Hydraulic engineering multi-target four-pre-scheduling method based on digital twinning

Abstract

The invention relates to a hydraulic engineering multi-target four-pre-scheduling method based on digital twinning. The method comprises the following steps: constructing a hydraulic engineering data base plate; executing an incoming water forecasting sub-process; executing a flood routing sub-process; a flood early warning sub-process; executing a multi-target intelligent scheduling sub-process; and executing the three-dimensional visualization sub-process. According to the invention, accurate and rapid circulation of data among the unit modules is realized; according to a rehearsal result, accurately mastering the duration time and the peak flood flow of the flood; the scheduling plan is optimized, and the flood control benefit-making and comprehensive scheduling effect of the water conservancy project is fully exerted; and the refined and scientific level of flood control benefit-making dispatching is improved, water resources are utilized to the maximum extent, and comprehensive operation benefits as large as possible are obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy and flood control scheduling, and in particular to a multi-objective four-pre-scheduling method for water conservancy projects based on digital twins. Background Art

[0002] The flood control and benefit dispatching of water conservancy projects consists of two parts: project flood dispatching and water resources comprehensive utilization dispatching. It aims to achieve effective utilization of flood resources while preventing and controlling flood disasters, and coordinate the restrictive relationship between flood control and benefit dispatching. The goal of project flood dispatching is to smoothly discharge upstream water under the premise of ensuring the safety of the project through the operation dispatching of flow-through structures. The benefit dispatching task is the comprehensive utilization of water resources. The main goal is to minimize water abandonment and achieve optimized dispatching through comprehensive benefit dispatching, while meeting the requirements of the project's own safety, flood prevention, irrigation, shipping, ecology, etc.;

[0003] The technical solutions of the existing flood control and water conservation dispatching methods are based on rich practical experience and hydrological data, and are flexibly adjusted according to the hydrological characteristics of different river basins and seasons;

[0004] The advantages of the existing technology are: for small reservoir projects, the dispatching method of the existing technology is relatively simple and practical;

[0005] However, with the combined effects of climate change and rapid economic and social development, the demands for water conservancy flood control, power generation, water supply, shipping, ecology, etc. are constantly increasing. The demand for refined and scientific decision-making on flood control and water conservancy projects is also constantly increasing, and the defects of existing technologies are gradually emerging.

[0006] The defects of the prior art are:

[0007] 1. As existing technologies are based on experience and historical data, they are unable to make quantitative predictions and are overly dependent on human experience, making them less adaptable to modern refined decision-making;

[0008] 2. As existing technologies rely on human experience to make decisions, they are not applicable to the scheduling needs of large-scale water conservancy projects;

[0009] 3. The existing technology cannot quantify the decision-making process, resulting in a short foresight period and limited scheduling accuracy;

[0010] 4. Due to the short foresight period and limited dispatching accuracy of existing technologies, it is difficult to cope with complex situations and it is difficult to coordinate the contradictions between flood prevention and economic development. Summary of the invention

[0011] In response to the above problems, the present invention provides a multi-objective four-pre-scheduling method for water conservancy projects based on digital twins, which aims to realize the accurate and rapid flow of data among various unit modules; accurately grasp the duration and peak flow of floods according to the preview results; optimize the scheduling plan and give full play to the comprehensive scheduling role of water conservancy projects in flood control and benefit development; improve the refinement and scientific level of flood control and benefit development scheduling, and maximize the use of water resources to obtain the greatest possible comprehensive operating benefits.

[0012] To solve the above problems, the technical solution provided by the present invention is:

[0013] A multi-objective four-pre-scheduling method for water conservancy projects based on digital twins includes the following steps:

[0014] S100. Build a water conservancy project data base;

[0015] S200. Execute the water forecast sub-process;

[0016] S300. Execute flood evolution sub-process;

[0017] S400. Execute flood warning subprocess;

[0018] S500. Execute multi-objective intelligent scheduling sub-process;

[0019] S600: Execute the 3D visualization sub-process.

[0020] Preferably, the water conservancy project data base includes database storage and data file storage, which are used to store DEM data, DOM data, underwater terrain data, and hydrological site monitoring data within the upstream flood impact range of the water conservancy project; wherein:

[0021] The accuracy of the DEM data within the upstream flood impact area is that the grid size is no more than 2m;

[0022] The accuracy of the DOM data is a resolution not greater than 0.5m spatial resolution;

[0023] The accuracy of the underwater topographic data is that the section interval is no more than 200m, the distance between measuring points is no more than 1m, and the grid size is no more than 5m;

[0024] The hydrological site monitoring data includes real-time rainfall data of each observation site within the monitoring range, historical rainfall data arranged in chronological order, evapotranspiration data, real-time water level of important control sections, historical water level of important control sections, and flow data.

[0025] Preferably, the water inflow forecast sub-process in S200 specifically includes the following steps:

[0026] S210. Based on the DEM data within the upstream flood impact range, the upstream flood impact range is divided into small watersheds through the hydrological analysis module of the GIS tool software, and the key confluence sections are determined; specifically, the following steps are included:

[0027] S211. Fill the depressions of the DEM basic data;

[0028] S212. Determine the direction of water flow;

[0029] S213. Calculate the cumulative amount of confluence;

[0030] S214. Determine the drainage area;

[0031] S215. Extract the river network and classify the river network;

[0032] S216. Finally, the upstream flood impact range is divided into the small watersheds, and the key confluence sections are determined;

[0033] S220. According to the basin characteristics of the upstream flood impact range obtained in S210, the Xin'anjiang model is used as the basis to construct a water inflow forecast model, and the parameters of the Xin'anjiang model are set at the same time; wherein: the parameters of the Xin'anjiang model include: basin area, basin evapotranspiration capacity refraction coefficient, basin total water storage capacity, water storage capacity parabolic index, basin average free water capacity, basin free water storage capacity curve index, free water storage capacity to subsoil flow outflow coefficient, free water storage capacity to groundwater outflow coefficient, deep water retreat coefficient, underground runoff retreat coefficient;

[0034] S230. Obtaining the optimal parameter value combination for the Xin'anjiang model; specifically comprising the following steps:

[0035] S231. Assigning a set of initial parameter values ​​to the parameters of the Xinanjiang model;

[0036] S232. Read the historical rainfall data and the evapotranspiration data within the monitoring range in the water conservancy project data base as the initial input values ​​of the model for simulation calculation, calculate and output the flow process of the key confluence section; the flow process of the key confluence section is a collection of flow values ​​arranged in chronological order;

[0037] S234. Compare the flow process of the key confluence section output by S232 with the historical measured flow process, use the Nash efficiency coefficient or the root mean square error to quantify the simulation accuracy of the Xin'anjiang model, and calibrate the parameters of the Xin'anjiang model;

[0038] S235. Repeat S231 to S234, gradually approaching until the optimal parameter value combination is obtained;

[0039] S240. Inputting the measured rainfall and the measured basin evapotranspiration into the Xin'anjiang model;

[0040] S250. Calculating the forecast flow process of the key confluence section within the upstream flood impact range through the Xin'anjiang model;

[0041] S260. Store the predicted flow process of the key confluence section obtained in S250 in real time in the water conservancy project data base as initial data of the flood evolution sub-process.

[0042] Preferably, the flood evolution sub-process specifically comprises the following steps:

[0043] S310. Generalize the key confluence section to the river channel in front of the engineering dam; specifically include the following steps:

[0044] S311. setting the inlet section as the key confluence section within the upstream flood influence range;

[0045] S312. Based on the underwater topographic data and the hydrological station monitoring data in the water conservancy project data base, simplify the river channel morphology into a line segment set or a surface area set with representative characteristics;

[0046] S313. Setting the river section, specifically including setting the value of the main stream control section spacing and the value of the tributary river control average section spacing;

[0047] S320. The hydrodynamic flood evolution model of a one-dimensional river channel is constructed using the unsimplified dynamic wave equation, which is expressed as follows:

[0048]

[0049] Where: x is the mileage, in meters; t is the time, in seconds; Z is the water level, in meters; B is the width of the water section, in meters; Q is the flow, in meters 3 / s; q is the lateral single-width flow rate, in m 2 / s; A is the cross-sectional area of ​​water flow, unit is m 2 ; g is the acceleration due to gravity, in m / s 2 ; u is the average flow velocity of the section, in m / s; β is the correction coefficient, dimensionless; R is the hydraulic radius, in m; c is the Xie Cai coefficient, expressed as follows:

[0050] c=R 1 / 6 / n

[0051] Where: n is the Manning roughness coefficient;

[0052] S330. Perform simulation calculation until the degree of agreement between the simulation result and the measured data reaches a manually preset agreement threshold; specifically comprising the following steps:

[0053] S331. Read the river longitudinal profile data, river cross-section data, historical flow rate, and water level data within the monitoring range in the water conservancy project data base as the initial input value of the model, input the one-dimensional river hydrodynamic flood evolution model, perform simulation calculation, and output the water level, flow rate, and flow velocity of each section of the river arranged in chronological order within a manually preset time period;

[0054] S332. Compare the water level, flow rate and flow velocity outputted by S331 with the historical measured data, and then automatically calibrate the key parameters using the Levenberg-Marquardt optimization algorithm; the key parameters include the Manning roughness coefficient;

[0055] S333. Compare the simulation results with the measured data to obtain a verification result of the degree of fit;

[0056] S333. Repeat S331 to S333 until the degree of agreement between the simulation result and the measured data reaches the agreement threshold;

[0057] S340. The process of reading the forecast flow rate of the key confluence section obtained in the water inflow forecast subprocess from the water conservancy project data baseboard;

[0058] Then, the hydrological change process of each section of the river and the section in front of the dam arranged in time sequence within a manually preset time period is calculated by the hydrodynamic flood evolution model of the one-dimensional river channel; the hydrological change process includes water level, flow rate and flow velocity;

[0059] S350. Simulate the flood evolution inundation range and calculate the inundation area and inundation depth; specifically including the following steps:

[0060] S351. Based on the DEM data within the upstream flood impact range, the impact area is gridded at a manually preset resolution;

[0061] S352. Read the hydrological process line data of each section passing through the grid from the hydrological process lines of each section input; then obtain the corresponding hydrological process line data in other grids through interpolation calculation;

[0062] S353. Compare the flood level value in each grid with the terrain elevation value of the grid one by one, and then perform the following operations according to the comparison result:

[0063] If the flood water level value of this grid is higher than the terrain elevation value of this grid, then it is determined that this grid is submerged by flood;

[0064] Otherwise, it is determined that the grid is not flooded;

[0065] S354. extracting and combining all the grids determined to be flooded in S353 to obtain the flooded range of the simulated flood evolution;

[0066] S355. Calculate the flooded area and the flooded depth of all grids determined to be flooded in S353 one by one;

[0067] S356. Package the calculation results of S355 and save them as a GIS layer of the flood evolution inundation range preview results; then store the GIS layer of the flood evolution inundation range preview results into the water conservancy project data base for subsequent three-dimensional visualization sub-process calls.

[0068] Preferably, S320 further includes a river coding loop structure for characterizing the river channel topology structure and a section coding loop structure for characterizing the relative position of the section; wherein: the section coding loop structure is nested in the river coding loop structure;

[0069] The river code loop structure includes the number of rivers, the river code, and the river code array; the river code array is written as: riverID[j] = {"1", "2", "3", ... "j"}; wherein:

[0070] j is a counter of the number of rivers; riverID is the river code; riverID[j] is the river code array;

[0071] The order of the river codes in the river code array is used to characterize the river channel topology of the river network;

[0072] The cross-section coding loop structure includes a cross-section number storage array on the river channel and a cross-section coding storage array; wherein:

[0073] The storage array of the number of sections on the river channel is written as: secNum[k]={W1, W2, ..., Wk}; wherein: k is a counter for the number of sections on the river channel; W is the total number of sections on the river channel;

[0074] The section code storage array is written as: sec1[W1]={"01-1","02-1",...,"W-1"}, sec2[W2]={"01-2","02-2",...,"W-2"},..., sec l[Wl]={"01-l","02-l",...,"Wl"}; wherein: l is a counter for the section code;

[0075] The order of the section codes in the section code storage array is used to characterize the relative position relationship between sections.

[0076] Preferably, the flood warning sub-process in S400 specifically includes the following steps:

[0077] S410. Set the flood control water level corresponding to each river embankment as the warning threshold; the flood control water level includes the defense water level, the warning water level, and the guaranteed water level;

[0078] S420. According to the results of the water inflow forecast sub-process and the flood evolution sub-process, the water level value of each river embankment passing through the grid unit is compared with the value of the flood control water level corresponding to the river embankment, and then the following operations are performed according to the comparison results:

[0079] If the water level value of the grid cell of the river embankment is lower than the defense water level corresponding to the grid cell of the river embankment, the flood risk level of the grid cell of the river embankment is set to blue;

[0080] If the water level value of the grid cell of the river embankment is not lower than the defense water level corresponding to the river embankment, and is lower than the warning water level corresponding to the grid cell of the river embankment, the flood risk level of the grid cell of the river embankment is set to yellow;

[0081] If the water level value of the grid cell of the river embankment is not lower than the warning water level corresponding to the river embankment, and is lower than the guaranteed water level corresponding to the grid cell of the river embankment, the flood risk level of the grid cell of the river embankment is set to orange;

[0082] If the water level value of the grid cell of the river embankment is higher than the guaranteed water level corresponding to the grid cell of the river embankment, the flood risk level of the grid cell of the river embankment is set to red;

[0083] S430. Issue the result of S420 in corresponding colors as a warning.

[0084] Preferably, the multi-objective intelligent scheduling sub-process in S500 specifically includes the following steps:

[0085] S510. According to the flood flow process obtained by the inflow forecast sub-process and the flood evolution sub-process, the downstream flow process is determined according to the flood control constraint requirements;

[0086] S520. Using the ecological scheduling model, irrigation scheduling model, shipping scheduling model, power generation scheduling model, and multi-objective coordination model, the downstream flow process is respectively determined according to the target requirements to determine the power generation flow process and the sluice flow process;

[0087] S530. Based on S520, formulate an optimized power generation plan and a sluice gate optimized control plan.

[0088] Preferably, in S520, the ecological scheduling model is expressed as follows:

[0089]

[0090] in: is the total discharge flow, in m 3 / s;Q rmin is the minimum ecological flow requirement, in m 3 / s;

[0091] In S520, the irrigation scheduling model is expressed as follows:

[0092]

[0093] Where: Z u,t is the upstream water level in period t, in m; It is the artificially preset minimum upstream irrigation water level, in meters;

[0094] In S520, the objective requirement of the shipping scheduling model is to maximize the minimum navigable depth of upstream and downstream within the time period; the objective function of the shipping scheduling model is expressed as follows:

[0095]

[0096] Where: obj1 represents the objective function of the shipping scheduling model; T is the total time period; h u is the upstream navigable depth in time period t, in meters; h d is the downstream navigable depth in time period t, in meters;

[0097] The constraints of the shipping scheduling model include the following:

[0098] The water balance equation of the project reservoir area is expressed as follows:

[0099] Q gen +Q irri +Q z +R spill =I

[0100] The upstream and downstream navigation water level constraints are expressed as follows:

[0101]

[0102] The upstream and downstream head constraints are expressed as follows:

[0103] ΔZ≤ΔZ max

[0104] The downstream flow rate amplitude constraint is expressed as follows:

[0105] ΔR≤R range

[0106] The relationship curve between the water level in the project reservoir and the navigation depth, the relationship curve between the downstream water level and the navigation depth, and non-negative constraints;

[0107] In the above constraints: Q gen is the power generation flow, in m 3 / s;Q irri is the irrigation flow, in m 3 / s;Q z is the discharge flow of the sluice gate, in m 3 / s; R spill is the floodplain flow, in m 3 / s; I is the incoming flow, unit is m 3 / s; Z u,min is the lowest navigable water level upstream, in meters; Z u,max is the highest navigable water level upstream, in meters; Z u,ship is the upstream navigation water level, in m; Z d,min is the lowest navigable water level downstream, in meters; Z d,max is the highest navigable water level downstream, in meters; Z d,ship is the downstream navigation water level, in meters; ΔZ is the upstream and downstream water head, in meters; ΔZ max is the maximum upstream and downstream head specified for the project, in m; ΔR is the difference in discharge flow between adjacent time periods, in m 3 / s; R range It is the maximum manually preset downstream flow rate fluctuation, in m 3 / s;

[0108] In S520, the objective function of the power generation scheduling model is expressed as follows:

[0109]

[0110] Wherein: obj2 represents the objective function of the power generation scheduling model; ρ(t) is the electricity price coefficient of time period t; N(t) is the output of the power station in time period t; Δt is the time interval within time period t;

[0111] The constraints of the power generation scheduling model include the following:

[0112] The water balance equation of the project reservoir area is expressed as follows:

[0113] Q gen +Q irri +Q z=I

[0114] The power station generating head constraint is expressed as follows:

[0115] ΔZ min ≤ΔZ≤ΔZ max

[0116] The control water level constraint of the project reservoir area is expressed as follows:

[0117] Z u ≤Z o

[0118] The power generation flow constraint of the power station is expressed as follows:

[0119] Q gen ≤Q genmax

[0120] The power station output constraint is expressed as follows:

[0121] N min ≤N≤N max

[0122] Non-negative constraints;

[0123] In the above constraints: ΔZ min The minimum upstream and downstream head specified for the project, in meters; Z u is the upstream water level, in m; Z o is the normal water storage level of the project, in m; Q genmax The maximum power generation flow specified in the project, in m 3 / s.

[0124] Preferably, in S520, the multi-objective coordination model uses a membership function to characterize the degree of achievement and satisfaction of the desired objectives, which is expressed as follows:

[0125]

[0126] Among them, f i (Z i ) is the membership function of the i-th objective function; Z i is the function value of the i-th objective function; is the optimal value of the i-th objective function; is the worst value of the i-th objective function, or an artificially preset lower limit;

[0127] The optimal and worst values ​​of each objective include the following:

[0128] Ecological goal: Downflow flow is greater than or equal to Q rmin When the membership is 1; less than Q rmin When the membership is 0;

[0129] Irrigation target: water level is greater than or equal to Z o When , the membership is 1; the lower limit constraint of the objective function is manually preset and set to 0 in combination with the engineering requirements;

[0130] Shipping objective: The membership function is 1 when the minimum upstream water level in each period of the scheduling period is maximized; the lower limit constraint of the objective function is manually preset and the membership is set to 0;

[0131] Power generation target: The membership is 1 when the power generation is maximum during the dispatch period; the lower limit constraint of the objective function is manually preset and set to 0 based on the project requirements.

[0132] Preferably, in the three-dimensional visualization sub-process in S600, the visualization engine is used to call the engineering scene data and the water conservancy professional model calculation result data in the water conservancy engineering data base, and then render and present them; wherein:

[0133] The engineering scene data includes DEM data, DOM data, and underwater terrain data within the upstream flood impact range of the water conservancy project;

[0134] The calculation result data of the water conservancy professional model includes the GIS layer of the flood evolution inundation range preview result, river water level, flow rate, water volume, cross-section water level change, and water flow direction;

[0135] In the three-dimensional visualization sub-process, water body modeling is formed based on the DEM data within the upstream flood impact range, the DOM data, the underwater terrain data, the flood evolution inundation range preview result GIS layer, river water level, flow velocity, water inflow, cross-section water level change, and water flow direction, and a visual flood forecast preview is performed in a three-dimensional scene.

[0136] Compared with the prior art, the present invention has the following advantages:

[0137] 1. The present invention uses the digital twin data base to gather high-precision basic data such as spatial data, monitoring data, historical data, etc. required for the four-dimensional multi-objective scheduling of water conservancy projects for flood control and benefit development, and writes the model calculation results data in real time during the scheduling process for other modules to call, thereby realizing the accurate and rapid flow of data between each unit module;

[0138] 2. Since the present invention uses the flood forecasting model and the flood evolution model to forecast and rehearse the water inflow conditions upstream of the water conservancy project, the duration and peak flow of the flood can be accurately grasped according to the rehearsal results;

[0139] 3. Since the present invention relies on a multi-objective intelligent scheduling model and performs a preview operation scheduling, it realizes an optimized scheduling plan and gives full play to the comprehensive scheduling function of flood control and benefit development of water conservancy projects;

[0140] 4. Since the present invention relies on a multi-objective intelligent scheduling model, it improves the refinement and scientific level of flood control and benefit-promoting scheduling, and maximizes the use of water resources to obtain the greatest possible comprehensive operating benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0141] Figure 1 It is a schematic diagram of the overall process of a specific embodiment of the present invention;

[0142] Figure 2 It is a schematic diagram of the water inflow forecast sub-process of a specific embodiment of the present invention;

[0143] Figure 3 It is a schematic diagram of the flood evolution sub-process of a specific embodiment of the present invention. DETAILED DESCRIPTION

[0144] The present invention is further explained below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0145] like Figure 1 As shown in the figure, a multi-objective four-pre-scheduling method for water conservancy projects based on digital twins includes the following steps:

[0146] S100. Build a water conservancy project data base.

[0147] In this specific embodiment, the water conservancy project data base includes database storage and data file storage, which are used to store DEM data, DOM data, underwater terrain data, and hydrological site monitoring data within the upstream flood impact range of the water conservancy project; wherein:

[0148] The accuracy of DEM data within the upstream flood impact area is that the grid size is no more than 2m.

[0149] The accuracy of DOM data is no more than 0.5m spatial resolution.

[0150] The accuracy of underwater topographic data is that the section interval is no more than 200m, the measurement point spacing is no more than 1m, and the grid size is no more than 5m.

[0151] The monitoring data of hydrological stations include real-time rainfall data of each observation station within the monitoring range, historical rainfall data arranged in chronological order, evapotranspiration data, real-time water level of important control sections, historical water level of important control sections, and flow data.

[0152] It should be noted that S100 builds a water conservancy project data base to provide the necessary basic data for model algorithms such as water inflow forecasting, flood evolution, and multi-objective intelligent scheduling. At the same time, the calculation results of the model algorithm are stored in the data base for other applications to call.

[0153] It should be further explained that the higher the accuracy of the data in the water conservancy project data base, the more accurate the flood control calculation results will be.

[0154] like Figure 2 As shown, S200. Execute the water inflow forecast sub-process.

[0155] It should be noted that the purpose of the inflow forecast sub-process is to use the flood forecast model to forecast the water level and flow of the inflow at the key upstream inlet section of the water conservancy project, and to use the error information between the forecast value and the measured value in the data base to timely correct and improve the forecast estimate or the parameters in the flood forecast model using the model theoretical method, so as to reduce the forecast error in the later stage.

[0156] In this specific embodiment, the water inflow forecast sub-process in S200 specifically includes the following steps:

[0157] S210. Based on the DEM data within the upstream flood impact range, the upstream flood impact range is divided into small watersheds through the hydrological analysis module of the GIS tool software, and the key confluence sections are determined; specifically, the following steps are included:

[0158] S211. Fill the depressions in the DEM basic data.

[0159] S212. Determine the direction of water flow.

[0160] S213. Calculate and obtain the cumulative amount of confluence.

[0161] S214. Determine the watershed area.

[0162] S215. Extract the river network and classify the river network.

[0163] S216. Finally, the upstream flood impact area is divided into small watersheds and the key confluence sections are determined.

[0164] S220. According to the basin characteristics of the upstream flood impact range obtained in S210, the Xin'anjiang model is used as the basis to construct a water inflow forecast model, and the parameters of the Xin'anjiang model are set at the same time; wherein: the parameters of the Xin'anjiang model include: basin area, basin evapotranspiration capacity refraction coefficient, basin total water storage capacity, water storage capacity parabolic index, basin average free water capacity, basin free water storage capacity curve index, free water storage capacity to subsoil flow outflow coefficient, free water storage capacity to groundwater outflow coefficient, deep water recession coefficient, groundwater runoff recession coefficient.

[0165] In this specific embodiment, the value ranges of the parameters in the Xinanjiang model are as follows:

[0166] The basin evapotranspiration capacity refraction coefficient, denoted as KC, is generally between 0.9 and 1.6, and the specific value needs to be determined through model calibration.

[0167] The total water storage capacity of a watershed, denoted as WM, is approximately 120-180 mm, depending on the wetness and soil properties of the watershed.

[0168] The parabolic index of water storage capacity, denoted as B, reflects the uneven distribution of water storage capacity in the basin. In hilly areas, the B value of a very small area (a few square kilometers) is about 0.1, the B value of a medium area (within 300 square kilometers) is about 0.2-0.3, and the B value of a large area (thousands of square kilometers) is about 0.3-0.4.

[0169] The mean free water capacity of a basin is denoted as SM. When the calculation period is day, the SM value of a general basin is about 10-50 mm.

[0170] The free water storage capacity curve index of a basin, denoted as EX, represents the uneven distribution of free water capacity and usually takes values ​​between 1 and 1.5.

[0171] The outflow coefficient of free water storage capacity to subsoil flow is denoted by KI.

[0172] The outflow coefficient of free water storage capacity to groundwater is denoted as KG.

[0173] The sum of the two parameters KI and KG represents the speed of free water outflow, generally KG+KI=0.7.

[0174] The deep water recession coefficient, denoted as CI, tends to 0 when there is no deep soil interflow and tends to 0.9 when deep soil interflow is very abundant.

[0175] The groundflow recession coefficient, denoted as CG, generally ranges from 0.9 to 0.999.

[0176] S230. Obtaining the optimal parameter value combination for the Xinanjiang model; specifically comprising the following steps:

[0177] S231. Assign a set of initial values ​​to the parameters of the Xinanjiang model.

[0178] S232. Read the historical rainfall data and evapotranspiration data within the monitoring range in the water conservancy project data base as the initial input values ​​of the model for simulation calculation, calculate and output the flow process of the key confluence section; the flow process of the key confluence section is a collection of flow values ​​arranged in chronological order.

[0179] S234. Compare the flow process of the key confluence section output by S232 with the historical measured flow process, use the Nash efficiency coefficient or root mean square error to quantify the simulation accuracy of the Xin'anjiang model, and calibrate the parameters of the Xin'anjiang model.

[0180] S235. Repeat S231 to S234, gradually approaching until the optimal parameter value combination is obtained.

[0181] It should be noted that the calibration of the above parameters and the adjustment of the parameter values ​​of the model need to be repeated, and the optimal parameter combination can be gradually approached through multiple simulations and evaluations.

[0182] S240. Input the measured rainfall and basin evapotranspiration into the Xin'anjiang model.

[0183] S250. The predicted flow process of the key confluence sections within the upstream flood impact range is calculated through the Xin'anjiang model.

[0184] S260. The predicted flow process of the key confluence section obtained in S250 is stored in real time in the water conservancy project data base as the initial data of the flood evolution sub-process.

[0185] like Figure 3 As shown, S300. Execute the flood evolution sub-process.

[0186] It should be noted that the principle of the flood evolution sub-process is to use the DEM data and underwater terrain data in the data base to analyze the characteristics of the river channel from the key confluence section to the front of the project dam for reasonable generalization, build a hydrodynamic model and use historical flood data to calibrate the model parameters. Call the inflow forecast result data to perform flood evolution calculation and analysis, obtain the total amount of engineering flood, peak flow and its occurrence time, flood process data and GIS layer data and store them in the data base.

[0187] In this specific embodiment, the flood evolution sub-process in S300 specifically includes the following steps:

[0188] S310. Generalize the key confluence section to the river channel in front of the engineering dam; specifically includes the following steps:

[0189] S311. Set the inlet section as the key confluence section within the impact range of the upstream flood.

[0190] S312. Based on the underwater topographic data and hydrological station monitoring data in the water conservancy project data base, the river channel morphology is simplified into a set of line segments or surface areas with representative characteristics.

[0191] S313. Setting the river section, specifically including setting the value of the main stream control section spacing and the value of the tributary river control average section spacing.

[0192] In this specific embodiment, the value of the main stream control section spacing is 1km to 2km, and the value of the tributary river channel control average section spacing is 0.5km to 1km.

[0193] S320. The unsimplified dynamic wave equation, namely the Saint-Venant equations, is used to construct a hydrodynamic flood evolution model for a one-dimensional river channel, which is expressed as follows:

[0194]

[0195] Where: x is the mileage, in meters; t is the time, in seconds; Z is the water level, in meters; B is the width of the water section, in meters; Q is the flow, in meters 3 / s; q is the lateral single-width flow rate, in m 2 / s; A is the cross-sectional area of ​​water flow, unit is m 2 ; g is the acceleration due to gravity, in m / s 2 ; u is the average flow velocity of the section, in m / s; β is the correction coefficient, dimensionless; R is the hydraulic radius, in m; c is the Xie Cai coefficient, expressed as follows:

[0196] c=R 1 / 6 / n(2)

[0197] Where: n is the Manning roughness coefficient.

[0198] In this specific embodiment, S320 also includes a river coding loop structure for characterizing the river channel topology structure and a section coding loop structure for characterizing the relative position of the section; wherein: the section coding loop structure is nested in the river coding loop structure.

[0199] The river code loop structure includes the river number, river code, and river code array; the river code array is written as: riverID[j] = {"1", "2", "3", ... "j"}; where:

[0200] j is a counter of the number of rivers; riverID is the river code; riverID[j] is the river code array.

[0201] The order of river codes in the river code array is used to characterize the river channel topology of the river network.

[0202] The cross-section coding loop structure includes a cross-section number storage array and a cross-section coding storage array on the river channel; among them:

[0203] The storage array of the number of sections on the river channel is written as: secNum[k] = {W1, W2, ..., Wk}; where: k is a counter for the number of sections on the river channel; W is the total number of sections on the river channel.

[0204] The section code storage array is written as: sec1[W1] = {"01-1", "02-1", ..., "W-1"}, sec2[W2] = {"01-2", "02-2", ..., "W-2"}, ..., sec l[Wl] = {"01-l", "02-l", ..., "Wl"}; where: l is a counter for the section code.

[0205] The order of section codes in the section code storage array is used to characterize the relative position relationship between sections.

[0206] S330. Perform simulation calculation until the degree of agreement between the simulation result and the measured data reaches a manually preset agreement threshold; specifically comprising the following steps:

[0207] S331. Read the river longitudinal profile data, river cross-section data, historical flow, and water level data within the monitoring range in the water conservancy project data base as the initial input value of the model, input the one-dimensional river hydrodynamic flood evolution model, perform simulation calculations, and output the water level, flow, and flow velocity of each section of the river arranged in chronological order within a manually preset time period.

[0208] S332. Compare the water level, flow rate and velocity output by S331 with the historical measured data, and then use the Levenberg-Marquardt optimization algorithm to automatically calibrate the key parameters; the key parameters include the Manning roughness coefficient.

[0209] S333. Compare the simulation results with the measured data to obtain a verification result of the degree of consistency.

[0210] S333. Repeat S331 to S333 until the degree of fit between the simulation result and the measured data reaches a fit threshold.

[0211] S340. The process of reading the forecast flow of the key confluence section obtained in the water inflow forecast sub-process from the water conservancy project data base.

[0212] Then, the hydrological change process of each section of the river and the section in front of the dam is calculated in chronological order within an artificially preset time period through the one-dimensional river hydrodynamic flood evolution model; the hydrological change process includes water level, flow rate and flow velocity.

[0213] S350. Simulate the flood evolution inundation range and calculate the inundation area and inundation depth; specifically including the following steps:

[0214] S351. Based on the DEM data within the upstream flood impact area, the affected area is gridded according to an artificially preset resolution.

[0215] S352. Read the hydrological process line data of each section passing through the grid from the input hydrological change process lines of each section; then obtain the corresponding hydrological process line data in other grids through interpolation calculation.

[0216] S353. Compare the flood level value in each grid with the terrain elevation value of the grid one by one, and then perform the following operations according to the comparison result:

[0217] If the flood water level value of this grid is higher than the terrain elevation value of this grid, then this grid is determined to be flooded.

[0218] Otherwise, it is determined that the grid is not flooded.

[0219] S354. Extract and aggregate all grids determined to be flooded in S353 to obtain the flooding range of the simulated flood evolution.

[0220] S355. Calculate the flooded area and flooded depth of all grids determined to be flooded in S353 one by one.

[0221] S356. Package the calculation results of S355 and save them as a GIS layer of the flood evolution inundation range preview results; then store the GIS layer of the flood evolution inundation range preview results into the water conservancy project data base for subsequent three-dimensional visualization sub-process calls.

[0222] S400. Execute the flood warning sub-process.

[0223] In this specific embodiment, the flood warning sub-process in S400 specifically includes the following steps:

[0224] S410. Set the flood control water level corresponding to each river embankment as the warning threshold; the flood control water level includes the defense water level, warning water level, and guaranteed water level.

[0225] S420. Based on the results of the inflow forecast sub-process and the flood evolution sub-process, the water level value of each river embankment passing through the grid unit is compared with the value of the flood control level corresponding to the river embankment, and then the following operations are performed based on the comparison results:

[0226] If the water level value of the grid cell of the river embankment is lower than the defense water level corresponding to the grid cell of the river embankment, the flood risk level of the grid cell of the river embankment is set to blue.

[0227] If the water level value of the grid cell of the river embankment is not lower than the defense water level corresponding to the river embankment, and is lower than the warning water level corresponding to the grid cell of the river embankment, the flood risk level of the grid cell of the river embankment is set to yellow.

[0228] If the water level value of the grid cell of the river embankment is not lower than the warning water level corresponding to the river embankment, and is lower than the guaranteed water level corresponding to the grid cell of the river embankment, the flood risk level of the grid cell of the river embankment is set to orange.

[0229] If the water level value of the grid cell of the river embankment is higher than the guaranteed water level corresponding to the grid cell of the river embankment, the flood risk level of the grid cell of the river embankment is set to red.

[0230] S430. Issue the result of S420 in corresponding colors as a warning.

[0231] S500. Execute the multi-objective intelligent scheduling sub-process.

[0232] In this specific embodiment, the multi-objective intelligent scheduling sub-process in S500 specifically includes the following steps:

[0233] S510. According to the flood flow process obtained by the inflow forecast sub-process and the flood evolution sub-process, the downstream flow process is determined in accordance with the flood control constraint requirements.

[0234] S520. Utilize ecological dispatching model, irrigation dispatching model, shipping dispatching model, power generation dispatching model and multi-objective coordination model to determine the power generation flow process and sluice gate flow process respectively according to the target requirements.

[0235] In this specific embodiment, in S520, the ecological scheduling model is expressed as follows:

[0236]

[0237] in: is the total discharge flow, in m 3 / s;Q rmin is the minimum ecological flow requirement, in m3 / s.

[0238] It should be noted that under normal scheduling conditions, ecological requirements only serve as constraints to ensure that the downstream flow meets the minimum requirements.

[0239] In S520, the irrigation scheduling model is expressed as follows:

[0240]

[0241] Where: Z u,t is the upstream water level in period t, in m; It is the artificially preset minimum upstream irrigation water level, in meters.

[0242] It should be noted that under normal scheduling conditions, irrigation requirements are only used as constraints to ensure that the upstream water level meets the irrigation and drinking water requirements in the irrigation area.

[0243] In S520, the objective requirement of the shipping scheduling model is to maximize the minimum navigable depth of upstream and downstream within the time period; the objective function of the shipping scheduling model is expressed as formula (5):

[0244]

[0245] Where: obj1 represents the objective function of the shipping scheduling model; T is the total time period; h u is the upstream navigable depth in time period t, in meters; h d is the downstream navigable depth during time period t, in meters.

[0246] The constraints of the shipping scheduling model include the following:

[0247] The water balance equation of the project reservoir area is expressed as follows:

[0248] Q gen +Q irri +Q z +R spill =I(6)

[0249] The upstream and downstream navigation water level constraints are expressed as follows:

[0250]

[0251] The upstream and downstream head constraints are expressed as follows:

[0252] ΔZ≤ΔZ max (8)

[0253] The downstream flow rate amplitude constraint is expressed as follows:

[0254] ΔR≤R range(9)

[0255] The relationship curve between the water level in the project reservoir and the navigation depth, the relationship curve between the downstream water level and the navigation depth, and non-negative constraints.

[0256] In the above constraints: Q gen is the power generation flow, in m 3 / s;Q irri is the irrigation flow, in m 3 / s;Q z is the discharge flow of the sluice gate, in m 3 / s; R spill is the floodplain flow, in m 3 / s; I is the incoming flow, unit is m 3 / s; Z u,min is the lowest navigable water level upstream, in meters; Z u,max is the highest navigable water level upstream, in meters; Z u,ship is the upstream navigation water level, in m; Z d,min is the lowest navigable water level downstream, in meters; Z d,max is the highest navigable water level downstream, in meters; Z d,ship is the downstream navigation water level, in meters; ΔZ is the upstream and downstream water head, in meters; ΔZ max is the maximum upstream and downstream head specified for the project, in m; ΔR is the difference in discharge flow between adjacent time periods, in m 3 / s; R range It is the maximum manually preset downstream flow rate fluctuation, in m 3 / s.

[0257] It should be noted that the limited navigation capacity of a river depends on the minimum navigable depth in the upstream and downstream.

[0258] In S520, the objective function of the power generation scheduling model is expressed as follows:

[0259]

[0260] Where: obj2 represents the objective function of the power generation scheduling model; ρ(t) is the electricity price coefficient in period t; N(t) is the output of the power station in period t; Δt is the time interval within period t.

[0261] The constraints of the power generation scheduling model include the following:

[0262] The water balance equation of the project reservoir area is expressed as follows:

[0263] Q gen +Q irri +Q z =I(11)

[0264] The power station generating head constraint is expressed as follows:

[0265] ΔZ min ≤ΔZ≤ΔZ max (12)

[0266] The control water level constraint of the project reservoir area is expressed as follows:

[0267] Z u ≤Z o (13)

[0268] The power generation flow constraint of the power station is expressed as follows:

[0269] Q gen ≤Q genmax (14)

[0270] The power station output constraint is expressed as follows:

[0271] N min ≤N≤N max (15)

[0272] Non-negativity constraint.

[0273] In the above constraints: ΔZ min The minimum upstream and downstream head specified for the project, in meters; Z u is the upstream water level, in m; Z o is the normal water storage level of the project, in m; Q genmax The maximum power generation flow specified in the project, in m 3 / s.

[0274] It should be noted that the hydrological change process of the dam front section obtained by the initial water level of the project reservoir during the control period and the flood evolution, while meeting various constraints, maximizes the sum of the power generation of the hydropower station during the control period and the residual benefit at the end of the control period, so as to make full use of water energy.

[0275] In this specific embodiment, in S520, the multi-objective coordination model uses a membership function to characterize the degree of achievement and satisfaction of the desired goal, which is expressed as follows:

[0276]

[0277] Among them, f i (Z i ) is the membership function of the i-th objective function; Z i is the function value of the i-th objective function; is the optimal value of the i-th objective function; It is the worst value of the i-th objective function, or an artificially preset lower limit.

[0278] The optimal and worst values ​​of each objective include the following:

[0279] Ecological goal: Downflow flow is greater than or equal to Q rmin When the membership is 1; less than Q rmin When the membership is 0.

[0280] Irrigation target: water level is greater than or equal to Z o When , the membership is 1; the lower limit constraint of the objective function is manually preset and set to 0 in combination with the engineering requirements.

[0281] Shipping objective: The membership function is 1 when the minimum upstream water level in each period of the scheduling period is maximized; the lower limit constraint of the objective function is preset manually and the membership is set to 0.

[0282] Power generation target: The membership is 1 when the power generation is maximum during the dispatch period; the lower limit constraint of the objective function is manually preset and set to 0 based on the project requirements.

[0283] It should be noted that the present invention adopts fuzzy target programming method for the above multi-objective coordination, calculates the optimal solution and the worst solution of each objective function, or gives a lower limit, and characterizes the degree of achievement and satisfaction of the expected goal through the membership function.

[0284] S530. Based on S520, formulate an optimized power generation plan and a sluice gate optimized control plan.

[0285] It should be noted that the multi-objective coordination model takes the weighted maximization of membership and the maximization of the minimum membership as the goal, and adopts the dynamic programming method to solve it. Combined with the actual project, the weight coefficients of each objective are given, and the hydrological change process of the dam section obtained according to the evolution of the flood is taken as the stage variable. The state variable is the upstream water level Z u , the decision variables are the weighted values ​​of each target membership within the time period and the minimum value of each target membership. In this way, the optimal water allocation plan for multi-objective scheduling such as engineering ecology, irrigation, shipping and power generation is formulated.

[0286] S600: Execute the 3D visualization sub-process.

[0287] In this specific embodiment, in the three-dimensional visualization sub-process in S600, the visualization engine is used to call the engineering scene data and the water conservancy professional model calculation result data in the water conservancy project data base, and then render and present them; wherein:

[0288] The engineering scenario data includes DEM data, DOM data, and underwater terrain data within the upstream flood impact range of the water conservancy project.

[0289] The calculation results of the water conservancy professional model include the GIS layer of flood evolution and inundation range preview results, river water level, flow velocity, water inflow, cross-section water level changes, and water flow direction.

[0290] In the 3D visualization sub-process, water body modeling is formed based on the DEM data, DOM data, underwater terrain data, flood evolution and inundation range preview results GIS layer, river water level, flow velocity, water inflow, cross-section water level changes, and water flow direction within the upstream flood impact area, and a visual flood forecast preview is performed in a 3D scene.

[0291] In the above detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that the embodiments of the claimed subject matter require more features than are clearly stated in each claim. On the contrary, as reflected in the appended claims, the invention is in a state of having less than all the features of the disclosed individual embodiments. Therefore, the appended claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0292] The disclosed embodiments are described above to enable any person skilled in the art to implement or use the present invention. Various modifications of these embodiments are obvious to those skilled in the art, and the general principles defined herein may also be applied to other embodiments without departing from the spirit and scope of the present disclosure. Therefore, the present disclosure is not limited to the embodiments given herein, but is consistent with the broadest scope of the principles and novel features disclosed in this application.

[0293] The above description includes examples of one or more embodiments. Of course, it is impossible to describe all possible combinations of components or methods for the purpose of describing the above embodiments, but it should be recognized by those skilled in the art that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to cover all such changes, modifications and variations that fall within the scope of protection of the appended claims. In addition, with respect to the term "comprising" used in the specification or claims, the word is covered in a manner similar to the term "including", just as "including," is explained as a transitional word in the claims. In addition, any term "or" used in the specification of the claims is intended to mean "non-exclusive or".

[0294] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A multi-objective four-pre-scheduling method for water conservancy projects based on digital twins, characterized by: The following steps are involved: S100. Build a water conservancy project data base; S200. Execute the water forecast sub-process; S300. Execute flood evolution sub-process; S400. Execute flood warning subprocess; S500. Execute multi-objective intelligent scheduling sub-process; S600: Execute the 3D visualization sub-process.

2. The multi-objective four-pre-scheduling method for water conservancy projects based on digital twins according to claim 1 is characterized by: The water conservancy project data base includes database storage and data file storage, which are used to store DEM data, DOM data, underwater terrain data, and hydrological site monitoring data within the upstream flood impact range of the water conservancy project; wherein: The accuracy of the DEM data within the upstream flood impact area is that the grid size is no more than 2m; The accuracy of the DOM data is a resolution not greater than 0.5m spatial resolution; The accuracy of the underwater topographic data is that the section interval is no more than 200m, the distance between measuring points is no more than 1m, and the grid size is no more than 5m; The hydrological site monitoring data includes real-time rainfall data of each observation site within the monitoring range, historical rainfall data arranged in chronological order, evapotranspiration data, real-time water level of important control sections, historical water level of important control sections, and flow data.

3. The multi-objective four-pre-scheduling method for water conservancy projects based on digital twins according to claim 2 is characterized by: The water inflow forecast sub-process in S200 specifically includes the following steps: S210. Based on the DEM data within the upstream flood impact range, the upstream flood impact range is divided into small watersheds through the hydrological analysis module of the GIS tool software, and the key confluence sections are determined; specifically, the following steps are included: S211. Fill the depressions of the DEM basic data; S212. Determine the direction of water flow; S213. Calculate the cumulative amount of confluence; S214. Determine the drainage area; S215. Extract the river network and classify the river network; S216. Finally, the upstream flood impact range is divided into the small watersheds, and the key confluence sections are determined; S220. According to the basin characteristics of the upstream flood impact range obtained in S210, the Xin'anjiang model is used as the basis to construct a water inflow forecast model, and the parameters of the Xin'anjiang model are set at the same time; wherein: the parameters of the Xin'anjiang model include: basin area, basin evapotranspiration capacity refraction coefficient, basin total water storage capacity, water storage capacity parabolic index, basin average free water capacity, basin free water storage capacity curve index, free water storage capacity to subsoil flow outflow coefficient, free water storage capacity to groundwater outflow coefficient, deep water retreat coefficient, underground runoff retreat coefficient; S230. Obtaining the optimal parameter value combination for the Xin'anjiang model; specifically comprising the following steps: S231. Assigning a set of initial parameter values ​​to the parameters of the Xinanjiang model; S232. Read the historical rainfall data and the evapotranspiration data within the monitoring range in the water conservancy project data base as the initial input values ​​of the model for simulation calculation, calculate and output the flow process of the key confluence section; the flow process of the key confluence section is a collection of flow values ​​arranged in chronological order; S234. Compare the flow process of the key confluence section output by S232 with the historical measured flow process, use the Nash efficiency coefficient or the root mean square error to quantify the simulation accuracy of the Xin'anjiang model, and calibrate the parameters of the Xin'anjiang model; S235. Repeat S231 to S234, gradually approaching until the optimal parameter value combination is obtained; S240. Inputting the measured rainfall and the measured basin evapotranspiration into the Xin'anjiang model; S250. Calculating the forecast flow process of the key confluence section within the upstream flood impact range through the Xin'anjiang model; S260. Store the predicted flow process of the key confluence section obtained in S250 in real time in the water conservancy project data base as initial data of the flood evolution sub-process.

4. The multi-objective four-pre-scheduling method for water conservancy projects based on digital twins according to claim 3 is characterized by: The flood evolution sub-process specifically includes the following steps: S310. Generalize the key confluence section to the river channel in front of the engineering dam; specifically include the following steps: S311. setting the inlet section as the key confluence section within the upstream flood influence range; S312. Based on the underwater topographic data and the hydrological station monitoring data in the water conservancy project data base, simplify the river channel morphology into a line segment set or a surface area set with representative characteristics; S313. Setting the river section, specifically including setting the value of the main stream control section spacing and the value of the tributary river control average section spacing; S320. The hydrodynamic flood evolution model of a one-dimensional river channel is constructed using the unsimplified dynamic wave equation, which is expressed as follows: Where: x is the mileage, in meters; t is the time, in seconds; Z is the water level, in meters; B is the width of the water section, in meters; Q is the flow, in meters 3 / s; q is the lateral single width flow rate, unit is m 2 / s; A is the cross-sectional area of ​​water flow, unit is m 2 ; g is the acceleration due to gravity, in m / s 2 ; u is the average flow velocity of the section, in m / s; β is the correction coefficient, dimensionless; R is the hydraulic radius, in m; c is the Xie Cai coefficient, expressed as follows: c=R 1 / 6 / n Where: n is the Manning roughness coefficient; S330. Perform simulation calculation until the degree of agreement between the simulation result and the measured data reaches a manually preset agreement threshold; specifically comprising the following steps: S331. Read the river longitudinal profile data, river cross-section data, historical flow rate, and water level data within the monitoring range in the water conservancy project data base as the initial input value of the model, input the one-dimensional river hydrodynamic flood evolution model, perform simulation calculation, and output the water level, flow rate, and flow velocity of each section of the river arranged in chronological order within a manually preset time period; S332. Compare the water level, flow rate and flow velocity outputted by S331 with the historical measured data, and then automatically calibrate the key parameters using the Levenberg-Marquardt optimization algorithm; the key parameters include the Manning roughness coefficient; S333. Compare the simulation results with the measured data to obtain a verification result of the degree of fit; S333. Repeat S331 to S333 until the degree of agreement between the simulation result and the measured data reaches the agreement threshold; S340. The process of reading the forecast flow rate of the key confluence section obtained in the water inflow forecast subprocess from the water conservancy project data baseboard; Then, the hydrological change process of each section of the river and the section in front of the dam arranged in time sequence within a manually preset time period is calculated by the hydrodynamic flood evolution model of the one-dimensional river channel; the hydrological change process includes water level, flow rate and flow velocity; S350. Simulate the flood evolution inundation range and calculate the inundation area and inundation depth; specifically including the following steps: S351. Based on the DEM data within the upstream flood impact range, the impact area is gridded at a manually preset resolution; S352. Read the hydrological process line data of each section passing through the grid from the hydrological process lines of each section input; then obtain the corresponding hydrological process line data in other grids through interpolation calculation; S353. Compare the flood level value in each grid with the terrain elevation value of the grid one by one, and then perform the following operations according to the comparison result: If the flood water level value of this grid is higher than the terrain elevation value of this grid, then it is determined that this grid is submerged by flood; Otherwise, it is determined that the grid is not flooded; S354. extracting and combining all the grids determined to be flooded in S353 to obtain the flooded range of the simulated flood evolution; S355. Calculate the flooded area and the flooded depth of all grids determined to be flooded in S353 one by one; S356. Package the calculation results of S355 and save them as a GIS layer of the flood evolution inundation range preview results; then store the GIS layer of the flood evolution inundation range preview results into the water conservancy project data base for subsequent three-dimensional visualization sub-process calls.

5. The multi-objective four-pre-scheduling method for water conservancy projects based on digital twins according to claim 4 is characterized by: S320 also includes a river coding loop structure for characterizing the river channel topology structure and a section coding loop structure for characterizing the relative position of the section; wherein: the section coding loop structure is nested in the river coding loop structure; The river code loop structure includes the number of rivers, the river code, and the river code array; the river code array is written as: riverID[j] = {"1", "2", "3", ... "j"}; wherein: j is a counter of the number of rivers; riverID is the river code; riverID[j] is the river code array; The order of the river codes in the river code array is used to characterize the river channel topology of the river network; The cross-section coding loop structure includes a cross-section number storage array on the river channel and a cross-section coding storage array; wherein: The storage array of the number of sections on the river channel is written as: secNum[k]={W1, W2, ..., Wk}; wherein: k is a counter for the number of sections on the river channel; W is the total number of sections on the river channel; The section code storage array is written as: sec1[W1]={"01-1","02-1",...,"W-1"}, sec2[W2]={"01-2","02-2",...,"W-2"},...,sec1[Wl]={"01-l","02-l",...,"Wl"}; wherein: l is a counter for the section code; The order of the section codes in the section code storage array is used to characterize the relative position relationship between sections.

6. The multi-objective four-pre-scheduling method for water conservancy projects based on digital twins according to claim 5 is characterized by: The flood warning sub-process in S400 specifically includes the following steps: S410. Set the flood control water level corresponding to each river embankment as the warning threshold; the flood control water level includes the defense water level, the warning water level, and the guaranteed water level; S420. According to the results of the water inflow forecast sub-process and the flood evolution sub-process, the water level value of each river embankment passing through the grid unit is compared with the value of the flood control water level corresponding to the river embankment, and then the following operations are performed according to the comparison results: If the water level value of the grid cell of the river embankment is lower than the defense water level corresponding to the grid cell of the river embankment, the flood risk level of the grid cell of the river embankment is set to blue; If the water level value of the grid cell of the river embankment is not lower than the defense water level corresponding to the river embankment, and is lower than the warning water level corresponding to the grid cell of the river embankment, the flood risk level of the grid cell of the river embankment is set to yellow; If the water level value of the grid cell of the river embankment is not lower than the warning water level corresponding to the river embankment, and is lower than the guaranteed water level corresponding to the grid cell of the river embankment, the flood risk level of the grid cell of the river embankment is set to orange; If the water level value of the grid cell of the river embankment is higher than the guaranteed water level corresponding to the grid cell of the river embankment, the flood risk level of the grid cell of the river embankment is set to red; S430. Issue the result of S420 in corresponding colors as a warning.

7. The multi-objective four-pre-scheduling method for water conservancy projects based on digital twins according to claim 6 is characterized by: The multi-objective intelligent scheduling sub-process in S500 specifically includes the following steps: S510. According to the flood flow process obtained by the inflow forecast sub-process and the flood evolution sub-process, the downstream flow process is determined according to the flood control constraint requirements; S520. Using the ecological scheduling model, irrigation scheduling model, shipping scheduling model, power generation scheduling model, and multi-objective coordination model, the downstream flow process is respectively determined according to the target requirements to determine the power generation flow process and the sluice flow process; S530. Based on S520, formulate an optimized power generation plan and a sluice gate optimized control plan.

8. The multi-objective four-pre-scheduling method for water conservancy projects based on digital twins according to claim 7 is characterized by: In S520, the ecological scheduling model is expressed as follows: in: is the total discharge flow, in m 3 / s;Q rmin is the minimum ecological flow requirement, in m 3 / s; In S520, the irrigation scheduling model is expressed as follows: Z u,t ≥ Z u,irri Where: Z u,t is the upstream water level in period t, in m; Z u,irri It is the artificially preset minimum upstream irrigation water level, in meters; In S520, the objective requirement of the shipping scheduling model is to maximize the minimum navigable depth of upstream and downstream within the time period; the objective function of the shipping scheduling model is expressed as follows: Where: obj1 represents the objective function of the shipping scheduling model; T is the total time period; h u is the upstream navigable depth in time period t, in meters; h d is the downstream navigable depth in time period t, in meters; The constraints of the shipping scheduling model include the following: The water balance equation of the project reservoir area is expressed as follows: Q gen +Q irri +Q z +R spill =I The upstream and downstream navigation water level constraints are expressed as follows: The upstream and downstream head constraints are expressed as follows: ΔZ≤ΔZ max The downstream flow rate amplitude constraint is expressed as follows: ΔR≤R range The relationship curve between the water level in the project reservoir and the navigation depth, the relationship curve between the downstream water level and the navigation depth, and non-negative constraints; In the above constraints: Q gen is the power generation flow, in m 3 / s;Q irri is the irrigation flow, in m 3 / s;Q z is the discharge flow of the sluice gate, in m 3 / s; R spill is the floodplain flow, in m 3 / s; I is the incoming flow, unit is m 3 / s; Z u,min is the lowest navigable water level upstream, in meters; Z u,max is the highest navigable water level upstream, in meters; Z u,ship is the upstream navigation water level, in m; Z d,min is the lowest navigable water level downstream, in meters; Z d,max is the highest navigable water level downstream, in meters; Z d,ship is the downstream navigation water level, in meters; ΔZ is the upstream and downstream water head, in meters; ΔZ max is the maximum upstream and downstream head specified for the project, in m; ΔR is the difference in discharge flow between adjacent time periods, in m 3 / s; R range It is the maximum manually preset downstream flow rate fluctuation, in m 3 / s; In S520, the objective function of the power generation scheduling model is expressed as follows: Wherein: obj2 represents the objective function of the power generation scheduling model; ρ(t) is the electricity price coefficient of time period t; N(t) is the output of the power station in time period t; Δt is the time interval within time period t; The constraints of the power generation scheduling model include the following: The water balance equation of the project reservoir area is expressed as follows: Q gen +Q irri +Q z =I The power station generating head constraint is expressed as follows: ΔZ min ≤ΔZ≤ΔZ max The control water level constraint of the project reservoir area is expressed as follows: WITH u ≤Z o The power generation flow constraint of the power station is expressed as follows: Q gen ≤Q genmax The power station output constraint is expressed as follows: N min ≤N≤N max Non-negative constraints; In the above constraints: ΔZ min The minimum upstream and downstream head specified for the project, in meters; Z u is the upstream water level, in m; Z o is the normal water storage level of the project, in m; Q genmax The maximum power generation flow specified in the project, in m 3 / s.

9. The multi-objective four-pre-scheduling method for water conservancy projects based on digital twins according to claim 8 is characterized by: In S520, the multi-objective coordination model uses a membership function to characterize the degree of achievement and satisfaction of the desired objectives, which is expressed as follows: Among them, f i (Z i ) is the membership function of the i-th objective function; Z i is the function value of the i-th objective function; is the optimal value of the i-th objective function; is the worst value of the i-th objective function, or an artificially preset lower limit; The optimal and worst values ​​of each objective include the following: Ecological goal: Downflow flow is greater than or equal to Q rmin When the membership is 1; less than Q rmin When the membership is 0; Irrigation target: water level is greater than or equal to Z o When , the membership is 1; the lower limit constraint of the objective function is manually preset and set to 0 in combination with the engineering requirements; Shipping objective: The membership function is 1 when the minimum upstream water level in each period of the scheduling period is maximized; the lower limit constraint of the objective function is manually preset and the membership is set to 0; Power generation target: The membership is 1 when the power generation is maximum during the dispatch period; the lower limit constraint of the objective function is manually preset and set to 0 based on the project requirements.

10. The multi-objective four-pre-scheduling method for water conservancy projects based on digital twins according to claim 9 is characterized by: In the three-dimensional visualization sub-process in S600, the visualization engine is used to call the engineering scene data and the water conservancy professional model calculation result data in the water conservancy engineering data base, and then render and present them; wherein: The engineering scene data includes DEM data, DOM data, and underwater terrain data within the upstream flood impact range of the water conservancy project; The calculation result data of the water conservancy professional model includes the GIS layer of the flood evolution inundation range preview result, river water level, flow rate, water volume, cross-section water level change, and water flow direction; In the three-dimensional visualization sub-process, water body modeling is formed based on the DEM data within the upstream flood impact range, the DOM data, the underwater terrain data, the flood evolution inundation range preview result GIS layer, river water level, flow velocity, water inflow, cross-section water level change, and water flow direction, and a visual flood forecast preview is performed in a three-dimensional scene.

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