Silt accumulation condition monitoring method and device, electronic equipment and medium

Abstract

The application discloses a kind of silt deposition condition monitoring 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 by three-dimensional design software;Multiple sonars are laid along dam axis direction in dam front dam surface of hydropower station engineering according to preset interval, and point cloud data is obtained by sonar;According to point cloud data, obtain deposition section information;Deposition section information is superimposed to BIM model;The route of unmanned ship is planned according to the result of grid processing to conventional detection area;Drive unmanned ship to sail along route, obtain underwater silt deposition feature point three-dimensional information;Drive unmanned ship to hover at hovering measurement point, and obtain deposition three-dimensional information of hovering measurement point by single-beam depth sounding system and positioning system;Underwater silt deposition feature point three-dimensional information and deposition three-dimensional information are superimposed to BIM model.The method can track and accurately detect silt deposition condition in real time.

Description

Technical Field

[0001] This invention relates to the field of sedimentation monitoring technology, and in particular to a method, device, electronic equipment, and medium for monitoring sedimentation. 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 monitoring 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 method for monitoring siltation according to embodiments of the present invention includes the following steps: Based on the design of the hydropower station project, a BIM model of the hydropower station project was created using 3D design software; Multiple sonars are deployed along the dam axis at preset intervals on the dam face in front of the hydropower station project to obtain point cloud data. Based on the point cloud data, information on the siltation cross-section is obtained; The siltation cross-section information is superimposed onto the BIM model to obtain the current status and changes of siltation in front of the dam of the hydropower station project; Obtain historical monitoring data of the hydropower station project, and determine the key monitoring areas and routine monitoring areas of the reservoir area of ​​the hydropower station project based on the historical monitoring data; The conventional detection area is gridded, and the route of the unmanned vessel is planned based on the gridding results; the unmanned vessel is equipped with a single-beam echo sounder, a positioning system, and a sound velocity profiler. The unmanned vessel is driven to navigate along the route, and during the navigation, the depth of the seabed is obtained through the single-beam echo sounder system, and the positioning information obtained by the positioning system is superimposed to obtain three-dimensional information of the sediment deposition feature points on the seabed. Based on the key detection areas, determine the hovering measurement points of the unmanned vessel; The unmanned vessel is driven to hover at the hovering measurement point, and the three-dimensional information of the sedimentation at the hovering measurement point is obtained through the single-beam echo sounder and the positioning system. The three-dimensional information of the sediment deposition feature points at the bottom of the water and the three-dimensional information of the sediment deposition are superimposed on the BIM model to obtain the current status and changes of sediment deposition in the reservoir area of ​​the hydropower station project.

[0005] According to some embodiments of the present invention, the method further includes a step of lightweighting the BIM model, specifically including: Obtain the geometric data and material information of the BIM model; Based on the geometric data, determine whether the BIM model has triangular faces. If it does, merge the triangular faces and simplify the boundary lines to obtain a simplified BIM model. Based on the material information, the simplified BIM model is assigned the corresponding material.

[0006] 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.

[0007] 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.

[0008] According to some embodiments of the present invention, the step of superimposing the three-dimensional information of the sediment deposition feature points at the bottom of the water body and the three-dimensional information of the sediment deposition onto the BIM model to obtain the current status and changes of sediment deposition in the reservoir area of ​​the hydropower station project includes: The system acquires the positioning information and detection data sent back by the unmanned vessel, and preprocesses the detection data. The preprocessed detection data is iteratively matched with the detection data of the previous frame; Determine whether the iteration matching is successful based on the preset iteration termination condition and iteration matching condition; When it is determined that the iterative matching is successful, the key frame of the probe data is determined; Calculate the nearest point between the probe data of the key frame and all probe data preceding the key frame for key frame correction; The point cloud data is stitched together based on the corrected keyframes to obtain the three-dimensional information of the underwater sediment deposition feature points and the three-dimensional information of the deposition. The three-dimensional information of the sediment deposition feature points at the bottom of the water and the three-dimensional information of the sediment deposition are superimposed on the BIM model to obtain the current status and changes of sediment deposition in the reservoir area of ​​the hydropower station project.

[0009] According to some embodiments of the present invention, the method of driving the unmanned vessel to hover at the hovering measurement point and obtaining the siltation three-dimensional information of the hovering measurement point through the single-beam echo sounder and the positioning system includes: 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 system; 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, a corresponding forward propulsion is applied to the unmanned vessel, causing it to hover at the hovering measurement point.

[0010] According to some embodiments of the present invention, the step of performing gridding processing on the conventional detection area and planning the route of the unmanned vessel based on the gridding processing result includes: Detect whether there are obstacles in the conventional detection 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 conventional detection area other than the annular grid, several rectangular grids are created; wherein, some of the rectangular grids partially overlap with the annular route. 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] Secondly, according to an embodiment of the present invention, a siltation monitoring device 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 monitoring method described in the first aspect embodiment.

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

[0014] The siltation monitoring 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 in hydropower station projects 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.

[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 flowchart illustrating the steps of the siltation monitoring method according to an embodiment of the present invention. Figure 2 This is a diagram showing the effect of linear interpolation on a two-dimensional profile. Figure 3 This is a diagram showing the effect of cubic spline interpolation on a two-dimensional profile. Figure 4 This is a rendering of a 3D point cloud model; Figure 5 A flowchart outlining the steps involved in lightweighting a BIM model; Figure 6 A schematic diagram illustrating the specific process of lightweighting a BIM model; Figure 7This is a schematic diagram of the siltation monitoring 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, device, electronic equipment, and medium for monitoring siltation. For the dam front and reservoir area of ​​hydropower projects, fixed sonar detection and unmanned surface vessel (USV) mobile detection methods are employed respectively. By combining these two methods, comprehensive detection of siltation in hydropower projects is achieved. Simultaneously, the detected data is integrated into a BIM model, allowing users to visually view changes in siltation through the BIM model and receive timely warnings when abnormal siltation occurs.

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

[0024] On the one hand, embodiments of the present invention propose a method for monitoring siltation, such as... Figure 1 As shown, the method includes the following steps: Step S100: Based on the design of the hydropower station project, establish a BIM model of the hydropower station project 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, allowing the monitored data to be overlaid onto the BIM model for a more intuitive representation of sediment deposition.

[0025] Step S200: Multiple sonars are deployed along the dam axis at preset intervals on the dam face in front of the hydropower station project to obtain point cloud data of siltation surface information through 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 on the point cloud data to obtain siltation cross-section information. Based on this siltation cross-section information and the elevation of the sonar itself, the elevation of the underwater siltation cross-section can be calculated, thereby obtaining information on sediment deposition and realizing an automated data acquisition, transmission, processing, and display process.

[0027] Step S400: Overlay the siltation cross-section information onto the BIM model to obtain the current status and changes of siltation in front of the dam of the hydropower station project; By overlaying the acquired siltation cross-section information onto the BIM model, the siltation situation at the bottom of the water in front of the dam can be viewed intuitively on the BIM model. By comparing this data with historical and design data, the changes in sedimentation can be obtained. Specialized software is used to integrate the installation locations of various sonars, converting acoustic image data into topographic coordinate data to draw underwater topographic cross-sections. Then, the current siltation cross-section data, historical data, and design data are used to analyze the current status and changes in sedimentation, achieving the purpose of sedimentation monitoring and analyzing underwater siltation.

[0028] Step S500: Obtain historical monitoring data of the hydropower station project, and determine the key monitoring areas and routine monitoring areas of the reservoir area of ​​the hydropower station project based on the historical monitoring data; In this application, fixed sonar detection methods were adopted for the dam front and reservoir area of ​​the hydropower station project, respectively, along with mobile detection methods for the reservoir area. For the dam front, multiple fixed sonar arrays were used to monitor sediment deposition. For the reservoir area, unmanned surface vessels (USVs) were used for mobile detection, ensuring comprehensive monitoring of all areas within the reservoir. Furthermore, based on historical monitoring data from the hydropower station project, it was determined which areas of the reservoir were prone to severe sediment deposition and which were less prone to it. This allowed for the division of the reservoir area into key monitoring areas and routine monitoring areas, for which different monitoring methods were employed.

[0029] Step S600: The routine detection area is gridded, and the route of the unmanned vessel is planned according to the gridding result; the unmanned vessel is equipped with a single-beam echo sounder, a positioning system and a sound velocity profiler. Specifically, the detection boundaries of the conventional detection area are first defined, then the conventional detection area is converted into a grid map with attributes, and the grid map is uniformly converted into the coordinate system of the hydropower station project. The optimal path of the unmanned vessel is planned by using the A* algorithm or the intelligent water droplet algorithm, thereby determining the route of the unmanned vessel.

[0030] Step S700: Drive the unmanned vessel to navigate along the route, and during the navigation, obtain the bottom depth through the single-beam echo sounder system, and superimpose the positioning information obtained by the positioning system to obtain three-dimensional information of the bottom sediment deposition feature points; Specifically, the unmanned surface vessel (USV) is equipped with a single-beam echo sounder, a positioning system, 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 system is superimposed to calculate the three-dimensional information of sediment deposition feature points on the seabed. This allows for 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 multi-beam systems, features lower power consumption, smaller size, and greater 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 unmanned surface vessel (USV) hull rolling due to wind and waves affecting the data acquisition accuracy of single-beam depth sounding systems, the USV integrates attitude sensors within its hull. These attitude sensors correct for depth sounding beam deflection through real-time synchronization of attitude information with depth sounding data.

[0031] Step S800: Determine the hovering measurement points of the unmanned vessel based on the key detection areas; For designated key detection areas, unmanned surface vessels (USVs) are required for focused exploration. To ensure accuracy, the USVs need to hover and measure at appropriate points, enabling long-term, accurate detection. The USVs are equipped with adaptive water flow hovering technology, allowing them to conduct normal detection even in windy and wavy conditions on the reservoir surface.

[0032] Step S900: Drive the unmanned vessel to hover at the hovering measurement point and obtain the three-dimensional information of the sedimentation at the hovering measurement point through the single-beam echo sounder and positioning system; After the unmanned vessel hovers at the hovering measurement point, it transmits sound waves to the seabed through the transducer of the single-beam echo sounder and receives the echoes. The depth of the seabed is calculated based on the time difference, and the positioning information of the positioning system is superimposed to solve the three-dimensional information of sedimentation at the hovering measurement point. This enables the rapid acquisition of sedimentation information in key monitoring areas of the reservoir, and the depth sounding data results are visualized on the BIM model to draw the underwater elevation topographic map, achieving the goal of real-time mobile monitoring of sedimentation data.

[0033] Step S1000: Overlay the three-dimensional information of the sediment deposition feature points and the three-dimensional information of sediment deposition onto the BIM model to obtain the current status and changes of sediment deposition in the reservoir area of ​​the hydropower station project.

[0034] By overlaying the three-dimensional information of sediment deposition feature points and the three-dimensional information of sediment deposition onto the BIM model, staff can intuitively view the sediment deposition situation in front of the dam and in the reservoir area of ​​the hydropower station project, thereby achieving real-time monitoring of sediment deposition and ensuring the normal operation of the hydropower station.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] In some embodiments of this application, step S200 above, which involves deploying multiple sonars along the dam axis at preset intervals on the dam face in front of the hydropower station and obtaining point cloud data through the sonars, 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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:

[0047] Make:

[0048] It can be proven that the polynomial satisfying equation (2) is unique. If we take the condition that satisfies the polynomial:

[0049] A set of nth degree polynomials As a basis for all n+1-dimensional linear spaces, the Lagrange polynomial can be derived:

[0050] in:

[0051] We use piecewise quadratic Lagrange polynomial interpolation, i.e., linear interpolation, and the resulting graph is shown below. Figure 2 As shown. From Figure 2 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.

[0052] In each sub-interval superior, In the form of:

[0053] in There are n undetermined coefficients, and there are n subintervals, so there are a total of 4n undetermined coefficients.

[0054] 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. The above is continuous. Therefore, according to the definition of cubic spline interpolation function (2) and (3), these undetermined coefficients should satisfy 4n-2 equations:

[0055] 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:

[0056] The final result is as follows: Figure 3 As shown.

[0057] 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 densification 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 4 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.

[0058] In some embodiments of this application, step S1000 above—overlaying the three-dimensional information of sediment deposition feature points and the three-dimensional information of sediment deposition onto the BIM model to obtain the current status and changes of sediment deposition in the reservoir area of ​​the hydropower station project—includes the following seven steps: Step S1010: Obtain the positioning information and detection data sent back by the unmanned vessel, and preprocess the detection data; Step S1020: Iteratively match the preprocessed detection data with the detection data of the previous frame; Step S1030: Determine whether the iteration matching is successful based on the preset iteration termination condition and iteration matching condition; Step S1040: When it is determined that the iterative matching is successful, determine the key frame of the probe data; Step S1050: Calculate the nearest point between the probe data of the keyframe and all probe data before the keyframe for keyframe correction; Step S1060: Based on the corrected keyframes, stitch together the point cloud data to obtain the three-dimensional information of the feature points of sediment deposition at the bottom of the water and the three-dimensional information of sediment deposition; Step S1070: Overlay the three-dimensional information of the sediment deposition feature points and the three-dimensional information of sediment deposition onto the BIM model to obtain the current status and changes of sediment deposition in the reservoir area of ​​the hydropower station project.

[0059] When the unmanned surface vessel (USV) completes its survey of the reservoir area, it sends the survey data and positioning information to the host platform. After integrating the positioning information and survey data, geometric imaging is performed, and the resulting geometric structure is fused into the BIM model. The core technology here 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 for precise matching of the current real-time 3D point cloud to its corresponding 3D environment. Second, attitude estimation is performed by aligning one point cloud A with another point cloud B. This generates the attitude information of point cloud A relative to point cloud B, which can be used for decision-making by the USV.

[0060] 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.

[0061] (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.

[0062] (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.

[0063] (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.

[0064] (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.

[0065] (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.

[0066] (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.

[0067] (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.

[0068] (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.

[0069] (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.

[0070] Furthermore, in some embodiments of this application, the method for monitoring siltation further includes a step of lightweighting the BIM model, specifically including the following three steps: Obtain the geometric data and material information of the BIM model; Based on the geometric data, determine whether there are triangular faces in the BIM model. If so, merge the triangular faces and simplify the boundary lines to obtain the simplified BIM model. Based on the material information, assign the corresponding materials to the simplified BIM model.

[0071] It should be noted that BIM models come in various file formats, such as IFC and FBX. The software will support importing models in multiple file formats. By importing BIM model files, the software extracts geometric and material information of structures, creates a database-based model data storage system, and reorganizes data relationships, laying the foundation for efficient use of model data in the future. Model data is primarily used for drawing 2D and 3D graphics and for coordinate calibration of other data. Based on data utilization, a data caching layer is built to improve data access speed.

[0072] Simultaneously, based on the model data, relevant feature values ​​are extracted and calculated, and the entropy-weighted grey relational model is used to calculate the possible scenarios of model congestion. Then, combining user interaction data and sonar data of the current model location, a fusion model of Gradient Boosting Decision Tree (GBDT) and Logistic Regression (LR) is used to infer the congestion situation at the current model location. Finally, the combination ratio of the two recommendation levels is dynamically adjusted according to the size of the training dataset to achieve accurate analysis and localization of congestion, optimize the working settings of the sonar acquisition equipment, and improve equipment efficiency. The final output BIM model can guarantee: ① The optimization of BIM model data does not affect the review of model information.

[0073] ②The BIM model does not lose important geometric features after simplification.

[0074] ③ The platform can determine whether geometric data needs to be optimized based on model size and rendering efficiency.

[0075] The specific process is as follows: Figure 5 and Figure 6As shown. Simultaneously, a dynamic preprocessing mechanism will be employed for batch rendering. After the culling operation is completed, the rendering data is submitted to the rendering pipeline for drawing operations. Before data processing, each object requires a drawing operation, and the drawing operation for each object needs to be repeated to complete one frame of the scene. Therefore, the data after the culling operation can undergo a batch merging operation, merging objects with the same rendering state. The processed data is stored in a cache for the drawing operation thread to access. This operation significantly reduces the time spent by the drawing operation thread when rendering one frame by reducing the number of drawing command calls, thereby improving rendering efficiency. Based on information in the BIM model, triangular mesh data of primitives with the same rendering state data in the scene primitives are batch merged and passed to the graphics card.

[0076] Using the above method, the BIM model is processed in a lightweight manner, and the optimization efficiency is not affected by the model's specialization or the size of the data. This satisfies the requirements of the software's overall running efficiency and effect speed, and reserves sufficient computing performance for the subsequent overlay of point cloud data.

[0077] Furthermore, in some embodiments of this application, step S900 above—driving the unmanned vessel to hover at the hovering measurement point and obtaining the three-dimensional information of sedimentation at the hovering measurement point through a single-beam echo sounder and a positioning system—includes the following six steps: Step S910: Drive the unmanned vessel toward the hovering measurement point; Step S920: 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 system; Step S930: Acquire the displacement trajectory of the unmanned vessel within a preset time period; Step S940: Calculate the flow velocity and direction angle of the water flow based on the preset time period and displacement trajectory; Step S950: Control the unmanned vessel to face the opposite direction of the heading angle, and drive the unmanned vessel to the hovering measurement point; Step S960: Apply corresponding forward propulsion to the unmanned vessel according to the flow velocity, so that the unmanned vessel hovers at the hovering measurement point.

[0078] Specifically, to control the unmanned surface vessel (USV) to hover at the designated measurement point, the USV is first propelled towards the measurement point. 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 through a positioning system. Next, within a preset time period, the positioning system continuously monitors the USV's position changes to obtain its displacement trajectory, thereby calculating the water flow velocity and direction angle. After determining the water flow direction angle, the USV's bow is oriented opposite to the direction angle of the water flow, propelling it to the hovering measurement point. Once the USV reaches the measurement point, forward propulsion is applied to counteract the water flow's movement, ensuring hovering. Simultaneously, to prevent positional shifts during hovering, the positioning system monitors the USV's position in real time. If a shift is detected, appropriate power is applied to adjust the USV based on the direction and degree of the shift.

[0079] In some embodiments of this application, step S600 above—grid-processing the conventional detection area and planning the unmanned vessel's route based on the grid processing results—includes the following five steps: Step S610: Detect whether there are obstacles in the regular detection area; Step S620: When an obstacle exists, determine the area around the obstacle; Step S630: When the area around the obstacle is greater than the preset area, establish a ring grid centered on the obstacle; Step S640: For the regular detection area other than the circular grid, create several rectangular grids; some of the rectangular grids overlap with the circular route. Step S650: Based on the rectangular grid and the circular grid, plan the straight-line navigation route and the circular navigation route of the unmanned vessel.

[0080] It should be noted that obstacles may exist in the routine inspection areas 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 areas. If there are no obstacles in the routine inspection area, the entire area is divided into multiple rectangular grids. A path optimization algorithm is used to plan the shortest path for the USV, ensuring that the USV can detect all areas while minimizing the path, thus improving detection efficiency. It should 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 current. If obstacles exist in the routine inspection area, it is determined whether there is sufficient space around the obstacle. If so, a circular grid centered on the obstacle is established, 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 a straight-line route is planned based on these grids. This combines straight-line and circular navigation for comprehensive detection of the routine inspection area.

[0081] 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.

[0082] According to the siltation monitoring method of this invention, fixed sonar detection and unmanned surface vessel (USV) mobile detection methods are used for the dam front and reservoir area of ​​hydropower station projects, respectively. By combining these two methods, comprehensive detection of siltation in hydropower station projects is achieved. Simultaneously, the detected data is integrated into a BIM model, allowing users to intuitively view changes in siltation through the BIM model and receive timely warnings when abnormal siltation occurs.

[0083] On the other hand, embodiments of the present invention also provide a device for monitoring siltation, such as... Figure 7 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.

[0084] On the other hand, embodiments of the present invention also provide an electronic device, including the above-mentioned siltation monitoring device.

[0085] 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 monitoring siltation.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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 monitoring siltation, characterized in that, Includes the following steps: Based on the design of the hydropower station project, a BIM model of the hydropower station project was created using 3D design software; Multiple sonars are deployed along the dam axis at preset intervals on the dam face in front of the hydropower station project to obtain point cloud data. Based on the point cloud data, information on the siltation cross-section is obtained; The siltation cross-section information is superimposed onto the BIM model to obtain the current status and changes of siltation in front of the dam of the hydropower station project; Obtain historical monitoring data of the hydropower station project, and determine the key monitoring areas and routine monitoring areas of the reservoir area of ​​the hydropower station project based on the historical monitoring data; The conventional detection area is gridded, and the route of the unmanned vessel is planned based on the gridding results; the unmanned vessel is equipped with a single-beam echo sounder, a positioning system, and a sound velocity profiler. The unmanned vessel is driven to navigate along the route, and during the navigation, the depth of the seabed is obtained through the single-beam echo sounder system, and the positioning information obtained by the positioning system is superimposed to obtain three-dimensional information of the sediment deposition feature points on the seabed. Based on the key detection areas, determine the hovering measurement points of the unmanned vessel; The unmanned vessel is driven to hover at the hovering measurement point, and the three-dimensional information of the sedimentation at the hovering measurement point is obtained through the single-beam echo sounder and the positioning system. The three-dimensional information of the sediment deposition feature points at the bottom of the water and the three-dimensional information of the sediment deposition are superimposed on the BIM model to obtain the current status and changes of sediment deposition in the reservoir area of ​​the hydropower station project.

2. The method for monitoring siltation according to claim 1, characterized in that, The method further includes a step of lightweighting the BIM model, specifically including: Obtain the geometric data and material information of the BIM model; Based on the geometric data, determine whether the BIM model has triangular faces. If it does, merge the triangular faces and simplify the boundary lines to obtain a simplified BIM model. Based on the material information, the simplified BIM model is assigned the corresponding material.

3. The method for monitoring siltation 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.

4. The method for monitoring siltation according to claim 3, 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.

5. The method for monitoring siltation according to claim 1, characterized in that, The process of overlaying the three-dimensional information of the sediment deposition feature points at the bottom of the water body and the three-dimensional information of the sediment deposition onto the BIM model to obtain the current status and changes of sediment deposition in the reservoir area of ​​the hydropower station project includes: The system acquires the positioning information and detection data sent back by the unmanned vessel, and preprocesses the detection data. The preprocessed detection data is iteratively matched with the detection data of the previous frame; Determine whether the iteration matching is successful based on the preset iteration termination condition and iteration matching condition; When it is determined that the iterative matching is successful, the key frame of the probe data is determined; Calculate the nearest point between the probe data of the key frame and all probe data preceding the key frame for key frame correction; The point cloud data is stitched together based on the corrected keyframes to obtain the three-dimensional information of the underwater sediment deposition feature points and the three-dimensional information of the deposition. The three-dimensional information of the sediment deposition feature points at the bottom of the water and the three-dimensional information of the sediment deposition are superimposed on the BIM model to obtain the current status and changes of sediment deposition in the reservoir area of ​​the hydropower station project.

6. The method for monitoring siltation according to claim 1, characterized in that, The unmanned surface vessel is driven to hover at the hovering measurement point, and the siltation three-dimensional information of the hovering measurement point is obtained through the single-beam echo sounder and the positioning system, including: 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 system; 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, a corresponding forward propulsion is applied to the unmanned vessel, causing it to hover at the hovering measurement point.

7. The method for monitoring siltation according to claim 1, characterized in that, The step of performing grid-based processing on the conventional detection area and planning the unmanned surface vessel's route based on the grid-based processing results includes: Detect whether there are obstacles in the conventional detection 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 conventional detection region 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 device for monitoring siltation, 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 to enable the at least one control processor to perform the siltation monitoring method as described in any one of claims 1 to 7.

9. An electronic device, characterized in that, Includes the siltation monitoring 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 execute the siltation monitoring method according to any one of claims 1-7.

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