Sediment deposition detection method and device, electronic equipment and medium

Abstract

The application discloses a kind of silt deposition detection method, device, electronic equipment and medium, it is related to silt deposition monitoring technical field.The method comprises: establishing the BIM model of hydropower station engineering in visual platform by three-dimensional design software;Multiple sonars of sonar detection module are laid along dam axis direction on the dam face before the dam of hydropower station engineering, point cloud data is obtained by sonar, and deposition section information is obtained;The navigation path of unmanned ship of unmanned ship detection module is planned, unmanned ship is driven to navigate along the route, in the navigation process, the depth of water bottom is obtained by single-beam depth sounding unit, and the positioning information obtained by superposition positioning unit is obtained, and the three-dimensional information of silt deposition feature point of water bottom is obtained;Deposition section information and the three-dimensional information of silt deposition feature point of water bottom are superimposed to BIM model;The deposition change curve of dam and reservoir area in time period is displayed in visual platform;BIM model is lightweighted using edge folding mode.The method can accurately detect silt deposition condition.

Description

Technical Field

[0001] This invention relates to the field of sedimentation monitoring technology, and in particular to a sedimentation detection method, device, electronic equipment, and medium. Background Technology

[0002] For hydropower stations, the high sediment content of the flowing water easily leads to a certain degree of siltation, which can adversely affect the long-term safe and stable operation of the reservoir. In order to more accurately grasp the siltation situation in the reservoir area and the working status of the desilting holes, ensure the reservoir capacity required for the normal operation of the power station, and dynamically monitor the siltation situation in key areas in front of the dam, it is necessary to conduct real-time tracking and accurate detection of siltation in front of the dam and in the reservoir area. Summary of the Invention

[0003] This invention aims to solve at least one of the technical problems existing in the prior art. To this end, this invention proposes a method, device, electronic equipment, and medium for detecting sediment deposition, which can track and accurately detect sediment deposition in front of dams and reservoir areas in real time.

[0004] In a first aspect, the siltation detection method according to an embodiment of the present invention is applied to a siltation monitoring system, the siltation monitoring system including a sonar detection module, an unmanned vessel detection module, and a visualization platform, the method comprising the following steps: Based on the design of the hydropower station project, a BIM model of the hydropower station project is created on the visualization platform using 3D design software; Multiple sonar detectors of the sonar detection module are deployed along the dam axis on the dam face in front of the dam of the hydropower station project at preset intervals, and point cloud data is obtained through the sonar. Based on the point cloud data, information on the siltation cross-section is obtained; Based on the reservoir area conditions of the hydropower station project, the navigation path of the unmanned vessel detection module is planned; the unmanned vessel includes a single-beam depth sounding unit, a positioning unit, and a sound velocity profiler. The unmanned vessel is driven to navigate along the route, and during the navigation, the depth of the bottom is obtained through the single-beam echo sounder unit, and the positioning information obtained by the positioning unit is superimposed to obtain three-dimensional information of the sediment deposition feature points at the bottom of the water. The information on the siltation cross section and the three-dimensional information on the siltation feature points at the bottom of the water are superimposed on the BIM model to obtain the current status and changes of siltation in front of the dam and in the reservoir area of ​​the hydropower station project. Based on the selected time period, the visualization platform displays the siltation change curves in front of the dam and in the reservoir area during that time period. The BIM model is lightweighted by using edge-folding.

[0005] According to some embodiments of the present invention, the method of lightweighting the BIM model by edge folding includes: Based on the component types of the BIM model, each component type is assigned a corresponding importance level; Based on the importance level of each component type, configure a corresponding target threshold for each component type; Export different types of components as OBJ format files respectively; Based on the OBJ format file, obtain the network model of the corresponding component; All collapsible edges of all triangles in the network model are determined, and the folding order of all collapsible edges is determined. The collapsible edges are then folded sequentially according to the folding order to reduce the number of triangles in the network model until the number of triangles in the network model reaches the corresponding target threshold.

[0006] According to some embodiments of the present invention, determining all foldable edges of all triangles in the network model, determining the folding order of all foldable edges, and folding the foldable edges sequentially according to the folding order to reduce the number of triangles in the network model until the number of triangles in the network model reaches the corresponding target threshold includes: Calculate the error matrix for all collapsible edges; Based on the error matrix, the optimal folding vertex for each of the collapsible edges is determined; the optimal folding vertex represents the point with the smallest error value after merging the two vertices of the collapsible edge into a new vertex. Based on the error matrix and the optimal folding vertex, determine the error value generated after folding the two vertices of each foldable edge into the optimal folding vertex; Based on the magnitude of the error value, the folding order of each of the foldable edges is sorted. According to the sorting results, the foldable edges are folded sequentially. After each fold, the error matrix of the new foldable edge connected to the optimal folded vertex and the error value of the new foldable edge are calculated. The sorting result is updated based on the new error value of the collapsible edge, and the step of sorting the folding order of each collapsible edge according to the magnitude of the error value is returned until the number of triangles in the network model reaches the corresponding target threshold.

[0007] According to some embodiments of the present invention, the step of deploying multiple sonars along the dam axis at preset intervals on the dam face in front of the hydropower station project, and obtaining point cloud data through the sonars, includes: Multiple sonars are deployed along the dam axis on the dam face in front of the hydropower station project at preset intervals, wherein every two sonars are located on orthogonal axes and detect the same position from two different perspectives. Point cloud data is acquired using the sonar, and the point cloud data is filtered using the PCL point cloud data processing library. A two-dimensional profile is established using the filtered point cloud data. The two-dimensional profile is interpolated to obtain a three-dimensional point cloud model.

[0008] According to some embodiments of the present invention, the step of interpolating the two-dimensional profile to obtain a three-dimensional point cloud model includes: The two-dimensional profile is subjected to linear interpolation and point cloud densification in the longitudinal direction; The three-dimensional point cloud model is obtained by performing cubic spline interpolation on the two-dimensional cross-section in the horizontal direction.

[0009] According to some embodiments of the present invention, the method further includes: Based on the historical monitoring data of the hydropower station project, the hovering measurement points in the reservoir area were determined; Drive the unmanned vessel toward the hovering measurement point; When the distance between the unmanned vessel and the hovering measurement point is less than a preset distance, the application of power to the unmanned vessel is stopped, and the current coordinate information of the unmanned vessel is obtained through the positioning unit; Within a preset time period, the displacement trajectory of the unmanned vessel is acquired; Calculate the flow velocity and direction angle of the water flow based on the preset time period and the displacement trajectory; Control the bow of the unmanned vessel to face the opposite direction of the azimuth angle, and drive the unmanned vessel to the hovering measurement point; Based on the flow velocity, apply corresponding forward propulsion to the unmanned vessel to make it hover at the hovering measurement point; The unmanned vessel conducts detection at the hovering measurement point and sends the detection results to the visualization platform.

[0010] According to some embodiments of the present invention, the step of planning the navigation path of the unmanned vessel detection module based on the reservoir area conditions of the hydropower station project includes: Detect whether there are any obstacles in the storage area; When an obstacle is present, determine the area of ​​the region surrounding the obstacle; When the area around the obstacle is greater than the preset area, a ring grid is established with the obstacle as the center; For the remaining area of ​​the reservoir area other than the annular grid, several rectangular grids are created; wherein some of the rectangular grids partially overlap with the annular grid. Based on the rectangular grid and the circular grid, the straight-line navigation route and the circular navigation route of the unmanned vessel are planned.

[0011] In a second aspect, the siltation detection device according to an embodiment of the present invention includes at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed by the at least one control processor, enable the at least one control processor to perform the siltation detection method described in the first aspect embodiment.

[0012] Thirdly, the electronic device according to embodiments of the present invention includes the siltation detection device described in the second aspect embodiment.

[0013] Fourthly, according to an embodiment of the present invention, the storage medium stores computer-executable instructions for causing a computer to execute the sedimentation detection method described in the first aspect embodiment.

[0014] The siltation detection method, apparatus, electronic equipment, and medium according to embodiments of the present invention have at least the following beneficial effects: For the dam front and reservoir area of ​​hydropower station projects, fixed sonar detection methods and unmanned surface vessel (USV) mobile detection methods are employed respectively. By combining these two methods, comprehensive detection of siltation conditions at the hydropower station project is achieved. Simultaneously, the detected data is integrated into the BIM model, allowing users to intuitively view changes in siltation through the BIM model and receive timely warnings when siltation conditions are abnormal. Furthermore, by streamlining the BIM model, the visualization platform can smoothly load and display the BIM model.

[0015] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0016] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a schematic diagram of the siltation monitoring system according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the steps of the sediment deposition detection method according to an embodiment of the present invention. Figure 3 This is a diagram showing the effect of linear interpolation on a two-dimensional profile. Figure 4 This is a diagram showing the effect of cubic spline interpolation on a two-dimensional profile. Figure 5 This is a rendering of a 3D point cloud model; Figure 6 This is a schematic diagram of the siltation detection device according to an embodiment of the present invention. Detailed Implementation

[0017] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0018] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0019] The terms "first," "second," "third," and "fourth," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0020] In this invention, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0021] For hydropower stations, the high sediment content of the flowing water easily leads to a certain degree of siltation, which can adversely affect the long-term safe and stable operation of the reservoir. In order to more accurately grasp the siltation situation in the reservoir area and the working status of the desilting holes, ensure the reservoir capacity required for the normal operation of the power station, and dynamically monitor the siltation situation in key areas in front of the dam, it is necessary to conduct real-time tracking and accurate detection of siltation in front of the dam and in the reservoir area.

[0022] To address this, embodiments of the present invention provide a method, apparatus, electronic device, and medium for detecting sediment deposition. For the dam front and reservoir area of ​​hydropower station projects, a fixed sonar detection method and an unmanned surface vessel (USV) mobile detection method are employed respectively. By combining these two methods, comprehensive detection of sediment deposition in hydropower station projects is achieved. Simultaneously, the detected data is integrated into a BIM model, allowing users to intuitively view changes in sediment deposition through the BIM model and receive timely warnings when abnormal sediment deposition occurs. Furthermore, by streamlining the BIM model, the visualization platform can smoothly load and display the BIM model.

[0023] The following describes in detail, with reference to the accompanying drawings, the siltation detection method, apparatus, electronic equipment, and medium of the present invention.

[0024] On the one hand, embodiments of the present invention propose a method for detecting sediment deposition, such as... Figure 1 As shown, this method is applied to a sedimentation monitoring system, which includes a sonar detection module 100, an unmanned surface vessel detection module 200, and a visualization platform 300. Figure 2 As shown, the method includes the following steps: Step S100: Based on the design of the hydropower station project, a BIM model of the hydropower station project is created on the visualization platform 300 using 3D design software; It should be noted that BIM model refers to Building Information Modeling, a digital model that integrates all physical information (dimensions, materials, location) and functional information (performance, parameters, relationships) throughout the entire lifecycle of an engineering project (design, construction, operation and maintenance) using 3D digital technology. It connects data, processes, and resources at different stages of the building's lifecycle, providing a complete description of the engineering object and supporting visualized information management throughout the entire phase of a hydropower station project. Addressing the systematic need for sediment deposition detection in hydropower station projects, research was conducted on sediment deposition visualization technology based on measured data. Combining the design results of a specific hydropower station project, a detailed 3D model of the hydropower station project was established using 3D design software. This model serves as a 3D visualization platform for the system. Subsequently, monitored data can be overlaid onto the BIM model, enabling a direct visualization of sediment deposition on the Format Visualization Platform 300.

[0025] Step S200: Multiple sonar detectors of the sonar detection module 100 are deployed along the dam axis on the dam face in front of the hydropower station project at preset intervals to obtain point cloud data of siltation surface information through the sonar. Specifically, based on the actual conditions of the hydropower station, multiple sonars are deployed along the dam axis at appropriate intervals on the dam face in front of the dam. At the same time, information on the surface of siltation is collected in real time to obtain point cloud data. The collected data can be uploaded to the cloud in real time through a 100 Mbps network host. Monitoring personnel can download the collected data by logging into the cloud storage.

[0026] Step S300: Obtain siltation cross-section information based on point cloud data; It should be noted that after obtaining point cloud data, filtering, processing, and calculations can be performed to obtain siltation cross-section information. Based on this siltation cross-section information and the sonar's own elevation, the elevation of the underwater siltation cross-section can be calculated, thereby obtaining information on sediment deposition and achieving automated data acquisition, transmission, processing, and display. Specialized software is used to integrate the installation locations of various sonars, converting acoustic image data into topographic coordinate data, drawing underwater topographic cross-sections, and then comparing the current siltation cross-section data with historical data and design parameters to assess the current status and changes in sediment deposition, achieving the purpose of sediment deposition monitoring and analyzing underwater siltation conditions.

[0027] Step S400: Based on the reservoir area conditions of the hydropower station project, plan the navigation path of the unmanned vessel detection module 200; the unmanned vessel includes a single-beam echo sounder, a positioning unit, and a sound velocity profiler. In this application, fixed sonar detection methods were adopted for the dam front and reservoir area of ​​the hydropower station project, respectively, and mobile detection methods were adopted for the reservoir area. For the dam front, multiple fixed sonars were used to monitor sediment deposition, while for the reservoir area, unmanned surface vessels were used for mobile detection to ensure that all areas of the reservoir area could be detected.

[0028] Step S500: Drive the unmanned vessel to navigate along the route, and during the navigation, obtain the bottom depth through the single beam echo sounder unit, and superimpose the positioning information obtained by the positioning unit to obtain three-dimensional information of the bottom sediment deposition feature points. Specifically, the unmanned surface vessel (USV) is equipped with sensing devices such as a single-beam echo sounder, a positioning unit, and a sound velocity profiler. The basic principle is that the transducer of the single-beam echo sounder emits sound waves to the seabed and receives the echoes. The depth is calculated based on the time difference, and the positioning information from the positioning unit is superimposed to calculate the three-dimensional information of sediment deposition feature points on the seabed. This enables rapid acquisition of sediment deposition information in specific areas of the reservoir, and the echo sounding data is visualized on a BIM model to create a seabed elevation topographic map, achieving real-time mobile monitoring of sediment deposition data. As the main onboard measurement unit of the USV, the single-beam echo sounder, compared to shipborne multibeam systems, features lower power consumption, smaller size, and higher ease of use. While ensuring data acquisition accuracy, it is more suitable for the long-term, long-distance, and large-area sediment detection operations required by USVs, making it a better choice for the routine use of sediment deposition mobile monitoring technology. The sound velocity profiler can measure how the speed of sound propagation in seawater changes with water depth, thereby correcting the curvature of the sound wave propagation path and ensuring the accuracy of underwater depth sounding, positioning, and other data. Meanwhile, to address the issue of wind and waves causing the unmanned surface vessel (USV) to roll and affect the data acquisition accuracy of the single-beam depth sounding unit, the USV integrates an attitude sensor within its hull. This sensor corrects the deflection of the depth sounding beam through real-time synchronization of attitude information and depth sounding data.

[0029] Step S600: Overlay the siltation cross-section information and the three-dimensional information of the sedimentation feature points on the bottom of the water body into the BIM model to obtain the current status and changes of siltation in front of the dam and in the reservoir area of ​​the hydropower station project; By overlaying the obtained siltation cross-section information and the three-dimensional information of the sediment deposition feature points on the bottom of the water onto the BIM model, the sedimentation situation in front of the dam and the bottom of the reservoir can be viewed intuitively on the BIM model. By combining historical data and design data for comparison, the changes in sediment deposition can be obtained.

[0030] Step S700: Based on the selected time period, display the siltation change curves in front of the dam and the reservoir area during that time period on the visualization platform 300; Specifically, on the visualization platform 300, any time period can be selected, and the BIM model can then display the changes in siltation within that time period.

[0031] Step S800: Lighten the BIM model by using edge folding.

[0032] It's important to note that the dam and river channel constitute a massive and complex engineering project. Under the increasingly widespread framework of large-scale platforms, the digital delivery of various engineering projects necessitates the smooth simulation and display of massive BIM model data on simulation platforms. In practical applications, the inability to load large-scale models or their frequent slow loading times during 3D model browsing lead to poor usability. To enable browsing of often enormous BIM models on large platforms, both from a design and application perspective, loading and display delays hinder BIM from realizing its advantages in design, construction, and operation. Unlightened BIM models encounter numerous performance-related issues, such as large file sizes leading to high storage and transmission costs, and difficulties in reviewing data on lightweight rendering platforms. In particular, the large number of triangular meshes and complex texture styles make convenient and quick browsing and transmission difficult in practical use. Furthermore, loading and rendering BIM models often places high demands on the equipment itself, further hindering the original intention of convenient BIM model development. Therefore, this application adopts a side-folding method to lighten the BIM model, so that the BIM model can be smoothly displayed on the visualization platform 300.

[0033] Furthermore, in some embodiments of this application, step S800 specifically includes the following five steps: Step S810: Based on the component types in the BIM model, assign corresponding importance levels to each component type; Step S820: Configure a corresponding target threshold for each component type based on its importance level; Step S830: Export the different types of components as OBJ format files respectively; Step S840: Obtain the network model of the corresponding component based on the OBJ format file; Step S850: Determine all collapsible edges of all triangles in the network model, determine the collapsible edge folding order, and fold the collapsible edges sequentially according to the folding order to reduce the number of triangles in the network model until the number of triangles in the network model reaches the corresponding target threshold.

[0034] Specifically, the importance of component types in the BIM model is first ranked. Component types in the BIM model can include the dam core area, monitoring supports, decorative components, etc. Based on the function of different components, their importance is ranked, and a corresponding importance level is assigned to each component type. Then, based on the importance level of each component type, a corresponding target threshold is assigned. It should be noted that this target threshold represents the number of triangles in the final network model of that component type that needs to be reduced. The higher the importance level, the higher the target threshold, and the more triangles are retained, allowing for more detailed visualization; conversely, the lower the importance level, the lower the target threshold, and the fewer triangles are retained, requiring less detail to be displayed. Next, different types of components are exported as OBJ format files. OBJ format refers to an open-source, cross-platform 3D model text file format. Exporting OBJ format files allows the acquisition of all point, edge, and face information, i.e., obtaining the component's network model. Next, all triangular faces in the network model are identified, and the collapsible edges of all triangular faces are determined. It's important to note that during the lightweighting of the BIM model, certain critical parts need to be protected to prevent damage to the original building's structural features after folding. Therefore, edges related to core areas, key geometric features, areas with special materials and textures, and non-manifold surfaces are designated as non-collapable edges and are not folded during the lightweighting process. Similarly, edges exceeding a certain length threshold are also designated as non-collapable. After determining all collapsible edges, the folding order needs to be determined, and these edges are folded according to this order to reduce the number of triangular faces in each network model until the number of triangular faces in the network model reaches the target threshold. When folding collapsible edges, they are sorted according to the degree of impact on the BIM model after folding; edges with greater impact are placed further down the list, and those with less impact are placed earlier. By lightweighting the BIM model, the loading and display of the visualization platform 300 become smoother when browsing the BIM model.

[0035] Furthermore, in some embodiments of this application, step S850 specifically includes the following seven steps: Step S851: Calculate the error matrix for all collapsible edges; Step S852: Based on the error matrix, determine the optimal folding vertex for each foldable edge; the optimal folding vertex represents the point with the smallest error value after merging the two endpoints of the foldable edge into a new vertex. Step S852: Based on the error matrix and the optimal folding vertex, determine the folding error value generated after folding the two endpoints of each foldable edge into the optimal folding vertex; Step S854: Determine the folding order of each foldable edge based on the magnitude of the folding error value; Step S855: Fold the foldable edges sequentially according to the folding order; Step S856: Calculate the error matrix of the new collapsible edge connected to the optimal collapsible vertex after collapsing, and the collapsible error value of the new collapsible edge; Step S857: Update the sorting results based on the new edge error values, and return the steps of sorting the folding order of each foldable edge according to the size of the folding error value, until the number of triangular patches in the network model reaches the corresponding target threshold.

[0036] Specifically, first, we establish the plane equation for each triangular facet, denoted as: ; Where n = (a, b, c) is the normal vector of the triangular facet, and d is the distance from the triangular facet to the origin.

[0037] Obtain the coordinates of the vertex of each collapsible edge, denoted as (x, y, z, 1), and the plane equation is represented as (a, b, c, d). Thus, the quadratic error matrix of the triangles connected to each vertex is represented as: ; It should be noted that each vertex may be connected to multiple triangular faces. Therefore, the total quadratic error matrix of each vertex should be the sum of the quadratic error matrices of all the triangular faces, denoted as: ; Each collapsible edge contains two vertices. Assuming the total quadratic error matrices of the two vertices are Q1 and Q2, the error matrix of the collapsible edge is denoted as Q1 + Q2.

[0038] After obtaining the error matrix of the collapsible edges, it is necessary to determine the optimal collapsing vertex for each collapsible edge. Assuming the two vertices of a collapsible edge are V1 and V2, the two vertices are merged into a new vertex, denoted as V. new The combined error value is denoted as: ; The vertex that minimizes the aforementioned error value is the optimal folding vertex to be determined. It should be noted that if the above formula does not have a suitable solution, then the point with the smallest error among the two vertices or the midpoint of the edge is selected as the optimal folding vertex.

[0039] After obtaining the optimal folding vertices for all collapsible edges, the error value after folding each collapsible edge is determined. The error values ​​of all collapsible edges are then sorted in ascending order of error value, with smaller error values ​​appearing earlier and larger error values ​​appearing later. Then, the collapsible edges are folded sequentially according to the folding order. It should be noted that folding a collapsible edge generates a new vertex (i.e., the optimal folding vertex of that edge). This new vertex inherits all triangles connected to the original two vertices. Therefore, the folding error of all edges connected to the new vertex needs to be recalculated, and the folding order of all edges to be folded is re-sorted based on the calculated error values. This process is repeated until the number of triangles in the network model is reduced to a set target threshold. Using this method, the BIM model can be lightweighted while retaining as many necessary structural features as possible, making it easier for users to view siltation conditions through the BIM model.

[0040] It should be noted that sedimentation monitoring indicators are important indicators for evaluating and monitoring the operational status of the dam's intake and the reservoir's capacity. Formulating sedimentation monitoring indicators requires establishing a reliable early warning model based on historical operational monitoring data of the hydropower station. Personalized monitoring and early warning values ​​should be set for different key monitoring locations. When the reported sediment surface depth approaches or exceeds the preset early warning value within a certain range, the system can automatically issue an alarm to the user and promptly display the sedimentation impact status of the warning location on the visualization platform. Simultaneously, based on the changing trends of sedimentation in front of the dam over a long-term monitoring period, research on the accumulation patterns of sedimentation in front of the dam should be conducted, and predictions of sedimentation thickness variation curves in front of key structures should be made, providing reliable data support for the hydropower station's reservoir scheduling plan.

[0041] Real-time monitoring of sediment deposition is conducted at key locations upstream of the power plant intake dam. Sediment deposition in these areas is crucial for decisions regarding water diversion, power generation, flood discharge, and sediment removal, thus requiring focused monitoring. The current status and changes in sediment deposition provide guidance for power plant operation and scheduling; therefore, the accuracy of sediment deposition monitoring is critical. To this end, an automatic acoustic sediment tracking and monitoring system is constructed using multiple fixed, high-resolution sonar arrays upstream of the dam. The sonars acquire high-resolution three-dimensional topographic data of underwater structures and terrain through 360-degree scanning. The distance accuracy is better than 2mm in the 1-4m range and better than 10mm in the range greater than 5m, easily distinguishing subtle changes in underwater sediment deposition. Through continuous data acquisition, the changes and current status of underwater sediment can be analyzed in real time. This accuracy meets the highest requirements of current reservoir hydrological sediment observation standards. During the installation of sonar equipment, its horizontal position and elevation will be accurately located using a total station and a level. The starting point for the positioning coordinates will be the coordinate point of the monitoring and control network in the reservoir head area to ensure the accuracy of the sonar equipment's installation location and to ensure that the measured underwater topographic points and elevations meet the specifications.

[0042] Regarding the power supply for sonar: The underwater power supply is crucial to the continuity and immediacy of data acquisition. Therefore, the underwater sonar requires an uninterrupted power supply. Simultaneously, the surface signal and data transmission systems need to continuously send data acquisition commands underwater and transmit the acquired data back to the control center, making uninterrupted power supply equally important. Therefore, the power supply for the underwater and surface sonar equipment employs a dual-protection approach: onshore power is connected via cable to the plant's mains power supply, providing uninterrupted power; solar and wind power are used as charging power sources, and batteries are provided. In environments with plant power, 220V, 50Hz AC power is used to power the underwater components. In environments without plant power, wind and solar power are combined for electricity generation and storage. The batteries are continuously charged under normal circumstances, ensuring a 3-day power supply in the event of a power outage.

[0043] The acoustic sediment tracking and monitoring system can automatically collect data at preset intervals or collect data in real time via manual settings. After data collection, the data is transmitted back to the backend data processing, analysis, and early warning platform in real time. Therefore, the timeliness and effectiveness of data transmission are crucial. The underwater equipment is equipped with a surface-based data transmission terminal. The data transmission terminal employs a dual-security method: wireless transmission using 5G network signals, and wired transmission using fiber optic cables.

[0044] Because hydropower station projects involve water with high sediment content and strong corrosiveness, the equipment experiences severe erosion and wear. The underwater components of the acoustic sediment tracking and monitoring system, once installed, may remain submerged for extended periods. Therefore, ensuring the durability of the underwater equipment and implementing corrosion prevention measures are crucial. To this end, all underwater equipment is equipped with anti-corrosion measures, including applying lead-containing paint to the external structure and installing As2S3 to prevent plankton adhesion and corrosion.

[0045] Meanwhile, to ensure the long-term use of the underwater equipment, multiple internal monitoring sensors are installed to sense its operating status. The underwater equipment is equipped with temperature, humidity, and leakage protection sensors to monitor internal temperature and humidity and provide leakage protection.

[0046] Furthermore, due to the large amount of debris carried in the water, and the fact that this debris often accumulates in this area with the current, underwater equipment is inevitably subject to impacts from underwater structures. Therefore, to ensure continuous operation of the equipment, impact-resistant measures must be taken for underwater components. The main load-bearing structure for the fixed installation of the underwater equipment is made of 1Cr18Ni9 0.1%. Meanwhile, all underwater metal components use anodic AL-MG 6 series non-load-bearing fixing structures, and all underwater malleable soft materials use ABS (Acrylonitrile Butadiene Styrene) for fixing and sealing. Finally, underwater cables use double-protected corrosion-resistant rubber material.

[0047] In some embodiments of this application, step S200 above, which involves deploying multiple sonar detection modules 100 along the dam axis at preset intervals on the dam face in front of the hydropower station project and obtaining point cloud data through the sonar, includes the following four steps: Step S210: Multiple sonars are deployed along the dam axis on the dam face in front of the hydropower station project at preset intervals, wherein every two sonars are located on the orthogonal axis and detect the same position from two different perspectives. Step S220: Acquire point cloud data using sonar and filter the point cloud data using the PCL point cloud data processing library; Step S230: Establish a two-dimensional profile using the filtered point cloud data; Step S240: Interpolate the two-dimensional profile to obtain a three-dimensional point cloud model.

[0048] It should be noted that the sonar is a scanning depth-sensing device. After being fixed in place, the detected depth is compared with the actual depth in the BIM model to calculate the elevation data of the cross-section. Because the ambiguity of the elevation angle associated with sonar beam imaging and sonar observations poses a challenge to the accurate fusion of 3D geometric information, a pair of sonars on orthogonal axes with uncertainties are used to independently observe the same point in the environment from two perspectives and correlate these observations. Using these parallel observations, a dense, fully defined point cloud can be created at each moment to aid in the construction and fusion of geometric information of the underwater scene.

[0049] To achieve better integration, based on the on-site sonar sampling and the complexity of the geometric distribution of key areas in front of the dam, the PCL point cloud data processing library was ultimately selected. When acquiring point cloud data using sonar, factors such as equipment accuracy, operator experience, environmental conditions, changes in the surface properties of the measured object, and the impact of data stitching and registration operations are all significant. Furthermore, point clouds acquired at different times or from different viewpoints will inevitably contain some noise points during registration and geometric information generation. In practical applications, in addition to noise points caused by random measurement errors, external interference such as line-of-sight obstruction and obstacles often results in outliers far from the main point cloud. In the point cloud processing workflow, filtering, as the first step in preprocessing, significantly impacts subsequent processing (registration, feature extraction, surface reconstruction). The PCL point cloud filtering module provides many flexible and practical filtering algorithms, such as bilateral filtering, Gaussian filtering, conditional filtering, pass-through filtering, and voxel filtering. After filtering, a two-dimensional profile can be built using the filtered points. Then, the two-dimensional profile is subjected to interpolation to obtain the required three-dimensional point cloud model.

[0050] In some embodiments of this application, step S240 above, which involves interpolating the two-dimensional profile to obtain a three-dimensional point cloud model, includes the following two steps: Linear interpolation and point cloud densification are performed on the two-dimensional profile in the longitudinal direction. A three-dimensional point cloud model is obtained by performing cubic spline interpolation on the two-dimensional cross-section in the horizontal direction.

[0051] Specifically, when performing linear interpolation on a two-dimensional profile, Lagrange interpolation can be used: We represent the two-dimensional profile curve using an algebraic polynomial of degree no higher than n, that is:

[0052] Make:

[0053] It can be proven that the polynomial satisfying equation (2) is unique. If we take the condition that satisfies the polynomial:

[0054] A set of nth degree polynomials As a basis for all n+1-dimensional linear spaces, the Lagrange polynomial can be derived:

[0055] in:

[0056] We use piecewise quadratic Lagrange polynomial interpolation, i.e., linear interpolation, and the resulting graph is shown below. Figure 3 As shown. From Figure 3 As can be seen, while piecewise quadratic interpolation is computationally convenient and has guaranteed convergence, the two-dimensional profile produced by linear interpolation has many jagged edges, making it difficult to determine the smoothness of the curve. Therefore, cubic spline interpolation is also needed laterally. (Definition) For a known data pair ),when If the function Conditions met: (1) In each sub-interval All of the above are polynomials of degree no higher than three. (2) In the interval Upper continuous; (3) ; Then it is called For nodes The cubic spline interpolation function.

[0057] In each sub-interval superior, In the form of:

[0058] in There are n undetermined coefficients, and the total number of undetermined coefficients is 4n.

[0059] On the other hand, it requires a piecewise cubic polynomial. and its second derivative in the interval Continuity is defined as the connection points of their respective subintervals. Assuming continuity, and based on the definition of cubic spline interpolation function (2) and (3), these undetermined coefficients should satisfy 4n-2 equations:

[0060] Since there are 4n undetermined coefficients, two more equations are needed: boundary conditions or endpoint conditions. We will use the first derivative values ​​at the endpoints:

[0061] The final result is as follows: Figure 4 As shown.

[0062] As can be seen, the curve obtained by this interpolation method has excellent smoothness. For the surface of a silt layer, we can better simulate realistic undulations. Therefore, after performing linear interpolation and point cloud refinement in the longitudinal direction, we perform cubic spline interpolation in the transverse direction to obtain a 3D point cloud model, as shown in the image. Figure 5 As shown. After obtaining the 3D point cloud model, the model is mapped into the BIM model, which can more accurately and intuitively present the cross-sectional elevation, and also provide a data foundation for the evolution of silt data.

[0063] When the unmanned surface vessel (USV) completes its survey of the reservoir area, it sends the survey data and positioning information to the visualization platform 300. After integrating the positioning information and survey data, geometric imaging is performed, and the presented geometric structure is fused into the BIM model. The core technology of this part is to merge partial scanned point clouds of the same 3D scene or object into a complete 3D point cloud. First, 3D positioning is performed using an USV equipped with GNSS capabilities for intelligent inspection. By comparing the coordinate data with the coordinate points in the BIM model, and adding possible delay compensation, the sonar geometric information in the original map is registered with the single-beam sonar geometric information of the USV. This allows the current real-time 3D point cloud to be accurately matched to its corresponding 3D environment. Second, attitude estimation is performed by aligning one point cloud A with another point cloud B to generate the attitude information of point cloud A relative to point cloud B. This attitude information can be used for decision-making by the USV.

[0064] The specific operation process is as follows: (1) Acquire GNSS positioning and point cloud data sent back by the unmanned vessel, and preprocess the point cloud data; perform iterative matching between the preprocessed point cloud data and the point cloud data of the previous frame; determine whether the iterative matching is successful according to the preset iteration termination condition and iterative matching condition; when it is determined that the iterative matching is successful, determine the key frame of the point cloud data; calculate the nearest point between the point cloud data of the key frame and all point cloud data before the key frame for key frame correction; and stitch the point cloud data according to the corrected key frame.

[0065] (2) Iterative matching of the preprocessed point cloud data with the point cloud data of the original BIM model, including: extracting point pairs with consistent two-dimensional coordinates from the preprocessed point cloud data and the original point cloud data as initial point pairs; calculating the initial transformation matrix using singular value decomposition based on the correspondence of the initial point pairs; transforming the preprocessed point cloud data based on the calculated initial transformation matrix; calculating the nearest point between the transformed point cloud data and the original point cloud data as valid point pairs; and calculating the transformation matrix based on the valid point pairs and the cost function.

[0066] (3) Preprocessing the point cloud data, including: calculating the neighborhood topology of all points in the point cloud data; calculating the normal of all points in the point cloud data; and identifying and filtering outliers in the point cloud data.

[0067] (4) The preset iteration termination conditions include the number of iterations, the similarity of the matrices of two adjacent iterations and the distance between two adjacent point pairs. The iteration matching conditions include the mean square error of the point pair distance and the overlap rate of the cloud data adjacent to the point.

[0068] (5) Determine the key frames of the point cloud data, including: performing rigid body transformation and normal transformation on the acquired point cloud data; calculating the overlap rate between the point cloud data after rigid body transformation and normal transformation and the point cloud data of the previous frame; and determining the key frames of the point cloud data based on the calculated overlap rate.

[0069] (6) The modules involved in the whole process include: a preprocessing module, used to acquire point cloud data of 3D scanning and preprocess the point cloud data; an iterative matching module, used to iteratively match the point cloud data preprocessed by the preprocessing module with the point cloud data of the previous frame; an iterative judgment module, used to determine whether the iterative matching is successful according to the preset iterative termination condition and iterative matching condition; a key frame determination module, used to determine the key frame of the point cloud data when the iterative judgment module determines that the iterative matching module has successfully matched; a key frame correction module, used to calculate the nearest point between the point cloud data of the key frame determined by the key frame determination module and all point cloud data before the key frame, so as to perform key frame correction; and a point cloud data stitching module, used to stitch the point cloud data according to the key frame corrected by the key frame correction module.

[0070] (7) The iterative matching module includes: an initial point pair extraction unit, used to extract point pairs with consistent two-dimensional coordinates in the point cloud data preprocessed by the preprocessing module and the point cloud data of the previous frame as initial point pairs; an initial transformation matrix calculation unit, used to calculate the initial transformation matrix using singular value decomposition based on the correspondence of the initial point pairs extracted by the initial point pair extraction unit; a point cloud data transformation unit, used to transform the point cloud data preprocessed by the preprocessing module based on the initial transformation matrix calculated by the initial transformation matrix calculation unit; an effective point pair calculation unit, used to calculate the nearest point between the point cloud data transformed by the point cloud data transformation unit and the point cloud data of the previous frame as an effective point pair; and a transformation matrix calculation unit, used to calculate the transformation matrix based on the effective point pairs calculated by the effective point pair calculation unit and the cost function.

[0071] (8) The preprocessing module includes: a domain calculation unit for calculating the domain topology of all points in the point cloud data; a normal calculation unit for calculating the normal of all points in the point cloud data; and an outlier detection and filtering unit for detecting and filtering outliers in the point cloud data.

[0072] (9) The preset iteration termination conditions include the number of iterations, the similarity of two adjacent iteration matrices and the distance between two adjacent point pairs. The iteration matching conditions include the mean square error of the point pair distance and the overlap rate of adjacent cloud data.

[0073] (10) The key frame determination module includes: a rigid body normal transformation unit, used to perform rigid body transformation and normal transformation on the point cloud data preprocessed by the preprocessing module; an overlap rate calculation unit, used to calculate the overlap rate between the point cloud data after rigid body transformation and normal transformation by the rigid body normal transformation unit and the point cloud data of the previous frame; and a key frame determination unit, used to determine the key frame of the point cloud data according to the overlap rate calculated by the overlap rate calculation unit.

[0074] Furthermore, in some embodiments of this application, the siltation detection method further includes the following seven steps: Step S910: Determine the hovering measurement points in the reservoir area based on the historical monitoring data of the hydropower station project; Step S920: Drive the unmanned vessel toward the hovering measurement point; Step S930: When the distance between the unmanned vessel and the hovering measurement point is less than the preset distance, stop applying power to the unmanned vessel and obtain the current coordinate information of the unmanned vessel through the positioning unit; Step S940: Acquire the displacement trajectory of the unmanned vessel within a preset time period; Step S950: Calculate the flow velocity and direction angle of the water flow based on the preset time period and displacement trajectory; Step S960: Control the unmanned vessel to face the opposite direction of the heading angle, and drive the unmanned vessel to the hovering measurement point; Step S970: Apply corresponding forward propulsion to the unmanned vessel according to the flow velocity, so that the unmanned vessel hovers at the hovering measurement point; Step S980: The unmanned vessel is used to explore the hovering measurement point for silt and mud, and the exploration results are sent to the visualization platform 300.

[0075] Specifically, the process begins by acquiring historical monitoring data for the hydropower station project to identify key monitoring locations (i.e., areas prone to siltation). This data is used to determine hovering measurement points, allowing the unmanned surface vessel (USV) to focus its investigation on these points. To enable the USV to hover at these measurement points, it is first propelled towards them. When the distance between the USV and the measurement point is less than a preset distance, power is stopped, and the USV's current coordinates are obtained via a positioning unit. Then, within a preset time period, the positioning unit continuously monitors the USV's position changes to obtain its displacement trajectory, allowing for the calculation of the water flow velocity and direction angle. After determining the water flow direction angle, the USV's bow is oriented opposite to the direction of the water flow, propelling it to the hovering measurement point. Once at the measurement point, to ensure hovering, forward propulsion is applied to counteract the water flow's movement. Meanwhile, to prevent the unmanned surface vessel (USV) from shifting its position during hovering, a positioning unit monitors its position changes in real time. When a shift is detected, appropriate power is applied to adjust the USV based on the direction and degree of the shift. This method enables the USV to hover at the designated measurement point, allowing it to investigate sediment deposition and obtain the results. These results are then sent to the visualization platform 300 for overlay with the BIM model.

[0076] In some embodiments of this application, step S400 above, which involves planning the navigation path of the unmanned vessel for the unmanned vessel detection module based on the reservoir conditions of the hydropower station project, includes the following five steps: Step S410: Detect whether there are obstacles in the storage area; Step S420: When an obstacle exists, determine the area of ​​the region surrounding the obstacle; Step S430: When the area around the obstacle is greater than the preset area, establish a ring grid centered on the obstacle; Step S440: For the remaining area of ​​the reservoir area excluding the circular grid, create several rectangular grids; some of the rectangular grids overlap with the circular route. Step S450: Based on the rectangular grid and the circular grid, plan the straight-line navigation route and the circular navigation route of the unmanned vessel.

[0077] It should be noted that obstacles may exist in the reservoir area of ​​hydropower station projects. When planning the navigation route of the unmanned surface vessel (USV), these obstacles should be avoided. Simultaneously, the system should be able to detect the areas surrounding the obstacles to avoid missing any detection zones. If there are no obstacles in the reservoir area, the entire reservoir area is divided into multiple rectangular grids, and the shortest path for the USV is planned using a path optimization algorithm. This ensures that the USV can detect all areas while ensuring the shortest path, thereby improving detection efficiency. It should also be noted that the USV maintains a straight-line navigation throughout its journey, and its navigation direction is parallel to the water flow direction to avoid lateral deviation due to the water flow. If obstacles exist in the reservoir area, it is determined whether there is sufficient space around the obstacle. If there is sufficient space, a circular grid is established around the obstacle, allowing the USV to navigate in a circular pattern around the obstacle, gradually approaching it from the outermost edge, thus detecting the area around the obstacle. For other areas, several rectangular grids are created, and straight-line routes are planned based on these grids. This allows the USV to combine straight-line and circular navigation for comprehensive detection of the reservoir area. If there is not enough space around the obstacle, the entire reservoir area is divided into rectangular grids, and the rectangular grids around the obstacle are smaller, making the detection range of the unmanned vessel around the obstacle more compact.

[0078] It should be noted that the unmanned surface vessel (USV) is lightweight and easy to transport. It can easily begin measurement work upon launch and can strictly follow the planned route, avoiding repeated measurements and thus improving work efficiency. The USV is equipped with absolute straight-line measurement technology, adaptive flow velocity technology, and automatic hovering technology, fully covering the test area and making the measurement data more accurate.

[0079] According to the siltation detection method of this invention, for the dam front and reservoir area of ​​a hydropower station project, a fixed sonar detection method and an unmanned surface vessel (USV) mobile detection method are used respectively. By combining the two methods, a comprehensive detection of siltation in the hydropower station project is achieved. Simultaneously, the detected data is integrated into the BIM model, allowing users to intuitively view changes in siltation through the BIM model and receive timely warnings when abnormal siltation occurs. Furthermore, the BIM model is lightweighted for easy loading and display on the visualization platform 300.

[0080] On the other hand, embodiments of the present invention also provide a siltation detection device, such as... Figure 6 As shown, the device includes: The processor 101 can be implemented using a general-purpose central processing unit (CPU), microprocessor, application specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 102 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 102 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 102 and called and executed by the processor 101 using the sheet metal stamping method of the embodiments of this application. Input / output interface 103 is used to implement information input and output; The communication interface 104 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 105 transmits information between various components of the device (e.g., processor 101, memory 102, input / output interface 103, and communication interface 104); The processor 101, memory 102, input / output interface 103 and communication interface 104 are connected to each other within the device via bus 105.

[0081] On the other hand, embodiments of the present invention also provide an electronic device, including the above-mentioned siltation detection device.

[0082] On the other hand, embodiments of the present invention also provide a storage medium, which is a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for detecting sediment deposition.

[0083] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof. The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separate, and may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0084] Although specific embodiments are described herein, those skilled in the art will recognize that many other modifications or alternative embodiments are also within the scope of this disclosure. For example, any of the functions and / or processing capabilities described in connection with a particular device or component can be performed by any other device or component. Furthermore, while various exemplary embodiments and architectures have been described according to embodiments of this disclosure, those skilled in the art will recognize that many other modifications to the exemplary embodiments and architectures described herein are also within the scope of this disclosure.

[0085] The foregoing description, with reference to block diagrams and flowcharts of systems, methods, systems, and / or computer program products according to exemplary embodiments, has described certain aspects of this disclosure. It should be understood that one or more blocks in the block diagrams and flowcharts, as well as combinations of blocks in the block diagrams and flowcharts, can be implemented by executing computer-executable program instructions, respectively. Similarly, according to some embodiments, some blocks in the block diagrams and flowcharts may not need to be executed in the order shown, or may not all need to be executed. Furthermore, additional components and / or operations beyond those shown in the blocks in the block diagrams and flowcharts may exist in some embodiments.

[0086] Therefore, blocks in block diagrams and flowcharts support combinations of means for performing a specified function, combinations of elements or steps for performing a specified function, and program instruction means for performing a specified function. It should also be understood that each block in a block diagram and flowchart, and combinations of blocks in block diagrams and flowcharts, can be implemented by a dedicated hardware computer system or a combination of dedicated hardware and computer instructions that performs a specific function, element, or step.

[0087] The program modules, applications, etc., described herein may include one or more software components, including, for example, software objects, methods, data structures, etc. Each such software component may include computer-executable instructions that, in response to execution, cause at least a portion of the functionality described herein (e.g., one or more operations of the exemplary methods described herein) to be performed.

[0088] Software components can be coded using any of a variety of programming languages. An exemplary programming language could be a low-level programming language, such as assembly language associated with a specific hardware architecture and / or operating system platform. Software components including assembly language instructions may need to be converted into executable machine code by an assembler before being executed by the hardware architecture and / or platform. Another exemplary programming language could be a higher-level programming language that is portable across multiple architectures. Software components including higher-level programming languages ​​may need to be converted into an intermediate representation by an interpreter or compiler before execution. Other examples of programming languages ​​include, but are not limited to, macro languages, shell or command languages, job control languages, scripting languages, database query or search languages, or report writing languages. In one or more exemplary embodiments, a software component containing instructions from one of the above-described programming language examples can be executed directly by the operating system or other software components without first being converted into another form.

[0089] Software components can be stored as files or other data storage structures. Software components of similar type or related function can be stored together in a specific directory, folder, or library. Software components can be static (e.g., pre-defined or fixed) or dynamic (e.g., created or modified at runtime).

[0090] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A method for detecting sediment deposition, characterized in that, An application is made in a sedimentation monitoring system, which includes a sonar detection module, an unmanned surface vessel detection module, and a visualization platform. The method includes the following steps: Based on the design of the hydropower station project, a BIM model of the hydropower station project is created on the visualization platform using 3D design software; Multiple sonar detectors of the sonar detection module are deployed along the dam axis on the dam face in front of the dam of the hydropower station project at preset intervals, and point cloud data is obtained through the sonar. Based on the point cloud data, information on the siltation cross-section is obtained; Based on the reservoir area conditions of the hydropower station project, the navigation path of the unmanned vessel detection module is planned; the unmanned vessel includes a single-beam depth sounding unit, a positioning unit, and a sound velocity profiler. The unmanned vessel is driven to navigate along the route, and during the navigation, the depth of the bottom is obtained through the single-beam echo sounder unit, and the positioning information obtained by the positioning unit is superimposed to obtain three-dimensional information of the sediment deposition feature points at the bottom of the water. The information on the siltation cross section and the three-dimensional information on the siltation feature points at the bottom of the water are superimposed on the BIM model to obtain the current status and changes of siltation in front of the dam and in the reservoir area of ​​the hydropower station project. Based on the selected time period, the visualization platform displays the siltation change curves in front of the dam and in the reservoir area during that time period. The BIM model is lightweighted by using edge-folding.

2. The method for detecting sediment deposition according to claim 1, characterized in that, The method of using edge folding to lighten the BIM model includes: Based on the component types of the BIM model, each component type is assigned a corresponding importance level; Based on the importance level of each component type, configure a corresponding target threshold for each component type; Export different types of components as OBJ format files respectively; Based on the OBJ format file, obtain the network model of the corresponding component; All collapsible edges of all triangles in the network model are determined, and the folding order of all collapsible edges is determined. The collapsible edges are then folded sequentially according to the folding order to reduce the number of triangles in the network model until the number of triangles in the network model reaches the corresponding target threshold.

3. The method for detecting sediment deposition according to claim 2, characterized in that, The process of determining all collapsible edges of all triangles in the network model, determining the collapsible edge folding order, and folding the collapsible edges sequentially according to the folding order to reduce the number of triangles in the network model until the number of triangles in the network model reaches the corresponding target threshold includes: Calculate the error matrix for all collapsible edges; Based on the error matrix, the optimal folding vertex for each of the collapsible edges is determined; the optimal folding vertex represents the point with the smallest error value after merging the two vertices of the collapsible edge into a new vertex. Based on the error matrix and the optimal folding vertex, determine the error value generated after folding the two vertices of each foldable edge into the optimal folding vertex; Based on the magnitude of the error value, the folding order of each of the foldable edges is sorted. According to the sorting results, the foldable edges are folded sequentially. After each fold, the error matrix of the new foldable edge connected to the optimal folded vertex and the error value of the new foldable edge are calculated. The sorting result is updated based on the new error value of the collapsible edge, and the step of sorting the folding order of each collapsible edge according to the magnitude of the error value is returned until the number of triangles in the network model reaches the corresponding target threshold.

4. The method for detecting sediment deposition according to claim 1, characterized in that, The method involves deploying multiple sonars along the dam axis on the dam face in front of the hydropower station at preset intervals, and obtaining point cloud data through the sonars, including: Multiple sonars are deployed along the dam axis on the dam face in front of the hydropower station project at preset intervals, wherein every two sonars are located on orthogonal axes and detect the same position from two different perspectives. Point cloud data is acquired using the sonar, and the point cloud data is filtered using the PCL point cloud data processing library. A two-dimensional profile is established using the filtered point cloud data. The two-dimensional profile is interpolated to obtain a three-dimensional point cloud model.

5. The method for detecting sediment deposition according to claim 4, characterized in that, The step of interpolating the two-dimensional profile to obtain a three-dimensional point cloud model includes: The two-dimensional profile is subjected to linear interpolation and point cloud densification in the longitudinal direction; The three-dimensional point cloud model is obtained by performing cubic spline interpolation on the two-dimensional cross-section in the horizontal direction.

6. The method for detecting sediment deposition according to claim 1, characterized in that, The method further includes: Based on the historical monitoring data of the hydropower station project, the hovering measurement points in the reservoir area were determined; Drive the unmanned vessel toward the hovering measurement point; When the distance between the unmanned vessel and the hovering measurement point is less than a preset distance, the application of power to the unmanned vessel is stopped, and the current coordinate information of the unmanned vessel is obtained through the positioning unit; Within a preset time period, the displacement trajectory of the unmanned vessel is acquired; Calculate the flow velocity and direction angle of the water flow based on the preset time period and the displacement trajectory; Control the bow of the unmanned vessel to face the opposite direction of the azimuth angle, and drive the unmanned vessel to the hovering measurement point; Based on the flow velocity, apply corresponding forward propulsion to the unmanned vessel to make it hover at the hovering measurement point; The unmanned vessel conducts detection at the hovering measurement point and sends the detection results to the visualization platform.

7. The method for detecting sediment deposition according to claim 1, characterized in that, The step of planning the navigation path of the unmanned surface vessel (USV) for the unmanned surface vessel (USV) detection module based on the reservoir area conditions of the hydropower station project includes: Detect whether there are any obstacles in the storage area; When an obstacle is present, determine the area of ​​the region surrounding the obstacle; When the area around the obstacle is greater than the preset area, a ring grid is established with the obstacle as the center; For the remaining area of ​​the reservoir area other than the annular grid, several rectangular grids are created; wherein some of the rectangular grids partially overlap with the annular grid. Based on the rectangular grid and the circular grid, the straight-line navigation route and the circular navigation route of the unmanned vessel are planned.

8. A siltation detection device, characterized in that, It includes at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed by the at least one control processor, enable the at least one control processor to perform the sedimentation detection method as described in any one of claims 1 to 7.

9. An electronic device, characterized in that, Including the siltation detection device as described in claim 8.

10. A storage medium, characterized in that, The storage medium stores computer-executable instructions, which are used to cause a computer to perform the sedimentation detection method according to any one of claims 1-7.

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