Reservoir capacity curve high-precision calculation method and system based on three-dimensional model

Abstract

The invention relates to the technical field of hydraulic engineering, and discloses a reservoir capacity curve high-precision calculation method and system based on a three-dimensional model, and the method comprises the steps: carrying out the self-adaptive sampling and optimization of original terrain data, constructing a three-dimensional terrain surface model, carrying out the self-adaptive discretization grid division, and carrying out the volume analysis error correction. And high-precision reservoir capacity estimation is realized. A data integrity detection and repair mechanism is introduced, the calculation efficiency is improved by combining a parallel processing technology, and the result reliability is ensured by adopting cross validation. Meanwhile, a three-dimensional visual model is generated, layered display of topographic features is realized, a topographic change adaptability analysis function is realized, and a capacity calculation result can be dynamically updated. According to the method, the accuracy and efficiency of reservoir capacity calculation are remarkably improved, and reliable data support and a visual visualization tool are provided for hydraulic engineering planning and management.

Description

Technical Field

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

[0002] Reservoir management and planning, as a core part of the water conservancy project field, is of irreplaceable significance for ensuring the reasonable allocation of water resources, flood control and disaster reduction, and ecological protection. Therefore, scientifically and accurately evaluating reservoir capacity and topographic features will directly relate to the safety and operation efficiency of engineering design, and is a key link in promoting regional economic development and social stability.

[0003] Currently, in reservoir capacity calculation and topographic modeling, traditional methods often rely on simple two-dimensional measurements or rough estimations, making it difficult to comprehensively reflect the true characteristics of complex terrains, resulting in large errors in calculation results. Especially when facing areas with severe terrain undulations or incomplete data, the accuracy and reliability often cannot meet the actual needs.

[0004] In an existing technology, the field of reservoir topographic modeling and capacity calculation faces significant technical challenges. The first and foremost is how to accurately discretize complex three-dimensional terrains for detailed volume analysis. Due to the complexity and irregularity of terrain data, traditional uniform partitioning methods often cannot adapt to terrain changes, resulting in the loss of local details and further affecting the accuracy of overall calculations. This problem further gives rise to the difficult problem of balancing calculation efficiency and accuracy. Because when pursuing higher accuracy, finer grid partitioning of the terrain is required, which inevitably increases the computational amount. How to achieve efficient and accurate analysis with limited computing resources has become a technical bottleneck to be solved urgently.

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

[0006] The present invention provides a high-precision calculation method and system for reservoir storage capacity curves based on three-dimensional models to accurately calculate reservoir capacity.

[0007] In a first aspect, to solve the above technical problems, the present invention provides a high-precision calculation method for reservoir storage capacity curves based on three-dimensional models, including: Preprocessing the collected original terrain data to obtain a terrain point cloud data set; Constructing a first three-dimensional terrain surface model based on the terrain point cloud data set and obtaining a second three-dimensional terrain surface model; Obtain a hierarchically optimized grid model based on the second three-dimensional terrain surface model; Obtain a first reservoir capacity estimation value based on the grid model; Perform numerical filling on the first reservoir capacity estimation value to obtain a corrected second reservoir capacity estimation value; Perform block calculation on the second reservoir capacity estimation value to obtain a third reservoir capacity estimation value; Determine whether the third reservoir capacity estimation value meets the conditions. If so, determine that the third reservoir capacity estimation value is reliable output capacity calculation data. If not, return to execute the step of obtaining a hierarchically optimized grid model based on the second three-dimensional terrain surface model; Generate a first three-dimensional visualization model based on the reliable output capacity calculation data, compress the rendering data scale, and obtain a second three-dimensional visualization model; Dynamically update 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 the preset update threshold, trigger the adaptive grid division algorithm to recalculate and obtain real-time updated reservoir capacity data.

[0008] Preferably, the preprocessing of the collected original terrain data to obtain a terrain point cloud data set includes: Obtain initial point cloud information from the original terrain data, perform denoising and format unification processing on the initial point cloud information using a preprocessing method, and perform regional division on the processed point cloud information through a grid division 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 it exceeds the slope threshold, mark it as a complex terrain area. If it does not exceed the slope threshold, mark it as a flat area to obtain a classified terrain area label; Increase the sampling point distribution in the complex terrain area, adjust the density of the complex terrain area through a point cloud interpolation tool, reduce the number of sampling points in the flat area, and process the flat area using a point cloud sparsification tool to obtain an adjusted point cloud density distribution; Perform data fusion on the point cloud density distribution through a point cloud integration tool to obtain an optimized terrain point cloud data set, and use a data verification tool to verify the accuracy of the optimized data set to determine a preliminary optimized terrain point cloud data set.

[0009] Preferably, the constructing a first three-dimensional terrain surface model based on the terrain point cloud data set and obtaining a second three-dimensional terrain surface model includes: Perform regional segmentation on the preliminarily optimized topographic point cloud dataset using a grid division tool, classify and label the topographic features of different regions to obtain a set of divided topographic regions, and supplement local data for the set of divided topographic regions through a surface interpolation tool; If the data density of a region is lower than a preset density threshold, perform interpolation processing on the region to obtain a supplemented topographic data distribution. Use a triangular network construction tool to construct a surface for the supplemented topographic data distribution, and perform adaptive adjustment on regions with obvious topographic changes to obtain a first three-dimensional topographic surface model; Optimize the first three-dimensional topographic surface model through a smoothing tool to obtain the second three-dimensional topographic surface model.

[0010] Preferably, obtaining a hierarchically optimized grid model according to the second three-dimensional topographic surface model includes: According to the second three-dimensional topographic surface model, use a curvature analysis tool to divide regions based on the curvature changes of the topographic surface, obtain classified curvature distribution information, and determine the boundaries of high-curvature regions and low-curvature regions; Use a grid adjustment tool on the classified curvature distribution information to encrypt the grid for the high-curvature region. If the curvature value of one of the regions is detected to be higher than a preset curvature threshold, add grid cells to the region to obtain encrypted grid distribution information; Use a grid optimization tool on the encrypted grid distribution information to sparsify the grid for the low-curvature region. If the curvature value of another region is detected to be lower than a preset curvature threshold, reduce the grid cells of the other region to obtain sparsely adjusted grid distribution information; Use a hierarchical integration tool on the sparsely adjusted grid distribution information to uniformly process the grid distributions of different regions to obtain hierarchically optimized grid distribution data; Obtaining a first reservoir capacity estimation value according to the grid model includes: Use a data extraction tool on the hierarchically optimized grid distribution data to obtain corresponding geometric parameter information from each grid cell, and perform preliminary processing on each grid cell through the geometric parameter information to obtain initial volume distribution data; Use an integral calculation tool on the initial volume distribution data to randomly sample each grid cell, and perform multiple independent estimations on each grid cell during the sampling to obtain sampled volume distribution information; Use a data integration tool on the sampled volume distribution information to accumulate the volume estimation values of all grid cells. If the accumulated value exceeds a preset accumulated value threshold, resample the grid cells in the region where the accumulated value threshold is exceeded to determine the adjusted accumulated volume data; Apply a deviation analysis tool to 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 capacity of the first reservoir.

[0011] Preferably, the numerical filling of the estimated value of the capacity of the first reservoir to obtain the corrected estimated value of the capacity of the second reservoir includes: Obtain the original terrain distribution record of the target area from the repository, and use a data scanning tool to check the integrity of each sub-area of the original terrain distribution record to determine whether there are data missing or abnormal points, and obtain a preliminary integrity assessment result; If the data missing area in the integrity assessment result exceeds the preset missing threshold, preliminarily mark the missing area through a data repair tool, and use an interpolation calculation tool to generate corresponding filling data to obtain the repaired terrain distribution record; Apply a data integration tool to the repaired terrain distribution record to fuse the filling data with the original terrain distribution record, and perform a consistency check on the fused data to obtain the corrected terrain distribution set; Apply a volume estimation tool to the corrected terrain distribution set for local adjustment to obtain the estimated value of the capacity of the second reservoir; The block calculation of the estimated value of the capacity of the second reservoir to obtain the estimated value of the capacity of the third reservoir includes: Obtain the corrected data record of the target area from the repository, and use a data segmentation tool to perform grid block processing on the corrected data record to obtain a segmented data set; Apply a task allocation tool to the segmented data set to dynamically adjust each sub-area unit, combine the preset resource limit conditions, and use a load balancing tool to allocate the calculation tasks to determine the allocated task list; Apply a parallel processing tool to the allocated task list to synchronously calculate the data of each sub-area unit. If it is detected that the processing speed of one of the sub-areas is lower than the preset speed threshold, re-allocate the tasks through a dynamic adjustment tool to obtain the adjusted calculation progress; Apply a data integration tool to the adjusted calculation progress to fuse the calculation results of each sub-area unit, and perform a consistency check on the fused data to obtain the estimated value of the capacity of the third reservoir.

[0012] Preferably, the judgment of whether the estimated value of the capacity of the third reservoir meets the conditions includes: Obtain the final optimized data record of the target area, and perform multiple rounds of cross-checking processing on the optimized data record using a data comparison tool to obtain the deviation value data for 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, then conduct a detailed assessment of the data consistency through a deviation analysis tool to obtain a classification list; Determine whether it is necessary to adjust the grid density; According to the classification list, use a grid adjustment tool to dynamically update the grid density of the target area, obtain the adjusted discretized data record, and determine the new calculation output range; According to the new calculation output range, use a data integration tool to perform consistency verification on the adjusted discretized data record to obtain the final capacity verification result, and determine whether the reliability of the capacity verification result meets the conditions; The generating the first three-dimensional visualization model according to the capacity calculation data output reliably, compressing the rendering data scale, and obtaining the second three-dimensional visualization model includes: Obtain the terrain feature record of the target area from the repository, and perform preliminary stratification processing on the terrain features using volume data technology to obtain a stratified terrain data set; According to the stratified terrain data set, use a data compression tool to reduce the scale of the rendering data; If it is found that the data scale exceeds the preset scale threshold during the reduction, then use a block processing tool to split the rendering data to determine the compressed rendering data range; According to the compressed rendering data range, use a three-dimensional construction tool to construct a visualization model for the terrain features to obtain a preliminary three-dimensional visualization result that meets the analysis requirements; According to the preliminary three-dimensional visualization result, use a storage optimization tool to adjust the data format. If the storage requirement does not meet the preset storage standard, then perform secondary optimization through a format conversion tool to obtain the final three-dimensional visualization result.

[0013] Preferably, the dynamically updating the terrain data and the capacity calculation result according to the second three-dimensional visualization model. If the detected terrain data update frequency is higher than the preset update threshold, then trigger the adaptive grid division algorithm to recalculate to obtain real-time updated reservoir capacity data, including: According to the association between the terrain data and the terrain change, obtain the latest terrain record of the target area from the repository; Use a data comparison tool to perform change detection on the latest terrain record. If it is detected that the terrain change exceeds a preset change threshold, trigger the subsequent processing flow to obtain a set of terrain data after the change; According to the set of terrain data after the change, combined with the association between dynamic update and real-time data, use a data synchronization tool to integrate the changed data with the existing records; If the update frequency of the integrated data is higher than the preset update threshold, mark it through a frequency monitoring tool to determine the range of areas that need to be recalculated; Use an adaptive grid division tool for the marked area range to re-segment the area range, obtain the grid cell data after segmentation, and determine the grid distribution suitable for capacity calculation; According to the grid distribution, combined with the association between capacity calculation and reservoir capacity, use a volume estimation tool to perform capacity numerical calculation on the grid cell data to obtain the finally updated reservoir capacity record.

[0014] In a second aspect, the present invention provides a high-precision calculation of a reservoir storage capacity curve based on a three-dimensional model, including: A first acquisition module for preprocessing the collected original terrain data to obtain a terrain point cloud data set; A second acquisition module for constructing a first three-dimensional terrain surface model according to the terrain point cloud data set and obtaining a second three-dimensional terrain surface model; A third acquisition module for obtaining a hierarchically optimized grid model according to the second three-dimensional terrain surface model; A fourth acquisition module for obtaining a first reservoir capacity estimation value according to the grid model; A fifth acquisition module for numerically filling the first reservoir capacity estimation value to obtain a corrected second reservoir capacity estimation value; A sixth acquisition module for performing block calculation on the second reservoir capacity estimation value to obtain a third reservoir capacity estimation value; A judgment module for judging whether the third reservoir capacity estimation value meets the conditions. If so, judge that the third reservoir capacity estimation value is reliable output capacity calculation data. If not, return to execute the step of obtaining a hierarchically optimized grid model according to the second three-dimensional terrain surface model; A seventh acquisition module for generating a first three-dimensional visualization model according to the reliable output capacity calculation data, compressing the rendering data scale, and obtaining a second three-dimensional visualization model; An update module for dynamically updating terrain data and capacity calculation results according to the second three-dimensional visualization model. If the detected terrain data update frequency is higher than the preset update threshold, trigger an adaptive grid division algorithm to recalculate to obtain real-time updated reservoir capacity data.

[0015] In a third aspect, the present invention further provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the high-precision calculation method of the reservoir storage curve based on the three-dimensional model described in any one of the above is implemented.

[0016] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program. Wherein, when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the high-precision calculation method of the reservoir storage curve based on the three-dimensional model described in any one of the above.

[0017] Compared with the prior art, the present invention provides a high-precision calculation method and system for the reservoir storage curve based on a three-dimensional model. By adaptively sampling and optimizing the original terrain data, constructing a three-dimensional terrain surface model, using adaptive discretization grid division, and performing volume analysis error correction, high-precision reservoir capacity estimation is achieved. The present invention introduces a data integrity detection and repair mechanism, combines parallel processing technology to improve calculation 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, realizes hierarchical 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 calculation, providing reliable data support and intuitive visualization tools for water conservancy project planning and management. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a schematic flowchart of a high-precision calculation method for a reservoir storage curve based on a three-dimensional model provided by the first embodiment of the present invention; Figure 2 is a schematic structural diagram of a high-precision calculation system for a reservoir storage curve based on a three-dimensional model provided by the second embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0020] Refer to Figure 1 , the first embodiment of the present invention provides a flowchart of a high-precision calculation method for a reservoir storage curve based on a three-dimensional model, including the following steps: S11. Preprocess the collected original terrain data to obtain a terrain point cloud dataset; S12. Construct a first three-dimensional terrain surface model based on the terrain point cloud dataset and obtain a second three-dimensional terrain surface model; S13. Obtain a hierarchically optimized grid model based on the second three-dimensional terrain surface model; S14. Obtain a first reservoir capacity estimation value based on the grid model; S15. Perform numerical filling on the first reservoir capacity estimation value to obtain a corrected second reservoir capacity estimation value; S16. Perform block calculation on the second reservoir capacity estimation value to obtain a third reservoir capacity estimation value; S17. Determine whether the third reservoir capacity estimation value meets the conditions. If so, determine that the third reservoir capacity estimation value is reliable output capacity calculation data. If not, return to execute the step of obtaining a hierarchically optimized grid model based on the second three-dimensional terrain surface model; S18. Generate a first three-dimensional visualization model based on the reliable output capacity calculation data, compress the rendering data scale, and obtain a second three-dimensional visualization model; S19. 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, trigger the adaptive grid division algorithm to recalculate and obtain real-time updated reservoir capacity data.

[0021] In step S11, preprocess the collected original terrain data to obtain a terrain point cloud dataset.

[0022] Preprocessing the collected original terrain data to obtain a terrain point cloud dataset includes: Obtain initial point cloud information from the original terrain data, perform denoising and format unification processing on the initial point cloud information using a preprocessing method, and perform regional division on the processed point cloud information through a grid division 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 it exceeds the slope threshold, mark it as a complex terrain area. If it does not exceed the slope threshold, mark it as a flat area to obtain a classified terrain area label; Increase the sampling point distribution in the complex terrain area, adjust the density of the complex terrain area through a point cloud interpolation tool, reduce the number of sampling points in the flat area, and process the flat area using a point cloud sparsification tool to obtain an adjusted point cloud density distribution; The point cloud density distribution is data - fused through a point cloud integration tool to obtain an optimized terrain point cloud data set, and a data verification tool is used to verify the accuracy of the optimized data set to determine a preliminarily optimized terrain point cloud data set.

[0023] For example, when processing the original terrain data, the initial point cloud information can be obtained from a high - precision lidar device. Suppose the data covers a mountainous terrain area, containing millions of points, and each point has three - dimensional coordinates and intensity information. The initial point cloud may have noise, such as abnormal points caused by device jitter or spurious points generated by tree occlusion. In the pre - processing stage, a statistical filtering method can be used, setting a radius of 0.5 meters to remove isolated points, and at the same time unifying the data format into a standard three - dimensional coordinate system to ensure consistency in subsequent processing. This can effectively improve the data quality and lay a foundation for subsequent analysis.

[0024] In a possible implementation, the grid division tool can divide the processed point cloud information into regions according to a grid of 10 meters by 10 meters, obtaining multiple terrain units. Suppose the total area is 1 square kilometer, which can be divided into 10,000 units. This division method is convenient for analyzing the terrain features of each unit one by one, reducing the computational complexity, while ensuring that the regional features are not lost, which helps with accurate classification.

[0025] Specifically, for the application of the slope calculation tool, the slope value of each unit can be calculated through the height difference between adjacent points. Suppose the height difference within a certain unit is 5 meters and the horizontal distance is 10 meters, and the slope change is significant. If the preset threshold is 30 degrees and the slope of this unit exceeds this value, it is marked as a complex terrain area; if the slope of another unit is only 5 degrees, it is marked as a flat area. This classification method can effectively distinguish the terrain complexity, provide a basis for subsequent sampling optimization, and improve the terrain modeling accuracy.

[0026] For example, when increasing the sampling point distribution in complex terrain areas, new points can be generated through a point cloud interpolation tool. Suppose the original density is 10 points per square meter, and after adjustment, it increases to 20 points per square meter to ensure more comprehensive detail capture. In flat areas, the point cloud sparsification tool is used to reduce the density from 10 points per square meter to 5 points per square meter, reducing redundant data and the storage and computational burden. This density adjustment strategy can optimize resource allocation and improve the overall efficiency.

[0027] In a possible implementation, the point cloud integration tool can data - fuse the adjusted density distribution, unify the coordinate system and the point cloud format, ensuring seamless connection between complex areas and flat areas. Suppose the fused data set contains 5 million points, reducing 20% of the redundant points compared to the original data, but the details of key areas are retained. This integration method can improve data consistency and provide a reliable basis for subsequent verification.

[0028] Specifically, the data verification tool can verify the accuracy of the optimized point cloud dataset by comparing it with the field measurement data. Suppose the height error of the point cloud in a complex area is controlled within 0.1 meter, meeting the engineering requirements, while the error in a flat area is 0.2 meter, which is also within the acceptable range. This verification method ensures the reliability of the final terrain optimization result and helps with terrain analysis and planning in practical applications.

[0029] It can be understood that through hierarchical processing and targeted optimization, the above method not only improves the accuracy and usability of terrain data 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.

[0030] 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. 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: Using a grid division tool to perform regional segmentation on the preliminarily optimized terrain point cloud dataset, classifying and labeling the terrain features of different regions to obtain a set of divided terrain regions, and supplementing local data for the set of divided terrain regions through a surface interpolation tool. If the data density of a region is lower than a preset density threshold, interpolation processing is performed on the region to obtain a supplemented terrain data distribution. A triangular network construction tool is used to construct the surface for the supplemented terrain data distribution, and adaptive adjustment is performed on regions with obvious terrain changes to obtain a first three-dimensional terrain surface model. The first three-dimensional terrain surface model is optimized through a smoothing processing tool to obtain a second three-dimensional terrain surface model.

[0031] For example, when processing based on the preliminarily optimized terrain point cloud dataset, the application of the grid division tool can be focused on first. Grid division is the process of dividing large-scale terrain data into small regions, facilitating the analysis of the terrain characteristics of different regions one by one. Suppose the dataset of a mountainous terrain covers an area of 2 square kilometers and can be divided into grid cells of 20 meters by 20 meters, generating a total of 5000 small regions. This division method facilitates subsequent feature extraction and classification labeling for each unit. For example, units with a large terrain slope can be labeled as steep slope areas, while those with a small slope can be labeled as gentle slope areas.

[0032] For example, for the set of terrain regions after division, the application of the surface interpolation tool is particularly important. Due to limited original data collection in some regions, there may be insufficient data density. Suppose the preset data density threshold is 8 points per square meter, while the density in a steep slope region is only 3 points per square meter. At this time, new data points can be generated through interpolation methods and supplemented to be close to the threshold level. This method can fill in the data blank areas and ensure the continuity of terrain features, especially avoiding information loss in complex terrains.

[0033] For example, when constructing the three-dimensional terrain surface morphology, the triangular network construction tool is the core means. Its basic principle is to connect the point cloud data into a triangular mesh to form a preliminary terrain surface. For regions with obvious terrain changes, such as a cliff area with a height difference of 10 meters, the density of the triangular mesh can be adjusted to increase the number of local meshes to more finely depict the terrain changes. This adaptive adjustment can better reflect the true shape of the terrain.

[0034] For example, when optimizing the preliminary three-dimensional terrain surface morphology through the smoothing tool, the focus is on dealing with discontinuous regions of the surface. Suppose at the junction of two terrain units, due to differences in data density, there are obvious mutations on the surface. The smoothing algorithm can be used to perform transitional processing on the junction area to make the height change more natural. For example, smoothly adjust a mutation height difference of 2 meters to a gradual transition to ensure that the final three-dimensional terrain surface structure is more coherent visually and in terms of data. This processing method helps to enhance the realism of the terrain model and is also convenient for data analysis and planning in subsequent applications.

[0035] For example, in each of the above steps, whether it is grid division, surface interpolation, triangular network construction, or smoothing processing, each step closely focuses on the optimization of terrain data. By reasonably dividing regions, terrain characteristics can be more accurately identified; by interpolating to supplement data, information loss can be avoided; by constructing triangular networks and making adaptive adjustments, the true appearance of the terrain can be restored; and through smoothing processing, the continuity and usability of the model are further enhanced. These steps are closely linked and jointly lay the foundation for constructing a high-quality three-dimensional terrain surface structure.

[0036] In step S13, a hierarchically optimized grid model is obtained according to the second three-dimensional terrain surface model.

[0037] Obtaining a hierarchically optimized grid model according to the second three-dimensional terrain surface model includes: According to the second three-dimensional terrain surface model, use the curvature analysis tool to divide the terrain surface curvature changes into regions, obtain the classified curvature distribution information, and determine the boundaries of high-curvature regions and low-curvature regions; For the classified curvature distribution information, use a grid adjustment tool to densify the grid in high-curvature regions. If the curvature value of one of the regions is detected to be higher than the preset curvature threshold, add grid cells to the region to obtain the densified grid distribution information; For the densified grid distribution information, use a grid optimization tool to sparsify the grid in low-curvature regions. If the curvature value of another region is detected to be lower than the preset curvature threshold, reduce the grid cells of the other region to obtain the sparsely adjusted grid distribution information; For the sparsely adjusted grid distribution information, use a hierarchical integration tool to uniformly process the grid distributions of different regions to obtain the hierarchically optimized grid distribution data.

[0038] For example, when performing curvature analysis based on smoothed three-dimensional terrain surface data, the application of the curvature analysis tool is crucial. Curvature reflects the degree of bending of the terrain surface and is usually used to identify the complexity of terrain features. Suppose a terrain data covers an area of 1 square kilometer. Through the curvature analysis tool, the surface can be divided into multiple regions, and the curvature levels of each region can be classified. For example, the curvature value of a certain ridge region is high, indicating drastic terrain changes, while the curvature value of a certain flat valley bottom is low, indicating relatively gentle terrain. This classification helps to take differential treatments for different regions subsequently.

[0039] For example, for the classified curvature distribution information, the grid adjustment tool plays an important role in densifying the grid in high-curvature regions. Suppose the preset curvature threshold is 0.5, and the curvature value of a certain hillside region reaches 0.8, which is significantly higher than the threshold. At this time, the grid adjustment tool can be used to increase the number of grid cells in this region. For example, adjust the original one grid cell per 100 square meters to one grid cell per 50 square meters. This densification process can more precisely capture the change characteristics of the terrain surface, especially in regions with large terrain undulations, ensuring the accuracy and integrity of the data.

[0040] For example, in the process of sparsifying the grid in low-curvature regions, the application of the grid optimization tool is also indispensable. Suppose the curvature value of a certain plain region is only 0.1, which is much lower than the preset threshold of 0.5. At this time, the number of grid cells in this region can be reduced. For example, adjust the original one grid cell per 50 square meters to one grid cell per 200 square meters. This sparsification process can reduce the burden of data storage and processing while ensuring the basic terrain features remain unchanged, especially suitable for regions with small terrain changes, thus optimizing the overall resource allocation.

[0041] For example, for the sparse-adjusted grid distribution information, the use of a hierarchical integration tool can achieve unified processing of grid distributions in different regions. Suppose there is a large difference in grid density at the boundary between a high-curvature region and a low-curvature region, which may lead to unnatural data connection. At this time, the boundary region can be transitionally adjusted through the hierarchical integration tool, such as gradually adjusting the size of grid cells to smoothly transition from high-density grids to low-density grids. This way of hierarchical optimization can ensure the coherence of the entire terrain surface data and avoid surface discontinuity problems caused by sudden changes in grid density.

[0042] For example, from the overall process, curvature analysis, grid encryption, grid sparsification, and hierarchical integration form a complete processing chain. Curvature analysis provides a basis for subsequent grid adjustment, while grid encryption and sparsification are targeted optimizations for the terrain characteristics of different regions. Finally, hierarchical integration ensures the unity and consistency of the data. Such a processing method not only improves the fineness of terrain data but also achieves a reasonable balance in resource allocation. Especially in complex terrain environments, it can better adapt to the diverse terrain feature requirements and lay a solid foundation for subsequent applications.

[0043] In step S14, a first reservoir capacity estimation value is obtained according to the grid model.

[0044] Obtaining a first reservoir capacity estimation value according to the grid model includes: Using a data extraction tool for the hierarchically optimized grid distribution data to obtain corresponding geometric parameter information from each grid cell, and performing preliminary processing on each grid cell through the geometric parameter information to obtain initial volume distribution data; Using an integral calculation tool for the initial volume distribution data, randomly sampling each grid cell, and performing multiple independent estimations on each grid cell during the sampling to obtain the volume distribution information after sampling; Using a data integration tool for the volume distribution information after sampling, 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 region where the accumulated value threshold is exceeded to determine the adjusted accumulated volume data; Using a deviation analysis tool for the adjusted accumulated volume data to correct the overall capacity estimation value and perform local adjustment on abnormal regions to obtain the first reservoir capacity estimation value.

[0045] For example, when dealing with grid-distributed data of a hierarchical structure, the application of data extraction tools is crucial. Data extraction tools are mainly used to obtain geometric parameter information from each grid cell, such as basic data like the height and slope of the terrain surface. Suppose in a terrain area with a coverage area of 2 square kilometers, the grid cells are divided into one cell per 100 square meters. Through the data extraction tool, the height value range of each cell can be quickly obtained. For example, the height value in 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.

[0046] For example, for the initial volume distribution data, the integral calculation tool plays an important role in random sampling processing. The purpose of random sampling is to reduce the error that may be brought by a single calculation through multiple independent estimations. Suppose 10 independent samples are taken for a certain grid cell, and its volume data is estimated each time. A range value may be obtained, such as between 500 cubic meters and 550 cubic meters. By taking the average value or weighted processing, volume distribution information closer to the real situation can be obtained. This method can effectively improve the reliability of the estimation, especially in areas with complex terrain, avoiding the overall error caused by the deviation of a single sample.

[0047] For example, in the processing of the volume distribution information after sampling, the data integration tool is used to accumulate the volume estimation values of all grid cells. If the accumulated value exceeds the preset threshold, for example, the set threshold is 10,000 cubic meters and the actual accumulated value reaches 12,000 cubic meters, then the relevant area needs to be resampled. Suppose the volume estimation value of a certain hillside area is on the high side. By resampling, its data range can be adjusted, such as from the original 800 cubic meters to 750 cubic meters, and finally the overall accumulated value is close to the threshold. This adjustment mechanism can ensure the rationality of the data and prevent the overall result from being affected by local anomalies.

[0048] For example, for the adjusted accumulated volume data, the deviation analysis tool is crucial when correcting the overall capacity estimation value. The deviation analysis tool is mainly used to identify abnormal areas and make local adjustments. Suppose in a certain flat area, the volume estimation value is significantly low, only 200 cubic meters, while the average value of the surrounding area is 400 cubic meters. Through the deviation analysis tool, this area can be corrected, such as adjusting the data to 350 cubic meters to conform to the overall trend. This correction process can ensure the balance of the final capacity estimation data, especially in a terrain environment with uneven data distribution, which helps to improve the credibility of the overall data.

[0049] For example, from the perspective of the overall process, data extraction, random sampling, data integration, and deviation correction form a complete processing chain. Each link closely focuses on the processing requirements of terrain data. For example, from the initial extraction of geometric parameters to the final correction of capacity estimation, each step ensures the improvement of data quality. This hierarchical processing method can better adapt to diverse data characteristics in complex terrain environments, lay a solid foundation for subsequent terrain analysis and applications, and effectively avoid potential problems caused by data deviation.

[0050] In step S15, numerical filling is performed on the estimated value of the capacity of the first reservoir to obtain the corrected estimated value of the capacity of the second reservoir.

[0051] Performing numerical filling on the estimated value of the capacity of the first reservoir to obtain the corrected estimated value of the capacity of the second reservoir includes: Obtain the original terrain distribution record of the target area from the repository, and use a data scanning tool to check the integrity of each sub-area of the original terrain distribution record to determine whether there are data missing or abnormal points, and obtain a preliminary integrity assessment result; If it is found in the integrity assessment result that the data missing area exceeds the preset missing threshold, the missing area is preliminarily marked by a data repair tool, and interpolation calculation tools are used to generate corresponding filling data to obtain the repaired terrain distribution record; Use a data integration tool for the repaired terrain distribution record to fuse the filling data with the original terrain distribution record, and perform consistency verification on the fused data to obtain the corrected terrain distribution set; Use a volume estimation tool to perform local adjustment on the corrected terrain distribution set to obtain the estimated value of the capacity of the second reservoir.

[0052] For example, when processing terrain data, obtaining the original terrain distribution record of the target area from the repository is the primary link. This process involves the invocation of historical data, usually including information such as terrain height and boundary range. Suppose the target area is the terrain around a reservoir with a coverage area of 5 square kilometers. The repository records the terrain height data of this area, but there may be blanks in some sub-areas due to historical collection problems. The use of a data scanning tool is particularly important. It can check the data integrity of each sub-area one by one. For example, if it is found that the missing ratio of the height data in a 0.5-square-kilometer sub-area reaches 30%, exceeding the preset threshold of 10%, further processing is required.

[0053] For example, regarding the data missing problem, the data repair tool will initially mark the missing areas and generate filled data in combination with the interpolation calculation tool. Suppose in the above-mentioned sub-region of 0.5 square kilometers, the missing data is mainly concentrated in the edge areas. The interpolation calculation tool will infer reasonable filling values based on the height values of the surrounding areas, such as 50 meters to 60 meters, and set the height of the missing points to, for example, 55 meters. This method can quickly complete the data filling and lay a foundation for subsequent analysis.

[0054] For example, in the data integration stage, the data integration tool will fuse the filled data with the original records to ensure the consistency of the overall data. Suppose the height of a certain sub-region in the original record is 58 meters, while the filled data is 55 meters. After fusion, the average value may be taken or adjusted to 56 meters according to the terrain trend. Subsequently, the consistency check will examine the rationality of the fused data, such as ensuring that the height difference between adjacent sub-regions does not exceed a preset range, such as 5 meters, to avoid the impact of mutation points on the accuracy of the overall terrain distribution set.

[0055] For example, for the calibrated terrain distribution set, the volume estimation tool is used to recalculate the capacity of the target reservoir area. Suppose the total area of the reservoir area is 3 square kilometers, and the terrain distribution set shows that the depth ranges from 10 meters to 30 meters. The estimation tool will calculate the capacity based on these characteristic data. At the same time, if it is found that the depth of a certain sub-region is abnormally low, such as only 5 meters, while the surrounding areas are all around 20 meters, then local adjustment will be made to this region, such as correcting the depth to 18 meters to be closer to the overall trend. This adjustment can effectively improve the reliability of the capacity calculation data.

[0056] For example, throughout the process, the implementation of each technical theme is closely centered around terrain data processing. From data scanning to repair, then to integration and calibration, each step aims to ensure the integrity and consistency of the data. Suppose in the final capacity calculation, the adjusted data increases the reservoir capacity estimation value from the initial 8000 cubic meters to 8500 cubic meters, being closer to the actual terrain characteristics. This meticulous processing method can not only optimize the data quality but also provide a more reliable basis for reservoir planning, fully reflecting the adaptability of hierarchical processing in a complex terrain environment.

[0057] In step S16, the second reservoir capacity estimation value is calculated in blocks to obtain the third reservoir capacity estimation value.

[0058] Calculating the second reservoir capacity estimation value in blocks to obtain the third reservoir capacity estimation value includes: Obtaining the calibrated data records of the target area from the repository, and using a data segmentation tool to perform grid block processing on the calibrated data records to obtain the segmented data set; For the segmented data set, use a task allocation tool to dynamically adjust each sub-region unit. Combine the preset resource limit conditions, and use a load balancing tool to allocate computing tasks to determine the task list after allocation; For the task list after allocation, use a parallel processing tool 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 the preset speed threshold, use a dynamic adjustment tool to re-allocate tasks to obtain the adjusted computing progress; For the adjusted computing progress, use a data integration tool to fuse the calculation results of each sub-region unit, and perform consistency verification on the fused data to obtain the estimated value of the third reservoir capacity.

[0059] For example, when processing the reservoir capacity calculation requirement, obtaining the corrected data record of the target area from the repository is the primary step. For the processing of the data record, a data segmentation tool will divide the entire area into multiple grid units for subsequent calculation. For instance, assume the total area of the target reservoir area is 6 square kilometers, and the segmentation tool may divide it into 36 sub-grids of 1 square kilometer each, forming a structured data set. This division method facilitates decomposing large-scale data into manageable units and lays the foundation for subsequent task allocation.

[0060] For example, for the segmented data set, the task allocation tool will make dynamic adjustments according to the data volume and complexity of each sub-region unit. Suppose some sub-regions have large terrain undulations and high data point densities. The task allocation tool will give priority to resource limit conditions, such as the processing capabilities of computing nodes, and reasonably allocate tasks to different nodes. The load balancing tool will ensure that the task volume of each node is close to avoid overloading a certain node. For example, allocate the tasks of 36 sub-grids to 4 computing nodes, with each node processing 9 sub-grids to ensure the full utilization of computing resources.

[0061] For example, after the task list is determined, the parallel processing tool will synchronously calculate the data of the sub-region units. Suppose during the calculation process, the processing speed of a certain sub-region is lower than the preset threshold due to overly dense data points, such as less than 50% of the expected value of the data volume processed per minute. The dynamic adjustment tool will intervene in a timely manner and re-allocate some tasks of this sub-region to other idle nodes. In this way, the computing progress is optimized to prevent the overall process from being blocked due to the delay of a certain link.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] Determine whether the estimated capacity of the third reservoir meets the requirements, including: 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; 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.

[0066] For example, in the business scenario of capacity verification, obtaining the final optimized data record of the target area from the repository is the primary step. This process involves extracting historical calculation results, aiming to provide a reliable data basis for subsequent verification. Suppose the target area is a reservoir, and the repository stores the capacity data records obtained through parallel processing and dynamic adjustment before. These data are stored in a grid form, and each grid cell contains specific topographic and capacity information.

[0067] For example, multiple rounds of cross-verification processing for data records can be achieved through a data comparison tool. Conceptually, cross-verification means comparing the data of the same area from different sources or different time points to discover potential deviations. Suppose in the first round of verification, the capacity data of a certain grid cell is 2500 cubic meters, while in the second round it is 2600 cubic meters, and the deviation value is 100 cubic meters. The set of deviation values of all grid cells will be summarized, and it will be analyzed whether its distribution range is within the preset threshold, such as the allowable deviation is 50 cubic meters. If it exceeds, further analysis is required.

[0068] For example, in the case where the deviation value exceeds the threshold, a deviation analysis tool will evaluate the data consistency. This tool will identify the inconsistent areas and generate a list of divisions. Suppose in the northwest corner area of the reservoir, the topographic data has a large deviation due to different collection times. The analysis tool will mark this area as an inconsistent area, and the list will list the specific grid cell numbers and details of the deviation values for subsequent processing.

[0069] For example, for the list of divisions of inconsistent areas, a grid adjustment tool will dynamically update the grid density. In principle, grid density adjustment means increasing the number of grid cells in the area with large data deviations to improve data accuracy. Suppose the original grid is one unit per square kilometer, and after adjustment, it is refined to one unit per 0.5 square kilometer within the inconsistent area, so as to obtain more detailed discretized data records. This way can better capture the topographic changes.

[0070] For example, through the adjusted discretized data record, the new calculation output range will be determined. This process involves re-analyzing the data of the updated grid. Suppose the capacity data in the northwest corner area changes from the original 2500 cubic meters to 2550 cubic meters after adjustment, and the overall output range is closer to the actual topographic features, laying a foundation for subsequent integration.

[0071] For example, in the data integration stage, a data integration tool will perform consistency verification on the adjusted data. Suppose during the integration process, it is found that the data of some grid cells still have large differences from other areas. The tool will perform smoothing processing through the average value of the surrounding data, and finally obtain the capacity verification result. Suppose the final result shows that the reservoir capacity is 9300 cubic meters, which is more reasonable compared with the historical data.

[0072] For example, when judging whether the reliability of the judgment result meets the standard, it can be evaluated through a preset error range. Suppose the standard requires the error to be within 2%, and the error of this verification result is 1.5%, then it meets the requirements. This method of multi-round verification and dynamic adjustment can effectively improve the data credibility and provide a solid basis for reservoir planning.

[0073] In step S18, a first three-dimensional visualization model is generated according to the data of the reliable output capacity, the scale of the rendering data is compressed, and a second three-dimensional visualization model is obtained.

[0074] Generating a first three-dimensional visualization model according to the data of the reliable output capacity, compressing the scale of the rendering data, and obtaining a second three-dimensional visualization model includes: Obtain the topographic feature records of the target area from the repository, and use volume data technology to perform preliminary stratification processing on the topographic features to obtain a set of stratified topographic data; According to the set of stratified topographic data, use a data compression tool to reduce the scale of the rendering data; If it is found that the data scale exceeds the preset scale threshold during reduction, the rendering data is segmented by a block processing tool to determine the range of the compressed rendering data; According to the range of the compressed rendering data, use a three-dimensional construction tool to construct a visualization model for the topographic features to obtain a preliminary three-dimensional visualization result that meets the analysis requirements; According to the preliminary three-dimensional visualization result, use a storage optimization tool to adjust the data format. If the storage requirement does not meet the preset storage standard, secondary optimization is performed through a format conversion tool to obtain the final three-dimensional visualization result.

[0075] For example, when processing the topographic feature records of a target reservoir, relevant data needs to be extracted from a pre-established repository first. This data usually includes the topographic elevation, slope distribution, and water depth information of the reservoir, etc. As a centralized data management platform, the repository can ensure the integrity and traceability of the data. Suppose the topographic data of the target reservoir contains 10,000 data points, and each data point records specific coordinates and elevation values, which lay the foundation for subsequent processing.

[0076] For example, for the preliminary stratification processing of topographic features, volume data technology is used to classify complex topographic data according to elevation intervals. Conceptually, volume data technology decomposes topographic features into multiple levels through the division of three-dimensional space for subsequent analysis. Suppose the topographic data of the reservoir is divided into 5 elevation layers, and each layer represents a different depth range, such as 0 - 5 meters, 5 - 10 meters, etc. This stratification method helps to understand the topographic changes more clearly.

[0077] For example, when reducing the scale of the stratified terrain data set, the data compression tool plays an important role. In principle, the compression tool removes redundant data points and retains key features. Suppose the original data size is 500 MB and the preset threshold is 200 MB. If the threshold is exceeded, the data is divided into multiple small blocks by a chunking tool, such as 50 MB per block, for compression block by block. Finally, the compressed data range is determined to be 180 MB.

[0078] For example, using the compressed rendering data range, the 3D construction tool can build a visualization model of the reservoir terrain features. Suppose during the construction process, the tool converts the terrain data into a 3D model containing elevation and texture. The preliminary results show that the distribution of the deep and shallow areas of the reservoir is clearly visible. This visualization method facilitates analysts to intuitively understand the terrain features.

[0079] For example, in the storage optimization phase, the storage optimization tool adjusts the data format of the preliminary 3D visualization results. Suppose the initial format occupies 300 MB of space, while the preset standard is 150 MB. If the requirement is not met, secondary optimization is performed through a format conversion tool, such as converting the data to a more efficient storage format, and the final occupied space is reduced to 140 MB. This optimization ensures the efficient storage and fast access of data.

[0080] For example, from the overall process, each of the above links is closely connected. From data extraction to hierarchical processing, and then to compression, construction, and optimization, a complete technical chain is formed. Suppose in practical applications, the 3D visualization results of the target reservoir are used for flood control measures planning. The clear terrain distribution information provides important reference for decision-making. This multi-step collaborative processing method not only improves the efficiency of data processing but also enhances the practicality of the analysis results, providing strong support for reservoir management.

[0081] In step S19, the terrain data and the capacity calculation results are dynamically updated according to the second 3D visualization model. If the detected update frequency of the terrain data is higher than the preset update threshold, the adaptive grid division algorithm is triggered to recalculate, and the real-time updated reservoir capacity data is obtained.

[0082] Dynamically updating the terrain data and the capacity calculation results according to the second 3D visualization model. If the detected update frequency of the terrain data is higher than the preset update threshold, the adaptive grid division algorithm is triggered to recalculate, and the real-time updated reservoir capacity data is obtained, including: Obtaining the latest terrain record of the target area from the repository according to the correlation between the terrain data and the terrain changes; Performing change detection on the latest terrain record using a data comparison tool. If the detected terrain changes exceed the preset change threshold, the subsequent processing flow is triggered to obtain the changed terrain data set; According to the changed terrain data set, in combination with the association of dynamic updates and real-time data, a data synchronization tool is used to integrate the changed data with the existing records; If the update frequency of the integrated data is higher than the preset update threshold, it is marked by a frequency monitoring tool to determine the area range that needs to be recalculated; An adaptive grid division tool is used for the marked area range to re-segment the area range, obtain the grid cell data after segmentation, and determine the grid distribution suitable for capacity calculation; According to the grid distribution, in combination 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 the finally updated reservoir capacity record.

[0083] For example, when processing the terrain data of the target reservoir, it is first necessary to obtain the latest terrain record from a pre-established repository. As a centralized management platform, the repository contains the historical terrain information of the reservoir, such as key data like elevation and slope. Suppose the repository stores the terrain records of the target reservoir for the past year, and each update contains approximately 5,000 data points, covering the entire area of the reservoir. This data acquisition method ensures the accuracy and integrity of subsequent analysis.

[0084] For example, for the change detection of the latest terrain record, a data comparison tool will compare the current data with the historical record point by point. Suppose the preset change threshold is that the elevation change exceeds 0.5 meters. If the elevation change in a certain area is detected to reach 0.8 meters, it will trigger the subsequent processing flow. This comparison method can quickly locate the key areas of terrain changes and provide a basis for subsequent data updates.

[0085] For example, in the data integration link, a data synchronization tool will fuse the changed terrain data with the existing records. Suppose the update frequency of the integrated data is once a day, while the preset threshold is once a week, then the data is marked by a frequency monitoring tool to determine the area range that needs to be recalculated. This marking mechanism helps to promptly discover the data update requirements and avoid resource waste.

[0086] For example, for the adaptive grid division of the marked area, the tool will re-segment the area according to the terrain complexity. Suppose the area of a certain marked area is 10 square kilometers, and the tool divides it into 100 grid cells, with each cell having an area of 0.1 square kilometers. This division method can better adapt to the terrain characteristics and ensure the accuracy of subsequent calculations.

[0087] For example, in the capacity calculation process, the volume estimation tool performs numerical estimation based on grid cell data. Assuming that the elevation and area data of each grid cell are known, the tool obtains the latest capacity record of the reservoir by accumulating the volumes of each cell one by one. This method can reflect the impact of terrain changes on the capacity and provide important reference for reservoir management.

[0088] For example, from the overall process perspective, the above-mentioned process forms a closed-loop processing chain from data acquisition to change detection, integration, division, and calculation. Assuming that the capacity record of the target reservoir is used for water resource scheduling, the updated data can help managers formulate more accurate allocation plans. This collaborative method of multiple links significantly improves the pertinence and practicality of data processing.

[0089] It should be noted that the combination of terrain change detection and data integration can effectively address the challenges brought by the dynamic changes of the reservoir terrain. Assuming that due to long-term rainfall, the elevation of some areas in a reservoir has decreased, the relevant records can be quickly updated through the above process to provide support for flood prevention early warning. This method has obvious advantages in ensuring the timeliness of data.

[0090] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structural or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be included in the patent protection scope of the present invention by the same token.

[0091] In summary, the present invention discloses a high-precision calculation method for the reservoir storage curve based on a three-dimensional model. By adaptively sampling and optimizing the original terrain data, constructing a three-dimensional terrain surface model, using adaptive discretized grid division, and performing volume analysis error correction, high-precision reservoir capacity estimation is achieved. The present invention introduces a data integrity detection and repair mechanism, combines parallel processing technology to improve the calculation 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, realizes hierarchical 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 calculation, and provides reliable data support and intuitive visualization tools for water conservancy project planning and management.

[0092] Referring to Figure 2 , the second embodiment of the present invention provides a high-precision calculation system for the reservoir storage curve based on a three-dimensional model, including: The first acquisition module 201 is used to preprocess the collected original terrain data to obtain a terrain point cloud data set; The second acquisition module 202 is used to construct a first three-dimensional terrain surface model according to the terrain point cloud data set and obtain a second three-dimensional terrain surface model; The third acquisition module 203 is configured to obtain a hierarchically optimized grid model according to the second three-dimensional terrain surface model; The fourth acquisition module 204 is configured to obtain a first reservoir capacity estimation value according to the grid model; The fifth acquisition module 205 is configured to perform numerical filling on the first reservoir capacity estimation value to obtain a corrected second reservoir capacity estimation value; The 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; The judgment module 207 is configured to judge 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 execute the step of obtaining a hierarchically optimized grid model according to the second three-dimensional terrain surface model; The seventh acquisition module 208 is configured to generate a first three-dimensional visualization model according to the reliable output capacity calculation data, compress the rendering data scale, and obtain a second three-dimensional visualization model; The update module 209 is configured to dynamically update the terrain data and the capacity calculation result according to the second three-dimensional visualization model. If the detected terrain data update frequency is higher than a preset update threshold, it triggers the adaptive grid division algorithm to recalculate and obtain real-time updated reservoir capacity data.

[0093] It should be noted that a high-precision calculation system for reservoir storage capacity curve based on a three-dimensional model provided by an embodiment of the present invention is used to execute all process steps of a high-precision calculation method for reservoir storage capacity curve based on a three-dimensional model in the above embodiment. The working principles and beneficial effects of the two correspond one by one, and thus will not be elaborated here.

[0094] 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 reservoir storage capacity curve based on a three-dimensional model. When the processor executes the computer program, it implements the steps in each of the above embodiments of the high-precision calculation method for reservoir storage capacity curve based on a three-dimensional model, such as Figure 1 The step S11 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in each of the above device embodiments, such as the first acquisition module.

[0095] Exemplarily, the computer program can be divided into one or more modules / units. One or more modules / units are stored in the memory and executed by the processor to complete the present invention. One or more modules / units can 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 the electronic device.

[0096] An electronic device can be a computing device such as a desktop computer, a notebook, a handheld computer, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above components are only examples of the electronic device and do not constitute a limitation on the electronic device. It may include more or fewer components than the above, or combine some components, or different components. For example, the electronic device may also include input / output devices, network access devices, a bus, etc.

[0097] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the electronic device and connects various parts of the entire electronic device through various interfaces and circuits.

[0098] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area may store data created according to the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory may include high-speed random access memory and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0099] Among them, if the modules / units integrated in the electronic device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice within the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0100] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that they have a communication connection, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0101] The above specific embodiments have further elaborated on the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. It is particularly pointed out that for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A high-precision calculation method for reservoir storage capacity curve based on 3D model, characterized in that, Including: Preprocess the collected original terrain data to obtain a terrain point cloud dataset; Construct a first three-dimensional terrain surface model based on the terrain point cloud dataset and obtain a second three-dimensional terrain surface model; Obtain a hierarchically optimized grid model based on the second three-dimensional terrain surface model; Obtain a first reservoir capacity estimation value based on the grid model; Perform numerical filling on the first reservoir capacity estimation value to obtain a corrected second reservoir capacity estimation value; Perform block calculation on the second reservoir capacity estimation value to obtain a third reservoir capacity estimation value; Judge whether the third reservoir capacity estimation value meets the conditions. If so, judge that the third reservoir capacity estimation value is reliable output capacity calculation data. If not, return to execute the step of obtaining a hierarchically optimized grid model based on the second three-dimensional terrain surface model; Generate a first three-dimensional visualization model based on the reliable output capacity calculation data, compress the rendering data scale, and obtain a second three-dimensional visualization model; Dynamically update 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 the preset update threshold, trigger the adaptive grid division algorithm to recalculate and obtain real-time updated reservoir capacity data.

2. The high-precision calculation method for reservoir storage capacity curve based on a three-dimensional model according to claim 1, characterized in that The preprocessing of the collected original terrain data to obtain a terrain point cloud dataset includes: Obtain initial point cloud information from the original terrain data, perform denoising and format unification processing on the initial point cloud information using a preprocessing method, and perform regional division on the processed point cloud information through a grid division tool to obtain a set of divided terrain units; Extract features from each unit of the terrain unit set and judge whether the slope change of each unit exceeds a preset slope threshold; If it exceeds the slope threshold, mark it as a complex terrain area. If it does not exceed the slope threshold, mark it as a flat area to obtain a classified terrain area label; Increase the sampling point distribution in the complex terrain area, adjust the density of the complex terrain area through a point cloud interpolation tool, reduce the number of sampling points in the flat area, and process the flat area using a point cloud sparsification tool to obtain an adjusted point cloud density distribution; Perform data fusion on the point cloud density distribution through a point cloud integration tool to obtain an optimized terrain point cloud dataset, and verify the accuracy of the optimized terrain point cloud dataset using a data verification tool to determine a preliminary optimized terrain point cloud dataset.

3. The high-precision calculation method for reservoir storage capacity curve based on 3D model according to claim 1, wherein The construction of a first three-dimensional terrain surface model based on the terrain point cloud dataset and obtaining a second three-dimensional terrain surface model includes: Perform regional segmentation on the preliminary optimized terrain point cloud dataset using a grid division tool, classify and label the terrain features of different regions to obtain a set of divided terrain regions, and supplement local data for the set of divided terrain regions through a surface interpolation tool; If the data density of the region is lower than the preset density threshold, perform interpolation processing on the region to obtain a supplemented terrain data distribution, construct a surface using a triangular network construction tool for the supplemented terrain data distribution, and perform adaptive adjustment on the region with obvious terrain changes to obtain a first three-dimensional terrain surface model; Optimize the first three-dimensional terrain surface model through a smoothing tool to obtain a second three-dimensional terrain surface model.

4. The high-precision calculation method for reservoir storage capacity curve based on a 3D model according to claim 1, wherein The method for obtaining a hierarchically optimized grid model based on the second three-dimensional terrain surface model includes: According to the second three-dimensional terrain surface model, use a curvature analysis tool to divide the curvature change of the terrain surface into regions, obtain the classified curvature distribution information, and determine the boundary between the high-curvature region and the low-curvature region; Use a grid adjustment tool for the classified curvature distribution information to encrypt the grid in the high-curvature region. If it is detected that the curvature value of one of the regions is higher than the preset curvature threshold, add grid cells to this region to obtain the encrypted grid distribution information; Use a grid optimization tool for the encrypted grid distribution information to sparsify the grid in the low-curvature region. If it is detected that the curvature value of another region is lower than the preset curvature threshold, reduce the grid cells in the other region to obtain the sparsely adjusted grid distribution information; Use a hierarchical integration tool for the sparsely adjusted grid distribution information to uniformly process the grid distributions in different regions to obtain hierarchically optimized grid distribution data; The method for obtaining the first reservoir capacity estimation value based on the grid model includes: Use a data extraction tool for the hierarchically optimized grid distribution data to obtain the corresponding geometric parameter information from each grid cell, and perform preliminary processing on each grid cell through the geometric parameter information to obtain the initial volume distribution data; Use an integral calculation tool for the initial volume distribution data to randomly sample each grid cell, and perform multiple independent estimations on each grid cell during the sampling to obtain the sampled volume distribution information; Use a data integration tool for the sampled volume distribution information to accumulate the volume estimation values of all grid cells. If the accumulated value exceeds the preset accumulated value threshold, resample the grid cells in the region where the accumulated value threshold is exceeded to determine the adjusted accumulated volume data; Use a deviation analysis tool for the adjusted accumulated volume data to correct the overall capacity estimation value and perform local adjustment on the abnormal region to obtain the first reservoir capacity estimation value.

5. The high-precision calculation method for reservoir storage capacity curve based on 3D model according to claim 1, characterized in that The method for numerically filling the first reservoir capacity estimation value to obtain the corrected second reservoir capacity estimation value includes: Obtain the original terrain distribution record of the target region from the repository, and use a data scanning tool to check the integrity of each sub-region of the original terrain distribution record to determine whether there are data missing or abnormal points, and obtain a preliminary integrity assessment result; If it is found in the preliminary integrity assessment result that the data missing region exceeds the preset missing threshold, preliminarily mark the data missing region through a data repair tool, and use an interpolation calculation tool to generate the corresponding filling data to obtain the repaired terrain distribution record; Use a data integration tool for the repaired terrain distribution record to fuse the filling data with the original terrain distribution record, and perform consistency verification on the fused data to obtain the corrected terrain distribution set; Use a volume estimation tool to perform local adjustment on the corrected terrain distribution set to obtain the second reservoir capacity estimation value; Performing block calculation on the estimated value of the second reservoir capacity to obtain the estimated value of the third reservoir capacity, including: Obtaining the corrected data record of the target area from the repository, and performing grid block processing on the corrected data record using a data segmentation tool to obtain the segmented data set; For the segmented data set, through a task allocation tool, dynamically adjusting each sub-region unit, combining the preset resource limit conditions, and using a load balancing tool to allocate the calculation tasks to determine the task list after allocation; Using a parallel processing tool for the task list after allocation 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 the preset speed threshold, re-allocate the tasks through a dynamic adjustment tool to obtain the adjusted calculation progress; Using a data integration tool for the adjusted calculation progress to fuse the calculation results of each sub-region unit, and performing consistency verification on the fused data to obtain the estimated value of the third reservoir capacity.

6. The high-precision calculation method for reservoir storage capacity curve based on a 3D model according to claim 1, characterized in that Judging whether the estimated value of the third reservoir capacity meets the conditions, including: Obtaining the final optimized data record of the target area, and performing multi-round cross-verification processing on the optimized data record using a data comparison tool to obtain the deviation value data of each round of verification, and determining the distribution range of the deviation value set; If at least one value in the deviation value set exceeds the preset deviation threshold, perform a detailed evaluation of the data consistency through a deviation analysis tool to obtain a division list; Judging whether it is necessary to adjust the grid density; According to the division list, using a grid adjustment tool to dynamically update the grid density of the target area to obtain the adjusted discretized data record, and determining the new calculation output range; According to the new calculation output range, using a data integration tool to perform consistency verification on the adjusted discretized data record to obtain the final capacity verification result, and judging whether the reliability of the capacity verification result meets the conditions; Generating the first three-dimensional visualization model according to the reliable output capacity calculation data, compressing the rendering data scale to obtain the second three-dimensional visualization model, including: Obtaining the terrain feature record of the target area from the repository, and performing preliminary stratification processing on the terrain features using volume data technology to obtain the stratified terrain data set; According to the stratified terrain data set, using a data compression tool to reduce the scale of the rendering data; If it is found that the scale of the rendering data exceeds the preset scale threshold during the reduction, segment the rendering data through a block processing tool to determine the range of the compressed rendering data; According to the range of the compressed rendering data, using a three-dimensional construction tool to construct a visualization model of the terrain features to obtain the preliminary result of the three-dimensional visualization that meets the analysis requirements; According to the preliminary result of the three-dimensional visualization, using a storage optimization tool to adjust the data format. If the storage requirement does not meet the preset storage standard, perform secondary optimization through a format conversion tool to obtain the final three-dimensional visualization result.

7. The high-precision calculation method for reservoir storage capacity curve based on a 3D model according to claim 1, characterized in that, Dynamically update 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 the preset update threshold, trigger the adaptive grid division algorithm to recalculate and obtain the real-time updated reservoir capacity data, including: Obtain the latest terrain record of the target area from the repository according to the association between the terrain data and the terrain change; Use a data comparison tool to perform change detection on the latest terrain record. If the detected terrain change exceeds the preset change threshold, trigger the subsequent processing flow to obtain the terrain data set after the change; According to the terrain data set after the change, combined with the association between dynamic update and real-time data, use a data synchronization tool to integrate the changed data with the existing records; If the update frequency of the integrated data is higher than the preset update threshold, mark it through a frequency monitoring tool to determine the area range that needs to be recalculated; Use an adaptive grid division tool for the marked area range to re-segment the area range, obtain the grid cell data after segmentation, and determine the grid distribution suitable for capacity calculation; According to the grid distribution, combined with the association between capacity calculation and reservoir capacity, use a volume estimation tool to perform capacity numerical calculation on the grid cell data after segmentation to obtain the finally updated reservoir capacity record.

8. A high-precision calculation system for reservoir storage capacity curves based on 3D models, characterized in that, Including: The first acquisition module is used to preprocess the collected original terrain data to obtain a terrain point cloud data set; The second acquisition module is used to construct a first three-dimensional terrain surface model according to the terrain point cloud data set and obtain a second three-dimensional terrain surface model; The third acquisition module is used to obtain a hierarchically optimized grid model according to the second three-dimensional terrain surface model; The fourth acquisition module is used to obtain a first reservoir capacity estimation value according to the grid model; The fifth acquisition module is used to perform numerical filling on the first reservoir capacity estimation value to obtain a corrected second reservoir capacity estimation value; The sixth acquisition module is used to perform block calculation on the second reservoir capacity estimation value to obtain a third reservoir capacity estimation value; The judgment module is used to judge whether the third reservoir capacity estimation value meets the conditions. If so, judge that the third reservoir capacity estimation value is the reliable output capacity calculation data. If not, return to execute the step of obtaining the hierarchically optimized grid model according to the second three-dimensional terrain surface model; The seventh acquisition module is used to generate a first three-dimensional visualization model according to the reliable output capacity calculation data, compress the rendering data scale, and obtain a second three-dimensional visualization model; The update module is used to dynamically update 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 the preset update threshold, trigger the adaptive grid division algorithm to recalculate and obtain the real-time updated reservoir capacity data.

9. An electronic device, characterized in that, Including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the high-precision calculation method for the reservoir storage capacity curve based on a three-dimensional model as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the high-precision calculation method for the reservoir storage curve based on a three-dimensional model according to any one of claims 1 to 7.

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