Three-dimensional modeling method and system for water gate structure based on BIM technology

Abstract

This application relates to the field of data processing technology, specifically to a three-dimensional modeling method for sluice gate structures based on BIM technology. The method includes: constructing a target geological model of the target river channel area based on remote sensing images and acoustic ranging data of that area; determining the riverbed slope inclination of each local area within the target river channel area based on the target riverbed elevation data of each pixel; determining the water level slope inclination of each local area based on the target water level height data of each pixel; determining the suitability for sluice gate construction in each local area based on the riverbed slope inclination and water level slope inclination; and determining candidate areas for sluice gate construction based on the suitability for sluice gate construction in each local area.

Description

Technical Field

[0001] This application relates to the field of image data processing technology, specifically to a method and system for three-dimensional modeling of sluice gate structures based on BIM technology. Background Technology

[0002] As an important water-retaining and discharge structure in water conservancy projects, the rational selection of the construction location of sluice gates directly affects the efficiency of water resource management and flood control safety. With the development of building information modeling (BIM) technology, three-dimensional modeling of sluice gate structures has become a core part of the project's entire life cycle management. By integrating remote sensing imagery and acoustic ranging data, it is possible to quickly construct river geological models, providing data support for sluice gate location selection.

[0003] In existing technologies, the location for sluice gate construction is usually selected by analyzing the elevation difference or water level changes of adjacent locations in a geological model.

[0004] However, the above methods fail to fully consider the integrity and correlation between river topography and hydrodynamics. Some areas with large elevation differences are not suitable for building sluice gates. Simply determining the location of a sluice gate as the location of a large elevation difference or a location with large water level changes is inaccurate. The sluice gate location selected based on this is often not accurate or reliable in terms of engineering suitability. Summary of the Invention

[0005] To address the technical problem that determining locations with significant elevation differences or large water level changes as sluice gate construction sites is inaccurate, this application aims to provide a 3D modeling method and system for sluice gate structures based on BIM technology. The specific technical solution adopted is as follows: This application provides a 3D modeling method for sluice gate structures based on BIM technology, comprising: constructing a target geological model of the target river channel area based on remote sensing images and acoustic ranging data of the target river channel area, wherein the target geological model includes target riverbed elevation data and target water level height data for each pixel in the remote sensing images; determining the riverbed slope inclination degree of each local area within the target river channel area based on the target riverbed elevation data of each pixel; determining the water level slope inclination degree of each local area based on the target water level height data of each pixel; determining the sluice gate construction suitability of each local area based on the riverbed slope inclination degree and the water level slope inclination degree of each local area; and determining candidate areas for sluice gate construction based on the sluice gate construction suitability of each local area.

[0006] Optionally, the acoustic ranging data includes elevation data, acoustic round-trip distance, and the displacement distance of the acoustic rangefinder. The construction of a target geological model for the target river channel area based on remote sensing imagery and acoustic ranging data includes: constructing an initial geological model of the target river channel area based on the elevation data and acoustic round-trip distance included in the remote sensing imagery and acoustic ranging data. This initial geological model includes the initial riverbed elevation and initial water level height for each pixel in the remote sensing imagery. The initial geological model is then corrected based on the acoustic round-trip distance and the displacement distance of the acoustic rangefinder included in the acoustic ranging data to obtain a corrected target geological model. This corrected target geological model includes target riverbed elevation data and target water level height data.

[0007] Optionally, the initial geological model is corrected based on the round-trip distance of the acoustic wave and the displacement distance of the acoustic rangefinder included in the acoustic ranging data to obtain a corrected target geological model. This includes: determining an error correction value based on the round-trip distance of the acoustic wave and the displacement distance of the acoustic rangefinder, which is used to quantify the measurement error caused by the displacement of the acoustic rangefinder; correcting the acoustic ranging data based on the error correction value to obtain corrected acoustic ranging data; and correcting the initial riverbed elevation and initial water level height in the initial geological model based on the corrected acoustic ranging data to obtain the target geological model.

[0008] Optionally, determining the riverbed slope inclination of each local area within the target river channel region based on the target riverbed elevation data of each pixel includes: determining the riverbed elevation gradient of each pixel based on the target riverbed elevation data of each pixel, wherein the riverbed elevation gradient includes the gradient direction and gradient magnitude; determining multiple local areas within the target river channel region based on the gradient direction of each pixel; and determining the riverbed slope inclination of each local area based on the gradient magnitude of the pixels included in each local area.

[0009] Optionally, determining multiple local regions within the target river channel area based on the gradient direction of each pixel includes: determining neighboring pixels of a first pixel, wherein the first pixel is any pixel in the target river channel area other than the already generated local regions; determining neighboring pixels whose gradient direction is less than a preset threshold as similar pixels of the first pixel, and determining the area containing the first pixel and its similar pixels as an initial region; if similar pixels exist among the boundary pixels of the initial region, updating the initial region and its boundary pixels based on the similar pixels, and redetermining the similar pixels among the updated boundary pixels; if no similar pixels exist among the boundary pixels of the initial region, determining the last updated initial region as a local region.

[0010] Optionally, determining the riverbed slope inclination of each local region based on the gradient magnitude of the pixels included in each local region includes: determining the riverbed gradient consistency and average riverbed inclination of each local region based on the gradient magnitude of the pixels included in each local region; determining the riverbed slope inclination of the first local region based on the riverbed gradient consistency and average riverbed inclination of the first local region, and the riverbed gradient consistency and average riverbed inclination of the adjacent regions of the first local region, wherein the first local region is any one of the plurality of local regions.

[0011] Optionally, determining the riverbed slope of the first local area based on the riverbed gradient consistency and average riverbed slope of the first local area, as well as the riverbed gradient consistency and average riverbed slope of the adjacent areas of the first local area, includes: determining the maximum value of the riverbed gradient consistency and the maximum value of the average riverbed slope in the adjacent areas; and determining the riverbed slope of the first local area based on the ratio of the riverbed gradient consistency to the maximum value of the riverbed gradient consistency in the first local area, and the difference between the average riverbed slope and the maximum value of the average riverbed slope in the first local area.

[0012] Optionally, determining the suitability for sluice gate construction in each local area based on the riverbed slope inclination and the water level slope inclination of each local area includes: determining a first mean, which is the average of the riverbed slope inclination and the water level slope inclination of the first local area; determining a first absolute difference, which is the absolute value of the difference between the riverbed slope inclination and the water level slope inclination of the first local area; and determining the suitability for sluice gate construction in the first local area based on the first mean and the first absolute difference.

[0013] Optionally, determining the sluice gate construction candidate area based on the sluice gate construction suitability of each local area includes: sorting the sluice gate construction suitability of the multiple local areas in descending order to obtain a local area sequence; and determining the top preset number of local areas in the local area sequence as sluice gate construction candidate areas.

[0014] This application also provides a 3D modeling system for sluice gate structures based on BIM technology, including a model building unit, a data processing unit, and a region determination unit. The model building unit is used to construct a target geological model of the target river channel area based on remote sensing imagery and acoustic ranging data of the target river channel area. The target geological model includes target riverbed elevation data and target water level height data for each pixel in the remote sensing imagery. The data processing unit is used to determine the riverbed slope inclination of each local area within the target river channel area based on the target riverbed elevation data of each pixel. The data processing unit is used to determine the water level slope inclination of each local area based on the target water level height data of each pixel. The data processing unit is used to determine the suitability for sluice gate construction in each local area based on the riverbed slope inclination and water level slope inclination. The region determination unit is used to determine candidate areas for sluice gate construction based on the suitability for sluice gate construction in each local area.

[0015] This application has the following beneficial effects: In this application, a target geological model is constructed by integrating remote sensing imagery and acoustic ranging data. Then, based on the riverbed elevation data and water level height data in the target geological model, the overall riverbed slope and water level slope of each local area are comprehensively evaluated to determine the suitability for sluice gate construction. This achieves a holistic and multi-dimensional assessment of the region, avoiding the limitations of existing technologies that rely solely on single data or human experience. It improves the comprehensiveness of sluice gate construction suitability assessment and further enhances the accuracy and effectiveness of determining candidate areas for sluice gate construction. Attached Figure Description

[0016] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This application provides a method for three-dimensional modeling of a sluice gate structure based on BIM technology, as one embodiment of the present application. Figure 2 Another method for three-dimensional modeling of sluice gate structures based on BIM technology is provided in one embodiment of this application; Figure 3 This application provides a 3D modeling system for sluice gate structures based on BIM technology, which is one embodiment of the present application. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a 3D modeling method and system for a sluice gate structure based on BIM technology proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0020] A sluice gate is a low-head hydraulic structure built on rivers, canals, reservoirs, and lakes to control water flow and regulate water levels. It functions to release or retain water by opening and closing the gates. Its applications in water conservancy projects are very widespread.

[0021] With the development of 3D modeling and visualization technology and related BIM modeling software, BIM technology is now involved in the engineering design, construction and management of sluice gate structures through 3D modeling data. It enables the sharing and transmission of planning, operation and maintenance data throughout the entire project lifecycle, effectively ensuring sluice gate construction efficiency, saving costs and greatly shortening the construction period.

[0022] Before creating a 3D model of a sluice gate, it is often necessary to first establish a geological model of the river area where the sluice gate will be built. Within this geological model, suitable locations for sluice gate construction are selected, paving the way for subsequent 3D modeling and actual construction. Currently, suitable locations are often selected manually from the geological model. While this method has a high degree of suitability, it consumes a significant amount of manpower and time, is inefficient, and easily leads to the omission of suitable areas.

[0023] Regarding the issue of selecting suitable locations for sluice gate construction based on geological modeling, related technologies often directly select locations based on the elevation and water level difference of adjacent locations in the riverbed in the obtained geological model. This approach does not take into account the fact that geological models obtained directly from real-scene remote sensing images and acoustic ranging data have certain errors, and that some areas with large local elevation differences are not suitable for building sluice gates, often resulting in missed or incorrect judgments.

[0024] To address the aforementioned issues, this application, based on existing technology, further incorporates data obtained from acoustic ranging to correct the riverbed elevation and water level data at various points in the geological model, and assesses the suitability of constructing a sluice gate based on the overall data changes between the corrected regions, thereby enabling more precise selection of sluice gate construction locations.

[0025] Current technology directly selects the location for sluice gate construction based on the differences between adjacent locations in the geological model obtained from real-scene remote sensing imagery and acoustic ranging data. However, the data used has a large margin of error, and the evaluation based solely on the differences between adjacent locations is rather one-sided. The results are often not the most suitable locations, and misjudgments and omissions are prone to occur. This necessitates subsequent manual comparisons, resulting in no significant improvement in efficiency.

[0026] The following description, in conjunction with the accompanying drawings, details the specific scheme of the 3D modeling method and system for sluice gate structures based on BIM technology provided in this application.

[0027] Please see Figure 1 The diagram illustrates a flowchart of a 3D modeling method for a sluice gate structure based on BIM technology, provided in one embodiment of this application.

[0028] like Figure 1 As shown, the 3D modeling method for sluice gate structures based on BIM technology includes S101-S105.

[0029] S101. Based on remote sensing images and acoustic ranging data of the target river area, construct a target geological model of the target river area.

[0030] The target geological model includes target riverbed elevation data and target water level height data for each pixel in the remote sensing image.

[0031] It should be understood that the target river area is a river area that requires the construction of sluice gates for water conservancy engineering management.

[0032] In one alternative implementation, remote sensing images of the target river area can be acquired via satellite, and the pixels of the target river area in the remote sensing images can be recorded. At the same time, an unmanned measuring vessel equipped with an acoustic rangefinder can be controlled to navigate along the target river on the water surface to acquire the acoustic ranging data.

[0033] It should be understood that the acoustic ranging data includes elevation data and acoustic round-trip distance. The elevation data is the elevation data of the location of the unmanned measuring vessel, and the acoustic round-trip distance is the round-trip distance that the acoustic wave travels from transmission to reception.

[0034] Alternatively, during navigation, the unmanned survey vessel can obtain the elevation data at each detection point via the global navigation satellite system (GNSS), and control the acoustic rangefinder to emit sound waves vertically downwards, recording the round-trip distance the sound waves travel from emission to reception after reflection from the riverbed.

[0035] Optionally, the acoustic ranging data also includes the displacement distance of the acoustic rangefinder, which is the horizontal displacement distance between the positions of the unmanned measuring vessel when the acoustic rangefinder emits and receives acoustic waves.

[0036] Optionally, the horizontal displacement distance between the two positions of the acoustic rangefinder at the time of transmitting the acoustic wave and the time of receiving the echo can be calculated by using a high-frequency inertial measurement unit (IMU) in conjunction with GNSS positioning data.

[0037] In one alternative implementation, an initial geological model of the target river channel area can be constructed based on the elevation data and acoustic ranging distance included in the remote sensing imagery and acoustic ranging data of the target river channel area. The initial geological model is then corrected based on the acoustic ranging distance and the displacement distance of the acoustic ranging instrument included in the acoustic ranging data to obtain the corrected target geological model.

[0038] The initial geological model includes the initial riverbed elevation and initial water level height for each pixel in the remote sensing image, while the corrected target geological model includes the target riverbed elevation and target water level height.

[0039] Optionally, the remote sensing imagery and elevation data can be processed and fused: the pixels of the remote sensing imagery are spatially registered with the track and detection points of the unmanned survey vessel. Then, based on the measurements of the detection points, spatial interpolation algorithms (such as Kriging interpolation and linear interpolation) are used to generate a riverbed elevation surface and a water level surface covering the entire target river channel area. Finally, these two continuous surfaces are resampled onto a pixel grid that is completely consistent with the remote sensing imagery, thereby obtaining the elevation data and acoustic round-trip distance corresponding to each pixel in the remote sensing imagery. Based on the elevation data and acoustic round-trip distance of each pixel, the initial riverbed elevation and initial water level height of each pixel are determined.

[0040] It should be understood that since the round-trip distance of a sound wave is the distance between the emission point and the reflection point, the initial water level height is half of the round-trip distance of the sound wave.

[0041] Optionally, the initial waterline height of a pixel satisfies the following formula: in, Represents pixels The initial water level height, Represents pixels The round-trip distance of the sound waves.

[0042] It should be understood that the initial riverbed elevation of a pixel is the height of the unmanned survey vessel minus the water level.

[0043] Optionally, the initial riverbed elevation of a pixel satisfies the following formula: in, Represents pixels The initial riverbed elevation, Represents pixels Elevation data of unmanned surveying vessels Represents pixels The initial water level height.

[0044] Optionally, after obtaining the initial water level height and initial riverbed elevation of each pixel, the spatial coordinate information of each pixel, as well as the initial riverbed elevation and initial water level height of each pixel, are imported into engineering software (such as AutoCAD Civil 3D) using 3D modeling software to generate 3D point cloud data of the target river area. Then, the initial water level height is imported into BIM modeling software (such as Revit) as a terrain base plate, thereby completing the construction of the initial geological model of the target river area.

[0045] Because unmanned survey vessels are subject to the effects of wind and waves when navigating on the water, the acoustic rangefinder is not located at the same point when emitting sound waves and receiving echoes. The displacement of the acoustic rangefinder during this process introduces measurement errors. Therefore, the initial geological model can be corrected based on this displacement distance to improve the accuracy of the initial geological model.

[0046] In one alternative implementation, an error correction value can be determined based on the round-trip distance of the acoustic wave and the displacement distance of the acoustic rangefinder; the acoustic ranging data can be corrected based on the error correction value to obtain corrected acoustic ranging data; and the initial riverbed elevation and initial water level height in the initial geological model can be corrected based on the corrected acoustic ranging data to obtain the target geological model.

[0047] The error correction value is used to quantify the measurement error caused by the displacement of the acoustic rangefinder.

[0048] Optionally, error correction values ​​can be calculated based on acoustic and geometric principles.

[0049] It should be understood that due to the swaying of the water platform, the propagation path of the sound waves is not an ideal vertical zigzag line, but rather the sum of the two sides of an isosceles triangle with the displacement as its base.

[0050] Optionally, the error correction value for a single pixel satisfies the following formula: in, Represents pixels Error correction value, Represents pixels The corresponding displacement distance of the acoustic rangefinder, Represents pixels The round-trip distance of the sound wave, that is, the measured round-trip distance of the sound wave.

[0051] In this formula, This represents the actual round-trip distance of the sound wave after considering displacement. The difference between the actual round-trip distance of the sound wave and the measured round-trip distance yields the path increase caused by displacement, i.e., the error correction value. .

[0052] Optionally, the error correction value is subtracted from the acoustic wave measurement distance of each pixel to obtain the corrected acoustic wave measurement distance of each pixel.

[0053] Optionally, after obtaining the corrected acoustic measurement distance for each pixel, the riverbed elevation data and water level height data for each pixel can be re-determined based on the corrected acoustic measurement distance, and the re-determined riverbed elevation data and water level height data can be used as the target riverbed elevation data and target water level height data.

[0054] Optionally, the initial geological model can be adjusted based on the target riverbed elevation data and the target water level height data to obtain the target geological model.

[0055] In this embodiment, an initial geological model is first constructed, and then corrected based on acoustic ranging data. This effectively compensates for errors during data acquisition, ensuring that the riverbed elevation and water level data in the target geological model are closer to the actual values. During the data correction process, error correction values ​​are calculated based on the acoustic round-trip distance and the displacement distance of the ranging instrument. This quantifies and eliminates the error, making the corrected acoustic ranging data more consistent with the actual physical scenario.

[0056] S102. Based on the target riverbed elevation data of each pixel, determine the riverbed slope inclination of each local area within the target river channel area.

[0057] Understandably, the purpose of constructing sluice gates is to prevent waterlogging and other natural disasters in rivers during certain seasons or rainy seasons when some waterways are unable to store large amounts of water. This is because such waterways need sluice gate structures to impound and store water, effectively preventing floods and storing water for release when needed. Therefore, areas with poor water storage capacity and prone to water loss are more suitable for constructing sluice gates.

[0058] It should be understood that riverbed slope affects the flow of water and influences the selection of sluice gate construction sites. When a local riverbed exhibits a continuous and consistent sloping trend, the gradient increases the gravitational potential energy of the water above the riverbed, prompting it to move rapidly along the slope and form a strong flow. Compared to other flatter or low-slope areas, the sloping riverbed terrain shortens the water retention time, resulting in a weaker ability to retain additional water resources such as precipitation and upstream water, making the area more prone to water loss.

[0059] Understandably, the target river channel area consists of multiple local regions. The riverbed slope inclination is used to characterize the inclination of the riverbed topography in each local region. The greater the inclination, the greater the relative risk of accelerated water loss due to the sloping topography. Based on this riverbed slope inclination, local regions with a significant and consistent downward slope trend can be identified from a continuous riverbed elevation field, and their prominence relative to the surrounding topography can be assessed.

[0060] In one alternative implementation, a general slope calculation algorithm (such as fitting calculations based on three-dimensional triangular surfaces) can be used to determine the average slope value of each local region. A larger average slope value indicates a steeper overall slope in that local region. The number of pixels in each local region can also be determined. Then, the ratio between the average slope value of each local region and the mean of the average slope values ​​of all local regions is used to determine the relative slope of that local region. The ratio between the number of pixels in each local region and the mean of the number of pixels in all local regions is used to determine the relative area of ​​that local region. The product of the relative slope and the relative area of ​​each local region is used to determine the riverbed slope inclination of that local region. It is understandable that if a local region has a steeper average slope and a larger area, the overall risk of rapid water flow and difficulty in water retention is higher.

[0061] S103. Based on the target water level height data of each pixel, determine the slope inclination of the water level in each local area.

[0062] Understandably, aside from the riverbed slope, the water in these areas is not stagnant; it exhibits flow and fluctuation. Areas with greater fluctuation have higher water flow energy and faster water movement, necessitating the construction of sluice gates for water resource management. During the operation of the unmanned survey vessel, the movement and bobbing caused by the water's fluidity result in greater variations in the water level data obtained in areas of greater fluctuation. Therefore, these water level changes reflect the degree of water flow fluctuation in the area. In conclusion, areas with greater fluctuations are more likely to require the construction of sluice gates for water conservancy management. Thus, areas with a trend of water level differences that are highly consistent with the elevation changes of the downstream riverbed are more suitable for constructing sluice gates for water control and discharge.

[0063] In one alternative implementation, the difference between the maximum and minimum values ​​of the target waterline height data for all pixels in each local region is determined, and then the ratio between this difference and the area of ​​the local region is determined as the slope of the waterline in that local region.

[0064] It should be understood that this difference reflects the vertical fluctuation range of the water level in that local area. The larger the difference, the more drastic the water level change and the greater the potential water flow energy. The greater the water level change in a local area, and the larger the area where this change occurs, the higher the kinetic energy or fluctuation energy of the water body in that area, and the higher the risk of rapid water loss.

[0065] S104. Based on the slope inclination of the riverbed and the slope inclination of the water level in each local area, determine the suitability of sluice gate construction in each local area.

[0066] It should be understood that when both the riverbed slope and the water level slope are relatively high in a local area, it can be determined that the local area is more suitable for building a sluice gate.

[0067] In one alternative implementation, the weights of the riverbed slope inclination and the water level slope inclination can be set separately, and then the weighted sum of the riverbed slope inclination and the water level slope inclination can be obtained to obtain the suitability of sluice gate construction in this local area.

[0068] Optionally, the weights of the riverbed slope inclination and the water level slope inclination can be adjusted according to actual needs. For example, in the absence of a particular inclination, the weights of the riverbed slope inclination and the water level slope inclination can be set to 0.5 respectively.

[0069] In another alternative implementation, a first mean and a first absolute difference are determined, and the suitability of sluice gate construction in a first local area is determined based on the first mean and the first absolute difference.

[0070] Wherein, the first mean is the average of the slope inclination of the riverbed and the slope inclination of the water level in the first local area, and the first absolute difference is the absolute value of the difference between the slope inclination of the riverbed and the slope inclination of the water level in the first local area.

[0071] It should be understood that the first mean is used to characterize the average risk intensity of water resource loss in the local area in terms of both topography and hydrology. The higher the average risk intensity, the more suitable the local area is for building a sluice gate. The first absolute difference is used to characterize the degree of difference in risk in the two dimensions of topography and hydrology. The smaller the difference, the closer the two risk indicators are, that is, the more synchronous and consistent the loss risk indicated by topography and hydrology is. In this case, the local area is more suitable for building a sluice gate.

[0072] Optionally, the suitability of sluice gate construction in a local area satisfies the following formula: in, Indicates a local area Suitability of sluice gate construction Indicates a local area The slope and inclination of the waterline. Indicates a local area The degree of inclination of the riverbed slope, This is a smoothing coefficient, and its value range can be set according to the basic fluctuation range of the river channel. When the value is large, the formula tends to prioritize screening areas with high slope and inclination; when the value is small, the formula tends to prioritize screening areas with high consistency. In this embodiment, the value is 1. Indicates a local area The average of the riverbed slope and the slope of the water level line. Indicates a local area The absolute value of the difference between the slope inclination of the riverbed and the slope inclination of the water level.

[0073] In this embodiment, the slope and inclination of the riverbed and water level are combined to assess their consistency and differences, avoiding the one-sidedness of a single indicator and enabling accurate identification of areas prone to water loss.

[0074] S105. Determine candidate areas for sluice gate construction based on the suitability of sluice gate construction in each local area.

[0075] In one alternative implementation, the suitability of the sluice gate construction in the multiple local areas is sorted in descending order to obtain a local area sequence; the top-ranked number of local areas in the local area sequence are determined as candidate areas for sluice gate construction.

[0076] Optionally, the preset number can be determined based on the actual number of sluice gates to be built. Since the selected sluice gate construction candidate area is only for reference, it can be set slightly higher than the actual number of sluice gates to be built, for example, twice the actual number of sluice gates to be built.

[0077] For example, assuming the actual number of sluice gates to be built is 5, the preset number can be 10.

[0078] In this embodiment, a target geological model is constructed by integrating remote sensing imagery and acoustic ranging data. Then, based on the riverbed elevation data and water level height data in the target geological model, the overall riverbed slope and water level slope of each local area are comprehensively evaluated to determine the suitability for sluice gate construction. This achieves a holistic and multi-dimensional assessment of the region, avoiding the limitations of relying solely on single data or human experience in existing technologies. It improves the comprehensiveness of sluice gate construction suitability assessment and further enhances the accuracy and effectiveness of determining candidate areas for sluice gate construction.

[0079] Combination Figure 1 ,like Figure 2 As shown, in one implementation of this application embodiment, the above S102 may specifically include S201-S203.

[0080] S201. Determine the riverbed elevation gradient of each pixel based on the target riverbed elevation data of each pixel.

[0081] The elevation gradient of the riverbed includes the gradient direction and the gradient magnitude.

[0082] It should be understood that the gradient magnitude is used to characterize the slope of a pixel, and the gradient direction points towards the steepest uphill direction of the riverbed.

[0083] Optionally, the riverbed elevation gradient of each pixel can be calculated based on the riverbed elevation data of each pixel and a spatial difference algorithm (such as the central difference method).

[0084] S202. Based on the gradient direction of each pixel, determine multiple local regions within the target river channel area.

[0085] It should be understood that a local area is composed of multiple pixels with similar gradient directions. Pixels with similar and adjacent gradient directions can be grouped together to form a continuous local area with consistent internal topographic or hydrological change trends.

[0086] In one optional implementation, neighboring pixels of the first pixel can be determined; neighboring pixels whose gradient direction is less than a preset threshold are identified as similar pixels of the first pixel, and the region containing the first pixel and its similar pixels is identified as an initial region; if similar pixels exist among the boundary pixels of the initial region, the initial region and its boundary pixels are updated based on the similar pixels, and the similar pixels among the updated boundary pixels are re-determined; if no similar pixels exist among the boundary pixels of the initial region, the last updated initial region is identified as a local region.

[0087] The first pixel is any pixel in the target river channel region other than the already generated local area.

[0088] Specifically, firstly, a pixel that has not yet been assigned to any generated local region (i.e., the first pixel) is randomly selected from the target river region. This first pixel is used as the starting pixel for the growth of the current local region. Then, the neighboring pixels of the starting pixel are determined. In this embodiment, the pixels in the eight-neighborhood of the starting pixel can be determined as the neighboring pixels.

[0089] Then, the angle between the gradient direction of each adjacent pixel and the starting pixel is determined sequentially. If the angle is less than a preset threshold, it means that the adjacent pixel is similar in direction to the starting pixel. The adjacent pixel can be included in the local region and determined as a similar pixel to the starting pixel. Furthermore, the determined similar pixel can be determined as the boundary pixel of the initial region.

[0090] After obtaining the initial region, iterative expansion of the initial region can be achieved by determining the boundary pixels of the initial region, determining the neighboring pixels of the boundary pixels, determining the similar pixels among the neighboring pixels, and expanding the initial region based on the similar pixels until there are no similar pixels at the boundary pixels. Once this process is complete, the initial region will no longer grow, and the initial region will be defined as a local region.

[0091] It should be understood that the preset threshold is the upper tolerance limit of the gradient direction angle, with an example value of 30°. It should be understood that after a local region is determined, a starting pixel can be randomly selected from the remaining pixels that have not been assigned to any generated local region, and the above steps can be repeated until each pixel is assigned to a local region.

[0092] In this embodiment, the local region is divided based on the similarity of gradient direction. This can divide areas with similar terrain change trends into a single local region, which is closer to natural laws. The resulting local region has more complete geographical features and the assessment is more accurate.

[0093] S203. Determine the riverbed slope inclination of each local region based on the gradient magnitude of the pixels included in each local region.

[0094] In one alternative implementation, the riverbed gradient consistency and average riverbed slope of each local region are determined based on the gradient magnitude included in each local region; the riverbed slope of the first local region is determined based on the riverbed gradient consistency and average riverbed slope of the first local region, as well as the riverbed gradient consistency and average riverbed slope of the adjacent regions of the first local region.

[0095] The first local region is any one of the multiple local regions, and the adjacent region of a local region is the local region among the multiple local regions that is connected to the boundary of the local region.

[0096] Optionally, the total riverbed elevation gradient of a local region can be determined based on the sum of the riverbed elevation gradients of all pixels included in each local region. The gradient consistency of a local region can be determined based on the riverbed elevation gradient of the center point of the local region, the total riverbed elevation gradient, the angle between the riverbed elevation gradient of the center point and the total riverbed elevation gradient, and the number of pixels included in the local region.

[0097] Alternatively, the geometric centroid of a local region can be determined as the center point of that local region.

[0098] Optionally, the gradient consistency of a local region satisfies the following formula: in, Indicates a local area gradient consistency, Indicates a local area Total riverbed elevation gradient, Indicates a local area The modulus of the total riverbed elevation gradient, Indicates a local area The riverbed elevation gradient (vector) at the center point. express The length of the mold, Indicates a local area The number of pixels included. express and The angle between them This represents the cosine value of the included angle.

[0099] Based on this formula, it should be understood that Characterizing local regions The overall trend of change Characterizing local regions The local representative change trend Characterizes the local region The degree of alignment between the overall trend of change and the local representative trend of change in direction. The larger the value, the more consistent the trend of change and the greater the consistency of the gradient; Characterizing the maximum possible total modulus of a theory, i.e., assuming a local region All pixels within the center point have the same riverbed elevation gradient as the center point. Therefore, the magnitudes of these riverbed elevation gradients are directly added together to obtain the value. The ratio between the modulus of the actual total riverbed elevation gradient and the total modulus under ideal conditions; the larger this value, the better the local area. The more consistent the riverbed elevation gradient of the internal pixels, the better.

[0100] Alternatively, the mean of the modulus of the riverbed elevation gradient of all pixels within a local area can be determined as the average slope of the riverbed.

[0101] Alternatively, the average slope of the riverbed in a local area satisfies the following formula: in, Indicates a local area The average slope of the riverbed, Indicates a local area Total riverbed elevation gradient, Indicates a local area The modulus of the total riverbed elevation gradient, Indicates a local area The number of pixels included.

[0102] In one alternative implementation, the maximum value of the riverbed gradient consistency and the maximum value of the average riverbed slope in adjacent regions can be determined; then, based on the ratio of the riverbed gradient consistency to the maximum value of the riverbed gradient consistency in the first local region, and the difference between the average riverbed slope and the maximum value of the average riverbed slope in the first local region, the degree of riverbed slope in the first local region can be determined.

[0103] Alternatively, the slope of a riverbed in a local area may satisfy the following formula: in, Indicates a local area The degree of inclination of the riverbed slope, Indicates a local area gradient consistency, Indicates a local area The maximum gradient consistency in adjacent regions, Indicates a local area The average slope of the riverbed, Indicates a local area The maximum average riverbed slope in the adjacent region, It is a sigmoid function used to convert values ​​that may be positive, negative, or zero. Mapped to [0, 1].

[0104] Based on this formula, it should be understood that Used to measure local areas The gradient consistency ratio represents the strength of gradient consistency relative to the maximum gradient consistency in its neighboring regions. A larger ratio indicates stronger gradient consistency in the local region. The more pronounced the gradient consistency of topographic trends in the surrounding environment; Characterizing local regions The difference between the average slope of the riverbed and the average slope of the steepest riverbed in the adjacent area. This value can be positive, zero, or negative. When the value is positive, it indicates that the local area... It is steeper than the steepest surrounding area. When this value is negative, it indicates that the local area is steeper than the steepest surrounding area. It is gentler than the steepest area in the surrounding region.

[0105] In this embodiment of the application, the riverbed slope is determined by comparing the maximum parameter values ​​of a local area with those of its neighboring areas. This allows the focus to be placed on key areas with the most significant slope and the highest gradient consistency within the local area, thereby enhancing the sensitivity to key areas and making the site selection of the sluice gate more engineering-oriented.

[0106] In one implementation of this application, the method for determining the slope inclination of the waterline in each local area is similar to that for determining the slope inclination of the riverbed, i.e., determining the waterline gradient of each pixel, and then determining the waterline gradient consistency and average slope of the waterline in each local area; based on the waterline gradient consistency and average slope of the waterline in the first local area, and the waterline gradient consistency and average slope of the waterline in the adjacent areas of the first local area, the slope inclination of the waterline in the first local area is determined.

[0107] Based on the methods provided in S201-S203 above, multiple local regions are determined by calculating the gradient modulus, and the slope inclination of the riverbed in each local region is evaluated by the gradient direction. This allows for the identification of potential fast-flow channels with intact slopes and consistent trends.

[0108] like Figure 3 As shown in the embodiment of this application, a 3D modeling system for sluice gate structures based on BIM technology is also provided. The 3D modeling system 30 for sluice gate structures based on BIM technology includes a model building unit 301, a data processing unit 302, and a region determination unit 303.

[0109] Model building unit 301 is used to build a target geological model of the target river area based on remote sensing images and acoustic ranging data of the target river area.

[0110] The target geological model includes target riverbed elevation data and target water level height data for each pixel in the remote sensing image.

[0111] The data processing unit 302 is used to determine the slope inclination of the riverbed in each local area within the target river channel area based on the target riverbed elevation data of each pixel.

[0112] The data processing unit 302 is used to determine the slope inclination of the water level line in each local area based on the target water level line height data of each pixel.

[0113] Data processing unit 302 is used to determine the suitability of sluice gate construction for each local area based on the riverbed slope inclination and water level slope inclination of each local area; The region determination unit 303 is used to determine the candidate region for sluice gate construction based on the suitability of sluice gate construction in each local region.

[0114] It should be understood that the BIM-based 3D modeling system 30 for sluice gate structures can implement any of the above-mentioned BIM-based 3D modeling methods for sluice gate structures.

[0115] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0116] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for three-dimensional modeling of sluice gate structures based on BIM technology, characterized in that, include: Based on remote sensing images and acoustic ranging data of the target river channel area, a target geological model of the target river channel area is constructed. The target geological model includes the target riverbed elevation data and target water level height data of each pixel in the remote sensing image. Based on the target riverbed elevation data of each pixel, the riverbed slope inclination of each local area within the target river channel is determined. Based on the target water level height data of each pixel, the slope inclination of the water level in each local area is determined. Based on the slope inclination of the riverbed and the slope inclination of the water level in each local area, the suitability for constructing a sluice gate in each local area is determined. Candidate areas for sluice gate construction are determined based on the suitability of sluice gate construction in each local area.

2. The method for three-dimensional modeling of sluice gate structures based on BIM technology according to claim 1, characterized in that, The acoustic ranging data includes elevation data, acoustic round-trip distance, and the displacement distance of the acoustic ranging instrument. The construction of a target geological model of the target river area based on remote sensing imagery and acoustic ranging data of the target river area includes: Based on the elevation data and acoustic round-trip distance included in the remote sensing image and acoustic ranging data of the target river channel area, an initial geological model of the target river channel area is constructed. The initial geological model includes the initial riverbed elevation and initial water level height of each pixel in the remote sensing image. The initial geological model is corrected based on the round-trip distance of the acoustic wave and the displacement distance of the acoustic rangefinder included in the acoustic ranging data to obtain the corrected target geological model, which includes target riverbed elevation data and target water level height data.

3. The method for three-dimensional modeling of sluice gate structures based on BIM technology according to claim 2, characterized in that, The initial geological model is corrected based on the round-trip distance of the acoustic waves and the displacement distance of the acoustic rangefinder included in the acoustic ranging data, resulting in a corrected target geological model, including: Based on the round-trip distance of the acoustic wave and the displacement distance of the acoustic rangefinder, an error correction value is determined, which is used to quantify the measurement error caused by the displacement of the acoustic rangefinder. The acoustic ranging data is corrected based on the error correction value to obtain the corrected acoustic ranging data. Based on the corrected acoustic ranging data, the initial riverbed elevation and initial water level in the initial geological model are corrected to obtain the target geological model.

4. The method for three-dimensional modeling of sluice gate structures based on BIM technology according to claim 1, characterized in that, The determination of the riverbed slope inclination degree of each local area within the target river channel region based on the target riverbed elevation data of each pixel includes: The riverbed elevation gradient of each pixel is determined based on the target riverbed elevation data of each pixel, and the riverbed elevation gradient includes the gradient direction and gradient magnitude. Multiple local regions within the target river channel area are determined based on the gradient direction of each pixel; The slope inclination of the riverbed in each local region is determined based on the gradient magnitude of the pixels included in each local region.

5. The method for three-dimensional modeling of sluice gate structures based on BIM technology according to claim 4, characterized in that, The determination of multiple local regions within the target river channel area based on the gradient direction of each pixel includes: Determine the neighboring pixels of the first pixel, wherein the first pixel is any pixel in the target river area excluding the already generated local area; Adjacent pixels whose gradient direction is less than a preset threshold are identified as similar pixels of the first pixel, and the region containing the first pixel and its similar pixels is identified as the initial region. If there are similar pixels at the boundary pixels of the initial region, the initial region and the boundary pixels of the initial region are updated based on the similar pixels, and the similar pixels of the updated boundary pixels are re-determined. If there are no similar pixels among the boundary pixels of the initial region, the last updated initial region is determined as a local region.

6. The method for three-dimensional modeling of sluice gate structures based on BIM technology according to claim 4, characterized in that, The determination of the riverbed slope inclination degree of each local region based on the gradient magnitude of the pixels included in each local region includes: The riverbed gradient consistency and average riverbed slope of each local region are determined based on the gradient modulus included in each local region. Based on the riverbed gradient consistency and average riverbed slope of the first local region, as well as the riverbed gradient consistency and average riverbed slope of the adjacent regions of the first local region, the riverbed slope inclination of the first local region is determined. The first local region can be any one of the plurality of local regions.

7. The method for three-dimensional modeling of sluice gate structures based on BIM technology according to claim 6, characterized in that, The determination of the riverbed slope in the first local area based on the riverbed gradient consistency and average riverbed slope of the first local area, and the riverbed gradient consistency and average riverbed slope of adjacent areas of the first local area, includes: Determine the maximum value of riverbed gradient consistency and the maximum value of average riverbed slope in adjacent regions; The degree of inclination of the riverbed slope in the first local area is determined based on the ratio of the riverbed gradient consistency to the maximum riverbed gradient consistency in the first local area, and the difference between the average riverbed slope and the maximum average riverbed slope in the first local area.

8. The method for three-dimensional modeling of sluice gate structures based on BIM technology according to claim 1, characterized in that, The determination of the suitability for sluice gate construction in each local area, based on the riverbed slope and water level slope, includes: A first mean is determined, which is the average of the riverbed slope inclination and the water level slope inclination in the first local area; Determine the first absolute difference, which is the absolute value of the difference between the slope inclination of the riverbed and the slope inclination of the water level in the first local area; Based on the first mean and the first absolute difference, the suitability of sluice gate construction in the first local area is determined.

9. The method for three-dimensional modeling of sluice gate structures based on BIM technology according to claim 1, characterized in that, The process of determining candidate areas for sluice gate construction based on the suitability of each local area includes: The suitability of sluice gate construction in multiple local areas is sorted from highest to lowest to obtain a local area sequence. The top-ranked number of local regions in the local region sequence are identified as candidate regions for sluice gate construction.

10. A 3D modeling system for sluice gate structures based on BIM technology, characterized in that, It includes a model building unit, a data processing unit, and a region determination unit; The model building unit is used to construct a target geological model of the target river area based on remote sensing images and acoustic ranging data of the target river area. The target geological model includes target riverbed elevation data and target water level height data for each pixel in the remote sensing image. The data processing unit is used to determine the slope inclination of the riverbed in each local area within the target river channel area based on the target riverbed elevation data of each pixel. The data processing unit is used to determine the slope inclination of the water level line in each local area based on the target water level line height data of each pixel. The data processing unit is used to determine the suitability of sluice gate construction for each local area based on the riverbed slope and water level slope of each local area. The region determination unit is used to determine candidate regions for sluice gate construction based on the suitability of sluice gate construction in each local region.

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