Sludge amount measuring method and system based on reservoir model

Abstract

The invention provides a sludge amount measurement method and system based on a reservoir model, and relates to the technical field of hydrographic survey, and the method comprises the steps: obtaining the operation data, basin hydrological data and reservoir topographic data of a target reservoir; based on the operation data, the watershed hydrological data and the reservoir topographic data, establishing a water balance equation and a sediment balance equation; establishing a reservoir model through a water balance equation and a sediment balance equation, and calculating a first predicted distribution quantity of sediment deposition in combination with a dispatching record and a sediment transportation quantity of a target reservoir; calculating a second predicted distribution quantity of the target reservoir at the prediction moment based on the historical sludge distribution quantity of the target reservoir; and if it is determined that the first predicted distribution quantity and the second predicted distribution quantity are within the preset range, determining that the sludge quantity of the target reservoir at the prediction moment is the first predicted distribution quantity. By introducing a coupling mechanism among multiple factors related to reservoir sludge amount prediction, the accuracy of reservoir sludge amount prediction is improved.

Description

Technical Field

[0001] This application relates to the technical field of hydrological measurement, and specifically relates to a method and system for measuring the silt volume based on a reservoir model. Background Art

[0002] The prediction of reservoir silt volume is a process of estimating future silt volume by analyzing the change in reservoir sediment deposition, combining various influencing factors such as hydrology, geology, meteorology, and operation conditions, and using methods such as mathematical modeling, statistical analysis, or machine learning. Its core purpose is to provide a scientific basis for reservoir operation management, optimize the scheduling plan, extend the service life of the reservoir, and reduce the impact on the downstream environment. Common methods include physical models, empirical formula methods, and artificial intelligence technologies based on big data, such as neural networks and time series analysis.

[0003] In existing reservoir silt volume prediction methods, individual influencing factors such as rainfall, incoming sediment concentration, and reservoir operation mode are often considered independently, while ignoring the coupling relationship and non-linear interaction between these factors. This simplification may lead to the model being difficult to comprehensively capture the complex dynamic process of sediment deposition, reducing the accuracy and applicability of the prediction. Therefore, in-depth study of the coupling mechanism between multiple factors and the introduction of modeling methods that can handle complex non-linear relationships are important directions for improving prediction accuracy. Summary of the Invention

[0004] This application provides a method and system for measuring the silt volume based on a reservoir model, which improves the accuracy of reservoir silt volume prediction by introducing the coupling mechanism between multiple factors related to reservoir silt volume prediction.

[0005] In the first aspect of this application, a method for measuring the silt volume based on a reservoir model is provided. The method includes:

[0006] Obtain the operation data, basin hydrological data, and reservoir terrain data for the target reservoir;

[0007] Based on the operation data and the basin hydrological data, establish a water balance equation, and based on the operation data, the basin hydrological data, and the reservoir terrain data, establish a sediment balance equation;

[0008] Establish a reservoir model through the water balance equation and the sediment balance equation, and combine the scheduling records and sediment transport volume of the target reservoir to calculate the first predicted distribution volume of sediment deposition;

[0009] Based on the historical silt distribution volume of the target reservoir, calculate the second predicted distribution volume of the target reservoir at the prediction time, where the prediction time is the time corresponding to when the silt volume of the target reservoir reaches the first predicted distribution volume;

[0010] If it is determined that the first predicted distribution amount is within the preset range from the first predicted distribution amount, then determine that the silt amount of the target reservoir at the prediction time is the first predicted distribution amount.

[0011] Based on the above technical solutions, preferably, establishing a water balance equation based on the operation data and the basin hydrological data specifically includes:

[0012] Calculating the inflow of the target reservoir at different times based on the basin rainfall-runoff model;

[0013] Performing time series analysis on the inflow to determine the change amount of the inflow water of the target reservoir;

[0014] Determining the change amount of the outflow water of the target reservoir according to the historical outflow law of the target reservoir;

[0015] Calculating the dynamic loss amount of the target reservoir based on the meteorological observation data and the water surface distribution in the reservoir area of the target reservoir, where the dynamic loss amount includes the dynamic evaporation amount and the dynamic infiltration amount;

[0016] Establishing the water balance equation according to the balance relationship among the change amount of the inflow water, the change amount of the outflow water, and the dynamic loss amount.

[0017] Based on the above technical solutions, preferably, establishing the water balance equation according to the balance relationship among the change amount of the inflow water, the change amount of the outflow water, and the dynamic loss amount, the water balance equation is expressed as follows:

[0018] V t+1 =V t +∫ t t+Δt Q in (t)dt-∫ t t+Δt Q out (t)dt-∫ A E(x,y,t)dxdy

[0019] Wherein, V t+1 is the water volume of the reservoir at time t + 1, V t is the water volume of the reservoir at time t, Q in (t) is the change amount of the inflow water, Q out (t) is the change amount of the outflow water, and E(x, y, t) is the dynamic loss amount.

[0020] Based on the above technical solutions, preferably, establishing a sediment balance equation based on the operation data, the basin hydrological data, and the reservoir topographic data specifically includes:

[0021] Introduce a seasonal variation term to simulate the fluctuations in the sediment concentration entering the target reservoir during the rainy season and the dry season. Describe the seasonal variation using a cosine function to reflect the fluctuations in water flow and sediment concentration over seasons, and obtain the first simulation result;

[0022] Introduce the dynamic variation based on rainfall events, use a Gaussian function to simulate the change in sediment input into the target reservoir after heavy rainfall, and obtain the second simulation result;

[0023] According to the first simulation result and the second simulation result, describe the process of the sediment concentration changing with time in the target reservoir after rainfall in different seasons, and obtain the change in sediment inflow into the target reservoir;

[0024] Based on the relationship between sediment concentration and water depth, combine the water depth in the flood discharge area of the target reservoir and the overall water depth of the reservoir area of the target reservoir to calculate the change in sediment outflow from the target reservoir;

[0025] Based on the reservoir area topography of the target reservoir, determine the sediment deposition rate;

[0026] Introduce sediment particle size and sediment critical velocity, and calculate the deposition change amount of sediment with different particle sizes at different flow velocities;

[0027] Based on the flow velocity and flow energy, simulate the movement and deposition laws of sediment;

[0028] According to the sediment deposition rate, the deposition change amount, and the movement and deposition laws of the sediment, determine the dynamic sediment deposition change amount of the target reservoir;

[0029] According to the change in sediment inflow, the change in sediment outflow, and the dynamic sediment deposition change amount, establish the sediment balance equation of the target reservoir.

[0030] On the basis of the above technical solutions, preferably, the establishment of the sediment balance equation of the target reservoir according to the change in sediment inflow, the change in sediment outflow, and the dynamic sediment deposition change amount is specifically expressed as follows:

[0031]

[0032] Among them, S is the sediment storage in the reservoir, C s,in (t) is the sediment inflow, C s,out (t) is the sediment outflow, D sediment (t) is the dynamic sediment deposition change amount.

[0033] Based on the above technical solutions, preferably, establishing a reservoir model through the water balance equation and the sediment balance equation, and combining the scheduling records and sediment transport laws of the target reservoir to calculate the first predicted distribution amount of sediment deposition, specifically including:

[0034] Using a hydrodynamic model to simulate the water flow field in the reservoir area of the target reservoir, determine the sediment transport path and stagnant area, and obtain the sediment transport law;

[0035] According to the water level change amount of the target reservoir, dynamically adjust the sediment transport law and deposition model, calculate the impact of each scheduling on the sediment distribution, and simulate the sediment deposition law under different scheduling scenarios;

[0036] Integrate the dynamic calculation results of the water balance equation and the sediment balance equation, and the dynamic calculation results include the input, output and deposition amounts of reservoir sediment within each time step;

[0037] Based on the sediment deposition law and the dynamic calculation results, predict the spatial distribution of sediment in each area of the reservoir area and determine the deposition thickness;

[0038] Superimpose the deposition thicknesses of multiple time steps to obtain the first predicted distribution amount of sediment deposition in the target reservoir during the entire prediction period.

[0039] Based on the above technical solutions, preferably, calculating the second predicted distribution amount of the target reservoir at the prediction moment based on the historical silt distribution amount of the target reservoir, specifically including:

[0040] Collect the historical silt distribution data of the target reservoir;

[0041] Compare the historical silt distribution data with the first predicted distribution amount for model calibration, and through regression analysis, find out the change trend of the reservoir sediment deposition amount over time and calculate the change amount of the sediment amount in the historical data;

[0042] Based on the corresponding relationship between the first predicted distribution amount and the historical silt distribution amount, dynamically adjust the silt deposition law at the prediction moment to obtain an adjustment factor;

[0043] Based on the scheduling records and historical silt distribution amount of the target reservoir, simulate the influence degree on the sediment distribution under different scheduling scenarios;

[0044] Introduce the adjustment factor and the influence degree into the change amount, calculate the distribution change of the silt amount under different schedulings, and obtain the second predicted distribution amount.

[0045] In the second aspect of the present application, a sediment volume measurement system based on a reservoir model is provided. The system includes an acquisition module, a processing module, and an output module, where:

[0046] The acquisition module is used to acquire operation data, basin hydrological data, and reservoir terrain data for the target reservoir;

[0047] The processing module is used to establish a water balance equation based on the operation data and the basin hydrological data, and establish a sediment balance equation based on the operation data, the basin hydrological data, and the reservoir terrain data;

[0048] The processing module is used to establish a reservoir model through the water balance equation and the sediment balance equation, and calculate the first predicted distribution amount of sediment deposition in combination with the scheduling records and sediment transport laws of the target reservoir;

[0049] The processing module is used to calculate the second predicted distribution amount of the target reservoir at the prediction time based on the historical sediment distribution amount of the target reservoir, and the prediction time is the time corresponding to when the sediment volume of the target reservoir reaches the first predicted distribution amount;

[0050] The output module is used to determine that the sediment volume of the target reservoir at the prediction time is the first predicted distribution amount if it is determined that the first predicted distribution amount is within a preset range from the first predicted distribution amount.

[0051] Based on the above technical solutions, preferably, the processing module is used to calculate the inflow of the target reservoir at different times based on a basin rainfall-runoff model;

[0052] The processing module is used to perform time series analysis on the inflow to determine the change amount of the inflow water of the target reservoir;

[0053] The processing module is used to determine the change amount of the outflow water of the target reservoir according to the historical outflow law of the target reservoir;

[0054] The processing module is used to calculate the dynamic loss amount of the target reservoir based on the meteorological observation data and the water surface distribution in the reservoir area of the target reservoir, and the dynamic loss amount includes dynamic evaporation amount and dynamic infiltration amount;

[0055] The processing module is used to establish the water balance equation according to the balance relationship among the change amount of the inflow water, the change amount of the outflow water, and the dynamic loss amount.

[0056] Based on the above technical solutions, preferably, the processing module is configured to establish a water balance equation according to the balance relationship among the inflow water change amount, the outflow water change amount, and the dynamic loss amount. The water balance equation is expressed as follows:

[0057] V t+1 =V t +∫ t t+Δt Q in (t)dt - ∫ t t+Δt Q out (t)dt - ∫ A E(x, y, t)dxdy

[0058] Wherein, V t+1 is the water volume of the reservoir at time t + 1, V t is the water volume of the reservoir at time t, Q in (t) is the inflow water change amount, Q out (t) is the outflow water change amount, and E(x, y, t) is the dynamic loss amount.

[0059] Based on the above technical solutions, preferably, the processing module is configured to introduce a seasonal change term to simulate the fluctuations in the sediment concentration of the target reservoir during the rainy season and the dry season. The seasonal change is described by a cosine function to reflect the fluctuations in water flow and sediment concentration with seasons, and obtain a first simulation result;

[0060] The processing module is configured to introduce a dynamic change based on rainfall events, use a Gaussian function to simulate the change of sediment input into the target reservoir after heavy rainfall, and obtain a second simulation result;

[0061] The processing module is configured to describe the process of the sediment concentration changing with time after rainfall in the target reservoir in different seasons according to the first simulation result and the second simulation result, and obtain the change amount of the sediment inflow into the target reservoir;

[0062] The processing module is configured to calculate the change amount of the sediment outflow from the target reservoir based on the relationship between the sediment concentration and the water depth, in combination with the water depth in the flood discharge area of the target reservoir and the overall water depth of the reservoir area of the target reservoir;

[0063] The processing module is configured to determine the sediment deposition rate based on the terrain of the reservoir area of the target reservoir;

[0064] The processing module is configured to introduce the sediment particle size and the sediment critical velocity, and calculate the deposition change amount of sediments with different particle sizes at different flow velocities;

[0065] The processing module is used to simulate the movement and deposition laws of sediment based on the flow velocity and flow energy;

[0066] The processing module is used to determine the dynamic sediment deposition change amount of the target reservoir according to the sediment deposition rate, the deposition change amount, and the movement and deposition laws of the sediment;

[0067] The processing module is used to establish a sediment balance equation for the target reservoir according to the incoming sediment change amount, the incoming sediment change amount, and the dynamic sediment deposition change amount.

[0068] Based on the above technical solutions, preferably, the processing module is used to establish a sediment balance equation for the target reservoir according to the incoming sediment change amount, the outgoing sediment change amount, and the dynamic sediment deposition change amount, which is specifically expressed as follows:

[0069]

[0070] Where S is the sediment storage in the reservoir, C s,in (t) is the incoming sediment change amount, C s,out (t) is the outgoing sediment change amount, D sediment (t) is the dynamic sediment deposition change amount.

[0071] Based on the above technical solutions, preferably, the processing module is used to use a hydrodynamic model to simulate the flow field of the water area of the target reservoir, determine the sediment transport path and stagnant area, and obtain the sediment transport law;

[0072] The processing module is used to dynamically adjust the sediment transport law and deposition model according to the water level change amount of the target reservoir, calculate the influence of each scheduling on the sediment distribution, and simulate the sediment deposition law under different scheduling scenarios;

[0073] The processing module is used to synthesize the dynamic calculation results of the water volume balance equation and the sediment balance equation, and the dynamic calculation results include the input, output, and deposition amounts of reservoir sediment in each time step;

[0074] The processing module is used to predict the spatial distribution of sediment in each area of the reservoir area based on the sediment deposition law and the dynamic calculation results, and determine the deposition thickness;

[0075] The processing module is used to superimpose the deposition thicknesses of multiple time steps to obtain the first predicted distribution amount of sediment deposition in the target reservoir during the entire prediction period.

[0076] Based on the above technical solutions, preferably, the acquisition module is used to collect the historical silt distribution data of the target reservoir;

[0077] The processing module is configured to compare the historical silt distribution data with the first predicted distribution quantity for model calibration, find out the change trend of the reservoir sediment deposition quantity over time through regression analysis, and calculate the change quantity of the sediment quantity in the historical data.

[0078] The output module is configured to dynamically adjust the silt deposition rule at the predicted moment based on the corresponding relationship between the first predicted distribution quantity and the historical silt distribution quantity, and obtain an adjustment factor.

[0079] The processing module is configured to simulate the influence degree on the sediment distribution under different scheduling scenarios based on the scheduling records of the target reservoir and the historical silt distribution quantity.

[0080] The output module is configured to introduce the adjustment factor and the influence degree into the change quantity, calculate the distribution change of the silt quantity under different scheduling, and obtain the second predicted distribution quantity.

[0081] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0082] 1. By introducing a multi-factor coupling mechanism related to the prediction of reservoir silt quantity, the accuracy of reservoir silt quantity prediction is improved. Specifically, first, by establishing a water balance equation and a sediment balance equation, and combining basin hydrological data, operation data, and reservoir topographic data, the movement laws of water flow and sediment in the reservoir are accurately simulated. In addition, by incorporating the scheduling records of the reservoir, sediment transport laws, and historical silt distribution quantity into the model, the influence of the dynamic changes in reservoir operation on sediment deposition is considered. This multi-factor coupling mechanism can more comprehensively reflect the physical processes inside the reservoir, and accurately simulate the input, output, and deposition distribution of sediment under different scheduling scenarios, thereby improving the accuracy and reliability of the prediction. Especially considering the influence of factors such as seasonal changes, flood scheduling, and historical data adjustment, the consistency between the prediction results and the actual silt quantity is ensured.

[0083] 2. By introducing factors such as a basin rainfall-runoff model, time series analysis, and the historical water discharge law of the reservoir, the water change quantity of the reservoir can be comprehensively and accurately simulated. By considering dynamic loss quantities such as dynamic evaporation and infiltration, and combining meteorological observation data and the water surface distribution in the reservoir area, the accuracy of the reservoir water balance is further improved.

[0084] 3. By comprehensively considering multiple factors such as seasonal variations, rainfall events, the relationship between sediment concentration and water depth, sediment particle size, and flow velocity, the dynamic change process of sediment in the reservoir can be accurately simulated and predicted. By introducing cosine and Gaussian functions to simulate the sediment concentration fluctuations during the rainy and dry seasons, as well as the sediment input after heavy rainfall, the change amount of sediment input can be more comprehensively reflected. At the same time, considering factors such as reservoir topography, flow velocity, and flow energy, the movement and deposition of sediment are simulated, improving the accuracy of predicting sediment deposition rate and spatial distribution. Finally, by establishing a sediment balance equation, a dynamic and accurate sediment volume prediction model is provided.

[0085] 4. By combining the water balance equation and the sediment balance equation, comprehensively considering the reservoir operation records, hydrodynamic model, sediment transport law, and the influence of different operation scenarios on sediment distribution, the sediment deposition process in the reservoir can be accurately simulated and predicted. By dynamically adjusting the sediment transport law and deposition model, and combining the calculation results of each time step, the spatial distribution and deposition thickness of sediment in each area of the reservoir area are accurately described. Finally, by superimposing the deposition thickness of multiple time steps, the first predicted distribution amount of sediment deposition during the entire prediction period is obtained.

[0086] 5. By combining historical silt distribution data with the first predicted distribution amount, using regression analysis and dynamic adjustment methods, the sediment deposition prediction model can be effectively calibrated and optimized. Through comprehensive analysis of historical silt distribution and operation records, the silt deposition law at the prediction moment is dynamically adjusted, and then the change of sediment distribution under different operation scenarios is predicted. By introducing adjustment factors and simulating the influence of operation scenarios, the prediction results are made more accurate and reliable, and finally a more accurate second predicted distribution amount can be obtained. Description of the Drawings

[0087] Figure 1 is a schematic flowchart of a method for measuring silt volume based on a reservoir model disclosed in an embodiment of the present application;

[0088] Figure 2 is a schematic block diagram of a system for measuring silt volume based on a reservoir model disclosed in an embodiment of the present application;

[0089] Description of the Reference Numerals: 201, acquisition module; 202, processing module; 203, output module. Detailed Embodiments

[0090] In order to enable those skilled in the art to better understand the technical solutions in this specification, the following will clearly and completely describe the technical solutions in the embodiments of this specification with reference to the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments.

[0091] In the description of the embodiments of the present application, words such as "for example" or "for instance" are used to give examples, illustrations or explanations. Any embodiment or design solution described as "for example" or "for instance" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0092] In the description of the embodiments of the present application, the term "a plurality of" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. In addition, the terms "first" and "second" are used only for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "include", "comprise", "have" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0093] Reservoir silt volume prediction aims to estimate future silt volume by combining various factors such as hydrology, geology, and meteorology through mathematical modeling or artificial intelligence technology, providing support for reservoir management and scheduling optimization. However, existing methods often consider each factor independently, ignoring their coupling relationships and non-linear interactions, and it is difficult to comprehensively capture the complex dynamics of sediment deposition. Therefore, studying the multi-factor coupling mechanism and introducing modeling methods for dealing with non-linear relationships is an important direction to improve prediction accuracy.

[0094] This embodiment discloses a method for measuring silt volume based on a reservoir model, referring to Figure 1 , and includes the following steps S110 - S150:

[0095] S110, obtain the operation data, basin hydrological data, and reservoir terrain data for the target reservoir.

[0096] A method for measuring silt volume based on a reservoir model disclosed in the embodiments of the present application is applied to a server. The server includes, but is not limited to, electronic devices such as mobile phones, tablet computers, wearable devices, and PCs (Personal Computers), and can also be a background server running a method for measuring silt volume based on a reservoir model. The server can be implemented by an independent server or a server cluster composed of multiple servers.

[0097] Through the reservoir management department or operation unit, retrieve the reservoir operation records (such as gate opening time, flood discharge volume, reservoir water level changes), daily operation logs, historical drainage flow rates, etc. Real-time operation status data can be collected in combination with automated monitoring equipment (such as water level gauges, flow meters, telemetry equipment). Obtain data such as regional rainfall, runoff, inflow, and sediment concentration in the reservoir from the basin hydrological station. When necessary, use remote sensing technology and hydrological models to supplement data in monitoring blind spots to ensure comprehensive and accurate basin hydrological information. Combine high-precision bathymetric surveying instruments and unmanned aerial vehicle (UAV) remote sensing mapping technology to collect reservoir bottom elevation data and topographic change information. At the same time, use historical topographic maps or digital elevation models (DEMs) to calibrate the current topographic change trend and form accurate topographic basic data.

[0098] S120. Based on the operation data and basin hydrological data, establish a water balance equation. Based on the operation data, basin hydrological data, and reservoir topographic data, establish a sediment balance equation.

[0099] In a possible implementation, the basin hydrological data includes the water balance equation established based on the operation data and basin hydrological data of the target reservoir, which specifically includes: calculating the inflow of the target reservoir at different times based on the basin rainfall-runoff model; performing time series analysis on the inflow to determine the change in the inflow water of the target reservoir; determining the change in the outflow water of the target reservoir according to the historical outflow pattern of the target reservoir; calculating the dynamic loss of the target reservoir based on the meteorological observation data of the target reservoir and the water surface distribution in the reservoir area, where the dynamic loss includes dynamic evaporation and dynamic seepage; establishing a water balance equation according to the balance relationship among the change in inflow water, the change in outflow water, and the dynamic loss.

[0100] Specifically, use the basin rainfall-runoff model, such as the SCS-CN model, HEC-HMS, or distributed hydrological model, take basin rainfall, underlying surface conditions such as land use type, soil characteristics, and basin area as input data, and calculate the runoff at different times. By simulating rainfall events and basin runoff processes, convert the basin runoff into the inflow of the target reservoir. Dynamically observe the rainfall intensity and distribution to ensure that the calculation can reflect the changes in actual flood peaks and dry seasons, which is specifically expressed as follows:

[0101]

[0102] Among them, Q in (t) is the change in inflow water, Q base is the base flow, representing the inflow of the reservoir under steady state, usually estimated based on multi-year average flow or historical data to ensure that the flow calculation has a basic reference value. Δt is the time step, α is the seasonal change amplitude coefficient, simulating the flow fluctuations in the dry season and rainy season, T year is the time period within a year, Qpeak is the peak flow rate, and t 0 is the time when the peak flow occurs, and σ is the peak duration, reflecting the flow attenuation rate.

[0103] In the formula, reflects the annual periodic change, such as seasonal peak flow. represents the event-driven flow, simulating the impact of emergencies (such as peak flow or emergency flood discharge), β represents the amplitude of the scheduling adjustment, and the normal distribution function describes the concentration and persistence of the scheduling demand over time.

[0104] According to the preset reservoir operation rules such as flood control limit water level, dead water level, flood scheduling frequency, and downstream demand, the historical water discharge pattern of the target reservoir is obtained, and the outflow rate at different times is calculated. For example, the flood discharge is increased during the flood control period, and the outflow is reduced during the dry period to maintain the reservoir water storage. At the same time, considering the dynamics of the regulation operation, the impact of the actual scheduling decision is simulated through scheduling algorithms (such as multi-objective optimization or simulated annealing algorithm). The outflow water volume of the target reservoir is dynamically adjusted according to the water level change and spillway frequency to ensure safe operation and efficient utilization of resources. The change in the outflow water of the target reservoir is expressed as follows:

[0105]

[0106] where Q out is the change in the outflow water volume, Q target (t) is the target flow rate, representing the outflow rate under normal conditions, determined by the scheduling rules, and β is the dynamic adjustment coefficient, reflecting the impact amplitude of the emergency scheduling, such as the flood discharge demand during a sudden peak flow. In the formula partially simulates the time distribution of the impact of emergencies on the flow adjustment.

[0107] The dynamic loss includes two parts: evaporation and seepage. For the evaporation amount, based on meteorological observation data such as air temperature, humidity, wind speed, etc., combined with the water surface distribution in the reservoir area, the Penman-Monteith or energy balance method is used to calculate the evaporation rate; for the seepage amount, combined with the reservoir geological conditions and water level changes, the leakage loss is calculated through empirical formulas or seepage models. The dynamic characteristics of evaporation and seepage changing with time and space are introduced into the calculation to accurately describe the total loss of the reservoir, which is specifically expressed as follows:

[0108] E(x, y, t) = ∫ A [k 1 (t, x, y) · E local (x, y, t) + k 2 (t, x, y) · H local (x, y, t)]dxdy

[0109] where E(x, y, t) is the dynamic loss, and k 1(t, x, y) is the dynamic adjustment coefficient corresponding to the local evaporation amount, E local (x, y, t) is the local evaporation amount, k 2 (t, x, y) is the dynamic adjustment coefficient corresponding to the local infiltration amount, H local (x, y, t) is the local infiltration amount.

[0110] Among them, the local evaporation amount can be calculated by the following formula:

[0111] E local (x, y, t) = f(U(x, y, t), T(x, y, t), RH(x, y, t))

[0112] Among them, E local (x, y, t) is the local evaporation amount, U is the wind speed, T is the air temperature, RH is the relative humidity, and this formula is a function determined by empirical formulas such as the Penman - Monteith equation.

[0113] Finally, according to the balance relationship among the change amount of water in storage, the change amount of water out of the reservoir, and the dynamic loss amount, a water balance equation is established, and the water balance equation is expressed as follows:

[0114] V t+1 = V t + ∫ t t+Δt Q in (t)dt - ∫ t t+Δt Q out (t)dt - ∫ A E(x, y, t)dxdy

[0115] Among them, V t+1 is the water volume of the reservoir at time t + 1, V t is the water volume of the reservoir at time t, Q in (t) is the change amount of water in storage, Q out (t) is the change amount of water out of the reservoir, and E(x, y, t) is the dynamic loss amount. This formula combines the three parts of water inflow, outflow, and loss, and can dynamically reflect the water balance change of the reservoir, providing a basis for accurate prediction and scheduling.

[0116] In a possible implementation manner, based on the operation data, the basin hydrological data, and the reservoir terrain data, a sediment balance equation is established, which specifically includes: introducing a seasonal variation term to simulate the fluctuations of the sediment concentration entering the target reservoir during the rainy season and the dry season. The seasonal variation is described by a cosine function to reflect the fluctuations of the water flow and sediment concentration with the seasons, and the first simulation result is obtained; introducing the dynamic variation based on rainfall events, and using a Gaussian function to simulate the change of sediment input into the target reservoir after heavy rainfall, and the second simulation result is obtained; according to the first simulation result and the second simulation result, the process of the sediment concentration changing with time after rainfall in the target reservoir in different seasons is described, and the change amount of the sediment entering the target reservoir is obtained; based on the relationship between the sediment concentration and the water depth, combined with the water depth in the flood discharge area of the target reservoir and the overall water depth of the reservoir area of the target reservoir, the change amount of the sediment entering the target reservoir is calculated; based on the terrain of the reservoir area of the target reservoir, the sediment deposition rate is determined; the sediment particle size and the critical sediment velocity are introduced to calculate the deposition change amount of sediments with different particle sizes at different flow velocities; based on the flow velocity and the flow energy, the movement and deposition laws of sediments are simulated; according to the sediment deposition rate, the deposition law, and the movement and deposition change amount of sediments, the dynamic sediment deposition law of the target reservoir is determined; according to the change amount of the sediment entering the reservoir, the change amount of the sediment entering the reservoir, and the dynamic sediment deposition change amount, the sediment balance equation of the target reservoir is established.

[0117] Specifically, collect the historical rainfall, runoff, and sediment concentration data of the target reservoir for many years, identify the typical characteristics of the rainy season and the dry season, and establish a model through a cosine function to set the seasonal variation range of the sediment concentration. Where C base is the average sediment concentration, α is the variation range, and T year is the one-year time period. Use the model to calculate the sediment concentration entering the target reservoir in different seasons, and obtain the first simulation result, which reflects the seasonal fluctuations.

[0118] Furthermore, according to the multi-year basin hydrological observation data, extract the change amount of the sediment concentration in the rainy season and the dry season of the target reservoir basin, and use a cosine function to simulate the seasonal change curve. By adjusting the amplitude and the change within a year, describe the periodic fluctuations of the sediment concentration and the flow rate with the seasons to form the first simulation result. Analyze the short-term impact of heavy rainfall events on the sediment concentration. Use a Gaussian function to simulate the temporal distribution change of the sediment concentration after rainfall events, and the parameters include rainfall intensity, duration, and sediment response delay time, etc. Superimpose the short-term dynamic effect of heavy rainfall to form the second simulation result. Superimpose the first simulation result (seasonal variation) and the second simulation result (dynamic effect of rainfall events) to obtain the complete sediment concentration change curve. Combine the inflow data to calculate the change amount of the sediment entering the reservoir at different times:

[0119]

[0120] Among them, C s,in (t) is the change in sediment inflow, C base is the basic sediment change, reflecting the sediment volume under stable conditions, γ 1 is the seasonal change amplitude coefficient, t rain is the time of heavy rainfall events, σ rain is the duration of rainfall intensity, reflecting the increase or decay rate of sediment over time, λ is the amplitude coefficient, reflecting the influence of seasons on sediment concentration, such as high concentration in the rainy season and low concentration in the dry season, T year is the time period within a year.

[0121] Based on the flood discharge outlet and reservoir water depth data, an inverse relationship model between sediment concentration and water depth is established. Combining the outflow and sediment concentration, the change in sediment outflow at different times is dynamically calculated, and its expression is:

[0122]

[0123] Among them, C s,out (t) is the change in sediment outflow, is the relationship between sediment concentration and water depth, H dam (t) is the water depth in the flood discharge outlet area of the target reservoir, H total (t) is the overall water depth of the reservoir area of the target reservoir. The concentration is inversely proportional to the water depth. The shallower the water depth, the higher the sediment concentration. The flood peak adjustment term is used to simulate the instantaneous adjustment of sediment concentration by sudden flood discharge, β represents the adjustment amplitude, σ peak controls the duration.

[0124] Collect the reservoir bottom topography data (including slope, water depth distribution, etc.), and combine with the sediment particle characteristics to estimate the deposition efficiency in different regions. Use the deposition rate formula to calculate the dynamic sediment deposition change, D sediment = k·S transport where S transport is the sediment transport volume, k is the deposition efficiency coefficient. Determine the spatial distribution of the deposition rate and simulate the sediment deposition law in the reservoir area. According to the sediment sample analysis, determine the particle size distribution of the sediment. Introduce the critical flow velocity model u c ∝d 0.5 (the critical flow velocity u c is proportional to the particle size d) to calculate the deposition conditions of sediments with different particle sizes. Simulate the deposition efficiency of sediments with different particle sizes when the flow velocity u is lower than the critical flow velocity. Collect the flow velocity and hydrodynamic data in the reservoir area, and calculate the sediment transport capacity Ts∝u^2·H, reflecting the relationship between the kinetic energy of water flow and sediment transport. Divide the sediment movement into three modes: suspension, rolling, and deposition, establish the sediment movement equation, and simulate the movement path and deposition process of sediment in different regions, specifically as follows:

[0125]

[0126] Where: D sediment (t) is the change in dynamic sediment deposition, φ(x, y) is the spatial distribution coefficient, which reflects the sediment deposition probability in different areas of the reservoir bottom and is usually related to the terrain, u(x, y, t) is the flow velocity, simulating the influence of hydrodynamic force on sediment deposition, u crit is the critical flow velocity, when the flow velocity exceeds this value, sediment is difficult to deposit, d p is the sediment particle diameter, d cri t is the critical particle diameter of sediment deposition (related to hydrodynamic force). This formula combines spatial distribution and particle characteristics to simulate the sediment deposition law and is applicable to three-dimensional sediment distribution modeling.

[0127] Finally, according to the sediment balance principle, a sediment mass conservation equation is established:

[0128]

[0129] Where, S is the sediment storage in the reservoir, C s,in (t) is the change in incoming sediment, C s,out (t) is the change in outgoing sediment, D sediment (t) is the change in dynamic sediment deposition. Through numerical integration with a time step of Δt, the sediment storage and deposition distribution in the reservoir are dynamically calculated.

[0130] S130. A reservoir model is established through the water balance equation and the sediment balance equation. Combining the scheduling records and sediment transport volume of the target reservoir, the first predicted distribution volume of sediment deposition is calculated.

[0131] In a possible implementation, a reservoir model is established through the water balance equation and the sediment balance equation. Combining the scheduling records and sediment transport volume of the target reservoir, the first predicted distribution volume of sediment deposition is calculated, which specifically includes: using a hydrodynamic model to simulate the flow field of the reservoir area of the target reservoir, determining the sediment transport path and stagnant area, and obtaining the sediment transport law; dynamically adjusting the sediment transport law and deposition model according to the change in the water level of the target reservoir, calculating the influence of each scheduling on the sediment distribution, and simulating the sediment deposition law under different scheduling scenarios; comprehensively considering the dynamic calculation results of the water balance equation and the sediment balance equation, where the dynamic calculation results include the input, output, and deposition volume of reservoir sediment within each time step; predicting the spatial distribution of reservoir sediment in each area based on the sediment deposition law and the dynamic calculation results, and determining the deposition thickness; superimposing the deposition thicknesses of multiple time steps to obtain the first predicted distribution volume of sediment deposition in the target reservoir during the entire prediction period.

[0132] Specifically, based on the topographic data of the reservoir and the basin hydrological data, a two-dimensional or three-dimensional hydrodynamic model, such as a model based on the shallow water equations, is used to simulate the flow field in the reservoir area. The flow velocity and flow direction distributions in the reservoir are determined, especially the stagnant areas where the flow velocity is low and sediment is prone to deposition. The influence of the reservoir area flow on sediment transport is simulated, and a dynamic relationship between the sediment concentration field and the flow velocity field is established to describe the sediment transport path and spatial distribution.

[0133] Operation data such as the water transfer plan, flood discharge frequency, and reservoir water level change records of the target reservoir are collected to study the influence of different scheduling rules on the reservoir flow field and water level fluctuations, and to determine the flow characteristics of the reservoir under different operation modes. The reservoir management objectives under normal operation, flood control scheduling, and other special operation scenarios are determined and their potential impact on sediment transport is evaluated.

[0134] The influence of the water level change amount is introduced into the sediment transport law and deposition model to dynamically adjust the sediment concentration, flow velocity field, and deposition rate. Combining the water transfer and flood discharge processes, analyze how these processes affect the spatial distribution and deposition amount of sediment in the reservoir area. Under different operation scenarios, simulate the dynamic changes of sediment deposition to generate the sediment deposition law for each scenario.

[0135] According to the water balance equation, the inflow water volume, outflow water volume, and reservoir capacity change in the reservoir for each time step are determined to provide the boundary conditions for sediment transport and deposition. According to the sediment balance equation, the sediment input amount, output amount, and deposition rate for each time step are calculated. Dynamically simulate the sediment balance during the reservoir operation process, and calculate the spatio-temporal changes of sediment deposition step by step in combination with the time step.

[0136] Based on the sediment transport law simulated by hydrodynamics, combined with the calculation results of water balance and sediment balance, predict the sediment deposition thickness in different regions. Considering parameters such as the particle size distribution and critical flow velocity of sediment particles, simulate the sediment deposition rate at different reservoir area locations. Using the reservoir area topographic data, superimpose the predicted sediment deposition thickness on the existing topography to generate the sediment spatial distribution map for each region.

[0137] Finally, accumulate the deposition thickness calculated for each time step to obtain the total sediment deposition amount during the entire prediction period. Perform time weighting on the sediment deposition law, focusing on the deposition changes during key periods (such as flood periods or when scheduling is frequent). By analyzing the distribution law of the total deposition amount, determine the spatial accumulation characteristics of sediment in the reservoir area and generate the first predicted distribution amount.

[0138] S140, based on the historical silt distribution amount of the target reservoir, calculate the second predicted distribution amount of the target reservoir at the prediction moment.

[0139] In a possible implementation, based on the historical silt distribution volume of the target reservoir, calculate the second predicted distribution volume of the target reservoir at the prediction moment, which specifically includes: collecting the historical silt distribution data of the target reservoir; comparing the historical silt distribution data with the first predicted distribution volume for model calibration, and through regression analysis, finding out the change trend of the reservoir sediment deposition volume over time, and calculating the change volume of the sediment volume in the historical data; based on the corresponding relationship between the first predicted distribution volume and the historical silt distribution volume, dynamically adjusting the silt deposition law at the prediction moment to obtain an adjustment factor; based on the operation records and historical silt distribution volume of the target reservoir, simulating the influence degree on the sediment distribution under different operation scenarios; introducing the adjustment factor and the influence degree into the change volume, calculating the distribution change of the silt volume under different operations, and obtaining the second predicted distribution volume.

[0140] Specifically, collect the historical silt distribution data of the target reservoir over the years, covering the silt deposition laws in different time periods (such as monthly, seasonally or annually) and different regions. The data sources can include the field investigation results of the reservoir, remote sensing monitoring data, historical silt measurement records, etc. Ensure the integrity and accuracy of the data to ensure that it can represent the silt deposition characteristics of the reservoir under different hydrological and climatic conditions.

[0141] Compare and analyze the collected historical silt distribution data with the first predicted distribution volume obtained based on the water balance equation and sediment balance equation. Through regression analysis (such as methods like linear regression, non-linear regression or least squares method), find out the differences between the historical data and the predicted distribution, and analyze and calibrate the deviation of the model. Through comparison, calculate the change trend of the sediment deposition volume over time to ensure that the prediction model can more accurately reflect the historical silt distribution law.

[0142] According to the results of the regression analysis, combined with the corresponding relationship between the first predicted distribution volume and the historical silt distribution volume, calculate an adjustment factor. This adjustment factor reflects the differences and trend changes between the historical silt volume and the predicted silt volume, and can be used to dynamically correct the silt deposition volume at the prediction moment. Dynamically adjust the silt deposition law at the prediction moment to more realistically reflect the actual deposition volume.

[0143] Collect and analyze the operation records of the target reservoir, clarify the operation parameters such as the water transfer plan, flood discharge frequency, and reservoir water level change. Simulate the influence on the sediment distribution under different operation scenarios (such as normal reservoir operation, flood operation, etc.), and use hydrodynamic and sediment deposition models to evaluate the change volume of the sediment distribution in each scenario. Combine the historical silt distribution data to quantitatively evaluate the long-term influence of each operation strategy on the sediment distribution, providing a basis for the dynamic adjustment of silt deposition.

[0144] Combine the adjustment factors obtained from the above steps with the impacts of the simulated scheduling scenarios to adjust the sediment deposition change amount, and consider the silt deposition amounts under different scheduling conditions. Based on the adjustment factors, dynamically simulate the sediment deposition and movement under different scheduling scenarios, and predict the changes in the silt amounts in each region. Calculate the spatial distribution of the sediment amounts in the reservoir area under different schedulings to obtain the silt change amounts under each scheduling scenario.

[0145] Finally, combine the calculated scheduling impacts, adjustment factors with the historical silt distribution amounts to generate the second predicted distribution amount of the target reservoir at the prediction time. The second predicted distribution amount should incorporate the reservoir operation rules, the change trends of historical data, and the silt deposition rules after dynamic adjustment. Through multiple simulations and verifications, ensure that this predicted distribution amount can accurately reflect the sediment deposition and distribution under different scheduling scenarios.

[0146] S150, if it is determined that the first predicted distribution amount is within the preset range, then determine the silt amount of the target reservoir at the prediction time as the first predicted distribution amount.

[0147] First, calculate the silt deposition rule of the reservoir at the prediction time by calculating the first predicted distribution amount. Then, set a preset range for judging whether the first predicted distribution amount is within the allowable error range. This preset range can be set according to historical silt data, the error of the physical model, or expert experience. When the first predicted distribution amount conforms to the set preset range, it indicates that the prediction result is relatively accurate and reliable, and thus determine the silt amount of the target reservoir at the prediction time as this first predicted distribution amount. If the first predicted distribution amount exceeds the preset range, further calibration or correction is required, and it may be necessary to re-analyze the input data or adjust the model parameters to ensure the accuracy and reliability of the prediction result. Finally, based on this adjusted prediction result, establish the accurate distribution amount of the reservoir silt deposition, providing a scientific basis for subsequent sediment management and scheduling decisions.

[0148] This embodiment also discloses a silt amount measurement system based on a reservoir model. Refer to Figure 2 , the system includes an acquisition module 201, a processing module 202, and an output module 203, where:

[0149] The acquisition module 201 is used to acquire the operation data, basin hydrological data, and reservoir terrain data for the target reservoir.

[0150] The processing module 202 is used to establish a water balance equation based on the operation data and basin hydrological data, and establish a sediment balance equation based on the operation data, basin hydrological data, and reservoir terrain data.

[0151] The processing module 202 is configured to establish a reservoir model through a water balance equation and a sediment balance equation, and calculate a first predicted distribution amount of sediment deposition by combining the scheduling records of the target reservoir and the sediment transport volume.

[0152] The processing module 202 is configured to calculate a second predicted distribution amount of the target reservoir at a predicted time based on the historical silt distribution amount of the target reservoir, where the predicted time is the time corresponding to when the silt amount of the target reservoir reaches the first predicted distribution amount.

[0153] The output module 203 is configured to, if it is determined that the first predicted distribution amount and the first predicted distribution amount are within a preset range, determine that the silt amount of the target reservoir at the predicted time is the first predicted distribution amount.

[0154] In a possible implementation manner, the processing module 202 is configured to calculate the inflow of the target reservoir at different times based on a basin rainfall-runoff model.

[0155] The processing module 202 is configured to perform time series analysis on the inflow to determine the change amount of the inflow water of the target reservoir.

[0156] The processing module 202 is configured to determine the change amount of the outflow water of the target reservoir according to the historical outflow law of the target reservoir.

[0157] The processing module 202 is configured to calculate the dynamic loss amount of the target reservoir based on the meteorological observation data of the target reservoir and the water surface distribution in the reservoir area, where the dynamic loss amount includes the dynamic evaporation amount and the dynamic infiltration amount.

[0158] The processing module 202 is configured to establish a water balance equation according to the balance relationship among the change amount of the inflow water, the change amount of the outflow water, and the dynamic loss amount.

[0159] In a possible implementation manner, the processing module 202 is configured to establish a water balance equation according to the balance relationship among the change amount of the inflow water, the change amount of the outflow water, and the dynamic loss amount. The water balance equation is expressed as follows:

[0160] V t+1 =V t +∫ t t+Δt Q in (t)dt-∫ t t+Δt Q out (t)dt-∫ A E(x,y,t)dxdy

[0161] Where, V t+1 is the reservoir water volume at time t + 1, V t is the reservoir water volume at time t, Q in (t) is the change amount of the inflow water, Qout (t) is the change in the amount of water out of the reservoir, and E(x, y, t) is the dynamic loss amount.

[0162] In a possible implementation, the processing module 202 is configured to introduce a seasonal variation term to simulate the fluctuations in the sediment concentration in the target reservoir during the rainy season and the dry season. The seasonal variation is described by a cosine function to reflect the fluctuations in water flow and sediment concentration over seasons, and a first simulation result is obtained.

[0163] The processing module 202 is configured to introduce a dynamic change based on rainfall events and use a Gaussian function to simulate the change in sediment input into the target reservoir after heavy rainfall, and a second simulation result is obtained.

[0164] The processing module 202 is configured to describe the process of sediment concentration changing over time in the target reservoir after rainfall in different seasons according to the first simulation result and the second simulation result, and obtain the change in sediment inflow into the target reservoir.

[0165] The processing module 202 is configured to calculate the change in sediment outflow from the target reservoir based on the relationship between sediment concentration and water depth, in combination with the water depth in the flood discharge area of the target reservoir and the overall water depth of the reservoir area of the target reservoir.

[0166] The processing module 202 is configured to determine the sediment deposition rate based on the reservoir area topography of the target reservoir.

[0167] The processing module 202 is configured to introduce sediment particle size and sediment critical velocity and calculate the deposition change amount of sediment with different particle sizes at different flow velocities.

[0168] The processing module 202 is configured to simulate the movement and deposition laws of sediment based on the flow velocity and flow energy.

[0169] The processing module 202 is configured to determine the dynamic sediment deposition change amount of the target reservoir according to the sediment deposition rate, the deposition change amount, and the movement and deposition laws of sediment.

[0170] The processing module 202 is configured to establish a sediment balance equation for the target reservoir according to the sediment inflow change amount, the sediment inflow change amount, and the dynamic sediment deposition change amount.

[0171] In a possible implementation, the processing module 202 is configured to establish a sediment balance equation for the target reservoir according to the sediment inflow change amount, the sediment outflow change amount, and the dynamic sediment deposition change amount, which is specifically expressed as follows:

[0172]

[0173] Among them, S is the sediment storage in the reservoir, C s,in (t) is the sediment inflow change amount, C s,out(t) is the change in sediment volume out of the reservoir, D sediment (t) is the change in dynamic sediment deposition.

[0174] In a possible implementation, the processing module 202 is configured to use a hydrodynamic model to simulate the water flow field in the reservoir area of the target reservoir, determine the sediment transport path and stagnant area, and obtain the sediment transport law.

[0175] The processing module 202 is configured to dynamically adjust the sediment transport law and deposition model according to the change in water level of the target reservoir, calculate the impact of each scheduling on the sediment distribution, and simulate the sediment deposition law under different scheduling scenarios.

[0176] The processing module 202 is configured to comprehensively calculate the dynamic results of the water balance equation and the sediment balance equation, and the dynamic calculation results include the input, output, and deposition amounts of reservoir sediment within each time step.

[0177] The processing module 202 is configured to predict the spatial distribution of sediment in each area of the reservoir area based on the sediment deposition law and the dynamic calculation results, and determine the deposition thickness.

[0178] The processing module 202 is configured to stack the deposition thicknesses of multiple time steps to obtain the first predicted distribution amount of sediment deposition in the target reservoir during the entire prediction period.

[0179] In a possible implementation, the acquisition module 201 is configured to collect historical silt distribution data of the target reservoir.

[0180] The processing module 202 is configured to compare the historical silt distribution data with the first predicted distribution amount for model calibration, find the change trend of the reservoir sediment deposition amount over time through regression analysis, and calculate the change amount of the sediment amount in the historical data.

[0181] The output module 203 is configured to dynamically adjust the silt deposition law at the prediction moment based on the correspondence between the first predicted distribution amount and the historical silt distribution amount to obtain an adjustment factor.

[0182] The processing module 202 is configured to simulate the influence degree on the sediment distribution under different scheduling scenarios based on the scheduling records of the target reservoir and the historical silt distribution amount.

[0183] The output module 203 is configured to introduce the adjustment factor and the influence degree into the change amount, calculate the distribution change of the silt amount under different scheduling, and obtain the second predicted distribution amount.

[0184] It should be noted that: when the system provided in the above embodiments realizes its functions, only the division of the above functional modules is used for illustration. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept. For the specific implementation process, please refer to the method embodiments, which will not be elaborated here.

Claims

1. A method for measuring silt volume based on a reservoir model, characterized in that: The method comprises: Obtaining operational data, watershed hydrological data, and reservoir topographic data for target reservoirs; Based on the operation data and the basin hydrological data, a water balance equation is established; based on the operation data, the basin hydrological data and the reservoir topographic data, a sediment balance equation is established; A reservoir model is established by using the water balance equation and the sediment balance equation, and a first predicted distribution amount of sediment deposition is calculated in combination with the dispatching record and sediment transport amount of the target reservoir; Based on the historical silt distribution amount of the target reservoir, calculating a second predicted distribution amount of the target reservoir at a predicted time, the predicted time being the time when the silt amount of the target reservoir reaches the time corresponding to the first predicted distribution amount; If it is determined that the first predicted distribution amount and the first predicted distribution amount are within a preset range, the silt amount of the target reservoir at the predicted time is determined to be the first predicted distribution amount.

2. A method for measuring silt volume based on a reservoir model according to claim 1, characterized in that: The establishing of a water balance equation based on the operation data and the basin hydrological data specifically includes: Calculate the inflow of the target reservoir at different times based on the basin rainfall-runoff model; Performing a time series analysis on the inflow flow to determine the change in the inflow water volume of the target reservoir; Determining the change in the outflow water volume of the target reservoir according to the historical outflow law of the target reservoir; Based on the meteorological observation data of the target reservoir and the water surface distribution of the reservoir area, the dynamic loss of the target reservoir is calculated, and the dynamic loss includes dynamic evaporation and dynamic infiltration; The water balance equation is established based on the balance relationship between the change in the amount of water entering the reservoir, the change in the amount of water leaving the reservoir, and the dynamic loss.

3. The method for measuring silt volume based on a reservoir model according to claim 2, characterized in that: The water balance equation is established according to the balance relationship between the change in the amount of water entering the reservoir, the change in the amount of water leaving the reservoir, and the dynamic loss. The water balance equation is expressed as follows: Among them, V t+1 is the reservoir water volume at time t+1, V t is the reservoir water volume at time t, Q in (t) is the change in the amount of water entering the reservoir, Q out (t) is the change in the outflow water volume, and E(x,y,t) is the dynamic loss volume.

4. The method for measuring silt volume based on a reservoir model according to claim 1, characterized in that: The establishing of a sediment balance equation based on the operation data, the watershed hydrological data and the reservoir topographic data specifically includes: A seasonal variation term is introduced to simulate the fluctuation of sediment concentration entering the target reservoir during the rainy season and the dry season. The seasonal variation is described by a cosine function to reflect the fluctuation of water flow and sediment concentration with seasonal changes, thereby obtaining a first simulation result. Introducing dynamic changes based on rainfall events, using a Gaussian function to simulate changes in sediment input into the target reservoir after heavy rainfall, and obtaining a second simulation result; According to the first simulation result and the second simulation result, describe the process of the sediment concentration of the target reservoir changing with time after rainfall in different seasons, and obtain the change amount of sediment entering the target reservoir; Based on the relationship between sediment concentration and water depth, combined with the water depth of the flood discharge area of ​​the target reservoir and the overall water depth of the reservoir area of ​​the target reservoir, the change in sediment discharge from the target reservoir is calculated; Determining a sediment deposition rate based on the reservoir topography of the target reservoir; The sediment particle size and sediment critical velocity are introduced to calculate the deposition variation of sediments with different particle sizes at different flow velocities. Based on flow velocity and flow energy, simulate the movement and deposition of sediment; Determining the dynamic sediment deposition change of the target reservoir according to the sediment deposition rate, the deposition change, and the movement and deposition law of the sediment; A sediment balance equation of the target reservoir is established according to the change in the amount of sediment entering the reservoir, the change in the amount of sediment entering the reservoir, and the change in the dynamic sediment deposition.

5. The method for measuring silt volume based on a reservoir model according to claim 4, characterized in that: The sediment balance equation of the target reservoir is established according to the change in the inflow sediment, the change in the outflow sediment and the change in the dynamic sediment deposition, which is specifically expressed as follows: Among them, S is the sediment storage in the reservoir, C s,in (t) is the change of sediment entering the reservoir, C s,out (t) is the change of sediment discharged from the reservoir, D sediment (t) is the dynamic sediment deposition change.

6. The method for measuring silt volume based on a reservoir model according to claim 1, characterized in that: The step of establishing a reservoir model by using the water balance equation and the sediment balance equation, and calculating a first predicted distribution of sediment deposition in combination with the dispatching record and sediment transport volume of the target reservoir, specifically includes: Using a hydrodynamic model, the water flow field of the target reservoir is simulated to determine the sediment transport path and stagnation area, and to obtain the sediment transport law; According to the water level change of the target reservoir, the sediment transport law and the sedimentation model are dynamically adjusted, the impact of each scheduling on the sediment distribution is calculated, and the sediment deposition law under different scheduling scenarios is simulated; The dynamic calculation results of the integrated water balance equation and the sediment balance equation include the input, output and deposition of reservoir sediment in each time step; Based on the sediment deposition law and the dynamic calculation results, predict the spatial distribution of sediment in each area of ​​the reservoir and determine the deposition thickness; The sediment thicknesses of multiple time steps are superimposed to obtain a first predicted distribution of sediment deposition in the target reservoir during the entire prediction period.

7. The method for measuring silt volume based on a reservoir model according to claim 1, characterized in that: The calculating, based on the historical silt distribution amount of the target reservoir, a second predicted distribution amount of the target reservoir at the predicted time specifically includes: Collecting historical silt distribution data for the target reservoir; Comparing the historical silt distribution data with the first predicted distribution amount to perform model calibration, finding out the change trend of reservoir sediment deposition over time through regression analysis, and calculating the change amount of sediment in the historical data; Based on the corresponding relationship between the first predicted distribution amount and the historical silt distribution amount, dynamically adjust the silt deposition law at the prediction moment to obtain an adjustment factor; Based on the dispatching records and historical silt distribution of the target reservoir, simulate the impact of different dispatching scenarios on sediment distribution; The adjustment factor and the degree of influence are introduced into the variation, and the distribution variation of the sludge amount under different scheduling is calculated to obtain the second predicted distribution amount.

8. A silt volume measurement system based on a reservoir model, characterized in that: The system comprises an acquisition module (201), a processing module (202) and an output module (203), wherein: The acquisition module (201) is used to acquire operation data, watershed hydrological data and reservoir topographic data for a target reservoir; The processing module (202) is used to establish a water balance equation based on the operation data and the watershed hydrological data, and to establish a sediment balance equation based on the operation data, the watershed hydrological data and the reservoir topographic data; The processing module (202) is used to establish a reservoir model through the water balance equation and the sediment balance equation, and calculate a first predicted distribution amount of sediment deposition in combination with the dispatching record and sediment transport amount of the target reservoir; The processing module (202) is used to calculate a second predicted distribution amount of the target reservoir at a predicted time based on the historical silt distribution amount of the target reservoir, wherein the predicted time is the time when the silt amount of the target reservoir reaches the time corresponding to the first predicted distribution amount; The output module (203) is used to determine that the silt volume of the target reservoir at the prediction time is the first predicted distribution volume if it is determined that the first predicted distribution volume is within a preset range.

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