A method and system for measuring silt volume based on a reservoir model

Abstract

This application provides a method and system for measuring sediment volume based on a reservoir model, relating to the technical field of hydrological measurement. The method includes: acquiring operational data, watershed hydrological data, and reservoir topographic data for a target reservoir; establishing water balance equations and sediment balance equations based on the operational data, watershed hydrological data, and reservoir topographic data; establishing a reservoir model using the water balance equations and sediment balance equations, and calculating a first predicted distribution of sediment deposition by combining the target reservoir's scheduling records and sediment transport; calculating a second predicted distribution of sediment at the prediction time based on the historical sediment distribution of the target reservoir; and determining the sediment volume of the target reservoir at the prediction time as the first predicted distribution if the first predicted distribution is within a preset range. This application improves the accuracy of reservoir sediment volume prediction by introducing a coupling mechanism among multiple factors related to reservoir sediment volume prediction.

Description

Technical Field

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

[0002] Reservoir siltation prediction is the process of estimating future siltation levels by analyzing changes in reservoir sediment deposition, considering various influencing factors such as hydrology, geology, meteorology, and operational conditions, and utilizing methods such as mathematical modeling, statistical analysis, or machine learning. Its core purpose is to provide a scientific basis for reservoir operation and management, optimize scheduling plans, extend reservoir lifespan, and reduce impacts on the downstream environment. Commonly used methods include physical models, empirical formulas, and big data-based artificial intelligence technologies, such as neural networks and time series analysis.

[0003] Existing methods for predicting reservoir sediment load often tend to consider individual influencing factors independently, such as rainfall, inflow sediment concentration, and reservoir operation mode, while neglecting the coupling relationships and nonlinear interactions between these factors. This simplification may lead to models that fail to fully capture the complex dynamic process of sediment deposition, reducing the accuracy and applicability of predictions. Therefore, in-depth research into the coupling mechanisms between multiple factors and the introduction of modeling methods capable of handling complex nonlinear relationships are important directions for improving prediction accuracy. Summary of the Invention

[0004] This application provides a method and system for measuring silt volume based on a reservoir model. By introducing a coupling mechanism among multiple factors related to reservoir silt volume prediction, the accuracy of reservoir silt volume prediction is improved.

[0005] The first aspect of this application provides a method for measuring silt volume based on a reservoir model, the method comprising:

[0006] Acquire operational data, watershed hydrological data, and reservoir topographic data for the target reservoir;

[0007] Based on the operational data and the watershed hydrological data, a water balance equation is established; based on the operational data, the watershed hydrological data, and the reservoir topographic data, a sediment balance equation is established.

[0008] A reservoir model is established using the water balance equation and the sediment balance equation. By combining the scheduling records and sediment transport of the target reservoir, the first predicted distribution of sediment deposition is calculated.

[0009] Based on the historical silt distribution of the target reservoir, a second predicted distribution of the target reservoir at the predicted time is calculated, wherein the predicted time is the time when the silt volume of the target reservoir reaches the first predicted distribution.

[0010] If it is determined that the first predicted distribution amount and the second predicted distribution amount are within a preset range, then the amount of silt in the target reservoir at the predicted time is determined to be the first predicted distribution amount.

[0011] Based on the above technical solutions, preferably, the step of establishing a water balance equation based on the operational data and the watershed hydrological data specifically includes:

[0012] The inflow rate to the target reservoir at different times was calculated based on the watershed rainfall-runoff model.

[0013] Time series analysis is performed on the inflow to determine the change in inflow to the target reservoir;

[0014] Based on the historical outflow patterns of the target reservoir, determine the change in outflow from the target reservoir;

[0015] Based on the meteorological observation data and water surface distribution of the target reservoir, the dynamic loss of the target reservoir is calculated, including dynamic evaporation and dynamic infiltration.

[0016] Based on the balance relationship between the changes in inflow water, the changes in outflow water, and the dynamic loss, the water balance equation is established.

[0017] Based on the above technical solution, preferably, the water balance equation is established according to the balance relationship between the changes in inflow water, the changes in outflow water, and the dynamic loss, and the water balance equation is expressed as follows:

[0018]

[0019] Among them, V t+1 Let V be the water volume in the reservoir at time t+1. t Let Q be the water volume in the reservoir at time t. in (t) represents the change in inflow water, Q out (t) represents the change in outflow water, and E(x,y,t) represents the dynamic loss.

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

[0021] A seasonal variation term is introduced to simulate the fluctuation of sediment concentration entering the target reservoir during the rainy and dry seasons. The seasonal variation is described by a cosine function, reflecting the fluctuation of water flow and sediment concentration with seasonal changes, and the first simulation result is obtained.

[0022] By introducing dynamic changes based on rainfall events, a Gaussian function is used to simulate the changes in sediment input to the target reservoir after heavy rainfall, resulting in a second simulation result.

[0023] Based on the first simulation results and the second simulation results, the process of sediment concentration change over time after rainfall in the target reservoir in different seasons is described, and the change in sediment inflow into the target reservoir is obtained.

[0024] Based on the relationship between sediment concentration and water depth, and combined with the water depth of the spillway area of ​​the target reservoir and the overall water depth of the reservoir area, the change in sediment discharge from the target reservoir is calculated.

[0025] Based on the reservoir topography of the target reservoir, the sediment deposition rate is determined;

[0026] By introducing the particle size and critical velocity of sediment, the depositional variation of sediment with different particle sizes at different flow velocities is calculated.

[0027] Based on flow velocity and flow energy, the movement and deposition patterns of sediment are simulated.

[0028] Based on the sediment deposition rate, the sedimentation change, and the movement and deposition patterns of the sediment, the dynamic sediment deposition change of the target reservoir is determined.

[0029] Based on the changes in inflow sediment, the changes in outflow sediment, and the changes in dynamic sediment deposition, a sediment balance equation for the target reservoir is established.

[0030] Based on the above technical solutions, preferably, the sediment balance equation for the target reservoir is established according to the changes in inflow sediment, the changes in outflow sediment, and the changes in dynamic sediment deposition, as specifically expressed below:

[0031]

[0032] Where S represents the sediment storage in the reservoir, and C s,in (t) represents the amount of sediment entering the reservoir, C s,out (t) represents the amount of sediment discharged from the reservoir, D sediment (t) represents the dynamic changes in sediment deposition.

[0033] Based on the above technical solutions, preferably, the step of establishing a reservoir model using the water balance equation and the sediment balance equation, and calculating the first predicted distribution of sediment deposition by combining the scheduling records and sediment transport patterns of the target reservoir, specifically includes:

[0034] Using a hydrodynamic model, the flow field of the target reservoir is simulated to determine the transport paths and stagnation areas of sediment, and the sediment transport patterns are obtained.

[0035] Based on the water level change of the target reservoir, the sediment transport pattern and sedimentation model are dynamically adjusted, the impact of each scheduling on sediment distribution is calculated, and the sediment deposition pattern under different scheduling scenarios is simulated.

[0036] The dynamic calculation results of the combined water balance equation and sediment balance equation include the input, output and deposition of sediment in the reservoir at each time step.

[0037] Based on the aforementioned sediment deposition patterns and the aforementioned dynamic calculation results, the spatial distribution of sediment in various regions of the reservoir area is predicted, and the deposition thickness is determined.

[0038] By superimposing the sediment thicknesses at multiple time steps, the first predicted distribution of sediment deposition in the target reservoir during the entire prediction period is obtained.

[0039] Based on the above technical solutions, preferably, the step of calculating the second predicted distribution of the target reservoir at the predicted time based on the historical silt distribution of the target reservoir specifically includes:

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

[0041] The historical silt distribution data is compared with the first predicted distribution amount for model calibration. Through regression analysis, the trend of silt deposition in the reservoir over time is identified, and the change in silt amount in historical data is calculated.

[0042] Based on the correspondence between the first predicted distribution and the historical silt distribution, the silt deposition pattern at the predicted time is dynamically adjusted to obtain the adjustment factor.

[0043] Based on the scheduling records and historical silt distribution of the target reservoir, the impact of different scheduling scenarios on sediment distribution is simulated.

[0044] By introducing the adjustment factor and the degree of influence into the change amount, the distribution change of silt volume under different scheduling is calculated to obtain the second predicted distribution amount.

[0045] A second aspect of this application provides a siltation measurement system based on a reservoir model, the system comprising an acquisition module, a processing module, and an output module, wherein:

[0046] The acquisition module is used to acquire operational data, watershed hydrological data, and reservoir topographic data for the target reservoir.

[0047] The processing module is used to establish a water balance equation based on the operational data and the watershed hydrological data, and to establish a sediment balance equation based on the operational data, the watershed hydrological data, and the reservoir topographic 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 of sediment deposition by combining the scheduling records and sediment transport patterns of the target reservoir.

[0049] The processing module is used to calculate the second predicted distribution amount of the target reservoir at the 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 first predicted distribution amount.

[0050] The output module is used to determine the amount of silt in the target reservoir at the prediction time as the first predicted distribution if the first predicted distribution and the second predicted distribution are within a preset range.

[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 the watershed rainfall-runoff model;

[0052] The processing module is used to perform time series analysis on the inflow to determine the change in inflow to the target reservoir.

[0053] The processing module is used to determine the change in outflow water of the target reservoir based on the historical outflow patterns of the target reservoir.

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

[0055] The processing module is used to establish the water balance equation based on the balance relationship between the changes in inflow water, the changes in outflow water, and the dynamic loss.

[0056] Based on the above technical solution, preferably, the processing module is used to establish the water balance equation according to the balance relationship between the change in inflow water, the change in outflow water, and the dynamic loss, and the water balance equation is expressed as follows:

[0057]

[0058] Among them, V t+1 Let V be the water volume in the reservoir at time t+1. t Let Q be the water volume in the reservoir at time t.in (t) represents the change in inflow water, Q out (t) represents the change in outflow water, and E(x,y,t) represents the dynamic loss.

[0059] Based on the above technical solutions, preferably, the processing module is used to introduce a seasonal variation term to simulate the fluctuation of sediment concentration entering the target reservoir during the rainy season and dry season. The seasonal variation is described by a cosine function to reflect the fluctuation of water flow and sediment concentration with seasonal changes, and the first simulation result is obtained.

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

[0061] The processing module is used to describe the process of sediment concentration change over time after rainfall in the target reservoir in different seasons based on the first simulation results and the second simulation results, and to obtain the change in sediment inflow into the target reservoir.

[0062] The processing module is used to calculate the change in sediment discharge from 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.

[0063] The processing module is used to determine the sediment deposition rate based on the reservoir topography of the target reservoir.

[0064] The processing module is used to introduce the particle size of sediment and the critical flow velocity of sediment, and calculate the depositional changes of sediment with different particle sizes at different flow velocities.

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

[0066] The processing module is used to determine the dynamic sediment deposition change of the target reservoir based on the sediment deposition rate, the sedimentation change, and the movement and deposition patterns of the sediment.

[0067] The processing module is used to establish the sediment balance equation of the target reservoir based on the changes in inflow sediment, the changes in inflow sediment, and the changes in dynamic sediment deposition.

[0068] Based on the above technical solution, preferably, the processing module is used to establish the sediment balance equation of the target reservoir according to the changes in inflow sediment, the changes in outflow sediment, and the changes in dynamic sediment deposition, as specifically expressed as follows:

[0069]

[0070] Where S represents the sediment storage in the reservoir, and C s,in (t) represents the change in sediment inflow, C s,out (t) represents the change in sediment outflow, D sediment (t) represents the dynamic changes in sediment deposition.

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

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

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

[0074] The processing module is used to predict the spatial distribution of sediment in various regions of the reservoir area and determine the sediment thickness based on the sediment deposition patterns and the dynamic calculation results.

[0075] The processing module is used to superimpose the deposition thicknesses of multiple time steps to obtain the first predicted distribution 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 historical silt distribution data of the target reservoir;

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

[0078] The output module is used to dynamically adjust the silt deposition pattern at the prediction time based on the correspondence between the first predicted distribution amount and the historical silt distribution amount, and obtain the adjustment factor.

[0079] The processing module is used to simulate the impact of different scheduling scenarios on sediment distribution based on the scheduling records and historical silt distribution of the target reservoir.

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

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

[0082] 1. This application improves the accuracy of reservoir sedimentation prediction by introducing a multi-factor coupling mechanism. Specifically, firstly, by establishing water balance and sediment balance equations and combining watershed hydrological data, operational data, and reservoir topographic data, the movement patterns of water flow and sediment within the reservoir are accurately simulated. Furthermore, by incorporating reservoir scheduling records, sediment transport patterns, and historical sediment distribution into the model, the impact of dynamic changes in reservoir operation on sediment deposition is considered. This multi-factor coupling mechanism can more comprehensively reflect the physical processes within the reservoir and accurately simulate sediment input, output, and deposition distribution under different scheduling scenarios, thereby improving the accuracy and reliability of the prediction. Especially when considering the influence of seasonal variations, flood control scheduling, and adjustments to historical data, it ensures consistency between the predicted results and the actual sedimentation volume.

[0083] 2. By incorporating factors such as watershed rainfall-runoff models, time series analysis, and historical outflow patterns from the reservoir, the water volume changes in the reservoir can be comprehensively and accurately simulated. Furthermore, by considering dynamic losses such as dynamic evaporation and infiltration, and combining this with meteorological observation data and the reservoir's water surface distribution, the accuracy of the reservoir's 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 changes in sediment within the reservoir can be accurately simulated and predicted. Introducing cosine and Gaussian functions to simulate sediment concentration fluctuations during the rainy and dry seasons, as well as sediment input after heavy rainfall, provides a more comprehensive reflection of sediment input variations. Simultaneously, considering factors such as reservoir topography, flow velocity, and flow energy, the simulation of sediment movement and deposition improves the accuracy of sediment deposition rate and spatial distribution predictions. 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 sediment balance equation, and comprehensively considering the reservoir's scheduling records, hydrodynamic models, sediment transport patterns, and the impact of different scheduling scenarios on sediment distribution, the sediment deposition process within the reservoir can be accurately simulated and predicted. By dynamically adjusting the sediment transport patterns and sedimentation models, and combining the calculation results at each time step, the spatial distribution and depositional thickness of sediment in different areas of the reservoir are accurately described. Finally, by superimposing the depositional thicknesses at multiple time steps, the first predicted distribution of sediment deposition over the entire prediction period is obtained.

[0086] 5. By combining historical silt distribution data with the first predicted distribution, regression analysis and dynamic adjustment methods can effectively calibrate and optimize the sediment deposition prediction model. Through comprehensive analysis of historical silt distribution and scheduling records, the silt deposition patterns at the prediction time are dynamically adjusted, thereby predicting changes in sediment distribution under different scheduling scenarios. Introducing adjustment factors and the influence of simulated scheduling scenarios makes the prediction results more accurate and reliable, ultimately yielding a more accurate second predicted distribution. Attached Figure Description

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

[0088] Figure 2 This is a schematic diagram of a silt measurement system based on a reservoir model disclosed in an embodiment of this application;

[0089] Explanation of reference numerals in the attached diagram: 201, acquisition module; 202, processing module; 203, output module. Detailed Implementation

[0090] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

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

[0092] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0093] Reservoir sedimentation prediction aims to estimate future sedimentation volumes by combining multiple factors such as hydrology, geology, and meteorology, using mathematical modeling or artificial intelligence techniques, to support reservoir management and scheduling optimization. However, existing methods often consider each factor independently, neglecting their coupling relationships and nonlinear interactions, making it difficult to comprehensively capture the complex dynamics of sediment deposition. Therefore, studying multi-factor coupling mechanisms and introducing modeling methods to handle nonlinear relationships is an important direction for improving prediction accuracy.

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

[0095] S110: Acquire operational data, watershed hydrological data, and reservoir topographic data for the target reservoir.

[0096] The silt measurement method based on a reservoir model disclosed in this application is applied to a server. The server includes, but is not limited to, electronic devices such as mobile phones, tablets, wearable devices, and PCs (Personal Computers), and can also be a backend server running a silt measurement method based on a reservoir model. The server can be implemented using a standalone server or a server cluster composed of multiple servers.

[0097] Retrieve reservoir scheduling records (such as gate opening time, flood discharge volume, and reservoir water level changes), daily operation logs, and historical drainage flows from reservoir management departments or operating units. Real-time operational status data can be collected in conjunction with automated monitoring equipment (such as water level gauges, flow meters, and telemetry devices). Data on regional rainfall, runoff, inflow, and sediment concentration in the reservoir can be obtained from watershed hydrological stations. When necessary, remote sensing technology and hydrological models can be used to supplement data in monitoring blind spots to ensure comprehensive and accurate watershed hydrological information. Combine high-precision depth sounders and UAV remote sensing mapping technology to collect reservoir bottom elevation data and topographic change information. Simultaneously, historical topographic maps or digital elevation models (DEMs) can be used to calibrate current topographic change trends, forming accurate basic topographic data.

[0098] S120 establishes a water balance equation based on operational data and watershed hydrological data, and a sediment balance equation based on operational data, watershed hydrological data, and reservoir topographic data.

[0099] In one possible implementation, the watershed hydrological data includes operational data and watershed hydrological data of the target reservoir. A water balance equation is established, specifically including: calculating the inflow to the target reservoir at different times based on a watershed rainfall-runoff model; performing time-series analysis on the inflow to determine the change in inflow to the target reservoir; determining the change in outflow to the target reservoir based on historical outflow patterns; calculating the dynamic loss to the target reservoir based on meteorological observation data and water surface distribution in the reservoir area, where dynamic loss includes dynamic evaporation and dynamic infiltration; and establishing a water balance equation based on the balance relationship between changes in inflow, changes in outflow, and dynamic loss.

[0100] Specifically, using watershed rainfall-runoff models, such as the SCS-CN model, HEC-HMS model, or distributed hydrological models, watershed rainfall, underlying surface conditions such as land use type, soil properties, and watershed area are used as input data to calculate runoff at different times. By simulating rainfall events and watershed confluence processes, the watershed runoff is converted into inflow into the target reservoir. Rainfall intensity and distribution are dynamically monitored to ensure that the calculations reflect actual flood peaks and dry season variations, as detailed below:

[0101]

[0102] Among them, Q in (t) represents the change in inflow water, Q base Base flow represents the inflow into the reservoir under steady-state conditions. It is typically estimated based on multi-year average flow or historical data to ensure that flow calculations have a basic reference value. Δt is the time step, α is the seasonal variation coefficient, simulating flow fluctuations during dry and rainy seasons, and T... year For a time period of one year, Q peak σ represents the peak flow rate, t0 represents the time of the peak flow, and σ represents the duration of the peak flow, reflecting the rate of flow attenuation.

[0103] In the formula, It reflects annual cyclical changes, such as seasonal flood peaks. This represents an event-driven flow that simulates the impact of sudden events (such as flood peaks or emergency flood discharges). β represents the magnitude of scheduling adjustments, and the normal distribution function describes the concentration and persistence of scheduling demands over time.

[0104] Based on preset reservoir operation rules such as flood control limit level, dead water level, flood control frequency, and downstream demand, the historical outflow patterns of the target reservoir are obtained, and the outflow at different times is calculated. For example, the outflow is increased during the flood season and decreased during the dry season to maintain reservoir storage. Simultaneously, considering the dynamic nature of regulation operations, the impact of actual scheduling decisions is simulated using scheduling algorithms (such as multi-objective optimization or simulated annealing). The outflow is dynamically adjusted according to water level changes and spillway frequency to ensure safe operation and effective resource utilization. The changes in the outflow of the target reservoir are expressed as follows:

[0105]

[0106] Among them, Q out Q represents the change in water outflow. target (t) represents the target flow rate, indicating the outflow rate under normal conditions, determined by the scheduling rules. β is a dynamic adjustment coefficient, reflecting the impact of emergency scheduling, such as the demand for flood discharge during sudden flood peaks. In the formula... The time distribution of traffic flow adjustments in some simulated emergencies.

[0107] Dynamic losses include both evaporation and infiltration. For evaporation, the Penman-Monteith method or energy balance method is used to calculate the evaporation rate based on meteorological observation data such as temperature, humidity, and wind speed, combined with the water surface distribution in the reservoir area. For infiltration, leakage losses are calculated using empirical formulas or infiltration models, taking into account the reservoir's geological conditions and water level changes. The dynamic characteristics of evaporation and infiltration changing with time and space are incorporated into the calculation to accurately describe the total losses in the reservoir, as detailed below:

[0108]

[0109] Where E(x,y,t) is the dynamic loss, and k1(t,x,y) is the dynamic adjustment coefficient corresponding to the local evaporation. local (x,y,t) represents the local evaporation rate, k2(t,x,y) represents the dynamic adjustment coefficient corresponding to the local infiltration rate, and H local (x,y,t) represents the local permeability.

[0110] The local evaporation rate can be calculated using the following formula:

[0111]

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

[0113] Finally, based on the balance relationship between changes in inflow water, changes in outflow water, and dynamic loss, a water balance equation is established, which is expressed as follows:

[0114]

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

[0116] In one possible implementation, a sediment balance equation is established based on operational data, watershed hydrological data, and reservoir topographic data. Specifically, this includes: introducing a seasonal variation term to simulate the fluctuations in sediment concentration entering the target reservoir during the rainy and dry seasons; using a cosine function to describe the seasonal variation, reflecting the fluctuations in water flow and sediment concentration with seasonal changes, thus obtaining a first simulation result; introducing dynamic changes based on rainfall events, using a Gaussian function to simulate the changes in sediment input to the target reservoir after heavy rainfall, thus obtaining a second simulation result; and based on the first and second simulation results, describing the process of sediment concentration change over time after rainfall in the target reservoir in different seasons, thus obtaining the change in sediment entering the target reservoir. Based on the relationship between sediment concentration and water depth, and combined with the water depth of the spillway area and the overall water depth of the reservoir, the change in inflow sediment of the target reservoir is calculated. Based on the topography of the reservoir area, the sediment deposition rate is determined. Introducing sediment particle size and critical sediment velocity, the depositional changes of sediment with different particle sizes at different flow velocities are calculated. Based on flow velocity and flow energy, the movement and deposition patterns of sediment are simulated. According to the sediment deposition rate, depositional patterns, and the changes in sediment movement and deposition, the dynamic sediment deposition patterns of the target reservoir are determined. Based on the changes in inflow sediment, the changes in inflow sediment, and the dynamic changes in sediment deposition, the sediment balance equation for the target reservoir is established.

[0117] Specifically, historical rainfall, runoff, and sediment concentration data for the target reservoir over many years are collected to identify typical characteristics of the rainy and dry seasons. The seasonal variation range of sediment concentration is then set using cosine function modeling. C base Let T be the average sediment concentration, α be the variation range, and T be the average sediment concentration. year The simulation period is one year. The model is used to calculate the inflow sediment concentration of the target reservoir in different seasons, and the first simulation results are obtained, which reflect the seasonal fluctuations.

[0118] Furthermore, based on multi-year watershed hydrological observation data, the sediment concentration changes during the rainy and dry seasons of the target reservoir watershed were extracted, and a cosine function was used to simulate the seasonal variation curve. By adjusting the amplitude and the corresponding annual variation, the periodic fluctuations of sediment concentration and flow with the seasons were described, forming the first simulation result. The short-term impact of heavy rainfall events on sediment concentration was analyzed. A Gaussian function was used to simulate the temporal distribution changes of sediment concentration after a rainfall event, with parameters including rainfall intensity, duration, and sediment response delay time. The short-term dynamic effects of heavy rainfall were superimposed to form the second simulation result. The first simulation result (seasonal variation) and the second simulation result (dynamic effects of rainfall events) were superimposed to obtain a complete sediment concentration variation curve. The inflow sediment changes at different times were calculated using inflow data.

[0119]

[0120] Among them, C s,in (t) represents the change in sediment inflow, C base The basic sediment variation reflects the sediment load under stable conditions, γ1 is the seasonal variation amplitude coefficient, and t rain For the duration of the heavy rainfall event, σ rain The duration of rainfall intensity reflects the rate of increase or decrease of sediment over time, and λ is the amplitude coefficient, reflecting the influence of season on sediment concentration, such as high concentration during the rainy season and low concentration during the dry season. year The time period is within one year.

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

[0122]

[0123] Among them, C s,out (t) represents the change in sediment output. To illustrate the relationship between sediment concentration and water depth, H dam (t) is the water depth in the flood discharge area of ​​the target reservoir, H total (t) represents the overall water depth of the target reservoir. Concentration is inversely proportional to water depth; the shallower the water, the higher the sediment concentration. Peak flow adjustment term. Used to simulate the instantaneous adjustment of sediment concentration caused by sudden flood discharge, β represents the adjustment range, σ peak Control the duration.

[0124] Collect topographic data of the reservoir bottom (including slope, water depth distribution, etc.) and, combined with sediment particle characteristics, estimate the deposition efficiency in different areas. Use the deposition rate formula to calculate the dynamic changes in sediment deposition. Stransport Let be the sediment transport rate, and k be the deposition efficiency coefficient. The spatial distribution of deposition rate is determined to simulate sediment deposition patterns within the reservoir area. Based on sediment sample analysis, the particle size distribution of the sediment is determined. A critical flow velocity model is introduced. (critical flow velocity u) c The deposition conditions of sediments with different particle sizes (proportional to particle size d) were calculated. The deposition efficiency of sediments with different particle sizes was simulated when the flow velocity u was below the critical velocity. Flow velocity and hydrodynamic data of the reservoir area were collected, and the sediment transport capacity T_s∝u^2⋅H was calculated, reflecting the relationship between water kinetic energy and sediment transport. Sediment movement was divided into three modes: suspension, rolling, and deposition. Sediment movement equations were established to simulate the movement paths and deposition processes of sediments in different regions, as specifically represented below:

[0125]

[0126] Where: D sediment (t) represents the dynamic variation in sediment deposition, ϕ(x,y) is the spatial distribution coefficient, reflecting the probability of sediment deposition in different areas of the reservoir bottom, which is usually related to topography, and u(x,y,t) is the flow velocity, simulating the influence of hydrodynamics on sediment deposition. crit This is the critical flow velocity; above this velocity, sediment is difficult to deposit. p d represents the diameter of the sediment particles. cri t is the critical particle diameter for sediment deposition (related to hydrodynamics). This formula combines spatial distribution and particle characteristics to simulate the sediment deposition pattern and is suitable for three-dimensional sediment distribution modeling.

[0127] Finally, based on the principle of sediment balance, the sediment conservation equation is established:

[0128]

[0129] Where S represents the sediment storage in the reservoir, and C s,in (t) represents the change in sediment inflow, C s,out (t) represents the change in sediment outflow, D sediment (t) represents the dynamic changes in sediment deposition. The sediment storage and distribution in the reservoir are dynamically calculated by numerical integration over a time step Δt.

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

[0131] In one possible implementation, a reservoir model is established using water balance and sediment balance equations. Combined with the target reservoir's scheduling records and sediment transport volume, the first predicted distribution of sediment deposition is calculated. Specifically, this includes: using a hydrodynamic model to simulate the reservoir's flow field, determining sediment transport paths and stagnation areas, and obtaining sediment transport patterns; dynamically adjusting the sediment transport patterns and deposition model based on the target reservoir's water level changes, calculating the impact of each scheduling on sediment distribution, and simulating sediment deposition patterns under different scheduling scenarios; integrating the dynamic calculation results of the water balance and sediment balance equations, including the reservoir's sediment input, output, and deposition volume at each time step; predicting the spatial distribution of sediment in various regions of the reservoir based on sediment deposition patterns and dynamic calculation results, and determining the deposition thickness; and superimposing the deposition thicknesses from multiple time steps to obtain the first predicted distribution of sediment deposition in the target reservoir throughout the entire prediction period.

[0132] Specifically, based on reservoir topographic data and watershed hydrological data, two-dimensional or three-dimensional hydrodynamic models, such as models based on shallow water equations, are used to simulate the reservoir flow field. The distribution of flow velocity and direction within the reservoir is determined, especially stagnant areas with low flow velocity and easy sediment deposition. The impact of reservoir flow on sediment transport is simulated, and the dynamic relationship between the sediment concentration field and the flow velocity field is established to describe the sediment transport path and spatial distribution.

[0133] This study collects operational data on the target reservoir, including water diversion plans, flood discharge frequencies, and reservoir water level changes. It investigates the impact of different scheduling rules on the reservoir's flow field and water level fluctuations, and determines the reservoir's flow characteristics under different operating modes. The study also identifies reservoir management objectives and their potential impacts on sediment transport under routine operation, flood control, and other special operating scenarios.

[0134] The influence of water level changes is incorporated into sediment transport patterns and sedimentation models, dynamically adjusting sediment concentration, velocity field, and deposition rate. By combining water regulation and flood discharge processes, the spatial distribution and deposition amount of sediment within the reservoir area are analyzed. Under different operational scenarios, the dynamic changes in sediment deposition are simulated, generating sediment deposition patterns for each scenario.

[0135] Based on the water balance equation, the inflow, outflow, and reservoir capacity changes of the reservoir are determined at each time step, providing boundary conditions for sediment transport and deposition. Based on the sediment balance equation, the sediment input, output, and deposition rate are calculated at each time step. The sediment balance during reservoir operation is dynamically simulated, and the spatiotemporal changes in sediment deposition are calculated step-by-step in conjunction with the time step.

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

[0137] Finally, the sediment thicknesses calculated within each time step are accumulated to obtain the total sediment deposition over the entire prediction period. Time-weighted processing is applied to the sediment deposition patterns, with a focus on sedimentary changes during key periods (such as flood seasons or periods of frequent water management). By analyzing the distribution patterns of the total sediment deposition, the spatial accumulation characteristics of sediment in the reservoir area are determined, generating the first predicted distribution.

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

[0139] In one possible implementation, based on the historical silt distribution of the target reservoir, a second predicted distribution of the target reservoir at the predicted time is calculated. Specifically, this includes: collecting historical silt distribution data of the target reservoir; comparing the historical silt distribution data with the first predicted distribution for model calibration, identifying the trend of sediment deposition over time through regression analysis, and calculating the change in sediment volume in the historical data; dynamically adjusting the silt deposition pattern at the predicted time based on the correspondence between the first predicted distribution and the historical silt distribution to obtain an adjustment factor; simulating the impact of different scheduling scenarios on sediment distribution based on the target reservoir's scheduling records and historical silt distribution; introducing the adjustment factor and the degree of impact into the change, calculating the distribution change of silt volume under different scheduling scenarios, and obtaining the second predicted distribution.

[0140] Specifically, historical silt distribution data for the target reservoir should be collected, covering different time periods (e.g., by month, season, or year) and different regions to illustrate silt deposition patterns. Data sources may include field surveys of the reservoir, remote sensing monitoring data, and historical silt measurement records. The completeness and accuracy of the data must be ensured to guarantee that it represents the silt deposition characteristics of the reservoir under different hydrological and climatic conditions.

[0141] The collected historical silt distribution data were compared and analyzed with the first predicted distribution obtained based on the water balance equation and sediment balance equation. Regression analysis (such as linear regression, nonlinear regression, or least squares method) was used to identify the differences between the historical data and the predicted distribution, analyzing and calibrating the model's bias. Through comparison, the trend of sediment deposition over time was calculated, ensuring that the prediction model can more accurately reflect the historical silt distribution patterns.

[0142] Based on the regression analysis results and the correspondence between the first predicted silt distribution and the historical silt distribution, an adjustment factor is calculated. This adjustment factor reflects the difference and trend changes between the historical and predicted silt amounts, and can be used to dynamically correct the silt deposition amount at the prediction time. This dynamic adjustment of the silt deposition pattern at the prediction time more accurately reflects the actual deposition amount.

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

[0144] The adjustment factors obtained from the above steps are combined with the effects of simulated scheduling scenarios to adjust for changes in sediment deposition, taking into account the amount of silt deposition under different scheduling conditions. Based on the adjustment factors, the sediment deposition and movement under different scheduling scenarios are dynamically simulated to predict the changes in silt volume in each region. The spatial distribution of sediment volume in the reservoir area under different scheduling conditions is calculated to obtain the amount of silt change under each scheduling scenario.

[0145] Finally, the calculated scheduling impact, adjustment factor, and historical silt distribution are combined to generate a second predicted distribution of the target reservoir at the predicted time. This second predicted distribution should incorporate reservoir operation rules, historical data trends, and dynamically adjusted silt deposition patterns. Through multiple simulations and verifications, it is ensured that this predicted distribution accurately reflects silt 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 the amount of silt in the target reservoir at the prediction time is determined as the first predicted distribution amount.

[0147] First, the sediment deposition pattern of the reservoir at the predicted time is obtained by calculating the first predicted distribution. Then, a preset range is set to determine whether the first predicted distribution is within the allowable error range. This preset range can be set based on historical sediment data, the error of the physical model, or expert experience. When the first predicted distribution matches the preset range, the prediction result is considered relatively accurate and reliable, thus determining the amount of sediment in the target reservoir at the predicted time as the first predicted distribution. If the first predicted distribution exceeds the preset range, further calibration or correction is required, which may require reanalyzing the input data or adjusting the model parameters to ensure the accuracy and reliability of the prediction results. Finally, based on the adjusted prediction results, the accurate distribution of sediment deposition in the reservoir is established, providing a scientific basis for subsequent sediment management and scheduling decisions.

[0148] This embodiment also discloses a siltation measurement system based on a reservoir model, referring to... Figure 2 The system includes an acquisition module 201, a processing module 202, and an output module 203, wherein:

[0149] The acquisition module 201 is used to acquire operational data, watershed hydrological data, and reservoir topographic data for the target reservoir.

[0150] The processing module 202 is used to establish a water balance equation based on operational data and watershed hydrological data, and to establish a sediment balance equation based on operational data, watershed hydrological data and reservoir topographic data.

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

[0152] The processing module 202 is used to calculate the second predicted distribution of the target reservoir at the predicted time based on the historical silt distribution of the target reservoir. The predicted time is the time when the silt volume of the target reservoir reaches the first predicted distribution.

[0153] The output module 203 is used to determine the amount of silt in the target reservoir at the prediction time as the first predicted distribution if the first predicted distribution and the second predicted distribution are within a preset range.

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

[0155] The processing module 202 is used to perform time series analysis on the inflow to determine the change in inflow to the target reservoir.

[0156] The processing module 202 is used to determine the change in the outflow of the target reservoir based on the historical outflow patterns of the target reservoir.

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

[0158] The processing module 202 is used to establish a water balance equation based on the balance relationship between changes in inflow water, changes in outflow water, and dynamic loss.

[0159] In one possible implementation, the processing module 202 is used to establish a water balance equation based on the balance relationship between changes in inflow water, changes in outflow water, and dynamic loss. The water balance equation is expressed as follows:

[0160]

[0161] Among them, V t+1 Let V be the water volume in the reservoir at time t+1. t Let Q be the water volume in the reservoir at time t. in (t) represents the change in inflow water, Q out (t) represents the change in water outflow, and E(x,y,t) represents the dynamic loss.

[0162] In one possible implementation, the processing module 202 is used to introduce a seasonal variation term to simulate the fluctuation of sediment concentration entering the target reservoir during the rainy and dry seasons. The seasonal variation is described by a cosine function to reflect the fluctuation of water flow and sediment concentration with seasonal changes, and the first simulation result is obtained.

[0163] Processing module 202 is used to introduce dynamic changes based on rainfall events, use Gaussian functions to simulate changes in sediment input to the target reservoir after heavy rainfall, and obtain the second simulation results.

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

[0165] The processing module 202 is used to calculate the change in sediment discharge from 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.

[0166] Processing module 202 is used to determine the sediment deposition rate based on the reservoir topography of the target reservoir.

[0167] The processing module 202 is used to input the particle size of sediment and the critical velocity of sediment flow, and to calculate the sedimentation changes of sediment with different particle sizes at different flow velocities.

[0168] Processing module 202 is used to simulate the movement and deposition patterns of sediment based on flow velocity and flow energy.

[0169] Processing module 202 is used to determine the dynamic sediment deposition change of the target reservoir based on the sediment deposition rate, sedimentation change, and sediment movement and deposition patterns.

[0170] The processing module 202 is used to establish the sediment balance equation of the target reservoir based on the changes in inflow sediment, the changes in inflow sediment, and the changes in dynamic sediment deposition.

[0171] In one possible implementation, the processing module 202 is used to establish a sediment balance equation for the target reservoir based on the changes in inflow sediment, changes in outflow sediment, and changes in dynamic sediment deposition, as specifically expressed below:

[0172]

[0173] Where S represents the sediment storage in the reservoir, and C s,in (t) represents the change in sediment inflow, C s,out (t) represents the change in sediment outflow, D sediment (t) represents the dynamic changes in sediment deposition.

[0174] In one possible implementation, the processing module 202 is used to simulate the flow field of the target reservoir using a hydrodynamic model, determine the transport path and stagnation area of ​​sediment, and obtain the sediment transport law.

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

[0176] The processing module 202 is used to integrate the dynamic calculation results of the water balance equation and the sediment balance equation. The dynamic calculation results include the input, output and sedimentation of the reservoir sediment in each time step.

[0177] The processing module 202 is used to predict the spatial distribution of sediment in various regions of the reservoir area and determine the sediment thickness based on the sediment deposition patterns and dynamic calculation results.

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

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

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

[0181] The output module 203 is used to dynamically adjust the silt deposition pattern at the prediction time based on the correspondence between the first predicted distribution amount and the historical silt distribution amount, and obtain the adjustment factor.

[0182] The processing module 202 is used to simulate the impact of different scheduling scenarios on sediment distribution based on the scheduling records and historical silt distribution of the target reservoir.

[0183] Output module 203 is used to introduce adjustment factors and the degree of influence to the amount of change, calculate the distribution change of silt under different scheduling, and obtain the second predicted distribution.

[0184] It should be noted that the system provided in the above embodiments is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be 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, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

Claims

1. A method for measuring silt volume based on a reservoir model, characterized in that, The method includes: Acquire operational data, watershed hydrological data, and reservoir topographic data for the target reservoir; Based on the operational data and the watershed hydrological data, a water balance equation is established; based on the operational data, the watershed hydrological data, and the reservoir topographic data, a sediment balance equation is established. A reservoir model is established using the water balance equation and the sediment balance equation. By combining the scheduling records and sediment transport of the target reservoir, the first predicted distribution of sediment deposition is calculated. Based on the historical silt distribution of the target reservoir, a second predicted distribution of the target reservoir at the predicted time is calculated, wherein the predicted time is the time when the silt volume of the target reservoir reaches the first predicted distribution. If it is determined that the first predicted distribution amount and the second predicted distribution amount are within a preset range, then the amount of silt in the target reservoir at the prediction time is determined to be the first predicted distribution amount. The process involves establishing a reservoir model using the water balance equation and the sediment balance equation, and then calculating the first predicted distribution of sediment deposition based on the target reservoir's scheduling records and sediment transport volume. Specifically, this includes: Using a hydrodynamic model, the flow field of the target reservoir is simulated to determine the transport paths and stagnation areas of sediment, and the sediment transport patterns are obtained. Based on the water level change of the target reservoir, the sediment transport pattern and sedimentation model are dynamically adjusted, the impact of each scheduling on sediment distribution is calculated, and the sediment deposition pattern under different scheduling scenarios is simulated. The dynamic calculation results of the combined water balance equation and sediment balance equation include the input, output and deposition of sediment in the reservoir at each time step. Based on the aforementioned sediment deposition patterns and the aforementioned dynamic calculation results, the spatial distribution of sediment in various regions of the reservoir area is predicted, and the deposition thickness is determined. By superimposing the sediment thicknesses at multiple time steps, the first predicted distribution of sediment deposition in the target reservoir during the entire prediction period is obtained.

2. The method for measuring silt volume based on a reservoir model according to claim 1, characterized in that, The establishment of a water balance equation based on the operational data and the watershed hydrological data specifically includes: The inflow rate to the target reservoir at different times was calculated based on the watershed rainfall-runoff model. Time series analysis is performed on the inflow to determine the change in inflow to the target reservoir; Based on the historical outflow patterns of the target reservoir, determine the change in outflow from the target reservoir; Based on the meteorological observation data and water surface distribution of the target reservoir, the dynamic loss of the target reservoir is calculated, including dynamic evaporation and dynamic infiltration. Based on the balance relationship between the changes in inflow water, the changes in outflow water, and the dynamic loss, the water balance equation is established.

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 based on the balance relationship between the changes in inflow water, the changes in outflow water, and the dynamic loss. The water balance equation is expressed as follows: ； Among them, V t+1 Let V be the water volume in the reservoir at time t+1. t Let Q be the water volume in the reservoir at time t. in (t) represents the change in inflow water, Q out (t) represents the change in outflow water, and E(x,y,t) represents the dynamic loss.

4. The method for measuring silt volume based on a reservoir model according to claim 1, characterized in that, The establishment of a sediment balance equation based on the operational 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 and dry seasons. The seasonal variation is described by a cosine function, reflecting the fluctuation of water flow and sediment concentration with seasonal changes, and the first simulation result is obtained. By introducing dynamic changes based on rainfall events, a Gaussian function is used to simulate the changes in sediment input to the target reservoir after heavy rainfall, resulting in a second simulation result. Based on the first simulation results and the second simulation results, the process of sediment concentration change over time after rainfall in the target reservoir in different seasons is described, and the change in sediment inflow into the target reservoir is obtained. Based on the relationship between sediment concentration and water depth, and combined with the water depth of the spillway area of ​​the target reservoir and the overall water depth of the reservoir area, the change in sediment discharge from the target reservoir is calculated. Based on the reservoir topography of the target reservoir, the sediment deposition rate is determined; By introducing the particle size and critical velocity of sediment, the depositional variation of sediment with different particle sizes at different flow velocities is calculated. Based on flow velocity and flow energy, the movement and deposition patterns of sediment are simulated. Based on the sediment deposition rate, the sedimentation change, and the movement and deposition patterns of the sediment, the dynamic sediment deposition change of the target reservoir is determined. Based on the changes in inflow sediment, the changes in outflow sediment, and the changes in dynamic sediment deposition, a sediment balance equation for the target reservoir is established.

5. The method for measuring silt volume based on a reservoir model according to claim 4, characterized in that, The sediment balance equation for the target reservoir is established based on the changes in inflow sediment, the changes in outflow sediment, and the changes in dynamic sediment deposition, as specifically expressed below: ； Where S represents the sediment storage in the reservoir, and C s,in (t) represents the change in sediment inflow, C s,out (t) represents the change in sediment outflow, D sediment (t) represents the dynamic changes in sediment deposition.

6. The method for measuring silt volume based on a reservoir model according to claim 1, characterized in that, The calculation of the second predicted distribution of the target reservoir at the predicted time, based on the historical silt distribution of the target reservoir, specifically includes: Collect historical silt distribution data for the target reservoir; The historical silt distribution data is compared with the first predicted distribution amount for model calibration. Through regression analysis, the trend of silt deposition in the reservoir over time is identified, and the change in silt amount in historical data is calculated. Based on the correspondence between the first predicted distribution and the historical silt distribution, the silt deposition pattern at the predicted time is dynamically adjusted to obtain the adjustment factor. Based on the scheduling records and historical silt distribution of the target reservoir, the impact of different scheduling scenarios on sediment distribution is simulated. By introducing the adjustment factor and the degree of influence into the change amount, the distribution change of silt volume under different scheduling is calculated to obtain the second predicted distribution amount.

7. A silt volume measurement system based on a reservoir model, characterized in that, The system is used to perform the method as described in any one of claims 1-6, the system comprising an acquisition module (201), a processing module (202), and an output module (203), wherein: The acquisition module (201) is used to acquire operational data, watershed hydrological data, and reservoir topographic data for the target reservoir. The processing module (202) is used to establish a water balance equation based on the operating data and the watershed hydrological data, and to establish a sediment balance equation based on the operating 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 the first predicted distribution of sediment deposition by combining the scheduling records and sediment transport of the target reservoir. The processing module (202) is used to calculate the second predicted distribution of the target reservoir at the predicted time based on the historical silt distribution of the target reservoir, wherein the predicted time is the time when the silt amount of the target reservoir reaches the first predicted distribution. The output module (203) is used to determine the amount of silt in the target reservoir at the prediction time as the first predicted distribution if the first predicted distribution amount and the second predicted distribution amount are determined to be within a preset range.

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