A high-precision calculation method and system for reservoir capacity curve based on three-dimensional model

Abstract

The present invention relates to the technical field of water conservancy projects, and discloses a high-precision calculation method and system for reservoir capacity curves based on a three-dimensional model. By adaptively sampling and optimizing the original terrain data, a three-dimensional terrain surface model is constructed, and adaptive discretization grid division is adopted to perform volume analysis error correction to achieve high-precision reservoir capacity estimation. The present invention introduces a data integrity detection and repair mechanism, combines parallel processing technology to improve computing efficiency, and uses cross-validation to ensure the reliability of the results. At the same time, the present invention generates a three-dimensional visualization model to achieve a layered display of terrain features, and has a terrain change adaptability analysis function, which can dynamically update the capacity calculation results. The present invention significantly improves the accuracy and efficiency of reservoir capacity calculations, and provides reliable data support and intuitive visualization tools for water conservancy project planning and management.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy projects, and in particular to a high-precision calculation method and system for a reservoir capacity curve based on a three-dimensional model. Background Art

[0002] As a core component of water conservancy projects, reservoir management and planning are of irreplaceable importance for ensuring the rational allocation of water resources, flood prevention and disaster reduction, and ecological protection. Therefore, scientifically and accurately assessing reservoir capacity and topographical characteristics is directly related to the safety and operational efficiency of project design and is a key link in promoting regional economic development and social stability.

[0003] Currently, traditional methods for reservoir capacity calculation and terrain modeling often rely on simple two-dimensional measurements or rough estimates, which make it difficult to fully reflect the true characteristics of complex terrain, resulting in large errors in the calculation results. Especially in areas with drastic terrain fluctuations or incomplete data, the accuracy and reliability often cannot meet actual needs.

[0004] In one existing technology, the field of reservoir terrain modeling and capacity calculation faces significant technical challenges. The first and foremost is how to accurately discretize the complex three-dimensional terrain to facilitate detailed volume analysis. Due to the complexity and irregularity of terrain data, the traditional uniform division method is often unable to adapt to terrain changes, resulting in the loss of local details, which in turn affects the accuracy of the overall calculation. This problem further leads to the difficult problem of balancing computational efficiency and accuracy, because in the pursuit of higher accuracy, the terrain needs to be divided into finer grids, which inevitably increases the amount of calculation. How to achieve efficient and accurate analysis with limited computing resources has become a technical bottleneck that needs to be solved urgently.

[0005] Therefore, how to construct an accurate three-dimensional terrain model and accurately calculate the reservoir capacity under complex terrain conditions through reasonable discretization methods and efficient calculation strategies has become a key issue that needs to be overcome in this study. Summary of the Invention

[0006] The present invention provides a high-precision calculation method and system for a reservoir capacity curve based on a three-dimensional model, so as to accurately calculate the reservoir capacity.

[0007] In a first aspect, in order to solve the above technical problems, the present invention provides a high-precision calculation method for a reservoir capacity curve based on a three-dimensional model, comprising:

[0008] Preprocess the collected original terrain data to obtain a terrain point cloud dataset;

[0009] constructing a first three-dimensional terrain surface model based on the terrain point cloud dataset, and obtaining a second three-dimensional terrain surface model;

[0010] Obtaining a hierarchically optimized grid model according to the second three-dimensional terrain surface model;

[0011] obtaining an estimated value of a first reservoir capacity according to the grid model;

[0012] Performing numerical filling on the first estimated value of reservoir capacity to obtain a corrected second estimated value of reservoir capacity;

[0013] Performing block calculation on the second reservoir capacity estimation value to obtain a third reservoir capacity estimation value;

[0014] determining whether the third reservoir capacity estimation value meets a condition; if so, determining whether the third reservoir capacity estimation value is reliable output capacity calculation data; if not, returning to the step of obtaining a hierarchically optimized grid model based on the second three-dimensional terrain surface model;

[0015] generating a first three-dimensional visualization model according to the reliably outputted capacity calculation data, compressing the rendering data scale, and obtaining a second three-dimensional visualization model;

[0016] The terrain data and capacity calculation results are dynamically updated according to the second three-dimensional visualization model. If the detected terrain data update frequency is higher than a preset update threshold, the adaptive meshing algorithm is triggered to recalculate and obtain real-time updated reservoir capacity data.

[0017] Preferably, the preprocessing of the collected original terrain data to obtain a terrain point cloud dataset includes:

[0018] Acquiring initial point cloud information from the original terrain data, performing denoising and formatting on the initial point cloud information using a preprocessing method, and performing regional division on the processed point cloud information using a meshing tool to obtain a set of divided terrain units;

[0019] Performing feature extraction on each unit of the terrain unit set to determine whether a slope change of each unit exceeds a preset slope threshold;

[0020] If the slope exceeds the threshold, it is marked as a complex terrain area; if it does not exceed the threshold, it is marked as a flat area, and a classified terrain area label is obtained;

[0021] Increasing the distribution of sampling points in the complex terrain area, adjusting the density of the complex terrain area using a point cloud interpolation tool, reducing the number of sampling points in the flat area, and processing the flat area using a point cloud thinning tool to obtain an adjusted point cloud density distribution;

[0022] The point cloud density distribution is fused using a point cloud integration tool to obtain an optimized terrain point cloud dataset. The optimized dataset is verified for accuracy using a data verification tool to determine it as a preliminary optimized terrain point cloud dataset.

[0023] Preferably, constructing a first three-dimensional terrain surface model based on the terrain point cloud dataset and obtaining a second three-dimensional terrain surface model includes:

[0024] Using a grid partitioning tool to segment the initially optimized terrain point cloud dataset, classifying and labeling the terrain features of different regions to obtain a set of divided terrain regions, and supplementing the set of divided terrain regions with local data using a surface interpolation tool;

[0025] If the data density of the area is lower than a preset density threshold, interpolation processing is performed on the area to obtain a supplemented terrain data distribution, and a triangulated network construction tool is used to perform surface construction on the supplemented terrain data distribution. Adaptive adjustments are performed on areas with obvious terrain changes to obtain a first three-dimensional terrain surface model.

[0026] The first three-dimensional terrain surface model is optimized by using a smoothing tool to obtain the second three-dimensional terrain surface model.

[0027] Preferably, the layered optimized grid model obtained according to the second three-dimensional terrain surface model includes:

[0028] Based on the second three-dimensional terrain surface model, using a curvature analysis tool to divide the terrain surface into regions according to curvature changes, obtain classified curvature distribution information, and determine boundaries between high curvature regions and low curvature regions;

[0029] Using a grid adjustment tool on the classified curvature distribution information, meshing the high curvature area, and if it is detected that the curvature value of one of the areas is higher than a preset curvature threshold, adding grid cells to the area to obtain mesh distribution information after encryption;

[0030] Using a grid optimization tool on the encrypted grid distribution information, performing grid thinning processing on the low curvature area, and if it is detected that the curvature value of another area is lower than a preset curvature threshold, reducing the grid units of the other area to obtain sparsely adjusted grid distribution information;

[0031] Using a hierarchical integration tool on the sparsely adjusted grid distribution information, uniformly processing the grid distributions of different regions to obtain hierarchically optimized grid distribution data;

[0032] The obtaining of a first reservoir capacity estimation value according to the grid model includes:

[0033] Using a data extraction tool on the hierarchically optimized grid distribution data, obtaining corresponding geometric parameter information from each grid cell, and performing preliminary processing on each grid cell based on the geometric parameter information to obtain initial volume distribution data;

[0034] Using an integral calculation tool on the initial volume distribution data, randomly sampling each grid cell, performing multiple independent estimations on each grid cell during the sampling process, and obtaining volume distribution information after sampling;

[0035] Using a data integration tool on the sampled volume distribution information, accumulating the volume estimation values ​​of all grid cells; if the accumulated value exceeds a preset accumulated value threshold, resampling the grid cells in the area exceeding the accumulated value threshold to determine adjusted accumulated volume data;

[0036] A deviation analysis tool is used on the adjusted accumulated volume data to correct the overall capacity estimate, perform local adjustments on abnormal areas, and obtain the first reservoir capacity estimate.

[0037] Preferably, performing numerical filling on the first estimated value of reservoir capacity to obtain a corrected second estimated value of reservoir capacity includes:

[0038] Obtaining the original terrain distribution records of the target area from the repository, performing integrity verification on each sub-area of ​​the original terrain distribution records using a data scanning tool to determine whether there are any missing data or abnormal points, and obtaining a preliminary integrity assessment result;

[0039] If the data missing area found in the integrity assessment result exceeds the preset missing threshold, the missing area is preliminarily marked using a data repair tool, and the corresponding filling data is generated using an interpolation calculation tool to obtain the repaired terrain distribution record;

[0040] Using a data integration tool on the restored terrain distribution record, fusing the filled data with the original terrain distribution record, performing consistency check on the fused data, and obtaining a corrected terrain distribution set;

[0041] Performing local adjustments on the corrected terrain distribution set using a volume estimation tool to obtain an estimated value of the second reservoir capacity;

[0042] The block-by-block calculation of the second reservoir capacity estimate to obtain a third reservoir capacity estimate includes:

[0043] Obtaining the corrected data records of the target area from a storage repository, and performing grid-block processing on the corrected data records using a data segmentation tool to obtain a segmented data set;

[0044] Dynamically adjust each sub-region unit of the segmented data set using a task allocation tool, allocate computing tasks using a load balancing tool in combination with preset resource constraints, and determine a task list after allocation;

[0045] Using a parallel processing tool on the assigned task list, synchronously calculating the data of each sub-region unit, and if it is detected that the processing speed of one of the sub-regions is lower than a preset speed threshold, reallocating the tasks through a dynamic adjustment tool to obtain an adjusted calculation progress;

[0046] A data integration tool is used for the adjusted calculation progress to fuse the calculation results of each sub-region unit, and the fused data is checked for consistency to obtain the estimated value of the third reservoir capacity.

[0047] Preferably, determining whether the third reservoir capacity estimated value meets a condition includes:

[0048] Obtaining the final optimized data record of the target area, performing multiple rounds of cross-check processing on the optimized data record using a data comparison tool, obtaining deviation value data from each round of verification, and determining the distribution range of the deviation value set;

[0049] If at least one value in the deviation value set exceeds a preset deviation threshold, a deviation analysis tool is used to conduct a detailed evaluation of the data consistency to obtain a partition list;

[0050] Determine whether the grid density needs to be adjusted;

[0051] According to the partition list, a grid adjustment tool is used to dynamically update the grid density of the target area, obtain the adjusted discretized data records, and determine the new calculation output range;

[0052] Based on the new calculation output range, a data integration tool is used to perform consistency check on the adjusted discretized data records to obtain a final capacity verification result, and determine whether the reliability of the capacity verification result meets the requirements;

[0053] The generating of a first three-dimensional visualization model based on the reliably outputted capacity calculation data, compressing the rendering data scale, and obtaining a second three-dimensional visualization model includes:

[0054] Obtaining terrain feature records of a target area from the storage library, and performing preliminary layering processing on the terrain features using volume data technology to obtain a layered terrain data set;

[0055] Using a data compression tool to reduce the size of the rendering data according to the layered terrain data set;

[0056] If it is found during the reduction that the data size exceeds a preset size threshold, the rendering data is segmented using a block processing tool to determine a range of the compressed rendering data;

[0057] Based on the compressed rendering data range, a three-dimensional construction tool is used to construct a visualization model of the terrain features to obtain preliminary three-dimensional visualization results that meet the analysis requirements;

[0058] According to the preliminary results of the three-dimensional visualization, the data format is adjusted using a storage optimization tool. If the storage requirements do not meet the preset storage standards, a secondary optimization is performed using a format conversion tool to obtain the final three-dimensional visualization results.

[0059] Preferably, the dynamically updating of terrain data and capacity calculation results according to the second three-dimensional visualization model, if the detected terrain data update frequency is higher than a preset update threshold, triggering the adaptive meshing algorithm to recalculate and obtain real-time updated reservoir capacity data, includes:

[0060] Retrieving the latest terrain record of the target area from the repository based on the association between the terrain data and terrain changes;

[0061] Performing change detection on the latest terrain record using a data comparison tool. If it is detected that the terrain change exceeds a preset change threshold, a subsequent processing flow is triggered to obtain a set of terrain data after the change.

[0062] Based on the changed terrain data set, combined with dynamic updates and association with real-time data, a data synchronization tool is used to integrate the changed data with existing records;

[0063] If the update frequency of the integrated data is higher than the preset update threshold, the frequency monitoring tool is used to mark the data and determine the area that needs to be recalculated;

[0064] Adopting an adaptive meshing tool to re-divide the marked area, obtaining mesh unit data after segmentation, and determining a mesh distribution suitable for capacity calculation;

[0065] According to the grid distribution, combined with the association between capacity calculation and reservoir capacity, a volume estimation tool is used to perform capacity numerical calculation on the grid cell data to obtain a final updated reservoir capacity record.

[0066] In a second aspect, the present invention provides a high-precision calculation method for a reservoir capacity curve based on a three-dimensional model, comprising:

[0067] The first acquisition module is used to pre-process the collected original terrain data to obtain a terrain point cloud dataset;

[0068] A second acquisition module, configured to construct a first three-dimensional terrain surface model based on the terrain point cloud dataset, and to acquire a second three-dimensional terrain surface model;

[0069] a third acquisition module, configured to obtain a hierarchically optimized grid model according to the second three-dimensional terrain surface model;

[0070] a fourth acquisition module, configured to acquire an estimated value of the first reservoir capacity according to the grid model;

[0071] a fifth acquisition module, configured to perform numerical filling on the first estimated value of the reservoir capacity to obtain a corrected estimated value of the second reservoir capacity;

[0072] a sixth acquisition module, configured to perform block calculation on the second reservoir capacity estimation value to obtain a third reservoir capacity estimation value;

[0073] a determination module, configured to determine whether the third reservoir capacity estimation value meets a condition; if so, determining whether the third reservoir capacity estimation value is reliable output capacity calculation data; if not, returning to the step of obtaining a hierarchically optimized grid model based on the second three-dimensional terrain surface model;

[0074] a seventh acquisition module, configured to generate a first three-dimensional visualization model based on the reliably outputted capacity calculation data, compress the rendering data scale, and acquire a second three-dimensional visualization model;

[0075] An update module is used to dynamically update terrain data and capacity calculation results based on the second three-dimensional visualization model. If the detected terrain data update frequency is higher than a preset update threshold, the adaptive meshing algorithm is triggered to recalculate and obtain real-time updated reservoir capacity data.

[0076] In a third aspect, the present invention also provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements any one of the above-mentioned high-precision calculation methods for reservoir capacity curves based on a three-dimensional model.

[0077] In a fourth aspect, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned high-precision calculation methods for reservoir capacity curves based on three-dimensional models.

[0078] Compared with the existing technology, the present invention provides a high-precision calculation method and system for reservoir capacity curves based on a three-dimensional model. By adaptively sampling and optimizing the original terrain data, a three-dimensional terrain surface model is constructed, and adaptive discretization grid division is adopted to perform volume analysis error correction to achieve high-precision reservoir capacity estimation. The present invention introduces a data integrity detection and repair mechanism, combines parallel processing technology to improve computing efficiency, and uses cross-validation to ensure the reliability of the results. At the same time, the present invention generates a three-dimensional visualization model to achieve a layered display of terrain features, and has a terrain change adaptability analysis function, which can dynamically update the capacity calculation results. The present invention significantly improves the accuracy and efficiency of reservoir capacity calculations, and provides reliable data support and intuitive visualization tools for water conservancy project planning and management. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 This is a flow chart of a high-precision calculation method for a reservoir capacity curve based on a three-dimensional model provided by the first embodiment of the present invention;

[0080] Figure 2 This is a schematic diagram of the structure of a high-precision calculation system for a reservoir capacity curve based on a three-dimensional model provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0081] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0082] Reference Figure 1 The first embodiment of the present invention provides a flow chart of a high-precision calculation method for a reservoir capacity curve based on a three-dimensional model, comprising the following steps:

[0083] S11, preprocessing the collected original terrain data to obtain a terrain point cloud dataset;

[0084] S12, constructing a first three-dimensional terrain surface model based on the terrain point cloud dataset, and obtaining a second three-dimensional terrain surface model;

[0085] S13, obtaining a hierarchically optimized grid model based on the second three-dimensional terrain surface model;

[0086] S14, obtaining an estimated value of the first reservoir capacity according to the grid model;

[0087] S15, performing numerical filling on the first reservoir capacity estimation value to obtain a corrected second reservoir capacity estimation value;

[0088] S16, performing block calculation on the second reservoir capacity estimation value to obtain a third reservoir capacity estimation value;

[0089] S17, determining whether the third reservoir capacity estimate meets the conditions; if so, determining whether the third reservoir capacity estimate is reliable output capacity calculation data; if not, returning to the step of obtaining a hierarchically optimized grid model based on the second three-dimensional terrain surface model;

[0090] S18, generating a first three-dimensional visualization model based on the reliably output capacity calculation data, compressing the rendering data scale, and obtaining a second three-dimensional visualization model;

[0091] S19, dynamically updating the terrain data and capacity calculation results according to the second three-dimensional visualization model. If the detected terrain data update frequency is higher than a preset update threshold, the adaptive meshing algorithm is triggered to recalculate and obtain real-time updated reservoir capacity data.

[0092] In step S11 , the collected original terrain data is preprocessed to obtain a terrain point cloud data set.

[0093] Preprocess the collected original terrain data to obtain a terrain point cloud dataset, including:

[0094] Initial point cloud information is obtained from the original terrain data. The initial point cloud information is denoised and formatted using a preprocessing method. The processed point cloud information is then divided into regions using a meshing tool to obtain a set of divided terrain units.

[0095] Extract features from each unit of the terrain unit set and determine whether the slope change of each unit exceeds a preset slope threshold;

[0096] If the slope threshold is exceeded, it is marked as a complex terrain area. If the slope threshold is not exceeded, it is marked as a flat area, and the classified terrain area label is obtained.

[0097] Increase the distribution of sampling points in complex terrain areas, adjust the density of complex terrain areas through point cloud interpolation tools, reduce the number of sampling points in flat areas, and use point cloud thinning tools to process flat areas to obtain the adjusted point cloud density distribution;

[0098] The point cloud density distribution is fused using the point cloud integration tool to obtain the optimized terrain point cloud dataset. The accuracy of the optimized dataset is verified using the data verification tool to determine it as the preliminary optimized terrain point cloud dataset.

[0099] For example, when processing raw terrain data, initial point cloud information can be obtained from a high-precision lidar device. Suppose the data covers a mountainous terrain and contains millions of points, each with 3D coordinates and intensity information. This initial point cloud may contain noise, such as outliers caused by device vibration or obscured by trees. During the preprocessing phase, a statistical filtering method can be used with a radius of 0.5 meters to remove isolated points. The data format is also standardized to a standard 3D coordinate system to ensure consistency in subsequent processing. This effectively improves data quality and lays the foundation for subsequent analysis.

[0100] In one possible implementation, the gridding tool can partition the processed point cloud into 10-meter by 10-meter grids, creating multiple terrain cells. For example, a total area of ​​1 square kilometer could be divided into 10,000 cells. This division facilitates cell-by-cell analysis of terrain features, reducing computational complexity while preserving regional features and facilitating accurate classification.

[0101] Specifically, the slope calculation tool calculates the slope of each cell based on the height difference between adjacent points. For example, suppose the height difference within a cell is 5 meters, and the horizontal distance is 10 meters, indicating significant slope variation. If the preset threshold is 30 degrees and the slope of that cell exceeds this value, it is marked as a complex terrain area. If the slope of another cell is only 5 degrees, it is marked as a flat area. This classification method effectively distinguishes terrain complexity, provides a basis for subsequent sampling optimization, and improves terrain modeling accuracy.

[0102] For example, when increasing the distribution of sampling points in areas with complex terrain, the Point Cloud Interpolation tool can be used to generate new points. For example, if the original density is 10 points per square meter, this can be increased to 20 points per square meter to ensure more comprehensive capture of details. For flat areas, the Point Cloud Sparsification tool can be used to reduce the density from 10 points per square meter to 5 points per square meter, reducing redundant data and lowering storage and computational burdens. This density adjustment strategy optimizes resource allocation and improves overall efficiency.

[0103] In one possible implementation, the point cloud integration tool can fuse the adjusted density distribution data, unifying the coordinate system and point cloud format to ensure seamless integration between complex and flat areas. Assuming the fused dataset contains 5 million points, this reduces redundant points by 20% compared to the original data while preserving key area details. This integration approach improves data consistency and provides a reliable foundation for subsequent verification.

[0104] Specifically, the data verification tool verifies the accuracy of the optimized point cloud dataset by comparing it with field measurement data. For example, the point cloud height error in a complex area is controlled within 0.1 meters, meeting engineering requirements, while the error in a flat area is 0.2 meters, also within an acceptable range. This verification method ensures the reliability of the final terrain optimization results and facilitates terrain analysis and planning in real-world applications.

[0105] It is understandable that the above method not only improves the accuracy and availability of terrain data through layered processing and targeted optimization, but also significantly reduces the consumption of computing resources, providing high-quality data support for subsequent application scenarios such as terrain modeling and engineering design, demonstrating the efficiency and practicality of the technology.

[0106] In step S12, a first three-dimensional terrain surface model is constructed based on the terrain point cloud dataset, and a second three-dimensional terrain surface model is obtained;

[0107] Constructing a first three-dimensional terrain surface model based on a terrain point cloud dataset and obtaining a second three-dimensional terrain surface model, including:

[0108] The initially optimized terrain point cloud dataset is segmented using a gridding tool, and the terrain features of different regions are classified and labeled to obtain a set of divided terrain regions. The surface interpolation tool is used to supplement the local data of the divided terrain region set.

[0109] If the data density of the area is lower than the preset density threshold, the area is interpolated to obtain the supplemented terrain data distribution, and the surface of the supplemented terrain data distribution is constructed using a triangulated network construction tool. Adaptive adjustments are made to areas with obvious terrain changes to obtain a first three-dimensional terrain surface model.

[0110] The first three-dimensional terrain surface model is optimized by a smoothing tool to obtain a second three-dimensional terrain surface model.

[0111] For example, when processing a pre-optimized terrain point cloud dataset, one can first focus on applying meshing tools. Meshing is the process of dividing large-scale terrain data into smaller regions, making it easier to analyze the terrain characteristics of each region. For example, if a dataset covering a mountainous terrain area covers 2 square kilometers, it can be divided into grid cells of 20 meters by 20 meters, generating a total of 5,000 small regions. This division method facilitates subsequent feature extraction and classification labeling for each cell, such as labeling cells with larger terrain slopes as steep slopes and cells with smaller slopes as gentle slopes.

[0112] For example, the application of surface interpolation tools is particularly important for delineating a collection of terrain regions. Due to limitations in original data collection, some areas may have insufficient data density. For example, if the preset data density threshold is 8 points per square meter, but the density in a steep slope area is only 3 points per square meter, new data points can be generated through interpolation to close the threshold. This approach fills in data gaps, ensures continuity of terrain features, and prevents information loss, especially in complex terrain.

[0113] For example, when constructing three-dimensional terrain surface morphology, triangulated mesh construction tools are a core tool. Their basic principle is to connect point cloud data into a triangular mesh to form a preliminary terrain surface. For areas with significant terrain variations, such as cliffs with a height difference of 10 meters, the density of the triangular mesh can be adjusted, increasing the number of local meshes to more accurately depict the terrain changes. This adaptive adjustment can better reflect the true shape of the terrain.

[0114] For example, when using smoothing tools to optimize the initial three-dimensional terrain surface morphology, the focus is on addressing areas of surface discontinuity. For example, if there's a significant surface change at the junction of two terrain units due to differences in data density, a smoothing algorithm can be used to transition the junction area, making the height change more natural. For example, a sudden height difference at a certain location can be smoothly adjusted from 2 meters to a gradual transition, ensuring that the final three-dimensional terrain surface structure is more coherent both visually and in terms of data. This processing method helps enhance the realism of the terrain model while facilitating data analysis and planning in subsequent applications.

[0115] For example, in each of the aforementioned steps, whether meshing, surface interpolation, triangulation, or smoothing, each step is closely centered around optimizing terrain data. Proper regional division allows for more accurate identification of terrain features; interpolation supplements data to avoid information gaps; triangulation and adaptive adjustments ensure a faithful restoration of terrain features; and smoothing further enhances the model's continuity and usability. These interconnected steps lay the foundation for constructing high-quality three-dimensional terrain surface structures.

[0116] In step S13, a hierarchically optimized grid model is obtained according to the second three-dimensional terrain surface model.

[0117] A hierarchically optimized grid model is obtained based on the second three-dimensional terrain surface model, including:

[0118] Based on the second three-dimensional terrain surface model, a curvature analysis tool is used to divide the curvature changes of the terrain surface into regions, obtain classified curvature distribution information, and determine the boundaries of high curvature regions and low curvature regions;

[0119] The classified curvature distribution information is subjected to a grid adjustment tool to encrypt the grid in the high curvature area. If the curvature value of one of the areas is detected to be higher than the preset curvature threshold, grid cells are added to the area to obtain the encrypted grid distribution information.

[0120] The mesh optimization tool is used to perform mesh thinning on the low curvature area after the mesh is encrypted. If the curvature value of another area is detected to be lower than the preset curvature threshold, the mesh units in the other area are reduced to obtain the mesh distribution information after the thinning adjustment.

[0121] The hierarchical integration tool is used to uniformly process the grid distribution information after sparse adjustment to obtain hierarchically optimized grid distribution data.

[0122] For example, when performing curvature analysis based on smooth three-dimensional terrain surface data, the application of curvature analysis tools is key. Curvature reflects the degree of curvature of the terrain surface and is often used to identify the complexity of terrain features. Assuming a piece of terrain data covers an area of ​​1 square kilometer, the curvature analysis tool can divide the surface into multiple regions and classify the curvature of each region. For example, a high curvature value in a ridge region indicates drastic terrain changes, while a low curvature value in a flat valley bottom indicates relatively flat terrain. This classification facilitates subsequent differentiated treatment of different regions.

[0123] For example, the grid adjustment tool plays a crucial role in refining the mesh in areas of high curvature, based on the classified curvature distribution information. For example, if the preset curvature threshold is 0.5, and the curvature value of a hillside area reaches 0.8, significantly exceeding the threshold, the grid adjustment tool can be used to increase the number of grid cells in that area, for example, from one cell per 100 square meters to one per 50 square meters. This refining process allows for more detailed capture of the changing characteristics of the terrain surface, especially in areas with significant topography, ensuring data accuracy and integrity.

[0124] For example, mesh optimization tools are also essential for thinning meshes in low-curvature areas. For example, if the curvature value of a plain area is only 0.1, well below the preset threshold of 0.5, the number of grid cells in that area can be reduced, for example, from one cell per 50 square meters to one per 200 square meters. This thinning process reduces the data storage and processing burden while maintaining relatively unchanged terrain characteristics. It is particularly suitable for areas with minimal terrain changes, thereby optimizing overall resource allocation.

[0125] For example, the use of hierarchical integration tools can achieve unified processing of mesh distribution in different areas for sparsely adjusted mesh distribution information. Assume that at the boundary between high-curvature and low-curvature areas, the mesh density is quite different, which may lead to unnatural data connection. In this case, the hierarchical integration tool can be used to make transition adjustments to the boundary area, such as gradually adjusting the size of the mesh cells to smoothly transition from high-density mesh to low-density mesh. This hierarchical optimization method can ensure the consistency of the entire terrain surface data and avoid surface discontinuities caused by sudden changes in mesh density.

[0126] For example, from a holistic perspective, curvature analysis, mesh refinement, mesh sparsification, and layered integration form a complete processing chain. Curvature analysis provides a basis for subsequent mesh adjustments, while mesh refinement and sparsification optimize the terrain characteristics of different regions. Ultimately, layered integration ensures data uniformity and consistency. This processing approach not only improves the refinement of terrain data but also achieves a reasonable balance in resource allocation. This approach, especially in complex terrain environments, can better adapt to the diverse terrain characteristics required, laying a solid foundation for subsequent applications.

[0127] In step S14, a first reservoir capacity estimation value is obtained according to the grid model.

[0128] Obtain the estimated capacity of the first reservoir based on the grid model, including:

[0129] A data extraction tool is used to extract the corresponding geometric parameter information from each grid cell of the hierarchical optimized grid distribution data. Each grid cell is preliminarily processed based on the geometric parameter information to obtain the initial volume distribution data.

[0130] The initial volume distribution data is subjected to an integral calculation tool, and each grid cell is randomly sampled. During the sampling, each grid cell is independently estimated multiple times to obtain the volume distribution information after sampling.

[0131] The volume distribution information after sampling is processed using a data integration tool to accumulate the volume estimates of all grid cells. If the accumulated value exceeds a preset accumulated value threshold, the grid cells in the area exceeding the accumulated value threshold are resampled to determine the adjusted accumulated volume data.

[0132] The deviation analysis tool is used on the adjusted cumulative volume data to correct the overall capacity estimate and make local adjustments to the abnormal areas to obtain the estimated value of the first reservoir capacity.

[0133] For example, when processing hierarchical grid-distributed data, the application of data extraction tools is key. Data extraction tools are primarily used to obtain geometric parameter information from each grid cell, such as basic data such as the height and slope of the terrain surface. Assuming that in a terrain area covering an area of ​​2 square kilometers, the grid cells are divided into units of 100 square meters. The data extraction tool can quickly obtain the height value range of each unit, for example, the height value of a certain area is between 50 meters and 80 meters. This geometric parameter information provides basic data support for subsequent processing and helps to more accurately reflect the terrain characteristics.

[0134] For example, for initial volume distribution data, integral calculation tools play an important role in random sampling processing. The purpose of random sampling is to reduce the errors that may be caused by a single calculation through multiple independent estimates. Assuming that a grid cell is sampled 10 times independently, each time its volume data is estimated, a range of values ​​may be obtained, such as from 500 cubic meters to 550 cubic meters. By taking the average or weighted processing, volume distribution information that is closer to the actual situation can be obtained. This method can effectively improve the reliability of the estimate, especially in areas with complex terrain, and avoid overall errors caused by single sampling deviations.

[0135] For example, in post-sampling volume distribution information processing, data integration tools are used to accumulate the volume estimates of all grid cells. If the accumulated value exceeds a preset threshold—for example, if the threshold is set at 10,000 cubic meters and the actual accumulated value reaches 12,000 cubic meters—then the relevant area needs to be resampled. For example, if the volume estimate for a hillside area is too high, resampling can adjust its data range, for example, from the original 800 cubic meters to 750 cubic meters, ultimately bringing the overall accumulated value closer to the threshold. This adjustment mechanism ensures data rationality and prevents local anomalies from affecting the overall results.

[0136] For example, the Deviation Analysis tool is crucial for correcting the overall capacity estimate after adjusting the cumulative volume data. Deviation Analysis is primarily used to identify outliers and make local adjustments. For example, if a flat area has a significantly lower volume estimate of 200 cubic meters, compared to the surrounding area's average of 400 cubic meters, the Deviation Analysis tool can be used to correct that area, for example, to 350 cubic meters to align with the overall trend. This correction ensures the balance of the final capacity estimate, especially in terrain with uneven data distribution, helping to improve the credibility of the overall data.

[0137] For example, from a holistic perspective, data extraction, random sampling, data integration, and bias correction form a complete processing chain. Each link is closely centered around the processing requirements of terrain data, from initial geometric parameter extraction to final capacity estimation and correction. Each step ensures improved data quality. This layered processing approach better adapts to diverse data characteristics in complex terrain environments, laying a solid foundation for subsequent terrain analysis and applications while effectively avoiding potential problems caused by data bias.

[0138] In step S15, the first estimated value of the reservoir capacity is numerically filled to obtain a corrected second estimated value of the reservoir capacity.

[0139] Perform numerical filling on the first reservoir capacity estimate to obtain a corrected second reservoir capacity estimate, including:

[0140] Obtain the original terrain distribution records of the target area from the repository, use data scanning tools to perform integrity checks on each sub-area of ​​the original terrain distribution records, determine whether there are missing data or abnormal points, and obtain preliminary integrity assessment results;

[0141] If the data missing area found in the integrity assessment results exceeds the preset missing threshold, the missing area will be preliminarily marked using the data repair tool, and the corresponding filling data will be generated using the interpolation calculation tool to obtain the repaired terrain distribution record;

[0142] The restored terrain distribution records are processed using data integration tools to fuse the filled data with the original terrain distribution records, and the fused data are checked for consistency to obtain the corrected terrain distribution set.

[0143] The corrected terrain distribution set is locally adjusted using the volume estimation tool to obtain the second reservoir capacity estimate.

[0144] For example, when processing terrain data, the first step is to obtain the original terrain distribution records of the target area from the repository. This process involves the call of historical data, which usually includes information such as terrain height and boundary range. Assume that the target area is the terrain around a reservoir, covering an area of ​​5 square kilometers. The terrain height data of the area is recorded in the repository, but some sub-areas may be blank due to historical collection problems. The use of data scanning tools is particularly important. It can check the data integrity of each sub-area one by one. For example, if the missing ratio of height data in a sub-area of ​​0.5 square kilometers reaches 30%, which exceeds the preset threshold of 10%, further processing is required.

[0145] For example, to address missing data, the data repair tool will initially mark the missing areas and, in conjunction with the interpolation tool, generate fill-in data. For example, if the missing data in the aforementioned 0.5 square kilometer subregion is primarily concentrated in the periphery, the interpolation tool will calculate a reasonable fill-in value based on the surrounding area's height, such as 50 to 60 meters, setting the missing point's height to 55 meters. This approach quickly completes data and lays the foundation for subsequent analysis.

[0146] For example, during the data integration phase, the data integration tool merges the infill data with the original records to ensure overall data consistency. For example, if the original record shows a sub-area elevation of 58 meters, while the infill data shows 55 meters, the fused data might take the average value or adjust it to 56 meters based on the terrain trend. Subsequently, a consistency check verifies the rationality of the merged data, for example, ensuring that the height difference between adjacent sub-areas does not exceed a preset range, such as 5 meters, to avoid sudden changes that could affect the accuracy of the overall terrain distribution set.

[0147] For example, the volume estimation tool recalculates the capacity of a target reservoir area based on the corrected terrain distribution set. Assuming the reservoir area has a total area of ​​3 square kilometers and the terrain distribution set indicates depths ranging from 10 to 30 meters, the estimation tool uses this characteristic data to estimate capacity. Furthermore, if a sub-area is found to have an unusually low depth—for example, only 5 meters, compared to the surrounding area's depth of around 20 meters—the tool will make a local adjustment, such as correcting the depth to 18 meters, to align with the overall trend. This adjustment effectively improves the reliability of the capacity calculation data.

[0148] For example, throughout the entire process, the implementation of every technical topic is closely centered around terrain data processing. From data scanning to restoration, integration, and correction, every step is designed to ensure data integrity and consistency. For example, in the final capacity calculation, adjusted data increases the reservoir capacity estimate from the initial 8,000 cubic meters to 8,500 cubic meters, more closely reflecting the actual terrain characteristics. This meticulous processing approach not only optimizes data quality but also provides a more reliable basis for reservoir planning, fully demonstrating the adaptability of layered processing in complex terrain environments.

[0149] In step S16, the second reservoir capacity estimation value is calculated in blocks to obtain a third reservoir capacity estimation value.

[0150] The estimated value of the second reservoir capacity is calculated in blocks to obtain the estimated value of the third reservoir capacity, including:

[0151] Obtain the corrected data records of the target area from the repository, and use the data segmentation tool to perform grid block processing on the corrected data records to obtain a segmented data set;

[0152] The task allocation tool is used to dynamically adjust each sub-region unit of the segmented data set. Based on the preset resource constraints, the load balancing tool is used to allocate computing tasks and determine the assigned task list.

[0153] The assigned task list is processed using a parallel processing tool to synchronously calculate the data of each sub-region unit. If the processing speed of one sub-region is detected to be lower than the preset speed threshold, the task is reallocated through a dynamic adjustment tool to obtain the adjusted calculation progress.

[0154] The data integration tool is used to integrate the calculation results of each sub-region unit with the adjusted calculation progress, and the consistency of the integrated data is checked to obtain the estimated value of the third reservoir capacity.

[0155] For example, when calculating reservoir capacity, obtaining corrected data records for the target area from the repository is a crucial step. To process these records, the data segmentation tool divides the entire area into multiple grid cells to facilitate subsequent calculations. For example, if the target reservoir area has a total area of ​​6 square kilometers, the segmentation tool might divide it into 36 1-square-kilometer subgrids, forming a structured data set. This division facilitates breaking down large areas of data into manageable units, laying the foundation for subsequent task allocation.

[0156] For example, for segmented data sets, the task allocation tool dynamically adjusts based on the data volume and complexity of each sub-region unit. For example, if some sub-regions have large terrain fluctuations and a high density of data points, the task allocation tool will prioritize resource constraints, such as the processing power of the computing nodes, and distribute tasks appropriately to different nodes. The load balancing tool ensures that the task volume of each node is close to that of the other nodes, avoiding overloading any one node. For example, tasks for 36 sub-grids can be allocated to four computing nodes, with each node processing nine sub-grids, ensuring full utilization of computing resources.

[0157] For example, after the task list is finalized, the parallel processing tool will perform simultaneous calculations on the data in the sub-regions. If, during the calculation process, the processing speed of a sub-region falls below a preset threshold due to excessive data points, such as processing less than 50% of the expected data volume per minute, the dynamic adjustment tool will intervene promptly and reallocate some of the tasks in that sub-region to other idle nodes. This optimizes the calculation progress and prevents the overall process from being hindered by delays in any particular link.

[0158] For example, based on the adjusted calculation schedule, the data integration tool will merge the calculation results of each sub-area unit. For example, suppose the estimated capacity of a sub-area is 2,000 cubic meters, while that of an adjacent sub-area is 2,100 cubic meters. The integration tool will aggregate these data and perform consistency checks to ensure the rationality of the results. For example, if the verification process finds that the data for a sub-area differs significantly from that of the surrounding area, a second verification will be triggered to ensure the reliability of the final data. This integration and verification mechanism effectively improves the overall quality of capacity calculations.

[0159] For example, after consistency verification, the final optimized capacity calculation data will provide an important reference for reservoir planning. Suppose, through the above processing, the target reservoir's capacity data is adjusted from the initial estimate of 9,000 cubic meters to 9,200 cubic meters, a result that better reflects the actual terrain characteristics. The implementation of each technical theme, such as segmentation, allocation, parallel processing, and integrated verification, ensures efficient and accurate data processing through detailed tasks and dynamic adjustments, providing solid support for subsequent decision-making.

[0160] In step S17, it is determined whether the third reservoir capacity estimation value meets the conditions. If so, it is determined that the third reservoir capacity estimation value is reliable output capacity calculation data. If not, it returns to the step of obtaining a hierarchically optimized grid model based on the second three-dimensional terrain surface model.

[0161] Determine whether the estimated capacity of the third reservoir meets the requirements, including:

[0162] Obtain the final optimized data records of the target area, perform multiple rounds of cross-checking on the optimized data records using data comparison tools, obtain the deviation value data of each round of verification, and determine the distribution range of the deviation value set;

[0163] If at least one value in the deviation value set exceeds the preset deviation threshold, a detailed evaluation of the data consistency is performed using the deviation analysis tool to obtain a partition list;

[0164] Determine whether the grid density needs to be adjusted;

[0165] According to the partition list, the grid density of the target area is dynamically updated using the grid adjustment tool, the adjusted discretized data records are obtained, and the new calculation output range is determined;

[0166] According to the new calculation output range, the data integration tool is used to perform consistency verification on the adjusted discretized data records to obtain the final capacity verification results and determine whether the reliability of the capacity verification results meets the requirements.

[0167] For example, in a capacity verification scenario, obtaining the final optimized data records for the target area from a repository is the first step. This process involves extracting historical calculation results to provide a reliable data foundation for subsequent verification. For example, let's assume the target area is a reservoir. The repository stores capacity data records previously generated through parallel processing and dynamic adjustments. This data is stored in a grid format, with each grid cell containing detailed topographic and capacity information.

[0168] For example, data comparison tools can be used to perform multiple rounds of cross-verification of data records. Conceptually, cross-verification involves comparing data from the same area across different sources or at different time points to identify potential deviations. For example, suppose the capacity of a grid cell is 2,500 cubic meters in the first round of verification, but 2,600 cubic meters in the second round, a deviation of 100 cubic meters. The set of deviations for all grid cells is aggregated and analyzed to see if their distribution falls within a pre-set threshold, such as a tolerance of 50 cubic meters. If not, further analysis is required.

[0169] For example, if the deviation value exceeds a threshold, the Deviation Analysis tool will assess data consistency. This tool will identify inconsistent areas and generate a demarcation list. For example, in the northwest corner of the reservoir, the terrain data has significant deviations due to different acquisition times. The analysis tool will mark this area as inconsistent and list the specific grid cell number and deviation value for subsequent processing.

[0170] For example, the grid adjustment tool dynamically updates the grid density for inconsistent areas. In principle, grid density adjustment involves increasing the number of grid cells in areas with large data deviations to improve data accuracy. For example, if the original grid had one cell per square kilometer, after adjustment, it would be refined to one cell per 0.5 square kilometer within the inconsistent areas, thereby obtaining a more detailed discretized data record. This approach better captures topographic variations.

[0171] For example, the new calculation output range will be determined by adjusting the discretized data records. This process involves reanalyzing the data of the updated grid. For example, after adjustment, the capacity data of the northwest corner area changes from the original 2500 cubic meters to 2550 cubic meters. The overall output range is closer to the actual terrain characteristics, laying the foundation for subsequent integration.

[0172] For example, during the data integration phase, the data integration tool performs a consistency check on the adjusted data. If the integration process reveals that the data for certain grid cells still differ significantly from that of other areas, the tool will smooth the data by averaging the surrounding data to obtain a final capacity verification result. Suppose the final result shows a reservoir capacity of 9,300 cubic meters, which is more reasonable than historical data.

[0173] For example, when determining whether the reliability of the results meets the standard, a preset error range can be used for evaluation. Assuming the standard requires an error of less than 2%, and the error in this verification result is 1.5%, it meets the requirement. This multi-round verification and dynamic adjustment method can effectively improve data credibility and provide a solid basis for reservoir planning.

[0174] In step S18, a first three-dimensional visualization model is generated according to the reliably outputted capacity calculation data, and the size of the rendering data is compressed to obtain a second three-dimensional visualization model.

[0175] Generating a first 3D visualization model based on the reliable output capacity calculation data, compressing the rendering data scale, and obtaining a second 3D visualization model, including:

[0176] Obtain the terrain feature records of the target area from the storage library, use volume data technology to perform preliminary layering processing on the terrain features, and obtain a layered terrain data set;

[0177] Based on the layered terrain data set, data compression tools are used to reduce the size of the rendering data;

[0178] If the data size exceeds the preset size threshold during reduction, the rendering data is segmented using a block processing tool to determine the range of the compressed rendering data;

[0179] Based on the compressed rendering data range, a 3D construction tool is used to construct a visualization model of the terrain features to obtain preliminary 3D visualization results that meet the analysis requirements.

[0180] Based on the preliminary results of 3D visualization, the data format is adjusted using a storage optimization tool. If the storage requirements do not meet the preset storage standards, secondary optimization is performed using a format conversion tool to obtain the final 3D visualization results.

[0181] For example, when processing the topographic features of a target reservoir, the first step is to extract relevant data from a pre-established repository. This data typically includes information such as the reservoir's terrain elevation, slope distribution, and water depth. As a centralized data management platform, the repository ensures data integrity and traceability. For example, suppose the topographic data for the target reservoir contains 10,000 data points, each with specific coordinates and elevation values. This data lays the foundation for subsequent processing.

[0182] For example, for the initial stratification of terrain features, volumetric data technology is used to classify complex terrain data according to elevation intervals. Conceptually, volumetric data technology decomposes terrain features into multiple layers by dividing the three-dimensional space for subsequent analysis. Assuming that the terrain data of a reservoir is divided into five elevation layers, each layer represents a different depth range, such as 0-5 meters, 5-10 meters, etc., this stratification method helps to more clearly understand the terrain changes.

[0183] For example, data compression tools play a crucial role in reducing the size of a layered terrain dataset. Essentially, compression tools remove redundant data points while preserving key features. For example, assuming the original data size is 500MB and the preset threshold is 200MB, if the data exceeds the threshold, a block processing tool will be used to split the data into smaller blocks, such as 50MB each, for compression. The final compressed data size is 180MB.

[0184] For example, using the compressed rendering data range, 3D construction tools can create a visual model of reservoir terrain features. Assuming the tool converts the terrain data into a 3D model with elevation and textures, the initial results clearly show the distribution of shallow and deep areas of the reservoir. This visualization method allows analysts to intuitively understand the terrain features.

[0185] For example, during storage optimization, the storage optimization tool adjusts the data format of the initial 3D visualization results. For example, if the initial format occupies 300MB of space, while the preset standard is 150MB, if this does not meet the requirements, the format conversion tool will perform secondary optimization, converting the data to a more efficient storage format, ultimately reducing the space usage to 140MB. This optimization ensures efficient data storage and fast access.

[0186] For example, from a holistic perspective, each of the aforementioned steps is tightly linked, from data extraction to layered processing, compression, construction, and optimization, forming a complete technical chain. Imagine, in a real-world application, that the 3D visualization of a target reservoir is used to plan flood control measures. The clear topographic distribution information provides a crucial reference for decision-making. This multi-step collaborative processing approach not only improves data processing efficiency but also enhances the practicality of the analysis results, providing strong support for reservoir management.

[0187] In step S19, the terrain data and capacity calculation results are dynamically updated according to the second three-dimensional visualization model. If the detected terrain data update frequency is higher than the preset update threshold, the adaptive meshing algorithm is triggered to recalculate and obtain real-time updated reservoir capacity data.

[0188] The terrain data and capacity calculation results are dynamically updated based on the second 3D visualization model. If the detected terrain data update frequency exceeds the preset update threshold, the adaptive meshing algorithm is triggered to recalculate and obtain real-time updated reservoir capacity data, including:

[0189] According to the correlation between the terrain data and the terrain changes, the latest terrain records of the target area are obtained from the repository;

[0190] Use data comparison tools to detect changes in the latest terrain records. If the terrain change exceeds the preset change threshold, the subsequent processing flow is triggered to obtain the changed terrain data set.

[0191] Based on the changed terrain data set, combined with dynamic updates and the association with real-time data, data synchronization tools are used to integrate the changed data with existing records;

[0192] If the update frequency of the integrated data is higher than the preset update threshold, it will be marked through the frequency monitoring tool to determine the area that needs to be recalculated;

[0193] Adaptive meshing tools are used to re-divide the marked area, obtain the mesh unit data after segmentation, and determine the grid distribution suitable for capacity calculation;

[0194] According to the grid distribution and the relationship between capacity calculation and reservoir capacity, the volume estimation tool is used to perform capacity numerical calculation on the grid cell data to obtain the final updated reservoir capacity record.

[0195] For example, when processing topographic data for a target reservoir, the first step is to retrieve the latest topographic records from a pre-established repository. This repository serves as a centralized management platform, containing historical topographic information about the reservoir, such as elevation, slope, and other key data. Assuming the repository contains the target reservoir's topographic records for the past year, each update contains approximately 5,000 data points, covering the entire reservoir area. This data acquisition approach ensures the accuracy and completeness of subsequent analysis.

[0196] For example, to detect changes in the latest topographic records, the data comparison tool compares the current data point by point with the historical records. Assuming the preset change threshold is an elevation change of more than 0.5 meters, if the elevation change detected in a certain area reaches 0.8 meters, subsequent processing will be triggered. This comparison method can quickly locate key areas of topographic change, providing a basis for subsequent data updates.

[0197] For example, during data integration, the data synchronization tool merges changed terrain data with existing records. Assuming the integrated data is updated daily, and the preset threshold is weekly, the frequency monitoring tool will flag the data to identify areas requiring recalculation. This flagging mechanism helps identify data update needs promptly, avoiding wasted resources.

[0198] For example, adaptive meshing of marked areas re-segments the area based on terrain complexity. For example, if a marked area is 10 square kilometers, the tool will divide it into 100 grid cells, each measuring 0.1 square kilometers. This division method better adapts to terrain characteristics and ensures the accuracy of subsequent calculations.

[0199] For example, when calculating capacity, the volume estimation tool uses grid cell data to perform numerical estimations. Assuming the elevation and area of ​​each grid cell are known, the tool accumulates the volume of each cell to obtain the latest reservoir capacity record. This method can reflect the impact of topographic changes on capacity, providing an important reference for reservoir management.

[0200] For example, from a holistic perspective, the aforementioned steps, from data acquisition to change detection, integration, segmentation, and calculation, form a closed-loop processing chain. For example, if the capacity records of a target reservoir are used for water resource scheduling, the updated data can help managers formulate more precise allocation plans. This multi-step collaborative approach significantly improves the targeted and practical nature of data processing.

[0201] It's important to note that the combination of terrain change detection and data integration can effectively address the challenges posed by dynamic changes in reservoir terrain. For example, if the elevation of a reservoir area drops due to prolonged rainfall, the above process can quickly update relevant records, providing support for flood prevention and early warning. This approach offers significant advantages in ensuring data timeliness.

[0202] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

[0203] In summary, the present invention discloses a high-precision calculation method for reservoir capacity curves based on a three-dimensional model. By adaptively sampling and optimizing the original terrain data, a three-dimensional terrain surface model is constructed, and adaptive discretization grid division is adopted to perform volume analysis error correction to achieve high-precision reservoir capacity estimation. The present invention introduces a data integrity detection and repair mechanism, combines parallel processing technology to improve computing efficiency, and uses cross-validation to ensure the reliability of the results. At the same time, the present invention generates a three-dimensional visualization model to achieve a layered display of terrain features, and has a terrain change adaptability analysis function, which can dynamically update the capacity calculation results. The present invention significantly improves the accuracy and efficiency of reservoir capacity calculations, and provides reliable data support and intuitive visualization tools for water conservancy project planning and management.

[0204] Reference Figure 2 The second embodiment of the present invention provides a high-precision calculation system for a reservoir capacity curve based on a three-dimensional model, comprising:

[0205] The first acquisition module 201 is used to pre-process the collected original terrain data to obtain a terrain point cloud dataset;

[0206] A second acquisition module 202 is configured to construct a first three-dimensional terrain surface model based on the terrain point cloud dataset and acquire a second three-dimensional terrain surface model;

[0207] A third acquisition module 203 is configured to obtain a hierarchically optimized grid model based on the second three-dimensional terrain surface model;

[0208] A fourth acquisition module 204 is configured to acquire an estimated value of the first reservoir capacity according to the grid model;

[0209] A fifth acquisition module 205 is configured to perform numerical filling on the first estimated value of the reservoir capacity to obtain a corrected second estimated value of the reservoir capacity;

[0210] A sixth acquisition module 206 is configured to perform block calculation on the second reservoir capacity estimation value to obtain a third reservoir capacity estimation value;

[0211] A determination module 207 is configured to determine whether the third reservoir capacity estimate meets the conditions; if so, determining whether the third reservoir capacity estimate is reliable output capacity calculation data; if not, returning to the step of obtaining a hierarchically optimized grid model based on the second three-dimensional terrain surface model;

[0212] A seventh acquisition module 208 is configured to generate a first three-dimensional visualization model based on the reliably outputted capacity calculation data, compress the rendering data size, and acquire a second three-dimensional visualization model;

[0213] The updating module 209 is used to dynamically update the terrain data and capacity calculation results according to the second 3D visualization model. If the detected terrain data update frequency is higher than the preset update threshold, the adaptive meshing algorithm is triggered to recalculate and obtain real-time updated reservoir capacity data.

[0214] It should be noted that the high-precision calculation system for reservoir capacity curve based on three-dimensional model provided in an embodiment of the present invention is used to execute all the process steps of the high-precision calculation method for reservoir capacity curve based on three-dimensional model in the above embodiment. The working principles and beneficial effects of the two correspond one to one, so they will not be repeated here.

[0215] An embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a high-precision calculation program for a reservoir capacity curve based on a three-dimensional model. When the processor executes the computer program, the steps in the above-mentioned embodiments of the method for high-precision calculation of a reservoir capacity curve based on a three-dimensional model are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the first acquisition module.

[0216] For example, a computer program may be divided into one or more modules / units, one or more of which are stored in a memory and executed by a processor to implement the present invention. One or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in an electronic device.

[0217] Electronic devices may be computing devices such as desktop computers, laptops, PDAs, and smart tablets. Electronic devices may include, but are not limited to, processors and memory. Those skilled in the art will appreciate that the aforementioned components are merely examples of electronic devices and do not constitute a limitation of electronic devices. Electronic devices may include more or fewer components than those described above, or combinations of certain components, or different components. For example, electronic devices may also include input / output devices, network access devices, buses, and the like.

[0218] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of an electronic device, connecting all parts of the electronic device using various interfaces and circuits.

[0219] The memory can be used to store computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and accessing the data stored in the memory. The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as sound playback or image playback); the data storage area may store data generated based on the use of the mobile phone (such as audio data and a phone book). Furthermore, the memory may include high-speed random access memory (RAM) and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a SmartMedia Card (SMC), a Secure Digital (SD) card, a flash memory card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0220] If the module / unit integrated into the electronic device is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. Computer-readable media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, removable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signals, telecommunications signals, and software distribution media. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased based on the requirements of legislation and patent practice within a jurisdiction. For example, in some jurisdictions, based on legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunications signals.

[0221] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0222] The above specific embodiments further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention for those skilled in the art.

Claims

1. A high-precision calculation method for reservoir capacity curve based on a three-dimensional model, characterized in that: include: Preprocess the collected original terrain data to obtain a terrain point cloud dataset; constructing a first three-dimensional terrain surface model based on the terrain point cloud dataset, and obtaining a second three-dimensional terrain surface model; Obtaining a hierarchically optimized grid model according to the second three-dimensional terrain surface model; obtaining an estimated value of the first reservoir capacity based on the grid model; Performing numerical filling on the first reservoir capacity estimate to obtain a corrected second reservoir capacity estimate; Perform block calculation on the estimated value of the second reservoir capacity to obtain an estimated value of the third reservoir capacity; determining whether the third reservoir capacity estimate meets the conditions; if so, determining whether the third reservoir capacity estimate is reliable output capacity calculation data; if not, returning to the step of obtaining a hierarchically optimized grid model based on the second three-dimensional terrain surface model; generating a first three-dimensional visualization model based on the reliably outputted capacity calculation data, compressing the rendering data scale, and obtaining a second three-dimensional visualization model; Dynamically update terrain data and capacity calculation results based on the second 3D visualization model. If the detected terrain data update frequency exceeds a preset update threshold, the adaptive meshing algorithm is triggered to recalculate and obtain real-time updated reservoir capacity data. The step of constructing a first three-dimensional terrain surface model based on a terrain point cloud dataset and obtaining a second three-dimensional terrain surface model includes: The initially optimized terrain point cloud dataset is segmented using a gridding tool, and the terrain features of different regions are classified and labeled to obtain a set of divided terrain regions. The surface interpolation tool is used to supplement the local data of the divided terrain region set. If the data density of a region is lower than a preset density threshold, the region is interpolated to obtain a supplemented terrain data distribution, and a triangulated network construction tool is used to construct a surface for the supplemented terrain data distribution. Adaptive adjustments are made to regions with significant terrain changes to obtain a first three-dimensional terrain surface model. Optimizing the first three-dimensional terrain surface model by using a smoothing tool to obtain a second three-dimensional terrain surface model; The step of obtaining a hierarchically optimized grid model based on the second three-dimensional terrain surface model includes: Based on the second three-dimensional terrain surface model, a curvature analysis tool is used to divide the curvature changes of the terrain surface into regions, obtain classified curvature distribution information, and determine the boundaries of high curvature regions and low curvature regions; The classified curvature distribution information is subjected to a grid adjustment tool to encrypt the grid in the high curvature area. If the curvature value of one area is detected to be higher than the preset curvature threshold, grid cells are added to the area to obtain the encrypted grid distribution information. Using a grid optimization tool on the encrypted grid distribution information, a grid thinning process is performed on the low curvature area. If it is detected that the curvature value of another area is lower than a preset curvature threshold, the grid cells of the other area are reduced to obtain the sparsely adjusted grid distribution information; The hierarchical integration tool is used to uniformly process the grid distribution information after sparse adjustment to obtain hierarchically optimized grid distribution data.

2. The high-precision calculation method for reservoir capacity curve based on three-dimensional model according to claim 1 is characterized in that: The preprocessing of the collected original terrain data to obtain a terrain point cloud dataset includes: Acquiring initial point cloud information from the original terrain data, performing denoising and formatting on the initial point cloud information using a preprocessing method, and performing regional division on the processed point cloud information using a meshing tool to obtain a set of divided terrain units; Extract features from each unit of the terrain unit set and determine whether the slope change of each unit exceeds a preset slope threshold; If the slope threshold is exceeded, it is marked as a complex terrain area. If the slope threshold is not exceeded, it is marked as a flat area, and the classified terrain area label is obtained. Increase the distribution of sampling points in complex terrain areas, adjust the density of complex terrain areas through point cloud interpolation tools, reduce the number of sampling points in flat areas, and use point cloud thinning tools to process flat areas to obtain the adjusted point cloud density distribution; The point cloud density distribution is fused using the point cloud integration tool to obtain the optimized terrain point cloud dataset. The accuracy of the optimized terrain point cloud dataset is verified using the data verification tool to determine it as the preliminary optimized terrain point cloud dataset.

3. The high-precision calculation method for reservoir capacity curve based on three-dimensional model according to claim 1 is characterized in that: The obtaining of the first reservoir capacity estimation value according to the grid model includes: A data extraction tool is used to extract the corresponding geometric parameter information from each grid cell of the hierarchical optimized grid distribution data. Each grid cell is preliminarily processed based on the geometric parameter information to obtain the initial volume distribution data. The initial volume distribution data is subjected to an integral calculation tool, and each grid cell is randomly sampled. During the sampling, each grid cell is independently estimated multiple times to obtain the volume distribution information after sampling. The volume distribution information after sampling is processed using a data integration tool to accumulate the volume estimates of all grid cells. If the accumulated value exceeds a preset accumulated value threshold, the grid cells in the area exceeding the accumulated value threshold are resampled to determine the adjusted accumulated volume data. The adjusted accumulated volume data is subjected to a deviation analysis tool to correct the overall capacity estimate, and local adjustments are made to abnormal areas to obtain the first reservoir capacity estimate.

4. The high-precision calculation method for reservoir capacity curve based on three-dimensional model according to claim 1 is characterized in that: The step of numerically filling the first estimated reservoir capacity value to obtain a corrected second estimated reservoir capacity value includes: Obtain the original terrain distribution records of the target area from the repository, use data scanning tools to perform integrity checks on each sub-area of ​​the original terrain distribution records, determine whether there are missing data or abnormal points, and obtain preliminary integrity assessment results; If the preliminary integrity assessment results show that the data missing area exceeds the preset missing threshold, the data repair tool will be used to preliminarily mark the data missing area, and the interpolation calculation tool will be used to generate the corresponding filling data to obtain the repaired terrain distribution record; The restored terrain distribution records are processed using data integration tools to fuse the filled data with the original terrain distribution records, and the fused data are checked for consistency to obtain the corrected terrain distribution set. The corrected terrain distribution set is locally adjusted using a volume estimation tool to obtain the second reservoir capacity estimate; The block-by-block calculation of the second reservoir capacity estimate to obtain the third reservoir capacity estimate includes: Obtain the corrected data records of the target area from the repository, and use the data segmentation tool to perform grid block processing on the corrected data records to obtain a segmented data set; The task allocation tool is used to dynamically adjust each sub-region unit of the segmented data set. Based on the preset resource constraints, the load balancing tool is used to allocate computing tasks and determine the assigned task list. A parallel processing tool is used on the assigned task list to synchronously calculate the data of each sub-region unit. If it is detected that the processing speed of one of the sub-regions is lower than a preset speed threshold, the task is reallocated through a dynamic adjustment tool to obtain an adjusted calculation progress; The data integration tool is used to integrate the calculation results of each sub-region unit with the adjusted calculation progress, and the consistency of the integrated data is checked to obtain the estimated value of the third reservoir capacity.

5. The high-precision calculation method for reservoir capacity curve based on three-dimensional model according to claim 1 is characterized in that: The determining whether the estimated value of the third reservoir capacity meets the conditions includes: Obtain the final optimized data records of the target area, perform multiple rounds of cross-checking on the optimized data records using data comparison tools, obtain the deviation value data of each round of verification, and determine the distribution range of the deviation value set; If at least one value in the deviation value set exceeds the preset deviation threshold, a detailed evaluation of the data consistency is performed using the deviation analysis tool to obtain a partition list; Determine whether the grid density needs to be adjusted; According to the partition list, the grid density of the target area is dynamically updated using the grid adjustment tool, the adjusted discretized data records are obtained, and the new calculation output range is determined; Based on the new calculation output range, use data integration tools to perform consistency check on the adjusted discretized data records to obtain the final capacity verification results and determine whether the reliability of the capacity verification results meets the requirements; The method of generating a first three-dimensional visualization model based on the reliably outputted capacity calculation data, compressing the rendering data scale, and obtaining a second three-dimensional visualization model includes: Obtain the terrain feature records of the target area from the storage library, use volume data technology to perform preliminary layering processing on the terrain features, and obtain a layered terrain data set; Based on the layered terrain data set, data compression tools are used to reduce the size of the rendering data; If the size of the rendering data exceeds the preset size threshold during reduction, the rendering data is segmented using a block processing tool to determine the range of the compressed rendering data; Based on the compressed rendering data range, a 3D construction tool is used to construct a visualization model of the terrain features to obtain preliminary 3D visualization results that meet the analysis requirements. Based on the preliminary results of 3D visualization, the data format is adjusted using a storage optimization tool. If the storage requirements do not meet the preset storage standards, secondary optimization is performed using a format conversion tool to obtain the final 3D visualization results.

6. The high-precision calculation method for reservoir capacity curve based on three-dimensional model according to claim 1 is characterized in that: The method of dynamically updating the terrain data and the capacity calculation results based on the second three-dimensional visualization model, and triggering the adaptive meshing algorithm to recalculate if the detected terrain data update frequency is higher than a preset update threshold, thereby obtaining real-time updated reservoir capacity data, includes: According to the correlation between the terrain data and the terrain changes, the latest terrain records of the target area are obtained from the repository; Use data comparison tools to detect changes in the latest terrain records. If the terrain change exceeds the preset change threshold, the subsequent processing flow is triggered to obtain the changed terrain data set. Based on the changed terrain data set, combined with dynamic updates and the association with real-time data, data synchronization tools are used to integrate the changed data with existing records; If the update frequency of the integrated data is higher than the preset update threshold, it will be marked through the frequency monitoring tool to determine the area that needs to be recalculated; Adaptive meshing tools are used to re-divide the marked area, obtain the mesh unit data after segmentation, and determine the grid distribution suitable for capacity calculation; According to the grid distribution and the relationship between capacity calculation and reservoir capacity, the volume estimation tool is used to perform capacity numerical calculation on the segmented grid unit data to obtain the final updated reservoir capacity record.

7. A high-precision calculation system for reservoir capacity curve based on a three-dimensional model, characterized in that: A method for high-precision calculation of a reservoir capacity curve based on a three-dimensional model according to any one of claims 1 to 6, comprising: The first acquisition module is used to pre-process the collected original terrain data to obtain a terrain point cloud dataset; A second acquisition module is used to construct a first three-dimensional terrain surface model based on the terrain point cloud data set and obtain a second three-dimensional terrain surface model; A third acquisition module is used to obtain a hierarchically optimized grid model according to the second three-dimensional terrain surface model; a fourth acquisition module, configured to acquire an estimated value of the first reservoir capacity according to the grid model; a fifth acquisition module, configured to perform numerical filling on the first reservoir capacity estimation value to obtain a corrected second reservoir capacity estimation value; a sixth acquisition module, configured to perform block calculation on the second reservoir capacity estimation value to obtain a third reservoir capacity estimation value; a determination module, configured to determine whether the third reservoir capacity estimation value meets the conditions; if so, determining whether the third reservoir capacity estimation value is reliable output capacity calculation data; if not, returning to the step of obtaining a hierarchically optimized grid model based on the second three-dimensional terrain surface model; a seventh acquisition module, configured to generate a first three-dimensional visualization model based on the reliably outputted capacity calculation data, compress the rendering data scale, and acquire a second three-dimensional visualization model; The update module is used to dynamically update the terrain data and capacity calculation results based on the second three-dimensional visualization model. If the detected terrain data update frequency is higher than the preset update threshold, the adaptive meshing algorithm is triggered to recalculate and obtain real-time updated reservoir capacity data.

8. An electronic device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the high-precision calculation method of the reservoir capacity curve based on the three-dimensional model as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the high-precision calculation method for reservoir capacity curve based on a three-dimensional model as described in any one of claims 1 to 6.

Images