Rapid determination method, apparatus and computer equipment for cavitation damage of turbine runners

Abstract

This application relates to a method, apparatus, and computer equipment for rapid determination of cavitation damage in a turbine runner. The method includes: acquiring point cloud data collected by a 3D laser scanner on the surface of the turbine runner; determining the cavitation erosion region based on the geometric features of the point cloud data; determining the cavitation damage area, cavitation damage depth, and cavitation damage volume based on the point cloud data corresponding to the cavitation erosion region; and determining the cavitation damage measurement result of the turbine runner based on the cavitation erosion region, cavitation damage area, cavitation damage depth, and cavitation damage volume. This method leverages the high precision and anti-interference characteristics of the 3D laser scanner to identify cavitation erosion areas using the collected point cloud data, calculate the area, depth, and volume of the cavitation erosion region, and generate accurate cavitation damage measurement results for the turbine runner. This avoids the limitations imposed by the detection environment and operating conditions on cavitation damage detection, achieving efficient cavitation damage detection and thus improving the safety of hydroelectric equipment.

Description

Technical Field

[0001] This application relates to the field of water turbine equipment maintenance and testing technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium and computer program product for rapid determination of cavitation damage of water turbine runner. Background Technology

[0002] The turbine is the core equipment of a hydropower station, and its performance and reliability directly affect the power generation efficiency and safety of the station. However, due to the impact of high-speed water flow during long-term operation, the turbine runner surface is prone to cavitation erosion. Cavitation erosion is caused by the micro-impact force resulting from the collapse of air bubbles in the water flow. Over time, this can gradually damage the material surface, manifesting as pitting, cratering, and even peeling, severely affecting the turbine's lifespan and performance.

[0003] Traditional techniques for detecting cavitation damage in turbine runners require a long testing time and are limited by the testing environment and operating conditions. This is especially true for on-site testing of large turbines, where the operation is inconvenient and inefficient, which is detrimental to improving the safety of hydropower equipment. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for rapidly determining cavitation damage of turbine runners that can improve the safety of hydroelectric equipment, in response to the above-mentioned technical problems.

[0005] In a first aspect, this application provides a method for rapid determination of cavitation damage in a turbine runner, comprising:

[0006] Acquire point cloud data from a 3D laser scanner on the surface of a water turbine runner;

[0007] Based on the geometric characteristics of the point cloud data, the cavitation region is determined;

[0008] Based on the point cloud data corresponding to the cavitation erosion area, the cavitation erosion damage area, cavitation erosion damage depth, and cavitation erosion damage volume are determined.

[0009] The cavitation damage measurement results of the turbine runner are determined based on the cavitation area, 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. Determining the erosion region based on the geometric features of the point cloud data includes:

[0011] Based on the 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 regions.

[0012] Based on the normal vector information, curvature information and depth information of each point in the data set, the candidate cavitation region corresponding to the data set is determined;

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

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

[0015] Based on the point cloud data collected from the surface of the turbine runner, a point cloud model is determined;

[0016] The point cloud model is triangulated to identify the boundary of the cavitation region. A surface is fitted at the boundary points corresponding to the boundary using interpolation to determine the repair area. The area of ​​the repair area is then used as the cavitation damage area.

[0017] Based on the point cloud data corresponding to the cavitation region, a reference surface is determined, and based on the distance between each point in the point cloud data corresponding to the cavitation region and the reference surface, the cavitation damage depth is determined, and the cavitation damage volume is calculated using an integral method.

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

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

[0020] Based on the point cloud data corresponding to the cavitation area, a mesh model of the cavitation area is constructed;

[0021] Based on the turbine runner cavitation damage measurement report and the grid model of the cavitation area, the measurement results of the turbine runner cavitation damage are determined.

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

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

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

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

[0026] Secondly, this application also provides a rapid measurement device for cavitation damage of a turbine runner, comprising:

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

[0028] The region analysis module is used to determine the cavitation region based on the geometric characteristics of the point cloud data;

[0029] The parameter calculation module is used to determine the cavitation damage area, cavitation damage depth, and cavitation damage volume based on the point cloud data corresponding to the cavitation erosion area.

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

[0031] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program that, when executed by the processor, implements the steps of the method described above.

[0032] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the above-described method.

[0033] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the above-described method.

[0034] The aforementioned method, apparatus, computer equipment, computer-readable storage medium, and computer program product for rapid determination of cavitation damage in turbine runners acquire point cloud data collected by a 3D laser scanner on the surface of the turbine runner. This allows for high-precision scanning of the turbine runner surface using a 3D laser scanner, generating accurate point cloud data. Based on the geometric characteristics of the point cloud data, cavitation erosion areas are determined, and the geometric features of the point cloud data are analyzed to identify areas of cavitation erosion within the turbine runner. Based on the point cloud data corresponding to the cavitation erosion areas, 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 erosion areas. Finally, based on the cavitation erosion areas, cavitation damage area, cavitation damage depth, and cavitation damage volume, the cavitation damage of the turbine runner is determined. The damage measurement results, combined with the cavitation erosion area and its damage area, depth, and volume, determine accurate measurement results, enabling rapid measurement of cavitation erosion damage in turbine runners. Leveraging the high precision and anti-interference characteristics of 3D laser scanners, accurate point cloud data is collected from the turbine runner surface using a 3D laser scanner. By analyzing the geometric features of the point cloud data, the cavitation erosion area is identified, and the damage area, depth, and volume of the cavitation erosion area are calculated. This comprehensive analysis of the cavitation erosion area and its damage area, depth, and volume generates accurate cavitation erosion damage measurement results for the turbine runner. This avoids limitations imposed by the testing environment and operating conditions on cavitation erosion damage detection, achieving efficient cavitation erosion damage detection and ultimately improving the safety of hydroelectric equipment. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is an application environment diagram of a method for rapid determination of cavitation damage in a water turbine runner, as shown in one embodiment.

[0037] Figure 2 This is a flowchart illustrating a method for rapid determination of cavitation damage in a water turbine runner, as described in one embodiment.

[0038] Figure 3 This is a schematic diagram of a process for rapidly determining cavitation damage to a turbine runner in one embodiment;

[0039] Figure 4 This is a structural block diagram of a rapid cavitation damage measurement device for a water turbine runner in one embodiment;

[0040] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0041] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0042] The rapid determination method for cavitation damage of turbine runners provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed in the cloud or on other network servers. Server 104 acquires point cloud data collected by a 3D laser scanner from the surface of the turbine runner; server 104 determines the cavitation erosion area based on the geometric characteristics of the point cloud data; server 104 determines the cavitation damage area, cavitation damage depth, and cavitation damage volume based on the point cloud data corresponding to the cavitation erosion area; server 104 determines the cavitation damage measurement results of the turbine runner based on the cavitation erosion area, cavitation damage area, cavitation damage depth, and cavitation damage volume. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, portable wearable devices, and dashboards. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0043] In an exemplary embodiment, Figure 2 As shown, a rapid method for determining cavitation damage in a water turbine runner is provided. Taking the application of this method to a server as an example, the method includes the following steps S202 to S208. Wherein:

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

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

[0046] The turbine runner surface can refer to the outer surface of the components in a turbine that are in direct contact with water flow and utilize the energy of the water flow to rotate.

[0047] Point cloud data refers 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 water turbine runner). The three-dimensional coordinate points are determined by emitting a laser beam from the 3D laser scanner and measuring the time or phase change of the laser beam reflected back from the object surface. Each point in the point cloud represented by the point cloud data can contain three basic coordinate values ​​(X, Y, Z) and other information. 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 features high precision and anti-interference capabilities, enabling it to operate normally under complex lighting and humidity conditions. The 3D laser scanner can emit a laser beam onto the surface of a water turbine runner and measure the time or phase change of the laser beam reflected back from the runner's surface. This allows it to determine the coordinates and other data of at least one point on the runner's surface. The coordinates and other data of several points on the runner's surface output by the 3D laser scanner can serve as point cloud data collected by the scanner. The server can then send a data acquisition request to the 3D laser scanner. In response, the scanner sends the point cloud data to the server, allowing the server to acquire the point cloud data collected by the scanner from the runner's surface.

[0049] Step S204: Determine the cavitation region based on the geometric characteristics of the point cloud data.

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

[0051] Among them, the cavitation area can refer to the area on the surface of the turbine runner where cavitation occurs.

[0052] As an example, the server can first determine the geometric features of the point cloud data, such as the three-dimensional coordinates, normal vectors, curvature, and density distribution, based on the point cloud data. Then, it can compare the geometric features of each point represented by the point cloud data pairwise, 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: Determine the cavitation damage area, cavitation damage depth, and cavitation damage volume based on the point cloud data corresponding to the cavitation erosion area.

[0054] Among them, the cavitation damage area can refer to information that characterizes the area of ​​the cavitation erosion region.

[0055] Among them, cavitation damage depth can refer to information that characterizes the depth of the cavitation erosion area.

[0056] Among them, cavitation damage volume can refer to information that characterizes the volume of the cavitation erosion region.

[0057] As an example, for each cavitation erosion 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 erosion area. The server can also 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 erosion area. Furthermore, the server can construct a three-dimensional model based on the point cloud data corresponding to the cavitation erosion area and use the three-dimensional model to determine the cavitation damage volume.

[0058] Step S208: Determine the cavitation damage measurement results of the turbine runner based on the cavitation area, cavitation damage area, cavitation damage depth, and cavitation damage volume.

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

[0060] As an example, the server can generate textual descriptions of the location of each cavitation region on the turbine runner surface, as well as the measurement results of the cavitation damage area, depth, and volume of each cavitation region, based on the cavitation erosion area, cavitation damage area, cavitation damage depth, and cavitation damage volume. This allows the server to determine the turbine runner cavitation damage measurement results. Alternatively, the server can generate charts or graphs displaying the location of each cavitation region on the turbine runner surface, as well as the measurement results of the cavitation damage area, depth, and volume of each cavitation region, based on the same data. This also allows the server to determine the turbine runner cavitation damage measurement results.

[0061] In the aforementioned rapid determination method for cavitation damage of a turbine runner, point cloud data collected by a 3D laser scanner on the surface of the turbine runner is acquired. This allows for high-precision scanning of the turbine runner surface using the 3D laser scanner, generating accurate point cloud data. Based on the geometric characteristics of the point cloud data, cavitation areas are determined. Further analysis of these geometric characteristics identifies the regions within the turbine runner where cavitation exists. The cavitation damage area, depth, and volume are determined based on the corresponding point cloud data, enabling accurate analysis and calculation of these damage parameters. Finally, the cavitation damage measurement results are determined based on the cavitation area, damage area, depth, and volume, thus combining the cavitation damage data with the determination of the cavitation damage... This method accurately measures the damage area, depth, and volume of cavitation erosion zones in turbine runners, enabling rapid measurement. Leveraging the high precision and interference resistance of 3D laser scanners, it collects accurate point cloud data from the turbine runner surface. By analyzing the geometric features of this point cloud data, it identifies cavitation erosion areas and calculates the damage area, depth, and volume. This comprehensive analysis of the cavitation erosion zones and their damage areas and volumes generates accurate measurement results for turbine runner cavitation erosion damage. This approach avoids limitations imposed by environmental and operational conditions on cavitation erosion damage detection, achieving efficient detection and ultimately improving the safety of hydroelectric equipment.

[0062] In an exemplary embodiment, the geometric features include normal vector information, curvature information, and depth information. Determining the cavitation region based on the geometric features of the point cloud data includes: dividing 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; different data sets represent different turbine runner surface regions; determining candidate cavitation regions corresponding to each data set based on the normal vector information, curvature information, and depth information of each point in the data set; and determining the cavitation region corresponding to the data set based on the intersection of the candidate cavitation regions.

[0063] The normal vector information can refer to the normal vector. In practical applications, the normal vector information can characterize the local direction at each point on the point cloud surface.

[0064] Curvature information can refer to the degree of curvature of a point cloud surface at a certain point. In practical applications, curvature information can include the average curvature or Gaussian curvature of the point and its neighborhood.

[0065] Depth information can refer to the coordinate data of each point in the three-dimensional coordinates of a specific coordinate axis. In practical applications, depth information can 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 region can refer to the difference between the geometric features of each point represented by the point cloud data or the similarity between the geometric features of each point represented by the point cloud data and the preset cavitation region analysis basis. The region corresponding to the points selected from the points represented by the point cloud data on the surface of the turbine runner. In practical applications, the candidate cavitation region can be regarded as a region where cavitation may occur.

[0067] As an example, the server can 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 characteristics (such as geometric shape and concavity / convexity distribution) of different regions on the surface of a turbine runner can be different, the geometric characteristics of the point cloud data corresponding to different regions on the surface of the turbine runner can also be different. In this case, the server can compare the normal vector information, curvature information, and depth information of each point in the point cloud data pairwise to determine whether the normal vector information meets the preset normal vector similarity condition, whether the curvature information meets the preset curvature similarity condition, and whether the depth information meets the preset depth similarity condition. The set of point cloud data of points that meet the corresponding similarity conditions is then taken as a data set. At this time, different data sets can represent different regions on the surface of the turbine runner. For each dataset, the server can determine the candidate void regions corresponding to the dataset based on the normal vector information, curvature information, and depth information of each point in the dataset. For example, the server can determine the first candidate void region corresponding to the dataset based on the normal vector information of each point in the dataset. Similarly, the server can determine the second candidate void region corresponding to the dataset based on the curvature information of each point in the dataset. The server can determine the third candidate void region corresponding to the dataset based on the depth information of each point in the dataset. Then, the server can determine the intersection between the candidate void regions and take the intersection between the candidate void regions as the void region corresponding to the dataset.

[0068] In this embodiment, the point cloud data is divided 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. Different data sets represent different surface regions of the turbine runner. For each data set, candidate cavitation regions are determined based on the 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 based on the intersection between the candidate cavitation regions. This method can first divide the point cloud data into data sets representing different regions based on the geometric features of the point cloud data, and then carefully analyze the geometric features of the point cloud data in each data set to determine the candidate cavitation regions. Finally, by combining the intersection between the candidate cavitation regions, the cavitation regions on the surface of the turbine runner can be accurately determined, thereby improving the accuracy of cavitation region detection.

[0069] In some embodiments, determining the cavitation damage area, cavitation damage depth, and cavitation damage volume based on the point cloud data corresponding to the cavitation erosion area includes: determining a point cloud model based on point cloud data collected from the surface of the turbine runner; triangulating the point cloud model to identify the boundary of the cavitation erosion area, fitting a surface at the boundary points corresponding to the boundary using an interpolation method to determine 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 erosion area, and determining the cavitation damage depth based on the distance between each point in the point cloud data corresponding to the cavitation erosion area and the reference surface, and calculating the cavitation damage volume using an integral method.

[0070] Among them, the point cloud model can refer to the collection of point cloud data collected from the surface of the turbine runner.

[0071] The repaired area can refer to the area in the surface that needs to be repaired after fitting the surface.

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

[0073] As an example, the server can determine a point cloud model based on point cloud data collected from the surface of a water turbine runner, and then triangulate this model. Point cloud model triangulation (also known as meshing or surface reconstruction) converts discrete point cloud data into a continuous three-dimensional surface model. The server can then use the triangulated result (e.g., the three-dimensional surface model) to identify the boundaries of cavitation erosion areas. Next, the server can fit a surface at the boundary points using interpolation, obtaining the surface fitting result. Based on this result, the server determines the repair area and calculates its area as the cavitation damage area. Alternatively, the server can analyze the points corresponding to the boundaries of the cavitation erosion area based on the geometric features (e.g., depth information) of the point cloud data. The server uses the plane containing the points at the edge of the cavitation erosion area as the reference surface. Then, the server calculates the distances between each point in the point cloud data corresponding to the cavitation erosion area and the reference surface, and uses the maximum distance between these distances as the cavitation damage depth. Finally, the server can integrate the cavitation damage depth using an integral method to calculate the cavitation damage volume. In practical applications, the server can also construct a mesh model of the cavitation erosion area based on the point cloud data corresponding to the cavitation erosion area, and determine the cavitation damage volume of each cavitation erosion 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 the turbine runner. The point cloud model is triangulated to identify the boundaries of the cavitation erosion area. A surface is fitted at the boundary points corresponding to the boundaries using interpolation 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 erosion area. The cavitation damage depth is determined based on the distance between each point in the point cloud data corresponding to the cavitation erosion area and the reference surface. The cavitation damage volume is calculated using an integral method. This method can accurately calculate the cavitation damage area, cavitation damage depth, and cavitation damage volume, thereby improving the accuracy of the turbine runner cavitation damage measurement results.

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

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

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

[0078] As an example, the server can first obtain a preset report template, which can be used to record data such as cavitation damage area, cavitation damage depth, and cavitation damage volume. The server can then fill in the corresponding positions in the preset report template with the cavitation damage area, cavitation damage depth, and cavitation damage volume corresponding to the cavitation area to obtain a turbine runner cavitation damage measurement report. The server can also construct a mesh model of the cavitation area based on the point cloud data corresponding to the cavitation area. Afterward, the server can generate the turbine runner cavitation damage measurement results 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 erosion region into a preset report template. A mesh model of the cavitation erosion region is constructed based on the point cloud data corresponding to the cavitation erosion region. The turbine runner cavitation damage measurement result is determined based on the turbine runner cavitation damage measurement report and the mesh model of the cavitation erosion region. This allows for the generation of a damage measurement report based on the cavitation damage area, cavitation damage depth, and cavitation damage volume corresponding to the cavitation erosion region, the construction of a mesh model of the cavitation erosion region based on the point cloud data corresponding to the cavitation erosion region, and the combination of the damage measurement report and the mesh model to determine an accurate turbine runner cavitation damage measurement result, thereby improving the accuracy of the turbine runner cavitation damage measurement result.

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

[0081] As an example, to avoid the influence of irrelevant data on cavitation damage detection, after the server obtains the raw point cloud data collected by the 3D laser scanner on the surface of the turbine runner, it can perform noise reduction and other simplification processes on the raw point cloud data to obtain the point cloud data collected on the surface of the turbine runner, thereby reducing redundant data and optimizing computational efficiency.

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

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

[0084] The monitoring terminal can refer to the device used to display the measurement results of cavitation damage of the turbine runner 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 cavitation damage measurement results of the turbine runner, it can send the results to the monitoring terminal. The monitoring terminal can then display these results to the user. In practical applications, before sending the cavitation damage measurement results to the monitoring terminal, the server can also encrypt them. The encrypted results are then sent to the monitoring terminal. After successful decryption, the monitoring terminal can display the cavitation damage measurement results to the user in text / chart format.

[0086] In this embodiment, the cavitation damage measurement results of the turbine runner are sent to the monitoring terminal. The monitoring terminal is used to display the cavitation damage measurement results of the turbine runner to the user. It can send the cavitation damage measurement results of the turbine runner to the monitoring terminal in a timely manner, so as to display the cavitation damage measurement results of the turbine runner to the user in a timely manner and improve the display efficiency of the cavitation damage measurement results of the turbine runner.

[0087] In some embodiments, conventional techniques such as ultrasonic flaw detection, infrared thermal imaging, image analysis, and sensor monitoring for cavitation damage determination have certain shortcomings in terms of cost, ease of operation, detection accuracy, or real-time performance. Therefore, to effectively improve the efficiency of cavitation damage determination in turbine runners and further enhance the safety and reliability of hydropower equipment, a pre-constructed rapid cavitation damage determination system for turbine runners can be used for cavitation damage detection. This rapid cavitation damage determination system can implement the aforementioned rapid cavitation damage determination method for turbine runners. The system may include a 3D laser scanner, a point cloud data processing module, a data transmission module, and a host computer monitoring terminal. The 3D laser scanner is used to acquire point cloud data of the turbine runner surface. The scanner features high precision and anti-interference capabilities, enabling it to operate normally under complex lighting and humidity conditions. The point cloud data processing module has functions for simplification, segmentation, cavitation area identification, and data calculation of point cloud data, allowing for rapid processing and analysis of 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 monitoring and storage. The host computer monitoring terminal receives, displays, and stores the detection data, allowing operators to 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 ​​a 3D laser scanner ensures complete coverage of the cavitation surface of the rotating wheel. Compared to traditional detection methods, laser scanning can not only capture minute cavitation pits and irregular shapes on the wheel surface but also generate high-precision 3D models, laying the foundation for subsequent accurate analysis. The 3D laser scanner can connect to a point cloud data processing module via USB or Ethernet to transmit the scanned point cloud data. The point cloud data processing module can perform data simplification, segmentation, identification of cavitation areas, and calculation of parameters such as damage area, depth, and volume. High-performance MCUs or embedded computing modules (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 operation and errors, and improving the reliability of detection results. The point cloud data processing module can receive point cloud data from 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. It transmits the processed cavitation detection data to the host computer via wireless (e.g., Wi-Fi, 4G) or wired connections. The data transmission module can use common wireless communication modules (e.g., ESP8266, 4G modules). It can also connect 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. It receives, displays, and stores the detection data and generates detection reports. The host computer monitoring terminal can be an industrial computer or a PC host with monitoring software. It can receive the cavitation detection results sent by the data transmission module via a network interface.

[0089] In practical implementation, a high-precision industrial-grade laser scanner (such as FARO or Leica) can be selected for the 3D laser scanner, which has strong environmental adaptability and meets on-site scanning requirements. The point cloud data processing module can use an embedded processing platform (such as NVIDIA Jetson Nano / AGX) or an AI-accelerated MCU. Internally, the point cloud data processing module includes a CPU / GPU, data storage chip, and interface control chip for rapidly processing large-scale point cloud data. If rapid transmission is required, a Wi-Fi / 4G module can be used. Internally, the data transmission module consists of a microcontroller and communication chip (such as ESP8266 or SIM7600) responsible for data uploading. The host computer monitoring terminal can use a common industrial computer, coupled with custom monitoring software.

[0090] like Figure 3The diagram illustrates a process for rapidly determining cavitation damage on a turbine runner. First, equipment initialization and site preparation are performed: a 3D laser scanner is installed at a suitable location near the turbine runner, ensuring it can cover the cavitation areas on the runner surface. The 3D laser scanner is then started for calibration and initialization. The scanner then begins scanning the turbine runner surface, acquiring point cloud data. This point cloud data provides detailed three-dimensional information about the runner surface, including the microscopic morphology of the cavitation damage areas. Point cloud data processing includes data simplification, data reduction, and data calculation. Data simplification automatically reduces noise and simplifies the original point cloud data to minimize redundant data and optimize computational efficiency. Segmentation and identification are based on geometric features such as depth variations. By segmenting the point cloud data on the runner surface into different regions and identifying damage areas with cavitation characteristics, high-precision identification of cavitation areas is ensured. After the point cloud data from different regions is transmitted to the data processing module, the module automatically and accurately calculates damage parameters such as the area, maximum depth, average depth, and cavitation volume of the cavitation damage areas. Subsequently, the detection results of cavitation erosion damage, including damage parameters, can be transmitted in real time to the host computer monitoring terminal via the data transmission module, allowing operators to view and analyze them at any time. The host computer displays the detected cavitation erosion damage data in graphical and numerical form, including a 3D model of the cavitation erosion area, a depth distribution map, and a damage parameter table. The host computer can also generate a complete detection report for archiving and subsequent analysis. Similarly, the server can also display the detected cavitation erosion damage data in graphical and numerical form, including a 3D model of the cavitation erosion area, a depth distribution map, and a damage parameter table. The server can also generate a detection report and send the 3D model of the cavitation erosion area, the depth distribution map, the damage parameter table, and the detection report to the host computer monitoring terminal.

[0091] In this embodiment, data denoising and sampling are used to reduce the amount of point cloud data, decrease redundant points, and improve processing speed. Subsequently, the point cloud data is segmented based on geometric features and depth variations, dividing the wheel surface into regions and extracting point cloud data from cavitation damage areas. This achieves efficient data management and provides a concise and clear data foundation for cavitation identification and subsequent calculations. Through specific graphics and depth algorithms, the processing program can automatically identify regions with cavitation characteristics. This identification process determines cavitation damage areas based on the depth gradient and surface morphology changes in the point cloud data. After identifying the cavitation erosion area, the program automatically extracts its area and boundary contour, and identifies the maximum and average values ​​of the damage depth. Following the identification of the cavitation erosion area, the program analyzes the point cloud data of the cavitation erosion area to accurately calculate the damage volume. It fully considers the irregularity of the cavitation erosion area and performs integration based on a 3D model to ensure the accuracy of the volume calculation. During the detection process, all processed cavitation erosion data is transmitted to the host computer in real time via wireless or wired means, allowing for immediate viewing of on-site detection results. This avoids the cumbersome data storage and organization steps of traditional detection methods, providing immediate decision support for maintenance personnel. The turbine runner cavitation damage rapid measurement system has a compact design, facilitating on-site detection at the turbine. It can be modularly assembled and equipped with a portable power supply, adapting to various detection environments. By simplifying the operation process, it improves the convenience of the detection equipment, making it suitable for the frequent and rapid cavitation damage measurement needs of hydropower plants.

[0092] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0093] Based on the same inventive concept, this application also provides a device for rapidly determining cavitation damage of a turbine runner, used to implement the aforementioned method for rapidly determining cavitation damage of a turbine runner. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the device for rapidly determining cavitation damage of a turbine runner provided below can be found in the limitations of the method for rapidly determining cavitation damage of a turbine runner described above, and will not be repeated here.

[0094] In an exemplary embodiment, Figure 4 As shown, a rapid measurement 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 the 3D laser scanner on the surface of the turbine runner.

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

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

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

[0099] In one exemplary embodiment, the geometric features include normal vector information, curvature information, and depth information. The region analysis module 404 is further configured to 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; different data sets represent different turbine runner surface regions; determine candidate cavitation regions corresponding to the data sets based on the normal vector information, curvature information, and depth information of each point in the data sets; and determine the cavitation region corresponding to the data set based on the intersection of the candidate cavitation regions.

[0100] In one exemplary embodiment, the parameter calculation module 406 is further configured 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 to identify the boundary of the cavitation erosion region; fit a surface at the boundary points corresponding to the boundary using an interpolation method to determine the repair area; and use the area of ​​the repair area as the cavitation erosion damage area; determine a reference surface based on the point cloud data corresponding to the cavitation erosion region; and determine the cavitation erosion damage depth based on the distance between each point in the point cloud data corresponding to the cavitation erosion region and the reference surface, and calculate the cavitation erosion damage volume using an integral method.

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

[0102] In one exemplary embodiment, the data acquisition module 402 is further configured to acquire raw point cloud data collected by a 3D laser scanner on the surface of a water turbine runner; and to perform noise reduction processing on the raw point cloud data to obtain the point cloud data collected on the surface of the water turbine runner.

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

[0104] Each module in the aforementioned rapid determination device for cavitation damage of turbine runners can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0105] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 5 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores point cloud data, etc. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for rapid determination of cavitation damage in a water turbine runner.

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

[0107] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0108] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

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

[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] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this 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. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0112] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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 merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for rapid determination of cavitation damage in a water turbine runner, characterized in that, The method includes: Acquire point cloud data from a 3D laser scanner on the surface of a water turbine runner; Based on the geometric features of the point cloud data, cavitation erosion regions are determined. These geometric features include normal vector information, curvature information, and depth information. Determining cavitation erosion regions based on the geometric features of the point cloud data includes: dividing 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; different data sets represent different turbine runner surface regions; determining candidate cavitation erosion regions corresponding to each data set based on the normal vector information, curvature information, and depth information of each point in the data set; and determining the cavitation erosion region corresponding to the data set based on the intersection of the candidate cavitation erosion regions. Based on the point cloud data corresponding to the cavitation erosion area, the cavitation erosion damage area, cavitation erosion damage depth, and cavitation erosion damage volume are determined, 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 to convert the point cloud data into a three-dimensional surface model; identifying the boundary of the cavitation erosion area based on the three-dimensional surface model, fitting a surface at the boundary points corresponding to the boundary using interpolation to determine the repair area, and using the area of ​​the repair area as the cavitation erosion damage area; using the point cloud data corresponding to the cavitation erosion area, using the plane where the points corresponding to the edge of the cavitation erosion area are located as the reference surface, and determining the maximum distance between each point in the point cloud data corresponding to the cavitation erosion area and the reference surface as the cavitation erosion damage depth; and integrating the cavitation erosion damage depth using an integral method to obtain the cavitation erosion damage volume. The cavitation damage measurement results of the turbine runner are determined based on the cavitation area, the cavitation damage area, the cavitation damage depth, and the cavitation damage volume.

2. The method according to claim 1, characterized in that, The determination of the turbine runner cavitation damage measurement results based on the cavitation erosion region, the cavitation damage area, the cavitation damage depth, and the cavitation damage volume includes: Fill in the cavitation damage area, cavitation damage depth and cavitation damage volume corresponding to the cavitation erosion area into the preset report template to obtain the turbine runner cavitation damage measurement report. Based on the point cloud data corresponding to the cavitation area, a mesh model of the cavitation area is constructed; Based on the turbine runner cavitation damage measurement report and the grid model of the cavitation area, the measurement results of the turbine runner cavitation damage are determined.

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

4. The method according to claim 1, characterized in that, After the step of determining the cavitation damage measurement results of the turbine runner, the method further includes: The cavitation damage measurement results of the turbine runner are sent to the monitoring terminal; the monitoring terminal is used to display the cavitation damage measurement results of the turbine runner to the user.

5. A rapid measurement device for cavitation damage of a water turbine runner, characterized in that, The device includes: The data acquisition module is used to acquire point cloud data collected by a 3D laser scanner from the surface of the turbine runner; The region analysis module is used to determine cavitation erosion regions based on the geometric features of the point cloud data. The geometric features include normal vector information, curvature information, and depth information. Determining the cavitation erosion regions based on the geometric features of the point cloud data includes: dividing 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; different data sets represent different turbine runner surface regions; determining candidate cavitation erosion regions corresponding to each data set based on the normal vector information, curvature information, and depth information of each point in the data set; and determining the cavitation erosion region corresponding to the data set based on the intersection of the candidate cavitation erosion regions. The parameter calculation module is used to determine the cavitation damage area, cavitation damage depth, and cavitation damage volume based on the point cloud data corresponding to the cavitation erosion area; it is also 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 to convert the point cloud data into a three-dimensional surface model; identify the boundary of the cavitation erosion area based on the three-dimensional surface model; fit a surface at the boundary points corresponding to the boundary using interpolation to determine the repair area; and use the area of ​​the repair area as the cavitation damage area; based on the point cloud data corresponding to the cavitation erosion area, use the plane where the points corresponding to the edge of the cavitation erosion area are located as the reference surface; determine the maximum distance between each point in the point cloud data corresponding to the cavitation erosion area and the reference surface as the cavitation damage depth; and integrate the cavitation damage depth using an integral method to obtain the cavitation damage volume. The result determination module is used to determine the cavitation damage measurement results of the turbine runner based on the cavitation area, the cavitation damage area, the cavitation damage depth, and the cavitation damage volume.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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