a 3D laser scanner on the surface of a turbine runner;

[0007] Determining a cavitation area according to geometric features of the point cloud data;

[0008] Determining the cavitation damage area, cavitation damage depth and cavitation damage volume according to the point cloud data corresponding to the cavitation area;

[0009] The measurement result of the cavitation damage of the turbine runner is determined according to the cavitation region, the cavitation damage area, the cavitation damage depth and the cavitation damage volume.

[0010] In one embodiment, the geometric features include normal vector information, curvature information and depth information, and determining the cavitation area according to the geometric features of the point cloud data includes:

[0011] According to normal vector information, curvature information and depth information of each point in the point cloud data, the point cloud data is divided into at least one data set; different data sets represent different turbine runner surface areas;

[0012] Determine a candidate cavitation region corresponding to the data set according to normal vector information, curvature information and depth information of each point in the data set;

[0013] The cavitation region corresponding to the data set is determined according to the intersection between the candidate cavitation regions.

[0014] In one embodiment, determining the cavitation damage area, the cavitation damage depth and the cavitation damage volume according to the point cloud data corresponding to the cavitation area includes:

[0015] Determining a point cloud model according to the point cloud data collected from the surface of the turbine runner;

[0016] triangulate the point cloud model, identify the boundary of the cavitation area, fit a surface at the boundary points corresponding to the boundary according to the interpolation method, determine a repair area, and use the area of ​​the repair area as the cavitation damage area;

[0017] A reference curved surface is determined according to the point cloud data corresponding to the cavitation area, and the cavitation damage depth is determined according to the distance between each point in the point cloud data corresponding to the cavitation area and the reference curved surface, and the cavitation damage volume is calculated by an integral method.

[0018] In one embodiment, determining the turbine runner cavitation damage measurement result according to the cavitation region, the cavitation damage area, the cavitation damage depth and the cavitation damage volume includes:

[0019] Fill the cavitation damage area, cavitation damage depth and cavitation damage volume corresponding to the cavitation area into a preset report template to obtain a turbine runner cavitation damage measurement report;

[0020] Constructing a mesh model of the cavitation area according to the point cloud data corresponding to the cavitation area;

[0021] The water turbine runner cavitation damage measurement result is determined according to the water turbine runner cavitation damage measurement report and the grid model of the cavitation area.

[0022] In one embodiment, obtaining point cloud data collected by a 3D laser scanner on the surface of a turbine runner includes:

[0023] Obtaining original point cloud data collected by a 3D laser scanner on the surface of a turbine runner;

[0024] The original point cloud data is subjected to noise reduction processing to obtain the point cloud data collected on the surface of the turbine runner.

[0025] In one of the embodiments, after the step of determining the measurement result of the cavitation damage of the turbine runner, the method further includes: sending the measurement result of the cavitation damage of the turbine runner to a monitoring terminal; the monitoring terminal is used to display the measurement result of the cavitation damage of the turbine runner to a user.

[0026] In a second aspect, the present application also provides a rapid determination device for cavitation damage of a turbine runner, comprising:

[0027] A data acquisition module, used to acquire point cloud data collected by a 3D laser scanner on the surface of a turbine runner;

[0028] A regional analysis module, used for determining a cavitation region according to geometric features of the point cloud data;

[0029] A parameter calculation module, used to determine the cavitation damage area, cavitation damage depth and cavitation damage volume according to the point cloud data corresponding to the cavitation area;

[0030] The result determination module is used to determine the cavitation damage measurement result of the turbine runner according to the cavitation region, the cavitation damage area, the cavitation damage depth and the cavitation damage volume.

[0031] In a third aspect, the present application further provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the above method are implemented.

[0032] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0033] In a fifth aspect, the present application further provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0034] The above-mentioned method, device, computer equipment, computer-readable storage medium and computer program product for rapid determination of cavitation damage of a turbine runner obtain point cloud data collected by a 3D laser scanner on the surface of a turbine runner, thereby using a 3D laser scanner to perform high-precision scanning on the surface of the turbine runner to obtain accurate point cloud data; according to the geometric characteristics of the point cloud data, the cavitation area is determined, thereby analyzing the geometric characteristics of the point cloud data to determine the area where cavitation exists in the turbine runner; according to the point cloud data corresponding to the cavitation area, the cavitation damage area, cavitation damage depth and cavitation damage volume are determined, thereby accurately analyzing and calculating the damage area, damage depth and damage volume of the cavitation area; according to the cavitation area, cavitation damage area, cavitation damage depth and cavitation damage volume, the cavitation of the turbine runner is determined. The damage measurement results can be combined with the cavitation area and the damage area, damage depth and damage volume of the cavitation area to determine the accurate measurement results, and realize the rapid measurement of cavitation damage of the turbine runner. Based on the high precision and anti-interference characteristics of the 3D laser scanner, the 3D laser scanner can be used to collect accurate point cloud data for the surface of the turbine runner, and the point cloud data can be analyzed in combination with the geometric characteristics of the point cloud data to determine the area where cavitation exists, and calculate the damage area, damage depth and damage volume of the cavitation area, so as to comprehensively consider the cavitation area and the damage area, damage depth and damage volume of the cavitation area to generate accurate cavitation damage measurement results for the turbine runner, avoid the limitations of the detection environment and operating conditions on cavitation damage detection, realize efficient cavitation damage detection, and thus improve the safety of hydropower equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 A diagram showing an application environment of a method for rapidly determining cavitation damage of a turbine runner in one embodiment;

[0037] Figure 2 A schematic diagram of a process for rapidly determining cavitation damage of a turbine runner in one embodiment;

[0038] Figure 3 A schematic diagram of a process for quickly determining cavitation damage of a turbine runner in one embodiment;

[0039] Figure 4 A structural block diagram of a device for quickly determining cavitation damage of a turbine runner in one embodiment;

[0040] Figure 5 The figure is a diagram of the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0042] The method for quickly determining cavitation damage of a turbine runner provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The server 104 obtains the point cloud data collected by the 3D laser scanner for the surface of the turbine runner; the server 104 determines the cavitation area according to the geometric characteristics of the point cloud data; the server 104 determines the cavitation damage area, cavitation damage depth and cavitation damage volume according to the point cloud data corresponding to the cavitation area; the server 104 determines the cavitation damage measurement result of the turbine runner according to the cavitation area, cavitation damage area, cavitation damage depth and cavitation damage volume. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablets, Internet of Things devices, portable wearable devices and dashboards. The server 104 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0043] In an exemplary embodiment, Figure 2 As shown, a method for quickly determining cavitation damage of a turbine runner is provided, and the method is applied to a server as an example for explanation, including the following steps S202 to S208. Among them:

[0044] Step S202, obtaining point cloud data collected by a 3D laser scanner on the surface of the turbine runner.

[0045] Among them, a 3D laser scanner may refer to a device that uses laser technology to accurately measure the surface shape and size of an object. In practical applications, a 3D laser scanner can be used to obtain three-dimensional data of an object.

[0046] The turbine runner surface may refer to the outer surface of a component in the turbine that is in direct contact with the water flow and uses the energy of the water flow to rotate.

[0047] Among them, point cloud data can refer to the data output by a 3D laser scanner. In practical applications, point cloud data can represent a set of several three-dimensional coordinate points. Each three-dimensional coordinate point can represent a position on the surface of the scanned object (such as the surface of a turbine runner). The three-dimensional coordinate point is determined by the 3D laser scanner emitting a laser beam and measuring the time or phase change of the laser beam reflected from the surface of the object. Each point in the point cloud represented by the point cloud data can contain information such as three basic coordinate values ​​(X, Y, Z). The basic coordinate values ​​(X, Y, Z) can represent the specific position of each point in three-dimensional space.

[0048] As an example, a 3D laser scanner has the characteristics of high precision and anti-interference, and can work normally under complex light and humidity conditions on site. The 3D laser scanner can emit a laser beam to the surface of a turbine runner and measure the time or phase change of the laser beam reflected from the surface of the turbine runner, and determine the coordinates and other data of at least one point on the surface of the turbine runner. The coordinates and other data of several points on the surface of the turbine runner output by the 3D laser scanner can be used as point cloud data collected by the 3D laser scanner for the surface of the turbine runner. Then the server can send a data acquisition request to the 3D laser scanner. In response to the data acquisition request, the 3D laser scanner sends the point cloud data to the server, so that the server obtains the point cloud data collected by the 3D laser scanner for the surface of the turbine runner.

[0049] Step S204: determining the cavitation area according to the geometric features of the point cloud data.

[0050] The geometric features of the point cloud data may include, but are not limited to, information such as the three-dimensional coordinates, normal vector, curvature, density distribution, etc. of each point represented by the point cloud data.

[0051] The cavitation area may refer to an area on the surface of the turbine runner where cavitation occurs.

[0052] As an example, the server can first determine the three-dimensional coordinates, normal vector, curvature, density distribution and other geometric features corresponding to the point cloud data based on the point cloud data, and then compare the geometric features of each point represented by the point cloud data in pairs, analyze the differences between the points represented by the point cloud data, and filter out target points from the points represented by the point cloud data. The set of target points on the surface of the turbine runner can be used as the area where cavitation occurs on the surface of the turbine runner, thereby determining the cavitation area where cavitation occurs on the surface of the turbine runner.

[0053] Step S206, determining the cavitation damage area, cavitation damage depth and cavitation damage volume according to the point cloud data corresponding to the cavitation area.

[0054] The cavitation damage area may refer to information representing the area of ​​the cavitation region.

[0055] The cavitation damage depth may refer to information characterizing the depth of the cavitation area.

[0056] The cavitation damage volume may refer to information characterizing the volume of the cavitation region.

[0057] As an example, for each cavitation area, the server can determine the cavitation damage area based on the area of ​​the set of points represented by the point cloud data corresponding to the cavitation area. The server can determine the cavitation damage depth based on the three-dimensional coordinate data of the points represented by the point cloud data corresponding to the cavitation area. The server can also construct a three-dimensional model based on the point cloud data corresponding to the cavitation area, and use the three-dimensional model to determine the cavitation damage volume.

[0058] Step S208, determining the measurement result of the cavitation damage of the turbine runner according to the cavitation region, the cavitation damage area, the cavitation damage depth and the cavitation damage volume.

[0059] Among them, the turbine runner cavitation damage measurement results may refer to information used to display the location of each cavitation area on the turbine runner surface and the cavitation damage area, cavitation damage depth and cavitation damage volume of each cavitation area. In practical applications, the presentation of the turbine runner cavitation damage measurement results may include but is not limited to text, charts or reports.

[0060] As an example, the server can generate a textual description of the position of each cavitation area on the surface of the turbine runner and the measurement results of the cavitation damage area, cavitation damage depth and cavitation damage volume of each cavitation area based on the cavitation area, cavitation damage area, cavitation damage depth and cavitation damage volume, thereby determining the measurement result of the cavitation damage of the turbine runner. The server can also generate an image / table form of a graph showing the position of each cavitation area on the surface of the turbine runner and the measurement results of the cavitation damage area, cavitation damage depth and cavitation damage volume of each cavitation area based on the cavitation area, cavitation damage area, cavitation damage depth and cavitation damage volume, thereby determining the measurement result of the cavitation damage of the turbine runner.

[0061] In the above-mentioned method for rapid determination of cavitation damage of a turbine runner, point cloud data collected by a 3D laser scanner on the surface of the turbine runner is obtained, so that the surface of the turbine runner is scanned with high precision using a 3D laser scanner to obtain accurate point cloud data; the cavitation area is determined according to the geometric characteristics of the point cloud data, so as to analyze the geometric characteristics of the point cloud data and determine the area where cavitation exists in the turbine runner; the cavitation damage area, cavitation damage depth and cavitation damage volume are determined according to the point cloud data corresponding to the cavitation area, so as to accurately analyze and calculate the damage area, damage depth and damage volume of the cavitation area; the cavitation damage measurement result of the turbine runner is determined according to the cavitation area, cavitation damage area, cavitation damage depth and cavitation damage volume, so as to combine the cavitation The damage area, damage depth and damage volume of the cavitation area can be determined to determine the accurate measurement results, and the cavitation damage of the turbine runner can be quickly determined. Based on the high precision and anti-interference characteristics of the 3D laser scanner, the 3D laser scanner can be used to collect accurate point cloud data for the surface of the turbine runner, and the point cloud data can be analyzed in combination with the geometric characteristics of the point cloud data to determine the area where cavitation exists, and the damage area, damage depth and damage volume of the cavitation area can be calculated. Therefore, the cavitation area and the damage area, damage depth and damage volume of the cavitation area can be comprehensively considered to generate accurate cavitation damage measurement results for the turbine runner, avoid the limitations of the detection environment and operating conditions on cavitation damage detection, realize efficient cavitation damage detection, and thus improve the safety of hydropower equipment.

[0062] In an exemplary embodiment, the geometric features include normal vector information, curvature information and depth information, and the cavitation area is determined according to the geometric features of the point cloud data, including: dividing the point cloud data into at least one data set according to the normal vector information, curvature information and depth information of each point in the point cloud data; different data sets represent different turbine runner surface areas; determining the candidate cavitation area corresponding to the data set according to the normal vector information, curvature information and depth information of each point in the data set; and determining the cavitation area corresponding to the data set according to the intersection between the candidate cavitation areas.

[0063] The normal vector information may refer to a normal vector. In practical applications, the normal vector information may represent the local direction of each point on the surface of the point cloud.

[0064] The curvature information may refer to the information about the curvature of the point cloud surface at a certain point. In practical applications, the curvature information may include the average curvature or Gaussian curvature within the point and its neighborhood.

[0065] The depth information may refer to the coordinate data on a specific coordinate axis in the three-dimensional coordinates of each point in the point cloud data. In practical applications, the depth information may include the coordinate data of each point in the point cloud data in a direction perpendicular to the surface of the turbine runner.

[0066] Among them, the candidate cavitation area can refer to the area corresponding to the points on the surface of the turbine runner selected from the points represented by the point cloud data based on the differences between the geometric features of the points represented by the point cloud data or the similarity between the geometric features of the points represented by the point cloud data and the preset cavitation area analysis basis. In practical applications, the candidate cavitation area can be used as an area where cavitation may exist.

[0067] As an example, the server may divide the point cloud data into at least one data set based on the normal vector information, curvature information and depth information of each point in the point cloud data. For example, since the morphological features (such as geometric shape, concave-convex distribution) of different areas on the surface of a turbine runner may be different, the geometric features of the point cloud data of points corresponding to different areas on the surface of the turbine runner may be different. In this case, the server may compare the normal vector information, curvature information and depth information of each point in the point cloud data in pairs to determine whether the normal vector information satisfies a preset normal vector similarity condition, whether the curvature information satisfies a preset curvature similarity condition, and whether the depth information satisfies a preset depth similarity condition, and take the set of point cloud data of points that meet the corresponding similarity conditions as a data set. In this case, different data sets can represent different areas on the surface of a turbine runner. For each data set, the server can determine the candidate cavitation region corresponding to the data set based on the normal vector information, curvature information and depth information of each point in the data set. For example, the server can determine the first candidate cavitation region corresponding to the data set based on the normal vector information of each point in the data set. Similarly, the server can determine the second candidate cavitation region corresponding to the data set based on the curvature information of each point in the data set. The server can determine the third candidate cavitation region corresponding to the data set based on the depth information of each point in the data set. The server can then determine the intersection between the candidate cavitation regions and use the intersection between the candidate cavitation regions as the cavitation region corresponding to the data set.

[0068] In this embodiment, the point cloud data is divided into at least one data set according to the normal vector information, curvature information and depth information of each point in the point cloud data; different data sets represent different turbine runner surface areas; for each data set, the candidate cavitation area corresponding to the data set is determined according to the normal vector information, curvature information and depth information of each point in the data set; the cavitation area corresponding to the data set is determined according to the intersection between the candidate cavitation areas. Based on the geometric features of the point cloud data, the point cloud data can be first divided into data sets representing different areas, and the geometric features of the point cloud data in each data set can be carefully analyzed to determine the candidate cavitation areas, and the cavitation areas on the turbine runner surface can be accurately determined in combination with the intersection between the candidate cavitation areas, thereby improving the accuracy of cavitation area detection.

[0069] In some embodiments, the cavitation damage area, cavitation damage depth and cavitation damage volume are determined based on the point cloud data corresponding to the cavitation area, including: determining a point cloud model based on the point cloud data collected from the surface of the turbine runner; triangulating the point cloud model, identifying the boundary of the cavitation area, fitting a surface according to the interpolation method at the boundary points corresponding to the boundary, determining the repair area, and using the area of ​​the repair area as the cavitation damage area; determining a reference surface based on the point cloud data corresponding to the cavitation area, and determining the cavitation damage depth based on the distance between each point in the point cloud data corresponding to the cavitation area and the reference surface, and calculating the cavitation damage volume using the integral method.

[0070] The point cloud model may refer to a set of point cloud data collected from the surface of a turbine runner.

[0071] The repair area may refer to an area that needs to be repaired in the surface obtained after fitting the surface.

[0072] The reference curved surface may refer to a curved surface where the edge of the cavitation region is located.

[0073] As an example, the server can determine the point cloud model based on the point cloud data collected from the surface of the turbine runner, and triangulate the point cloud model. The point cloud model triangulation (also known as meshing or surface reconstruction) can convert discrete point cloud data into a continuous three-dimensional surface model. Then the server can use the point cloud model triangulation result (such as a three-dimensional surface model) to identify the boundary of the cavitation area. Then the server can fit the surface according to the interpolation method at the boundary points corresponding to the boundary to obtain the surface fitting result, and determine the repair area according to the surface fitting result. Then the server can calculate the area of ​​the repair area and use the area of ​​the repair area as the cavitation damage area. The server can first analyze the points corresponding to the boundary of the cavitation area according to the geometric features (such as depth information) of the point cloud data corresponding to the cavitation area, and use the plane where the points corresponding to the edge of the cavitation area are located as the reference surface. Then the server can calculate the distance between each point in the point cloud data corresponding to the cavitation area and the reference surface, and use the maximum value of the distance between each point in the point cloud data corresponding to the cavitation area and the reference plane as the cavitation damage depth. Then the server can also use the integration method to integrate the cavitation damage depth and calculate the cavitation damage volume. In practical applications, the server can also construct a mesh model of the cavitation area based on the point cloud data corresponding to the cavitation area, and determine the cavitation damage volume of each cavitation area based on the mesh model.

[0074] In this embodiment, a point cloud model is determined based on point cloud data collected from the surface of a turbine runner; the point cloud model is triangulated to identify the boundary of the cavitation area, and a surface is fitted at the boundary points corresponding to the boundary according to the interpolation method to determine the repair area, and the area of ​​the repair area is used as the cavitation damage area; a reference surface is determined based on the point cloud data corresponding to the cavitation area, and the cavitation damage depth is determined based on the distance between each point in the point cloud data corresponding to the cavitation area and the reference surface, and the cavitation damage volume is calculated using the integration method. The cavitation damage area, cavitation damage depth and cavitation damage volume can be accurately calculated, thereby improving the accuracy of the cavitation damage measurement results of the turbine runner.

[0075] In some embodiments, the cavitation damage measurement result of the turbine runner is determined based on the cavitation region, cavitation damage area, cavitation damage depth and cavitation damage volume, including: filling the cavitation damage area, cavitation damage depth and cavitation damage volume corresponding to the cavitation region into a preset report template to obtain a cavitation damage measurement report for the turbine runner; constructing a mesh model of the cavitation region based on the point cloud data corresponding to the cavitation region; and determining the cavitation damage measurement result of the turbine runner based on the cavitation damage measurement report for the turbine runner and the mesh model of the cavitation region.

[0076] Among them, the turbine runner cavitation damage measurement report may refer to a report obtained by filling in data such as the cavitation damage area, cavitation damage depth and cavitation damage volume corresponding to the cavitation area into the corresponding positions in the preset report template.

[0077] Among them, the mesh model of the cavitation area may refer to a model constructed based on point cloud data corresponding to the cavitation area according to a preset mesh model construction method. In practical applications, the mesh model of the cavitation area may include but is not limited to a three-dimensional model.

[0078] As an example, the server may first obtain a preset report template, which may be used to record data such as cavitation damage area, cavitation damage depth, and cavitation damage volume. The server may fill in the cavitation damage area, cavitation damage depth, and cavitation damage volume corresponding to the cavitation area to the corresponding position in the preset report template to obtain a turbine runner cavitation damage measurement report. The server may also construct a mesh model of the cavitation area based on the point cloud data corresponding to the cavitation area, and then the server may generate a turbine runner cavitation damage measurement result based on the turbine runner cavitation damage measurement report and the mesh model of the cavitation area.

[0079] In this embodiment, a turbine runner cavitation damage measurement report is obtained by filling in the cavitation damage area, cavitation damage depth and cavitation damage volume corresponding to the cavitation area into a preset report template; a grid model of the cavitation area is constructed according to the point cloud data corresponding to the cavitation area; the turbine runner cavitation damage measurement result is determined according to the turbine runner cavitation damage measurement report and the grid model of the cavitation area, and a damage measurement report can be generated based on the cavitation damage area, cavitation damage depth and cavitation damage volume corresponding to the cavitation area, and a grid model of the cavitation area can be constructed based on the point cloud data corresponding to the cavitation area, and an accurate turbine runner cavitation damage measurement result can be determined in combination with the damage measurement report and the grid model, thereby improving the accuracy of the turbine runner cavitation damage measurement result.

[0080] In some embodiments, obtaining point cloud data collected by a 3D laser scanner for the surface of a turbine runner includes: obtaining original point cloud data collected by a 3D laser scanner for the surface of the turbine runner; and performing noise reduction processing on the original point cloud data to obtain point cloud data collected for the surface of the turbine runner.

[0081] As an example, in order to avoid the influence of irrelevant data on cavitation damage detection, after obtaining the original point cloud data collected by the 3D laser scanner for the surface of the turbine runner, the server can perform streamlining processing such as noise reduction on the original point cloud data to obtain the point cloud data collected for the surface of the turbine runner, thereby reducing redundant data and optimizing computing efficiency.

[0082] In this embodiment, the original point cloud data collected by the 3D laser scanner on the surface of the turbine runner is obtained; the original point cloud data is denoised to obtain the point cloud data collected on the surface of the turbine runner. The original point cloud data can be denoised to reduce redundant data and exclude irrelevant data, thereby improving the detection efficiency and accuracy of cavitation damage detection.

[0083] In some embodiments, after the step of determining the measurement result of the cavitation damage of the turbine runner, the above method also includes: sending the measurement result of the cavitation damage of the turbine runner to a monitoring terminal; the monitoring terminal is used to display the measurement result of the cavitation damage of the turbine runner to a user.

[0084] The monitoring terminal may refer to a device used to display the measurement results of the turbine runner cavitation damage to the user. In practical applications, the monitoring terminal may include but is not limited to a host computer.

[0085] As an example, after the server determines the measurement result of the cavitation damage of the turbine runner, the server can send the measurement result of the cavitation damage of the turbine runner to the monitoring terminal, and the monitoring terminal can display the measurement result of the cavitation damage of the turbine runner to the user. In actual applications, the server can also encrypt the measurement result of the cavitation damage of the turbine runner before sending the measurement result of the cavitation damage of the turbine runner to the monitoring terminal, and then send the encrypted measurement result of the cavitation damage of the turbine runner to the monitoring terminal. After the monitoring terminal decrypts the encrypted measurement result of the cavitation damage of the turbine runner and the decryption is successful, it can display the measurement result of the cavitation damage of the turbine runner to the user in the form of text / charts.

[0086] In this embodiment, the turbine runner cavitation damage measurement result is sent to the monitoring terminal; the monitoring terminal is used to display the turbine runner cavitation damage measurement result to the user, and the turbine runner cavitation damage measurement result can be sent to the monitoring terminal in time, so that the turbine runner cavitation damage measurement result can be displayed to the user in time, thereby improving the display efficiency of the turbine runner cavitation damage measurement result.

[0087] In some embodiments, the conventional techniques of ultrasonic flaw detection, infrared thermal imaging, image analysis and sensor monitoring for cavitation damage measurement have certain deficiencies in terms of cost, ease of operation, detection accuracy or real-time performance. Therefore, in order to effectively improve the measurement efficiency of cavitation damage of turbine runners and further improve the safety and reliability of hydropower equipment, cavitation damage detection of turbine runners can be performed through a pre-built rapid measurement system for cavitation damage of turbine runners. The rapid measurement system for cavitation damage of turbine runners can implement the above-mentioned rapid measurement method for cavitation damage of turbine runners. The rapid measurement system for cavitation damage of turbine runners can include a 3D laser scanner, a point cloud data processing module, a data transmission module and a host computer monitoring terminal. Among them, the 3D laser scanner is used to obtain point cloud data on the surface of the turbine runner. The scanner has the characteristics of high precision and anti-interference, and can work normally under complex light and humidity conditions on site; the point cloud data processing module has the functions of point cloud data simplification, segmentation, cavitation area identification and data calculation, and can quickly process and analyze large-scale point cloud data; the data transmission module is responsible for transmitting the processed cavitation damage data to the host computer in real time for easy monitoring and storage; the host computer monitoring terminal is used to receive, display and store detection data, so that operators can view detailed information on cavitation damage and generate detection reports. Operators can adjust detection strategies or parameters in real time based on damage depth, damage area, damage volume and detection reports.

[0088] In practical applications, 3D laser scanners can include handheld scanners. The scanning area of ​​3D laser scanners ensures that the cavitation surface of the runner can be fully covered. Compared with traditional detection methods, laser scanning can not only capture the subtle cavitation pits and irregular morphology on the surface of the runner, but also generate high-precision three-dimensional models, laying the foundation for subsequent accurate analysis. The 3D laser scanner can be connected to the point cloud data processing module via USB or Ethernet to transmit the scanned point cloud data. The point cloud data processing module can realize the functions of data simplification, segmentation, identification of cavitation areas, and calculation of parameters such as damage area, depth and volume. High-performance MCU or embedded computing module (such as NVIDIA Jetson) can quickly process point cloud data through the point cloud data processing module, making the detection process more automated, reducing human operations and errors, and improving the reliability of detection results. The point cloud data processing module can receive the point cloud data of the 3D laser scanner and transmit the processing results to the data transmission module. The data transmission module can be integrated with the point cloud data processing module, or located near the control console. The data transmission module can transmit the processed cavitation detection data to the host computer via wireless (such as Wi-Fi, 4G) or wired mode. The data transmission module can use common wireless communication modules (such as ESP8266, 4G modules). The data transmission module can be connected to the point cloud data processing module to transmit the results to the host computer monitoring terminal. The host computer monitoring terminal can be located in the control room where the operator is located, and is used to receive, display and store the detection data and generate the detection report. The host computer monitoring terminal can use an industrial computer, or a PC host with monitoring software. The host computer monitoring terminal can receive the cavitation detection results sent by the data transmission module through the network interface.

[0089] In the specific implementation, 3D laser scanners can use high-precision industrial-grade laser scanners (such as FARO, Leica), which have strong environmental adaptability and meet the requirements of on-site scanning. The point cloud data processing module can use an embedded processing platform (such as NVIDIA Jetson Nano / AGX) or an MCU with AI acceleration. The point cloud data processing module includes a CPU / GPU, a data storage chip, and an interface control chip for fast processing of large-scale point cloud data. If fast transmission is required, the data transmission module can use a Wi-Fi / 4G module. The data transmission module is composed of a microcontroller and a communication chip (such as ESP8266 or SIM7600) to upload data. The host monitoring terminal can use a common industrial computer with customized monitoring software.

[0090] like Figure 3As shown, a schematic diagram of a process for quickly determining cavitation damage of a turbine runner is provided. First, the equipment is initialized and the site is prepared: the 3D laser scanner is installed at a suitable position near the turbine runner to ensure that the scanner can cover the cavitation area on the runner surface. The 3D laser scanner is started, calibrated and initialized. After that, the laser scanner starts to scan the surface of the turbine runner and collect point cloud data. The point cloud data is the fine three-dimensional information of the runner surface, including the microscopic morphology of the cavitation damage area. Point cloud data processing can include data reduction, data simplification and data calculation, wherein data reduction can automatically reduce noise and simplify the original point cloud data to reduce redundant data and optimize calculation efficiency. Segmentation and recognition can be based on geometric features such as geometric features and depth changes, by segmenting the point cloud data of the runner surface into different areas, and identifying the damaged area with cavitation characteristics, thereby ensuring high-precision identification of the cavitation area. After the point cloud data of different areas are transmitted to the data processing module, the data processing module can automatically and accurately calculate the damage parameters such as the damaged area, maximum depth, average depth and cavitation volume for the cavitation damage area. Afterwards, the detection results of cavitation damage such as damage parameters can be transmitted to the host computer monitoring terminal in real time through the data transmission module, so that the operator can view and analyze at any time. The host computer displays the detected cavitation damage data in the form of graphics and numbers, including the three-dimensional model of the cavitation area, the depth distribution map and the damage parameter table. The host computer can also generate a complete detection report for archiving and subsequent analysis. It is understandable that the server can also display the detected cavitation damage data in the form of graphics and numbers, including the three-dimensional model of the cavitation area, the depth distribution map and the damage parameter table. The server can also generate a detection report and send the three-dimensional model of the cavitation area, the depth distribution map and the damage parameter table and the detection report to the host computer monitoring terminal.

[0091] In this embodiment, the amount of point cloud data is reduced through data denoising and sampling, redundant points are reduced, and processing speed is improved. Subsequently, the point cloud data is segmented based on geometric features and depth changes, the wheel surface is divided into regions, and the point cloud data of the cavitation damage area is extracted, which realizes efficient data management and provides a concise and clear data basis for cavitation identification and subsequent calculations. Through specific graphics and depth algorithms, the processing program can automatically identify areas with cavitation characteristics. This recognition process determines the cavitation damage area based on the depth gradient of the point cloud data and the change of surface morphology. After identifying the cavitation area, the program automatically extracts its area, boundary contour, and identifies the maximum and average values ​​of the damage depth. After identifying the cavitation area, the point cloud data of the cavitation area is analyzed to accurately calculate the volume of the damage, fully considering the irregularity of the cavitation area, and integrating based on the three-dimensional model to ensure the accuracy of the volume calculation. During the detection process, all processed cavitation data will be transmitted to the host computer in real time via wireless or wired means, so that the on-site detection results can be viewed instantly, avoiding the tedious steps of data storage and organization in traditional detection methods, and providing immediate decision support for operation and maintenance personnel. The turbine runner cavitation damage rapid determination system is compactly designed, easy to detect on-site turbines, can be modularly assembled, and is equipped with a portable power supply to adapt to a variety of detection environments. By simplifying the operating process, the convenience of the detection equipment is improved, which is suitable for the frequent and rapid cavitation damage determination needs of hydropower plants.

[0092] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0093] Based on the same inventive concept, the embodiment of the present application also provides a device for quickly determining cavitation damage of a turbine runner for implementing the method for quickly determining cavitation damage of a turbine runner involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the method above, so the specific limitations in one or more embodiments of the device for quickly determining cavitation damage of a turbine runner provided below can refer to the limitations of the method for quickly determining cavitation damage of a turbine runner above, and will not be repeated here.

[0094] In an exemplary embodiment, Figure 4 As shown, a rapid determination device for cavitation damage of a turbine runner is provided, comprising: a data acquisition module 402, a region analysis module 404, a parameter calculation module 406 and a result determination module 408, wherein:

[0095] The data acquisition module 402 is used to acquire point cloud data collected by a 3D laser scanner on the surface of the turbine runner.

[0096] The region analysis module 404 is used to determine the cavitation region according to the geometric features of the point cloud data.

[0097] The parameter calculation module 406 is used to determine the cavitation damage area, the cavitation damage depth and the cavitation damage volume according to the point cloud data corresponding to the cavitation area.

[0098] The result determination module 408 is used to determine the cavitation damage measurement result of the turbine runner according to the cavitation region, the cavitation damage area, the cavitation damage depth and the cavitation damage volume.

[0099] In one of the exemplary embodiments, the geometric features include normal vector information, curvature information and depth information, and the regional analysis module 404 is specifically used to divide the point cloud data into at least one data set according to the normal vector information, curvature information and depth information of each point in the point cloud data; different data sets represent different turbine runner surface areas; determine the candidate cavitation areas corresponding to the data sets according to the normal vector information, curvature information and depth information of each point in the data sets; and determine the cavitation areas corresponding to the data sets according to the intersection between the candidate cavitation areas.

[0100] In one of the exemplary embodiments, the parameter calculation module 406 is specifically used to determine a point cloud model based on the point cloud data collected from the surface of the turbine runner; triangulate the point cloud model, identify the boundary of the cavitation area, fit the surface according to the interpolation method at the boundary points corresponding to the boundary, determine the repair area, and use the area of ​​the repair area as the cavitation damage area; determine a reference surface based on the point cloud data corresponding to the cavitation area, and determine the cavitation damage depth based on the distance between each point in the point cloud data corresponding to the cavitation area and the reference surface, and calculate the cavitation damage volume using the integration method.

[0101] In one of the exemplary embodiments, the result determination module 408 is specifically used to fill in the cavitation damage area, cavitation damage depth and cavitation damage volume corresponding to the cavitation region into a preset report template to obtain a turbine runner cavitation damage measurement report; construct a mesh model of the cavitation region based on the point cloud data corresponding to the cavitation region; determine the turbine runner cavitation damage measurement result based on the turbine runner cavitation damage measurement report and the mesh model of the cavitation region.

[0102] In one of the exemplary embodiments, the data acquisition module 402 is specifically used to acquire the original point cloud data collected by the 3D laser scanner for the surface of the turbine runner; perform noise reduction processing on the original point cloud data to obtain the point cloud data collected for the surface of the turbine runner.

[0103] In one of the exemplary embodiments, the device further includes a result sending module, which is specifically used to send the turbine runner cavitation damage measurement result to a monitoring terminal; the monitoring terminal is used to display the turbine runner cavitation damage measurement result to a user.

[0104] Each module in the above-mentioned rapid determination device for cavitation damage of a turbine runner can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0105] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store point cloud data, etc. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for quickly determining cavitation damage of a turbine runner is implemented.

[0106] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0107] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0108] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0109] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0110] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0111] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0112] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0113] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for quickly determining cavitation damage of a turbine runner, characterized in that: The method comprises: Obtain point cloud data collected by a 3D laser scanner on the surface of a turbine runner; Determining a cavitation area according to geometric features of the point cloud data; Determining the cavitation damage area, cavitation damage depth and cavitation damage volume according to the point cloud data corresponding to the cavitation area; The measurement result of the cavitation damage of the turbine runner is determined according to the cavitation region, the cavitation damage area, the cavitation damage depth and the cavitation damage volume.

2. The method according to claim 1, characterized in that The geometric features include normal vector information, curvature information and depth information. The step of determining the cavitation area according to the geometric features of the point cloud data includes: According to normal vector information, curvature information and depth information of each point in the point cloud data, the point cloud data is divided into at least one data set; different data sets represent different turbine runner surface areas; Determine a candidate cavitation region corresponding to the data set according to normal vector information, curvature information and depth information of each point in the data set; The cavitation region corresponding to the data set is determined according to the intersection between the candidate cavitation regions.

3. The method according to claim 1, characterized in that Determining the cavitation damage area, the cavitation damage depth and the cavitation damage volume according to the point cloud data corresponding to the cavitation area includes: Determining a point cloud model according to the point cloud data collected from the surface of the turbine runner; triangulate the point cloud model, identify the boundary of the cavitation area, fit a surface at the boundary points corresponding to the boundary according to the interpolation method, determine a repair area, and use the area of ​​the repair area as the cavitation damage area; A reference curved surface is determined according to the point cloud data corresponding to the cavitation area, and the cavitation damage depth is determined according to the distance between each point in the point cloud data corresponding to the cavitation area and the reference curved surface, and the cavitation damage volume is calculated by an integral method.

4. The method according to claim 1, characterized in that: Determining the measurement result of the cavitation damage of the turbine runner according to the cavitation region, the cavitation damage area, the cavitation damage depth and the cavitation damage volume includes: Fill the cavitation damage area, cavitation damage depth and cavitation damage volume corresponding to the cavitation area into a preset report template to obtain a turbine runner cavitation damage measurement report; Constructing a mesh model of the cavitation area according to the point cloud data corresponding to the cavitation area; The water turbine runner cavitation damage measurement result is determined according to the water turbine runner cavitation damage measurement report and the grid model of the cavitation area.

5. The method according to claim 1, characterized in that The step of acquiring point cloud data collected by a 3D laser scanner on the surface of a turbine runner includes: Obtaining original point cloud data collected by a 3D laser scanner on the surface of a turbine runner; The original point cloud data is subjected to noise reduction processing to obtain the point cloud data collected on the surface of the turbine runner.

6. The method according to claim 1, characterized in that After the step of determining the measurement result of the turbine runner cavitation damage, the method further comprises: The water turbine runner cavitation damage measurement result is sent to a monitoring terminal; the monitoring terminal is used to display the water turbine runner cavitation damage measurement result to a user.

7. A rapid determination device for cavitation damage of a turbine runner, characterized in that: The device comprises: A data acquisition module, used to acquire point cloud data collected by a 3D laser scanner on the surface of a turbine runner; A regional analysis module, used for determining a cavitation region according to geometric features of the point cloud data; A parameter calculation module, used to determine the cavitation damage area, cavitation damage depth and cavitation damage volume according to the point cloud data corresponding to the cavitation area; The result determination module is used to determine the cavitation damage measurement result of the turbine runner according to the cavitation region, the cavitation damage area, the cavitation damage depth and the cavitation damage volume.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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