Wind, light and water resource element simulation method and device for optimizing key parameters of confluence mode

Abstract

The invention discloses a wind, light and water resource element simulation method and device for optimizing key parameters of a confluence mode. The method comprises the following steps: acquiring a river network database and gridded hydrotopographic data, and interpolating the gridded hydrotopographic data to a refined confluence grid with a target resolution to obtain interpolated elevation data; according to the altitude data, the flow direction data and the cumulative flow after interpolation, determining vector data of a river network center line and a water collection area boundary, segmenting the river network according to the Strahler river level, and determining river reach of different Strahler levels; inputting the vector data of the central line of the river network and the boundary of the water collecting area into a pre-established river network confluence model, and simulating by adopting different Manning coefficients for river reaches with different Strahler levels to obtain simulated flow; and through a dynamic dimension search algorithm, the Manning coefficients of the river reaches with different Strahler levels are optimized respectively, so that the objective function is minimum.

Description

Technical Field

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

[0002] The Manning coefficient is a key parameter that describes the roughness of a river channel and directly affects water flow resistance and flow calculation. Parameter sensitivity analysis shows that the Manning coefficient is one of the most important parameters affecting the accuracy of flow simulation. Accurate estimation of the Manning coefficient is crucial to improving the accuracy of hydrological model simulation. The traditional method of obtaining the Manning coefficient through hydrological field measurements has great limitations and is difficult to generalize to the entire river network. The Manning coefficient of the spatial distribution of the river channel varies greatly, and field observations have great representativeness issues. Existing indirect estimation methods such as remote sensing images and geographic information require a lot of calibration and verification work.

[0003] By calibrating the parameters of the hydrological model, the optimized Manning coefficient can be indirectly inverted. However, the Manning coefficient obtained by this method may not represent the actual river conditions well. A comprehensive analysis combining measured data, empirical formulas and theoretical models is required. Existing technologies cannot effectively obtain Manning coefficient data across the entire river network, and lack a reliable and efficient method for observing and estimating the Manning coefficient. Innovative technical means are urgently needed to solve this problem.

[0004] Therefore, in view of the scarcity of direct observations of the Manning coefficient for the entire river network, it is of great practical significance to develop a new method for observing and estimating the Manning coefficient. Summary of the invention

[0005] The purpose of the embodiments of the present application is to provide a method and device for simulating wind and solar water resources elements for optimizing key parameters of confluence patterns, 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 grade to determine the river sections of different Strahler grades;

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

[0011] Through the dynamic dimensional search algorithm, the Manning coefficients of river sections with different Strahler grades are optimized 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-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 terrain 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 according to the interpolated elevation data, flow direction data and cumulative flow, and to segment the river network according to the Strahler river grade and determine the river sections of different Strahler grades;

[0016] An 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 simulate the river sections of different Strahler grades using different Manning coefficients to obtain 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, which includes a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the program 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, and 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, comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction 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 is 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 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 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 Manning coefficient is optimized in sections to better reflect the hydrological characteristics of river sections of different levels and improve the reliability of simulation results. Compared with simulation using a unified Manning coefficient, section-wise optimization can simulate the flow process more accurately. The dynamic dimensional search algorithm can dynamically increase or decrease the search dimension during the search process, and optimize the Manning coefficients of river sections of different Strahler levels respectively. The search strategy can be adaptively adjusted to improve the search efficiency, so that the objective function of the flow simulation error used to characterize the simulated flow and the observed flow is minimized. In the embodiments 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 It is a flow chart of a method for simulating wind, solar and water resources elements for optimizing key parameters of a confluence mode provided in an embodiment of the present application;

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

[0025] Figure 3 It is a structural diagram of a wind-solar-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 drawings of the embodiments of the present application to clearly describe the technical solutions of the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present application belong to the scope of protection of this application.

[0028] The terms "first", "second", etc. in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

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

[0030] Parameter sensitivity analysis studies have shown that the Manning coefficient is a key parameter affecting flow simulation. However, direct observations of the Manning coefficient for the entire river network are scarce. The Manning coefficient is a parameter used in hydraulics to describe the roughness of a river or channel, and it plays a vital role in river flow simulation. Due to the high sensitivity of the Manning coefficient to flow simulation, its accuracy directly affects the reliability of the simulation results. However, direct observations of the Manning 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 in a river system. The Strahler scale starts at 1 and increases gradually, indicating the confluence and branching of rivers. Rivers with higher Strahler scales generally have wider riverbeds, deeper channels, and more complex river networks.

[0032] The Manning coefficients of river sections of different Strahler grades will vary due to differences in riverbed materials, vegetation cover, river channel morphology, etc. The following are some general descriptions of the Manning coefficients of river sections of different Strahler grades:

[0033] Strahler Grade 1, used to identify the smallest tributaries. Usually headwater streams, with a bed composed of fine sediment and possibly some vegetation cover. Manning's Coefficient: Usually higher because of greater roughness of the bed and banks. Typical values ​​may be between 0.030 and 0.050.

[0034] Strahler Class 2, 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. Manning's coefficient: may be slightly lower than that of Class 1 streams, with typical values ​​probably ranging from 0.025 to 0.045.

[0035] Strahler Grade 3, used to identify medium tributaries. Formed by the confluence of two Strahler Grade 2 tributaries, the channel may begin to widen and the bed material may include more gravel and pebbles. Manning's coefficient: may decrease further, and typical values ​​may be between 0.020 and 0.040.

[0036] Strahler scale 4 and above are used to identify major channels. For example, major channels have wide riverbeds, riverbed materials may include large pieces of gravel, pebbles or even boulders, and vegetation cover may be reduced. Manning coefficient: Usually low because the roughness of the riverbed and banks is relatively small. Typical values ​​may be between 0.015 and 0.035.

[0037] It should be noted that the above range of the Manning coefficient is for reference only, and the actual Manning coefficient will be affected by many factors, including the specific conditions of the river, seasonal changes, human activities, etc. Therefore, in practical 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 in the related technologies, the embodiments of the present application provide a method and device for simulating wind, solar and water resources elements for optimizing the key parameters of the confluence pattern, which can solve the problem in the related technologies that direct observation of the Manning coefficient is scarce, posing challenges to flow simulation.

[0039] The wind, solar and water resources element simulation method for optimizing the key parameters of the confluence mode provided in the embodiment of the present application is 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, obtaining a river network database and gridded hydrological and topographic data, wherein 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 coverage 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 data are classified using the Strahler river classification method to obtain the river grade corresponding to each river to obtain the river network database.

[0047] The Strahler river classification method is a commonly used river classification method that defines the level of a river by the confluence and branching of the river. The Strahler classification starts with the smallest tributary (level 1) and gradually increases the level 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 method:

[0048] First, a directed graph is constructed to represent the river network, and then the Strahler classification of each river is calculated. These classification results are added to the previously extracted river network. Next, a database is created, which includes three fields: river identification information, river classification, and geometric information. Then, the river network is traversed and the river classification and geometric information of each river are inserted into the database. In this way, the river network database based on the Strahler river classification is completed. This database can be used for subsequent river analysis, modeling and other applications.

[0049] Specifically, river data is collected, and the river data includes: terrain data, remote sensing data, riverbed data, vegetation coverage data, etc.

[0050] Among them, terrain data can be obtained through digital elevation model (DEM) data to extract river networks. Remote sensing data: Use satellite remote sensing images or aerial photography images to identify and extract river information. Field survey: Conduct field surveys to collect actual information about rivers, such as riverbed materials, vegetation cover, etc.

[0051] River extraction, 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 unjoined tributary is marked as Strahler Class 1. Confluence rule: when two rivers of the same class merge, a higher class river is formed; when two rivers of different classes merge, the class remains unchanged, and the higher class river remains unchanged. Stepwise classification: apply the above rules step by step along the river network until the entire river network is classified.

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

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

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

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

[0057] Flow Accumulation Grid is an important concept in hydrological analysis. It represents the accumulation of water flowing from high places to low places in a digital elevation model (DEM). Flow Accumulation Grid is usually used together with Flow Direction Grid to simulate and analyze surface runoff, extract river networks, divide watershed boundaries, etc.

[0058] Each cell value in the confluence grid represents the cumulative amount of water flowing into that cell from all upstream cells. This value is usually expressed as the number of cells or area. The confluence grid is usually used in conjunction with the flow direction grid, which indicates the direction of water flow from each cell to the adjacent cells. The resolution of the confluence grid is consistent with the resolution of the DEM, usually ranging from tens of meters to hundreds of meters. The confluence grid can cover the entire world or a specific area, 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 in the DEM, flow direction calculation, etc.

[0061] Flow direction calculation: Calculate the flow direction of each cell based on the DEM after filling. The flow direction is usually determined using the D8 method (eight-direction method), which means that water can only flow to 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 the cumulative flow, the river network can be extracted. Cells with cumulative flow reaching a certain threshold are considered to be part of the river. Using the confluence grid and the flow direction grid, the watershed boundary can be delineated. The watershed boundary is usually the catchment area of ​​the cell with the maximum cumulative flow.

[0065] Confluence grids are key data in hydrological analysis. By representing the accumulation of water flow, they support a variety of applications such as river network extraction, watershed delineation, hydrological simulation, and ecological assessment. By generating and using confluence 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 the 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 grade to determine river sections of different Strahler grades;

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

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

[0069] Step 220, calculating the cumulative flow data of each confluence grid according to 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 watershed boundary.

[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] Stream Network or River Network refers to the central axis of a river system, which indicates the main flow path of the river. The extraction of stream network centerline is usually based on digital elevation model (DEM) and hydrological analysis tools.

[0075] The steps to extract 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 the 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] The watershed boundary (watershed boundary or catchment boundary) refers to the boundary of the watershed, which separates the geographical area where surface water and groundwater flow to the same outlet. The extraction of the watershed boundary is also based on DEM and hydrological analysis tools.

[0077] The steps to extract the watershed boundary include: first, fill the DEM. Calculate the flow direction of each cell. Calculate the cumulative flow of each cell. Select one or more exit points (such as the confluence or estuary of a river), and based on the flow direction information, track the cells where the cumulative flow reaches the maximum value to determine the boundary of the watershed.

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

[0079] Based on the flow direction data, the cumulative flow of each grid cell is calculated. The cumulative flow represents the cumulative amount of water flowing from upstream to the current grid cell. Based on the cumulative flow data, the centerline of the river network and the boundary of the catchment area are determined. Usually, grid cells with cumulative flow reaching a certain threshold are considered to be 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. By extracting these elements, we can better understand and simulate surface runoff processes, thereby 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 hydrological topographic data onto the 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, 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 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] Iterate the optimization step and calculate the objective function value (flow simulation error) based on the current solution.

[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 for river sections of 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 coefficients of different Strahler grade river sections 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. The DDS algorithm is often used to solve multidimensional optimization problems, especially when the objective function is complex or the search space dimension is high.

[0097] The DDS algorithm can dynamically increase or decrease the search dimension 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 the current search results and historical information to improve the search efficiency. The DDS algorithm has a strong global search capability and can find the global optimal solution or approximate optimal solution in a complex search space. The DDS algorithm can be designed for parallel execution to speed up the search by searching multiple dimensions or multiple solutions at the same time.

[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 the search dimensions based on the search results. If the search effect on a certain dimension is not good, you can reduce the search weight of that dimension or remove it completely; conversely, if the search effect on a certain dimension is good, you can increase the search weight of that dimension.

[0102] Convergence judgment: judge 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 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. The DDS algorithm has a wide range of applications in many fields, especially when high-dimensional optimization problems need to be dealt with. By properly designing and implementing the DDS algorithm, the efficiency and quality of solving optimization problems can be significantly improved.

[0105] The flow simulation error of the river network confluence model is a complex optimization problem that requires considering different attributes of the river, such as landforms, vegetation, hydrology, etc. The dynamic dimension search algorithm can solve this problem well.

[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 relative errors between the simulated flow and the observed flow.

[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 flow and the observed flow, 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]

[0113] Where r is the correlation coefficient between the simulated value and the observed value, α = σ m / σ. is the standard deviation of the simulation value σ m The ratio of the standard deviation of the observed value σ. β=μ m / μ. is the simulated mean μ m The ratio of KGE to the observed mean μ. The value range of KGE is (-∞, 7]. The closer KGE is to 1, the better the simulation effect.

[0114] In a possible embodiment, step 150 includes:

[0115] A dynamic dimension search algorithm is used to optimize the Manning coefficient of river sections with different Strahler grades.

[0116] 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.

[0117] According to the change of the objective function value, decide whether to accept the adjustment and select the next river section for optimization;

[0118] The optimization is iterated continuously until the objective function reaches the iteration stopping condition.

[0119] A river network confluence model was established, and the flow simulation error was calculated based on the measured data as the objective function.

[0120] The dynamic dimension search algorithm is used to optimize the Manning coefficient of different Strahler grade river sections at the same time. The dynamic dimension search algorithm can adaptively adjust the number of dimensions of the optimization space to avoid falling into the local optimal solution.

[0121] For example, there is a watershed containing 10 river sections 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 section.

[0122] The optimization steps using the dynamic dimension search algorithm are as follows:

[0123] 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 (flow simulation error) of the current solution; 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.

[0124] Repeat the above iterative optimization steps until the stopping condition is met (such as the maximum number of iterations or the objective function value is less than the threshold), and output the final optimization result, that is, the optimal Manning coefficient of the 10 river sections.

[0125] In a possible embodiment, the optimization is iterated continuously until the objective function reaches an iteration stop condition, including:

[0126] At each time step the following steps are performed:

[0127] Randomly select a dimension;

[0128] Randomly perturb the parameters of this dimension to obtain new parameter values;

[0129] Calculate the objective function value using the new parameter values;

[0130] If the new objective function value is less than the current value, the disturbance is accepted and the parameters are updated;

[0131] Otherwise, the disturbance is discarded and the current parameters are retained until the objective function value no longer changes or the maximum number of iterations is reached.

[0132] In the initialization step, the initial Manning coefficient value is set;

[0133] Calculate the initial flow simulation error as the objective function value; perform the following steps at each time step:

[0134] Randomly select a dimension, i.e. the Manning coefficient of a river section; randomly perturb the parameters of this dimension to obtain new parameter values; use the 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 keep the current parameters. Repeat the above steps until the objective function value no longer changes or the maximum number of iterations is reached.

[0135] 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.

[0136] The following are the steps to implement an optimization algorithm based on random perturbations:

[0137] 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]

[0138] 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 of 10 river sections are initialized. Then, the main optimization loop is entered. In each iterative step:

[0139] Randomly select a river section for parameter perturbation and calculate the objective function value (flow simulation error) after the perturbation. If the new objective function value is smaller, accept the perturbation and update the current Manning coefficient and objective function value. This process will be repeated 1000 times (or until the objective function value no longer changes). Finally, the optimized Manning coefficient and the minimum objective function value are output.

[0140] 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 are interpolated onto a refined confluence grid of a target resolution to obtain interpolated elevation data; according to 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 simulation results can be improved. Compared with the 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.

[0141] The wind-solar water resource element simulation method for optimizing the key parameters of the confluence mode provided in the embodiment of the present application can be executed by a wind-solar water resource element simulation device for optimizing the key parameters of the confluence mode. In the embodiment of the present application, the wind-solar water resource element simulation method for optimizing the key parameters of the confluence mode is executed by a wind-solar water resource element simulation device for optimizing the key parameters of the confluence mode as an example to illustrate the wind-solar water resource element simulation device for optimizing the key parameters of the confluence mode provided in the embodiment of the present application.

[0142] Figure 4 4 is a block diagram of a wind-solar-water resource element simulation device for optimizing key parameters of a confluence mode provided in an embodiment of the present application. The device 400 includes:

[0143] The acquisition module 410 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;

[0144] The interpolation module 420 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;

[0145] A determination module 430 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 grade to determine river sections of different Strahler grades;

[0146] An input module 440 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 different Manning coefficients for river sections of different Strahler grades to obtain simulated flow;

[0147] The optimization module 450 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.

[0148] In a possible embodiment, the acquisition module 410 is specifically configured to:

[0149] Collect river data of each river in the preset area; river data includes: terrain data, remote sensing data, riverbed data and vegetation coverage data, etc.

[0150] The river data is classified to obtain the river grade corresponding to each river, so as to obtain the river network database.

[0151] In a possible embodiment, the interpolation module 420 is specifically configured to:

[0152] According to the interpolated elevation data, the flow direction data of each confluence grid is calculated. The flow direction data is used to indicate the direction of water flow from the current grid unit to the adjacent confluence grid.

[0153] 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;

[0154] Determine the river network centerline and catchment area boundaries based on the accumulated flow data;

[0155] Track the confluence grids where the cumulative flow data reaches the preset threshold and extract the vector data of the river network centerline;

[0156] Grid cells whose accumulated flow data reaches a preset threshold are identified and their watershed boundaries are traced to extract vector data of the catchment area boundaries.

[0157] In a possible embodiment, the optimization module 450 is further configured to:

[0158] The objective function is to minimize the sum of squares of the relative errors between the simulated flow and the observed flow.

[0159] In a possible embodiment, the optimization module 450 is specifically configured to:

[0160] A dynamic dimension search algorithm is used to optimize the Manning coefficient of river sections with different Strahler grades.

[0161] 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.

[0162] According to the change of the objective function value, decide whether to accept the adjustment and select the next river section for optimization;

[0163] The optimization is iterated continuously until the objective function reaches the iteration stopping condition.

[0164] In a possible embodiment, the optimization module 450 is specifically configured to:

[0165] At each time step the following steps are performed:

[0166] Randomly select a dimension;

[0167] Randomly perturb the parameters of this dimension to obtain new parameter values;

[0168] Calculate the objective function value using the new parameter values;

[0169] If the new objective function value is less than the current value, the disturbance is accepted and the parameters are updated;

[0170] Otherwise, the disturbance is discarded and the current parameters are retained until the objective function value no longer changes or the maximum number of iterations is reached.

[0171] In a possible embodiment, the acquisition module 410 is further configured to:

[0172] 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 section of each river section;

[0173] Different initial Manning coefficients are set according to different Strahler grade river sections.

[0174] 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 are interpolated onto a refined confluence grid of a target resolution to obtain interpolated elevation data; according to 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, and for different Strahler grades, the vector data of the river network centerline and the catchment area boundary are calculated. Different Manning coefficients are used to simulate river sections of different Strahler levels to obtain simulated flow. By optimizing the Manning coefficient in sections, the hydrological characteristics of river sections of different levels can be better reflected and the reliability of 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 levels 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.

[0175] 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 again here.

[0176] Optionally, Figure 4 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application is shown.

[0177] The electronic device may include a processor 401 and a memory 402 storing computer program instructions.

[0178] 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.

[0179] 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. In appropriate cases, the memory 402 may include a removable or non-removable (or fixed) medium. In appropriate cases, 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). In appropriate cases, 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.

[0180] The processor 401 implements any one of the methods in the embodiments shown in the figure by reading and executing the computer program instructions stored in the memory 402.

[0181] 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.

[0182] 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.

[0183] Bus 410 includes hardware, software or both, and the parts of electronic equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industrial 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 appropriate cases, bus 410 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the application considers any suitable bus or interconnection.

[0184] The electronic device can execute the method in the embodiment of the present application, thereby realizing the combination Figure 1 Describe the method.

[0185] 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.

[0186] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0187] 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, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, 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 capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), 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.

[0188] 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, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0189] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity 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 protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A method for simulating wind and solar water resources elements by optimizing key parameters of confluence mode, characterized in that: 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 grade to determine the river sections of different Strahler grades; The vector data of the river network centerline and catchment area boundary are input into the pre-established river network confluence model. Different Manning coefficients are used to simulate the river sections of different Strahler grades to obtain the simulated flow. Through the dynamic dimensional search algorithm, the Manning coefficients of river sections with different Strahler grades are optimized to minimize the objective function, which is the flow simulation error between the simulated flow and the observed flow.

2. The method according to claim 1, characterized in that The obtaining 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 coverage data, etc. 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: According to the interpolated elevation data, the flow direction data of each confluence grid is calculated. The flow direction data is used to indicate the direction of water flow from the current grid unit 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 the accumulated 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; Grid cells whose accumulated flow data reaches a preset threshold are identified and their watershed boundaries are traced to extract vector data of the catchment area boundaries.

4. The method according to claim 1, characterized in that 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 relative errors between the simulated flow and the observed flow.

5. The method according to claim 1, characterized in that: The dynamic dimension search algorithm is used to optimize the Manning coefficients 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 model is rerun to obtain new flow simulation results. According to the change of 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.

6. The method according to claim 1, characterized in that The continuous iterative optimization, until the objective function reaches the iteration stop 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 disturbance is discarded and the current parameters are retained until the objective function value no longer changes or the maximum number of iterations is reached.

7. 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 comprises: 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 section of each river section; Different initial Manning coefficients are set according to different Strahler grade river sections.

8. A wind-solar-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 terrain 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 according to the interpolated elevation data, flow direction data and cumulative flow, and to segment the river network according to the Strahler river grade and determine the river sections of different Strahler grades; An 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 simulate the river sections of different Strahler grades using different Manning coefficients to obtain 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.

9. The device according to claim 8, characterized in that The determination module is specifically used for: According to the interpolated elevation data, the flow direction data of each confluence grid is calculated. The flow direction data is used to indicate the direction of water flow from the current grid unit 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 the accumulated 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; Grid cells whose accumulated flow data reaches a preset threshold are identified and their watershed boundaries are traced to extract vector data of the catchment area boundaries.

10. An electronic device, characterized in that: The electronic device comprises: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the method for simulating wind, solar and water resources elements for optimizing key parameters of the confluence mode as described in any one of claims 1 to 7 is implemented.

11. 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 method for simulating wind, solar and water resources elements for optimizing key parameters of a confluence pattern as described in any one of claims 1 to 7.

Images