Intelligent simulation method for spatial optimization of silt dam system based on high-resolution images

Abstract

The application discloses a kind of silt dam dam system space optimization intelligent simulation method based on high-resolution image, it is related to silt dam system planning technical field, including the geographic registration after the multiple high-resolution remote sensing images of target river basin, extract three-dimensional terrain information, generate surface deformation elevation difference grid through pixel level difference operation, extract the measured sediment discharge of each sub-basin, and combine with the rainfall data corresponding to different time, construct the sediment transport ratio model of each sub-basin, generate the actual sediment yield contribution intensity spatial distribution diagram of target river basin, it is used as input, in the spatial range of target river basin, with dam system overall service life maximization as target intelligent simulation optimization is carried out, at least including dam site coordinates and dam height dam system space layout parameter is solved, provide a kind of can analyze the dynamic process of river basin sediment yield and siltation, realize river basin sediment yield contribution intensity fine mapping, and can carry out system level optimization to dam system.The method.

Description

Technical Field

[0001] This invention belongs to the field of silt-retention dam system planning technology, specifically relating to an intelligent simulation method for spatial optimization of silt-retention dam systems based on high-resolution images. Background Technology

[0002] Silt-retaining dams are a widely adopted soil and water conservation engineering measure in areas with severe soil erosion, such as the Loess Plateau. By constructing dams within gullies, they serve multiple functions, including intercepting sediment, creating farmland through siltation, and mitigating flooding. A silt-retaining dam system is an engineering system composed of multiple dams connected upstream and downstream. The rationality of its spatial layout directly determines the overall sediment retention efficiency of the watershed and the service life of the project.

[0003] Traditional methods for planning silt-retention dam systems primarily rely on static topographic analysis using digital elevation models (DEMs) acquired at a single time point. Specifically, engineers extract the watershed channel network and catchment area boundaries from DEM data for a given period, and then, combined with empirical values ​​of regional soil erosion modulus or simplified runoff plot observation data, estimate the average annual sediment yield of each tributary, thereby determining the dam location, dam height, and upstream / downstream cascading distance. However, this approach has the following significant limitations: First, static topographic data cannot reflect the dynamic process of sediment production and deposition in the basin, making it difficult for dam system planning schemes based on this method to adapt to the actual water and sediment dynamics of the basin.

[0004] Second, existing technologies for estimating watershed sediment yield mostly employ empirical formulas based on watershed area or channel density. These methods treat the entire watershed as a homogeneous unit, failing to accurately quantify the actual sediment contribution intensity of each sub-watershed to the downstream dam site.

[0005] Third, the spatial layout design of cascade dam systems lacks a system-level optimization method. Traditional planning typically adopts empirical layout principles from "bottom to top" or "top to bottom," such as setting dam sites according to the channel gradient or determining dam spacing by analogy with existing projects in neighboring watersheds. This approach fails to consider the dam system as an interconnected organic whole and lacks a comprehensive consideration of multiple objectives such as the overall service life of the dam system, the total amount of sediment interception, engineering investment, and inundation losses. In particular, when the upstream dam fails due to siltation, its protective function for the downstream dam disappears, potentially leading to a sudden increase in sediment load on the downstream dam, a drastic reduction in the siltation period, or even cascade failure. Traditional planning methods struggle to simulate and mitigate such systemic risks in advance during the planning stage.

[0006] Therefore, how to provide a method that can analyze the dynamic process of sediment yield and deposition in a watershed, achieve refined mapping of the intensity of sediment yield contribution, and perform system-level optimization of the dam system has become an important issue. Summary of the Invention

[0007] To address the aforementioned problems in the existing technology, this invention provides an intelligent simulation method for spatial optimization of silt-retaining dam systems based on high-resolution images.

[0008] The technical problem to be solved by this invention is achieved through the following technical solution: In a first aspect, the present invention provides an intelligent simulation method for spatial optimization of silt-retaining dam systems based on high-resolution images, the intelligent simulation method for spatial optimization of silt-retaining dam systems comprising: Acquire multiple high-resolution remote sensing images of the target watershed at different time phases, and perform georegistration on the multiple high-resolution remote sensing images; Three-dimensional terrain information is extracted from multiple registered high-resolution remote sensing images, and a surface deformation elevation difference raster map is generated through pixel-level difference calculation; the surface deformation elevation difference raster map is used to reflect the spatial distribution of siltation and erosion within the target watershed; Based on the surface deformation elevation difference raster map, the measured sediment transport of each sub-basin is extracted, and combined with the rainfall data corresponding to the different times, a sediment transport ratio model of each sub-basin is constructed to generate a spatial distribution map of the actual sediment yield contribution intensity of the target basin; the target basin includes multiple sub-basins. Using the actual spatial distribution map of sediment yield contribution intensity as input, intelligent simulation optimization is performed within the target watershed space with the goal of maximizing the overall service life of the dam system, to solve for the spatial layout parameters of the dam system; the spatial layout parameters of the dam system include at least the dam site coordinates and the dam height.

[0009] Secondly, the present invention provides an intelligent simulation device for optimizing the spatial structure of silt-retaining dam systems based on high-resolution images, the intelligent simulation device for optimizing the spatial structure of silt-retaining dam systems comprising: The acquisition module is used to acquire multiple high-resolution remote sensing images of the target watershed at different time phases and perform georegistration on the multiple high-resolution remote sensing images; The extraction module is used to extract three-dimensional terrain information based on multiple registered high-resolution remote sensing images, and generate a surface deformation elevation difference raster map through pixel-level difference calculation; the surface deformation elevation difference raster map is used to reflect the spatial distribution of siltation and erosion within the target watershed; The generation module is used to extract the measured sediment transport of each sub-basin based on the surface deformation elevation difference raster map, and combine it with the rainfall data corresponding to different times to construct the sediment transport ratio model of each sub-basin, and generate a spatial distribution map of the actual sediment yield contribution intensity of the target basin; the target basin includes multiple sub-basins. The optimization module is used to take the spatial distribution map of the actual sediment yield contribution intensity as input, and perform intelligent simulation optimization within the target watershed space with the goal of maximizing the overall service life of the dam system, to solve the spatial layout parameters of the dam system; the spatial layout parameters of the dam system include at least the dam site coordinates and the dam height.

[0010] Thirdly, the present invention provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a computer program stored in memory, it implements the steps of any of the above-mentioned intelligent simulation methods for spatial optimization of silt-retaining dam systems based on high-resolution images.

[0011] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described intelligent simulation methods for spatial optimization of silt-retaining dam systems based on high-resolution images.

[0012] This invention provides an intelligent simulation method for spatial optimization of silt-retaining dam systems based on high-resolution images. By georegistering multiple high-resolution remote sensing images from different time phases, three-dimensional topographic information is extracted from the registered images, and pixel-level interpolation is performed to generate a surface deformation elevation difference raster map. This enables dynamic monitoring of watershed siltation and erosion changes, significantly improving the spatiotemporal accuracy of sediment change detection. Based on the surface deformation elevation difference raster map, measured sediment transport rates for each sub-watershed are extracted. Combined with corresponding rainfall data from different time periods, a sediment transport ratio model for each sub-watershed is constructed, thereby generating a spatial distribution map of the actual sediment yield contribution intensity of the target watershed. This overcomes the shortcomings of insufficient spatial accuracy in traditional empirical formulas and achieves refined mapping of sediment yield contribution intensity. With the goal of maximizing the overall service life of the dam system, intelligent simulation optimization is used to solve the spatial layout parameters of the dam system. Compared with the traditional manual site selection method based on experience principles, this invention can automatically search for the globally optimal layout scheme under complex terrain and multiple constraints through intelligent optimization algorithm, realizing system-level optimization, which can effectively extend the overall service life of the dam system and reduce the risk of cascading failure.

[0013] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating an intelligent simulation method for spatial optimization of silt-retaining dam systems based on high-resolution images, provided in an embodiment of the present invention. Figure 2This is a schematic diagram of a high-resolution remote sensing image provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the process for extracting the measured sediment transport of each sub-basin provided in an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the construction process of the sediment transport ratio model for each sub-basin provided in the embodiments of the present invention; Figure 5 This is a schematic diagram of the structure of an intelligent simulation device for optimizing the spatial structure of silt-retaining dam systems based on high-resolution images, provided in an embodiment of the present invention. Figure 6 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0015] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0016] To address the shortcomings of existing spatial optimization methods for silt-retaining dam systems, such as difficulty in analyzing the dynamic processes of sediment yield and deposition in watersheds, inability to achieve refined mapping of sediment yield contribution intensity, and difficulty in performing system-level optimization of the dam system, this invention provides an intelligent simulation method for spatial optimization of silt-retaining dam systems based on high-resolution images. (See [link to relevant documentation]). Figure 1 , Figure 1 This is a flowchart illustrating an intelligent simulation method for spatial optimization of silt-retaining dam systems based on high-resolution images, provided by an embodiment of the present invention. The method specifically includes the following steps: Step S101: Acquire multiple high-resolution remote sensing images of the target watershed at different time phases, and perform georegistration on the multiple high-resolution remote sensing images.

[0017] In this embodiment of the invention, the high-resolution remote sensing image can be an aerial photograph acquired by a high-resolution optical camera mounted on a drone. See also Figure 2 , Figure 2 This is a schematic diagram of a high-resolution remote sensing image provided in an embodiment of the present invention.

[0018] To accurately quantify surface changes caused by siltation and erosion within a target watershed, this invention requires acquiring high-resolution remote sensing images that reflect temporal evolution, specifically significant changes in siltation and erosion. For example, a high-resolution remote sensing image before the rainy season can be used as the baseline period (T1), and a high-resolution remote sensing image after the rainy season as the change period (T2). The difference between the two time periods effectively reflects the increase in siltation and erosion caused by the rainy season. For long-term planning needs of silt-retaining dam systems, images from the same time period across different years can also be used. Furthermore, high-resolution remote sensing images from different time periods should ideally be in the same or similar phenological phases to reduce spurious changes introduced by seasonal variations in vegetation cover. NDVI (Normalized Difference Vegetation Index) masking technology can also be introduced to eliminate vegetation interference with the topography.

[0019] However, due to differences in satellite attitude, sensor angle, and other factors during the capture of high-resolution remote sensing images from different time periods, direct pixel-level comparisons can produce significant errors. Therefore, it is necessary to perform high-precision georegistration on these high-resolution remote sensing images first, ensuring that the same geographic coordinates point to the exact same physical location on all high-resolution remote sensing images.

[0020] In this embodiment of the invention, georegistration is the process of transforming high-resolution remote sensing images acquired at different times to the same geographic coordinate system so that they are precisely aligned in spatial location, providing a reliable geometric basis for subsequent pixel-level difference calculations.

[0021] Step S102: Extract three-dimensional terrain information based on multiple registered high-resolution remote sensing images, and generate a surface deformation elevation difference raster map through pixel-level difference calculation; the surface deformation elevation difference raster map is used to reflect the spatial distribution of siltation and erosion within the target watershed.

[0022] In this embodiment of the invention, three-dimensional terrain information is extracted based on multiple registered high-resolution remote sensing images, and a surface deformation elevation difference raster map is generated through pixel-level difference calculation, including: Based on multiple registered high-resolution remote sensing images, three-dimensional terrain information corresponding to different times is extracted to generate a digital elevation model. Pixel-level interpolation is performed on digital elevation models that correspond to different times to obtain the net change in surface elevation for each geographic coordinate. The net change in surface elevation corresponding to each geographic coordinate is used to generate a raster map of surface deformation elevation difference according to spatial location.

[0023] Each geographic coordinate refers to the spatial location coordinates of each pixel in the digital elevation model under a unified coordinate system after geographic registration.

[0024] Since multiple high-resolution remote sensing images have already been georeferenced, the coordinates in each high-resolution remote sensing image are: Each pixel corresponds to the same physical point. Based on this, using multiple registered high-resolution remote sensing images, corresponding 3D terrain information at different times is extracted to generate a digital elevation model.

[0025] In one implementation, three-dimensional terrain information is extracted based on multiple registered high-resolution remote sensing images, including: Based on multiple registered high-resolution remote sensing images, the shadow recovery shape method is used to extract three-dimensional terrain information and generate a digital elevation model.

[0026] Shape restoration by shadow (SFS) refers to the method of using the changes in brightness and darkness on the surface of an object under a single light source to infer the surface orientation and then reconstruct its three-dimensional shape. In high-resolution remote sensing images, the sun can be considered a fixed light source, with the sunlit side of a hillside being brighter and the shaded side being darker. By establishing a physical lighting model in advance that relates image brightness to surface slope and aspect, the elevation undulations of the surface can be deduced from the brightness gradient, thereby generating three-dimensional terrain information and obtaining corresponding digital elevation models at different times.

[0027] Then, pixel-level interpolation is performed on the corresponding digital elevation models at different times: ; ; ; in, Indicates coordinates The net change in surface elevation from the baseline period to the change period; Indicates the coordinates during the period of change. The surface elevation at that location; Indicates the coordinates during the reference period. The surface elevation at that location; The coordinates during the period of change are The grayscale value corresponding to the pixel; The coordinates in the reference period are The grayscale value corresponding to the pixel; Indicates a high-inversion operator; like This represents the coordinates during the time period from the baseline period to the change period. The corresponding ground elevation increases, leading to siltation, such as sediment deposition, and ground rise; if This represents the coordinates during the time period from the baseline period to the change period. The corresponding ground level decreases, leading to erosion, such as soil erosion, and the ground level drops. This indicates that the altitude has not changed significantly.

[0028] By performing pixel-level difference calculations, the net change in surface elevation corresponding to each geographic coordinate is obtained. These changes are then organized according to spatial location to generate a distribution map of watershed erosion-siltation elevation changes covering the entire target watershed. This map is a raster map of surface deformation elevation difference with geographic coordinates, which intuitively shows where siltation occurs and its thickness, and where erosion occurs and its depth within the target watershed.

[0029] Step S103: Based on the surface deformation elevation difference raster map, extract the measured sediment transport of each sub-basin, and combine it with the rainfall data corresponding to different times to construct the sediment transport ratio model of each sub-basin, and generate a spatial distribution map of the actual sediment production contribution intensity of the target basin; the target basin includes multiple sub-basins.

[0030] See Figure 3 , Figure 3 This is a schematic flowchart of the process for extracting the measured sediment transport of each sub-basin according to an embodiment of the present invention. Based on a surface deformation elevation difference raster map, the measured sediment transport of each sub-basin is extracted, including: Step S301: Spatial partitioning of the surface deformation elevation difference raster map according to sub-basin boundaries and sub-basin units in the channel network.

[0031] In this embodiment of the invention, the row and column numbers of the pixels in the surface deformation elevation difference raster map are respectively... , Its corresponding coordinates in the aforementioned high-resolution remote sensing image are Pixels.

[0032] For any pixel in the surface deformation elevation difference raster map Its corresponding ground area is Then, the period from the base period to the change period for that pixel Net sediment / erosion volume within for: ; in, for Representation within a raster map of surface deformation elevation difference; Image spatial resolution; In an embodiment of the present invention, Indicates siltation, Indicates erosion; The target watershed comprises multiple sub-watersheds. Based on the known digital river network and sub-watershed delineation layers of the target watershed, sub-watershed boundaries and channel networks are extracted through hydrological analysis. Then, the surface deformation elevation difference raster map is spatially partitioned according to the sub-watershed units in the extracted sub-watershed boundaries and channel networks. ; in, Indicates the first Each sub-basin Change in net sediment volume within the area; Indicates the first Each sub-basin.

[0033] Step S302: Identify the extent of sediment sinks within each sub-basin, extract positive pixels within the sediment sinks, and calculate the sedimentation volume.

[0034] For any sub-basin, the extent of sediment sinks within or at its outlet is first identified. A sediment sink is a geographical unit capable of collecting and depositing sediment from upstream, typically including the area of ​​an existing silt-retaining dam, a broad section at the outlet of a main channel, or a section of the channel with a sudden and gentle longitudinal gradient.

[0035] In this embodiment of the invention, the scope of the sediment sink is defined as follows: if there is already a silt-retaining dam in the sub-basin, the boundary vector data of the dam site is directly used as the scope of the sediment sink; if there is no existing silt-retaining dam project, the area with a continuous positive elevation difference is automatically searched and delineated along the channel network in the surface deformation elevation difference raster map as the scope of the sediment sink.

[0036] Based on this, extract the first k Sedimentary volume within the sedimentary sink of each sub-basin : ; Then, the sediment volume was converted into sediment mass, which was used as the measured sediment transport volume for each sub-basin.

[0037] In one implementation, the undeposited portion during transport is also considered. In actual hydrological processes, not all sediment entering the channel settles in the sedimentary sink. Some fine-grained sediment may pass through the sink as suspended sediment and continue downstream transport. Therefore, strictly speaking, the measured sediment transport should include both the deposited and the transgressed portion.

[0038] In this embodiment of the invention, the sedimentation volume is converted into sediment mass as the measured sediment transport of each sub-basin during the corresponding time period, including: Step S303: Obtain the dry density parameters of sediment in each sub-basin, and obtain the basic sediment mass based on the sedimentation volume and the dry density parameters of sediment.

[0039] Among them, the dry bulk density parameter of sediment can be used to convert volume into mass, and the specific value is determined by technicians based on the soil type of the target watershed.

[0040] Step S304: Correct the basic sediment mass according to the preset suspended sediment passage coefficient to obtain the measured sediment transport of each sub-basin.

[0041] Specifically, the calculation method for measured sediment transport is as follows: ; in, Indicates the first Measured sediment transport in each sub-basin; The preset suspended sediment penetration coefficient, which is an empirical value of the proportion of sediment transported downstream through the sediment sink, can be determined by technicians based on the soil texture and gradient of the watershed. This represents the dry bulk density parameter of sediment, used to convert volume to mass.

[0042] See Figure 4 , Figure 4 This is a schematic diagram illustrating the construction process of the sediment transport ratio model for each sub-basin provided in this embodiment of the invention. Combining rainfall data corresponding to different times, the sediment transport ratio model for each sub-basin is constructed, including: Obtain rainfall data corresponding to different times and calculate rainfall erosivity; Based on rainfall erosivity, the potential soil erosion in each sub-basin was calculated using the modified general soil loss equation. The sediment transport ratio of each sub-basin was calculated based on the measured sediment transport and potential soil erosion. Using the sediment transport ratio of each sub-basin as the dependent variable and the topographic and surface characteristics of each sub-basin as independent variables, a spatial regression model was established to obtain the sediment transport ratio model of each sub-basin.

[0043] In this embodiment of the invention, collection The corresponding rainfall data is used to calculate the rainfall erosivity. ,include: ; in, , express Total number of rainy days within the region; This indicates the daily rainfall exceeding the preset corrosive rainfall threshold; All parameters are based on regional experience.

[0044] If there are multiple rainy seasons between T1 and T2, they need to be calculated separately for each hydrological year or rainy season and then summed to ensure that the rainfall input strictly matches the sediment change period retrieved from the image.

[0045] In this embodiment of the invention, the sediment transport ratio model for each sub-basin is constructed as follows: First, the potential soil erosion in each sub-basin was calculated using the Revised Universal Soil Loss Equation (RUSLE). ,include: ; in, The soil erodibility factor can be determined based on the soil type of the target watershed; This represents the slope length and slope factor, determined based on three-dimensional terrain information. This represents vegetation cover and management factors, obtained from the aforementioned NDVI inversion; This represents a soil and water conservation measure factor, characterizing the ability of engineering / biological measures to intercept erosion, and can be determined based on the target watershed conditions; For the The sediment transport ratio of each watershed for: ; in, Indicates the first The total potential soil erosion amount corresponding to all pixels in each sub-basin.

[0046] Using the sediment transport ratios of each sub-basin calculated above as the dependent variable, and the topographic and surface characteristic variables of each sub-basin as independent variables, a spatial regression model, such as a geographically weighted regression model, is established to obtain the sediment transport ratio model for each sub-basin. This model can be used to predict the sediment transport ratio of sub-basins without measured data, thereby achieving full spatial coverage of the sediment transport ratio of the target watershed. The topographic and surface characteristic variables of each sub-basin can include average slope, gully density, confluence path length, soil texture, and vegetation cover, etc.

[0047] Because the sediment transport ratio at the sub-basin level cannot meet the pixel-level spatial accuracy required for dam system spatial optimization, and even if the sediment transport ratio of a certain sub-basin is known, the actual sediment transport efficiency at different locations within that sub-basin is obviously different. Therefore, the following downscaling operation is performed in this embodiment of the invention: Using the pixel as the basic unit, starting from the pixel The probability that eroded soil will eventually reach the sub-basin outlet or sediment sink can be used to... express.

[0048] Will Spatializing to the pixel scale, pixel-level soil erodibility factors, slope length and gradient factors, vegetation cover and management factors, and soil and water conservation measures factors are incorporated into the sediment transport ratio model to inversely derive pixel-level data. .

[0049] Then calculate the actual sediment yield contribution intensity for each pixel: ; Based on the actual sediment yield contribution intensity of each pixel, a spatial distribution map of the actual sediment yield contribution intensity of the target watershed was obtained. This map not only identifies the "sediment source" of strong erosion, but also the key pathways of sediment transport and the main sediment sinks.

[0050] Step S104: Using the spatial distribution map of the actual sediment yield contribution intensity as input, intelligent simulation optimization is performed within the target watershed space with the goal of maximizing the overall service life of the dam system, and the spatial layout parameters of the dam system are solved; the spatial layout parameters of the dam system include at least the dam site coordinates and the dam height.

[0051] In this embodiment of the invention, within the coverage area of ​​a high-resolution remote sensing image of the target watershed, a set of spatial layout parameters of the silt-retaining dam system are automatically solved to maximize the overall silt retention benefit and extend the service life of the dam system, while simultaneously satisfying the constraints of engineering feasibility and cascade coordination.

[0052] The input data includes the spatial distribution map of the actual sediment yield contribution intensity and three-dimensional topographic information, as well as the preset engineering constraints of existing or proposed dam sites, such as maximum dam height limits, minimum cascade spacing, and areas where dams cannot be built.

[0053] In this embodiment of the invention, the spatial distribution map of the actual sediment yield contribution intensity is used as input. Within the target watershed space, intelligent simulation optimization is performed with the goal of maximizing the overall service life of the dam system to solve for the spatial layout parameters of the dam system, including: Multiple candidate dam system layout schemes are generated within the target watershed, where each candidate dam system layout scheme includes the dam site coordinates and dam height of several dam bodies; For each candidate dam system layout scheme, the upstream catchment area of ​​each dam body is determined by the digital elevation model of the target watershed, and the sediment contribution intensity in the corresponding catchment area is extracted from the spatial distribution map of the actual sediment contribution intensity. The sediment amount in the upstream catchment area of ​​each dam body is then accumulated. Following the order from upstream to downstream, the expected siltation age of each dam body is calculated based on the cascade relationship and silt retention efficiency of each dam body, combined with the amount of incoming silt. The minimum value among the expected siltation ages of each dam body is taken as the overall service life of the dam system corresponding to the candidate dam system layout scheme. With the goal of maximizing the overall service life of the dam system, an intelligent optimization algorithm is used to iteratively search and update multiple candidate dam system layout schemes until the preset conditions are met, and then output the spatial layout parameters of the dam system.

[0054] Specifically, multiple candidate dam system layout schemes are first generated within the target watershed. Each candidate dam system layout scheme includes the dam site coordinates and dam heights of several dams.

[0055] Let the candidate dam system layout schemes be the decision vectors. : ; in, Indicates the first The dam site coordinates of the dam body, Indicates the first The height of the dam body; , This indicates the total number of dam components.

[0056] For the The pixel position of the dam body on the surface deformation elevation difference raster map is: ,from Starting from this point, trace upstream to all pixels that flowed into it, and obtain the first... The upstream catchment area of ​​the dam body, i.e., the set of pixels in the catchment area. ; for For each pixel in the dataset, its sediment contribution intensity is read from the spatial distribution map of actual sediment contribution intensity, and then summed to obtain the sediment load of the pixel set in the catchment area: ; Collect pixels of the catchment area Converting the amount of sand received into units of volume, we get: ; in, This indicates the dry bulk density of the sediment.

[0057] Since the dam system as a whole consists of multiple dam bodies in a cascaded relationship, the amount of sediment entering the downstream dam body needs to be reduced by the portion already intercepted by the upstream dam body.

[0058] So, for the upstream dam section, what is the amount of sediment it receives? for: ; in, This represents the amount of sediment entering the catchment area of ​​the first dam.

[0059] For the downstream The dam body, its catchment area is like a collection of elements. Including upstream The water catchment area of ​​the dam body is a collection of elements. , of which The dam body in the first On the upstream path of the dam body. After the upstream dam body intercepts the flow, it actually enters the... The amount of sediment entering the dam body is: ; in, Indicates that it is located at the th The collection of all dams upstream of the dam body and along its catchment path; Indicates the first The sand-trapping efficiency of a dam can be determined based on factors such as the height of the dam. Indicates the first The amount of sediment entering the dam.

[0060] Then, based on the actual amount of sediment entering each dam and the design reservoir capacity of each dam, the expected siltation period for each dam can be calculated sequentially; among which, the design reservoir capacity of each dam... It can be determined by the height of the dam. The terrain information represented by the surface deformation elevation difference raster map is calculated.

[0061] Considering the cascading effects of upstream dams on downstream dams within a dam system, and the fact that once an upstream dam becomes silted up and fails, all the sediment it intercepted will be transferred to the downstream dam, causing it to silt up prematurely. Therefore, the overall service life of the entire dam system depends on the dam with the shortest expected siltation period. Thus, in this embodiment of the invention, the minimum expected siltation period of each dam is used as the overall service life of the dam system corresponding to the candidate dam system layout scheme.

[0062] In one implementation, a genetic algorithm is used as the intelligent optimization algorithm. With the goal of maximizing the overall service life of the dam system, multiple candidate dam system layout schemes are iteratively searched and updated, as follows: Randomly generated within the target watershed. An individual that satisfies engineering constraints (such as maximum dam height, minimum cascade spacing). The initial population is formed; each individual represents a candidate dam system layout scheme; the overall service life of the dam system corresponding to the candidate dam system layout scheme is used as the fitness value of the individual.

[0063] For the initial population, intelligent simulation optimization is performed based on a preset objective function, aiming to maximize the overall service life of the dam system. The preset objective function includes: ; in, Indicates the first The number of years the dam body has been silted up. This represents the fitness value of an individual, which is the overall service life of the dam system corresponding to the candidate dam system layout scheme.

[0064] The objective function seeks to achieve the desired result by iteratively searching and updating the dam site coordinates and dam height. The optimal dam system layout is determined when preset conditions are met, i.e., the number of iterations reaches a preset number or the objective function converges. This yields the spatial layout parameters of the dam system. These parameters may include the geographical coordinates of each dam, its height, and the cascading spacing between adjacent dams.

[0065] In this embodiment of the invention, by georegistering multiple high-resolution remote sensing images from different time phases, three-dimensional topographic information is extracted from the registered high-resolution remote sensing images, and pixel-level difference calculations are performed to generate a surface deformation elevation difference raster map. This enables dynamic monitoring of watershed sedimentation and erosion changes, significantly improving the spatiotemporal accuracy of sediment change detection. Based on the surface deformation elevation difference raster map, the measured sediment transport of each sub-watershed is extracted, and combined with corresponding rainfall data from different time phases, a sediment transport ratio model for each sub-watershed is constructed. This generates a spatial distribution map of the actual sediment yield contribution intensity of the target watershed, overcoming the shortcomings of insufficient spatial accuracy in traditional empirical formulas and achieving refined mapping of sediment yield contribution intensity. With the goal of maximizing the overall service life of the dam system, intelligent simulation optimization is performed to solve for the spatial layout parameters of the dam system. Compared with traditional manual site selection methods based on empirical principles, this invention, through an intelligent optimization algorithm, can automatically search for the globally optimal layout scheme under complex terrain and multiple constraints, achieving system-level optimization. This can effectively extend the overall service life of the dam system and reduce the risk of cascading failures.

[0066] Based on the same inventive concept, this invention also provides an intelligent simulation device for spatial optimization of silt-retaining dam systems based on high-resolution images. See [link to relevant documentation]. Figure 5 , Figure 5 This is a schematic diagram of the structure of an intelligent simulation device for optimizing the spatial structure of silt-retaining dam systems based on high-resolution images, provided in an embodiment of the present invention. The intelligent simulation device for optimizing the spatial structure of silt-retaining dam systems includes: The acquisition module 501 is used to acquire multiple high-resolution remote sensing images of the target watershed at different time phases and perform georegistration on the multiple high-resolution remote sensing images; Extraction module 502 is used to extract three-dimensional terrain information based on multiple registered high-resolution remote sensing images, and generate a surface deformation elevation difference raster map through pixel-level difference calculation; the surface deformation elevation difference raster map is used to reflect the spatial distribution of siltation and erosion within the target watershed; The generation module 503 is used to extract the measured sediment transport of each sub-basin based on the surface deformation elevation difference raster map, and combine it with the corresponding rainfall data at different times to construct the sediment transport ratio model of each sub-basin, and generate a spatial distribution map of the actual sediment production contribution intensity of the target basin; the target basin includes multiple sub-basins. The optimization module 504 is used to take the spatial distribution map of the actual sediment yield contribution intensity as input, and perform intelligent simulation optimization within the target watershed space with the goal of maximizing the overall service life of the dam system, and solve the spatial layout parameters of the dam system; the spatial layout parameters of the dam system include at least the dam site coordinates and the dam height.

[0067] This invention also provides an electronic device, such as... Figure 6 As shown, Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, including a processor 601, a communication interface 602, a memory 603, and a communication bus 604, wherein the processor 601, the communication interface 602, and the memory 603 communicate with each other through the communication bus 604. Memory 603 is used to store computer programs; When the processor 601 executes the program stored in the memory 603, it implements the method steps of any of the above-mentioned intelligent simulation methods for spatial optimization of silt-retaining dam systems based on high-resolution images.

[0068] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0069] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0070] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0071] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0072] The present invention also provides a computer-readable storage medium. A computer program is stored in the computer-readable storage medium, and when executed by a processor, the computer program implements the method steps of any of the above-described intelligent simulation methods for spatial optimization of silt-retaining dam systems based on high-resolution images.

[0073] Optionally, the computer-readable storage medium may be non-volatile memory (NVM), such as at least one disk storage device.

[0074] Optionally, the aforementioned computer-readable storage medium may also be at least one storage device located remotely from the aforementioned processor.

[0075] In another embodiment of the present invention, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute the steps described in any of the above-described intelligent simulation methods for spatial optimization of silt-retaining dam systems based on high-resolution images.

[0076] It should be noted that the terms "first," "second," etc., are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention.

[0077] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0078] Although the invention has been described herein in conjunction with various embodiments, those skilled in the art will understand and implement other variations of the disclosed embodiments by reviewing the accompanying drawings and the disclosure in carrying out the claimed invention. In the description of the invention, the word "comprising" does not exclude other components or steps, "a" or "an" does not exclude a plurality, and "a plurality" means two or more, unless otherwise explicitly specified. Furthermore, while different embodiments may describe certain measures, this does not mean that these measures cannot be combined to produce good results.

[0079] The method provided in this invention can be applied to electronic devices. Specifically, the electronic device can be a desktop computer, a portable computer, a smart mobile terminal, a server, etc. No limitation is made herein; any electronic device that can implement this invention falls within the protection scope of this invention.

[0080] For the embodiments of the device / electronic device / storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and relevant parts can be referred to in the description of the method embodiments.

[0081] It should be noted that the device, electronic device and storage medium in the embodiments of the present invention are respectively the device, electronic device and storage medium for applying the above-mentioned intelligent simulation method for spatial optimization of silt-retaining dam system based on high-resolution images. Therefore, all embodiments of the above-mentioned intelligent simulation method for spatial optimization of silt-retaining dam system based on high-resolution images are applicable to the device, electronic device and storage medium, and can achieve the same or similar beneficial effects.

[0082] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A smart simulation method for spatial optimization of silt-retaining dam systems based on high-resolution images, characterized in that, The intelligent simulation method for optimizing the spatial structure of silt-retaining dam systems includes: Acquire multiple high-resolution remote sensing images of the target watershed at different time phases, and perform georegistration on the multiple high-resolution remote sensing images; Three-dimensional terrain information is extracted from multiple registered high-resolution remote sensing images, and a surface deformation elevation difference raster map is generated through pixel-level difference calculation; the surface deformation elevation difference raster map is used to reflect the spatial distribution of siltation and erosion within the target watershed; Based on the surface deformation elevation difference raster map, the measured sediment transport of each sub-basin is extracted, and combined with the rainfall data corresponding to the different times, a sediment transport ratio model of each sub-basin is constructed to generate a spatial distribution map of the actual sediment yield contribution intensity of the target basin; the target basin includes multiple sub-basins. Using the actual spatial distribution map of sediment yield contribution intensity as input, intelligent simulation optimization is performed within the target watershed spatial range with the goal of maximizing the overall service life of the dam system, to solve for the spatial layout parameters of the dam system; the spatial layout parameters of the dam system include at least the dam site coordinates and the dam height. By combining rainfall data corresponding to the different times, a sediment transport ratio model for each sub-basin is constructed, including: Obtain rainfall data corresponding to the different times, and calculate rainfall erosivity; Based on the rainfall erosivity, the potential soil erosion in each sub-basin is calculated using the modified general soil loss equation. The sediment transport ratio of each sub-basin was calculated based on the measured sediment transport and potential soil erosion. Using the sediment transport ratio of each sub-basin as the dependent variable and the topographic and surface characteristics of each sub-basin as independent variables, a spatial regression model was established to obtain the sediment transport ratio model of each sub-basin.

2. The intelligent simulation method for spatial optimization of silt-retaining dam systems according to claim 1, characterized in that, Three-dimensional terrain information is extracted from multiple registered high-resolution remote sensing images, and a surface deformation elevation difference raster map is generated through pixel-level interpolation, including: Based on multiple registered high-resolution remote sensing images, three-dimensional terrain information corresponding to different times is extracted to generate a digital elevation model. Pixel-level interpolation is performed on digital elevation models that correspond to different times to obtain the net change in surface elevation for each geographic coordinate. The net change in surface elevation corresponding to each geographic coordinate is used to generate a raster map of surface deformation elevation difference according to spatial location.

3. The intelligent simulation method for optimizing the spatial structure of silt-retaining dam systems according to claim 2, characterized in that, Based on multiple registered high-resolution remote sensing images, corresponding 3D terrain information at different times is extracted to generate digital elevation models, including: Based on multiple registered high-resolution remote sensing images, the shadow recovery shape method is used to extract three-dimensional terrain information and generate a digital elevation model.

4. The intelligent simulation method for spatial optimization of silt-retaining dam systems according to claim 1, characterized in that, Based on the aforementioned surface deformation elevation difference raster map, the measured sediment transport of each sub-basin is extracted, including: The surface deformation elevation difference raster map is spatially partitioned according to the sub-basin boundaries and sub-basin units in the gully network; The extent of sediment sinks within each sub-basin is identified, and positive pixels within the sediment sinks are extracted to calculate the sedimentation volume. The volume of sediment deposition is converted into sediment mass, which is used as the measured sediment transport volume for each sub-basin.

5. The intelligent simulation method for spatial optimization of silt-retaining dam systems according to claim 4, characterized in that, The sedimentation volume is converted into sediment mass, which is used as the measured sediment transport for each sub-basin, including: Obtain the dry bulk density parameters of sediment in each sub-basin, and obtain the basic sediment mass based on the sedimentation volume and dry bulk density parameters; The basic sediment mass is corrected based on the preset suspended sediment passage coefficient to obtain the measured sediment transport of each sub-basin.

6. The intelligent simulation method for spatial optimization of silt-retaining dam systems according to claim 1, characterized in that, Using the spatial distribution map of the actual sediment yield contribution intensity as input, intelligent simulation optimization is performed within the target watershed area with the goal of maximizing the overall service life of the dam system, to solve for the spatial layout parameters of the dam system, including: Multiple candidate dam system layout schemes are generated within the target watershed, wherein each candidate dam system layout scheme includes the dam site coordinates and dam height of several dam bodies; For each candidate dam system layout scheme, the upstream catchment area of ​​each dam body is determined using the digital elevation model of the target watershed, and the sediment contribution intensity in the corresponding catchment area is extracted from the spatial distribution map of the actual sediment contribution intensity. After summing, the amount of sediment inflow in the upstream catchment area of ​​each dam body is obtained. Following the order from upstream to downstream, the expected siltation age of each dam body is calculated based on the cascade relationship and silt retention efficiency of each dam body, combined with the amount of incoming silt. The minimum value among the expected siltation ages of each dam body is taken as the overall service life of the dam system corresponding to the candidate dam system layout scheme. With the goal of maximizing the overall service life of the dam system, an intelligent optimization algorithm is used to iteratively search and update the multiple candidate dam system layout schemes until the preset conditions are met, and the spatial layout parameters of the dam system are output.

7. A smart simulation device for optimizing the spatial structure of silt-retaining dam systems based on high-resolution images, characterized in that, The intelligent simulation device for optimizing the spatial structure of silt-retaining dam systems includes: The acquisition module is used to acquire multiple high-resolution remote sensing images of the target watershed at different time phases and perform georegistration on the multiple high-resolution remote sensing images; The extraction module is used to extract three-dimensional terrain information based on multiple registered high-resolution remote sensing images, and generate a surface deformation elevation difference raster map through pixel-level difference calculation; the surface deformation elevation difference raster map is used to reflect the spatial distribution of siltation and erosion within the target watershed; The generation module is used to extract the measured sediment transport of each sub-basin based on the surface deformation elevation difference raster map, and combine it with the rainfall data corresponding to different times to construct the sediment transport ratio model of each sub-basin, and generate a spatial distribution map of the actual sediment yield contribution intensity of the target basin; the target basin includes multiple sub-basins. The optimization module is used to take the actual spatial distribution map of sediment yield contribution intensity as input, and within the target watershed space, perform intelligent simulation optimization with the goal of maximizing the overall service life of the dam system, and solve for the spatial layout parameters of the dam system; the spatial layout parameters of the dam system include at least the dam site coordinates and the dam height. The generation module, combining rainfall data corresponding to the different times, constructs a sediment transport ratio model for each sub-basin, including: Obtain rainfall data corresponding to the different times, and calculate rainfall erosivity; Based on the rainfall erosivity, the potential soil erosion in each sub-basin is calculated using the modified general soil loss equation. The sediment transport ratio of each sub-basin was calculated based on the measured sediment transport and potential soil erosion. Using the sediment transport ratio of each sub-basin as the dependent variable and the topographic and surface characteristics of each sub-basin as independent variables, a spatial regression model was established to obtain the sediment transport ratio model of each sub-basin.

8. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a computer program stored in memory, it implements the intelligent simulation method for spatial optimization of silt-retaining dam systems based on high-resolution images as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the intelligent simulation method for spatial optimization of silt-retaining dam systems based on high-resolution images as described in any one of claims 1-6.

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