Reservoir flood control management method based on digital twinning

Abstract

The application provides a reservoir flood control management method based on digital twinning, which comprises the following steps: establishing a digital twinning water regime analysis model, establishing a digital twinning visual display model, forecasting of the twinning water regime analysis model, publishing a warning result of the S3, pre-rehearsal of the digital twinning visual display model, and executing a pre-rehearsal result of the S5; the digital twinning model is applied to support the function implementation of forecasting, warning, pre-rehearsal, and pre-plan, and to establish a four-pre mechanism for the safety of reservoir managers and downstream residents; the warning mechanism of a traditional reservoir manager mainly comprises publishing a warning work through rainfall forecasting or real-time water level forecasting.

Description

Technical Field

[0001] This invention relates to the field of reservoir flood control, and in particular to a reservoir flood control management method based on digital twins. Background Technology

[0002] The reservoir flood control management method and system are based on two core models: a digital twin hydrological analysis model and a digital twin visualization model, supporting the realization of "four predictions" (forecasting, early warning, rehearsal, and contingency planning). The digital twin hydrological analysis model is an algorithmic model established through the coupling of rainfall, hydrological, and hydrodynamic theories. After inputting hydrological data, it can simulate the hourly time series of flood levels in the reservoir basin and generate forecast results. The digital twin visualization model consists of a database management system and a WebGIS / WebGL visualization interface. The database supports reservoir flood control early warning and contingency planning management; the WebGIS / WebGL visualization interface supports reservoir flood control simulation and rehearsal work.

[0003] This invention patent provides a flood control scheduling method based on digital twins, comprising the following steps: S1, constructing a digital twin of the flood control scheduling system; S2, tracking and updating the digital twin of the flood control scheduling system to obtain a real-time synchronized digital twin; S3, based on the real-time synchronized digital twin of the flood control scheduling system, using meteorological models and multiple hydrological models to conduct ensemble meteorological and hydrological forecasts, predicting the flood control scheduling process of the target reservoir for the next week, and analyzing and judging the flood situation based on the forecast results, proposing adjustment schemes for the current reservoir scheduling rules. The advantages are: overcoming the shortcomings of existing flood control scheduling methods that cannot comprehensively reflect real-time flood control scheduling information, have short effective lead times for meteorological and hydrological forecasts, and have low forecast accuracy; and being able to accurately provide information on the flood control scheduling process, dynamically assess flood risks, and thus facilitate the smooth implementation of flood disaster prevention work.

[0004] This invention patent provides a scheduling method for inter-basin water transfer projects based on digital twin technology, comprising the following steps: Step 1: accurate prediction of water demand for each water user in the water-receiving area of ​​the inter-basin water transfer project; Step 2: derivation of key section control indicators for the inter-basin water transfer project based on digital twin theory; Step 3: accurate generation of a joint scheduling scheme for multiple reservoirs in the water transfer area; Step 4: data perception of the inter-basin water transfer project and real-time data interaction between it and the virtual digital environment; Step 5: prediction of system safety status and generation of future scheduling countermeasures. This invention achieves the goal of precise water supply to each water intake plant by jointly using reservoirs in the water supply area and the water transfer and distribution network; it can accurately simulate the entire operation process of the inter-basin water transfer project, accurately predict potential risks in the project operation, and generate corresponding countermeasures and contingency plans, realizing emergency management in the event of sudden accidents, and has significant advantages over conventional simulation scheduling models.

[0005] The aforementioned invention patents all apply digital twins to reservoir flood control work, starting from the aspect of reservoir water allocation. In recent years, frequent flash floods or unannounced reservoir discharges have exposed the public to risks and caused numerous accidents. However, there is still a lack of forecasting, early warning, rehearsal, and contingency plans for reservoir managers and downstream residents. These mechanisms are either too traditional, issue warnings too late, or have insufficient timescales to handle unpredictable sudden rainstorms.

[0006] This invention addresses the aforementioned problems in water flood control management by enabling more advanced forecasting, detailed early warning issuance, digital simulations of potential future situations, and ultimately, decision-making to implement appropriate contingency plans. The establishment of these four pre-planning mechanisms not only optimizes the management of reservoir flood control personnel but also raises downstream residents' awareness of potential risks and facilitates safer evacuation, establishing a sound interactive mechanism for reservoir flood control management and shifting from extensive to intensive management. Summary of the Invention

[0007] The purpose of this invention is to address the shortcomings of the prior art by providing a reservoir flood control management method based on digital twins. This method provides a reservoir flood control management mechanism with an hourly time scale for flood control management release, thus effectively addressing unpredictable and sudden rainfall events.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] This invention provides a reservoir flood control management method based on digital twins, comprising the following steps:

[0010] S1. Establish a digital twin hydrological analysis model;

[0011] S2. Establish a digital twin visualization model;

[0012] S3. The forecast of the twin hydrological analysis model;

[0013] S4. Issue an early warning based on the forecast results of S3;

[0014] S5. Pre-show of the digital twin visualization display model;

[0015] Furthermore, S1 specifically refers to:

[0016] S1.1 Collect historical rainfall observation time series and downstream water level observation time series of the reservoir catchment area, and perform data analysis, verification and supplementation on the time series;

[0017] S1.2 Establishing a hydrological model

[0018] The coupled model using the flow generation algorithm and the flow merging algorithm is f = f(R, θ).N (t));

[0019] The flow generation algorithm is as follows:

[0020]

[0021] Where R is the rainfall runoff; PE is the rainfall amount; W is the soil moisture content; WM is the average water storage capacity of the basin; WMM is the maximum water storage capacity of the basin; b is the water storage distribution coefficient, which is dimensionless, and the smaller the coefficient, the more uniform the water storage capacity distribution in the vadose zone; a is the water storage capacity curve, and the formula is:

[0022]

[0023] The merging algorithm is as follows:

[0024] θN(t)=θ(0)·Ф(t)

[0025] Where, θ N (t) represents the instantaneous unit hydrograph of the N-level river network; θ(0) represents the initial state probability vector; t represents time; Ф(t) is the internal transmission probability array, expressed as:

[0026] Ф(t)=eAt

[0027] Where A is the transmission rate array, defined as:

[0028]

[0029] Where pij is the probability of transmission from the i-th level river network to the j-th level river network; λ i The average residence time for Class I river networks;

[0030] S1.3. Establish the reservoir model, the expression is:

[0031]

[0032] in, This represents the average inbound flow rate over the specified time period. ΔV is the average outbound flow rate over the time period; ΔV is the difference in storage capacity at the beginning and end of the time period; Δt is the length of the time period; Z is the loss during the time period.

[0033] S1.4 Establish a flood model downstream of the reservoir.

[0034] The system is established using a hydrodynamic algorithm, which should include the topographic and elevation information of the downstream river channel and floodplain for use in simulating downstream floods of the reservoir.

[0035] The boundary condition in the hydrodynamic algorithm is the simulated time series of the reservoir's outflow.

[0036] The downstream flood model of the reservoir is calculated by hydrodynamic algorithms to obtain the simulated time series of water level downstream of the reservoir.

[0037] S1.5, Calibration and Verification of Digital Twin Hydrological Analysis Model

[0038] The calibration and verification process of the digital twin hydrological analysis model involves adjusting the parameters within the model and conducting calibration and verification evaluations until the evaluation results reach an acceptable level or the limit.

[0039] The calibration verification includes the correlation coefficient method, efficiency coefficient method, peak water level arrival time method, and peak water level error percentage method.

[0040] Furthermore, S1.2 specifically includes:

[0041] S1.2.1 Collect upstream geographical information of m reservoirs, and obtain the Horton River number X by analyzing the watershed of each reservoir through topographic elevation analysis. 1,m Area ratio X 2,m Average river length X 3,m River length rate X 4,m We obtain m upstream geographical information as B m ={X1,X2,X3,X4};

[0042] S1.2.2 Obtain the center points of l cluster categories, including the Horton River number center point c. 1,l , area ratio center point c 2,l The average length of the river, center point c 3,l River length rate center point c 4,l The cluster center of group l is C l ={c1,c2,c3,c4};

[0043] S1.2.3, C l ={c1,c2,c3,c4} selects the d-th category C d ={c 1,d ,c 2,d ,c 3,d ,c 4,d}, d = 1, 2, ..., l, respectively input the coupling model f = f(R, θ) of the S1.2 flow generation algorithm and flow merging algorithm. N (t)); The instantaneous unit hydrograph method of the confluence algorithm selects a river network of level 3 to N, and obtains St ,x ={(C d ,f x (C d )):C d C l}, x = 3, 4, ..., N;

[0044] And for St,x An evaluation was conducted, and N was obtained. d The instantaneous unit line of the river network topography is the most suitable for the d-th cluster category C. d →N d ;

[0045] S1.2.4 If there is geographical information about the upstream of the new reservoir, the number rate Y1, area rate Y2, average river length Y3, and river length rate Y4 of the Horton River can be obtained by analyzing the watershed of the new reservoir through topographic elevation analysis. The result is E = {Y1, Y2, Y3, Y4}.

[0046] C via S1.2.2 l ={c1,c2,c3,c4}, find the d′-th class such that min(EC) d C is obtained through S1.2.3. d →N d The d′ group of categories corresponds to the most suitable N d′ The instantaneous unit line method for river network geomorphology is the most suitable for use with the new reservoir.

[0047] Furthermore, the correlation coefficient method and the efficiency coefficient method are calculated and statistically analyzed using the observed time series of water levels downstream of the reservoir and the simulated time series of water levels downstream of the reservoir. The closer the calculated result is to 1, the better and more reliable the simulation result of the digital twin hydrological analysis model.

[0048] The flood peak arrival time method is calculated by subtracting the observed peak arrival time of the downstream water level from the simulated peak arrival time. The closer the calculated result is to 0, the better and more reliable the simulation result of the digital twin hydrological analysis model.

[0049] The percentage error method for flood peak water level is calculated by subtracting the peak value of the observed water level downstream of the reservoir from the simulated peak value of the water level downstream of the reservoir, and then dividing the result by the observed peak value of the water level downstream of the reservoir. The closer the result is to 0, the better and more reliable the simulation result of the digital twin hydrological analysis model.

[0050] Furthermore, the specific steps of S2 are as follows:

[0051] S2.1 Establish a hydrological analysis database:

[0052] Three sets of database interfaces were established, including the time series of the next hourly rainfall forecast, the time series of the downstream water level forecast of the reservoir, and the time series of the early warning issuance of the downstream of the reservoir.

[0053] S2.2, Establish a visual layer database;

[0054] It contains geographic information data, including GIS maps, 3D oblique photography, and 3D model layers;

[0055] S2.3 Establish an early warning and contingency plan database;

[0056] The database of early warning plans is provided by the reservoir manager and includes flood control emergency early warnings and flood control emergency plans.

[0057] The flood control emergency early warning information includes the following content as specified in the flood control emergency response level regulations: reservoir operation guidelines, normal water storage level, flood control high water level, design water level, check water level, flood limit water level, dike crest elevation, river channel flood control level, warning water level, and guaranteed water level.

[0058] S2.4. Establish a WebGIS / WebGL visualization interface:

[0059] Each database is input into the WebGIS / WebGL visualization engine for processing and displayed on the visualization interface server. The geographical relationship of hydrological analysis data in the visualization layer data is shown; the hydrological analysis data should be used for the release, deployment, and law enforcement information in the early warning and contingency plan data.

[0060] Furthermore, the specific steps of S3 are as follows:

[0061] S3.1 Collect the past hourly rainfall observation time series and the future hourly rainfall forecast time series within the reservoir catchment area, and perform data analysis, verification, and supplementation on the time series;

[0062] The sources of the past hourly rainfall observation time series include single or multiple rain gauges, radar echo rainfall, and rainfall simulated by atmospheric circulation models.

[0063] The time series data for the future hourly rainfall forecast includes rainfall forecasts from atmospheric circulation models, mechanistic and non-mechanistic forecast models, and radar echo rainfall forecast models.

[0064] S3.2 The time series input calls the digital twin hydrological analysis model for simulation, and finally produces the downstream water level forecast time series of the reservoir;

[0065] The digital twin hydrological analysis model is the digital twin hydrological analysis model that has completed the S1 calibration verification.

[0066] Furthermore, the specific steps of S4 are as follows:

[0067] S4.1 Input the downstream water level forecast time series of the reservoir as described in S3.2 and the flood control emergency response level as specified in S2.3;

[0068] S4.2. By using the water level values ​​at different time points in the downstream water level forecast time series, find the corresponding flood control emergency response level and generate a downstream early warning release time series.

[0069] The time series of early warnings issued downstream of the reservoir is entered into the established hydrological analysis database.

[0070] Furthermore, S5 specifically includes:

[0071] S5.1, Call the digital twin visualization display model;

[0072] After updating the hydrological analysis database in the digital twin visualization model, the downstream water level forecast time series and the downstream early warning release time series of the S2 hydrological analysis database are called.

[0073] Flood emergency warning analysis and mapping of the early warning plan database to generate flood emergency plans;

[0074] S5.2, Digital Twin Visualization Model Preview

[0075] The flood control simulation scenario calls upon the S2 visualization layer database and the WebGIS / WebGL visualization interface server to display the dynamic simulation results.

[0076] Furthermore, S6 specifically includes:

[0077] S6.1 Implementation of the reservoir flood control emergency plan;

[0078] The results of the digital twin visualization display model are used to assess the flood response capabilities of reservoir managers, form contingency plans to assist decision-making, and finally form the implementation of the reservoir flood control emergency plan.

[0079] S6.2 Reservoir flood control deployment and enforcement;

[0080] The implementation of the aforementioned reservoir flood control emergency plan will notify relevant reservoir managers and downstream residents of the deployment and enforcement of flood disaster prevention and mitigation measures.

[0081] The beneficial effects of this invention are: by applying a digital twin model, it supports the realization of forecasting, early warning, rehearsal, and contingency planning functions, and establishes a four-prevention mechanism for the safety of reservoir managers and downstream residents;

[0082] Traditional reservoir managers mainly rely on issuing warnings through rainfall forecasts or real-time water level forecasts.

[0083] The digital twin hydrological analysis model using this method can predict the future flood level and arrival time of the reservoir basin, with a forecast time scale of hours. This allows for more effective response to unpredictable sudden rainstorm events and optimizes the work of traditional reservoir managers.

[0084] By generating flood level and arrival time forecasts through digital twin hydrological analysis models and combining them with local flood control emergency early warning rules, real-time and timely early warnings can be issued, enabling reservoir managers and downstream residents to have a better sense of security and take precautions in advance.

[0085] The digital twin visualization model using this method can dynamically simulate and display the evolution of future floods on a large scale, around the clock, frequently, from multiple angles, quickly, and at low cost, thus reinforcing areas where flood warnings are insufficient. Finally, it can assist reservoir managers in decision-making, integrating with local flood control emergency plans for deployment and enforcement. Attached Figure Description

[0086] Figure 1 A flowchart of a reservoir flood control management method based on digital twins;

[0087] Figure 2 Flowchart for establishing a digital twin hydrological analysis model;

[0088] Figure 3 Flowchart for selecting the instantaneous unit line method for N-level river network geomorphology;

[0089] Figure 4 Flowchart for establishing a digital twin visualization model;

[0090] Figure 5 A flowchart for digital twin hydrological analysis model forecasting;

[0091] Figure 6 Flowchart for issuing early warnings based on forecast results;

[0092] Figure 7 A flowchart for the pre-visualization of the digital twin model;

[0093] Figure 8 A flowchart for implementing the contingency plan based on the results of the rehearsal. Detailed Implementation

[0094] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0095] Please see Figure 1 A reservoir flood control management method based on digital twins includes the following steps:

[0096] S1. Establish a digital twin hydrological analysis model;

[0097] S2. Establish a digital twin visualization model;

[0098] S3. The forecast of the twin hydrological analysis model;

[0099] S4. Issue an early warning based on the forecast results of S3;

[0100] S5. Pre-show of the digital twin visualization display model;

[0101] S6. Execute the pre-run results of S5.

[0102] Specifically, S1 refers to: Please refer to... Figure 2 ,

[0103] S1.1 Collect historical rainfall observation time series and downstream water level observation time series of the reservoir catchment area, and perform data analysis, verification and supplementation on the time series;

[0104] S1.2 Establishing a hydrological model

[0105] The coupled model using the flow generation algorithm and the flow merging algorithm is f = f(R, θ). N (t));

[0106] The flow generation algorithm is as follows:

[0107]

[0108] Where R is the rainfall runoff; PE is the rainfall amount; W is the soil moisture content; WM is the average water storage capacity of the basin; WMM is the maximum water storage capacity of the basin; b is the water storage distribution coefficient, which is dimensionless, and the smaller the coefficient, the more uniform the water storage capacity distribution in the vadose zone; a is the water storage capacity curve, and the formula is:

[0109]

[0110] The bus coupling algorithm is as follows:

[0111] θN(t)=θ(0)·Ф(t)

[0112] Where, θ N (t) represents the instantaneous unit hydrograph of the N-level river network; θ(0) represents the initial state probability vector; t represents time; Ф(t) is the internal transmission probability array, expressed as:

[0113] Ф(t)=eAt

[0114] Where A is the transmission rate array, defined as:

[0115]

[0116] Where pij is the probability of transmission from the i-th level river network to the j-th level river network; λ i The average residence time for Class I river networks;

[0117] The soil moisture content and average watershed water storage capacity information can be obtained through real-time observation or model simulation. The rainfall data is a historical rainfall observation time series, provided by S1.1.

[0118] The maximum water storage capacity, water storage distribution coefficient, and average flow velocity of the basin are obtained through model calibration and verification. The selection of the N-level river network geomorphology instantaneous unit hydrograph method can be determined by using an unsupervised clustering learning algorithm based on different Horton River number rates, area rates, average river lengths, and river length rates.

[0119] S1.3. Establish the reservoir model, the expression is:

[0120]

[0121] in, This represents the average inbound flow rate over the specified time period. ΔV is the average outbound flow rate over the time period; ΔV is the difference in storage capacity at the beginning and end of the time period; Δt is the length of the time period; Z is the loss during the time period.

[0122] S1.4 Establish a flood model downstream of the reservoir.

[0123] The system is established using a hydrodynamic algorithm, which should include the topographic and elevation information of the downstream river channel and floodplain for use in simulating downstream floods of the reservoir.

[0124] The boundary condition in the hydrodynamic algorithm is the simulated time series of the reservoir's outflow.

[0125] The downstream flood model of the reservoir is calculated by hydrodynamic algorithms to obtain the simulated time series of water level downstream of the reservoir.

[0126] S1.5, Calibration and Verification of Digital Twin Hydrological Analysis Model

[0127] The calibration and verification process of the digital twin hydrological analysis model involves adjusting the parameters within the model and conducting calibration and verification evaluations until the evaluation results reach an acceptable level or the limit.

[0128] The calibration verification includes the correlation coefficient method, efficiency coefficient method, peak water level arrival time method, and peak water level error percentage method.

[0129] Specifically, S1.2 is as follows: Please refer to [link / reference needed]. Figure 3 ,

[0130] S1.2.1 Collect upstream geographical information of m reservoirs, and obtain the Horton River number X by analyzing the watershed of each reservoir through topographic elevation analysis. 1,m Area ratio X 2,m Average river length X 3,m River length rate X4,m We obtain m upstream geographical information as B m ={X1,X2,X3,X4};

[0131] S1.2.2 Obtain the center points of l cluster categories, including the Horton River number center point c. 1,l , area ratio center point c 2,l The average length of the river, center point c 3,l River length rate center point c 4,l The cluster center of group l is C l ={c1,c2,c3,c4};

[0132] S1.2.3, C l ={c1,c2,c3,c4} selects the d-th category C d ={c 1,d ,c 2,d ,c 3,d ,c 4,d}, d = 1, 2, ..., l, respectively input the coupling model f = f(R, θ) of the S1.2 flow generation algorithm and flow merging algorithm. N (t)); The instantaneous unit hydrograph method for the geomorphology of the confluence algorithm is selected as a river network of level 3 to N, resulting in S t,x ={(C d ,f x (C d )):C d C l}, x = 3, 4, ..., N;

[0133] And for S t,x An evaluation was conducted, and N was obtained. d The instantaneous unit line of the river network topography is the most suitable for the d-th cluster category C. d →N d ;

[0134] S1.2.4 If there is geographical information about the upstream of the new reservoir, the number rate Y1, area rate Y2, average river length Y3, and river length rate Y4 of the Horton River can be obtained by analyzing the watershed of the new reservoir through topographic elevation analysis. The result is E = {Y1, Y2, Y3, Y4}.

[0135] C via S1.2.2 l ={c1,c2,c3,c4}, find the d′-th class such that min(EC) d C is obtained through S1.2.3. d →N d The d′ group of categories corresponds to the most suitable N d′ The instantaneous unit line method for river network geomorphology is the most suitable for use with the new reservoir.

[0136] The correlation coefficient method and the efficiency coefficient method are calculated and statistically analyzed using the observed time series of water levels downstream of the reservoir and the simulated time series of water levels downstream of the reservoir. The closer the calculated result is to 1, the better and more reliable the simulation result of the digital twin hydrological analysis model is.

[0137] The flood peak arrival time method is calculated by subtracting the observed peak arrival time of the downstream water level from the simulated peak arrival time. The closer the calculated result is to 0, the better and more reliable the simulation result of the digital twin hydrological analysis model.

[0138] The percentage error method for flood peak water level is calculated by subtracting the peak value of the observed water level downstream of the reservoir from the simulated peak value of the water level downstream of the reservoir, and then dividing the result by the observed peak value of the water level downstream of the reservoir. The closer the result is to 0, the better and more reliable the simulation result of the digital twin hydrological analysis model.

[0139] The specific steps in S2 are as follows: Please refer to... Figure 4 ,

[0140] S2.1 Establish a hydrological analysis database:

[0141] Three sets of database interfaces were established, including the time series of the next hourly rainfall forecast, the time series of the downstream water level forecast of the reservoir, and the time series of the early warning issuance of the downstream of the reservoir.

[0142] S2.2, Establish a visual layer database;

[0143] It contains geographic information data, including GIS maps, 3D oblique photography, and 3D model layers;

[0144] S2.3 Establish an early warning and contingency plan database;

[0145] The database of early warning plans is provided by the reservoir manager and includes flood control emergency early warnings and flood control emergency plans.

[0146] The flood control emergency early warning information includes the following content as specified in the flood control emergency response level regulations: reservoir operation guidelines, normal water storage level, flood control high water level, design water level, check water level, flood limit water level, dike crest elevation, river channel flood control level, warning water level, and guaranteed water level.

[0147] S2.4. Establish a WebGIS / WebGL visualization interface:

[0148] Each database is input into the WebGIS / WebGL visualization engine for processing and displayed on the visualization interface server. The geographical relationship of hydrological analysis data in the visualization layer data is shown; the hydrological analysis data should be used for the release, deployment, and law enforcement information in the early warning and contingency plan data.

[0149] The specific steps in S3 are as follows: Please refer to... Figure 5 ,

[0150] S3.1 Collect the past hourly rainfall observation time series and the future hourly rainfall forecast time series within the reservoir catchment area, and perform data analysis, verification, and supplementation on the time series;

[0151] The sources of the past hourly rainfall observation time series include single or multiple rain gauges, radar echo rainfall, and rainfall simulated by atmospheric circulation models.

[0152] The time series data for the future hourly rainfall forecast includes rainfall forecasts from atmospheric circulation models, mechanistic and non-mechanistic forecast models, and radar echo rainfall forecast models.

[0153] S3.2 The time series input calls the digital twin hydrological analysis model for simulation, and finally produces the downstream water level forecast time series of the reservoir;

[0154] The digital twin hydrological analysis model is the digital twin hydrological analysis model that has completed the S1 calibration verification.

[0155] For specific steps in S4, please refer to: Figure 6 ,

[0156] S4.1 Input the downstream water level forecast time series of the reservoir as described in S3.2 and the flood control emergency response level as specified in S2.3;

[0157] S4.2. By using the water level values ​​at different time points in the downstream water level forecast time series, find the corresponding flood control emergency response level and generate a downstream early warning release time series.

[0158] The time series of early warnings issued downstream of the reservoir is entered into the established hydrological analysis database.

[0159] Specifically, S5 refers to: Please refer to [link / reference needed]. Figure 7 ,

[0160] S5.1, Call the digital twin visualization display model;

[0161] After updating the hydrological analysis database in the digital twin visualization model, the downstream water level forecast time series and the downstream early warning release time series of the S2 hydrological analysis database are called.

[0162] Flood emergency warning analysis and mapping of the early warning plan database to generate flood emergency plans;

[0163] S5.2, Digital Twin Visualization Model Preview

[0164] The flood control simulation scenario calls upon the S2 visualization layer database and the WebGIS / WebGL visualization interface server to display the dynamic simulation results.

[0165] Specifically, S6 refers to: Please refer to... Figure 8 ,

[0166] S6.1 Implementation of the reservoir flood control emergency plan;

[0167] The results of the digital twin visualization display model are used to assess the flood response capabilities of reservoir managers, form contingency plans to assist decision-making, and finally form the implementation of the reservoir flood control emergency plan.

[0168] S6.2 Reservoir flood control deployment and enforcement;

[0169] The implementation of the aforementioned reservoir flood control emergency plan will notify relevant reservoir managers and downstream residents of the deployment and enforcement of flood disaster prevention and mitigation measures.

[0170] The embodiments described above are merely illustrative of implementation methods of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A reservoir flood control management method based on digital twins, characterized in that, Includes the following steps: S1. Establish a digital twin hydrological analysis model; S2. Establish a digital twin visualization model; S3. The forecast of the twin hydrological analysis model; S4. Issue an early warning based on the forecast results of S3; S5. Pre-show of the digital twin visualization display model; S6. Execute the pre-rehearsal results of S5; Specifically, S1 is: S1.1 Collect historical rainfall observation time series and downstream water level observation time series of the reservoir catchment area, and perform data analysis, verification and supplementation on the time series; S1.2 Establishing a hydrological model: The coupled model using the flow generation algorithm and the flow merging algorithm is as follows: ; The flow generation algorithm is as follows: ； in, For rainfall-generated flow; Rainfall; Soil moisture content; The average water storage capacity of the basin; This represents the maximum water storage capacity of the basin. The water storage distribution coefficient is dimensionless; the smaller the coefficient, the more uniform the water storage capacity distribution in the vadose zone. The water storage capacity curve is given by the following formula: ； The merging algorithm is as follows: ； in, for Instantaneous unit line of the topography of the river network; This is the initial state probability vector; For time; Internal transmission probability array, expressed as: ； in, For a transmission rate array, it is defined as: ； in, From Transmission of river network to The probability of a multi-level river network; for Average retention time in the river network; S1.

3. Establish the reservoir model, the expression is: ； in, This represents the average inbound flow rate over the specified time period. This represents the average outbound flow rate over the specified time period. The difference in storage capacity at the beginning and end of the time period; The time period is long; This represents the amount of loss during the time period; S1.4 Establish a downstream flood model for the reservoir: The system is established using a hydrodynamic algorithm, which should include the topographic and elevation information of the downstream river channel and floodplain for use in simulating downstream floods of the reservoir. The boundary condition in the hydrodynamic algorithm is the simulated time series of the reservoir's outflow. The downstream flood model of the reservoir is calculated by hydrodynamic algorithms to obtain the simulated time series of water level downstream of the reservoir. S1.

5. Calibration and Validation of Digital Twin Hydrological Analysis Model: The calibration and verification process of the digital twin hydrological analysis model involves adjusting the parameters within the model and conducting calibration and verification evaluations until the evaluation results reach an acceptable level or the limit. The calibration verification includes the correlation coefficient method, efficiency coefficient method, peak water level arrival time method, and peak water level error percentage method.

2. The reservoir flood control management method based on digital twins according to claim 1, characterized in that, Specifically, S1.2 is as follows: S1.2.1, Collection Geographical information upstream of each reservoir was used to analyze the watershed of each reservoir's basin through topographic elevation analysis to obtain the Horton River number. Area ratio Average length of rivers River Chief Rate ,have to One upstream geographic information is ; S1.2.2, obtained Cluster center points, including Horton River rate center points , area ratio center point Center point of average river length River Chief's Center Point , Cluster center points are ; S1.2.3, will Select the first Group Category , Input the coupled models of the S1.2 flow generation algorithm and flow merging algorithm respectively. The instantaneous unit line method for terrain in the confluence algorithm is selected as 3 to 1000. Level river network, obtained , ; And on To evaluate and obtain The instantaneous unit line of the first-order river network landform is the most suitable for this. Clustering categories are ; S1.2.4 If geographical information about the upstream area of ​​the new reservoir is available, the Horton River number can be obtained by analyzing the watershed of the new reservoir basin through topographic elevation analysis. Area ratio Average length of rivers River Chief Rate for ; According to S1.2.2 , find the Groups, making ; obtained through S1.2.3 , No. Group category corresponds to the most suitable The instantaneous unit line method for river network geomorphology is the most suitable for use with the new reservoir.

3. The reservoir flood control management method based on digital twin according to claim 2, characterized in that: The correlation coefficient method and the efficiency coefficient method are calculated and statistically analyzed using the observed time series of water levels downstream of the reservoir and the simulated time series of water levels downstream of the reservoir. The closer the calculated result is to 1, the better and more reliable the simulation result of the digital twin hydrological analysis model is. The flood peak arrival time method is calculated by subtracting the arrival time of the observed peak water level downstream of the reservoir from the simulated peak water level downstream of the reservoir. The closer the calculated result is to 0, the better and more reliable the simulation result of the digital twin hydrological analysis model. The percentage error method for flood peak water level is calculated by subtracting the peak value of the observed water level downstream of the reservoir from the simulated peak value of the water level downstream of the reservoir, and then dividing the result by the observed peak value of the water level downstream of the reservoir. The closer the result is to 0, the better and more reliable the simulation result of the digital twin hydrological analysis model.

4. The reservoir flood control management method based on digital twin according to claim 1, characterized in that, The specific steps of S2 are as follows: S2.1 Establish a hydrological analysis database: Three sets of database interfaces were established, including the time series of the next hourly rainfall forecast, the time series of the downstream water level forecast of the reservoir, and the time series of the early warning issuance of the downstream of the reservoir. S2.2, Establish a visual layer database; It contains geographic information data, including GIS maps, 3D oblique photography, and 3D model layers; S2.3 Establish an early warning and contingency plan database; The database of early warning plans is provided by the reservoir manager and includes flood control emergency early warnings and flood control emergency plans. The flood control emergency early warning information includes the following content as specified in the flood control emergency response level regulations: reservoir operation guidelines, normal water storage level, flood control high water level, design water level, check water level, flood limit water level, dike crest elevation, river channel flood control level, warning water level, and guaranteed water level. S2.

4. Establish a WebGIS / WebGL visualization interface: Each database is input into the WebGIS / WebGL visualization engine for processing and displayed on the visualization interface server, showing the geographical relationship of hydrological analysis data in the visualization layer data; Water situation analysis data should be included in early warning and emergency response plans, and used for law enforcement information.

5. A reservoir flood control management method based on digital twins according to claim 4, characterized in that, The specific steps of S3 are as follows: S3.1 Collect the past hourly rainfall observation time series and the future hourly rainfall forecast time series within the reservoir catchment area, and perform data analysis, verification, and supplementation on the time series; The sources of the past hourly rainfall observation time series include single or multiple rain gauges, radar echo rainfall, and rainfall simulated by atmospheric circulation models. The time series data for the future hourly rainfall forecast includes rainfall forecasts from atmospheric circulation models, mechanistic and non-mechanistic forecast models, and radar echo rainfall forecast models. S3.2 The time series input calls the digital twin hydrological analysis model for simulation, and finally produces the downstream water level forecast time series of the reservoir; The digital twin hydrological analysis model is the digital twin hydrological analysis model that has completed the S1 calibration verification.

6. A reservoir flood control management method based on digital twins according to claim 5, characterized in that, The specific steps of S4 are as follows: S4.1 Input the downstream water level forecast time series of the reservoir as described in S3.2 and the flood control emergency response level as specified in S2.3; S4.

2. By using the water level values ​​at different time points in the downstream water level forecast time series, find the corresponding flood control emergency response level and generate a downstream early warning release time series. The time series of early warnings issued downstream of the reservoir is entered into the established hydrological analysis database.

7. A reservoir flood control management method based on digital twins according to claim 6, characterized in that, Specifically, S5 is: S5.1, Call the digital twin visualization display model; After updating the hydrological analysis database in the digital twin visualization model, the downstream water level forecast time series and the downstream early warning release time series of the S2 hydrological analysis database are called. Flood emergency warning analysis and mapping of flood emergency plans from the early warning plan database, and generation of flood control simulation scenarios; S5.2, Digital Twin Visualization Model Preview The flood control simulation scenario calls upon the S2 visualization layer database and the WebGIS / WebGL visualization interface server to display the dynamic simulation results.

8. A reservoir flood control management method based on digital twins according to claim 7, characterized in that, Specifically, S6 is: S6.1 Implementation of the reservoir flood control emergency plan; The results of the digital twin visualization display model are used to assess the flood response capabilities of reservoir managers, form contingency plans to assist decision-making, and finally form the implementation of the reservoir flood control emergency plan. S6.2 Reservoir flood control deployment and enforcement; The implementation of the aforementioned reservoir flood control emergency plan will notify relevant reservoir managers and downstream residents of the deployment and enforcement of flood disaster avoidance measures.

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