CN119203811B - Wind and solar water resource element simulation method and device for optimizing key parameters of confluence model - Google Patents
Wind and solar water resource element simulation method and device for optimizing key parameters of confluence modelInfo
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Abstract
The application discloses a wind, light and water resource element simulation method and device for optimizing key parameters of a confluence mode. The method comprises the steps of obtaining a river network database and lattice-formed hydrographic terrain data, interpolating the lattice-formed hydrographic terrain data onto a refined confluence grid with target resolution to obtain interpolated elevation data, determining vector data of a river network center line and a water collecting area boundary according to the interpolated elevation data, flow direction data and accumulated flow, segmenting the river network according to Strahler river grades to determine river segments with different Strahler grades, inputting the vector data of the river network center line and the water collecting area boundary into a pre-established river network confluence model, simulating the river segments with different Strahler grades by adopting different Manning coefficients to obtain simulated flow, and respectively optimizing Manning coefficients of the river segments with different Strahler grades through a dynamic dimension search algorithm to minimize a target function.
Description
Technical Field
The application belongs to the technical field of water resource treatment, and particularly relates to a wind, light and water resource element simulation method and device for optimizing key parameters of a confluence mode.
Background
The Manning coefficient is a key parameter for describing the roughness of a river channel, and directly influences the water flow resistance and flow calculation. The parameter sensitivity analysis shows that the Manning coefficient is one of the most important parameters affecting the flow simulation accuracy. Accurate estimation of the Manning coefficient is important to improve the simulation accuracy of the hydrological model. The traditional method for obtaining the Manning coefficient through hydrologic actual measurement has large limitation and is difficult to popularize in the whole river network. The Manning coefficient of the river space distribution has larger change, and the field observation has great representative problem. The existing indirect estimation methods of remote sensing images, geographic information and the like need a great deal of correction and verification work.
The optimized Manning coefficient can be obtained through indirect inversion by calibrating parameters of the hydrologic model. The manning coefficients obtained by this method may not be a good representation of the actual channel conditions. Comprehensive analysis is required by combining measured data, an empirical formula and a theoretical model. The prior art cannot effectively acquire the Manning coefficient data in the whole river network range, and lacks a reliable and efficient Manning coefficient observation and estimation method. Innovative technical means are needed to solve this problem.
Therefore, aiming at the problem of scarcity of direct observation of the Manning coefficient of the whole river network, the development of a novel Manning coefficient observation and estimation method has important practical significance.
Disclosure of Invention
The embodiment of the application aims to provide a wind, light and water resource element simulation method and device for optimizing key parameters of a confluence mode, which can solve the problem that the direct observation of the Manning coefficient of a full river network is rare.
In a first aspect, an embodiment of the present application provides a method for simulating wind, light and water resource elements for optimizing key parameters in a confluence mode, where the method includes:
The method comprises the steps of obtaining a river network database and lattice-formed hydrographic topography data, wherein the river network database comprises river identification information and river grades of each river;
Interpolating the latticed hydrologic terrain data onto a refined confluence grid with target resolution to obtain interpolated elevation data;
According to the interpolated elevation data, flow direction data and accumulated flow, determining vector data of a central line of the river network and a boundary of a water collecting area, segmenting the river network according to Strahler river grades, and determining river segments with different Strahler grades;
Inputting vector data of the central line of the river network and the boundary of the water collecting area into a pre-established river network converging model, and simulating by adopting different Manning coefficients aiming at river segments with different Strahler grades to obtain simulated flow;
And respectively optimizing Manning coefficients of river reach with different Strahler grades through a dynamic dimension search algorithm to minimize an objective function, wherein the objective function is a flow simulation error of the simulated flow and the observed flow.
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, where the device includes:
The acquisition module is used for acquiring a river network database and lattice-formed hydrographic topographic data, wherein the river network database comprises river identification information and river grades of each river;
the interpolation module is used for interpolating the latticed hydrologic and topographic data to the refined confluence grid with the target resolution to obtain interpolated elevation data;
The determining module is used for determining vector data of a central line of the river network and a boundary of a water collecting area according to the interpolated elevation data, the flow direction data and the accumulated flow, segmenting the river network according to Strahler river grades, and determining river segments with different Strahler grades;
The input module is used for inputting vector data of the central line of the river network and the boundary of the water collecting area into a pre-established river network converging model, and simulating different Manning coefficients aiming at river segments of different Strahler grades to obtain simulated flow;
and the optimization module is used for respectively optimizing the Manning coefficients of the river reach with different Strahler grades through a dynamic dimension search algorithm to minimize an objective function, wherein the objective function is a flow simulation error of the simulated flow and the observed flow.
In a third aspect, an embodiment of the present application provides an electronic device comprising a processor and a memory storing a program or instructions executable on the processor, which when executed by the processor, implement the steps of the method as described in the first aspect.
In a fourth aspect, embodiments of the present application provide a readable storage medium having stored thereon a program or instructions which when executed by a processor perform the steps of the method according to the first aspect.
In a fifth aspect, an embodiment of the present application provides a chip, where the chip includes a processor and a communication interface, where the communication interface is coupled to the processor, and where the processor is configured to execute a program or instructions to implement a method according to the first aspect.
In a sixth aspect, embodiments of the present application provide a computer program product stored in a storage medium, the program product being executable by at least one processor to implement the method according to the first aspect.
In the embodiment of the application, the river network database comprises river identification information and river grades of each river by acquiring the river network database and the latticed hydrographic topography data; the hydrologic topography data comprises elevation data, flow direction data and accumulated flow, the lattice-ordered hydrologic topography data is interpolated on a refined confluence grid with target resolution to obtain interpolated elevation data, vector data of a river network center line and a water collecting area boundary are determined according to the interpolated elevation data, the flow direction data and the accumulated flow, river networks are segmented according to Strahler river grades to determine river reach with different Strahler grades, the vector data of the river network center line and the water collecting area boundary are input into a pre-established river network confluence model, different Manning coefficients are adopted for simulating the river reach with different Strahler grades to obtain simulated flow, the hydrologic characteristics of the river reach with different grades can be better reflected through segmented optimization Manning coefficients, the reliability of simulation results is improved, compared with the simulation with uniform Manning coefficients, the segmented optimization can simulate the flow process more accurately, the search dimension of different Strahler grade segments can be dynamically increased or decreased in the search process through a dynamic dimension search algorithm, the Manning coefficients are optimized respectively, the search efficiency can be adaptively adjusted, the simulation algorithm can be improved, the actual flow can be provided for the actual flow, the simulation results can be provided with the practical flow, the practical flow is improved, the practical flow is accurately represented by the simulation model, the simulation results can be provided, the practical flow-based on the simulation results, the simulation results are improved, the practical flow is accurately-based on the simulation results, and the simulation results are provided, and the simulation results are improved.
Drawings
FIG. 1 is a flow chart of a wind, light and water resource element simulation method for optimizing key parameters of a confluence mode, which is provided by the embodiment of the application;
FIG. 2 is a flow chart of a method for determining vector data of a river network centerline and a water-collecting region boundary according to an embodiment of the present application;
FIG. 3 is a block diagram of a wind-light-water resource element simulation device with optimized key parameters in a confluence mode, which is provided by the embodiment of the application;
fig. 4 is a schematic hardware structure of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions of the embodiments of the present application will be clearly described below with reference to the accompanying drawings of the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which are obtained by a person skilled in the art based on the embodiments of the present application, fall within the scope of protection of the present application.
The terms first, second and the like in the description and in the claims, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged, as appropriate, such that embodiments of the present application may be implemented in sequences other than those illustrated or described herein, and that the objects identified by "first," "second," etc. are generally of a type, and are not limited to the number of objects, such as the first object may be one or more. Furthermore, in the description and claims, "and/or" means at least one of the connected objects, and the character "/", generally means that the associated object is an "or" relationship.
The wind, light and water resource element simulation method for optimizing the key parameters of the confluence mode, which is provided by the embodiment of the application, can be at least applied to the following application scenes, and the following description is provided.
There is a parametric sensitivity analysis study that shows that the Manning coefficient is a key parameter that affects flow simulation. But direct observation of the full Henschel manning coefficient is rare. The Manning's coeffient (Manning's coefficient) is a parameter in hydraulics that is used to describe the roughness of a river or channel and plays a vital role in the simulation of river flow. Because of the higher sensitivity of the Manning coefficient to flow simulation, the accuracy directly affects the reliability of the simulation result. However, direct observation of the Manning coefficients is truly rare, which presents challenges for flow simulation.
Grade Strahler is a classification method used to describe river size and complexity in a river system. The Strahler scale starts from 1 and increases gradually, indicating the confluence and branching of the river. A Strahler-grade higher river reach typically has a wider riverbed, deeper riverways, and a more complex river network structure.
The Manning coefficients of river segments of different Strahler grades are different due to the differences of factors such as river bed materials, vegetation coverage, river channel forms and the like. The following are some general descriptions of the Manning coefficients of different Strahler grades:
Strahler, level 1, is used to identify the smallest tributary. Typically source streams, the riverbed consists of fine sediment, possibly covered with some vegetation. The Manning coefficient is generally higher because of the greater roughness of the riverbed and riverbank. Typical values may be between 0.030 and 0.050.
Strahler grade 2, for identifying smaller tributaries. By merging two Strahler grade 1 branches, the riverbed material may be more diversified and vegetation coverage may be increased. Manning coefficients, which may be slightly below grade 1 segments, may typically range from 0.025 to 0.045.
Strahler grade 3, for identifying medium tributaries. From the confluence of two Strahler grade 2 substreams, the river channel may begin to widen and the bed material may include more gravel and pebbles. The manning coefficient may be further reduced, and a typical value may be between 0.020 and 0.040.
Strahler grade 4 and above, for identifying the main river channel. For example, in a main river, the river bed is wide, the river bed material may include large blocks of gravel, pebbles, and even boulders, and vegetation coverage may be reduced. The Manning coefficient is generally low because of the relatively small roughness of the riverbed and the riverbank. Typical values may be between 0.015 and 0.035.
It should be noted that the above-mentioned range of Manning coefficients is for reference only, and the actual Manning coefficients may be affected by a variety of factors, including river specific conditions, seasonal variations, human activities, etc. Therefore, in practical applications, it is necessary to perform in-situ measurement according to circumstances or to refer to the related research results to determine a specific value of the manning coefficient.
Aiming at the problems of the related art, the embodiment of the application provides a wind, light and water resource element simulation method and device for optimizing key parameters of a confluence mode, which can solve the problems that direct observation of Manning coefficients is rare and challenges flow simulation in the related art.
The wind, light and water resource element simulation method for optimizing key parameters of the confluence mode provided by the embodiment of the application is described in detail through specific embodiments and application scenes thereof by combining the drawings.
Fig. 1 is a flowchart of a wind-light-water resource element simulation method for optimizing key parameters of a confluence mode, which is provided by the embodiment of the application.
As shown in fig. 1, the method for simulating wind, light and water resource elements optimized by key parameters of the confluence mode can include steps 110-150, and the method is applied to a wind, light and water resource element simulation device optimized by key parameters of the confluence mode, and specifically includes the following steps:
Step 110, acquiring a river network database and lattice-formed hydrographic topography data, wherein the river network database comprises river identification information and river grades of each river;
in one possible embodiment, step 110 includes:
river data of each river in the preset area are collected, wherein the river data comprise topographic data, remote sensing data, river bed data, vegetation coverage data and the like.
And (3) carrying out river grading on the river data to obtain river grades corresponding to all the rivers so as to obtain a river network database.
River data of each river in the preset area are collected, river classification is carried out on the river data by adopting a Strahler river classification method, and corresponding river grades of each river are obtained, so that a river network database is obtained.
Strahler river classification is a common river classification method that defines the class of a river by the confluence and branching of the river. The Strahler scale starts with the smallest tributary (level 1) and increases the scale step by step, representing the complexity and size of the river. The following steps are adopted to carry out river classification and river network database construction by adopting Strahler river classification method:
First, a directed graph is constructed to represent the river network, and then Strahler ratings for each river are calculated. These classification results are added to the previously extracted river network. Next, a database is created that includes three fields of river identification information, river level and geometric information. Then, the river network is traversed, and river level and geometric information of each river are inserted into the database. Thus, the construction of the river network database based on Strahler river classification is completed. This database may be used for subsequent river analysis, modeling, etc.
Specifically, river data is collected, wherein the river data comprises topographic data, remote sensing data, riverbed data, vegetation coverage data and the like.
The terrain data can be obtained through Digital Elevation Model (DEM) data and is used for extracting a river network. And the remote sensing data is that the river information is identified and extracted by utilizing satellite remote sensing images or aerial photographic images. And (3) field investigation, namely performing field investigation and collecting actual information of the river, such as river bed materials, vegetation coverage and the like.
River extraction and hydrologic analysis, namely performing hydrologic analysis by using GIS software (such as ArcGIS, QGIS and the like) and extracting a river network from DEM data. River network identification, namely identifying and verifying the river network according to the remote sensing image and the field investigation data.
Strahler river classification, initial classification, the smallest non-joined tributaries are labeled Strahler class 1. The merging rule is that when two rivers of the same grade are merged, a river of a higher grade is formed, and when two rivers of different grades are merged, the grade is kept unchanged, and the grade of the river of the higher grade is unchanged. Gradually grading, namely gradually applying the rule along the river network until the whole river network is graded.
And constructing a river network database, wherein the data structure is a structure for designing the river network database, and comprises river ID, strahler grade, river length, river bed width, river bed material, vegetation coverage and other attributes. And (3) data entry, namely entering river grading results and other related information into a database. And (3) data verification, namely verifying information in a database through field investigation and remote sensing images.
Therefore, river classification and river network database construction are a systematic process by adopting Strahler river classification method, and involve multiple steps of data collection, river extraction, classification, database construction and management. Through the process, a detailed and accurate river network database can be established, and scientific basis is provided for hydrologic simulation, ecological assessment and river management.
Step 120, interpolating the latticed hydrographic terrain data onto a refined confluence grid with target resolution to obtain interpolated elevation data;
MERIT-Hydro (Merit Hydrography) hydrographic topography data is a set of global hydrographic topography data sets of high accuracy aimed at providing improved river networks and related hydrographic characterization information. These datasets are corrected and optimized by a series of processing steps based on a variety of sources of data, including SRTM (Shuttle Radar Topography Mission), AW3D (ALOS World D), and TanDEM-X, to reduce systematic and random errors.
The sink grid (Flow Accumulation Grid) is an important concept in hydrologic analysis and represents the cumulative amount of water flowing from high to low in a Digital Elevation Model (DEM). The sink grid is typically used with a flow grid (Flow Direction Grid) for simulating and analyzing surface runoffs, extracting river networks, dividing river basin boundaries, and the like.
Each cell value in the sink grid represents the cumulative amount of water flow into that cell from all cells upstream. This value is typically expressed in terms of the number of cells or area. The busing grid is typically used in conjunction with a flow-direction grid that indicates the direction of water flow from each cell to an adjacent cell. The resolution of the grid is consistent with the resolution of the DEM, typically tens to hundreds of meters. The grid of confluences may cover a global area or a specific area, depending on the coverage of the DEM data.
The generating process of the confluence grid specifically comprises the following steps:
The DEM preprocessing comprises the steps of preprocessing DEM data, including filling the hollow (filling the hollow in the DEM), calculating the flow direction and the like.
And calculating the flow direction of each cell according to the DEM after filling. The flow direction is typically determined using the D8 method (eight direction method), i.e. the water flow can only flow to one of the eight adjacent cells.
And calculating the accumulated flow, namely gradually calculating the accumulated flow of each cell from the highest point of the DEM according to the flow direction information. The accumulated flow is accumulated based on the accumulated flow of the upstream cell and the contribution of the current cell.
Generating a confluence grid, namely distributing the accumulated flow values to corresponding cells to generate the confluence grid.
Thus, by setting the threshold value of the cumulative flow rate, the river network can be extracted. Cells where the cumulative flow reaches a certain threshold are considered to be part of a river. With the sink grid and the flow grid, the flow domain boundaries can be partitioned. The drainage basin boundary is typically the water collection area of the cell where the cumulative flow reaches a maximum.
The confluence grid is key data in hydrologic analysis, and supports various applications such as river network extraction, river basin division, hydrologic simulation and ecological assessment by representing the accumulated water flow quantity. By generating and using a converged grid, researchers and decision makers can better understand and simulate the surface runoff process, supporting water resource management and environmental protection.
130, Determining vector data of a central line of a river network and a boundary of a water collecting area according to the interpolated elevation data, flow direction data and accumulated flow, segmenting the river network according to Strahler river grades, and determining river segments with different Strahler grades;
In one possible embodiment, step 130 includes:
Step 210, calculating flow direction data of each confluence grid according to the interpolated elevation data, wherein the flow direction data is used for representing the direction of water flow from the current grid unit to the adjacent confluence grid;
Step 220, calculating accumulated flow data of each confluence grid according to the flow direction data, wherein the accumulated flow is used for representing the accumulated flow amount of water flow from the upstream to the current confluence grid;
step 230, determining the central line of the river network and the boundary of the water collecting area according to the accumulated flow data;
Step 240, tracking a confluence grid with accumulated flow data reaching a preset threshold value, and extracting vector data of a central line of the river network;
Step 250, identifying grid cells for which the accumulated flow data reaches a preset threshold and tracking the boundaries of the drainage basins, and extracting vector data of the boundaries of the water collection areas.
River network centreline and catchment area boundaries are two important concepts in hydrologic analysis that describe the centreline of the river network and the boundaries of the river basin, respectively.
The river network centerline (Stream Network or River Network) refers to the central axis of the river system, which represents the main flow path of the river. The extraction of the centerline of the river network is typically based on a Digital Elevation Model (DEM) and a hydrographic analysis tool.
The river network central line extraction step comprises the steps of filling the DEM with the depressions, eliminating the depressions in the data and ensuring the smooth flow of water flow. Flow direction calculation the flow direction of each cell is calculated using a hydrologic analysis tool (e.g., tauDEM, arcGIS hydrologic tool set, etc.). Based on the flow direction information, the cumulative flow rate of each cell is calculated, representing the cumulative amount of water flow from upstream to the cell. A threshold value for the cumulative flow is set and cells for which the cumulative flow exceeds the threshold value are considered to be part of a river. By tracking these cells, the river network centerline can be extracted.
The catchment boundary (WATERSHED BOUNDARY OR CATCHMENT BOUNDARY) refers to the boundary of a basin that separates surface and groundwater flow to the geographic area of the same outlet. The extraction of the catchment area boundary is also based on DEM and hydrologic analysis tools.
The collecting area boundary extraction step comprises the steps of firstly filling the DEM with a depression. The flow direction of each cell is calculated. The cumulative flow per cell is calculated. One or more outlet points (such as the junction or estuary of a river) are selected, and the cells where the cumulative flow reaches a maximum are tracked based on the flow direction information to determine the boundary of the catchment area.
And calculating the flow direction of each grid cell according to the interpolated elevation data. The flow direction generally indicates the direction of water flow from the current grid cell to the adjacent grid cell. Flow direction calculations are performed using a hydrologic analysis tool (e.g., tauDEM, arcGIS hydrologic toolset, etc.).
Based on the flow direction data, the cumulative flow per grid cell is calculated. The cumulative flow represents the cumulative amount of water flow from upstream to the current grid cell. And determining the central line of the river network and the boundary of the water collecting area according to the accumulated flow data. Typically, grid cells where the cumulative flow reaches a certain threshold are considered to be part of the river network.
Vector data of the river network center line is extracted, and the vector data can be achieved by tracking grid cells with accumulated flow reaching a threshold value. Vector data of the catchment area boundary is extracted, which can be achieved by identifying grid cells whose cumulative flow reaches a threshold and tracking their basin boundaries.
The central line of the river network and the boundary of the water collecting area are key elements in hydrologic analysis, and the surface runoff process can be better understood and simulated by extracting the elements, so that water resource management and environmental protection are supported. The digital elevation model and the hydrologic analysis tool can systematically extract the central line of the river network and the boundary of the water collecting area, thereby providing scientific basis for various hydrologic and ecological researches.
The grid-formed MERIT-Hydro hydrographic topography data is interpolated on the refined confluence grid, and the central line of the river network and the boundary of the water collecting area are extracted, so that the vector data of the river network and the water collecting area with high resolution can be obtained, and scientific basis is provided for hydrologic simulation, ecological assessment and river management.
Step 140, inputting vector data of the central line of the river network and the boundary of the water collecting area into a pre-established river network converging model, and simulating the river reach of different Strahler grades by adopting different Manning coefficients to obtain simulated flow;
in a possible embodiment, before step 140, the method further comprises:
establishing a river network converging model, and defining a river network structure in the river network converging model, wherein the river network structure comprises the length, gradient and section of each river segment;
Different initial Manning coefficients are set according to the Strahler-grade river reach.
And initializing, namely randomly generating a group of initial solutions including Manning coefficients of river reach with different Strahler grades.
And an iterative optimization step, wherein an objective function value (flow simulation error) is calculated according to the current solution.
And determining the optimized dimension number according to the dynamic dimension searching strategy.
Searching is carried out in the current dimension, and the Manning coefficient is updated.
And comparing the updated objective function values, and receiving a better solution.
And stopping the condition until the maximum iteration times or the objective function value is smaller than the set threshold.
And outputting a final optimization result, namely, the optimal Manning coefficients of the river reach with different Strahler grades.
The optimization method based on dynamic dimension search can effectively improve the flow simulation precision of the river network confluence model.
Step 150, respectively optimizing Manning coefficients of river reach with different Strahler grades through a dynamic dimension search algorithm to minimize an objective function, wherein the objective function is a stream objective function value KGE of the simulated flow and the observed flow and is used for representing an error value between the simulated value and the observed value.
The dynamic dimension search algorithm (Dynamic Dimension Search, DDS) is an optimization algorithm that finds the optimal solution by dynamically adjusting the search dimension in the search space. DDS algorithms are often used to solve multidimensional optimization problems, especially in cases where the objective function is complex or the search space dimension is high.
The DDS algorithm can dynamically increase or decrease search dimension in the search process so as to adapt to the complexity of the problem and the characteristic of the search space, and can adaptively adjust the search strategy according to the current search result and the historical information, thereby improving the search efficiency. The DDS algorithm has strong global searching capability, and can find a global optimal solution or an approximate optimal solution in a complex searching space. The DDS algorithm can be designed to be executed in parallel, and the searching speed is increased by searching a plurality of dimensions or a plurality of solutions simultaneously.
Basic steps of the dynamic dimension search algorithm are as follows:
Initialization, namely setting initial search dimension and other parameters such as search step length, iteration times and the like.
And (3) searching in each dimension, and evaluating the objective function value of the current solution.
And D, dynamically adjusting the search dimension according to the search result. If the searching effect on a certain dimension is not good, the searching weight of the dimension can be reduced or the dimension can be completely removed, otherwise, if the searching effect on the certain dimension is good, the searching weight of the dimension can be increased.
And (3) convergence judgment, namely judging whether a convergence condition is reached, if the change of the objective function value is smaller than a certain threshold value or the maximum iteration number is reached.
Outputting the result, namely outputting the found optimal solution or the approximate optimal solution.
The dynamic dimension search algorithm is a flexible and efficient optimization algorithm that adapts to the complexity of the problem and the nature of the search space by dynamically adjusting the search dimension. DDS algorithms find wide application in a variety of fields, particularly where high-dimensional optimization problems need to be addressed. The DDS algorithm is reasonably designed and implemented, so that the solving efficiency and quality of the optimization problem can be remarkably improved.
The flow simulation error of the river network confluence model is a complex optimization problem, and different attributes of the river, such as topography, vegetation, hydrology and the like, need to be considered at the same time. This problem can be solved well with dynamic dimension search algorithms.
In one possible embodiment, prior to step 150, the method further comprises:
And taking the least square sum of the daily average flow relative errors of the simulated flow and the observed flow as an objective function.
In one possible embodiment, the objective function is:
The objective function value KGE is used for representing the objective function value KGE and representing an error value between the simulated flow and the observed flow;
r is the correlation coefficient of the analog flow and the observed flow, alpha is the ratio of the standard deviation of the analog flow to the standard deviation of the observed flow, and beta is the ratio of the average value of the analog flow to the average value of the observed flow. The KGE has a value range (- ≡1), and the more the KGE is close to 1, the better the simulation effect is.
In one possible embodiment, step 150 includes:
And optimizing the Manning coefficients of the river reach with different Strahler grades by adopting a dynamic dimension search algorithm.
And in each iteration, a Manning coefficient of a river reach is selected for adjustment, and the model is rerun to obtain a new flow simulation result.
Determining whether to accept the adjustment according to the change condition of the objective function value, and selecting the next river reach for optimization;
and (5) continuously performing iterative optimization until the objective function reaches the iterative stopping condition.
And establishing a river network confluence model, and calculating a flow simulation error as an objective function according to the measured data.
Dynamic dimension search algorithm is used, and the Manning coefficients of river reach with different Strahler grades are optimized. The dynamic dimension searching algorithm can adaptively adjust the dimension number of the optimization space, and avoid sinking into a local optimal solution.
Illustratively, there is a river basin containing 10 segments of different Strahler grades. The optimization objective is to minimize the flow simulation error of the river network confluence model by optimizing the Manning coefficient of each river segment.
The optimization steps using the dynamic dimension search algorithm are as follows:
The method comprises the steps of constructing a river network confluence model, calculating a flow simulation error as an objective function according to measured data, randomly generating 10 Manning coefficients as an initial solution in an initialization step, calculating an objective function value (flow simulation error) of a current solution in an iterative optimization step, determining an optimized dimension number which is 2 or 3 according to a dynamic dimension searching strategy, searching in a selected dimension, updating the corresponding Manning coefficients, comparing the updated objective function values, and receiving a better solution.
Repeating the iterative optimization steps until the stopping condition (such as the maximum iterative times or the objective function value is smaller than the threshold value) is met, and outputting a final optimization result, namely the optimal Manning coefficients of 10 river segments.
In one possible embodiment, the iterative optimization is continued until the objective function reaches an iteration stop condition, comprising:
The following steps are performed at each time step:
randomly selecting a dimension;
randomly disturbing the parameters of the dimension to obtain a new parameter value;
calculating an objective function value using the new parameter value;
if the new objective function value is smaller than the current value, the disturbance is accepted, and the parameters are updated;
otherwise, discarding the disturbance, and retaining the current parameter until the objective function value is no longer changed or the maximum iteration number is reached.
In the initializing step, setting an initial Manning coefficient value;
calculating initial flow simulation error as objective function value, and executing the following steps in each time step:
Randomly selecting a Manning coefficient of a dimension, namely a river reach, randomly perturbing the parameter of the dimension to obtain a new parameter value, calculating a flow simulation error (objective function value) by using the new parameter value, accepting the perturbation if the new objective function value is smaller than the current value, updating the parameter, otherwise discarding the perturbation, and reserving the current parameter. Repeating the steps until the objective function value is not changed or the maximum iteration number is reached.
Illustratively, there is a model of a river network consisting of 10 river segments, and the Manning coefficients of each segment need to be optimized.
The following is an implementation step of an optimization algorithm based on random perturbation:
Setting initial Manning coefficient as [0.03,0.04,0.05,0.06,0.07,0.08,0.09,0.10,0.11,0.12]
And calculating an initial flow simulation error as an objective function value, starting iterative optimization, and initializing Manning coefficients of 10 river segments and corresponding initial flow simulation errors. Then, an optimized main loop is entered, in each iteration step:
Randomly selecting one river reach for parameter disturbance, and calculating the objective function value (flow simulation error) after disturbance. 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 may be repeated 1000 times (or until the objective function value is no longer changed). And finally outputting the optimized Manning coefficient and the minimum objective function value.
In the embodiment of the application, the river network database comprises river identification information and river grades of each river by acquiring a river network database and latticed hydrographic terrain data; the hydrologic topography data comprises elevation data, flow direction data and accumulated flow, the lattice hydrologic topography data is interpolated on a refined confluence grid with target resolution to obtain interpolated elevation data, vector data of a river network center line and a water collecting area boundary are determined according to the interpolated elevation data, the flow direction data and the accumulated flow, the river network is segmented according to Strahler river grades to determine river reach with different Strahler grades, the vector data of the river network center line and the water collecting area boundary are input into a pre-established river network confluence model, different Manning coefficients are adopted for simulating the river reach with different Strahler grades to obtain simulated flow, the hydrologic characteristics of the river reach with different grades can be better reflected through segmented optimization Manning coefficients, the reliability of simulation results is improved, compared with the simulation with the uniform Manning coefficients, the segmented optimization can more accurately simulate the flow process, the Manning coefficients of different Strahler grade segments can be dynamically increased or decreased in the searching process through dynamic dimension searching algorithm, the Manning coefficients representing the simulation flow and the river reach with different Strahler grades are optimized respectively, the actual monitoring and the actual monitoring results can be provided by the minimum measurement policy, the actual monitoring and the network is adjusted.
According to the wind-light-water resource element simulation method for optimizing the key parameters of the converging mode, which is provided by the embodiment of the application, the execution main body can be a wind-light-water resource element simulation device for optimizing the key parameters of the converging mode. In the embodiment of the application, the wind-light-water resource element simulation device for optimizing the key parameters of the confluence mode is taken as an example to execute the wind-light-water resource element simulation method for optimizing the key parameters of the confluence mode, and the wind-light-water resource element simulation device for optimizing the key parameters of the confluence mode provided by the embodiment of the application is explained.
Fig. 3 is a block diagram of a wind-light-water resource element simulation device for optimizing key parameters of a confluence mode, where the device 300 includes:
The acquisition module 310 is configured to acquire a river network database and lattice-formed hydrographic topography data, where the river network database includes river identification information and river grades of each river;
The interpolation module 320 is configured to interpolate the latticed hydrographic terrain data onto a refined convergence grid with the target resolution, so as to obtain interpolated elevation data;
The determining module 330 is configured to determine vector data of a river network center line and a boundary of a water collecting area according to the interpolated elevation data, the flow direction data and the accumulated flow, segment the river network according to Strahler river grades, and determine river segments with different Strahler grades;
The input module 340 is configured to input vector data of a river network center line and a boundary of a water collecting area into a pre-established river network convergence model, and perform simulation by adopting different manning coefficients for river segments of different Strahler grades to obtain a simulated flow;
And the optimization module 350 is used for respectively optimizing the Manning coefficients of the river reach with different Strahler grades through a dynamic dimension search algorithm to minimize an objective function, wherein the objective function is a flow simulation error of the simulated flow and the observed flow.
In one possible embodiment, the obtaining module 310 is specifically configured to:
river data of each river in the preset area are collected, wherein the river data comprise topographic data, remote sensing data, river bed data, vegetation coverage data and the like.
And (3) carrying out river grading on the river data to obtain river grades corresponding to all the rivers so as to obtain a river network database.
In one possible embodiment, the interpolation module 320 is specifically configured to:
calculating flow direction data of each confluence grid according to the interpolated elevation data, wherein the flow direction data is used for representing the direction of water flow from the current grid unit to the adjacent confluence grid;
calculating accumulated flow data of each confluence grid according to the flow direction data, wherein the accumulated flow is used for representing the accumulated flow amount of water flow from the upstream to the current confluence grid;
Determining a river network central line and a water collecting area boundary according to the accumulated flow data;
Tracking a confluence grid with accumulated flow data reaching a preset threshold value, and extracting vector data of a central line of the river network;
And identifying grid cells with accumulated flow data reaching a preset threshold value, tracking the boundary of a drainage basin, and extracting vector data of the boundary of the water collecting area.
In one possible embodiment, the optimization module 350 is further configured to:
And taking the least square sum of the daily average flow relative errors of the simulated flow and the observed flow as an objective function.
In one possible embodiment, the optimization module 350 is specifically configured to:
And optimizing the Manning coefficients of the river reach with different Strahler grades by adopting a dynamic dimension search algorithm.
And in each iteration, a Manning coefficient of a river reach is selected for adjustment, and the model is rerun to obtain a new flow simulation result.
Determining whether to accept the adjustment according to the change condition of the objective function value, and selecting the next river reach for optimization;
and (5) continuously performing iterative optimization until the objective function reaches the iterative stopping condition.
In one possible embodiment, the optimization module 350 is specifically configured to:
The following steps are performed at each time step:
randomly selecting a dimension;
randomly disturbing the parameters of the dimension to obtain a new parameter value;
calculating an objective function value using the new parameter value;
if the new objective function value is smaller than the current value, the disturbance is accepted, and the parameters are updated;
otherwise, discarding the disturbance, and retaining the current parameter until the objective function value is no longer changed or the maximum iteration number is reached.
In one possible embodiment, the obtaining module 310 is further configured to:
establishing a river network converging model, and defining a river network structure in the river network converging model, wherein the river network structure comprises the length, gradient and section of each river segment;
Different initial Manning coefficients are set according to the Strahler-grade river reach.
In the embodiment of the application, the river network database comprises river identification information and river grades of each river by acquiring the river network database and the grid-dotted hydrographic topographic data, wherein the hydrotopographic data comprises elevation data, flow direction data and accumulated flow rate, the grid-dotted hydrotopographic data is interpolated on a refined confluence grid with target resolution to obtain interpolated elevation data, vector data of a river network center line and a water collecting area boundary are determined according to the interpolated elevation data, flow direction data and accumulated flow rate, the river network is segmented according to Strahler river grades to determine river segments with different Strahler grades, the vector data of the river network center line and the water collecting area boundary are input into a pre-established river network confluence model, different Manning coefficients are adopted for simulating the river segments with different Strahler grades to obtain simulated flow rate, the hydrologic characteristics of the river segments with different grades can be better reflected through the segmented Manning coefficients, the reliability of the simulation result can be improved, compared with the simulation by adopting the unified Manning coefficients, the segmented optimization can be used for simulating the river network center line and the river segments, the river segments with different dimensions can be accurately optimized, the dynamic error can be accurately regulated and the dynamic error can be improved by the dynamic error is improved by the dynamic algorithm or the dynamic error is improved by the dynamic error is improved, and the dynamic error is better and the dynamic is better and improved by the dynamic and is better and is used by the dynamic and is better and improved by the method.
The device provided by the embodiment of the application can realize each process realized by the embodiment of the method, and in order to avoid repetition, the description is omitted.
Optionally, fig. 4 shows a schematic hardware structure of an electronic device according to an embodiment of the present application.
A processor 401 may be included in an electronic device as well as a memory 402 in which computer program instructions are stored.
In particular, the processor 401 may include a Central Processing Unit (CPU), or an Application SPECIFIC INTEGRATED Circuit (ASIC), or may be configured as one or more integrated circuits that implement embodiments of the present application.
Memory 402 may include mass storage for data or instructions. By way of example, and not limitation, memory 402 may comprise a hard disk drive (HARD DISK DRIVE, HDD), a floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of the foregoing. Memory 402 may include removable or non-removable (or fixed) media, where appropriate. Memory 402 may be internal or external to the integrated gateway disaster recovery device, where appropriate. In a particular embodiment, the memory 402 is a non-volatile solid state memory. In a particular embodiment, the memory 402 includes Read Only Memory (ROM). The ROM may be mask programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically Erasable PROM (EEPROM), electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these, where appropriate.
The processor 401 implements any of the methods of the illustrated embodiments by reading and executing computer program instructions stored in the memory 402.
In one example, the electronic device may also include a communication interface 404 and a bus 410. As shown in fig. 4, the processor 401, the memory 402, and the communication interface 404 are connected by a bus 410 and perform communication with each other.
Communication interface 404 is mainly used to implement communication between modules, devices, units, and/or apparatuses in the embodiments of the present application.
Bus 410 includes hardware, software, or both, coupling components of the electronic device to one another. By way of example, and not limitation, the buses may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a micro channel architecture (MCa) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus, or a combination of two or more of the above. Bus 410 may include one or more buses, where appropriate. Although embodiments of the application have been described and illustrated with respect to a particular bus, the application contemplates any suitable bus or interconnect.
The electronic device may perform the method according to the embodiments of the present application, thereby implementing the method described in connection with fig. 1.
In addition, in connection with the methods in the above embodiments, embodiments of the present application may be implemented by providing a computer-readable storage medium. The computer readable storage medium having stored thereon computer program instructions which when executed by a processor implement the method of fig. 1.
It should be understood that the application is not limited to the particular arrangements and instrumentality described above and shown in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. The method processes of the present application are not limited to the specific steps described and shown, but various changes, modifications and additions, or the order between steps may be made by those skilled in the art after appreciating the spirit of the present application.
The functional blocks shown in the above-described structural block diagrams may be implemented in hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and the like. The code segments may be downloaded via computer networks such as the internet, intranets, etc.
It should also be noted that the exemplary embodiments mentioned in this disclosure describe some methods or systems based on a series of steps or devices. The present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, or may be performed in a different order from the order in the embodiments, or several steps may be performed simultaneously.
In the foregoing, only the specific embodiments of the present application are described, and it will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein. It should be understood that the scope of the present application is not limited thereto, and any equivalent modifications or substitutions can be easily made by those skilled in the art within the technical scope of the present application, and they should be included in the scope of the present application.
Claims (10)
1. The wind, light and water resource element simulation method for optimizing key parameters of confluence mode is characterized by comprising the following steps:
The method comprises the steps of obtaining a river network database and lattice-formed hydrographic topography data, wherein the river network database comprises river identification information and river grades of each river;
Interpolating the latticed hydrologic terrain data onto a refined confluence grid with target resolution to obtain interpolated elevation data;
According to the interpolated elevation data, flow direction data and accumulated flow, determining vector data of a central line of the river network and a boundary of a water collecting area, segmenting the river network according to Strahler river grades, and determining river segments with different Strahler grades;
Inputting vector data of the central line of the river network and the boundary of the water collecting area into a pre-established river network converging model, and simulating by adopting different Manning coefficients aiming at river segments with different Strahler grades to obtain simulated flow;
the Manning coefficients of river reach with different Strahler grades are optimized respectively through a dynamic dimension search algorithm, so that an objective function is minimum, and the objective function is a flow simulation error of simulated flow and observed flow;
The method for optimizing the Manning coefficients of the river reach with different Strahler grades through the dynamic dimension search algorithm to minimize the objective function comprises the following steps:
optimizing Manning coefficients of river reach with different Strahler grades by adopting a dynamic dimension search algorithm;
In each iteration, a Manning coefficient of a river reach is selected for adjustment, and the river network confluence model is rerun to obtain a new flow simulation result;
Determining whether to accept the adjustment according to the change condition of the objective function value, and selecting the next river reach for optimization;
and (5) continuously performing iterative optimization until the objective function reaches the iterative stopping condition.
2. The method of claim 1, wherein the obtaining the river network database and the rasterized hydrographic terrain data comprises:
The river data of each river in the preset area are collected, wherein the river data comprise topographic data, remote sensing data, river bed data and vegetation coverage data;
And (3) carrying out river grading on the river data to obtain river grades corresponding to all the rivers so as to obtain a river network database.
3. The method of claim 1, wherein determining vector data for the river network centerline and the water-collecting region boundary based on the interpolated elevation data, flow direction data, and accumulated flow comprises:
calculating flow direction data of each confluence grid according to the interpolated elevation data, wherein the flow direction data is used for representing the direction of water flow from the current grid unit to the adjacent confluence grid;
calculating accumulated flow data of each confluence grid according to the flow direction data, wherein the accumulated flow is used for representing the accumulated flow amount of water flow from the upstream to the current confluence grid;
Determining a river network central line and a water collecting area boundary according to the accumulated flow data;
Tracking a confluence grid with accumulated flow data reaching a preset threshold value, and extracting vector data of a central line of the river network;
And identifying grid cells with accumulated flow data reaching a preset threshold value, tracking the boundary of a drainage basin, and extracting vector data of the boundary of the water collecting area.
4. The method of claim 1, wherein before optimizing the manning coefficients of the different Strahler-level segments, respectively, by the dynamic dimension search algorithm to minimize the objective function, the method further comprises:
And taking the least square sum of the daily average flow relative errors of the simulated flow and the observed flow as an objective function.
5. The method of claim 1, wherein the continually iteratively optimizing until the objective function reaches an iteration stop condition comprises:
The following steps are performed at each time step:
randomly selecting a dimension;
randomly disturbing the parameters of the dimension to obtain a new parameter value;
calculating an objective function value using the new parameter value;
if the new objective function value is smaller than the current value, the disturbance is accepted, and the parameters are updated;
otherwise, discarding the disturbance, and retaining the current parameter until the objective function value is no longer changed or the maximum iteration number is reached.
6. The method of claim 1, wherein before inputting the vector data of the river network center line and the water collection area boundary into the pre-established river network convergence model, the method further comprises:
establishing a river network converging model, and defining a river network structure in the river network converging model, wherein the river network structure comprises the length, gradient and section of each river segment;
Different initial Manning coefficients are set according to the Strahler-grade river reach.
7. Wind-light-water resource element simulation device with optimized key parameters of confluence mode is characterized by comprising:
The acquisition module is used for acquiring a river network database and lattice-formed hydrographic topographic data, wherein the river network database comprises river identification information and river grades of each river;
the interpolation module is used for interpolating the latticed hydrologic and topographic data to the refined confluence grid with the target resolution to obtain interpolated elevation data;
The determining module is used for determining vector data of a central line of the river network and a boundary of a water collecting area according to the interpolated elevation data, the flow direction data and the accumulated flow, segmenting the river network according to Strahler river grades, and determining river segments with different Strahler grades;
The input module is used for inputting vector data of the central line of the river network and the boundary of the water collecting area into a pre-established river network converging model, and simulating different Manning coefficients aiming at river segments of different Strahler grades to obtain simulated flow;
The optimization module is used for respectively optimizing Manning coefficients of river reach with different Strahler grades through a dynamic dimension search algorithm to minimize an objective function, wherein the objective function is a flow simulation error of a simulated flow and an observed flow;
the optimizing module is specifically configured to:
optimizing Manning coefficients of river reach with different Strahler grades by adopting a dynamic dimension search algorithm;
In each iteration, a Manning coefficient of a river reach is selected for adjustment, and the river network confluence model is rerun to obtain a new flow simulation result;
Determining whether to accept the adjustment according to the change condition of the objective function value, and selecting the next river reach for optimization;
and (5) continuously performing iterative optimization until the objective function reaches the iterative stopping condition.
8. The apparatus of claim 7, wherein the determining module is specifically configured to:
calculating flow direction data of each confluence grid according to the interpolated elevation data, wherein the flow direction data is used for representing the direction of water flow from the current grid unit to the adjacent confluence grid;
calculating accumulated flow data of each confluence grid according to the flow direction data, wherein the accumulated flow is used for representing the accumulated flow amount of water flow from the upstream to the current confluence grid;
Determining a river network central line and a water collecting area boundary according to the accumulated flow data;
Tracking a confluence grid with accumulated flow data reaching a preset threshold value, and extracting vector data of a central line of the river network;
And identifying grid cells with accumulated flow data reaching a preset threshold value, tracking the boundary of a drainage basin, and extracting vector data of the boundary of the water collecting area.
9. An electronic device comprising a processor and a memory storing computer program instructions, wherein the processor, when executing the computer program instructions, implements the method for simulating wind, light and water resource elements for optimizing key parameters of a confluence mode as claimed in any one of claims 1-6.
10. A computer readable storage medium, wherein computer program instructions are stored on the computer readable storage medium, and when executed by a processor, the computer program instructions implement the wind-light-water resource element simulation method for optimizing key parameters of a confluence mode according to any one of claims 1-6.
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