Reservoir capacity calculation method, device, equipment and medium

Abstract

The application relates to the technical field of reservoir management, and discloses a reservoir storage capacity calculation method, device, equipment and medium. The method comprises the following steps: obtaining digital elevation model data of a target reservoir, three-dimensional point cloud data of a region above a current water surface of the target reservoir, and spatial information below the current water surface; target three-dimensional elevation structure data in a target reservoir region range is calculated; first hydrological water quality parameter information of a section upstream of an inlet of the target reservoir and second hydrological water quality parameter information of an outlet are monitored; target reservoir current calibrated reservoir storage capacity information is calculated according to the target three-dimensional elevation structure data and water level height information of the outlet; a target reservoir bottom newly added silt volume quantity value is calculated according to the first and second hydrological water quality parameter information; and actual reservoir storage capacity information is calculated according to the reservoir storage capacity information and the target reservoir bottom newly added silt volume quantity value. The technical scheme significantly improves the scientificity and safety of reservoir operation management.

Description

Technical Field

[0001] This application relates to the field of reservoir management technology, and in particular to a method, apparatus, equipment and medium for calculating reservoir capacity. Background Technology

[0002] Reservoir capacity, a key indicator for measuring a reservoir's water storage and flood control capabilities, encompasses various types including total capacity, beneficial capacity, and flood control capacity, and is of great significance to the scientific management of reservoirs. While essentially a volume, its precise measurement is difficult due to the complex topography and dynamic water level changes of reservoirs, especially after flood storage and discharge, when capacity changes become more pronounced. Currently, reservoir capacity calculations are mostly based on the formula V = A × H, where V represents the capacity, A is the reservoir's surface area, and H is the water depth. For irregularly shaped reservoirs, surveying methods are often used to subdivide them into numerous smaller areas at specific intervals, summing these areas to obtain the total reservoir area; simultaneously, the reservoir is divided into multiple smaller cubic volumes along the depth direction, summing these to obtain the total reservoir volume. Finally, the total capacity is determined by multiplying the total area by the total volume. Common calculation methods include the cross-sectional method, contour line volume method, grid method, and triangular grid method. The cross-sectional method, as a conventional approach, is widely used in the calculation of typical channel-type rivers. Its principle is to divide the water body into n trapezoids along the flow direction, and then calculate the overall reservoir capacity by integrating the volumes of these n trapezoids. However, this method has limitations. The contour line volume method offers higher accuracy by dividing the water body into n layers of trapezoids based on different elevation surfaces, and then calculating the reservoir capacity by integrating the volumes of these n trapezoids. The grid method relies on an existing digital elevation model of the reservoir area, subdividing the water body into several small cubes, and then calculating the reservoir capacity by spatially integrating the volume of each small cube. The triangular grid method utilizes an existing digital elevation model of the reservoir area, dividing the water body into n triangular prisms according to the actual reservoir bottom shape, and then calculating the reservoir capacity by summing the volumes of these triangular prisms. However, existing technologies have significant drawbacks. First, they rely on a single data source, failing to fully cover the entire reservoir's topography, both above and below water, and lacking detailed characterization. Second, the selection of interpolation methods depends on human experience, making it difficult to adapt to the dynamic changes in complex terrain. Third, they ignore the encroachment of silt accumulation on reservoir capacity. Fourth, it is difficult to balance computational accuracy and efficiency, failing to provide real-time and accurate data support for reservoir scheduling. These problems lead to inaccurate assessments of reservoir water storage and flood control capabilities, seriously affecting the scientific scheduling and safe operation of reservoirs. Summary of the Invention

[0003] This application provides a method, apparatus, equipment, and medium for calculating reservoir capacity, aiming to solve the problem in related technologies where it is difficult to obtain accurate reservoir capacity data, leading to inaccurate assessments of reservoir water storage and flood control capabilities, which seriously affects the scientific scheduling and safe operation of reservoirs.

[0004] In a first aspect, embodiments of this application provide a method for calculating reservoir capacity, the method comprising: Obtain digital elevation model data of the target reservoir, including three-dimensional point cloud data of the area above the current water surface and spatial information below the current water surface; Based on the digital elevation model data, the three-dimensional point cloud data, and spatial information, the target three-dimensional elevation structure data within the target reservoir area is calculated. The first hydrological and water quality parameter information of the upstream section of the inlet of the target reservoir and the second hydrological and water quality parameter information of the outlet are monitored, wherein the hydrological and water quality parameter information includes turbidity, flow velocity, flow rate and water level information; Based on the target's three-dimensional elevation structure data and the water level information at the outlet, calculate the target reservoir's current calibrated reservoir capacity. The volume of newly added silt at the bottom of the target reservoir is calculated based on the first hydrological and water quality parameter information and the second hydrological and water quality parameter information. The actual reservoir capacity is calculated based on the reservoir capacity information and the volume of newly added silt at the bottom of the reservoir.

[0005] In one embodiment, optionally, the target three-dimensional elevation structure data within the target reservoir area is calculated based on the digital elevation model data, the three-dimensional point cloud data, and spatial information, including: By integrating the digital elevation model data, the three-dimensional point cloud data, and spatial information, initial three-dimensional elevation structure data within the target reservoir area are obtained; Based on the initial three-dimensional elevation structure data, the target areas where three-dimensional point cloud data and spatial information are missing are identified; The elevation structure data of the target area is supplemented by the digital elevation model data interpolation inversion method to obtain the target three-dimensional elevation structure data. The interpolation inversion method includes ordinary kriging interpolation and generalized kriging interpolation.

[0006] In one embodiment, optionally, the current calibrated reservoir capacity information of the target reservoir is calculated based on the target three-dimensional elevation structure data and the water level information of the outlet, including: Based on the target's three-dimensional elevation structure data, the target reservoir is marked using contour lines, and the area of ​​the target reservoir is divided into multiple hexagonal prisms according to the terrain complexity. The water surface position of the target reservoir is determined based on the water level information at the outlet, and the effective height of each hexagonal prism is determined based on the water surface position of the target reservoir. Based on the effective height and shape of each hexagonal prism, the volume of each hexagonal prism is calculated and summed to determine the total volume of the target reservoir; Based on the total volume of the target reservoir, determine the reservoir capacity information after calibration.

[0007] In one embodiment, optionally, calculating the volume of newly added silt at the bottom of the target reservoir based on the first hydrological and water quality parameter information and the second hydrological and water quality parameter information includes: Based on the first turbidity and first flow rate in the first hydrological and water quality parameter information, the second turbidity and second flow rate in the second hydrological and water quality parameter information, and the sediment deposition coefficient, the volume of newly added silt at the bottom of the reservoir per unit time is calculated.

[0008] In one embodiment, optionally, the actual reservoir capacity information is calculated based on the reservoir capacity information and the volume of newly added silt at the bottom of the reservoir, including: Calculate the total number of newly added silt volumes from the time after calibration to the current time based on the newly added silt volume at the bottom of the reservoir per unit time. The actual reservoir capacity at the current moment is calculated based on the sum of the reservoir capacity information and the volume of newly added silt.

[0009] In one embodiment, optionally, the elevation structure data of the target area is calculated and supplemented through the digital elevation model data interpolation inversion method to obtain the target three-dimensional elevation structure data, including: From the initial digital elevation model data, effective elevation data points of the surrounding area of ​​the target are extracted. The effective elevation data points include elevation data from digital elevation model data, three-dimensional point cloud data or underwater spatial information, and each effective elevation data point includes at least plane coordinates and corresponding elevation attribute values. Based on the effective elevation data points, the semivariogram is calculated to quantify spatial correlation, and the characteristic parameters of the semivariogram are determined by fitting the semivariogram curve. The target interpolation method is selected according to the preset judgment conditions, wherein the target interpolation method includes ordinary kriging interpolation or generalized kriging interpolation. If the target interpolation method is ordinary kriging interpolation, a set of kriging equations satisfying preset constraints is constructed based on the characteristic parameters of the semi-variogram. After solving for the weights of the known points, the weights are substituted into the ordinary kriging interpolation formula to calculate the elevation values ​​of the unknown points in the target area. If the target interpolation method is universal Kriging interpolation, the trend term in the elevation data is separated by linear regression fitting to obtain residual data that satisfies the stationarity assumption. Perform weighting and interpolation calculations on the residual data in the same manner as ordinary kriging interpolation, and then superimpose the residual estimates with the trend term predictions to obtain the elevation values ​​of the unknown points.

[0010] In one embodiment, optionally, selecting a target interpolation method based on preset judgment conditions includes: Determine whether the terrain within the target area exhibits a gradual change trend; In response to the gradual change in the terrain, the selected target interpolation method is generalized Kriging interpolation; Since the terrain does not exhibit a gradual change trend, the selected target interpolation method is ordinary Kriging interpolation. The determination of whether the terrain exhibits a gradual change trend includes at least one of the following: Determine the variation pattern of elevation attribute values ​​and plane coordinates of elevation data points. If the elevation attribute values ​​change regularly with the plane coordinates, it is determined that the terrain has a gradual change trend; otherwise, it is determined that the terrain does not have a gradual change trend. Determine the difference values ​​of the semivariogram characteristic parameters in different spatial directions. If the difference value is greater than a preset threshold, it is determined that the terrain has a gradual change trend; otherwise, it is determined that the terrain does not have a gradual change trend. Determine the degree of fit between the trend term and the elevation data in the initial three-dimensional elevation structure data. If the degree of fit is greater than a preset degree, determine that the terrain has a gradual trend; otherwise, determine that the terrain does not have a gradual trend.

[0011] Secondly, embodiments of this application provide a reservoir capacity calculation device, comprising: The acquisition module is used to acquire digital elevation model data of the target reservoir, three-dimensional point cloud data of the area above the current water surface of the target reservoir, and spatial information below the current water surface; The first calculation module is used to calculate the target three-dimensional elevation structure data within the target reservoir area based on the digital elevation model data, the three-dimensional point cloud data, and spatial information. The monitoring module is used to monitor the first hydrological and water quality parameters of the upstream section of the inlet of the target reservoir and the second hydrological and water quality parameters of the outlet, wherein the hydrological and water quality parameters include turbidity, flow velocity, flow rate and water level information; The second calculation module is used to calculate the current calibrated reservoir capacity information of the target reservoir based on the target three-dimensional elevation structure data and the water level height information of the outlet. The third calculation module is used to calculate the volume of newly added silt at the bottom of the target reservoir based on the first hydrological and water quality parameter information and the second hydrological and water quality parameter information. The fourth calculation module is used to calculate the actual reservoir capacity information based on the reservoir capacity information and the volume of newly added silt at the bottom of the reservoir.

[0012] Thirdly, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described reservoir capacity calculation method.

[0013] Fourthly, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the above-described method for calculating reservoir capacity.

[0014] The above-described scheme for calculating reservoir capacity involves acquiring digital elevation model data of the target reservoir, three-dimensional point cloud data of the area above the current water surface of the target reservoir, and spatial information below the current water surface; calculating the target three-dimensional elevation structure data within the target reservoir area based on the digital elevation model data, the three-dimensional point cloud data, and the spatial information; monitoring the first hydrological and water quality parameters of the upstream section of the inlet and the second hydrological and water quality parameters of the outlet of the target reservoir, wherein the hydrological and water quality parameters include turbidity, flow velocity, flow rate, and water level information; calculating the current calibrated reservoir capacity information of the target reservoir based on the target three-dimensional elevation structure data and the water level information of the outlet; calculating the volume of newly added silt at the bottom of the target reservoir based on the first hydrological and water quality parameters and the second hydrological and water quality parameters; and calculating the actual reservoir capacity information based on the reservoir capacity information and the volume of newly added silt at the bottom of the reservoir. By integrating digital elevation model data acquired through satellite remote sensing, 3D point cloud data of the above-water area obtained through 3D laser scanning, and underwater spatial information detected by unmanned surface vessel sonar, a high-precision target 3D elevation structure data covering the entire reservoir area is constructed, overcoming the limitations of traditional single data sources in terrain characterization. Combined with the outlet water level, the reservoir area is divided into hexagonal prisms, and the calibrated reservoir capacity is calculated, significantly improving the accuracy of reservoir capacity calculation under irregular terrain. Simultaneously, by real-time monitoring of hydrological and water quality parameters such as turbidity, flow velocity, and flow rate at the inlet and outlet, the volume of newly added silt at the reservoir bottom is dynamically quantified, enabling dynamic correction of the calibrated reservoir capacity. Ultimately, this method effectively solves the problem of reservoir capacity data distortion caused by silt accumulation at the reservoir bottom and changes in the reservoir shoreline in existing technologies. It can continuously output high-precision actual reservoir capacity information that closely matches the actual operating conditions of the reservoir, providing reliable data support for management decisions such as reservoir water storage scheduling, flood control and disaster reduction, and optimal allocation of water resources, significantly improving the scientific nature and safety of reservoir operation and management. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A schematic flowchart of a reservoir capacity calculation method according to an embodiment of this application is shown.

[0017] Figure 2 A schematic flowchart of step S102 in a reservoir capacity calculation method according to an embodiment of this application is shown.

[0018] Figure 3 A schematic flowchart of step S203 in a reservoir capacity calculation method according to an embodiment of this application is shown.

[0019] Figure 4 A schematic flowchart of step S104 in a reservoir capacity calculation method according to an embodiment of this application is shown.

[0020] Figure 5 A block diagram of a reservoir capacity calculation device according to an embodiment of this application is shown.

[0021] Figure 6 A block diagram of a computer device according to one embodiment of this application is shown. Detailed Implementation

[0022] To better understand the technical solution of this application, the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0023] It should be understood that the described embodiments are merely some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0024] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0025] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0026] Please see Figure 1 , Figure 1 A schematic flowchart of a reservoir capacity calculation method according to an embodiment of this application is shown.

[0027] like Figure 1 As shown, the method for calculating reservoir capacity includes: Step S101: Obtain digital elevation model data of the target reservoir, including three-dimensional point cloud data of the area above the current water surface and spatial information below the current water surface. The study employed satellite remote sensing technologies (such as Sentinel-2 optical remote sensing and TerraSAR-X radar remote sensing) to acquire medium-precision digital elevation model data of the target reservoir and its surrounding area. This data is stored in raster format, with each raster cell containing planar coordinates (X, Y) and the corresponding elevation value (Z). The coverage extends to the entire reservoir basin, but the data update frequency is low, and the elevation accuracy is approximately 1-5 meters. It is primarily used to provide a macroscopic framework reference for the reservoir's topography.

[0028] The system employs unmanned aerial vehicle-mounted LiDAR or a ground-based 3D laser scanner to scan the area above the current water level of the reservoir (including the reservoir bank, dam, adjacent mountains, and man-made structures). The equipment generates high-density 3D point cloud data by emitting laser beams and receiving reflected signals. Each point contains precise 3D coordinates, allowing for a complete reconstruction of the detailed features of the terrain above the water surface (such as changes in reservoir bank slope, dam cracks, and protruding rocks).

[0029] An unmanned surface vessel (USV) equipped with a multibeam sonar sensor navigates the reservoir surface along a pre-defined grid-like route (the route spacing is adjusted according to water depth: ≤5 meters for depths <10 meters, ≤10 meters for depths >10 meters). The sonar sensor emits sound waves underwater and receives reflected waves from terrain at different depths, calculating spatial information such as underwater elevation, slope, and substrate type to generate underwater 3D topographic data. This data fills gaps in underwater areas not covered by satellite remote sensing and laser scanning, with an elevation accuracy of approximately 0.1-0.5 meters.

[0030] This approach breaks through the limitations of traditional reservoir capacity calculations that rely on single DEM data (covering only macroscopic topography and lacking underwater and detailed topography). By combining multi-source data—satellite remote sensing, laser scanning, and sonar detection—it achieves full coverage of the reservoir's topography, both above and below water, and both macroscopic and microscopic. Specifically, DEM data provides a large-scale topographic framework, 3D point cloud data ensures detailed accuracy above the water surface, and underwater sonar data fills in the underwater topographic blind spots. These three types of data corroborate and complement each other, laying a data foundation for the subsequent construction of a high-precision 3D elevation model. This solves the core problem of existing technologies' incomplete depiction and lack of detail in complex reservoir topography.

[0031] Step S102: Based on the digital elevation model data, the 3D point cloud data, and spatial information, the target 3D elevation structure data within the target reservoir area is calculated. In one specific embodiment, the optimal interpolation method can be automatically matched using a random forest classification model based on the target area's terrain feature parameters and historical interpolation error data. After inputting terrain parameters, the model can output a "recommended interpolation method" and a "predicted interpolation error value." If a gradual change in terrain is determined, the model... The system learns its own rules for goodness of fit and semivariogram difference, eliminating the need for manual threshold setting. It automatically selects generalized Kriging interpolation and otherwise selects ordinary Kriging interpolation. The system also automatically optimizes the semivariogram fitting parameters using a particle swarm optimization algorithm, with the goal of minimizing the difference between the interpolation result and the measured elevation.

[0032] like Figure 2 As shown, in one embodiment, optionally, step S102 includes: Step S201: Integrate the digital elevation model data, the three-dimensional point cloud data, and spatial information to obtain the initial three-dimensional elevation structure data within the target reservoir area. The collected DEM data, 3D point cloud data, and underwater sonar data are combined into a preliminary set of 3D elevation data covering the entire reservoir area through techniques such as coordinate unification and data integration. This is the initial 3D elevation structure data.

[0033] First, the three types of data are transformed to the same geographic coordinate system (such as the WGS84 geodetic coordinate system or the local Gaussian plane coordinate system). Coordinate transformation parameters (such as seven-parameter transformation) are used to eliminate coordinate deviations in data collected by different devices, ensuring that all data are spatially aligned. Second, a "precision-first" weighted fusion algorithm is used to process overlapping areas of the three types of data: Overlapping areas above the water surface: 3D point cloud data (highest precision) is retained first, and raster cells in the DEM data with an elevation difference > 0.5 meters from the point cloud data are replaced with the elevation values ​​of the point cloud data; Overlapping areas underwater: Sonar data is retained first, and the elevation values ​​of the underwater portion of the DEM data (mostly estimated values) are corrected; Non-overlapping areas: Valid elevation information of each data type is directly retained (such as the suburban areas of the DEM data, the reservoir bank details of the point cloud data, and the entire underwater area of ​​the sonar data). The fused data is converted into a unified point cloud or raster format (such as LAS point cloud format or GeoTIFF raster format) to form initial three-dimensional elevation structure data containing the spatial location (X, Y) and corresponding elevation (Z) of the entire reservoir area.

[0034] Step S202: Based on the initial three-dimensional elevation structure data, determine the target area where the three-dimensional point cloud data and spatial information are missing; Spatial continuity and density detection are performed on the initial three-dimensional elevation structure data to identify gaps or abnormal areas in the elevation data caused by equipment limitations (such as blind spots in laser scanning or weak areas in sonar signals), and these areas are defined as target areas.

[0035] Specifically, the elevation difference analysis method between adjacent points is used to traverse the point cloud or raster cells in the initial data: For point cloud data: calculate the elevation difference between adjacent points (distance ≤ 1 meter). If the elevation difference between 3 or more consecutive point pairs is > 2 meters (exceeding the range of natural terrain changes), and there is no obvious terrain change (such as cliffs) in the area, it is determined to be a data missing area. For raster data: Calculate the elevation difference between adjacent raster cells (8 neighborhoods). If the elevation difference between a raster cell and more than 4 surrounding raster cells is greater than 1 meter and there is no reasonable terrain explanation, it is determined to be a data missing area.

[0036] Data density detection: statistical analysis of the point cloud density or raster resolution of the initial data. Areas above water: If the point cloud density is less than 20 points / square meter (unable to reflect terrain details), it is judged as a sparse data area (classified as the target area); Underwater area: If the raster resolution is greater than 5 meters per pixel (which cannot accurately depict the underwater terrain), it is judged as a sparse data area.

[0037] Target region labeling: The detected missing and sparse regions are labeled with vector boundaries (such as polygons) to clarify the spatial extent and missing type (complete missing / sparse missing) of each target region.

[0038] In this way, by detecting both continuity and density, defective areas in the initial data can be accurately identified, avoiding the blind application of subsequent interpolation repair. At the same time, the target areas marked by vector boundaries provide a clear scope for targeted repair, ensuring that interpolation resources are concentrated on areas that truly need supplementation, thus improving the efficiency and accuracy of subsequent data repair.

[0039] Step S203: Calculate and supplement the elevation structure data of the target area using the digital elevation model data interpolation inversion method to obtain the target three-dimensional elevation structure data. The interpolation inversion method includes ordinary kriging interpolation and generalized kriging interpolation.

[0040] To address the issue of missing elevation data in the target area, based on the valid elevation points in the initial data, geostatistical methods such as ordinary kriging interpolation or pan-kriging interpolation (selected according to terrain features) are used to calculate and supplement the elevation values ​​of the missing areas, ultimately forming a "target three-dimensional elevation structure data" that covers the entire reservoir area, has no data defects, and has consistent accuracy.

[0041] like Figure 3 As shown, in one embodiment, optionally, step S203 includes: Step S301: Extract effective elevation data points of the target surrounding area from the initial digital elevation model data. The effective elevation data points include elevation data from digital elevation model data, three-dimensional point cloud data or underwater spatial information, and each effective elevation data point includes at least plane coordinates and corresponding elevation attribute values. From the initial 3D elevation structure data, extract the effective elevation data points within the "buffer zone" surrounding the target area (based on the boundary of the target area, extended outward by twice the maximum side length of the target area to ensure data representativeness): Valid point selection criteria: Includes complete planar coordinates (X, Y) and elevation values ​​(Z), and the data source is DEM, 3D point cloud or sonar (excluding speculative data), and outlier points with elevation values ​​deviating from surrounding points by more than 1 meter (such as equipment mismeasurement values). Sample size requirement: ≥30 valid points (the minimum sample size to meet the requirements of geostatistical interpolation and ensure the reliability of interpolation).

[0042] Step S302: Based on the effective elevation data points, calculate the semivariogram to quantify spatial correlation, and determine the characteristic parameters of the semivariogram by fitting the semivariogram curve. First, calculate the semivariogram using the known points. The distance is described as The difference in attribute values ​​between two points:

[0043] in, N(h) represents the distance between two points (lag distance); N(h) represents the distance equal to... The number of point pairs; and Indicates distance The attribute values ​​of the two points.

[0044] Feature parameter extraction: By fitting the semivariogram curve (commonly using spherical or exponential models), three core parameters are obtained: Range: The lag distance at which the semivariogram reaches stability (reflecting the maximum distance at which spatial correlation exists in elevation data; points beyond this distance are not correlated). Sill value: The value at which the semivariogram curve tends to stabilize (reflecting the total degree of variation in elevation data, including structural and random variations); Gold nugget value: the semivariogram value at lag distance (h=0) (reflecting random variation within the smallest observation scale, such as measurement error and micro-topographic disturbance).

[0045] Step S303: Select a target interpolation method according to preset judgment conditions, wherein the target interpolation method includes ordinary kriging interpolation or generalized kriging interpolation. In one embodiment, optionally, selecting a target interpolation method based on preset judgment conditions includes: Determine whether the terrain within the target area exhibits a gradual change trend; In response to the gradual change in the terrain, the selected target interpolation method is generalized Kriging interpolation; Since the terrain does not exhibit a gradual change trend, the selected target interpolation method is ordinary Kriging interpolation. The determination of whether the terrain exhibits a gradual change trend includes at least one of the following: Determine the variation pattern of elevation attribute values ​​and plane coordinates of elevation data points. If the elevation attribute values ​​change regularly with the plane coordinates, it is determined that the terrain has a gradual change trend; otherwise, it is determined that the terrain does not have a gradual change trend. Determine the difference values ​​of the semivariogram characteristic parameters in different spatial directions. If the difference value is greater than a preset threshold, it is determined that the terrain has a gradual change trend; otherwise, it is determined that the terrain does not have a gradual change trend. Determine the degree of fit between the trend term and the elevation data in the initial three-dimensional elevation structure data. If the degree of fit is greater than a preset degree, determine that the terrain has a gradual trend; otherwise, determine that the terrain does not have a gradual trend.

[0046] In this invention, ordinary kriging interpolation and universal kriging interpolation are parallel interpolation schemes with the same core objective: to supplement areas where 3D point cloud data or underwater sonar sensing information is missing. This helps to fuse DEM elevation data, 3D point cloud data of the area above the reservoir surface, and underwater spatial information sensed by sonar below the water surface, ultimately obtaining high-precision 3D elevation structure data for the entire reservoir area. Ordinary kriging interpolation calculates the value of unknown points by weighted summation of known points (weights are solved using a semi-variogram and the kriging equations to satisfy the unbiasedness condition). Universal kriging interpolation, on the other hand, introduces a trend term based on ordinary kriging interpolation to conform to the gradual changes in reservoir topography. There is no primary or secondary distinction or dependency between the two; the appropriate method should be selected based on the actual characteristics of the reservoir topography, together forming a parallel interpolation solution for areas with missing data.

[0047] The parameters and core conditions for judging the gradual change trend of terrain include: The variation pattern of elevation value (Z) and plane coordinates (X, Y): If the Z value shows a regular change such as continuous increase / decrease with X or Y, it indicates that the terrain has a gradual change trend; if the Z value is randomly distributed and has no regularity, it indicates that there is no obvious gradual change trend.

[0048] Spatial correlation characteristics of semivariogram: Based on the spatial correlation function of semivariogram, we analyze its hysteresis, range, sill value and nugget value in different spatial directions (such as along the water flow direction and perpendicular to the water flow direction): if the characteristics of different directions are significantly different (anisotropy), it indicates that there is a gradual trend; if the characteristics of each direction are consistent (isotropy), it indicates that there is no obvious gradual trend.

[0049] Trend term fit: For the trend term introduced by universal kriging interpolation, if the trend term can effectively fit the terrain trend of the XYZ coordinate point set, it indicates that there is a gradual trend; if the fitting deviation is large and cannot explain the terrain changes, it indicates that there is no obvious gradual trend.

[0050] Based on the core objective of this invention, "integrating multi-source data to obtain high-precision three-dimensional elevation structure data of reservoirs," and referencing the dynamic adjustment mechanism of "calibrating the sediment settlement coefficient K through reservoir characteristics," and based on the technical logic of semi-variogram (quantifying spatial correlation) and trend term fitting (fitting the gradual change law of topography), the numerical standards shown in Table 1 are set (which can be further optimized by combining historical topographic measurement data of specific reservoirs): Table 1

[0051] The above four judgment parameters are all obtained using conventional calculation methods in the field of geostatistics. Technical personnel in the relevant field can refer to existing technical literature to implement them.

[0052] Step S304: If the target interpolation method is ordinary kriging interpolation, construct a set of kriging equations that satisfy the preset constraints based on the characteristic parameters of the semi-variogram function, solve for the weights of the known points, and then substitute them into the ordinary kriging interpolation formula to calculate the elevation values ​​of the unknown points in the target area. Method 1: Standard Kriging Unknown point elevation value The weights are obtained by weighted summation of known points, and the weights are solved using a semi-variogram function.

[0053] in, The elevation value of a known point i (derived from DEM data, 3D point cloud data, or underwater sonar data); The weights of point i are known (satisfying) The interpolation results are solved by using a system of Kriging equations constructed from semi-variograms to ensure unbiasedness. is the Lagrange multiplier (used to satisfy the unbiasedness condition); n is the number of known points involved in the interpolation calculation.

[0054] Step S305: If the target interpolation method is universal kriging interpolation, the trend term in the elevation data is separated by linear regression fitting to obtain residual data that satisfies the stationarity assumption. Method 2: Pan-Kriging A trend term is introduced based on ordinary Kriging interpolation to conform to the gradual changes in the reservoir topography.

[0055]

[0056] in, Unknown point The trend term at the point (in the form of a first-order polynomial) ), , , The trend term coefficient is obtained by linearly fitting known point elevation data with plane coordinates (x, y). The elevation residual value of point i is known; The weights of the residuals are determined by a semi-mutation function; the remaining parameters are defined in the same way as in ordinary kriging.

[0057] Step S306: Perform weighting and interpolation calculations on the residual data in the same manner as ordinary kriging interpolation, and superimpose the residual estimate with the trend term prediction to obtain the elevation value of the unknown point.

[0058] For residual data whose overall topographic trend has been eliminated, the standard procedure of ordinary kriging interpolation is followed to solve for weights and perform interpolation calculations: First, the semivariogram is calculated based on the effective points of the residual data to determine the spatial correlation parameters. Then, a set of kriging equations is constructed to satisfy "the sum of weights is 1 (unbiased)" and "the estimated variance is minimized". The weight coefficients of the known points of the residual data are obtained by solving the equations using linear algebra. Finally, the weights are substituted into the formula of the same form as ordinary kriging interpolation to calculate the residual estimate of the unknown points in the target area. Subsequently, the residual estimate is superimposed with the predicted value of the trend term of the unknown points to obtain the final elevation value of the unknown points.

[0059] When processing 3D point clouds (including X, Y, and Z coordinates), the distance between known and unknown points in the two interpolation methods mentioned above can be directly extended to 3D space, and the distance calculation is changed to 3D Euclidean distance:

[0060] Where (x,y,z) are the three-dimensional coordinates of the unknown point. Given the three-dimensional coordinates of point i, The distance between the two points is a three-dimensional distance.

[0061] Step S103: Monitor the first hydrological and water quality parameters of the upstream section of the inlet of the target reservoir and the second hydrological and water quality parameters of the outlet, wherein the hydrological and water quality parameters include turbidity, flow velocity, flow rate and water level information; By deploying monitoring equipment at the upstream section of the reservoir's inlet and outlet, key parameters reflecting the water's sediment content, flow status, and water level (turbidity, flow velocity, flow rate, and water level height) are collected in real time, providing dynamic data support for subsequent quantification of the volume of newly added silt at the bottom of the reservoir.

[0062] Upstream section of the inlet: A straight river section with stable water flow and no backflow can be selected 50-100 meters away from the inlet (to avoid water flow disturbance affecting data accuracy) to deploy monitoring equipment; Outlet: Select an outlet 20-50 meters downstream of the gate to ensure that the monitoring section completely covers the outflow and avoids the gate disturbance area.

[0063] First hydrological and water quality parameter information (upstream section of the inlet): First turbidity: An online turbidity sensor (measurement range 0-5000 NTU, accuracy ±2%) is used to monitor the concentration of suspended particles (including sediment) in the water in real time, with data output frequency once every 5 minutes; First flow velocity: A Doppler current meter (measurement range 0.01-5 m / s, accuracy ±1%) was installed at a depth of 0.6 times the water depth of the cross section (representative flow layer) to obtain the average flow velocity of the cross section; First water level height: A radar water level gauge (measuring range 0-30 m, accuracy ±2 mm) is installed on an unobstructed section of the bank to collect water level data in real time. First flow rate: calculated by "average flow velocity of cross section × cross section area" (the cross section area is determined based on the cross section morphology parameters of previous topographic surveys. If the cross section topography changes, it will be remeasured and calibrated every quarter).

[0064] Second hydrological and water quality parameter information (outlet): Second turbidity: Same as the turbidity sensor at the inlet, to monitor the sediment concentration in the effluent; Second flow rate: Prioritize using a pipeline flow meter (if the outlet is a pipeline) or an ultrasonic flow meter (if it is an open channel) to directly measure the outlet flow rate (accuracy ±2%), without needing to calculate the flow velocity; Second water level: The same type of radar water level gauge is used to monitor the water level at the outlet, which is used to determine the overall water surface position of the reservoir.

[0065] Step S104: Calculate the current calibrated reservoir capacity information of the target reservoir based on the target three-dimensional elevation structure data and the water level information of the outlet. like Figure 4 As shown, in one embodiment, optionally, step S104 includes: Step S401: Based on the target three-dimensional elevation structure data, mark the target reservoir according to contour lines, and divide the area of ​​the target reservoir into multiple hexagonal prisms according to the terrain complexity. Based on the target three-dimensional elevation structure data, the elevation layers of the reservoir topography are clearly defined by contour lines. Then, according to the differences in topographic complexity, the reservoir area is divided into multiple hexagonal prisms (computation units) of different sizes, providing a scientific spatial division basis for subsequent accurate calculation of reservoir capacity.

[0066] Contour lines of the reservoir terrain are extracted from the target three-dimensional elevation structure data. The contour interval is determined according to the terrain slope: 1 meter for terrain slope < 5° (gentle area); 0.5 meters for terrain slope 5°-15° (gentle slope area); and 0.2 meters for terrain slope > 15° (steep slope area), to ensure that the contour lines can accurately reflect the terrain undulations.

[0067] Hexagonal prism division: Division principle: The size of the prism is dynamically adjusted according to the complexity of the terrain. In areas with drastic terrain changes (such as steep reservoir banks and shallow underwater shoals), the side length of the hexagon is 20-50 meters (small unit, to improve calculation accuracy); in areas with gentle terrain (such as the deep water area in the center of the reservoir), the side length of the hexagon is 50-100 meters (large unit, to balance accuracy and efficiency). Division method: The "hexagonal grid generation algorithm" is adopted. With the reservoir boundary as a constraint, a hexagonal grid covering the entire reservoir area is generated. Each grid corresponds to a "hexagonal prism". The base of the prism is a hexagonal grid, and the height direction extends along the direction perpendicular to the contour lines (consistent with the changes in terrain elevation). Data recording: Record the vertex coordinates (X, Y), bottom elevation (based on the target three-dimensional elevation structure data), and side length of each hexagonal prism to form a prism parameter table.

[0068] Compared to the regular grid division used in traditional reservoir capacity calculations, this step achieves precise matching between computational cells and terrain features through contour marking and terrain-adaptive cell division: small cells are used for complex areas such as steep slopes and shallows to avoid errors caused by large cells obscuring terrain details; large cells are used for flat areas, improving computational efficiency while ensuring accuracy. This division method lays the spatial foundation for high-precision subsequent volume calculations and solves the problem of balancing accuracy and efficiency in traditional regular grid division.

[0069] Step S402: Determine the water surface position of the target reservoir based on the water level information of the outlet, and determine the effective height of each hexagonal prism based on the water surface position of the target reservoir; Using the water level monitored at the outlet as a benchmark, the water surface position of the entire reservoir area is determined (since the reservoir water bodies are interconnected, the water level at the outlet can represent the elevation of the entire water surface). Then, combined with the bottom elevation of each hexagonal prism, the height of the submerged part of the prism (effective height) is calculated, providing key parameters for subsequent volume calculations.

[0070] The water surface location has been determined: Since the reservoir is a connected body of water, the second water level height monitored at the outlet is the absolute elevation of the entire water surface. In the coordinate system of the target three-dimensional elevation structure data, a virtual water surface plane with an elevation of the second water level is constructed. The intersection of this plane and the reservoir topography is the current shoreline of the reservoir, and the area below the plane is the submerged area.

[0071] Calculation of the effective height of a prism: For each hexagonal prism, extract the average elevation of its base (denoted as Hbase, which is obtained by averaging the elevations of the vertices at the base of the prism). The effective height (Hexisting) is calculated as follows: Hexisting = max (Hwater - Hbottom, 0); if Hwater ≤ Hbottom, it means that the prism is not submerged, and Hexisting = 0 (not included in subsequent volume calculations).

[0072] By setting the water level at the outlet to represent the entire water surface, the errors of interpolation after multi-point water level measurement in traditional methods are avoided, ensuring the uniformity and accuracy of the water surface position. At the same time, the effective height is calculated based on the difference between the bottom elevation of the prism and the water surface elevation, which accurately reflects the actual inundation situation of each unit. This provides reliable parameters for accurate summation of each unit in subsequent volume calculations, and solves the problem of reservoir capacity calculation deviation caused by the traditional method assuming a horizontal water surface but ignoring local topographic differences.

[0073] Step S403: Calculate the volume of each hexagonal prism based on its effective height and shape, and sum the volumes to determine the total volume of the target reservoir. Based on the shape (regular hexagon or arbitrary hexagon) and effective height of each hexagonal prism, the submerged volume of a single prism is calculated using the corresponding volume formula. Then, the volumes of all prisms are summed to obtain the current "calibrated reservoir capacity" (i.e., the theoretical reservoir capacity without considering silt accumulation), which serves as the benchmark value for subsequent calculations of the actual reservoir capacity.

[0074] (1) If the hexagonal prism is a regular hexagon, the calculation formula is: Vprism=∑( ·S2)×H Where Vprism is the volume of the hexagonal prism; S is the side length of the hexagonal prism; and H is the height of the hexagonal prism.

[0075] (2) For any hexagon, it can be divided into multiple triangles, and then the sum of the areas of these triangles can be calculated.

[0076] Suppose the coordinates of the vertices of the hexagon are (x1, y1), (x2, y2), ..., (x6, y6). The area of ​​the hexagon is: A =

[0077] The volume of a hexagonal prism: Vprism = A × H.

[0078] Where A is the area of ​​the hexagon; Vprism is the volume of the hexagonal prism; and H is the height of the hexagonal prism.

[0079] Differentiated volume formulas are used for hexagonal prisms of different shapes, avoiding the errors of traditional one-size-fits-all volume calculations (such as uniformly estimating based on cubes). In particular, the shoelace formula is used to calculate the base area of ​​irregular hexagons, accurately restoring the unit morphology under complex terrain. By summing unit by unit, the reservoir capacity of the entire area is refined and accumulated. Compared with the integral estimation of traditional methods (such as cross-section method and contour line volume method), the calculation accuracy is improved by more than 30%. The calibrated reservoir capacity can accurately reflect the current theoretical water storage capacity of the reservoir, providing a reliable benchmark for subsequent deduction of silt volume to obtain the actual reservoir capacity.

[0080] Step S404: Determine the current calibrated reservoir capacity information of the target reservoir based on the total volume of the target reservoir.

[0081] Step S105: Calculate the volume of newly added silt at the bottom of the target reservoir based on the first hydrological and water quality parameter information and the second hydrological and water quality parameter information. In one embodiment, optionally, step S105 includes: Based on the first turbidity and first flow rate in the first hydrological and water quality parameter information, the second turbidity and second flow rate in the second hydrological and water quality parameter information, and the sediment deposition coefficient, the volume of newly added silt at the bottom of the reservoir per unit time is calculated.

[0082] Based on the turbidity and flow data monitored at the inlet and outlet, and combined with the sediment settling characteristics (sediment settling coefficient) of the reservoir area, the volume of newly added silt at the bottom of the reservoir per unit time is obtained by calculating the difference between the amount of sediment carried in the inflow and the amount of sediment discharged from the outflow. This provides a basis for quantifying the amount of silt encroaching on the reservoir capacity.

[0083] Among them, the sediment settling coefficient K value can be intelligently predicted by constructing a dynamic prediction model using a long short-term memory network. By inputting real-time hydrological parameters, environmental parameters and historical K value data, the model outputs a dynamic K value once per hour. At the same time, the Kalman filter algorithm is used to automatically correct the volume of newly added silt at the bottom of the reservoir per unit time, reducing the error of a single data source.

[0084] Among them, reservoirs can be divided into two types: reservoirs with inlets and outlets and reservoirs without inlets. The following calculation method is used for the two types. (1) Reservoirs with inlets and outlets

[0085] Where Vsilt is the volume of newly added silt at the bottom of the reservoir per unit time; Cin and Cout are the turbidity at the inlet and outlet; Qin and Qout are the flow rates at the inlet and outlet; and K is the sediment settling coefficient (calibrated by the sediment characteristics of the reservoir area).

[0086] (2) Reservoirs without inlets

[0087] Where Vsilt is the volume of newly added silt at the bottom of the reservoir per unit time (if the result is 0, it means that no new silt has been added); Cout is the turbidity at the outlet (consistent with the original formula, reflecting the concentration of sediment carried by the outlet); Qout is the flow rate at the outlet (consistent with the original formula, reflecting the flow velocity at the outlet); K is the sediment settling coefficient (consistent with the original formula, calibrated by the sediment characteristics of the reservoir area); max[·, 0] is the maximum value function. Since there is no external sediment input when there is no inlet, only the original sediment in the reservoir may be lost with the outflow (the calculation result is negative). At this time, there is no new silt, so the negative value is corrected to 0 to ensure that Vsilt only reflects the positively added silt volume.

[0088] Without an inlet, the reservoir receives no external sediment supply; only the amount of sediment carried out from the outlet needs to be calculated. Since there is no external sediment input, even if some of the original sediment is lost with the outflow, no "new" silt will be generated. Therefore, max[·,0] ensures that the final result is only a non-negative value (0 or the volume of new silt, which is usually 0 in actual scenarios).

[0089] The above-mentioned technical solution quantifies the difference in inflow and outflow sediment volume by "turbidity × flow rate" and combines it with the K value calibrated on-site to achieve dynamic and real-time calculation of silt volume. Compared with the traditional method of manually drilling cores to measure silt thickness and then estimating volume (which is time-consuming, destructive, and prone to errors), this solution is not only more than 10 times more efficient, but also allows for continuous monitoring of silt change trends. At the same time, differentiated formulas are designed for different reservoir types to ensure that the calculation logic matches the actual working conditions, accurately capturing the amount of silt encroaching on the reservoir capacity, and solving the core pain point of existing technologies that cannot quantify the impact of silt in real time and accurately.

[0090] Step S106: Calculate the actual reservoir capacity information based on the reservoir capacity information and the volume of newly added silt at the bottom of the reservoir.

[0091] In one embodiment, optionally, step S106 includes: Calculate the total number of newly added silt volumes from the time after calibration to the current time based on the newly added silt volume at the bottom of the reservoir per unit time. The actual reservoir capacity at the current moment is calculated based on the sum of the reservoir capacity information and the volume of newly added silt.

[0092] In this step, the reservoir capacity information is calculated based on the actual horizontal height. The actual reservoir capacity information at this moment is obtained by summing the volume of newly added silt from the time after the calibration to the current time range.

[0093] Vactual = Vprism - ∑Vsilt Where Vactual is the actual storage capacity; ∑Vsilt is the total volume of newly added silt from the later time to the current time range.

[0094] The above-described scheme for calculating reservoir capacity involves acquiring digital elevation model data of the target reservoir, three-dimensional point cloud data of the area above the current water surface of the target reservoir, and spatial information below the current water surface; calculating the target three-dimensional elevation structure data within the target reservoir area based on the digital elevation model data, the three-dimensional point cloud data, and the spatial information; monitoring the first hydrological and water quality parameters of the upstream section of the inlet and the second hydrological and water quality parameters of the outlet of the target reservoir, wherein the hydrological and water quality parameters include turbidity, flow velocity, flow rate, and water level information; calculating the current calibrated reservoir capacity information of the target reservoir based on the target three-dimensional elevation structure data and the water level information of the outlet; calculating the volume of newly added silt at the bottom of the target reservoir based on the first hydrological and water quality parameters and the second hydrological and water quality parameters; and calculating the actual reservoir capacity information based on the reservoir capacity information and the volume of newly added silt at the bottom of the reservoir. By integrating digital elevation model data acquired through satellite remote sensing, 3D point cloud data of the above-water area obtained through 3D laser scanning, and underwater spatial information detected by unmanned surface vessel sonar, a high-precision target 3D elevation structure data covering the entire reservoir area is constructed, overcoming the limitations of traditional single data sources in terrain characterization. Combined with the outlet water level, the reservoir area is divided into hexagonal prisms, and the calibrated reservoir capacity is calculated, significantly improving the accuracy of reservoir capacity calculation under irregular terrain. Simultaneously, by real-time monitoring of hydrological and water quality parameters such as turbidity, flow velocity, and flow rate at the inlet and outlet, the volume of newly added silt at the reservoir bottom is dynamically quantified, enabling dynamic correction of the calibrated reservoir capacity. Ultimately, this method effectively solves the problem of reservoir capacity data distortion caused by silt accumulation at the reservoir bottom and changes in the reservoir shoreline in existing technologies. It can continuously output high-precision actual reservoir capacity information that closely matches the actual operating conditions of the reservoir, providing reliable data support for management decisions such as reservoir water storage scheduling, flood control and disaster reduction, and optimal allocation of water resources, significantly improving the scientific nature and safety of reservoir operation and management.

[0095] Figure 5 A block diagram of a reservoir capacity calculation device according to an embodiment of this application is shown.

[0096] like Figure 5 As shown, in a second aspect, embodiments of this application provide a reservoir capacity calculation device 50, comprising: The acquisition module 51 is used to acquire digital elevation model data of the target reservoir, three-dimensional point cloud data of the area above the current water surface of the target reservoir, and spatial information below the current water surface. The first calculation module 52 is used to calculate the target three-dimensional elevation structure data within the target reservoir area based on the digital elevation model data, the three-dimensional point cloud data and spatial information. The monitoring module 53 is used to monitor the first hydrological and water quality parameter information of the upstream section of the inlet of the target reservoir and the second hydrological and water quality parameter information of the outlet, wherein the hydrological and water quality parameter information includes turbidity, flow velocity, flow rate and water level information; The second calculation module 54 is used to calculate the current calibrated reservoir capacity information of the target reservoir based on the target three-dimensional elevation structure data and the water level height information of the outlet. The third calculation module 55 is used to calculate the volume of newly added silt at the bottom of the target reservoir based on the first hydrological and water quality parameter information and the second hydrological and water quality parameter information. The fourth calculation module 56 is used to calculate the actual reservoir capacity information based on the reservoir capacity information and the volume of newly added silt at the bottom of the reservoir.

[0097] In one embodiment, optionally, the first computing module includes: The fusion unit is used to fuse the digital elevation model data, the three-dimensional point cloud data, and spatial information to obtain the initial three-dimensional elevation structure data within the target reservoir area. The region determination unit is used to determine the target region where the three-dimensional point cloud data and spatial information are missing, based on the initial three-dimensional elevation structure data; An interpolation unit is used to calculate and supplement the elevation structure data of the target area through the interpolation inversion method of the digital elevation model data, so as to obtain the target three-dimensional elevation structure data, wherein the interpolation inversion method includes ordinary kriging interpolation and universal kriging interpolation.

[0098] In one embodiment, optionally, the second computing module includes: The division unit is used to mark the target reservoir according to the contour lines based on the target three-dimensional elevation structure data, and to divide the area of ​​the target reservoir into multiple hexagonal prisms according to the terrain complexity. The height determination unit is used to determine the water surface position of the target reservoir based on the water level height information of the outlet, and to determine the effective height of each hexagonal prism based on the water surface position of the target reservoir. The volume calculation unit is used to calculate and sum the volumes of each hexagonal prism based on its effective height and shape, in order to determine the total volume of the target reservoir. The information determination unit is used to determine the current calibrated reservoir capacity information of the target reservoir based on the total volume of the target reservoir.

[0099] In one embodiment, optionally, the third computing module is used for: Based on the first turbidity and first flow rate in the first hydrological and water quality parameter information, the second turbidity and second flow rate in the second hydrological and water quality parameter information, and the sediment deposition coefficient, the volume of newly added silt at the bottom of the reservoir per unit time is calculated.

[0100] In one embodiment, optionally, the fourth computing module includes: The volume calculation unit is used to calculate the sum of the newly added silt volume values ​​from the time after calibration to the current time range, based on the newly added silt volume value at the bottom of the reservoir per unit time. The reservoir capacity calculation unit is used to calculate the actual reservoir capacity information at the current moment based on the sum of the reservoir capacity information and the volume of newly added silt.

[0101] In one embodiment, optionally, the interpolation unit is used for: From the initial digital elevation model data, effective elevation data points of the surrounding area of ​​the target are extracted. The effective elevation data points include elevation data from digital elevation model data, three-dimensional point cloud data or underwater spatial information, and each effective elevation data point includes at least plane coordinates and corresponding elevation attribute values. Based on the effective elevation data points, the semivariogram is calculated to quantify spatial correlation, and the characteristic parameters of the semivariogram are determined by fitting the semivariogram curve. The target interpolation method is selected according to the preset judgment conditions, wherein the target interpolation method includes ordinary kriging interpolation or generalized kriging interpolation. If the target interpolation method is ordinary kriging interpolation, a set of kriging equations satisfying preset constraints is constructed based on the characteristic parameters of the semi-variogram. After solving for the weights of the known points, the weights are substituted into the ordinary kriging interpolation formula to calculate the elevation values ​​of the unknown points in the target area. If the target interpolation method is universal Kriging interpolation, the trend term in the elevation data is separated by linear regression fitting to obtain residual data that satisfies the stationarity assumption. Perform weighting and interpolation calculations on the residual data in the same manner as ordinary kriging interpolation, and then superimpose the residual estimates with the trend term predictions to obtain the elevation values ​​of the unknown points.

[0102] In one embodiment, optionally, selecting a target interpolation method based on preset judgment conditions includes: Determine whether the terrain within the target area exhibits a gradual change trend; In response to the gradual change in the terrain, the selected target interpolation method is generalized Kriging interpolation; Since the terrain does not exhibit a gradual change trend, the selected target interpolation method is ordinary Kriging interpolation. The determination of whether the terrain exhibits a gradual change trend includes at least one of the following: Determine the variation pattern of elevation attribute values ​​and plane coordinates of elevation data points. If the elevation attribute values ​​change regularly with the plane coordinates, it is determined that the terrain has a gradual change trend; otherwise, it is determined that the terrain does not have a gradual change trend. Determine the difference values ​​of the semivariogram characteristic parameters in different spatial directions. If the difference value is greater than a preset threshold, it is determined that the terrain has a gradual change trend; otherwise, it is determined that the terrain does not have a gradual change trend. Determine the degree of fit between the trend term and the elevation data in the initial three-dimensional elevation structure data. If the degree of fit is greater than a preset degree, determine that the terrain has a gradual trend; otherwise, determine that the terrain does not have a gradual trend.

[0103] Thirdly, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described reservoir capacity calculation method.

[0104] Fourthly, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the above-described method for calculating reservoir capacity.

[0105] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the reservoir capacity calculation device and its modules described above can be referred to the corresponding process in the aforementioned reservoir capacity calculation method embodiment, and will not be repeated here.

[0106] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the model training device and each module described above can be referred to the corresponding process in the aforementioned reservoir capacity calculation method embodiment, and will not be repeated here.

[0107] The aforementioned reservoir capacity calculation device can be implemented as a computer program, which can be used in, for example... Figure 6 It runs on the computer device shown.

[0108] Figure 6 A block diagram of a computer device according to one embodiment of this application is shown.

[0109] See Figure 6 The computer device includes a processor, memory, and network interface connected via a system bus, wherein the memory may include storage media and internal memory.

[0110] The storage medium may store an operating system and a computer program. The computer program includes program instructions that, when executed, cause the processor to perform any of the multi-source data reservoir capacity calculation methods provided in the embodiments of this application.

[0111] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0112] The internal memory provides an environment for the execution of computer programs stored in the storage medium. When executed by a processor, this computer program enables the processor to perform any method for calculating reservoir capacity based on multi-source data. The storage medium can be non-volatile or volatile.

[0113] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0114] It should be understood that the processor can be a Central Processing Unit (CPU), but it can 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. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0115] In addition, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions for performing the steps of the method in the first aspect embodiment.

[0116] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or electronic device described above can be referred to the relevant descriptions in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0117] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0118] It should be understood that although the terms "first," "second," etc., may be used to describe the setting units in the embodiments of this application, these setting units should not be limited to these terms. These terms are only used to distinguish the setting units from each other. For example, without departing from the scope of the embodiments of this application, the first setting unit may also be referred to as the second setting unit, and similarly, the second setting unit may also be referred to as the first setting unit.

[0119] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0120] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0121] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional units.

[0122] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0123] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for calculating reservoir capacity, characterized in that, The method includes: Obtain digital elevation model data of the target reservoir, including three-dimensional point cloud data of the area above the current water surface and spatial information below the current water surface; Based on the digital elevation model data, the three-dimensional point cloud data, and spatial information, the target three-dimensional elevation structure data within the target reservoir area is calculated. The first hydrological and water quality parameter information of the upstream section of the inlet of the target reservoir and the second hydrological and water quality parameter information of the outlet are monitored, wherein the hydrological and water quality parameter information includes turbidity, flow velocity, flow rate and water level information; Based on the target's three-dimensional elevation structure data and the water level information at the outlet, calculate the target reservoir's current calibrated reservoir capacity. The volume of newly added silt at the bottom of the target reservoir is calculated based on the first hydrological and water quality parameter information and the second hydrological and water quality parameter information. The actual reservoir capacity is calculated based on the reservoir capacity information and the volume of newly added silt at the bottom of the reservoir.

2. The method according to claim 1, characterized in that, Based on the digital elevation model data, the three-dimensional point cloud data, and spatial information, the target three-dimensional elevation structure data within the target reservoir area is calculated, including: By integrating the digital elevation model data, the three-dimensional point cloud data, and spatial information, initial three-dimensional elevation structure data within the target reservoir area are obtained; Based on the initial three-dimensional elevation structure data, the target areas where three-dimensional point cloud data and spatial information are missing are identified; The elevation structure data of the target area is supplemented by the digital elevation model data interpolation inversion method to obtain the target three-dimensional elevation structure data. The interpolation inversion method includes ordinary kriging interpolation and generalized kriging interpolation.

3. The method according to claim 1, characterized in that, Based on the target's three-dimensional elevation structure data and the water level information at the outlet, the current calibrated reservoir capacity information of the target reservoir is calculated, including: Based on the target's three-dimensional elevation structure data, the target reservoir is marked using contour lines, and the area of ​​the target reservoir is divided into multiple hexagonal prisms according to the terrain complexity. The water surface position of the target reservoir is determined based on the water level information at the outlet, and the effective height of each hexagonal prism is determined based on the water surface position of the target reservoir. Based on the effective height and shape of each hexagonal prism, the volume of each hexagonal prism is calculated and summed to determine the total volume of the target reservoir; Based on the total volume of the target reservoir, determine the reservoir capacity information after calibration.

4. The method according to claim 1, characterized in that, The volume of newly added silt at the bottom of the target reservoir is calculated based on the first hydrological and water quality parameter information and the second hydrological and water quality parameter information, including: Based on the first turbidity and first flow rate in the first hydrological and water quality parameter information, the second turbidity and second flow rate in the second hydrological and water quality parameter information, and the sediment deposition coefficient, the volume of newly added silt at the bottom of the reservoir per unit time is calculated.

5. The method according to claim 4, characterized in that, Based on the reservoir capacity information and the volume of newly added silt at the bottom of the reservoir, the actual reservoir capacity information is calculated, including: Calculate the total number of newly added silt volumes from the time after calibration to the current time based on the newly added silt volume at the bottom of the reservoir per unit time. The actual reservoir capacity at the current moment is calculated based on the sum of the reservoir capacity information and the volume of newly added silt.

6. The method according to claim 2, characterized in that, The elevation structure data of the target area is calculated and supplemented by the digital elevation model data interpolation and inversion method to obtain the target three-dimensional elevation structure data, including: From the initial digital elevation model data, effective elevation data points of the surrounding area of ​​the target are extracted. The effective elevation data points include elevation data from digital elevation model data, three-dimensional point cloud data or underwater spatial information, and each effective elevation data point includes at least plane coordinates and corresponding elevation attribute values. Based on the effective elevation data points, the semivariogram is calculated to quantify spatial correlation, and the characteristic parameters of the semivariogram are determined by fitting the semivariogram curve. The target interpolation method is selected according to the preset judgment conditions, wherein the target interpolation method includes ordinary kriging interpolation or generalized kriging interpolation. If the target interpolation method is ordinary kriging interpolation, a set of kriging equations satisfying preset constraints is constructed based on the characteristic parameters of the semi-variogram. After solving for the weights of the known points, the weights are substituted into the ordinary kriging interpolation formula to calculate the elevation values ​​of the unknown points in the target area. If the target interpolation method is universal Kriging interpolation, the trend term in the elevation data is separated by linear regression fitting to obtain residual data that satisfies the stationarity assumption. Perform weighting and interpolation calculations on the residual data in the same manner as ordinary kriging interpolation, and then superimpose the residual estimates with the trend term predictions to obtain the elevation values ​​of the unknown points.

7. The method according to claim 6, characterized in that, The target interpolation method is selected based on preset judgment conditions, including: Determine whether the terrain within the target area exhibits a gradual change trend; In response to the gradual change in the terrain, the selected target interpolation method is generalized Kriging interpolation; Since the terrain does not exhibit a gradual change trend, the selected target interpolation method is ordinary Kriging interpolation. The determination of whether the terrain exhibits a gradual change trend includes at least one of the following: Determine the variation pattern of elevation attribute values ​​and plane coordinates of elevation data points. If the elevation attribute values ​​change regularly with the plane coordinates, it is determined that the terrain has a gradual change trend; otherwise, it is determined that the terrain does not have a gradual change trend. Determine the difference values ​​of the semivariogram characteristic parameters in different spatial directions. If the difference value is greater than a preset threshold, it is determined that the terrain has a gradual change trend; otherwise, it is determined that the terrain does not have a gradual change trend. Determine the degree of fit between the trend term and the elevation data in the initial three-dimensional elevation structure data. If the degree of fit is greater than a preset degree, determine that the terrain has a gradual trend; otherwise, determine that the terrain does not have a gradual trend.

8. A reservoir capacity calculation device, characterized in that, include: The acquisition module is used to acquire digital elevation model data of the target reservoir, three-dimensional point cloud data of the area above the current water surface of the target reservoir, and spatial information below the current water surface; The first calculation module is used to calculate the target three-dimensional elevation structure data within the target reservoir area based on the digital elevation model data, the three-dimensional point cloud data, and spatial information. The monitoring module is used to monitor the first hydrological and water quality parameters of the upstream section of the inlet of the target reservoir and the second hydrological and water quality parameters of the outlet, wherein the hydrological and water quality parameters include turbidity, flow velocity, flow rate and water level information; The second calculation module is used to calculate the current calibrated reservoir capacity information of the target reservoir based on the target three-dimensional elevation structure data and the water level height information of the outlet. The third calculation module is used to calculate the volume of newly added silt at the bottom of the target reservoir based on the first hydrological and water quality parameter information and the second hydrological and water quality parameter information. The fourth calculation module is used to calculate the actual reservoir capacity information based on the reservoir capacity information and the volume of newly added silt at the bottom of the reservoir.

9. A computer device, characterized in that, include: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, the instructions being configured to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The device stores computer-executable instructions for performing the method as described in any one of claims 1 to 7.

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