Intelligent auxiliary method and system for mattress sinking construction based on unmanned ship

Abstract

The invention provides an intelligent auxiliary method and system for mattress sinking construction based on an unmanned ship. The method comprises the steps that the unmanned ship is controlled to sail along a preset path, and ashore terrain laser point cloud data and underwater multi-beam scanning point cloud data are synchronously obtained; the method comprises the following steps: carrying three-dimensional laser radar equipment on an unmanned ship, synchronously collecting ashore topographic data in the process of underwater topographic measurement of a preset planned route, generating ashore underwater point cloud three-dimensional data through processing software, and constructing a continuous digital elevation model covering a whole river region; and applying the continuous digital elevation model to carry out auxiliary mattress sinking construction. The system comprises a data synchronous acquisition module, a data fusion processing module, a model generation module and an auxiliary sinking construction module. The precision of mattress sinking construction can be improved.

Description

Technical Field

[0001] This application relates to the field of riverbed submerged drainage construction, and more specifically, to an intelligent auxiliary method and system for submerged drainage construction based on unmanned vessels. Background Technology

[0002] Traditional riverbed topographic surveys before sump construction still largely rely on a combination of manual survey vessels and underwater surveys. This method carries high safety risks in complex hydrological environments. Furthermore, traditional single-beam echo sounders, which use a "line scanning" mode, cannot meet the high-precision requirements of sump construction. Data stitching also depends on manual experience, resulting in significant errors. Especially in areas with rapid currents or narrow bends, traditional methods struggle to obtain complete topographic data. Summary of the Invention

[0003] The purpose of this application is to provide an intelligent auxiliary method and system for submerged raft construction based on unmanned vessels, which can improve the accuracy of submerged raft construction.

[0004] This application is implemented as follows: Firstly, this application provides an intelligent auxiliary method for submerged embankment construction based on an unmanned vessel, comprising the following steps: Data Synchronous Acquisition: Control the unmanned vessel equipped with both multi-base 3D lidar scanning equipment and underwater multibeam detection equipment to navigate along a preset path, simultaneously acquiring onshore terrain lidar point cloud data and underwater multibeam scanning point cloud data; the preset path enables the simultaneous acquisition of onshore terrain data during underwater terrain acquisition, and generates onshore and underwater point cloud 3D data through processing software. Data fusion processing: The underwater multibeam scanning data is processed by software and merged into the land topographic map for graphic editing and edge stitching, drawing the waterline to obtain a complete topographic map; Model generation: Based on the complete topographic map, construct a continuous digital elevation model covering the entire river channel area; Assisted sinking construction: Continuous digital elevation model is used for assisted sinking construction.

[0005] Based on the first aspect, the auxiliary sinking and embankment construction includes embankment foundation clearing; specifically, it includes: Spatially align the measured continuous digital elevation model with the pre-designed continuous digital elevation model; The grid node difference calculation is performed on the aligned measured continuous digital elevation model and the pre-designed continuous digital elevation model, using the measured elevation value Z. 实测 Subtract the design elevation value Z 设计 The thickness value ΔH of the residue was obtained; Set the thickness value of all mesh nodes with a negative residual thickness value ΔH to zero, while retain the calculation results of mesh nodes with a positive value; Check the smoothness of the junction area between the onshore and underwater topographic data. If the smoothness does not meet the preset requirements, make local optimization adjustments to ensure that the residual thickness calculated on both the land and water sides is continuous and reliable in the junction area. Based on the processed effective residual thickness data, digital results that can be directly used in the project are generated.

[0006] Based on the first aspect, the steps for spatially aligning the measured continuous digital elevation model with the pre-designed continuous digital elevation model include: On the measured continuous digital elevation model and the pre-designed continuous digital elevation model, the same known reference points with stable positions are selected, the coordinate translation and rotation parameters between them are calculated, and these parameters are applied to register the measured continuous digital elevation model as a whole to the engineering coordinate system of the designed continuous digital elevation model.

[0007] Based on the first aspect, and based on the processed effective residual thickness data, the steps to generate digital results that can be directly used in the project include: Draw a cloud map of the residual thickness distribution, using color gradients to represent the thickness; identify and delineate the boundaries of areas where the thickness exceeds the design allowable threshold, and generate vector range lines; statistically analyze the over-limit area, average thickness, maximum thickness, and volume of residual material that needs to be cleaned by embankment section and station number.

[0008] Based on the first aspect, the auxiliary sedimentation construction includes calculating the amount of rock dumped; specifically, it includes: Field measurements were conducted before and after construction to construct a continuous digital elevation model before construction and a continuous digital elevation model after construction. The pre-construction continuous digital elevation model and the post-construction continuous digital elevation model are spatially aligned. After alignment, for each grid node covering the same area, the post-construction elevation value is subtracted from the pre-construction elevation value to obtain a raster data ΔZ representing the terrain change. 栅格 A positive value indicates that the grid node has been filled in. By setting a lifting threshold, minor measurement noise or natural disturbances are filtered out, and the effective stone-throwing variation zone is extracted. Each ΔZ in the effective boulder variation zone 栅格 A cell is considered as a vertical prism, and its volume is equal to the topographic change of that cell multiplied by the horizontal area of ​​that cell. For standard cells that are completely located on land or underwater, the volume is calculated directly. For cells that cross the boundary between land and water, the area ratio of the part above and below the water surface in that cell is determined based on the measured shoreline data, and different volume calculation models are applied for each. The calculated volumes of all effective cells are summed to obtain the total volume of riprap for the entire toe protection project.

[0009] Based on the first aspect, the steps for spatially aligning the pre-construction continuous digital elevation model with the post-construction continuous digital elevation model include: At least three stable and undisturbed reference points are selected at the same locations in both the pre-construction and post-construction continuous digital elevation models. The pre-construction and post-construction continuous digital elevation models are then registered to the same engineering coordinate system using least-squares rigid body transformation to eliminate minor displacement or rotational deviations.

[0010] Based on the first aspect, auxiliary submerged revetment construction includes deformation monitoring; specifically, it includes: Establish a stable monitoring benchmark network and acquire point cloud data for multiple periods; specifically, this includes: deploying multiple continuous monitoring benchmark stations in stable areas around the bank slope structure area and conducting regular precision measurements to ensure the reliability of their coordinates; using the same set of laser scanning equipment and multi-beam detectors to scan the bank slope structure along the same path at different monitoring points to acquire three-dimensional point cloud data for multiple periods. Unify the point cloud data from different periods into the same stable coordinate system and perform coarse registration; specifically, this includes: converting all point cloud data collected at different periods into the same stable engineering coordinate system defined by the reference network; after completing coordinate system one, perform manual or automatic initial alignment based on the overall range of the point cloud or multiple obvious, undeformed fixed feature points to eliminate large overall translation and rotation deviations, in preparation for subsequent fine registration. Fine registration is performed by selecting corresponding feature points in undeformed areas. Specifically, this includes: identifying and selecting stable areas on the bank slope structure that were determined to have no displacement or minimal deformation during the monitoring period; manually or automatically selecting multiple corresponding feature points or feature surfaces in these areas; using these corresponding feature points or features, calculating the optimal rigid body transformation parameters using a point cloud registration algorithm; and accurately registering the point cloud data from each period to the point cloud data of a certain reference period, ensuring that the point cloud heights in undeformed areas are highly coincident, thereby eliminating measurement system errors and reference residual errors. Calculate the spatial displacement vector field of the point cloud in the deformation area; specifically, for the slope structure area to be monitored, calculate the displacement of the grid nodes in three-dimensional space through a point cloud matching algorithm to generate dense displacement vector field data covering the entire surface of the monitored structure.

[0011] Secondly, this application provides an intelligent auxiliary system for submerged embankment construction based on an unmanned vessel, comprising: The data synchronization acquisition module is configured to: control the unmanned vessel equipped with both a multi-base 3D lidar scanning device and an underwater multibeam detector to navigate along a preset path, and simultaneously acquire onshore terrain lidar point cloud data and underwater multibeam scanning point cloud data; the preset path enables the simultaneous acquisition of onshore terrain data during the underwater terrain acquisition process, and generates onshore and underwater point cloud 3D data through processing software. The data fusion processing module is configured to: process underwater multibeam scanning data through software, merge it into a land topographic map for graphic editing and edge stitching, draw the waterline, and obtain a complete topographic map; The model generation module is configured to: build a continuous digital elevation model covering the entire river channel area based on a complete topographic map; The auxiliary sinking construction module is configured to use a continuous digital elevation model for auxiliary sinking construction.

[0012] Thirdly, this application provides an electronic device, comprising: Memory, used to store one or more programs; processor; The above method is implemented when one or more programs are executed by the processor.

[0013] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0014] Compared with the prior art, this application has at least the following advantages or beneficial effects: This application provides an intelligent auxiliary method and system for underwater rafting construction based on unmanned vessels, significantly improving the comprehensiveness and systematic nature of underwater topographic mapping. It effectively avoids the arbitrariness and uncertainty common in traditional surveying, reducing human error and resource waste. By simultaneously acquiring onshore topographic laser point cloud data and underwater multibeam scanning point cloud data using laser scanning equipment and multibeam scanning equipment, the underwater multibeam scanning data is processed by software and merged into a land topographic map for graphic editing and edge stitching, drawing the shoreline to obtain a complete topographic map. This achieves seamless integration of shoreline and underwater topography, eliminating data gaps in traditional methods, generating a continuous digital elevation model, improving data reliability and usability, and ensuring optimal coverage density in key areas. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of an embodiment of an intelligent auxiliary method for dredging construction based on an unmanned vessel, as proposed in this application. Figure 2 This is a flowchart of the dike foundation cleaning process in one embodiment of an intelligent auxiliary method for embankment construction based on unmanned vessels, as described in this application. Figure 3 This is a flowchart illustrating the statistical calculation of rock dumping volume in one embodiment of an intelligent auxiliary method for sump construction based on unmanned vessels, as described in this application. Figure 4 This is a flowchart of deformation monitoring in one embodiment of an intelligent auxiliary method for dredging construction based on an unmanned vessel, as described in this application. Figure 5 This is a structural schematic diagram of an embodiment of an intelligent auxiliary system for dredging construction based on an unmanned vessel, as described in this application. Figure 6 This is a schematic diagram of the structure of an electronic device according to this application.

[0017] icon: 1. Data synchronization acquisition module; 2. Data fusion processing module; 3. Model generation module; 4. Assisted sinking and damming construction module; 5. Processor; 6. Memory; 7. Communication interface. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0019] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the various embodiments and features described below can be combined with each other. Example

[0020] This application provides an intelligent auxiliary method for submerged raft construction based on unmanned vessels, which can improve the accuracy of submerged raft construction.

[0021] Please refer to Figure 1 This intelligent auxiliary method for submerged embankment construction based on unmanned vessels includes the following steps: S101: Data Synchronous Acquisition: Controls the unmanned vessel, which is equipped with both a multi-base 3D lidar scanning device and an underwater multibeam detector, to navigate along a preset path, simultaneously acquiring onshore terrain lidar point cloud data and underwater multibeam scanning point cloud data; the preset path enables the simultaneous acquisition of onshore terrain data during the underwater terrain acquisition process, and generates onshore and underwater point cloud 3D data through processing software. In this step, the laser scanner and multibeam detector have a highly consistent reference base in time and space. This is achieved by installing a high-precision, tightly coupled GNSS / IMU integrated navigation system at the core location on the ship. The pre-planned path planning method includes determining the overall direction of the survey line (parallel to the main river line or construction pipeline) based on the river's direction, width, depth changes, and key construction area characteristics such as pipeline laying lines, bridge pier perimeters, and shoal cross-sections, according to the maximum water depth and river width of the survey area. Intersections or densification of survey lines are designed based on topographic relief and key areas. The spacing between main survey lines is controlled at 10%–20% of the water depth (e.g., 0.5m–1m spacing when the water depth is 5m), and the spacing between survey lines in key areas is halved to ensure lateral overlap. The planned survey line coordinates (including start, end, and turning points) are converted into a GPX route file recognizable by the unmanned vessel. Before importing, the route is verified to maintain a safe distance of ≥1m from the shore / shoal / obstacles, confirming that the turning radius at turning points meets the minimum turning requirements for the vessel, and ensuring that the start and end points are easily accessible for shore-based or support vessel operations. The obstacle avoidance control system sets the sonar detection radius, the threshold for identifying floating objects on the water surface, and emergency stop logic. When encountering dynamic obstacles such as fishing nets during measurement, the system automatically veers or triggers manual intervention, while simultaneously recording the obstacle avoidance position coordinates for later data removal and supplementary measurements. During the synchronous startup phase, clock synchronization and sound velocity profile acquisition are performed on the multibeam sonar, GNSS / RTK, and IMU systems. During measurement, echo intensity, signal-to-noise ratio, positioning accuracy, and attitude data are transmitted back to the shore-based terminal in real time via wireless link. Operators continuously monitor the integrity of echo coverage, deviation of the actual navigation trajectory, and data frame loss anomalies. For pipeline submersion areas, bridge pier perimeters, and areas with abrupt terrain changes, dense strip or multi-angle oblique measurement methods are used to supplement measurements to eliminate acoustic blind spots and improve the model accuracy of key areas. This embodiment adopts a "bow-shaped" route and accurately calculates the spacing of each measurement line, ensuring that the laser scanning strips between adjacent routes and between the laser strip and the sonar strip meet or exceed the 30% overlap requirement on the horizontal projection plane. During navigation, the GNSS / IMU system provides precise hull position and attitude in real time, which is used to dynamically control scanning equipment (such as a stabilizing platform) or calculate sensor pointing in real time to ensure that the designed coverage is achieved.

[0022] S102: Data fusion processing: After processing the underwater multibeam scanning data with software, merge it into the land topographic map for graphic editing and edge stitching, draw the waterline, and obtain a complete topographic map; In this step, all data is processed uniformly based on a GNSS elevation datum. Specifically, the acquired raw laser point cloud (containing elevations of surface and shallow water topography), multibeam scan point cloud (underwater topographic depth), and auxiliary bathymetry data, while all possessing a unified spatial location reference (horizontal position), initially have elevation / depth values ​​relative to different reference surfaces (laser elevation based on ellipsoidal height or geodetic height, sonar depth based on instantaneous water surface). To eliminate "data gaps," this embodiment converts all data to a unified elevation datum (e.g., a national elevation datum or local mean sea level). This is achieved by accurately measuring the elevation difference between the GNSS antenna phase center and this unified elevation datum (e.g., through onshore leveling or using a precise tide level model). Combining the real-time GNSS measurements of geodetic height, antenna height, and a precise geoid model (or through tide gauge data), the elevation (or depth) values ​​in all point cloud data are rigorously converted and unified under the same elevation datum system. This allows surface topographic elevation and underwater topographic depth to be seamlessly connected under the same vertical reference system.

[0023] S103: Model generation: Based on the complete topographic map, construct a continuous digital elevation model covering the entire river area; In this step, due to the differences in observation angle, resolution, accuracy, and coverage of the two sensors for the same terrain feature, there may be points with the same name or approximately the same name within the boundary area. Accurate point cloud registration (such as using the ICP algorithm) is performed using this boundary area data to further optimize and eliminate residual systematic biases. Then, the data within the boundary area is fused. Weighted averaging or selection of optimal data points can be performed based on factors such as point cloud density and accuracy estimation to ensure a smooth transition and maximum accuracy in the terrain representation within the boundary area. Finally, the fused laser point cloud (shore + shallow water), multibeam scanning point cloud (underwater), and auxiliary bathymetry data are integrated together. Using methods such as triangulation network (TIN) construction or regular grid interpolation, a continuous and seamless digital elevation surface (DEM) covering the entire survey area (from land to water) is generated, completely eliminating the "data gap" at the land-water boundary.

[0024] S104: Assisted sinking construction: Continuous digital elevation model is used for assisted sinking construction.

[0025] In this step, a continuous digital elevation model constructed through actual measurements is used to assist in the sinking and shoveling construction.

[0026] This embodiment provides an intelligent auxiliary method for underwater rafting construction based on unmanned vessels, significantly improving the comprehensiveness and systematic nature of underwater topographic mapping. It effectively avoids the arbitrariness and uncertainty common in traditional surveying, reducing human error and resource waste. By simultaneously acquiring onshore topographic laser point cloud data and underwater multibeam scanning point cloud data using laser scanning and multibeam scanning equipment, the underwater multibeam scanning data is processed by software and merged into a land topographic map for graphic editing and edge stitching, drawing the waterline to obtain a complete topographic map. This achieves seamless integration of shoreline and underwater topography, eliminating data gaps in traditional methods, generating a continuous digital elevation model, improving data reliability and usability, and ensuring optimal coverage density in key areas.

[0027] Please refer to Figure 2 In one embodiment of the present invention, the auxiliary sinking and damming construction includes dam foundation cleaning; specifically including: S201: Spatially align the measured continuous digital elevation model with the pre-designed continuous digital elevation model; Specifically, the spatial alignment method includes: selecting the same known reference points (such as measurement control points, structural corner points, etc., at least 3) on the measured continuous digital elevation model and the pre-designed continuous digital elevation model, calculating the coordinate translation and rotation parameters between them (rigid body transformation can be performed using the least squares method), and applying these parameters to register the measured continuous digital elevation model as a whole to the engineering coordinate system of the designed continuous digital elevation model, ensuring that the two are perfectly aligned in spatial position, and controlling the registration error to the centimeter level (e.g., ±1cm).

[0028] S202: Perform grid node difference calculations on the aligned measured continuous digital elevation model and the pre-designed continuous digital elevation model, using the measured elevation value Z. 实测 Subtract the design elevation value Z 设计 The thickness value ΔH of the residue was obtained; Specifically, after achieving precise spatial alignment, the core thickness can be calculated. The elevation values ​​of the measured DEM mesh (or dense point cloud) covering the same area are compared point-by-point (or mesh node-by-mesh) with the designed surface mesh. Specifically, for each point at the same planar coordinate position (X,Y), the measured elevation value Z is used... 实测 Subtract the design elevation value Z 设计 The theoretical residue thickness at that point, ΔH=Z, is obtained. 实测 -Z 设计 This calculation will generate a thickness value grid (or point set) that corresponds to the original grid.

[0029] S203: Set the thickness value of all mesh nodes with negative residual thickness ΔH to zero, while retain the calculation results of mesh nodes with positive values; Specifically, the calculated ΔH value may contain negative numbers (indicating that the actual measured value is lower than the design value, i.e., over-excavation or failure to meet the standard). For residual thickness verification, we only care about the portion above the design surface (i.e., the residue that needs to be cleaned up). Therefore, the thickness value of all grid nodes with negative ΔH values ​​is set to zero (ΔH... 残留 =0), while positive points retain their calculation results (ΔH). 残留 =ΔH). This yields a distribution map or grid containing only non-negative values ​​of the "effective residual thickness". To eliminate data noise (such as individual outliers), simple spatial filtering (such as median filtering) can be applied for smoothing.

[0030] S204: Check the smoothness of the junction area between the onshore and underwater topographic data. If the smoothness does not meet the preset requirements, perform local optimization and adjustment to ensure that the residual thickness calculated on both the land and water sides is continuous and reliable in the junction area. Specifically, the focus is on verifying the reliability of thickness calculations at the land-water interface. Since the measured data originates from integrated land-water surveys, especially in the area where laser scanning and sonar detection intersect (the land-water interface), special attention needs to be paid to the continuity and reliability of the thickness calculation results. Differential thickness profiles are extracted near the interface (e.g., extending 5-10 meters from the shoreline to both land and water sides). The thickness calculated from the laser point cloud and the thickness calculated from the multi-beam scanning point cloud are carefully examined to ensure a smooth transition at the interface without significant jumps (e.g., abrupt changes greater than 3-5 cm). If significant differences are found, the original fused data and registration process are reviewed to check the accuracy of spatiotemporal synchronization, point cloud fusion registration, and coordinate transformation in that area. Local optimization adjustments are then made to ensure that the residual thickness calculated on both land and water sides is continuous and reliable at the interface.

[0031] Based on the processed effective residual thickness data, digital results that can be directly used in the project are generated.

[0032] Specifically, this includes drawing intuitive cloud maps of residual thickness distribution (using color gradients to represent thickness); automatically identifying and delineating the boundaries of areas where the thickness exceeds the design allowable threshold (such as 10cm or 15cm), and generating vector range lines; statistically analyzing the over-limit area, average thickness, maximum thickness, and volume (cubic meters) of residual material to be cleaned by embankment section and station number; and integrating these results into a BIM platform or mobile APP for on-site cleanup personnel to query and locate in real time, providing precise guidance for cleanup work.

[0033] Please refer to Figure 3 In some embodiments of the present invention, the auxiliary submerged revetment construction includes calculating the amount of rock dumped; specifically including: S301: Conduct field measurements before and after construction to construct a continuous digital elevation model before construction and a continuous digital elevation model after construction. Specifically, under the same set of precise spatiotemporal and elevation benchmarks, integrated land and water measurements were conducted on the original topography before construction and the stable topography after construction (after the riprap settlement stabilized) of the toe protection project area. The aforementioned seamless integration technology of laser scanning and sonar detection was used to generate high-precision and comparable digital elevation models (DEMs) for the two periods, ensuring that the range, resolution (such as grid size), and coordinate elevation system of the two measurements were completely consistent, laying a comparability foundation for subsequent change detection.

[0034] S302: Spatially align the pre-construction continuous digital elevation model with the post-construction continuous digital elevation model; after alignment, for each grid node covering the same area, subtract the pre-construction elevation value from the post-construction elevation value to obtain a raster data ΔZ representing the terrain change. 栅格 A positive value indicates that the grid node has been filled in. Specifically, the method for spatially aligning the pre-construction continuous digital elevation model (DEM) with the post-construction continuous DEM includes: selecting at least three stable, undisturbed benchmarks within the engineering area (such as fixed piles with known coordinates or bedrock feature points); and using least-squares rigid body transformation (including translation and rotation) to accurately register the two sets of data to the same engineering coordinate system, eliminating any possible minor displacement or rotational deviations. The registration accuracy must reach the centimeter level (e.g., ±2cm). After registration, for each grid node (or corresponding location point) covering the same area, the post-construction elevation value is subtracted from the pre-construction elevation value to obtain a raster data (ΔZ) representing the terrain change. 栅格 ), where a positive value indicates that the point has been raised (rock dumping), and a negative value indicates that it has been lowered (scour).

[0035] S303: By setting the lifting threshold, minute measurement noise or natural disturbances are filtered out to extract the effective stone-throwing variation area; Specifically, to identify the effective fill area truly caused by the riprap placement project, this embodiment sets a reasonable lift threshold (e.g., ΔZ > 5cm) to filter out minor measurement noise or natural disturbances. Simultaneously, combining the planned riprap placement range vector boundary determined by the engineering design drawings, the analysis range is limited to this boundary (the buffer zone can be appropriately expanded to cover the possible actual rockfall area). Furthermore, spatial filtering (such as morphological opening operations) is applied to remove isolated, excessively small lift points within the boundary, ultimately extracting a continuous, closed polygonal range of the effective riprap placement change area that conforms to the project's expectations.

[0036] S304: For each ΔZ in the effective boulder variation zone... 栅格A cell is considered as a vertical prism, and its volume is equal to the topographic change of that cell multiplied by the horizontal area of ​​that cell. For standard cells that are completely located on land or underwater, the volume is calculated directly. For cells that cross the boundary between land and water, the area ratio of the part above and below the water surface in that cell is determined based on the measured shoreline data, and different volume calculation models are applied for each. The calculated volumes of all effective cells are summed to obtain the total volume of riprap for the entire toe protection project.

[0037] Specifically, after determining the spatial range of the effective boulder variation zone, the boulder volume can be accurately calculated within this zone. Each ΔZ within the effective zone... 栅格 A pixel is considered a vertical prism, and its volume is equal to the topographic variation of that pixel (ΔZ value, only positive values ​​are taken) multiplied by the horizontal area of ​​that pixel (pixel size, such as 0.5m x 0.5m). For standard pixels located entirely on land or underwater, the calculation is performed directly. For pixels crossing the land-water boundary, the area ratio of the portion above and below the water surface needs to be determined based on measured shoreline data. Different volume calculation models are then applied for each (e.g., a standard prism is used above the water surface, while the effect of sound wave refraction is considered below the water surface, or a more refined trapezoidal prism correction model is used) to perform weighted volume calculations. Finally, the calculated volumes of all effective pixels (or sub-regions) are summed to obtain the total volume of riprap for the entire toe protection project.

[0038] Furthermore, to ensure the reliability of the three-dimensional volume calculation results, this embodiment can also deploy a certain density and representative verification grid (e.g., a 10m × 10m grid) within the effective riprap variation zone for manual field sampling measurements. Within each sampling grid, the thickness of the riprap layer at multiple points is manually measured, the average thickness of the grid is calculated, and compared with the average thickness value of the grid calculated by DEM differential. If a systematic deviation or relative error is found to exceed the engineering allowable range (e.g., >5%), it is necessary to retrospectively check the measurement data (especially underwater multibeam scanning point cloud density, sound velocity profile accuracy, and registration quality of the land-water interface) and the calculation process (e.g., spatial registration accuracy, land-water interface processing model), perform necessary parameter adjustments or local data reprocessing, and calibrate the preliminary volume calculation results. Finally, a verified or calibrated detailed riprap volume distribution map and total volume statistical report, divided by engineering station number or block, are output.

[0039] Please refer to Figure 4 In one embodiment of the present invention, the auxiliary submerged revetment construction includes deformation monitoring; specifically, it includes: S401: Establish a stable monitoring benchmark network and acquire point cloud data for multiple periods; specifically, this includes: deploying multiple continuous monitoring benchmark stations in stable areas (such as bedrock or deep-buried piles) around the bank slope structure area, preferably at least three continuous monitoring benchmark stations. These stations constitute a stable spatial reference frame, and their coordinates are regularly measured precisely to ensure reliability; at different monitoring points (e.g., before construction, during critical construction periods, and periodically after operation), the same set of laser scanning equipment and multibeam detectors are used to scan the bank slope structure along the same path to acquire multiple periods of three-dimensional point cloud data; each period of point cloud data is recorded in real time through this benchmark network to establish a precise spatiotemporal reference.

[0040] S402: Unify the point cloud data from different periods into the same stable coordinate system and perform coarse registration; specifically, this includes: converting all point cloud data collected at different times into the same stable engineering coordinate system defined by the reference network (e.g., the local engineering coordinate system under the WGS84 ellipsoidal projection); after completing coordinate system one, perform manual or automatic initial alignment based on the overall range of the point cloud or multiple obvious, undeformed fixed feature points (e.g., reference points far from the bank slope or corner points of stable structures) to eliminate large overall translation and rotation deviations, in preparation for subsequent fine registration; S403: Select corresponding feature points in undeformed areas for fine registration; specifically, this includes: identifying and selecting stable areas on the bank slope structure that were determined to have no displacement or minimal deformation during the monitoring period (e.g., stable bedrock far from the monitoring subject, deeply buried pile cap corners, etc.), manually or automatically selecting multiple corresponding feature points or feature surfaces in these areas; using these corresponding feature points or features, employing point cloud registration algorithms (such as the ICP - Iterative Closest Point algorithm or feature-based registration methods) to calculate the optimal rigid body transformation parameters, accurately registering the point cloud data from each period to the point cloud data of a certain reference period (e.g., accurately registering to the point cloud data of the first period), ensuring that the point cloud heights in undeformed areas are highly coincident, thereby eliminating measurement system errors and reference residual errors; S404: Calculate the spatial displacement vector field of the point cloud in the deformation area; specifically, it includes: for the slope structure area to be monitored, calculating the displacement of the grid nodes in three-dimensional space through the point cloud matching algorithm, and generating dense displacement vector field data covering the entire surface of the monitored structure.

[0041] Specifically, after all point clouds from different periods are accurately registered to the same spatial reference, the deformation of the main slope structure can be analyzed. For the slope structure area to be monitored (such as retaining wall panels, slope protection blocks, underwater riprap surfaces, etc.), a dense point cloud matching algorithm (e.g., finding the nearest neighbor point in the reference period point cloud for each point in the registered later point cloud) is used to calculate the displacement of that point in three-dimensional space. This displacement is a vector containing components (ΔX, ΔY, ΔZ) in the X (east-west), Y (north-south), and Z (vertical) directions, or expressed as the magnitude and direction of horizontal displacement, as well as settlement / uplift. This ultimately generates dense displacement vector field data covering the entire monitored structure surface.

[0042] Furthermore, this includes analyzing the displacement vector field to quantify key deformation parameters and outputting the results. Specifically, deformation analysis is performed based on the calculated dense displacement vector field data. Key parameters extracted include: calculating the three-dimensional displacement of specific monitoring points (such as preset deformation monitoring point markers); statistically analyzing the average displacement, maximum displacement, and their locations across the entire monitoring area or zone; calculating the relative displacement difference between adjacent points to identify potential cracks or slippage (shear deformation); analyzing the spatial distribution patterns of displacement vector directions (such as overall slippage, local bulging, uneven settlement, etc.); and calculating displacement components in specific directions (such as the direction perpendicular to the slope) to assess stability. These quantified deformation parameters, displacement contour maps, deformation trend maps, and early warning information (such as displacement rate exceeding a threshold) are integrated into a monitoring report to provide a basis for safety assessment and maintenance decisions for the slope structure.

[0043] Please refer to Figure 5 This application also provides an intelligent auxiliary system for submerged embankment construction based on unmanned vessels, including: The data synchronization acquisition module 1 is configured to: control the unmanned vessel equipped with both a multi-base 3D lidar scanning device and an underwater multibeam detector to navigate along a preset path, and simultaneously acquire onshore terrain lidar point cloud data and underwater multibeam scanning point cloud data; the preset path enables the simultaneous acquisition of onshore terrain data during the underwater terrain acquisition process, and generates onshore and underwater point cloud 3D data through processing software. Data fusion processing module 2 is configured to: process underwater multibeam scanning data with software, merge it into a land topographic map for graphic editing and edge stitching, draw the waterline, and obtain a complete topographic map; Model generation module 3 is configured to: construct a continuous digital elevation model covering the entire river channel area based on a complete topographic map; The auxiliary sinking construction module 4 is configured to use a continuous digital elevation model for auxiliary sinking construction.

[0044] For a detailed implementation of the intelligent auxiliary system for sump construction based on unmanned vessels, please refer to the above-mentioned implementation of an intelligent auxiliary method for sump construction based on unmanned vessels, which will not be elaborated upon here.

[0045] Please refer to Figure 6 This application also provides an electronic device, including: Memory 6 is used to store one or more programs; Processor 5; Processor 5 and memory 6 are connected via communication interface 7; When one or more programs are executed by processor 5, all or some of the above methods are implemented.

[0046] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor 5, implements all or part of the methods described above.

[0047] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within this application. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A smart auxiliary method for submerged raft construction based on unmanned vessels, characterized in that, Includes the following steps: Data Synchronous Acquisition: The unmanned vessel, equipped with both a multi-base 3D lidar scanning device and an underwater multibeam detector, is controlled to navigate along a preset path to simultaneously acquire onshore terrain lidar point cloud data and underwater multibeam scanning point cloud data. The preset path enables the simultaneous acquisition of onshore terrain data during the underwater terrain acquisition process, and the processing software generates 3D onshore and underwater point cloud data. Data fusion processing: The underwater multibeam scanning data is processed by software and merged into the land topographic map for graphic editing and edge stitching, drawing the waterline to obtain a complete topographic map; Model generation: Based on the complete topographic map, construct a continuous digital elevation model covering the entire river channel area; Assisted sinking construction: Continuous digital elevation model is used for assisted sinking construction.

2. The intelligent auxiliary method for submerged raft construction based on unmanned vessels according to claim 1, characterized in that, The auxiliary settling and drainage construction includes embankment foundation clearing; specifically, it includes: Spatially align the measured continuous digital elevation model with the pre-designed continuous digital elevation model; The grid node difference calculation is performed on the aligned measured continuous digital elevation model and the pre-designed continuous digital elevation model, using the measured elevation value Z. 实测 Subtract the design elevation value Z 设计 The thickness value ΔH of the residue was obtained; Set the thickness value of all mesh nodes with a negative residual thickness value ΔH to zero, while retain the calculation results of mesh nodes with a positive value; Check the smoothness of the junction area between the onshore and underwater topographic data. If the smoothness does not meet the preset requirements, make local optimization adjustments to ensure that the residual thickness calculated on both the land and water sides is continuous and reliable in the junction area. Based on the processed effective residual thickness data, digital results that can be directly used in the project are generated.

3. The intelligent auxiliary method for submerged raft construction based on unmanned vessels according to claim 2, characterized in that, The step of spatially aligning the measured continuous digital elevation model with the pre-designed continuous digital elevation model includes: On the measured continuous digital elevation model and the pre-designed continuous digital elevation model, the same known reference points with stable positions are selected, the coordinate translation and rotation parameters between them are calculated, and these parameters are applied to register the measured continuous digital elevation model as a whole to the engineering coordinate system of the designed continuous digital elevation model.

4. The intelligent auxiliary method for submerged raft construction based on unmanned vessels according to claim 3, characterized in that, The steps for generating digital results that can be directly used in the project based on the processed effective residual thickness data include: Draw a cloud map of the residual thickness distribution, using color gradients to represent the thickness; identify and delineate the boundaries of areas where the thickness exceeds the design allowable threshold, and generate vector range lines; statistically analyze the over-limit area, average thickness, maximum thickness, and volume of residual material that needs to be cleaned by embankment section and station number.

5. The intelligent auxiliary method for submerged raft construction based on unmanned vessels according to claim 1, characterized in that, The auxiliary sedimentation construction includes calculating the amount of rock dumped; specifically, it includes: Field measurements were conducted before and after construction to construct a continuous digital elevation model before construction and a continuous digital elevation model after construction. The pre-construction continuous digital elevation model and the post-construction continuous digital elevation model are spatially aligned. After alignment, for each grid node covering the same area, the post-construction elevation value is subtracted from the pre-construction elevation value to obtain a raster data ΔZ representing the terrain change. 栅格 A positive value indicates that the grid node has been filled in. By setting a lifting threshold, minor measurement noise or natural disturbances are filtered out, and the effective stone-throwing variation zone is extracted. Each ΔZ in the effective boulder variation zone 栅格 A cell is considered as a vertical prism, and its volume is equal to the topographic change of that cell multiplied by the horizontal area of ​​that cell. For standard cells that are completely located on land or underwater, the volume is calculated directly. For cells that cross the boundary between land and water, the area ratio of the part above and below the water surface in that cell is determined based on the measured shoreline data, and different volume calculation models are applied for each. The calculated volumes of all effective cells are summed to obtain the total volume of riprap for the entire toe protection project.

6. The intelligent auxiliary method for submerged raft construction based on unmanned vessels according to claim 5, characterized in that, The steps for spatially aligning the pre-construction continuous digital elevation model with the post-construction continuous digital elevation model include: At least three stable and undisturbed reference points are selected at the same locations in both the pre-construction and post-construction continuous digital elevation models. The pre-construction and post-construction continuous digital elevation models are then registered to the same engineering coordinate system using least-squares rigid body transformation to eliminate minor displacement or rotational deviations.

7. The intelligent auxiliary method for submerged raft construction based on unmanned vessels according to claim 1, characterized in that, The auxiliary submerged embankment construction includes deformation monitoring; Specifically, it includes: Establish a stable monitoring benchmark network and acquire point cloud data for multiple periods; specifically, this includes: deploying multiple continuous monitoring benchmark stations in stable areas around the bank slope structure area and conducting regular precision measurements to ensure the reliability of their coordinates; using the same set of laser scanning equipment and multi-beam detectors to scan the bank slope structure along the same path at different monitoring points to acquire three-dimensional point cloud data for multiple periods. Unify the point cloud data from different periods into the same stable coordinate system and perform coarse registration; specifically, this includes: converting all point cloud data collected at different periods into the same stable engineering coordinate system defined by the reference network; after completing coordinate system one, perform manual or automatic initial alignment based on the overall range of the point cloud or multiple obvious, undeformed fixed feature points to eliminate large overall translation and rotation deviations, in preparation for subsequent fine registration. Fine registration is performed by selecting corresponding feature points in undeformed areas. Specifically, this includes: identifying and selecting stable areas on the bank slope structure that were determined to have no displacement or minimal deformation during the monitoring period; manually or automatically selecting multiple corresponding feature points or feature surfaces in these areas; using these corresponding feature points or features, calculating the optimal rigid body transformation parameters using a point cloud registration algorithm; and accurately registering the point cloud data from each period to the point cloud data of a certain reference period, ensuring that the point cloud heights in undeformed areas are highly coincident, thereby eliminating measurement system errors and reference residual errors. Calculate the spatial displacement vector field of the point cloud in the deformation area; specifically, for the slope structure area to be monitored, calculate the displacement of the grid nodes in three-dimensional space through a point cloud matching algorithm to generate dense displacement vector field data covering the entire surface of the monitored structure.

8. An intelligent auxiliary system for submerged raft construction based on unmanned vessels, characterized in that, include: The data synchronization acquisition module is configured to control an unmanned vessel equipped with both a multi-base 3D lidar scanning device and an underwater multibeam detector to navigate along a preset path, simultaneously acquiring onshore terrain lidar point cloud data and underwater multibeam scanning point cloud data; the preset path enables the simultaneous acquisition of onshore terrain data during the underwater terrain acquisition process, and the generation of onshore and underwater point cloud 3D data through processing software. The data fusion processing module is configured to: process underwater multibeam scanning data through software, merge it into a land topographic map for graphic editing and edge stitching, draw the waterline, and obtain a complete topographic map; The model generation module is configured to: build a continuous digital elevation model covering the entire river channel area based on a complete topographic map; The auxiliary sinking construction module is configured to use a continuous digital elevation model for auxiliary sinking construction.

9. An electronic device, characterized in that, include: Memory, used to store one or more programs; processor; When the one or more programs are executed by the processor, the method as described in any one of claims 1-7 is implemented.

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

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