A reservoir capacity measurement method and system based on remote sensing and point cloud data

Abstract

The present invention relates to the technical field of reservoir capacity measurement, specifically a reservoir capacity measurement method and system based on remote sensing and point cloud data, comprising the following steps: extracting a set of terrain surface points based on input remote sensing image data and point cloud data, and comparing and classifying the curvature and density data by calculating the three-dimensional coordinate value, curvature value and point density value of each point in combination with the change of the reservoir boundary. In the present invention, through the comprehensive application of remote sensing image data and point cloud data, three-dimensional coordinates, curvature values ​​and point density values ​​are used to accurately extract terrain features, so as to achieve scientific division and feature description of sub-regions, generate refined three-dimensional grid parameters through fitting and interpolation calculation, improve terrain modeling accuracy and spatial geometric description capabilities of complex regions, dynamically adjust boundary node coordinates and normal vectors, optimize grid continuity, ensure the integrity of boundary transition regions, and accurately describe boundary complex volumes by using unit volume partitioning and boundary correction.

Description

Technical Field

[0001] The present invention relates to the technical field of reservoir capacity measurement, and in particular to a reservoir capacity measurement method and system based on remote sensing and point cloud data. Background Art

[0002] The field of reservoir capacity measurement technology includes related methods and systems for measuring and analyzing reservoir capacity using various technical means. The core content of this technical field is to accurately calculate the volume of the reservoir and the distribution of water resources by acquiring, processing and analyzing the spatial morphology and water level change data of the reservoir. The overall technical field covers reservoir morphology measurement based on remote sensing technology, three-dimensional model construction based on point cloud data, development and application of reservoir capacity calculation algorithms, etc. By integrating spatial data acquisition and processing technology, the field of reservoir capacity measurement technology aims to provide scientific data support for water resources management and scheduling.

[0003] Among them, the reservoir capacity measurement method refers to a method of quantitatively calculating the reservoir capacity using technical means such as remote sensing data and point cloud data. This patent subject is aimed at the spatial measurement of reservoir capacity. It mainly generates a terrain model of the bottom and surrounding areas of the reservoir by collecting remote sensing data and combining it with three-dimensional point cloud data, and realizes the measurement of reservoir capacity by calculating the spatial volume between the water level line and the terrain model. At the same time, the method is based on the volume calculation method based on the mathematical model, and the terrain data of the reservoir is associated with the water level change information, and the quantitative analysis of the reservoir capacity is realized through precise geometric calculation.

[0004] In the existing technology, remote sensing data and point cloud data have not been deeply integrated, resulting in insufficient accurate extraction of complex terrain details and boundary features, and it is difficult to reflect scientific regional division. The single geometric fitting method has low adaptability when dealing with boundaries and transition areas, poor model continuity, and complex areas are prone to error accumulation. The reservoir capacity calculation method lacks refined subdivision and boundary correction, and the boundary area error in volume measurement is large, especially in scenes with complex terrain or large water level changes. The lack of volume distribution difference verification and partition correction leads to inconsistent partition data, affecting the reliability of the overall measurement results and the scientific nature of water resources management decisions. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a reservoir capacity measurement method and system based on remote sensing and point cloud data.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a reservoir capacity measurement method based on remote sensing and point cloud data, comprising the following steps:

[0007] S1: Based on the input remote sensing image data and point cloud data, extract the terrain surface point set, calculate the three-dimensional coordinate value, curvature value and point density value of each point, combine the changes in the reservoir boundary, compare and classify the curvature and density data, determine the regional division of the reservoir boundary, and generate the terrain sub-region distribution value;

[0008] S2: Based on the distribution value of the terrain sub-region, the three-dimensional coordinate points in each sub-region are fitted, the control point set is extracted and combined with the reservoir bottom terrain, the three-dimensional coordinate values ​​and curvature values ​​of the control points are calculated, and interpolation operations are performed to generate the three-dimensional grid parameter values ​​of the sub-region;

[0009] S3: based on the three-dimensional grid parameter values ​​of the sub-regions, extract the three-dimensional coordinate values ​​and normal vector values ​​of the boundary grid nodes of the multiple sub-regions, combine the boundary coordinate values ​​of the reservoir area division, adjust the three-dimensional coordinates and normal vectors of the nodes, smooth the boundary node data, and generate a global three-dimensional grid continuous value;

[0010] S4: Based on the global three-dimensional grid continuous value, according to the partitioning rules of the reservoir volume calculation unit, extract the three-dimensional node coordinate values ​​and the unit volume boundary values, calculate the volume of each unit, accumulate and correct the volume value of the boundary area, and generate the total reservoir capacity value of the terrain model;

[0011] S5: Based on the total storage capacity value of the terrain model, perform partition verification of the global storage capacity data, extract the volume distribution value of the partition boundary, verify the volume difference of the reservoir storage capacity boundary transition, correct the multi-partition volume data, and generate the reservoir storage capacity measurement value.

[0012] The terrain sub-area distribution value specifically refers to the three-dimensional coordinate value, curvature value, and point density value. The sub-area three-dimensional grid parameter value specifically refers to the control point set and three-dimensional grid interpolation. The global three-dimensional grid continuous value includes the three-dimensional coordinates of the boundary grid nodes, the normal vector value, and the node data smoothing processing. The total storage capacity value of the terrain model specifically refers to the unit volume boundary value and the calculation of each unit volume. The reservoir storage capacity measurement value specifically refers to the partition verification volume value and the boundary volume correction.

[0013] As a further solution of the present invention, the step of obtaining the terrain sub-region distribution value is specifically as follows:

[0014] S111: extracting a set of terrain surface points from the input remote sensing image data and point cloud data, calculating the displacement vectors of the points in the x-direction, y-direction and z-direction by analyzing the three-dimensional coordinate values ​​point by point, analyzing the relative distribution and variation range of multi-directional displacements, integrating the three-dimensional coordinate displacement data, and generating the three-dimensional coordinate value distribution of the terrain points;

[0015] S112: based on the three-dimensional coordinate value distribution of the terrain points, the principal curvature and Gaussian curvature of each point are calculated in turn, the difference between the curvatures is analyzed in combination with the local gradient change of the multi-curvature values, the area with obvious curvature change is screened point by point and the boundary characteristics are judged, the boundary difference data is used to mark the reservoir boundary change area, and the curvature distribution of the terrain points is generated;

[0016] S113: Density calculation is performed on the curvature distribution of the terrain points, and the distribution distance and quantity characteristics of all points in the area are analyzed point by point. Based on the ratio of the volume of points in the local area to the number of points, the formula is adopted:

[0017] ;

[0018] Calculate point density and generate terrain point density value distribution;

[0019] in, represents the density value of the i-th point, represents the distance between the i-th point and the j-th point in the neighborhood, represents the weight parameter of the jth point, represents the volume of the area where the i-th point is located, Represents the sum of all points in the neighborhood;

[0020] S114: Based on the joint analysis of the curvature distribution of the terrain points and the density value distribution of the terrain points, the point density distribution areas whose curvature values ​​exceed the threshold are screened, and the interval range intersection operation is performed on the curvature distribution points and the point density distribution points. The variation range of the curvature value and the density value is compared and analyzed, and the distribution of the reservoir boundary area is determined by determining the point classification boundary area that meets the boundary characteristic conditions, and the terrain sub-area distribution value is generated.

[0021] As a further solution of the present invention, the step of obtaining the sub-region three-dimensional grid parameter value is specifically as follows:

[0022] S211: Based on the terrain sub-region distribution value, the three-dimensional coordinate point set in each sub-region is analyzed, and the distribution characteristics of the three-dimensional coordinate points in the sub-region are calculated point by point. By analyzing the distribution law of the point set and combining the geometric relationship between the points in the local region, the control point distribution characteristics are analyzed, and the control point set of the sub-region is determined by a local fitting method to generate the distribution value of the sub-region control point set;

[0023] S212: Based on the distribution value of the control point set in the sub-region, the three-dimensional coordinate value of the control point is calculated point by point in combination with the terrain at the bottom of the reservoir, and the geometric change characteristics of the control point in the local area are calculated using the coordinate value. By screening the key values ​​of the principal curvature and Gaussian curvature point by point, the curvature characteristics in the control point set are extracted to generate the curvature distribution of the control point;

[0024] S213: performing interpolation operation on the curvature distribution of the control points by calculating a weighted combination of the curvature value of the control points and the three-dimensional coordinate value, using the formula:

[0025] ;

[0026] Generate sub-region three-dimensional grid parameter values;

[0027] in, Represents the parameter value of the 3D grid of the sub-region after interpolation, represents the weight parameter of the control point, represents the curvature value of the control point, A vector representing the three-dimensional coordinate values ​​of the control points, Indicates weighted calculation of all points in the control point set. Represents the number of control points.

[0028] As a further solution of the present invention, the step of obtaining the global three-dimensional grid continuous value is specifically as follows:

[0029] S311: extracting the three-dimensional coordinate values ​​and normal vector values ​​of the grid nodes at the boundaries of the multiple sub-regions from the three-dimensional grid parameter values ​​of the sub-regions, and by analyzing the geometric distribution relationship of the boundary nodes, for the grid nodes at the intersection of the multiple sub-regions, according to the adjacency characteristics of the boundary surface nodes, calculating the coordinates and normal vector distribution characteristics of the intersection point by point, and generating the three-dimensional coordinates and normal vector values ​​of the grid nodes at the boundaries of the multiple sub-regions;

[0030] S312: Based on the three-dimensional coordinates and normal vector values ​​of the boundary grid nodes of the multiple sub-regions, combined with the boundary coordinate values ​​of the reservoir area division, the geometric positions of the nodes are adjusted point by point, using the formula:

[0031] ;

[0032] Calculate and generate the three-dimensional coordinates and normal vector values ​​of the nodes after boundary smoothing;

[0033] in, Represents the smoothed The three-dimensional coordinates of the nodes, Represents the first The three-dimensional coordinates of the nodes, For the adjacent The three-dimensional coordinates of the nodes, is the weight coefficient of the adjacent node, is the weighted sum of adjacent nodes, is the total number of adjacent nodes;

[0034] S313: Based on the three-dimensional coordinates and normal vector values ​​of the nodes after boundary smoothing, through continuous interpolation calculation of all sub-region grid nodes, the connection characteristics of the multi-region grid nodes in the transition area are adjusted point by point, the node distribution values ​​are optimized according to the geometric features between adjacent areas, the transition area of ​​the multi-region grid is smoothed, and the global three-dimensional grid continuous value is generated.

[0035] As a further solution of the present invention, the steps for obtaining the total reservoir capacity value of the terrain model are specifically as follows:

[0036] S411: based on the global three-dimensional grid continuous value, according to the division rule of the reservoir volume calculation unit, gradually extract the three-dimensional node coordinate values ​​in the divided unit, analyze the boundary attributes of the divided unit, and calculate the volume boundary characteristics of the divided unit by comparing the geometric relationship between the unit boundary and the adjacent unit node by node, so as to generate the three-dimensional node coordinate values ​​and the unit volume boundary values;

[0037] S412: Based on the three-dimensional node coordinate values ​​and the unit volume boundary values, the geometric coordinates of the unit nodes are analyzed unit by unit, and the vector relationship of the three points in the unit is constructed by using the formula:

[0038] ;

[0039] Calculate the volume value of each unit and generate all unit volume values;

[0040] in, Representative The volume of a unit, Indicates The three-dimensional vector of the reference point in the cell, and represents the three-dimensional vector from the reference point to the other two points, represents the vector dot product, represents the vector cross product, Represents absolute value operation;

[0041] S413: Based on the volume values ​​of all the units, the volume is accumulated unit by unit, the volume value of the boundary unit is re-corrected, the volume contribution of the boundary area is adjusted according to the partitioning rule, and the overall volume is calibrated after the accumulation is completed to generate the total storage capacity value of the terrain model.

[0042] As a further solution of the present invention, the steps for obtaining the reservoir capacity measurement value are specifically as follows:

[0043] S511: Based on the total storage capacity value of the terrain model, perform a partition check on the global storage capacity data, calculate the volume distribution point by point for each partition by analyzing the geometric characteristics of the partition boundary, mark the volume distribution features of multiple partition boundaries, and generate the volume distribution value of the partition boundary;

[0044] S512: Based on the volume distribution value of the partition boundary, verify the volume difference of the reservoir storage capacity boundary transition, and analyze the difference distribution characteristics of each partition by comparing the volume value differences of adjacent partition boundaries, using the formula:

[0045] ;

[0046] Calculate the total volume correction value of the boundary area to generate corrected multi-partition volume data;

[0047] in, Indicates the total volume correction value, , Respectively represent and The volume value of the partition, is the correction factor used to adjust the influence weight of the volume difference of the partition boundary. is the total number of partitions;

[0048] S513: Based on the corrected multi-partition volume data, gradually accumulate the corrected volume values ​​of the multi-partitions, adjust the volume data of the boundary area by accumulating and integrating the global partition data, calibrate the integrity of the boundary volume correction, complete the full storage capacity calibration, and generate the reservoir capacity measurement value.

[0049] A reservoir capacity measurement system based on remote sensing and point cloud data, the reservoir capacity measurement system based on remote sensing and point cloud data is used to execute the above-mentioned reservoir capacity measurement method based on remote sensing and point cloud data, the system comprises:

[0050] The terrain point extraction module extracts terrain surface points based on remote sensing images and point cloud data, calculates three-dimensional coordinate values, curvature values, and point density values, and generates terrain sub-area distribution values ​​through classification and judgment in combination with reservoir boundary change information;

[0051] The regional grid fitting module fits the point set in the sub-region based on the distribution value of the terrain sub-region, extracts the control point set, combines the terrain information at the bottom of the reservoir, and generates the sub-region three-dimensional grid parameter value through interpolation operation;

[0052] The boundary node adjustment module extracts the three-dimensional coordinate values ​​and normal vector values ​​of the boundary grid nodes based on the three-dimensional grid parameter values ​​of the sub-region, combines the boundary coordinate values ​​of the reservoir region, and generates a global three-dimensional grid continuous value through smoothing calculation of data between nodes;

[0053] The unit volume calculation module is based on the global three-dimensional grid continuous value and the reservoir volume calculation rule, extracts the node coordinate value and the volume boundary value, calculates each unit volume, accumulates all unit volumes, corrects the boundary area volume data, and generates the total reservoir capacity value of the terrain model;

[0054] The partition verification and correction module extracts the partition boundary volume distribution value based on the total storage capacity value of the terrain model, analyzes the transition volume difference of the storage capacity boundary, calculates and corrects the partition volume data, and generates the reservoir storage capacity measurement value.

[0055] Compared with the prior art, the advantages and positive effects of the present invention are:

[0056] In the present invention, through the comprehensive application of remote sensing image data and point cloud data, three-dimensional coordinates, curvature values ​​and point density values ​​are used to accurately extract terrain features, thereby realizing the scientific division and feature description of sub-regions. Through fitting and interpolation calculations, refined three-dimensional grid parameters are generated to improve the accuracy of terrain modeling and the ability to describe the spatial geometry of complex areas. The coordinates and normal vectors of boundary nodes are dynamically adjusted to optimize the continuity of the grid and ensure the integrity of the boundary transition area. Unit volume subdivision and boundary correction are used to accurately describe the complex volume of the boundary, and the partition verification and volume distribution difference correction are combined to ensure the consistency of volume data and the accuracy of measurement results, meeting the quantitative analysis needs in complex water resource scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0058] Figure 2 A flow chart of the steps for obtaining the terrain sub-region distribution value of the present invention;

[0059] Figure 3 A flowchart of the steps for obtaining the sub-region three-dimensional grid parameter values ​​of the present invention;

[0060] Figure 4 A flowchart of steps for obtaining continuous values ​​of a global three-dimensional grid according to the present invention;

[0061] Figure 5 A flow chart of the steps for obtaining the total reservoir capacity value of the terrain model of the present invention;

[0062] Figure 6 The present invention is a flow chart of the steps for obtaining the reservoir capacity measurement value. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0064] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0065] Embodiment 1

[0066] See also Figure 1 The present invention provides a technical solution: a reservoir capacity measurement method based on remote sensing and point cloud data, comprising the following steps:

[0067] S1: Based on the input remote sensing image data and point cloud data, extract the terrain surface point set, calculate the three-dimensional coordinate value, curvature value and point density value of each point, combine the changes in the reservoir boundary, compare and classify the curvature and density data, determine the regional division of the reservoir boundary, and generate the terrain sub-region distribution value;

[0068] S2: Based on the distribution value of the terrain sub-region, the three-dimensional coordinate points in each sub-region are fitted, the control point set is extracted and combined with the reservoir bottom terrain, the three-dimensional coordinate values ​​and curvature values ​​of the control points are calculated, and interpolation operations are performed to generate the three-dimensional grid parameter values ​​of the sub-region;

[0069] S3: Based on the sub-region 3D grid parameter values, extract the 3D coordinate values ​​and normal vector values ​​of the multi-sub-region boundary grid nodes, combine the boundary coordinate values ​​of the reservoir area division, adjust the 3D coordinates and normal vectors of the nodes, smooth the boundary node data, and generate the global 3D grid continuous value;

[0070] S4: Based on the global three-dimensional grid continuous value and in accordance with the division rules of the reservoir volume calculation unit, the three-dimensional node coordinate values ​​and unit volume boundary values ​​are extracted, the volume of each unit is calculated, the volume value of the boundary area is accumulated and corrected, and the total reservoir capacity value of the terrain model is generated;

[0071] S5: Based on the total storage capacity value of the terrain model, perform partition verification of the global storage capacity data, extract the volume distribution value of the partition boundary, verify the volume difference of the reservoir storage capacity boundary transition, correct the multi-partition volume data, and generate the reservoir storage capacity measurement value.

[0072] The terrain sub-area distribution values ​​specifically refer to the three-dimensional coordinate values, curvature values, and point density values. The sub-area three-dimensional grid parameter values ​​specifically refer to the control point set and three-dimensional grid interpolation. The global three-dimensional grid continuous values ​​include the three-dimensional coordinates of the boundary grid nodes, the normal vector values, and the node data smoothing processing. The total storage capacity value of the terrain model specifically refers to the unit volume boundary value and the volume calculation of each unit. The reservoir capacity measurement value specifically refers to the partition verification volume value and the boundary volume correction.

[0073] See also Figure 2 , the specific steps for obtaining the terrain sub-region distribution value are:

[0074] S111: extracting a set of terrain surface points from the input remote sensing image data and point cloud data, calculating the displacement vectors of the points in the x-direction, y-direction and z-direction by analyzing the three-dimensional coordinate values ​​point by point, analyzing the relative distribution and variation range of multi-directional displacements, integrating the three-dimensional coordinate displacement data, and generating the three-dimensional coordinate value distribution of the terrain points;

[0075] The position of each pixel in the remote sensing image is calculated based on its spectral reflectance value. Combined with the three-dimensional distribution of points in the point cloud data, the three-dimensional coordinate value of each point is constructed with its longitude, latitude, and altitude. Then, for the point cloud data, the displacement vectors in the x, y, and z directions in the three-dimensional space are constructed, and the relative displacement of each point in space is calculated using geometric formulas. , , The displacement is analyzed point by point in a 3D way, the distribution direction of the points in the 3D space and their relative displacement range are calculated, and finally the analyzed 3D coordinate displacement data are combined into the 3D coordinate value distribution data of the terrain points as the basic data set for subsequent analysis.

[0076] S112: based on the three-dimensional coordinate value distribution of the terrain points, the principal curvature and Gaussian curvature of each point are calculated in turn, the difference between the curvatures is analyzed in combination with the local gradient change of the multi-curvature values, the area with obvious curvature change is screened point by point and the boundary characteristics are judged, the boundary difference data is used to mark the reservoir boundary change area, and the curvature distribution of the terrain points is generated;

[0077] According to the neighborhood distribution relationship of the points in the three-dimensional coordinate data, the set of neighboring points of each point in its local area is screened, and the principal curvature value is obtained by calculating the normal vector difference between the neighboring points. The curvature formula is used The principal curvature values ​​are calculated point by point, where is the normal vector difference, The Gaussian curvature value of the point is obtained by multiplying the principal curvature for the Gaussian curvature value of the point. The curvature value of each point is further analyzed for gradient change. The points whose curvature change gradient is greater than the threshold are selected as preliminary boundary change points. Combined with the spatial distribution characteristics of each point and the relative position differences between boundary point areas, the reservoir boundary change area data is marked to generate the curvature distribution of terrain points.

[0078] S113: Density calculation is performed on the curvature distribution of terrain points, and the distribution distance and quantity characteristics of all points in the area are analyzed point by point. Based on the ratio of the volume of points in the local area to the number of points, the formula is used:

[0079] ;

[0080] Calculate point density and generate terrain point density value distribution;

[0081] in, represents the density value of the i-th point, represents the distance between the i-th point and the j-th point in the neighborhood, represents the weight parameter of the jth point, represents the volume of the area where the i-th point is located, Represents the sum of all points in the neighborhood;

[0082] formula:

[0083] ;

[0084] The benefit of the formula is that, through the joint calculation of the distance between points, weight parameters and regional volume, the density distribution of each point in the local area can be evaluated more accurately, thereby improving the accuracy of the density assessment of regional boundary points.

[0085] Detailed explanation of the formula and the process of formula calculation and derivation:

[0086] represents the density value of the i-th point, Represents the distance between the i-th point and the j-th point in its neighborhood, which is obtained by screening the neighborhood points within a range of 0.5 meters in the point cloud. Represents the weight parameter of the jth point. The weight is set as a function value that decreases according to the distance between points. The formula is used. ,in is the adjustment coefficient, representing the decay rate of density to distance, is the volume of the local area where the i-th point is located, calculated based on the point cloud envelope volume within its neighborhood points. Represents the sum operation of all points in the neighborhood.

[0087] Substitute the specific data into the calculation: Assume that the neighborhood point set of point i is 5 points, and the distances between the points are meters, and the corresponding weight parameter is calculated as

[0088] , the regional volume of the neighborhood points cubic meters, substituting into the formula gives:

[0089] ;

[0090] ;

[0091] ;

[0092] The calculation results show that the local density value of the i-th point is 5.853, and this result is used to determine whether the point belongs to the dense area.

[0093] S114: Based on the joint analysis of the terrain point curvature distribution and the terrain point density value distribution, screen the point density distribution areas where the curvature value exceeds the threshold, perform an intersection operation on the interval ranges of the curvature distribution points and the point density distribution points, compare and analyze the change ranges of the curvature value and the density value, and determine the reservoir boundary area distribution by judging the point classification boundary area that meets the boundary characteristic conditions, and generate the terrain sub-region distribution value.

[0094] By judging whether each point density value is within the dense area range, jointly screen the points with density values higher than the average value and the points with curvature value changes, combine the curvature-density joint distribution matrix of the points in the boundary change area, perform intersection analysis using the relative gradient change range between the curvature value and the density value, mark the areas with significant curvature-density differences, and determine the regional division result by calculating the spatial neighborhood overlap degree of the adjacent distribution points of the boundary points, and generate the terrain sub-region distribution value of the reservoir boundary area.

[0095] Please refer to Figure 3 , and the steps for obtaining the three-dimensional grid parameter values of the sub-region are specifically as follows:

[0096] S211: Based on the terrain sub-region distribution value, analyze the set of three-dimensional coordinate points in each sub-region, calculate the distribution characteristics of the three-dimensional coordinate points in the sub-region point by point, determine the distribution law of the point set through analysis, analyze the distribution characteristics of the control points in combination with the geometric relationship between the points in the local area, and use the local fitting method to determine the control point set of the sub-region, and generate the sub-region control point set distribution value;

[0097] By calculating the geometric relationship between each point in the point set and its surrounding points point by point, judge the relative position and distance characteristics between the points, and through the distance calculation formula The distance between each pair of points is calculated, and the geometric changes of the local area are analyzed in combination with the distance characteristics. The distribution of the points in the local area is clustered, and the control points of the local fitting are determined by calculating the centroid coordinate values ​​in the point cluster. The distribution curvature in the area is judged by fitting, and the geometric model generated by fitting the three-dimensional coordinate point set in the area is adjusted according to the control points. The optimal control point position is obtained through local adjustment of the geometric model, and the distribution value of the sub-area control point set is generated.

[0098] S212: Based on the distribution value of the control point set in the sub-region, the three-dimensional coordinate value of the control point is calculated point by point in combination with the terrain at the bottom of the reservoir, and the geometric change characteristics of the control point in the local area are calculated using the coordinate value. By screening the key values ​​of the principal curvature and Gaussian curvature point by point, the curvature characteristics in the control point set are extracted to generate the curvature distribution of the control point;

[0099] The change characteristics of the coordinate values ​​of each control point in the local area are calculated in turn, and the principal curvature and Gaussian curvature are calculated by extracting the gradient of the three-dimensional coordinates. The curvature calculation formula is: , the three-dimensional coordinate value parameters of the control points are input point by point to obtain the curvature value, and the key characteristics of the geometric changes in the region are determined by calculating the distribution trends of the principal curvature and Gaussian curvature of the control point set. The areas with drastic curvature changes are screened and marked, and the control point set is further classified and analyzed through the regional division characteristics to generate the curvature distribution of the control points.

[0100] S213: interpolating the curvature distribution of the control points by calculating the weighted combination of the curvature value of the control points and the three-dimensional coordinate value, using the formula:

[0101] ;

[0102] Generate sub-region three-dimensional grid parameter values;

[0103] in, Represents the parameter value of the 3D grid of the sub-region after interpolation, represents the weight parameter of the control point, represents the curvature value of the control point, A vector representing the three-dimensional coordinate values ​​of the control points, Indicates weighted calculation of all points in the control point set. Represents the number of control points.

[0104] formula:

[0105] ;

[0106] The benefit of the formula is that, through the weighted combination operation of the curvature value and the three-dimensional coordinate value of the control point, the accuracy of the grid parameterization in the region is further optimized, the consistency of the grid division and the distribution of the control points is improved, and the relative influence trade-off between multiple points is achieved by using the weight parameters.

[0107] Detailed explanation of the formula and the process of formula calculation and derivation:

[0108] Represents the parameter value of the three-dimensional grid of the sub-region after interpolation. The value range is obtained by measuring the distribution of control points in the region. Represents the weight parameter of the control point. The weight parameter is set based on the spatial contribution ratio of the control point to the grid point. The weight is calculated by the formula calculate, is the distance between the control point and the interpolation point, and the value range is obtained by calculating the distance relationship between any points in the control point set; is the curvature value of the control point, and the curvature value is calculated by the formula Calculate and obtain, is the three-dimensional coordinate value vector of the control point, which is obtained by actual measurement and gradient calculation of the coordinate point. Indicates weighted calculation of all points in the control point set. is the number of control points in the region, obtained by direct numerical statistics.

[0109] Assume that the sub-region contains 5 control points, and the curvature values ​​are , the three-dimensional coordinate vectors are , the distances between the interpolation points and the control points are , through the formula Calculate the weights and get the weight values ​​respectively , put all the parameters into the formula and calculate as follows:

[0110] ;

[0111] Expand step-by-step calculation:

[0112] ;

[0113] ;

[0114] The results show that the three-dimensional grid parameter values ​​of the sub-area after interpolation have optimized coordinate parameters in spatial distribution, and the spatial influence of the control points is reflected through weight distribution. The generated three-dimensional grid parameter values ​​of the sub-area can more accurately describe the terrain characteristics in the area.

[0115] See also Figure 4 , the steps for obtaining the continuous value of the global three-dimensional grid are as follows:

[0116] S311: extracting the three-dimensional coordinate values ​​and normal vector values ​​of the mesh nodes at the boundaries of the multiple sub-regions from the three-dimensional mesh parameter values ​​of the sub-regions, analyzing the geometric distribution relationship of the boundary nodes, and calculating the coordinates and normal vector distribution characteristics of the mesh nodes at the intersection of the multiple sub-regions point by point according to the adjacency characteristics of the boundary surface nodes, and generating the three-dimensional coordinates and normal vector values ​​of the mesh nodes at the boundaries of the multiple sub-regions;

[0117] The coordinates of the boundary nodes of each sub-region are screened point by point, and the geometric distribution relationship between the nodes is calculated by combining the boundary adjacency characteristics. For example, the coordinate values ​​of the nodes contained in the boundary grid of a sub-region are extracted, and they can be paired with the grid nodes of the adjacent sub-region. The Euclidean distance is calculated by the difference in the three-dimensional coordinate values ​​of the nodes. The formula is: in, and are the three-dimensional coordinates of the grid nodes of the sub-region and the adjacent sub-region respectively. The value of is used to filter out the nodes in the boundary transition area. Then, combined with the boundary normal vector value, the angle between the node normal vectors can be calculated using the formula: in, and is the normal vector value of the two nodes, is the modulus of the vector, through Determine the adjacent nodes with smaller normal vector differences. The above calculation can analyze the distribution characteristics of the grid nodes at the junction of multiple sub-regions. For example: Assume that the boundary nodes of sub-region 1 are , the adjacent sub-region boundary nodes are , the Euclidean distance between the two , if its normal vector is and , angle , which satisfies the condition that it is less than the set threshold, so the two nodes are considered to constitute an adjacent boundary, and the three-dimensional coordinates and normal vector values ​​of the multi-sub-region boundary grid nodes are obtained.

[0118] S312: Based on the three-dimensional coordinates and normal vector values ​​of the boundary grid nodes of the multi-sub-regions, combined with the boundary coordinate values ​​of the reservoir area division, the geometric positions of the nodes are adjusted point by point, using the formula:

[0119] ;

[0120] Calculate and generate the three-dimensional coordinates and normal vector values ​​of the nodes after boundary smoothing;

[0121] in, Represents the smoothed The three-dimensional coordinates of the nodes, Represents the first The three-dimensional coordinates of the nodes, is the three-dimensional coordinates of the adjacent th node, is the weight coefficient of the adjacent node, is the weighted sum of the adjacent nodes, is the total number of adjacent nodes;

[0122] Formula:

[0123] ;

[0124] The benefit of the formula is that by introducing the weight coefficient of the adjacent node calculate the smoothing adjustment value of each node, while maintaining the overall geometric consistency between the node and its neighborhood, and improving the boundary smoothing effect.

[0125] Detailed explanation of the formula and the derivation process of the formula calculation:

[0126] In the formula, represents the three-dimensional coordinates of the th node after smoothing, represents the three-dimensional coordinates of the th node before adjustment, is the three-dimensional coordinates of the adjacent th node, is the weight coefficient of the adjacent node, is the weighted sum of the adjacent nodes, is the total number of adjacent nodes.

[0127] Parameter acquisition: and The three-dimensional coordinates of are extracted from the three-dimensional coordinates of the sub-region boundary nodes. is the inverse weight of the node distance, and the formula is: where is the node and Euclidean distance between, which is obtained through the above calculation.

[0128] Calculation process:

[0129] Assume that the coordinates of node are , and the coordinates of the adjacent node are , through the formula , the distance , get . Substitute into the formula:

[0130] ;

[0131] Calculate to get .

[0132] This result shows that by smoothing the node coordinates from becomes , satisfying the consistency of the geometric relationship with the adjacent nodes, and generating the three-dimensional coordinates and normal vector values ​​of the nodes after boundary smoothing.

[0133] S313: Based on the three-dimensional coordinates and normal vector values ​​of the nodes after boundary smoothing, the connection characteristics of the multi-region grid nodes in the transition area are adjusted point by point through continuous interpolation calculation of all sub-region grid nodes, the node distribution value is optimized according to the geometric features between adjacent areas, the transition area of ​​the multi-region grid is smoothed, and the global three-dimensional grid continuous value is generated.

[0134] By analyzing the geometric distribution of multi-region transition nodes, the transition nodes at the boundaries of adjacent regions are interpolated point by point. First, for each node, the geometric midpoint of its neighboring nodes is calculated. The formula is: in is the geometric midpoint of the node neighborhood, For the The node coordinates are calculated by adjusting the smooth connection characteristics of the nodes in the transition area, and continuous interpolation nodes are obtained. For example: If the node coordinates of the sub-region mesh boundary are , the neighborhood contains three nodes, whose coordinates are , , , substitute into the formula to calculate the geometric midpoint , the nodes of the final optimized transition area are obtained through multiple interpolations, and the global three-dimensional grid continuous values ​​are generated.

[0135] See also Figure 5 , the specific steps for obtaining the total reservoir capacity value of the terrain model are:

[0136] S411: based on the global three-dimensional grid continuous value, according to the division rule of the reservoir volume calculation unit, gradually extract the three-dimensional node coordinate values ​​in the divided unit, analyze the boundary properties of the divided unit, and calculate the volume boundary characteristics of the divided unit by comparing the geometric relationship between the unit boundary and the adjacent unit node by node, and generate the three-dimensional node coordinate values ​​and unit volume boundary values;

[0137] Extract the unit node and boundary characteristics under the partitioning rules, gradually analyze the geometric characteristics of the partitioned unit, analyze the relative position of the three-dimensional coordinates and their adjacency relationship node by node, calculate the minimum closed area of ​​each unit boundary by analyzing the geometric relationship within the unit boundary after partitioning, use the volume boundary attribute of the partitioned boundary, combine the three-dimensional coordinate points inside the unit, use the geometric attributes to analyze the spatial relationship between the points, lines and surfaces of each boundary, process the boundary closure of the partitioned unit one by one, construct the volume constraint of each partitioned unit through continuous partitioning of multiple boundaries, analyze the range of volume boundary values, and extract the spatial coordinate nodes inside each unit, screen the boundaries of each unit through the distribution position of the nodes, adjust the partition boundaries that do not meet the constraint rules, and correct their boundary closure characteristics, verify the volume distribution in the partitioned unit through the integrity check of the partitioning rules, and obtain the three-dimensional node coordinate values ​​and unit volume boundary values.

[0138] S412: Based on the three-dimensional node coordinate values ​​and the unit volume boundary values, the geometric coordinates of the unit nodes are analyzed unit by unit, and the vector relationship of the three points in the unit is constructed using the formula:

[0139] ;

[0140] Calculate the volume value of each unit and generate all unit volume values;

[0141] in, Representative The volume of a unit, Indicates The three-dimensional vector of the reference point in the cell, and represents the three-dimensional vector from the reference point to the other two points, represents the vector dot product, represents the vector cross product, Represents absolute value operation;

[0142] formula:

[0143] ;

[0144] The benefit of the formula is that, through the combination of dot products and cross products based on the spatial geometric relationship of three points, the volume inside the subdivision unit can be accurately calculated, and at the same time the spatial constraints of the boundary area can be clearly defined, thereby improving the accuracy of volume calculation and the stability of boundary processing.

[0145] Detailed explanation of the formula and the process of formula calculation and derivation:

[0146] , , The three-dimensional vector relationship of the three coordinate points in the extracted unit is obtained respectively. Assuming that the node coordinates in a certain subdivision unit are , , ,but:

[0147] ;

[0148] calculate and :

[0149] ;

[0150] calculate The cross product of:

[0151] ;

[0152] Calculating the dot product :

[0153] ;

[0154] Substituting into the formula:

[0155] ;

[0156] The result shows that the volume of the subdivision unit is 0.6667. By calculating this volume value, the total volume value of all subdivision units can be further accumulated for subsequent boundary volume correction and overall volume calculation.

[0157] S413: Based on the volume values ​​of all units, the volume accumulation is performed unit by unit, and the volume value of the boundary unit is re-corrected, and the volume contribution of the boundary area is adjusted according to the subdivision rule. After the accumulation is completed, the overall volume is calibrated to generate the total reservoir capacity value of the terrain model.

[0158] For the boundary area subdivision units, by analyzing the boundary position of each unit and the volume distribution difference with the adjacent units, combined with the boundary node coordinates of the subdivision units, the geometric constraints of the boundary units are checked point by point, and the subdivision volume of the boundary area is corrected using the volume distribution weight. According to the volume distribution rule of the internal area of ​​the subdivision boundary, the contribution of each boundary subdivision unit to the overall volume is redistributed by adjusting the distribution of the boundary point position weight value, and the corrected volume of the boundary area is supplemented and superimposed on the basis of the accumulation of all subdivision unit volumes to complete the calibration of the overall reservoir volume and obtain the total reservoir value of the terrain model.

[0159] See also Figure 6 , the specific steps for obtaining the reservoir capacity measurement value are:

[0160] S511: Based on the total storage capacity value of the terrain model, perform a partition check on the global storage capacity data, calculate the volume distribution point by point for each partition by analyzing the geometric characteristics of the partition boundary, mark the volume distribution features of multiple partition boundaries, and generate the volume distribution value of the partition boundary;

[0161] The volume distribution value of each partition boundary is extracted through partition verification, and the volume distribution of each partition boundary area is calculated point by point. First, the three-dimensional node coordinates of the partition are analyzed, and the volume-related parameters of each boundary node are extracted through the spatial position relationship of the coordinate points. The volume difference between the nodes on the boundary and the adjacent nodes is calculated in turn, and the difference data is accumulated according to the node position to obtain the preliminary volume distribution of the boundary nodes; for the boundary area between different partitions, the intersection points of adjacent partitions are extracted, and the intersection point positions are paired with the corresponding volume parameters. By calculating the geometric position difference between the partition nodes and combining the geometric curvature of the partition boundary, the geometric characteristic parameters of the boundary are extracted and the volume distribution characteristics are associated. Combined with the boundary point density analysis results, the volume weight values ​​of the boundary nodes are marked through the change trend of the point cloud data density in the adjacent area, and the volume distribution of the partition boundary is calculated by weighted accumulation of the weights; after completing the extraction of the partition volume distribution parameters, the boundary volume distribution data is aggregated within the region, and the consistency with the global model storage capacity data is checked point by point. Combined with the global volume distribution verification results, the volume distribution of abnormal boundary nodes is calculated again to generate the volume distribution value of the partition boundary.

[0162] S512: Based on the volume distribution value of the partition boundary, verify the volume difference of the reservoir storage capacity boundary transition, and analyze the difference distribution characteristics of each partition by comparing the volume value differences of adjacent partition boundaries, using the formula:

[0163] ;

[0164] Calculate the total volume correction value of the boundary area to generate corrected multi-partition volume data;

[0165] in, Indicates the total volume correction value, , Respectively represent and The volume value of the partition, is the correction factor used to adjust the influence weight of the volume difference of the partition boundary. is the total number of partitions;

[0166] formula:

[0167] ;

[0168] The benefit of the formula is that it increases the sensitivity of volume differences by introducing a combination of square and root signs, and also adds a weight adjustment factor The larger volume differences are corrected smoothly, thereby enhancing the flexibility of adjusting the transition volume of the partition boundary and reducing the interference of single-point volume anomalies on the total volume correction results.

[0169] Detailed explanation of the formula and the process of formula calculation and derivation:

[0170] Represents the total volume correction value, which is calculated by adding the correction values ​​of each partition. and Respectively represent and The volume value of the partition, assuming that the collected partition volume data are , , , , weight adjustment coefficient According to the regional density volatility setting, the volume correction process is as follows:

[0171] First calculate the correction between partition 1 and partition 2:

[0172] ;

[0173] Calculate the correction between partitions 2 and 3:

[0174] ;

[0175] Calculate the correction between partitions 3 and 4:

[0176] ;

[0177] Cumulative calculation of total volume correction value:

[0178] ;

[0179] The result shows that the total volume correction value is 46.28 units. The error caused by the discontinuity of the partition boundary volume is reduced through correction. The generated corrected data will serve as the basis for multi-partition volume data.

[0180] S513: Based on the corrected multi-partition volume data, gradually accumulate the corrected volume values ​​of the multi-partitions, adjust the volume data of the boundary area by accumulating and integrating the global partition data, calibrate the integrity of the boundary volume correction, complete the full storage capacity calibration, and generate the reservoir capacity measurement value.

[0181] Firstly, the overall partition volume is associated with the partition node data of the boundary area, and the corrected partition volume data are merged one by one to complete the global calibration of the partition volume; for the corrected volume data of adjacent partitions, the influence of the corrected volume value on the node volume distribution in the overall partition is analyzed in turn, and the range of volume data variation within the partition is calculated through the distribution weight of the corrected data in the region, and the node volume distribution of each region is smoothed during the accumulation process; the partition data after the global volume correction are accumulated and the full reservoir capacity is calculated in combination with the volume distribution of the partition boundary nodes; after completing the correction of the full reservoir capacity volume, the accumulated partition volume results are compared and calibrated with the actual reservoir measurement data to calibrate the reservoir capacity measurement value.

[0182] A reservoir capacity measurement system based on remote sensing and point cloud data, the reservoir capacity measurement system based on remote sensing and point cloud data is used to execute the above-mentioned reservoir capacity measurement method based on remote sensing and point cloud data, the system comprises:

[0183] The terrain point extraction module extracts terrain surface points based on remote sensing images and point cloud data, calculates three-dimensional coordinate values, curvature values, and point density values, and generates terrain sub-area distribution values ​​through classification and judgment in combination with reservoir boundary change information;

[0184] The regional grid fitting module fits the point set in the sub-region based on the distribution value of the terrain sub-region, extracts the control point set, combines the terrain information at the bottom of the reservoir, and generates the sub-region three-dimensional grid parameter value through interpolation operation;

[0185] The boundary node adjustment module extracts the 3D coordinate values ​​and normal vector values ​​of the boundary grid nodes based on the 3D grid parameter values ​​of the sub-region, combines the boundary coordinate values ​​of the reservoir region, and generates the global 3D grid continuous value through smoothing calculation of the data between nodes;

[0186] The unit volume calculation module is based on the global three-dimensional grid continuous value and the reservoir volume calculation rules. It extracts the node coordinate value and volume boundary value, calculates each unit volume, accumulates all unit volumes, corrects the boundary area volume data, and generates the total reservoir capacity value of the terrain model.

[0187] The partition verification and correction module extracts the partition boundary volume distribution value based on the total storage capacity value of the terrain model, analyzes the transition volume difference of the storage capacity boundary, calculates and corrects the partition volume data, and generates the reservoir storage capacity measurement value.

[0188] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A reservoir capacity measurement method based on remote sensing and point cloud data, characterized in that: The following steps are involved: S1: Based on the input remote sensing image data and point cloud data, extract the terrain surface point set, calculate the three-dimensional coordinate value, curvature value and point density value of each point, combine the changes in the reservoir boundary, compare and classify the curvature and density data, determine the regional division of the reservoir boundary, and generate the terrain sub-region distribution value; S2: Based on the distribution value of the terrain sub-region, the three-dimensional coordinate points in each sub-region are fitted, the control point set is extracted and combined with the reservoir bottom terrain, the three-dimensional coordinate values ​​and curvature values ​​of the control points are calculated, and interpolation operations are performed to generate the three-dimensional grid parameter values ​​of the sub-region; S3: based on the three-dimensional grid parameter values ​​of the sub-regions, extract the three-dimensional coordinate values ​​and normal vector values ​​of the boundary grid nodes of the multiple sub-regions, combine the boundary coordinate values ​​of the reservoir area division, adjust the three-dimensional coordinates and normal vectors of the nodes, smooth the boundary node data, and generate a global three-dimensional grid continuous value; S4: Based on the global three-dimensional grid continuous value, according to the partitioning rules of the reservoir volume calculation unit, extract the three-dimensional node coordinate values ​​and the unit volume boundary values, calculate the volume of each unit, accumulate and correct the volume value of the boundary area, and generate the total reservoir capacity value of the terrain model; S5: Based on the total storage capacity value of the terrain model, perform a partition check on the global storage capacity data, extract the volume distribution value of the partition boundary, check the volume difference of the reservoir storage capacity boundary transition, correct the multi-partition volume data, and generate the reservoir storage capacity measurement value; The terrain sub-area distribution value specifically refers to the three-dimensional coordinate value, curvature value, and point density value. The sub-area three-dimensional grid parameter value specifically refers to the control point set and three-dimensional grid interpolation. The global three-dimensional grid continuous value includes the three-dimensional coordinates of the boundary grid nodes, the normal vector value, and the node data smoothing processing. The total storage capacity value of the terrain model specifically refers to the unit volume boundary value and the calculation of each unit volume. The reservoir storage capacity measurement value specifically refers to the partition verification volume value and the boundary volume correction.

2. The reservoir capacity measurement method based on remote sensing and point cloud data according to claim 1 is characterized in that: The steps for obtaining the terrain sub-region distribution value are specifically as follows: S111: extracting a set of terrain surface points from the input remote sensing image data and point cloud data, calculating the displacement vectors of the points in the x-direction, y-direction and z-direction by analyzing the three-dimensional coordinate values ​​point by point, analyzing the relative distribution and variation range of multi-directional displacements, integrating the three-dimensional coordinate displacement data, and generating the three-dimensional coordinate value distribution of the terrain points; S112: based on the three-dimensional coordinate value distribution of the terrain points, the principal curvature and Gaussian curvature of each point are calculated in turn, the difference between the curvatures is analyzed in combination with the local gradient change of the multi-curvature values, the area with obvious curvature change is screened point by point and the boundary characteristics are judged, the boundary difference data is used to mark the reservoir boundary change area, and the curvature distribution of the terrain points is generated; S113: Density calculation is performed on the curvature distribution of the terrain points, and the distribution distance and quantity characteristics of all points in the area are analyzed point by point. Based on the ratio of the volume of points in the local area to the number of points, the formula is adopted: ； Calculate point density and generate terrain point density value distribution; in, represents the density value of the i-th point, represents the distance between the i-th point and the j-th point in the neighborhood, represents the weight parameter of the jth point, represents the volume of the area where the i-th point is located, Represents the sum of all points in the neighborhood; S114: Based on the joint analysis of the curvature distribution of the terrain points and the density value distribution of the terrain points, the point density distribution areas whose curvature values ​​exceed the threshold are screened, and the interval range intersection operation is performed on the curvature distribution points and the point density distribution points. The variation range of the curvature value and the density value is compared and analyzed, and the distribution of the reservoir boundary area is determined by determining the point classification boundary area that meets the boundary characteristic conditions, and the terrain sub-area distribution value is generated.

3. The reservoir capacity measurement method based on remote sensing and point cloud data according to claim 2 is characterized in that: The steps for obtaining the sub-region three-dimensional grid parameter value are specifically as follows: S211: Based on the terrain sub-region distribution value, the three-dimensional coordinate point set in each sub-region is analyzed, and the distribution characteristics of the three-dimensional coordinate points in the sub-region are calculated point by point. By analyzing the distribution law of the point set and combining the geometric relationship between the points in the local region, the control point distribution characteristics are analyzed, and the control point set of the sub-region is determined by a local fitting method to generate the distribution value of the sub-region control point set; S212: Based on the distribution value of the control point set in the sub-region, the three-dimensional coordinate value of the control point is calculated point by point in combination with the terrain at the bottom of the reservoir, and the geometric change characteristics of the control point in the local area are calculated using the coordinate value. By screening the key values ​​of the principal curvature and Gaussian curvature point by point, the curvature characteristics in the control point set are extracted to generate the curvature distribution of the control point; S213: performing interpolation operation on the curvature distribution of the control points by calculating a weighted combination of the curvature value of the control points and the three-dimensional coordinate value, using the formula: ； Generate sub-region three-dimensional grid parameter values; in, Represents the parameter value of the 3D grid of the sub-region after interpolation, represents the weight parameter of the control point, represents the curvature value of the control point, A vector representing the three-dimensional coordinate values ​​of the control points, Indicates weighted calculation of all points in the control point set. Represents the number of control points.

4. The reservoir capacity measurement method based on remote sensing and point cloud data according to claim 3 is characterized in that: The steps for obtaining the continuous value of the global three-dimensional grid are specifically as follows: S311: extracting the three-dimensional coordinate values ​​and normal vector values ​​of the grid nodes at the boundaries of the multiple sub-regions from the three-dimensional grid parameter values ​​of the sub-regions, and by analyzing the geometric distribution relationship of the boundary nodes, for the grid nodes at the intersection of the multiple sub-regions, according to the adjacency characteristics of the boundary surface nodes, calculating the coordinates and normal vector distribution characteristics of the intersection point by point, and generating the three-dimensional coordinates and normal vector values ​​of the grid nodes at the boundaries of the multiple sub-regions; S312: Based on the three-dimensional coordinates and normal vector values ​​of the boundary grid nodes of the multiple sub-regions, combined with the boundary coordinate values ​​of the reservoir area division, the geometric positions of the nodes are adjusted point by point, using the formula: ； Calculate and generate the three-dimensional coordinates and normal vector values ​​of the nodes after boundary smoothing; in, Represents the smoothed The three-dimensional coordinates of the nodes, Represents the first The three-dimensional coordinates of the nodes, For the adjacent The three-dimensional coordinates of the nodes, is the weight coefficient of the adjacent node, is the weighted sum of adjacent nodes, is the total number of adjacent nodes; S313: Based on the three-dimensional coordinates and normal vector values ​​of the nodes after boundary smoothing, through continuous interpolation calculation of all sub-region grid nodes, the connection characteristics of the multi-region grid nodes in the transition area are adjusted point by point, the node distribution values ​​are optimized according to the geometric features between adjacent areas, the transition area of ​​the multi-region grid is smoothed, and the global three-dimensional grid continuous value is generated.

5. The reservoir capacity measurement method based on remote sensing and point cloud data according to claim 4 is characterized in that: The steps for obtaining the total reservoir capacity value of the terrain model are specifically as follows: S411: based on the global three-dimensional grid continuous value, according to the division rule of the reservoir volume calculation unit, gradually extract the three-dimensional node coordinate values ​​in the divided unit, analyze the boundary attributes of the divided unit, and calculate the volume boundary characteristics of the divided unit by comparing the geometric relationship between the unit boundary and the adjacent unit node by node, so as to generate the three-dimensional node coordinate values ​​and the unit volume boundary values; S412: Based on the three-dimensional node coordinate values ​​and the unit volume boundary values, the geometric coordinates of the unit nodes are analyzed unit by unit, and the vector relationship of the three points in the unit is constructed by using the formula: ； Calculate the volume value of each unit and generate all unit volume values; in, Representative The volume of a unit, Indicates The three-dimensional vector of the reference point in the cell, and represents the three-dimensional vector from the reference point to the other two points, represents the vector dot product, represents the vector cross product, Represents absolute value operation; S413: Based on the volume values ​​of all the units, the volume is accumulated unit by unit, the volume value of the boundary unit is re-corrected, the volume contribution of the boundary area is adjusted according to the partitioning rule, and the overall volume is calibrated after the accumulation is completed to generate the total storage capacity value of the terrain model.

6. The reservoir capacity measurement method based on remote sensing and point cloud data according to claim 5 is characterized in that: The steps for obtaining the reservoir capacity measurement value are specifically as follows: S511: Based on the total storage capacity value of the terrain model, perform a partition check on the global storage capacity data, calculate the volume distribution point by point for each partition by analyzing the geometric characteristics of the partition boundary, mark the volume distribution features of multiple partition boundaries, and generate the volume distribution value of the partition boundary; S512: Based on the volume distribution value of the partition boundary, verify the volume difference of the reservoir storage capacity boundary transition, and analyze the difference distribution characteristics of each partition by comparing the volume value differences of adjacent partition boundaries, using the formula: ； Calculate the total volume correction value of the boundary area to generate corrected multi-partition volume data; in, Indicates the total volume correction value, , Respectively represent and The volume value of the partition, is the correction factor used to adjust the influence weight of the volume difference of the partition boundary. is the total number of partitions; S513: Based on the corrected multi-partition volume data, gradually accumulate the corrected volume values ​​of the multi-partitions, adjust the volume data of the boundary area by accumulating and integrating the global partition data, calibrate the integrity of the boundary volume correction, complete the full storage capacity calibration, and generate the reservoir capacity measurement value.

7. A reservoir capacity measurement system based on remote sensing and point cloud data, characterized in that: According to the reservoir capacity measurement method based on remote sensing and point cloud data according to any one of claims 1 to 6, the system comprises: The terrain point extraction module extracts terrain surface points based on remote sensing images and point cloud data, calculates three-dimensional coordinate values, curvature values, and point density values, and generates terrain sub-area distribution values ​​through classification and judgment in combination with reservoir boundary change information; The regional grid fitting module fits the point set in the sub-region based on the distribution value of the terrain sub-region, extracts the control point set, combines the terrain information at the bottom of the reservoir, and generates the sub-region three-dimensional grid parameter value through interpolation operation; The boundary node adjustment module extracts the three-dimensional coordinate values ​​and normal vector values ​​of the boundary grid nodes based on the three-dimensional grid parameter values ​​of the sub-region, combines the boundary coordinate values ​​of the reservoir region, and generates a global three-dimensional grid continuous value through smoothing calculation of data between nodes; The unit volume calculation module is based on the global three-dimensional grid continuous value and the reservoir volume calculation rule, extracts the node coordinate value and the volume boundary value, calculates each unit volume, accumulates all unit volumes, corrects the boundary area volume data, and generates the total reservoir capacity value of the terrain model; The partition verification and correction module extracts the partition boundary volume distribution value based on the total storage capacity value of the terrain model, analyzes the transition volume difference of the storage capacity boundary, calculates and corrects the partition volume data, and generates the reservoir storage capacity measurement value.

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