Reservoir capacity measurement method based on Gaussian integral point

Abstract

The invention discloses a reservoir capacity measurement method based on Gaussian integral points, belongs to the technical field of reservoir capacity measurement, and aims to solve the problems that an existing reservoir capacity measurement method is large in number of measurement points and large in later data processing workload. A Gaussian integral point mathematical method is combined with an unmanned ship with water depth measurement and GPS functions to realize autonomous measurement, and then the reservoir capacity can be obtained through simple calculation. According to the method, the reservoir capacity is calculated through Gaussian integral points, the number of measuring points is greatly reduced, and the later-period calculation amount is effectively reduced. The automatic processing level is high, path planning, measurement, calculation and the like are directly completed by a computer, and the workload of operators is greatly reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of reservoir capacity measurement, and in particular relates to the design of a reservoir capacity measurement method based on Gaussian integral points. Background Art

[0002] my country is rich in water resources, and reservoir capacity measurement is crucial for water resource management and planning. It serves as a key indicator for assessing reservoir water supply, flood management, and water ecological restoration. Accurately and precisely measuring reservoir capacity is crucial for ensuring the rational use and sustainable development of water resources.

[0003] Currently, there are a variety of methods for measuring reservoir capacity. Most methods rely on using underwater topographic data to differentiate the storage space between the water level and the bottom into multiple subspaces. The volumes of all these subspaces are then calculated and summed to arrive at the estimated reservoir capacity. Currently, different methods for calculating reservoir capacity based on differentiation methods can be categorized as the contour method, the cross-section method, the square grid method, and the irregular triangulated network method.

[0004] Among them, the cross-section method was first used to estimate the reserves of mineral blocks between two adjacent sections and is widely used in the measurement of earthwork. The contour method consists of multiple sets of contour lines with different altitudes and is often used to represent the ups and downs of terrain. Therefore, the contour method can also be used to calculate earthwork and stone, and is suitable for terrain with gentle terrain and regular contour changes. The square grid method divides the water plane into squares of equal size. The sum of the volumes of the four prisms corresponding to all squares is the calculated value of the reservoir capacity. It is suitable for small and medium-sized reservoirs with gentle underwater terrain. The irregular triangulated network method divides the water area into non-overlapping and closely adjacent irregular triangulated networks. The sum of the volumes of the three prisms corresponding to all triangular grids is the calculated value of the reservoir capacity. In theory, it is applicable to underwater terrain under any circumstances and has the highest measurement accuracy. The disadvantage is that the triangular grid generation algorithm is relatively complex and the calculation time is long. Summary of the Invention

[0005] The purpose of the present invention is to solve the problem that the existing reservoir capacity measurement method has a large number of measurement points and a large workload of subsequent data processing, and proposes a reservoir capacity measurement method based on Gaussian integral points.

[0006] The technical solution of the present invention is: a reservoir capacity measurement method based on Gaussian integral points, comprising the following steps: S1. Use a drone to take aerial photos of the reservoir to obtain a water surface map of the reservoir.

[0007] S2. Obtain multiple mapping areas from the water surface map.

[0008] S3. Map the selected Gaussian integral points to the vertical direction of each mapping area to obtain the water depth points to be measured.

[0009] S4. According to the water depth point to be measured, a path planning algorithm is used to obtain the optimal measurement path of the unmanned boat.

[0010] S5. Use an unmanned vessel to measure along the optimal measurement path to obtain the water depth corresponding to each Gaussian integral point.

[0011] S6. Calculate the capacity of the reservoir to be measured based on the water depth corresponding to each Gaussian integral point.

[0012] Furthermore, step S2 includes the following sub-steps: S21. Determine the area to be measured based on the edge of the reservoir on the water surface map.

[0013] S22. The area to be measured is evenly divided into 1 km*1 km square areas, and the incomplete square areas beyond the area to be measured are completed.

[0014] S23. Divide each square area horizontally to obtain 10 mapping areas of 100m*1000m.

[0015] Furthermore, step S4 includes the following sub-steps: S41. Use a clustering algorithm to assign the water depth points to be measured to clusters with the number of unmanned vessels.

[0016] S42. Use the simulated annealing algorithm to solve the shortest path in each cluster in turn.

[0017] S43. Combining the shortest paths in each cluster to obtain the optimal measurement path of the unmanned ship.

[0018] Furthermore, step S41 includes the following sub-steps: S411. Randomly select the water depth points to be measured with the number of unmanned boats as cluster centers.

[0019] S412: Allocate each remaining water depth point to be measured to the cluster represented by the cluster center closest to it.

[0020] S413 . For each cluster, update the cluster center according to the water depth points to be measured in the cluster, so that the total distance from all the water depth points to be measured in the cluster to the cluster center is minimized.

[0021] S414: Determine whether the preset number of iterations of the clustering algorithm has been reached. If so, proceed to step S42; otherwise, return to step S412.

[0022] Furthermore, step S42 includes the following sub-steps: S421. Randomly generate an initial path in each cluster and calculate its length as the energy function.

[0023] S422. Randomly select two water depth points to be measured in the initial path and exchange their positions to obtain a new path.

[0024] S423: Determine whether the energy function of the new path is smaller than the energy function of the original path. If so, replace the original path with the new path; otherwise, retain the original path.

[0025] S424: Determine whether the preset number of iterations of the simulated annealing algorithm has been reached. If so, obtain the shortest path in each cluster and proceed to step S43; otherwise, return to step S422.

[0026] Furthermore, the calculation formula for the capacity of the reservoir to be measured in step S6 is: in represents the capacity of the reservoir to be measured, Indicates the i Gaussian integration points, Indicates the i The water depth corresponding to the Gaussian integration point is Indicates the i The quadrature coefficients of Gaussian integration points are n Indicates the number of Gaussian integration points selected.

[0027] The beneficial effects of the present invention are: (1) The present invention uses Gaussian integral points to calculate reservoir capacity, which greatly reduces the number of measurement points and effectively reduces the amount of subsequent calculations.

[0028] (2) The present invention does not require three-dimensional modeling of point clouds, and only requires simple calculations to obtain the results.

[0029] (3) The present invention has a high level of automation processing. Path planning, measurement, calculation, etc. are all completed directly by the computer, which greatly reduces the workload of operators. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 The figure shows a reservoir capacity measurement method based on Gaussian integral points provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be understood that the embodiments shown and described in the accompanying drawings are merely exemplary and are intended to illustrate the principles and spirit of the present invention, rather than to limit the scope of the present invention.

[0032] The embodiment of the present invention provides a reservoir capacity measurement method based on Gaussian integral points, such as Figure 1 As shown, the following steps S1 to S6 are included: S1. Use a drone to take aerial photos of the reservoir to obtain a water surface map of the reservoir.

[0033] S2. Obtain multiple mapping areas from the water surface map.

[0034] Step S2 includes the following sub-steps S21 to S23: S21. Determine the area to be measured based on the edge of the reservoir on the water surface map.

[0035] S22. The area to be measured is evenly divided into 1 km*1 km square areas, and the incomplete square areas beyond the area to be measured are completed.

[0036] S23. Divide each square area horizontally to obtain 10 mapping areas of 100m*1000m.

[0037] S3. Map the selected Gaussian integral points to the vertical direction of each mapping area to obtain the water depth points to be measured.

[0038] Gaussian integration points are a technique used in numerical integration methods to improve the accuracy and stability of numerical integration. Gaussian integration points are obtained by solving a specific differential equation. The specific process is well-known and will not be described here. Typically, points within the interval where the integrand reaches an extreme value are selected as Gaussian integration points.

[0039] In the embodiment of the present invention, the water depth of the Gaussian integral points outside the area to be measured is directly set to 0, and no measurement is performed.

[0040] S4. According to the water depth point to be measured, a path planning algorithm is used to obtain the optimal measurement path of the unmanned boat.

[0041] In the embodiment of the present invention, in addition to the water depth points to be measured, the input of the path planning algorithm should also include parameters such as the edge of the reservoir surface and obstacles on the water surface.

[0042] Step S4 includes the following sub-steps S41 to S43: S41. Use a clustering algorithm to assign the water depth points to be measured to clusters with the number of unmanned vessels.

[0043] Step S41 includes the following sub-steps S411 to S414: S411. Randomly select the water depth points to be measured with the number of unmanned boats as cluster centers.

[0044] S412: Allocate each remaining water depth point to be measured to the cluster represented by the cluster center closest to it.

[0045] S413 . For each cluster, update the cluster center according to the water depth points to be measured in the cluster, so that the total distance from all the water depth points to be measured in the cluster to the cluster center is minimized.

[0046] S414: Determine whether the preset number of iterations of the clustering algorithm has been reached. If so, proceed to step S42; otherwise, return to step S412.

[0047] S42. Use the simulated annealing algorithm to solve the shortest path in each cluster in turn.

[0048] Step S42 includes the following sub-steps S421 to S424: S421. Randomly generate an initial path in each cluster and calculate its length as the energy function.

[0049] S422. Randomly select two water depth points to be measured in the initial path and exchange their positions to obtain a new path.

[0050] S423: Determine whether the energy function of the new path is smaller than the energy function of the original path. If so, replace the original path with the new path; otherwise, retain the original path.

[0051] S424: Determine whether the preset number of iterations of the simulated annealing algorithm has been reached. If so, obtain the shortest path in each cluster and proceed to step S43; otherwise, return to step S422.

[0052] S43. Combining the shortest paths in each cluster to obtain the optimal measurement path of the unmanned ship.

[0053] S5. Use an unmanned vessel to measure along the optimal measurement path to obtain the water depth corresponding to each Gaussian integral point.

[0054] S6. Calculate the capacity of the reservoir to be measured based on the water depth corresponding to each Gaussian integral point.

[0055] In the embodiment of the present invention, the calculation formula for the capacity of the reservoir to be measured is: in represents the capacity of the reservoir to be measured, Indicates the i Gaussian integration points, Indicates the i The water depth corresponding to the Gaussian integration point is Indicates the i The quadrature coefficients of Gaussian integration points are n Indicates the number of Gaussian integral points selected. In the embodiment of the present invention, the number of Gaussian integral points selected n= 7, because according to experiments, 7 Gaussian integration points have high accuracy and the amount of calculation is not particularly large.

[0056] In the embodiment of the present invention, 7 Gaussian integration points and its corresponding quadrature coefficient The values ​​are shown in Table 1.

[0057] Table 1 Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the present invention.

Claims

1. A reservoir capacity measurement method based on Gaussian integral points, characterized in that: The following steps are involved: S1. Use a drone to take aerial photos of the reservoir to obtain a water surface map of the reservoir; S2, obtaining multiple mapping areas from the water surface map; S3, mapping the selected Gaussian integral points to the vertical direction of each mapping area to obtain the water depth point to be measured; S4. Based on the water depth points to be measured, a path planning algorithm is used to obtain the optimal measurement path for the unmanned vessel; S5. Use an unmanned vessel to measure along the optimal measurement path to obtain the water depth corresponding to each Gaussian integral point; S6. Calculate the capacity of the reservoir to be measured based on the water depth corresponding to each Gaussian integral point.

2. The reservoir capacity measurement method based on Gaussian integral points according to claim 1, characterized in that: The step S2 comprises the following sub-steps: S21. Determine the area to be measured based on the edge of the reservoir on the water surface map; S22, evenly divide the area to be measured into 1km*1km square areas, and complete the incomplete square areas beyond the area to be measured; S23. Divide each square area horizontally to obtain 10 mapping areas of 100m*1000m.

3. The reservoir capacity measurement method based on Gaussian integral points according to claim 1, characterized in that: The step S4 comprises the following sub-steps: S41, using a clustering algorithm to assign the water depth points to be measured to clusters corresponding to the number of unmanned vessels; S42, using a simulated annealing algorithm to sequentially solve the shortest path in each cluster; S43. Combining the shortest paths in each cluster to obtain the optimal measurement path of the unmanned ship.

4. The reservoir capacity measurement method based on Gaussian integral points according to claim 3, characterized in that: The step S41 includes the following sub-steps: S411, randomly selecting the water depth points to be measured with the number of unmanned boats as cluster centers; S412, assigning each remaining water depth point to be measured to the cluster represented by the cluster center closest to it; S413: For each cluster, update the cluster center according to the water depth points to be measured in the cluster, so that the total distance from all water depth points to be measured in the cluster to the cluster center is minimized; S414: Determine whether the preset number of iterations of the clustering algorithm has been reached. If so, proceed to step S42; otherwise, return to step S412.

5. The reservoir capacity measurement method based on Gaussian integral points according to claim 3, characterized in that: The step S42 includes the following sub-steps: S421, randomly generate an initial path in each cluster and calculate its length as the energy function; S422, randomly selecting two water depth points to be measured in the initial path and exchanging their positions to obtain a new path; S423, determining whether the energy function of the new path is less than the energy function of the original path, if so, replacing the original path with the new path, otherwise retaining the original path; S424: Determine whether the preset number of iterations of the simulated annealing algorithm has been reached. If so, obtain the shortest path in each cluster and proceed to step S43; otherwise, return to step S422.

6. The reservoir capacity measurement method based on Gaussian integral points according to claim 1, characterized in that: The calculation formula for the capacity of the reservoir to be measured in step S6 is: in represents the capacity of the reservoir to be tested, Indicates the i Gaussian integration points, Indicates the i The water depth corresponding to the Gaussian integration point is Indicates the i The quadrature coefficients of Gaussian integration points are n Indicates the number of Gaussian integration points selected.

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