Dam body detection control method and system of intelligent hydropower station

Abstract

The invention discloses a dam body detection control method and system for an intelligent hydropower station, and relates to the technical field of image processing, and the method comprises the steps: carrying out the image collection of a dam body based on an unmanned plane, and obtaining a real-time image of the dam body; and obtaining to-be-verified data of the dam body. Real-time image acquisition is carried out on the surface of the dam body through the unmanned aerial vehicle, feature extraction is carried out on the real-time image of the dam body through the convolutional neural network algorithm, position information corresponding to the to-be-verified features of the dam body is determined, and the crack depth of the to-be-verified features is measured through the laser range finder. Judging the to-be-verified features in combination with the depth of a dam body decoration layer, determining dam body abnormal features, and finally, obtaining a dam body construction quality first parameter and a dam body construction quality second parameter by combining the dam body abnormal features with dam body construction parameters and dam body composition material parameters; the dam body construction quality is determined through the dam body construction quality first parameter and the dam body construction quality second parameter, the troubleshooting period is shortened, and meanwhile the risk that workers are injured is reduced.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to a dam body detection control method and system for a smart hydropower station. Background Art

[0002] Smart hydropower station is an intelligent system that combines modern information technology with hydropower station operation and management, aiming to improve the operating efficiency, management level and safety of hydropower stations.

[0003] The existing dam body inspection adopts slow manual inspection. When the dam body is too long, the manual inspection cycle is too long, which increases the workload of workers. In addition, the two sides of the dam body are mostly set vertically, and workers may be injured during the inspection. Summary of the invention

[0004] In order to solve the above technical problems, a dam body detection control method and system for a smart hydropower station are provided. This technical solution solves the problem that the existing dam body detection proposed in the above background technology adopts slow manual inspection. When the dam body is too long, the manual inspection cycle is too long, which increases the work intensity of the workers. In addition, the two sides of the dam body are mostly vertically arranged, and workers may be injured during inspection.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is: A dam body detection and control method for a smart hydropower station, comprising: Based on the UAV, the image of the dam body is collected to obtain the real-time image of the dam body; Based on the convolutional neural network algorithm, feature extraction is performed on the real-time image of the dam body to obtain the data to be verified for the dam body, wherein the data to be verified for the dam body includes features to be verified for the dam body and position information corresponding to the features to be verified for the dam body; Based on the laser rangefinder and the position information corresponding to the feature to be verified of the dam body, the feature to be verified of the dam body is analyzed and processed to determine abnormal data of the dam body, wherein the abnormal data of the dam body includes the position of the crack of the dam body and the depth of the crack of the dam body; Based on the abnormal data of the dam body, the quality of the dam body is analyzed and processed to determine the construction quality of the dam body.

[0006] Preferably, the method of collecting images of the dam body based on a drone to obtain a real-time image of the dam body specifically comprises the following steps: Based on the database system, data is extracted from the database system to obtain the original image of the dam body; Based on the original image of the dam body, data is retrieved from the database system to determine the original shooting parameters and the original shooting position corresponding to the original image of the dam body; Based on the original shooting parameters, adjust the parameters of the drone's shooting equipment; Based on the UAV and the original shooting position, real-time image collection is performed on the dam body to obtain the real-time image of the dam body.

[0007] Preferably, the method of extracting features from the real-time image of the dam body based on a convolutional neural network algorithm and obtaining the data to be verified of the dam body specifically comprises the following steps: Based on the convolutional neural network algorithm, feature extraction is performed on the real-time image of the dam body to obtain the features of the dam body to be verified; A rectangular coordinate system is constructed based on the real-time image of the dam body, with the lower left corner of the real-time image of the dam body as the coordinate origin, the horizontal direction of the real-time image of the dam body as the X-axis, and the vertical direction of the real-time image of the dam body as the Y-axis; Based on the features of the dam body to be verified, feature positioning is performed on the real-time image of the dam body in the rectangular coordinate system to obtain feature coordinate information; Analyze and process the characteristic coordinate information to obtain the position information corresponding to the characteristics to be verified on the dam body.

[0008] Preferably, the step of analyzing and processing the characteristic coordinate information to obtain the position information corresponding to the characteristic to be verified of the dam body specifically comprises the following steps: Perform parameter analysis based on the original shooting parameters to obtain the scaling ratio of the dam image; Calculate and process according to the feature coordinate information and the dam body image scaling ratio to obtain the position information corresponding to the feature to be verified on the dam body; The specific calculation formula for obtaining the position information corresponding to the feature to be verified of the dam body is:

[0009] In the formula, is the position information corresponding to the feature to be verified on the dam body; X is the horizontal coordinate of the feature coordinate information; Y is the vertical coordinate of the feature coordinate information; The scaling ratio of the dam image.

[0010] Preferably, the analyzing and processing of the features to be verified of the dam body based on the position information corresponding to the features to be verified of the dam body and determining the abnormal data of the dam body specifically comprises the following steps: Based on the laser rangefinder, the position information corresponding to the features to be verified on the dam body is measured and processed to obtain the depth of the cracks to be verified on the dam body; Based on the database system, data is retrieved from the database system to obtain the depth of the dam body decoration layer; Based on the depth of the dam body's decorative layer, the depth of the cracks to be verified in the dam body is analyzed and processed to obtain abnormal data of the dam body; Among them, the specific calculation model for obtaining the depth of cracks to be verified in the dam body is:

[0011] In the formula, The depth of the cracks in the dam body to be verified; The number of different depths of cracks to be verified for each dam body; is the depth of each crack to be verified at different positions of the dam body; max() is the maximum value function.

[0012] Preferably, the step of analyzing and processing the depth of the cracks to be verified in the dam body based on the depth of the dam body decoration layer to obtain abnormal data of the dam body specifically comprises the following steps: Make judgment and handle according to the depth of the dam body's decorative layer and the depth of the cracks to be verified in the dam body; If the depth of the crack to be verified in the dam body is less than or equal to the depth of the dam body decoration layer, the feature to be verified in the dam body is a normal feature, and the output dam body is normal; If the depth of the crack to be verified on the dam body is greater than the depth of the dam body decorative layer, the feature to be verified on the dam body is an abnormal feature, and the position and depth of the crack on the dam body are recorded.

[0013] Preferably, the analyzing and processing of the dam body quality based on the dam body abnormal data to determine the dam body construction quality specifically comprises the following steps: Based on the database system, data extraction and processing are performed on the database system to obtain the dam body construction parameters and dam body constituent material parameters; Conduct data analysis based on dam construction parameters and dam material parameters to determine the dam quality setting threshold; Based on the dam body crack depth and the dam body quality threshold, analysis and processing are performed to obtain the first parameter of the dam body construction quality; Based on the location of the cracks in the dam body, the database system is searched and processed to obtain the time when the cracks appeared; Analyze and process the crack appearance time and the depth of the dam crack to determine the average crack growth rate; Analyze and process the average crack growth rate to obtain the second parameter of the dam construction quality; The dam body construction quality is determined by conducting a comprehensive analysis based on the first parameter of the dam body construction quality and the second parameter of the dam body construction quality.

[0014] Preferably, the analysis and processing based on the dam body crack depth and the dam body quality setting threshold to obtain the first parameter of the dam body construction quality specifically includes the following steps: Set thresholds based on the depth of dam cracks and dam quality for judgment and processing; If the depth of the dam body cracks is less than the set threshold of the dam body quality, and the number of dam body cracks is less than or equal to the set first threshold, the output first construction quality of the dam body is excellent; If the depth of the dam body cracks is equal to the set threshold of the dam body quality, and the number of dam body cracks is less than or equal to the set first threshold, the output first construction quality of the dam body is good; If the depth of the cracks in the dam body is greater than the set threshold value of the dam body quality, or the number of cracks in the dam body is greater than the set first threshold value, the first construction quality of the dam body is output as poor.

[0015] Preferably, the analyzing and processing according to the average crack growth rate to obtain the second parameter of the dam body construction quality specifically comprises the following steps: Perform judgment processing according to the average crack growth rate and the set second threshold; If the average crack growth rate is greater than the set second threshold, the second construction quality of the dam body is output as poor; If the average crack growth rate is equal to the set second threshold, the second construction quality of the dam body is output as good; If the average crack growth rate is less than the set second threshold, the second construction quality of the dam body is output as excellent.

[0016] Furthermore, a dam body detection control system of a smart hydropower station is proposed, which is used to implement the dam body detection control method of a smart hydropower station as described above, including: A database system, the database system is used to store the original image of the dam body, the depth of the dam body decoration layer, the dam body construction parameters and the dam body constituent material parameters; A drone, wherein the drone is used to collect images of the dam body and obtain real-time images of the dam body; A laser rangefinder, which is used to measure and process the position information corresponding to the feature to be verified on the dam body, and obtain the depth of the crack to be verified on the dam body; A feature extraction module, which is used to extract features from the real-time image of the dam body to obtain features of the dam body to be verified; A data calculation module, which performs analysis and processing based on the crack occurrence time and the crack depth of the dam body to determine the average crack growth rate; A data analysis module, wherein the data analysis module analyzes and processes the features to be verified of the dam body according to the position information corresponding to the features to be verified of the dam body, and determines abnormal data of the dam body; A comparison and judgment module, wherein the comparison and judgment module analyzes and processes the quality of the dam body according to the abnormal data of the dam body to determine the construction quality of the dam body; A control module is used to realize data transmission and information interaction between modules.

[0017] Compared with the prior art, the present invention provides a dam body detection and control method and system for a smart hydropower station, which has the following beneficial effects: The present invention collects real-time images of the surface of the dam body through a drone, and then extracts features from the real-time images of the dam body through a convolutional neural network algorithm, determines the position information corresponding to the feature to be verified of the dam body, measures the crack depth of the feature to be verified through a laser rangefinder, and then judges the feature to be verified in combination with the depth of the dam body decorative layer to determine the abnormal feature of the dam body. Finally, the abnormal feature of the dam body is combined with the dam body construction parameters and the dam body constituent material parameters to obtain the first dam body construction quality parameter and the second dam body construction quality parameter, and the dam body construction quality is determined by the first dam body construction quality parameter and the second dam body construction quality parameter, thereby shortening the investigation period and reducing the risk of workers being injured. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of steps S100-S400 in a dam body detection and control method for a smart hydropower station proposed by the present invention; Figure 2 This is a flow chart of steps S101-S104 in a dam body detection and control method for a smart hydropower station proposed by the present invention; Figure 3 This is a flow chart of steps S201-S204 in a dam body detection and control method for a smart hydropower station proposed by the present invention; Figure 4 This is a flow chart of steps S2041-S2042 in a dam body detection and control method for a smart hydropower station proposed by the present invention; Figure 5 This is a flow chart of steps S301-S303 in a dam body detection and control method for a smart hydropower station proposed by the present invention; Figure 6 This is a flow chart of steps S3031-S3033 in a dam body detection and control method for a smart hydropower station proposed by the present invention; Figure 7 This is a flow chart of steps S401-S407 in a dam body detection and control method for a smart hydropower station proposed by the present invention; Figure 8 A flow chart of steps S4031-S4034 in a dam body detection and control method for a smart hydropower station proposed by the present invention; Fig. 9 This is a flow chart of steps S4061-S4064 in a dam body detection and control method for a smart hydropower station proposed by the present invention; Fig.10 This is a structural block diagram of a dam body detection and control system for a smart hydropower station proposed by the present invention. DETAILED DESCRIPTION

[0019] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0020] Reference Figure 1 As shown, a dam body detection and control method for a smart hydropower station includes: S100, based on UAV, collect images of the dam body and obtain real-time images of the dam body; S200, based on a convolutional neural network algorithm, extracting features from the real-time image of the dam body to obtain data to be verified for the dam body, wherein the data to be verified for the dam body includes features to be verified for the dam body and position information corresponding to the features to be verified for the dam body; S300, based on the laser rangefinder and the position information corresponding to the feature to be verified of the dam body, analyzing and processing the feature to be verified of the dam body, and determining abnormal data of the dam body, wherein the abnormal data of the dam body includes the position and depth of the cracks in the dam body; S400, analyzing and processing the dam body quality based on the abnormal data of the dam body to determine the construction quality of the dam body; It can be understood by those skilled in the art that the dams of smart hydropower stations are mostly used to intercept water sources. Therefore, some areas on both sides of the dam body are mostly set vertically, and the height of the dam body is set relatively high. Real-time image acquisition of both sides of the dam body by drones reduces the risk of injury to workers. A convolutional neural network algorithm is used to extract features from the real-time images of the dam body to obtain the possible abnormal locations and features of the dam body. A laser rangefinder is then used to measure the possible abnormal locations to determine the abnormal data of the dam body. Finally, the dam body construction parameters and the parameters of the dam body constituent materials are combined to analyze and determine the construction quality of the dam body, and different maintenance plans are designed according to the construction quality of the dam body.

[0021] Reference Figure 2 As shown in the figure, based on the UAV, the image acquisition of the dam body and the acquisition of the real-time image of the dam body specifically include the following steps: S101, based on the database system, extracting data from the database system to obtain the original image of the dam body; S102, based on the original image of the dam body, performing data retrieval on the database system to determine the original shooting parameters and the original shooting position corresponding to the original image of the dam body; S103, adjusting parameters of the shooting equipment of the drone based on the original shooting parameters; S104, based on the UAV and the original shooting position, real-time image acquisition is performed on the dam body to obtain a real-time image of the dam body; In this embodiment, in order to improve the accuracy of dam body detection, it is necessary to keep the shooting parameters of the real-time image of the dam body and the original image of the dam body consistent, and the shooting position must be the same. If the shooting parameters and the shooting position are different, when analyzing the real-time image of the dam body, it is impossible to determine whether the dam body abnormality occurred during construction or after the dam body was completed and put into use. Therefore, in order to avoid the above situation, it is necessary to adjust the parameters of the drone shooting equipment, and the drone is required to collect real-time images at the same location.

[0022] Reference Figure 3 As shown, based on the convolutional neural network algorithm, feature extraction is performed on the real-time image of the dam body, and obtaining the data to be verified of the dam body specifically includes the following steps: S201, based on the convolutional neural network algorithm, extracting features of the real-time image of the dam body to obtain features of the dam body to be verified; S202, constructing a rectangular coordinate system based on the real-time image of the dam body, with the lower left corner of the real-time image of the dam body as the coordinate origin, the horizontal direction of the real-time image of the dam body as the X axis, and the vertical direction of the real-time image of the dam body as the Y axis; S203, based on the features of the dam body to be verified, feature positioning is performed on the real-time image of the dam body in the rectangular coordinate system to obtain feature coordinate information; S204, analyzing and processing the feature coordinate information to obtain the position information corresponding to the feature to be verified of the dam body.

[0023] Reference Figure 4 As shown, the analysis and processing based on the feature coordinate information to obtain the position information corresponding to the feature to be verified on the dam body specifically includes the following steps: S2041, performing parameter analysis according to the original shooting parameters to obtain the scaling ratio of the dam body image; S2042, performing calculation processing according to the feature coordinate information and the dam body image scaling ratio to obtain the position information corresponding to the feature to be verified on the dam body; The specific calculation formula for obtaining the position information corresponding to the feature to be verified of the dam body is:

[0024] In the formula, is the position information corresponding to the feature to be verified on the dam body; X is the horizontal coordinate of the feature coordinate information; Y is the vertical coordinate of the feature coordinate information; is the scaling ratio of the dam image; In this embodiment, there may be errors in the positions of the features to be verified on the dam body in the image and the features to be verified on the actual dam body, so a rectangular coordinate system is constructed for the real-time image of the dam body, and then the coordinates of the features to be verified in the rectangular coordinate system and the scaling ratio of the dam body image are used to calculate and determine the positions of the features to be verified on the actual dam body. It is worth noting that a rectangular coordinate system is also constructed for the actual dam body, and the construction method is the same as that of the rectangular coordinate system of the real-time image of the dam body.

[0025] Reference Figure 5 As shown, based on the position information corresponding to the laser rangefinder and the features to be verified of the dam body, the features to be verified of the dam body are analyzed and processed, and the abnormal data of the dam body is determined, which specifically includes the following steps: S301, using a laser rangefinder, measuring and processing the position information corresponding to the feature to be verified on the dam body, and obtaining the depth of the crack to be verified on the dam body; S302, based on the database system, performing data retrieval on the database system to obtain the depth of the dam body decoration layer; S303, based on the depth of the dam body decoration layer, analyzing and processing the depth of the cracks to be verified in the dam body, and obtaining abnormal data of the dam body; Among them, the specific calculation model for obtaining the depth of cracks to be verified in the dam body is:

[0026] In the formula, The depth of the cracks in the dam body to be verified; The number of different depths of cracks to be verified for each dam body; is the depth of each crack to be verified at different positions of the dam body; max() is the maximum value function; A crack is a characteristic that spreads from the center to the surrounding areas, and the depths of different areas within the crack may be different. In order to improve the accuracy of dam body detection, the depths of different areas within each crack are compared, and the deepest crack depth is selected as analysis data.

[0027] Reference Figure 6 As shown, based on the depth of the dam body decoration layer, the depth of the cracks to be verified in the dam body is analyzed and processed, and the abnormal data of the dam body is obtained, which specifically includes the following steps: S3031, judging and processing according to the depth of the dam body decoration layer and the depth of the cracks to be verified in the dam body; S3032. If the depth of the crack to be verified in the dam body is less than or equal to the depth of the dam body decoration layer, the feature to be verified in the dam body is a normal feature, and the output is that there is no abnormality in the dam body; S3033. If the depth of the crack to be verified in the dam body is greater than the depth of the dam body decoration layer, the feature to be verified in the dam body is an abnormal feature, and the position and depth of the crack in the dam body are recorded; In this embodiment, after the construction of the dam body is completed, a decorative layer will be set on the surface. Some cracks appear on the decorative layer and do not penetrate into the interior of the dam body, while some cracks penetrate into the interior of the dam body. In order to remove the cracks on the surface of the decorative layer, the depth of the cracks is measured by a laser rangefinder, and then the depth of the cracks is analyzed. The crack characteristics on the surface of the decorative layer are removed, making subsequent analysis more convenient and quick.

[0028] Reference Figure 7 As shown in the figure, based on the abnormal data of the dam body, the dam body quality is analyzed and processed to determine the construction quality of the dam body, which specifically includes the following steps: S401, based on the database system, extracting data from the database system to obtain dam body construction parameters and dam body constituent material parameters; S402, performing data analysis based on dam body construction parameters and dam body constituent material parameters to determine a dam body quality setting threshold; S403, analyzing and processing based on the dam body crack depth and the dam body quality setting threshold, and obtaining the first dam body construction quality parameter; S404, based on the location of the cracks in the dam body, searching the database system to obtain the time when the cracks appeared; S405, analyzing and processing the crack occurrence time and the dam body crack depth to determine the average crack growth rate; S406, analyzing and processing according to the average growth rate of the cracks to obtain a second parameter of the dam body construction quality; S407, performing a comprehensive analysis based on the first dam body construction quality parameter and the second dam body construction quality parameter to determine the dam body construction quality; In this embodiment, the occurrence of cracks in the dam body and the average growth rate of the cracks are related to the construction parameters of the dam body and the parameters of the materials constituting the dam body. Therefore, the depth of the dam body cracks and the average growth rate of the cracks are analyzed to determine the construction quality of the dam body. When the first construction quality of the dam body and the second construction quality of the dam body are both excellent, the construction quality of the dam body is excellent. When the first construction quality of the dam body and the second construction quality of the dam body have one good or two good qualities, the construction quality of the dam body is good. When the first construction quality of the dam body and the second construction quality of the dam body have one poor quality, the construction quality of the dam body is poor. If the construction quality of the dam body is excellent, the maintenance period of the dam body is shorter. If the construction quality of the dam body is poor, the maintenance period of the dam body is longer.

[0029] Reference Figure 8 As shown, based on the dam body crack depth and the dam body quality setting threshold, analysis and processing are performed to obtain the first parameter of the dam body construction quality, specifically including the following steps: S4031, setting a threshold value for judging and processing according to the depth of the dam body cracks and the dam body quality; S4032. If the depth of the dam body cracks is less than the set threshold value of the dam body quality, and the number of dam body cracks is less than or equal to the set first threshold value, the first construction quality of the dam body is output as excellent; S4033. If the depth of the dam body cracks is equal to the set threshold value of the dam body quality, and the number of dam body cracks is less than or equal to the set first threshold value, output that the first construction quality of the dam body is good; S4034. If the depth of the cracks in the dam body is greater than the set threshold value of the dam body quality, or the number of cracks in the dam body is greater than the set first threshold value, the first construction quality of the dam body is output as poor.

[0030] Reference Fig. 9 As shown, the analysis and processing based on the average crack growth rate to obtain the second parameter of the dam construction quality specifically includes the following steps: S4061, performing judgment processing according to the average crack growth rate and setting a second threshold; S4062, if the average crack growth rate is greater than the set second threshold, outputting the second construction quality of the dam body as poor; S4063. If the average crack growth rate is equal to the set second threshold, the second construction quality of the dam body is output as good; S4064: If the average crack growth rate is less than the set second threshold, the second construction quality of the dam body is output as excellent; In this embodiment, when a crack appears at a certain position of the dam body, the crack will be affected by water pressure and the external environment and gradually become larger. Therefore, it is necessary to analyze and process the average growth rate of the crack to determine the second construction quality of the dam body, and then design a maintenance plan for the dam body in combination with the first construction quality of the dam body.

[0031] Reference Fig.10 As shown, a dam body detection control system of a smart hydropower station is used to implement the dam body detection control method of a smart hydropower station as described above, including: A database system, the database system is used to store the original image of the dam body, the depth of the dam body decoration layer, the dam body construction parameters and the dam body constituent material parameters; A drone, wherein the drone is used to collect images of the dam body and obtain real-time images of the dam body; A laser rangefinder, which is used to measure and process the position information corresponding to the feature to be verified on the dam body, and obtain the depth of the crack to be verified on the dam body; A feature extraction module, which is used to extract features from the real-time image of the dam body to obtain features of the dam body to be verified; A data calculation module, which performs analysis and processing based on the crack occurrence time and the crack depth of the dam body to determine the average crack growth rate; A data analysis module, wherein the data analysis module analyzes and processes the features to be verified of the dam body according to the position information corresponding to the features to be verified of the dam body, and determines abnormal data of the dam body; A comparison and judgment module, wherein the comparison and judgment module analyzes and processes the quality of the dam body according to the abnormal data of the dam body to determine the construction quality of the dam body; A control module is used to realize data transmission and information interaction between modules.

[0032] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A dam body detection and control method for a smart hydropower station, characterized in that: include: Based on the UAV, the image of the dam body is collected to obtain the real-time image of the dam body; Based on the convolutional neural network algorithm, feature extraction is performed on the real-time image of the dam body to obtain the data to be verified for the dam body, wherein the data to be verified for the dam body includes features to be verified for the dam body and position information corresponding to the features to be verified for the dam body; Based on the laser rangefinder and the position information corresponding to the feature to be verified of the dam body, the feature to be verified of the dam body is analyzed and processed to determine abnormal data of the dam body, wherein the abnormal data of the dam body includes the position of the crack of the dam body and the depth of the crack of the dam body; Based on the abnormal data of the dam body, the quality of the dam body is analyzed and processed to determine the construction quality of the dam body.

2. The dam body detection and control method of a smart hydropower station according to claim 1 is characterized in that: The method of collecting images of the dam body based on the drone to obtain the real-time image of the dam body specifically includes the following steps: Based on the database system, data is extracted from the database system to obtain the original image of the dam body; Based on the original image of the dam body, data is retrieved from the database system to determine the original shooting parameters and the original shooting position corresponding to the original image of the dam body; Based on the original shooting parameters, adjust the parameters of the drone's shooting equipment; Based on the UAV and the original shooting position, real-time image collection is performed on the dam body to obtain the real-time image of the dam body.

3. The dam body detection and control method of a smart hydropower station according to claim 1 is characterized in that: The method of extracting features from the real-time image of the dam body based on the convolutional neural network algorithm and obtaining the data to be verified of the dam body specifically includes the following steps: Based on the convolutional neural network algorithm, feature extraction is performed on the real-time image of the dam body to obtain the features of the dam body to be verified; A rectangular coordinate system is constructed based on the real-time image of the dam body, with the lower left corner of the real-time image of the dam body as the coordinate origin, the horizontal direction of the real-time image of the dam body as the X-axis, and the vertical direction of the real-time image of the dam body as the Y-axis; Based on the features of the dam body to be verified, feature positioning is performed on the real-time image of the dam body in the rectangular coordinate system to obtain feature coordinate information; Analyze and process the characteristic coordinate information to obtain the position information corresponding to the characteristics to be verified on the dam body.

4. The dam body detection and control method of a smart hydropower station according to claim 3 is characterized in that: The analysis and processing based on the characteristic coordinate information to obtain the position information corresponding to the feature to be verified of the dam body specifically includes the following steps: Perform parameter analysis based on the original shooting parameters to obtain the scaling ratio of the dam image; Calculate and process according to the feature coordinate information and the dam body image scaling ratio to obtain the position information corresponding to the feature to be verified on the dam body; The specific calculation formula for obtaining the position information corresponding to the feature to be verified of the dam body is: In the formula, is the position information corresponding to the feature to be verified on the dam body; X is the horizontal coordinate of the feature coordinate information; Y is the vertical coordinate of the feature coordinate information; The scaling ratio of the dam image.

5. The dam body detection and control method of a smart hydropower station according to claim 1 is characterized in that: The analyzing and processing of the features to be verified of the dam body based on the position information corresponding to the laser rangefinder and the features to be verified of the dam body to determine the abnormal data of the dam body specifically comprises the following steps: Based on the laser rangefinder, the position information corresponding to the features to be verified on the dam body is measured and processed to obtain the depth of the cracks to be verified on the dam body; Based on the database system, data is retrieved from the database system to obtain the depth of the dam body decoration layer; Based on the depth of the dam body's decorative layer, the depth of the cracks to be verified in the dam body is analyzed and processed to obtain abnormal data of the dam body; Among them, the specific calculation model for obtaining the depth of cracks to be verified in the dam body is: In the formula, The depth of the cracks in the dam body to be verified; The number of different depths of cracks to be verified for each dam body; is the depth of each crack to be verified at different positions of the dam body; max() is the maximum value function.

6. The dam body detection and control method of a smart hydropower station according to claim 5 is characterized in that: The method of analyzing and processing the depth of the cracks to be verified in the dam body based on the depth of the dam body decoration layer and obtaining the abnormal data of the dam body specifically includes the following steps: Make judgment and handle according to the depth of the dam body's decorative layer and the depth of the cracks to be verified in the dam body; If the depth of the crack to be verified in the dam body is less than or equal to the depth of the dam body decoration layer, the feature to be verified in the dam body is a normal feature, and the output dam body is normal; If the depth of the crack to be verified on the dam body is greater than the depth of the dam body decorative layer, the feature to be verified on the dam body is an abnormal feature, and the position and depth of the crack on the dam body are recorded.

7. The dam body detection and control method of a smart hydropower station according to claim 1 is characterized in that: The analysis and processing of the dam body quality based on the abnormal dam body data to determine the dam body construction quality specifically includes the following steps: Based on the database system, data extraction and processing are performed on the database system to obtain the dam body construction parameters and dam body constituent material parameters; Conduct data analysis based on dam construction parameters and dam material parameters to determine the dam quality setting threshold; Based on the dam body crack depth and the dam body quality threshold, analysis and processing are performed to obtain the first parameter of the dam body construction quality; Based on the location of the cracks in the dam body, the database system is searched and processed to obtain the time when the cracks appeared; Analyze and process the crack appearance time and the depth of the dam crack to determine the average crack growth rate; Analyze and process the average crack growth rate to obtain the second parameter of the dam construction quality; The dam body construction quality is determined by conducting a comprehensive analysis based on the first parameter of the dam body construction quality and the second parameter of the dam body construction quality.

8. The dam body detection and control method of a smart hydropower station according to claim 7 is characterized in that: The analysis and processing based on the dam body crack depth and the dam body quality setting threshold to obtain the first parameter of the dam body construction quality specifically includes the following steps: Set thresholds based on the depth of dam cracks and dam quality for judgment and processing; If the depth of the dam body cracks is less than the set threshold of the dam body quality, and the number of dam body cracks is less than or equal to the set first threshold, the output first construction quality of the dam body is excellent; If the depth of the dam body cracks is equal to the set threshold of the dam body quality, and the number of dam body cracks is less than or equal to the set first threshold, the output first construction quality of the dam body is good; If the depth of the cracks in the dam body is greater than the set threshold value of the dam body quality, or the number of cracks in the dam body is greater than the set first threshold value, the first construction quality of the dam body is output as poor.

9. The dam body detection and control method of a smart hydropower station according to claim 7, characterized in that: The analysis and processing based on the average crack growth rate to obtain the second parameter of the dam body construction quality specifically includes the following steps: Perform judgment processing according to the average crack growth rate and the set second threshold; If the average crack growth rate is greater than the set second threshold, the second construction quality of the dam body is output as poor; If the average crack growth rate is equal to the set second threshold, the second construction quality of the dam body is output as good; If the average crack growth rate is less than the set second threshold, the second construction quality of the dam body is output as excellent.

10. A dam body detection control system of a smart hydropower station, used to implement a dam body detection control method of a smart hydropower station as claimed in any one of claims 1 to 9, characterized in that: include: A database system, the database system is used to store the original image of the dam body, the depth of the dam body decoration layer, the dam body construction parameters and the dam body constituent material parameters; A drone, wherein the drone is used to collect images of the dam body and obtain real-time images of the dam body; A laser rangefinder, which is used to measure and process the position information corresponding to the feature to be verified on the dam body, and obtain the depth of the crack to be verified on the dam body; A feature extraction module, which is used to extract features from the real-time image of the dam body to obtain features of the dam body to be verified; A data calculation module, which performs analysis and processing based on the crack occurrence time and the crack depth of the dam body to determine the average crack growth rate; A data analysis module, wherein the data analysis module analyzes and processes the features to be verified of the dam body according to the position information corresponding to the features to be verified of the dam body, and determines abnormal data of the dam body; A comparison and judgment module, wherein the comparison and judgment module analyzes and processes the quality of the dam body according to the abnormal data of the dam body to determine the construction quality of the dam body; A control module is used to realize data transmission and information interaction between modules.

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