Wind and solar water resource element simulation method and device for optimizing key parameters of confluence model

Abstract

This application discloses a method and device for simulating wind, solar, and water resource elements by optimizing key parameters of a confluence model. The method comprises: obtaining a river network database and gridded hydrological and topographic data, interpolating the gridded hydrological and topographic data onto a refined confluence grid of a target resolution to obtain interpolated elevation data; determining vector data of the river network centerline and catchment boundary based on the interpolated elevation data, flow direction data, and cumulative flow, and segmenting the river network according to the Strahler river grade to determine river sections of different Strahler grades; inputting the vector data of the river network centerline and catchment boundary into a pre-established river network confluence model, simulating river sections of different Strahler grades using different Manning coefficients to obtain simulated flow; and optimizing the Manning coefficients of river sections of different Strahler grades using a dynamic dimensional search algorithm to minimize the objective function.

Description

Technical Field

[0001] The present application belongs to the field of water resource treatment technology, and specifically relates to a method and device for simulating wind and solar water resource elements by optimizing key parameters of a confluence pattern. Background Art

[0002] The Manning coefficient is a key parameter describing the roughness of a river channel, directly affecting flow resistance and flow calculations. Parameter sensitivity analysis shows that the Manning coefficient is one of the most important parameters affecting flow simulation accuracy. Accurately estimating the Manning coefficient is crucial for improving the accuracy of hydrological model simulations. Traditional methods of deriving the Manning coefficient through field hydrological measurements have significant limitations and are difficult to generalize to entire river networks. The Manning coefficient varies significantly across the spatial distribution of river channels, and field observations have significant representativeness issues. Existing indirect estimation methods, such as remote sensing imagery and geographic information, require extensive calibration and validation.

[0003] By calibrating the parameters of the hydrological model, an optimized Manning's coefficient can be indirectly inverted. However, the Manning's coefficient obtained by this method may not accurately represent actual river conditions. A comprehensive analysis combining measured data, empirical formulas, and theoretical models is necessary. Existing technologies are unable to effectively obtain Manning's coefficient data for the entire river network, and a reliable and efficient method for observing and estimating the Manning's coefficient is lacking. Innovative technical solutions are urgently needed to address this problem.

[0004] Therefore, in view of the scarcity of direct observations of the Manning coefficient for the entire river network, developing a new method for observing and estimating the Manning coefficient has important practical significance. Summary of the Invention

[0005] The purpose of the embodiments of the present application is to provide a method and device for simulating wind, solar and water resources elements with optimized key parameters of the confluence pattern, which can solve the problem of scarcity of direct observation of the Manning coefficient of the entire river network.

[0006] In a first aspect, an embodiment of the present application provides a method for simulating wind, solar and water resources elements by optimizing key parameters of a confluence mode, the method comprising:

[0007] Obtain river network database and gridded hydrological and topographic data. The river network database includes: river identification information and river level of each river; hydrological and topographic data includes: elevation data, flow direction data, and cumulative flow;

[0008] Interpolate the gridded hydrological and topographic data onto the refined confluence grid of the target resolution to obtain the interpolated elevation data;

[0009] Based on the interpolated elevation data, flow direction data and cumulative flow, the vector data of the river network centerline and catchment boundary are determined, and the river network is segmented according to the Strahler river class to determine the river sections of different Strahler classes;

[0010] The vector data of the river network centerline and catchment boundary are input into the pre-established river network confluence model. Different Manning coefficients are used to simulate the river sections with different Strahler grades to obtain the simulated flow.

[0011] The Manning coefficients of river sections with different Strahler grades are optimized separately by a dynamic dimensional search algorithm to minimize the objective function, which is the flow simulation error between the simulated flow and the observed flow.

[0012] In a second aspect, an embodiment of the present application provides a wind, solar and water resource element simulation device for optimizing key parameters of a confluence mode, the device comprising:

[0013] The acquisition module is used to obtain the river network database and gridded hydrological and topographic data. The river network database includes: river identification information and river level of each river; the hydrological and topographic data includes: elevation data, flow direction data, and cumulative flow;

[0014] The interpolation module is used to interpolate the gridded hydrological and topographic data onto the refined confluence grid of the target resolution to obtain the interpolated elevation data;

[0015] A determination module is used to determine the vector data of the river network centerline and the catchment area boundary based on the interpolated elevation data, flow direction data and cumulative flow, and to segment the river network according to the Strahler river level and determine the river sections of different Strahler levels;

[0016] The input module is used to input the vector data of the river network centerline and the catchment area boundary into the pre-established river network confluence model, and use different Manning coefficients to simulate the river sections of different Strahler grades to obtain the simulated flow;

[0017] The optimization module is used to optimize the Manning coefficient of different Strahler grade river sections through a dynamic dimensional search algorithm to minimize the objective function, which is the flow simulation error between the simulated flow and the observed flow.

[0018] In a third aspect, an embodiment of the present application provides an electronic device comprising a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the steps of the method described in the first aspect are implemented.

[0019] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0020] In a fifth aspect, an embodiment of the present application provides a chip, which includes a processor and a communication interface, the communication interface and the processor are coupled, and the processor is used to run programs or instructions to implement the method described in the first aspect.

[0021] In a sixth aspect, an embodiment of the present application provides a computer program product, which is stored in a storage medium and executed by at least one processor to implement the method described in the first aspect.

[0022] In an embodiment of the present application, by acquiring a river network database and gridded hydrological and topographic data, the river network database includes: river identification information and river grades of each river; the hydrological and topographic data include: elevation data, flow direction data, and cumulative flow; the gridded hydrological and topographic data are interpolated onto a refined confluence grid of a target resolution to obtain interpolated elevation data; based on the interpolated elevation data, flow direction data, and cumulative flow, vector data of the river network centerline and the watershed boundary are determined, and the river network is segmented according to the Strahler river grade to determine river sections of different Strahler grades; the vector data of the river network centerline and the watershed boundary are input into a pre-established river network confluence model, and different Manning systems are used for river sections of different Strahler grades. The simulation is performed to obtain the simulated flow; by optimizing the Manning coefficient in sections, the hydrological characteristics of river sections of different grades can be better reflected, and the reliability of the simulation results can be improved. Compared with the simulation using a unified Manning coefficient, the segmented optimization can simulate the flow process more accurately. By using the dynamic dimension search algorithm, the search dimension can be dynamically increased or decreased during the search process, and the Manning coefficients of river sections of different Strahler grades can be optimized respectively. The search strategy can be adaptively adjusted to improve the search efficiency, and the objective function of the flow simulation error used to characterize the simulated flow and the observed flow is minimized. In the embodiment of the present application, the accurate river network confluence simulation results obtained can provide more accurate parameter input for flow simulation, thereby significantly improving the simulation accuracy of the hydrological model and providing support for practical applications such as water resources management. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of a method for simulating wind, solar and water resources elements with optimized key parameters of a confluence mode provided by an embodiment of the present application;

[0024] Figure 2 This is a flow chart of a vector data method for determining a river network centerline and a catchment area boundary provided by an embodiment of the present application;

[0025] Figure 3 This is a structural diagram of a wind, solar and water resource element simulation device for optimizing key parameters of a confluence mode provided in an embodiment of the present application;

[0026] Figure 4 It is a schematic diagram of the hardware structure of the electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] The following will be combined with the accompanying drawings of the embodiments of the present application to clearly describe the technical solutions of the embodiments of the present application. Obviously, the embodiments described are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0028] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of this application can be implemented in an order other than that illustrated or described herein, and that the objects distinguished by "first," "second," and the like are generally of the same type, and do not limit the number of objects; for example, the first object can be one or more. In addition, the term "and / or" in the specification and claims refers to at least one of the connected objects, and the character " / " generally indicates that the objects connected are in an "or" relationship.

[0029] The wind, solar and water resource element simulation method for optimizing the key parameters of the confluence mode provided in the embodiment of the present application can be applied to at least the following application scenarios, which are explained below.

[0030] Parameter sensitivity analysis studies have shown that the Manning's coefficient is a key parameter influencing flow simulation. However, direct observations of the Manning's coefficient for entire river networks are scarce. The Manning's coefficient, a parameter used in hydraulics to describe the roughness of a river or channel, plays a crucial role in river flow simulation. Due to its high sensitivity to flow simulations, its accuracy directly impacts the reliability of simulation results. However, direct observations of the Manning's coefficient are indeed scarce, which poses a challenge to flow simulation.

[0031] The Strahler Scale is a classification used to describe the size and complexity of rivers within a river system. The Strahler Scale starts at 1 and increases progressively, indicating the extent of confluences and branching in a river. Rivers with higher Strahler Scales typically have wider beds, deeper channels, and more complex river networks.

[0032] River sections of different Strahler grades have different Manning coefficients due to differences in riverbed material, vegetation cover, river channel morphology, and other factors. The following is a general description of the Manning coefficients of river sections of different Strahler grades:

[0033] Strahler Grade 1 identifies the smallest tributaries. These are typically headwater streams with a bed composed of fine sediment and possibly some vegetation. Manning's Coefficient: Typically higher due to greater roughness of the bed and banks. Typical values ​​may range from 0.030 to 0.050.

[0034] Strahler Class 2 is used to identify smaller tributaries. Formed by the confluence of two Strahler Class 1 tributaries, the streambed material may be more diverse and vegetation cover may be increased. The Manning coefficient may be slightly lower than that of a Class 1 stream, with typical values ​​ranging from 0.025 to 0.045.

[0035] Strahler Grade 3 is used to identify medium-sized tributaries. Formed by the confluence of two Strahler Grade 2 tributaries, the channel may begin to widen, and the bed material may consist of more gravel and pebbles. The Manning coefficient may decrease further, with typical values ​​likely ranging from 0.020 to 0.040.

[0036] Strahler Scale 4 and above are used to identify major channels. For example, major channels have wide riverbeds, and the bed material may include large gravel, pebbles, or even boulders. Vegetation cover may be reduced. The Manning coefficient is typically low because the riverbed and banks have relatively low roughness. Typical values ​​may range from 0.015 to 0.035.

[0037] It should be noted that the above range of the Manning coefficient is for reference only. The actual Manning coefficient is affected by many factors, including specific river conditions, seasonal variations, and human activities. Therefore, in actual applications, it is necessary to conduct field measurements based on specific circumstances or refer to relevant research results to determine the specific value of the Manning coefficient.

[0038] In response to the problems arising from related technologies, the embodiments of the present application provide a method and device for simulating wind, solar and water resources elements by optimizing the key parameters of the confluence pattern, which can solve the problem in related technologies that direct observation of the Manning coefficient is scarce, posing challenges to flow simulation.

[0039] The wind, solar and water resource element simulation method for optimizing the key parameters of the confluence mode provided in the embodiment of the present application will be described in detail below with reference to the accompanying drawings through specific embodiments and their application scenarios.

[0040] Figure 1 A flow chart of a method for simulating wind, solar and water resources elements by optimizing key parameters of a confluence pattern provided in an embodiment of the present application.

[0041] like Figure 1As shown, the wind-solar-water resource element simulation method for optimizing the key parameters of the confluence mode may include steps 110 to 150. The method is applied to the wind-solar-water resource element simulation device for optimizing the key parameters of the confluence mode, as shown below:

[0042] Step 110: Acquire a river network database and gridded hydrological and topographic data. The river network database includes: river identification information and river level of each river; the hydrological and topographic data includes: elevation data, flow direction data, and cumulative flow;

[0043] In a possible embodiment, step 110 includes:

[0044] Collect river data of each river in the preset area; river data includes: terrain data, remote sensing data, riverbed data and vegetation cover data, etc.

[0045] The river data is classified to obtain the river grade corresponding to each river, so as to obtain the river network database.

[0046] The river data of each river in the preset area are collected and the river classification method of Strahler is used to classify the river data to obtain the river grade corresponding to each river to obtain the river network database.

[0047] The Strahler river classification system is a commonly used river classification method that defines the class of a river based on its confluences and branches. The Strahler classification starts with the smallest tributaries (class 1) and gradually increases in class to indicate the complexity and size of the river. The following are the steps for river classification and river network database construction using the Strahler river classification system:

[0048] First, a directed graph representing the river network was constructed, and the Strahler classification of each river was calculated. These classification results were added to the previously extracted river network. Next, a database was created, containing three fields: river identification information, river classification, and geometric information. The river network was then traversed, and the river classification and geometric information for each river were inserted into the database. This completed the construction of a river network database based on the Strahler classification. This database can be used for subsequent applications such as river analysis and modeling.

[0049] Specifically, river data is collected, including terrain data, remote sensing data, riverbed data, vegetation cover data, etc.

[0050] Topographic data can be obtained through digital elevation model (DEM) data and used to extract river networks. Remote sensing data: Utilize satellite remote sensing imagery or aerial photography to identify and extract river information. Field surveys: Conduct field surveys to collect actual river information, such as riverbed material and vegetation cover.

[0051] River extraction and hydrological analysis: Use GIS software (such as ArcGIS, QGIS, etc.) to perform hydrological analysis and extract river networks from DEM data. River identification: Identify and verify river networks based on remote sensing images and field survey data.

[0052] Strahler River Classification, Initial Classification: The smallest unmerged tributary is designated Strahler Class 1. Confluence Rule: When two rivers of equal class merge, a higher-class river is formed; when two rivers of different classes merge, the class remains unchanged, and the class of the higher-class river remains unchanged. Stepwise Classification: Apply the above rule stepwise along the river network until the entire network is classified.

[0053] River network database construction and data structure: Design the river network database structure, including river ID, Strahler class, river length, riverbed width, riverbed material, vegetation cover, and other attributes. Data entry: Enter river classification results and other relevant information into the database. Data verification: Verify the database information through field surveys and remote sensing imagery.

[0054] Therefore, using the Strahler river classification method to classify rivers and construct a river network database is a systematic process involving multiple steps, including data collection, river extraction, classification, database construction, and management. Through this process, a detailed and accurate river network database can be established, providing a scientific basis for hydrological modeling, ecological assessment, and river management.

[0055] Step 120 , interpolating the gridded hydrological and topographic data onto a refined confluence grid of target resolution to obtain interpolated elevation data;

[0056] MERIT-Hydro (Merit Hydrography) is a high-precision global hydrotopographic dataset designed to provide improved information on river networks and related hydrological characteristics. These datasets are based on data from multiple sources, including SRTM (Shuttle Radar Topography Mission), AW3D (ALOS World 3D), and TanDEM-X elevation data. These datasets are corrected and optimized through a series of processing steps to reduce systematic and random errors.

[0057] A flow accumulation grid is an important concept in hydrological analysis. It represents the cumulative amount of water flowing from high to low locations in a digital elevation model (DEM). A flow accumulation grid is often used together with a flow direction grid to simulate and analyze surface runoff, extract river networks, and delineate watershed boundaries.

[0058] Each cell in a catchment grid represents the cumulative flow into that cell from all upstream cells. This value is typically expressed as the number of cells or their area. A catchment grid is often used in conjunction with a flow direction grid, which indicates the direction of flow from each cell to its neighbors. The resolution of the catchment grid matches that of the DEM, typically ranging from tens to hundreds of meters. The catchment grid can cover the entire globe or a specific region, depending on the coverage of the DEM data.

[0059] The generation process of the confluence grid can specifically include the following steps:

[0060] DEM preprocessing: First, the DEM data is preprocessed, including filling potholes (filling potholes in DEM), flow direction calculation, etc.

[0061] Flow direction calculation: Based on the DEM after filling, the flow direction of each cell is calculated. The flow direction is usually determined using the D8 method (eight-direction method), which means that water can only flow into one of the eight adjacent cells.

[0062] Cumulative flow calculation: Based on the flow direction information, starting from the highest point of the DEM, the cumulative flow of each cell is calculated step by step. The cumulative flow is obtained by adding the cumulative flow of the upstream cell and the contribution of the current cell.

[0063] Generate confluence grid: Assign the accumulated flow values ​​to the corresponding cells to generate the confluence grid.

[0064] Thus, by setting a threshold for cumulative flow, a river network can be extracted. Cells with cumulative flow reaching a certain threshold are considered part of a river. Using the confluence grid and the flow direction grid, watershed boundaries can be delineated. The watershed boundary is typically the catchment area of ​​the cell with the maximum cumulative flow.

[0065] Catchment grids are key data in hydrological analysis. By representing the cumulative amount of water flow, they support a variety of applications, including river network extraction, watershed delineation, hydrological simulation, and ecological assessment. By generating and using catchment grids, researchers and decision-makers can better understand and simulate surface runoff processes, thereby supporting water resource management and environmental protection.

[0066] Step 130 , determining vector data of the river network centerline and catchment area boundary based on the interpolated elevation data, flow direction data, and cumulative flow, and segmenting the river network according to the Strahler river class to determine river sections of different Strahler classes;

[0067] In a possible embodiment, step 130 includes:

[0068] Step 210, calculating the flow direction data of each confluence grid based on the interpolated elevation data, where the flow direction data is used to indicate the direction of water flow from the current grid cell to the adjacent confluence grid;

[0069] Step 220: Calculate the cumulative flow data of each confluence grid based on the flow direction data. The cumulative flow is used to represent the cumulative amount of water flow from the upstream to the current confluence grid.

[0070] Step 230, determining the river network centerline and catchment area boundary based on the accumulated flow data;

[0071] Step 240 , tracking the confluence grids whose cumulative flow data reaches a preset threshold, and extracting vector data of the river network centerline;

[0072] Step 250 , identifying grid cells whose cumulative flow data reaches a preset threshold and tracing their watershed boundaries, and extracting vector data of the catchment area boundaries.

[0073] River network centerline and catchment boundary are two important concepts in hydrological analysis, which describe the centerline of the river network and the boundary of the catchment respectively.

[0074] A stream network (or river network) is the central axis of a river system, representing the primary flow path of a river. Stream network centerlines are typically extracted based on digital elevation models (DEMs) and hydrological analysis tools.

[0075] The steps for extracting the centerline of the river network include: filling the DEM to eliminate potholes in the data and ensure that the water can flow smoothly. Flow direction calculation: Use hydrological analysis tools (such as TauDEM, ArcGIS Hydrology Toolset, etc.) to calculate the flow direction of each cell. Based on the flow direction information, calculate the cumulative flow of each cell, which represents the cumulative amount of water flow from upstream to the cell. Set a cumulative flow threshold, and cells with cumulative flow exceeding the threshold are considered to be part of the river. By tracing these cells, the centerline of the river network can be extracted.

[0076] A watershed boundary (or catchment boundary) is the boundary of a watershed that separates the geographical area where surface water and groundwater flow to the same outlet. The extraction of the catchment boundary is also based on the DEM and hydrological analysis tools.

[0077] The steps for extracting the catchment boundary include: first, filling the DEM. Then, calculating the flow direction for each cell. Then, calculating the cumulative flow for each cell. Then, selecting one or more outlet points (such as river confluences or estuaries), and based on the flow direction information, tracing the cells with the maximum cumulative flow to determine the catchment boundary.

[0078] Calculate the flow direction for each grid cell based on the interpolated elevation data. Flow direction typically indicates the direction of water flow from the current grid cell to the adjacent grid cell. Use hydrological analysis tools (such as TauDEM or the ArcGIS Hydrology toolset) to calculate flow direction.

[0079] Based on the flow direction data, the cumulative flow is calculated for each grid cell. Cumulative flow represents the cumulative amount of water flowing from upstream to the current grid cell. Based on this cumulative flow data, the centerline of the river network and the boundaries of the catchment area are determined. Typically, grid cells with cumulative flow exceeding a certain threshold are considered part of the river network.

[0080] Extract vector data of river network centerlines by tracing grid cells where cumulative flow reaches a threshold. Extract vector data of catchment boundaries by identifying grid cells where cumulative flow reaches a threshold and tracing their catchment boundaries.

[0081] River network centerlines and catchment boundaries are key elements in hydrological analysis. Extracting these elements allows for a better understanding and simulation of surface runoff processes, supporting water resource management and environmental protection. Using digital elevation models and hydrological analysis tools, we can systematically extract river network centerlines and catchment boundaries, providing a scientific basis for various hydrological and ecological studies.

[0082] By interpolating the gridded MERIT-Hydro hydrotopographic data onto a refined confluence grid and extracting the river network centerlines and their catchment boundaries, high-resolution river network and catchment vector data can be obtained, providing a scientific basis for hydrological simulation, ecological assessment, and river management.

[0083] Step 140 , inputting the vector data of the river network centerline and the catchment area boundary into a pre-established river network confluence model, and using different Manning coefficients to simulate river sections of different Strahler grades to obtain simulated flow;

[0084] In a possible embodiment, before step 140, the method further includes:

[0085] Establish a river network confluence model and define the river network structure in the river network confluence model. The river network structure includes the length, slope and cross-section of each river section.

[0086] Different initial Manning coefficients are set according to different Strahler grade river sections.

[0087] In the initialization step, a set of initial solutions is randomly generated, including the Manning coefficients of river sections with different Strahler grades.

[0088] Iterative optimization step, based on the current solution, calculates the objective function value (flow simulation error).

[0089] Determine the optimized number of dimensions based on the dynamic dimension search strategy.

[0090] Search in the current dimension and update the Manning coefficient.

[0091] Compare the updated objective function values ​​and accept the better solution.

[0092] Stop condition: reaching the maximum number of iterations or the objective function value is less than the set threshold.

[0093] Output the final optimization result, that is, the optimal Manning coefficient of river sections with different Strahler grades.

[0094] This optimization method based on dynamic dimension search can effectively improve the flow simulation accuracy of river network confluence models.

[0095] Step 150, through the dynamic dimension search algorithm, optimize the Manning coefficient of the river sections of different Strahler grades respectively to minimize the objective function, where the objective function is the flow objective function value KGE of the simulated flow and the observed flow, which is used to characterize the error value between the simulated value and the observed value.

[0096] Dynamic Dimension Search (DDS) is an optimization algorithm that finds the optimal solution by dynamically adjusting the search dimension in the search space. DDS is often used to solve multidimensional optimization problems, especially when the objective function is complex or the search space is high-dimensional.

[0097] The DDS algorithm can dynamically increase or decrease search dimensions during the search process to adapt to the complexity of the problem and the characteristics of the search space. It can adaptively adjust the search strategy based on current search results and historical information to improve search efficiency. The DDS algorithm has strong global search capabilities and can find the global optimal solution or a near-optimal solution in a complex search space. The DDS algorithm can be designed for parallel execution, accelerating the search by simultaneously searching multiple dimensions or multiple solutions.

[0098] The basic steps of the dynamic dimension search algorithm are:

[0099] Initialization: Set the initial search dimension and other parameters, such as search step size, number of iterations, etc.

[0100] Search process: Search in each dimension and evaluate the objective function value of the current solution.

[0101] Dimension Adjustment: Dynamically adjust search dimensions based on search results. If a dimension performs poorly, you can reduce its search weight or remove it entirely. Conversely, if a dimension performs well, you can increase its search weight.

[0102] Convergence judgment: Determine whether the convergence conditions are met, such as whether the change in the objective function value is less than a certain threshold or the maximum number of iterations is reached.

[0103] Output result: Output the optimal solution or approximate optimal solution found.

[0104] The Dynamic Dimension Search (DDS) algorithm is a flexible and efficient optimization algorithm that adapts to the complexity of the problem and the characteristics of the search space by dynamically adjusting the search dimension. DDS has a wide range of applications in various fields, particularly when dealing with high-dimensional optimization problems. Properly designed and implemented, DDS can significantly improve the efficiency and quality of solving optimization problems.

[0105] Optimizing flow simulation errors in river network confluence models is a complex optimization problem that requires considering various river attributes, such as topography, vegetation, and hydrology. A dynamic dimension search algorithm can effectively address this problem.

[0106] In a possible embodiment, before step 150, the method further includes:

[0107] The objective function is to minimize the sum of squares of the daily average flow rate relative errors between the simulated flow rate and the observed flow rate.

[0108] In one possible embodiment, the objective function is:

[0109]

[0110] Among them, the objective function value KGE is used to characterize the objective function value KGE, which is used to characterize the error value between the simulated flow and the observed flow;

[0111] r is the correlation coefficient between the simulated and observed flows, α is the ratio of the standard deviation of the simulated flow to the standard deviation of the observed flow, and β is the ratio of the mean of the simulated flow to the mean of the observed flow. The value range of KGE is (-∞, 1). The closer KGE is to 1, the better the simulation effect.

[0112] In a possible embodiment, step 150 includes:

[0113] A dynamic dimension search algorithm is used to optimize the Manning coefficient of river sections with different Strahler grades.

[0114] In each iteration, the Manning coefficient of a river section is selected for adjustment and the model is rerun to obtain new flow simulation results.

[0115] Based on the change in the objective function value, decide whether to accept the adjustment and select the next river section for optimization;

[0116] The optimization is iterated continuously until the objective function reaches the iteration stopping condition.

[0117] A river network confluence model is established, and the flow simulation error is calculated based on the measured data as the objective function.

[0118] The Manning coefficients of different Strahler-grade river sections are optimized simultaneously using a dynamic dimensionality search algorithm. The dynamic dimensionality search algorithm can adaptively adjust the number of dimensions in the optimization space to avoid falling into local optimal solutions.

[0119] For example, there is a river basin containing 10 river segments of different Strahler grades. The optimization goal is to minimize the flow simulation error of the river network confluence model by optimizing the Manning coefficient of each river segment.

[0120] The optimization steps using the dynamic dimension search algorithm are as follows:

[0121] Construct a river network confluence model and calculate the flow simulation error as the objective function based on the measured data; in the initialization step, randomly generate 10 Manning coefficients as the initial solution; in the iterative optimization step, calculate the objective function value of the current solution (flow simulation error); according to the dynamic dimension search strategy, determine the number of dimensions to be optimized, which may be 2 or 3; search on the selected dimension and update the corresponding Manning coefficient; compare the updated objective function values ​​and accept the better solution.

[0122] The above iterative optimization steps are repeated until the stopping condition is met (such as the maximum number of iterations or the objective function value is less than the threshold), and the final optimization result, that is, the optimal Manning coefficient of the 10 river sections, is output.

[0123] In a possible embodiment, continuously iterating the optimization until the objective function reaches an iteration stopping condition includes:

[0124] At each time step, the following steps are performed:

[0125] Randomly select a dimension;

[0126] Randomly perturb the parameters of this dimension to obtain new parameter values;

[0127] Calculate the objective function value using the new parameter values;

[0128] If the new objective function value is less than the current value, the disturbance is accepted and the parameters are updated;

[0129] Otherwise, the perturbation is discarded and the current parameters are retained until the objective function value no longer changes or the maximum number of iterations is reached.

[0130] In the initialization step, the initial Manning coefficient value is set;

[0131] Calculate the initial flow simulation error as the objective function value; perform the following steps at each time step:

[0132] Randomly select a dimension, i.e., the Manning coefficient for a river reach; randomly perturb the parameters of that dimension to obtain new parameter values; use these new parameter values ​​to calculate the flow simulation error (objective function value); if the new objective function value is less than the current value, accept the perturbation and update the parameters; otherwise, discard the perturbation and retain the current parameters. Repeat these steps until the objective function value stops changing or the maximum number of iterations is reached.

[0133] For example, there is a river network model consisting of 10 river sections, and the Manning coefficient of each river section needs to be optimized.

[0134] The following are the steps to implement an optimization algorithm based on random perturbations:

[0135] Set the initial Manning coefficient to [0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.10, 0.11, 0.12]

[0136] The initial flow simulation error is calculated as the objective function value and iterative optimization begins. First, the Manning coefficients and the corresponding initial flow simulation errors for the 10 river sections are initialized. Then, the main optimization loop is entered. In each iteration step:

[0137] A random river section is perturbed and the resulting objective function value (flow simulation error) is calculated. If the new objective function value is smaller, the perturbation is accepted and the current Manning coefficient and objective function value are updated. This process is repeated 1000 times (or until the objective function value no longer changes). The optimized Manning coefficient and the minimum objective function value are finally output.

[0138] In an embodiment of the present application, by acquiring a river network database and gridded hydrological and topographic data, the river network database includes: river identification information and river grades of each river; the hydrological and topographic data includes: elevation data, flow direction data, and cumulative flow; the gridded hydrological and topographic data is interpolated onto a refined confluence grid of a target resolution to obtain interpolated elevation data; based on the interpolated elevation data, flow direction data, and cumulative flow, vector data of the river network centerline and the catchment area boundary are determined, and the river network is segmented according to the Strahler river grade to determine river sections of different Strahler grades; the vector data of the river network centerline and the catchment area boundary are input into a pre-established river network confluence model, Different Manning coefficients are used to simulate river sections of different Strahler grades to obtain simulated flow. By optimizing the Manning coefficient in sections, the hydrological characteristics of river sections of different grades can be better reflected, and the reliability of the simulation results can be improved. Compared with the simulation using a unified Manning coefficient, the section-wise optimization can simulate the flow process more accurately. Through the dynamic dimension search algorithm, the search dimension can be dynamically increased or decreased during the search process, and the Manning coefficients of river sections of different Strahler grades can be optimized separately, so that the objective function of the flow simulation error used to characterize the simulated flow and the observed flow is minimized. The search strategy can be adaptively adjusted to improve the search efficiency. Therefore, the accurate river network confluence simulation results can provide support for practical applications such as water resources management.

[0139] The wind-solar-water resource element simulation method for optimizing the key parameters of the convergence mode provided in the embodiments of the present application can be executed by a wind-solar-water resource element simulation device for optimizing the key parameters of the convergence mode. In the embodiments of the present application, the wind-solar-water resource element simulation method for optimizing the key parameters of the convergence mode is executed by the wind-solar-water resource element simulation device for optimizing the key parameters of the convergence mode as an example to illustrate the wind-solar-water resource element simulation method for optimizing the key parameters of the convergence mode provided in the embodiments of the present application.

[0140] Figure 3 : This is a structural diagram of a wind, solar and water resource element simulation device for optimizing key parameters of a confluence mode provided in an embodiment of the present application. The device 300 includes:

[0141] The acquisition module 310 is used to acquire the river network database and gridded hydrological and topographic data. The river network database includes: river identification information and river level of each river; the hydrological and topographic data includes: elevation data, flow direction data, and cumulative flow;

[0142] The interpolation module 320 is used to interpolate the gridded hydrological and topographic data onto the refined confluence grid of the target resolution to obtain interpolated elevation data;

[0143] a determination module 330 for determining vector data of the river network centerline and the catchment area boundary based on the interpolated elevation data, flow direction data, and accumulated flow, and for segmenting the river network according to the Strahler river class to determine river sections of different Strahler classes;

[0144] An input module 340 is used to input vector data of the river network centerline and the catchment area boundary into a pre-established river network confluence model, and simulate river sections of different Strahler grades using different Manning coefficients to obtain simulated flow;

[0145] The optimization module 350 is used to optimize the Manning coefficients of different Strahler grade river sections through a dynamic dimensional search algorithm to minimize the objective function, where the objective function is the flow simulation error between the simulated flow and the observed flow.

[0146] In a possible embodiment, the acquisition module 310 is specifically configured to:

[0147] Collect river data of each river in the preset area; river data includes: terrain data, remote sensing data, riverbed data and vegetation cover data, etc.

[0148] The river data is classified to obtain the river grade corresponding to each river, so as to obtain the river network database.

[0149] In a possible embodiment, the interpolation module 320 is specifically configured to:

[0150] Calculate the flow direction data of each confluence grid based on the interpolated elevation data. The flow direction data is used to indicate the direction of water flow from the current grid cell to the adjacent confluence grid.

[0151] According to the flow direction data, the cumulative flow data of each confluence grid is calculated. The cumulative flow is used to represent the cumulative amount of water flow from the upstream to the current confluence grid;

[0152] Determine the river network centerline and catchment area boundaries based on cumulative flow data;

[0153] Track the confluence grids where the cumulative flow data reaches the preset threshold and extract the vector data of the river network centerline;

[0154] Identify grid cells whose accumulated flow data reaches a preset threshold and trace their watershed boundaries to extract vector data of the catchment area boundaries.

[0155] In a possible embodiment, the optimization module 350 is further configured to:

[0156] The objective function is to minimize the sum of squares of the daily average flow rate relative errors between the simulated flow rate and the observed flow rate.

[0157] In a possible embodiment, the optimization module 350 is specifically configured to:

[0158] A dynamic dimension search algorithm is used to optimize the Manning coefficient of river sections with different Strahler grades.

[0159] In each iteration, the Manning coefficient of a river section is selected for adjustment and the model is rerun to obtain new flow simulation results.

[0160] Based on the change in the objective function value, decide whether to accept the adjustment and select the next river section for optimization;

[0161] The optimization is iterated continuously until the objective function reaches the iteration stopping condition.

[0162] In a possible embodiment, the optimization module 350 is specifically configured to:

[0163] At each time step, the following steps are performed:

[0164] Randomly select a dimension;

[0165] Randomly perturb the parameters of this dimension to obtain new parameter values;

[0166] Calculate the objective function value using the new parameter values;

[0167] If the new objective function value is less than the current value, the disturbance is accepted and the parameters are updated;

[0168] Otherwise, the perturbation is discarded and the current parameters are retained until the objective function value no longer changes or the maximum number of iterations is reached.

[0169] In a possible embodiment, the acquisition module 310 is further configured to:

[0170] Establish a river network confluence model and define the river network structure in the river network confluence model. The river network structure includes the length, slope and cross-section of each river section.

[0171] Different initial Manning coefficients are set according to different Strahler grade river sections.

[0172] In an embodiment of the present application, by obtaining a river network database and gridded hydrological and topographic data, the river network database includes: river identification information and river grades of each river; the hydrological and topographic data include: elevation data, flow direction data, and cumulative flow; the gridded hydrological and topographic data are interpolated onto a refined confluence grid of a target resolution to obtain interpolated elevation data; based on the interpolated elevation data, flow direction data, and cumulative flow, the vector data of the river network centerline and the watershed boundary are determined, and the river network is segmented according to the Strahler river grade to determine river sections of different Strahler grades; the vector data of the river network centerline and the watershed boundary are input into a pre-established river network confluence model, and for different Strahler grades, ... the pre-established river network confluence model, and the vector data of the river network centerline and the watershed boundary are Different Manning coefficients are used to simulate river sections of different Strahler grades to obtain simulated flow rates. By optimizing the Manning coefficient in sections, the hydrological characteristics of river sections of different grades can be better reflected, and the reliability of the simulation results can be improved. Compared with simulation using a unified Manning coefficient, section-wise optimization can simulate the flow process more accurately. Through the dynamic dimensional search algorithm, the search dimension can be dynamically increased or decreased during the search process, and the Manning coefficients of river sections of different Strahler grades can be optimized respectively, so that the objective function of the flow simulation error used to characterize the simulated flow and the observed flow is minimized, and the search strategy can be adaptively adjusted to improve the search efficiency. Therefore, accurate river network confluence simulation results can provide support for practical applications such as water resources management.

[0173] The device provided in the embodiment of the present application can implement each process implemented in the above method embodiment. To avoid repetition, it will not be described here.

[0174] Optionally, Figure 4 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application is shown.

[0175] The electronic device may include a processor 401 and a memory 402 storing computer program instructions.

[0176] Specifically, the processor 401 may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0177] The memory 402 may include a large-capacity memory for data or instructions. By way of example and not limitation, the memory 402 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 402 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 402 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 402 is a non-volatile solid-state memory. In a specific embodiment, the memory 402 includes a read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or a flash memory, or a combination of two or more of these.

[0178] The processor 401 implements any one of the methods in the embodiments shown in the figures by reading and executing computer program instructions stored in the memory 402 .

[0179] In one example, the electronic device may further include a communication interface 404 and a bus 410. Figure 4 As shown, the processor 401 , the memory 402 , and the communication interface 404 are connected via a bus 410 and communicate with each other.

[0180] The communication interface 404 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0181] Bus 410 comprises hardware, software or both, couples the parts of electronic equipment to each other.For example, and not limitation, bus can comprise accelerated graphics port (AGP) or other graphics bus, enhanced industry standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industry standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations.In suitable cases, bus 410 can comprise one or more buses.Although the present application embodiment describes and shows specific bus, the application considers any suitable bus or interconnection.

[0182] The electronic device can execute the method in the embodiment of the present application, thereby realizing the combination Figure 1 Described method.

[0183] In addition, in combination with the method in the above embodiment, the embodiment of the present application can provide a computer readable storage medium to implement. The computer readable storage medium stores computer program instructions; when the computer program instructions are executed by the processor, the Figure 1 method.

[0184] It should be understood that the present application is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated. Those skilled in the art can make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present application.

[0185] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium that can store or transmit information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0186] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps. In other words, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0187] The above description is only a specific embodiment of the present application. Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the scope of protection of the present application.

Claims

1. A wind-solar-water resource element simulation method for optimizing key parameters of confluence mode, characterized by: The method comprises: Obtain river network database and gridded hydrological and topographic data. The river network database includes: river identification information and river level of each river; hydrological and topographic data includes: elevation data, flow direction data, and cumulative flow; Interpolate the gridded hydrological and topographic data onto the refined confluence grid of the target resolution to obtain the interpolated elevation data; Based on the interpolated elevation data, flow direction data and cumulative flow, the vector data of the river network centerline and catchment boundary are determined, and the river network is segmented according to the Strahler river class to determine the river sections of different Strahler classes; The vector data of the river network centerline and catchment boundary are input into the pre-established river network confluence model. Different Manning coefficients are used to simulate the river sections with different Strahler grades to obtain the simulated flow. The Manning coefficients of different Strahler grade river sections are optimized by a dynamic dimensional search algorithm to minimize the objective function, which is the flow simulation error between the simulated flow and the observed flow. The dynamic dimension search algorithm is used to optimize the Manning coefficient of different Strahler grade river sections to minimize the objective function, including: A dynamic dimension search algorithm is used to optimize the Manning coefficient of river sections with different Strahler grades; In each iteration, the Manning coefficient of a river section is selected for adjustment, and the river network confluence model is re-run to obtain new flow simulation results; Based on the change in the objective function value, decide whether to accept the adjustment and select the next river section for optimization; The optimization is iterated continuously until the objective function reaches the iteration stopping condition.

2. The method according to claim 1, characterized in that The acquisition of the river network database and gridded hydrological and topographic data includes: Collect river data of each river in the preset area; river data includes: terrain data, remote sensing data, riverbed data and vegetation cover data; The river data is classified to obtain the river grade corresponding to each river, so as to obtain the river network database.

3. The method according to claim 1, characterized in that The vector data of the river network centerline and the catchment area boundary are determined based on the interpolated elevation data, flow direction data and cumulative flow, including: Calculate the flow direction data of each confluence grid based on the interpolated elevation data. The flow direction data is used to indicate the direction of water flow from the current grid cell to the adjacent confluence grid. According to the flow direction data, the cumulative flow data of each confluence grid is calculated. The cumulative flow is used to represent the cumulative amount of water flow from the upstream to the current confluence grid; Determine the river network centerline and catchment area boundaries based on cumulative flow data; Track the confluence grids where the cumulative flow data reaches the preset threshold and extract the vector data of the river network centerline; Identify grid cells whose accumulated flow data reaches a preset threshold and trace their watershed boundaries to extract vector data of the catchment area boundaries.

4. The method according to claim 1, wherein Before optimizing the Manning coefficients of river sections of different Strahler grades by the dynamic dimensional search algorithm to minimize the objective function, the method further includes: The objective function is to minimize the sum of squares of the daily average flow rate relative errors between the simulated flow rate and the observed flow rate.

5. The method according to claim 1, wherein The continuous iterative optimization, until the objective function reaches the iteration stopping condition, includes: At each time step, the following steps are performed: Randomly select a dimension; Randomly perturb the parameters of this dimension to obtain new parameter values; Calculate the objective function value using the new parameter values; If the new objective function value is less than the current value, the disturbance is accepted and the parameters are updated; Otherwise, the perturbation is discarded and the current parameters are retained until the objective function value no longer changes or the maximum number of iterations is reached.

6. The method according to claim 1, characterized in that Before inputting the vector data of the river network centerline and the catchment area boundary into the pre-established river network confluence model, the method further includes: Establish a river network confluence model and define the river network structure in the river network confluence model. The river network structure includes the length, slope and cross-section of each river section. Different initial Manning coefficients are set according to different Strahler grade river sections.

7. A wind, solar and water resource element simulation device for optimizing key parameters of confluence mode, characterized in that: The device comprises: The acquisition module is used to obtain the river network database and gridded hydrological and topographic data. The river network database includes: river identification information and river level of each river; the hydrological and topographic data includes: elevation data, flow direction data, and cumulative flow; The interpolation module is used to interpolate the gridded hydrological and topographic data onto the refined confluence grid of the target resolution to obtain the interpolated elevation data; A determination module is used to determine the vector data of the river network centerline and the catchment area boundary based on the interpolated elevation data, flow direction data and cumulative flow, and to segment the river network according to the Strahler river level and determine the river sections of different Strahler levels; The input module is used to input the vector data of the river network centerline and the catchment area boundary into the pre-established river network confluence model, and use different Manning coefficients to simulate the river sections of different Strahler grades to obtain the simulated flow; The optimization module is used to optimize the Manning coefficient of different Strahler grade river sections through a dynamic dimensional search algorithm to minimize the objective function, which is the flow simulation error between the simulated flow and the observed flow; The optimization module is specifically used to: A dynamic dimension search algorithm is used to optimize the Manning coefficient of river sections with different Strahler grades; In each iteration, the Manning coefficient of a river section is selected for adjustment, and the river network confluence model is re-run to obtain new flow simulation results; Based on the change in the objective function value, decide whether to accept the adjustment and select the next river section for optimization; The optimization is iterated continuously until the objective function reaches the iteration stopping condition.

8. The device according to claim 7, characterized in that The determining module is specifically configured to: Calculate the flow direction data of each confluence grid based on the interpolated elevation data. The flow direction data is used to indicate the direction of water flow from the current grid cell to the adjacent confluence grid. According to the flow direction data, the cumulative flow data of each confluence grid is calculated. The cumulative flow is used to represent the cumulative amount of water flow from the upstream to the current confluence grid; Determine the river network centerline and catchment area boundaries based on cumulative flow data; Track the confluence grids where the cumulative flow data reaches the preset threshold and extract the vector data of the river network centerline; Identify grid cells whose accumulated flow data reaches a preset threshold and trace their watershed boundaries to extract vector data of the catchment area boundaries.

9. An electronic device, characterized in that: The electronic device includes: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, it implements the wind, solar and water resource element simulation method for optimizing the key parameters of the confluence mode as described in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement a wind, solar, and water resource element simulation method for optimizing key parameters of a confluence pattern as described in any one of claims 1 to 6.

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