Dynamic prediction method for accumulated erosion and deposition amount of downstream river channel of reservoir

Abstract

The invention provides a reservoir downstream river channel accumulated erosion and deposition amount dynamic prediction method, which comprises the steps of collecting hydrological data of a target river reach, and preprocessing the collected data to enable the collected data to meet preset requirements; wherein the hydrological data comprises flow data, sand content data and bed sand particle size; analyzing water and sand conditions of the target river reach in the flood season according to the preprocessed hydrological data, and determining a sand coming coefficient and water flow scouring strength in the flood season; determining the erosion and deposition amount caused by the combined action of the riverway incoming water and incoming sand and the riverbed boundary according to the incoming sand coefficient, and determining the erosion and deposition amount caused by the influence of the upstream incoming sand amount condition according to the water flow erosion strength and the bed sand particle size; and determining the accumulative erosion and deposition amount of the target river reach according to the erosion and deposition amount caused by the combined action of the riverway incoming water and sand and the riverbed boundary, the erosion and deposition amount caused by the influence of the upstream incoming sand amount condition and the time effect.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hydraulic engineering, and particularly relates to a dynamic prediction method for cumulative erosion and deposition of a reservoir downstream river channel based on multi-parameter coupling. BACKGROUND

[0002] The erosion and deposition evolution of the reservoir downstream river channel is one of the core problems in the fields of river dynamics and hydraulic engineering, which directly affects the river regime stability, navigation channel navigation, flood control safety and the sustainability of the ecological system. The scouring or deposition of the riverbed changes the river channel gradient, cross-section shape and flow pattern, and further affects the distribution of water flow and scouring capacity. The change of the depth and width of the navigation channel caused by the erosion and deposition evolution directly affects the ship's traffic capacity and shipping efficiency; the erosion and deposition process of the riverbed changes the habitat conditions of the river, affecting the habitat, reproduction and survival of aquatic organisms. Therefore, in-depth study of the erosion and deposition evolution mechanism and reasonable regulation of the erosion and deposition process have important theoretical and practical significance for guaranteeing the river regime stability, navigation channel navigation, flood control safety and the sustainability of the ecological system.

[0003] The erosion and deposition evolution of the reservoir downstream river channel is a complex problem involving multiple factors and processes, and involves multiple disciplines such as hydrodynamics, riverbed evolution, ecology and engineering management. At present, the prediction of the erosion and deposition of the downstream of the dam mainly includes three methods: empirical formula method, numerical simulation method and physical model test. The numerical simulation method couples the hydrodynamic model with the sediment transport module to simulate the erosion and deposition process by solving the control equation. Although this method has a physical basis, it simplifies the assumptions, often assumes that the bed sand gradation is fixed or only divides a limited number of groups, and ignores the dynamic feedback of bed sand coarsening / fining during the scouring process. The time discretization is biased, and the calculation is not accurate. The physical model test reproduces the river channel erosion and deposition process through a scaled flume experiment, which can intuitively reflect the mechanism, but it is difficult to satisfy the similarity law of water flow and sediment movement at the same time, resulting in distortion of the experimental conclusion when extrapolated to the prototype and large-scale experiments consuming time and cost, which cannot be used as a normal prediction tool. The empirical formula method is based on the statistical relationship established by historical observation data (such as Engelund-Hansen formula, Ackers-White formula), which estimates the sediment transport rate through a single variable of flow intensity parameter (such as shear stress, flow velocity) or sediment inflow. Compared with the above two methods, the empirical formula method is based on the statistical relationship established by historical observation data, which is simple and accurate in calculation, and can better reflect the real law. However, this method has certain limitations and problems. This method only considers a single dominant factor (such as water flow scouring capacity or sediment inflow), ignoring the synergistic or antagonistic effect between multiple parameters. For example, high sediment inflow may exacerbate deposition, but if it is accompanied by strong water flow scouring, the actual deposition amount may be lower than expected. In addition, the empirical formula needs to be calibrated for a specific river section, and the accuracy decreases when transplanted to other river basins, especially in river channels with large differences in bed sand composition, and often ignores the influence of time effect on the nonlinear transformation in the erosion and deposition process.

[0004] To solve the above problems, the existing prediction methods are mostly based on a single parameter or a simplified model, for example, a static model only considering the amount of sand or the scouring capacity of water flow, ignoring the dynamic coupling effect between multiple parameters, resulting in insufficient long-term prediction accuracy, and ignoring the nonlinear cumulative effect of time effect on the scouring and silting process. SUMMARY

[0005] To solve the above technical problems, the present application proposes a dynamic prediction method for cumulative scouring and silting volume downstream of a reservoir based on multi-parameter coupling. The core of this method is to couple multiple key parameters affecting the scouring and silting process to build a more refined dynamic prediction model, thereby effectively reflecting the spatio-temporal variation characteristics of the scouring and silting process.

[0006] In some optional embodiments, the method comprises the following steps: Collecting hydrological data of the target river section and pre-processing the collected data to meet the preset requirements; wherein the hydrological data includes flow data, sediment concentration data and bed sand particle size; According to the pre-processed hydrological data, the water and sediment conditions of the target river section during the flood season are analyzed to determine the sediment coefficient and the scouring intensity of the water flow during the flood season; According to the sediment coefficient, the scouring and silting volume caused by the joint action of the incoming water and sediment of the river channel and the riverbed boundary is determined, and according to the scouring intensity of the water flow and the bed sand particle size, the scouring and silting volume caused by the influence of the upstream sediment amount is determined; According to the scouring and silting volume caused by the joint action of the incoming water and sediment of the river channel and the riverbed boundary, the scouring and silting volume caused by the influence of the upstream sediment amount, and the time effect, the cumulative scouring and silting volume of the target river section is determined.

[0007] In some optional embodiments, according to the following formula, the cumulative scouring and silting volume of the target river section is determined according to the scouring and silting volume caused by the joint action of the incoming water and sediment of the river channel and the riverbed boundary, the scouring and silting volume caused by the influence of the upstream sediment amount, and the time effect:

[0008] In the formula, is the cumulative scouring and silting volume of the target river section; is the scouring and silting volume caused by the joint action of the incoming water and sediment of the river channel and the riverbed boundary; is the scouring and silting volume caused by the influence of the upstream sediment amount, is a coefficient, and T is the year serial number.

[0009] In some optional embodiments, according to the following formula, the scouring and silting volume caused by the joint action of the incoming water and sediment of the river channel and the riverbed boundary is determined:

[0010] In the formula, is the scouring and silting amount caused by the interaction of the incoming water and sediment and the riverbed boundary; a, b, c are coefficients; is the median particle size of the riverbed sand after the flood season; is the scouring intensity of the water flow in the flood season.

[0011] In some optional embodiments, the scouring and silting amount caused by the upstream incoming sediment amount is determined according to the following formula:

[0012] In the formula, 2 is the scouring and silting amount caused by the upstream incoming sediment amount; d is a coefficient; is the incoming sediment coefficient in the flood season, and is balanced to the order of magnitude so as to be enlarged by 10000 times.

[0013] In some optional embodiments, the incoming sediment coefficient is determined according to the following formula:

[0014] In the formula, is the incoming sediment coefficient, is the average sediment concentration, .

[0015] In some optional embodiments, the scouring intensity of the water flow is determined according to the following formula:

[0016] In the formula, F is the scouring intensity of the water flow, is the average sediment concentration, .

[0017] In some optional embodiments, the pre-processing of the collected data includes data cleaning, data standardization and data integration.

[0018] At least one embodiment of the present application also provides an electronic device, characterized by comprising: at least one processor; and a memory in communication connection with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the steps of the method for dynamically predicting the cumulative scouring and silting amount of the river channel downstream of the reservoir as described above.

[0019] At least one embodiment of the present application also provides a computer readable storage medium storing a computer program, characterized in that the computer program is executed by a processor to implement the steps of the method for dynamically predicting the cumulative scouring and silting amount of the river channel downstream of the reservoir as described above.

[0020] At least one embodiment of the present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the method for dynamically predicting the cumulative erosion and deposition amount of a river channel downstream of a reservoir as described above.

[0021] Compared with the prior art, the embodiment of the present application provides a method for dynamically predicting the cumulative erosion and deposition amount of a river channel downstream of a reservoir, which has the following beneficial effects: (1) The present application aims to overcome the problem of insufficient accuracy of traditional erosion and deposition amount prediction methods in dealing with complex water and sediment conditions, and proposes a new dynamic prediction method based on multi-parameter coupling. This method comprehensively considers key factors such as sediment concentration, flow scouring intensity, bed sediment median particle size, and time coefficient, which can more comprehensively reflect the complexity of the erosion and deposition process. Compared with traditional single-parameter methods, this method improves the prediction accuracy while significantly enhancing the adaptability of the model to different water and sediment conditions.

[0022] (2) The present application aims to overcome the deficiency of traditional erosion and deposition amount prediction methods in dynamic response capability, and proposes a new dynamic prediction method by introducing a time coefficient. This method considers the cumulative effect and hysteresis of the erosion and deposition process, which can reflect the trend of the change of erosion and deposition amount in real time, and realize the dynamic tracking of the evolution of erosion and deposition downstream of the reservoir. Compared with traditional static prediction methods, this method ensures the reliability of the results while significantly improving the prediction ability of long-term erosion and deposition changes.

[0023] (3) The present application aims to overcome the limitations of traditional erosion and deposition amount prediction methods in data requirements and computational efficiency, and proposes a new efficient prediction method based on multi-parameter coupling. This method optimizes the model structure and parameter fitting process, significantly reducing the demand for a large amount of historical data, and reducing the computational complexity. Compared with traditional methods, this method maintains high accuracy while significantly shortening the calculation time and improving the feasibility of engineering application. BRIEF DESCRIPTION OF DRAWINGS

[0024] One or more embodiments are illustrated by way of example in the accompanying drawings that are not intended to be limiting of the embodiments.

[0025] Figure 1 The flowchart of the seismic migration velocity modeling and imaging method with fusion layer information adopted by the embodiments of the present application; Figure 2 is a schematic diagram of the sediment concentration parameter obtained by the second embodiment of the present application; Figure 3 is a schematic diagram of the flow scouring intensity parameter obtained by the second embodiment of the present application; Figure 4 is a schematic diagram of the calculated value and the actual value obtained by the second embodiment of the present application; Figure 5 are the coefficient values of the parameter calculation formula obtained in Embodiment Two of the present application. DETAILED DESCRIPTION

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the embodiments of the present application will be described in detail below with reference to the drawings. However, those skilled in the art can understand that, in the embodiments of the present application, many technical details are presented in order to make the readers better understand the present application. However, the claimed technical solutions of the present application can be implemented even without these technical details and based on various changes and modifications of the following embodiments. The following division of the embodiments is for the convenience of description, and should not constitute any limitation on the specific implementation of the present application, and the embodiments can be combined with each other and referred to each other without contradiction.

[0027] The implementation details of the above method will be specifically described below through embodiments. The following content only provides implementation details for the convenience of understanding, and is not essential for implementing the present solution.

[0028] Embodiment One According to the actual data, the cumulative erosion and deposition amount calculation empirical formula is constructed according to the previous results and the foregoing analysis. The river erosion and deposition amount is mainly affected in the physical mechanism, which is divided into two parts: one part is that the erosion and deposition process of the river is affected by the upstream sediment amount, which directly affects the sediment amount and deposition process in the river section, and it is represented by the sediment coefficient (C); the other part is that the water and sediment in the river channel and the riverbed boundary jointly determine the hydrodynamic characteristics of the river, which affects the sedimentation and scouring behavior of the sediment in the river section, and the ability of the river flow to scour and carry sediment is represented by the flow scouring intensity (u*), and the riverbed boundary can use the median bed sand (d50) as the constraint of the river channel, and the time effect is considered from the perspective of calculating the cumulative erosion and deposition amount, and the cumulative influence years (T) is used as the time factor.

[0029] Based on the above considerations, the present embodiment proposes a dynamic prediction method for the cumulative erosion and deposition amount of the river channel downstream of the reservoir, which specifically includes the following steps (as shown in Figure 1 ): S1. Data collection and preprocessing Before predicting the erosion and deposition amount downstream of the reservoir, comprehensive and accurate hydrological data need to be collected first. These hydrological data are the basis for model construction and prediction, and the data quality directly affects the reliability of the prediction results. The following is the type of main data that needs to be collected in the present embodiment and its source: ​​​​Flow data (Q) is the volume of water passing through a cross section per unit time, usually expressed in cubic meters per second (m3 / s), and originates from hydrological monitoring stations; Sediment content (S) is the weight of sediment contained in a unit volume of water, usually expressed in kilograms per cubic meter (kg / m3), and is derived from measured data from hydrological monitoring stations. Bed sediment particle size (D50) is the median particle size of riverbed sediment, which is the value of 50% of the sediment particles on the cumulative particle size distribution curve that are smaller than this particle size. It is usually expressed in millimeters (mm). Bed sediment particle size directly affects the erosion resistance and scouring and deposition rate of the riverbed and is an important indicator for assessing riverbed stability.

[0030] Then, the collected data is preprocessed, including three steps: data cleaning, data standardization, and data integration.

[0031] In this embodiment, outliers are removed during data cleaning. Outliers (such as negative values ​​or values ​​outside a reasonable range) are checked in the data and corrected or deleted. For example, traffic data may contain abnormal peak values ​​caused by equipment malfunctions. Missing values ​​are filled in; for missing data points, interpolation methods (such as linear interpolation or spline interpolation) or the average value of adjacent data points can be used to fill in the missing values.

[0032] In this embodiment, data standardization converts all data into a uniform unit and unifies the time resolution of the data to the same interval (such as day, month, year).

[0033] In this embodiment, data integration combines data from different monitoring stations and different time points into a unified database. Depending on research needs, the data is grouped by time (e.g., season, year) or space (e.g., different river sections) for further analysis.

[0034] S2. Determination and Calculation of Key Parameters Given that the cumulative scouring and deposition changes in the downstream river channel of the reservoir mainly occur during the flood season, the water and sediment conditions during the flood season can be assessed using the sediment inflow coefficient (SFC). ) and water flow scouring intensity ( These two important parameters are used to quantify the characteristics of the incoming water and the scouring capacity of the water flow, providing basic data support for the analysis of scouring and sedimentation evolution.

[0035] In this embodiment, the actual calculation process for these two parameters is as follows: The sediment coefficient ( ) is the average sediment content ( ) and average flow ( The ratio of ρ to ρ is used to quantify the relative content of sediment in incoming water. The formula for calculation is: The sediment coefficient ( ) reflects the amount of sediment carried per unit flow, and is an important indicator of the sediment characteristics of incoming water. Among them, the higher incoming sediment coefficient ( ) indicates that the sediment content in the incoming water is rich, which may have a greater impact on the siltation of the downstream river channel; lower incoming sediment coefficient indicates that the sediment content in the incoming water is less.

[0036] Flow scouring intensity ( ) is a quantitative indicator of the scouring ability of the flow to the riverbed, which is defined by the ratio of the average flow and the average sediment content during the flood season. Its calculation formula is:

[0037] The flow scouring intensity reflects the relationship between the kinetic energy of the flow and the sediment content. Among them, the higher scouring intensity ( ) indicates that the flow has strong scouring ability and can more effectively erode the riverbed; lower scouring intensity indicates that the scouring ability of the flow is weak, which may not be enough to overcome the anti-erosion ability of the riverbed.

[0038] S3. Model coupling and calculation The river channel erosion and deposition amount is mainly affected by the following two parts in the physical mechanism: one part is the erosion and deposition process of the river affected by the amount of incoming sediment from the upstream, which directly affects the amount of sediment in the river section and the deposition process, which is characterized by the incoming sediment coefficient ( ); the other part is the interaction of incoming water and sediment in the river channel and the riverbed boundary, which determines the hydrodynamic characteristics of the river, and affects the sedimentation and scouring behavior of the sediment in the river section. The ability of the river flow to scour and carry sediment is characterized by the flow scouring intensity ( ), and the riverbed boundary can be represented by the median bed sand ( ) as the constraint of the river channel. The physical mechanism of these two parts comes from the response of the sediment discharge ratio SDR to the water and sediment conditions and the cross-section shape in the wandering section of the lower Yellow River by Xia Junqiang. The response formula of the sediment discharge ratio SDR to the water and sediment coefficient is:

[0039] Based on the above considerations, a new dynamic prediction method based on multi-parameter coupling is proposed in this embodiment. For the interaction of incoming water and sediment in the river channel and the riverbed boundary, the following equation is used:

[0040] to represent the influence of the incoming sediment amount from the upstream on the erosion and deposition process of the river, and the linear relationship formula of the incoming sediment coefficient ( ) is used to express.

[0041] Furthermore, regarding the impact of time effects on sedimentation, this embodiment proposes a dynamic prediction method for cumulative sedimentation in the downstream channel of a reservoir, employing a lag response model for evaluation. Specifically, based on the lag response single-step model, the study period ( Divided into multiple sub-time periods The cumulative siltation at the end of the nth time period It can be represented as:

[0042] In the formula: Let be the adjustment rate parameter for riverbed scouring and deposition during time period n, representing how quickly the scouring and deposition volume adjusts. The larger the volume, the faster the siltation can be adjusted. The smaller the value, the slower the adjustment of the silt flushing volume; This represents the equilibrium value of the cumulative siltation volume over time period n. This represents the cumulative sedimentation amount at the end of the previous time period. Based on the aforementioned time effect, this embodiment uses... Changes as a time term.

[0043] The cumulative siltation and sedimentation coefficient Nonlinear relationship, cumulative siltation volume ( ) and water flow scouring intensity ( ) and bed sand median ( The logarithmic relationship between the two, and the time effect ( These four factors regress the cumulative siltation volume of each segment in a polynomial form as follows:

[0044] In the formula: The cumulative scouring and sedimentation volume of the river section is expressed in units of 10⁸ m³; lowercase letters (a, b, c, d, e, ...) represent the volume of sediment deposited in the river section. () represents the coefficient; The value of bed sand after the flood season is expressed in mm; The intensity of water scouring during the annual flood season; The annual sediment load coefficient during the flood season is increased by 10,000 times to balance the order of magnitude; the year T is (1, 2, 3...) instead of the year sequence (2003, 2004, 2005...).

[0045] Example 2 To further illustrate the beneficial effects of the technical solution of the present invention, this embodiment applies the above method to the study of the downstream river channel of the Three Gorges Reservoir.

[0046] The erosion process of the Yichang-Shashi river section in the lower reaches of the Three Gorges Reservoir from 2003 to 2023 is simulated. The measured data of water and sediment and bed sediment data of two hydrological stations in the main stream of the middle and lower reaches of the Yangtze River are collected. The implementation of the dynamic prediction method of cumulative erosion and deposition in the lower reaches of the reservoir based on multi-parameter coupling is carried out through the above steps, as follows: S1. Two stations in the near-dam river channel of the middle and lower reaches of the Yangtze River (Yichang, Zhicheng), Yichang-Zhicheng river section (60.8 km). Among them, the daily flow and daily sediment concentration data of two stations from 2003 to 2023 are collected, the data are cleaned and combined, and the database is arranged, as well as the annual erosion and deposition data and bed sediment gradation of two different river sections in the downstream of the dam , which is prepared for the calculation of the second step.

[0047] S2. The sediment arrival coefficient is the ratio of average sediment concentration to average flow ( ). This coefficient can reflect the amount of sediment in the incoming water. The flow erosion intensity can be represented as:

[0048] In the formula: and are the average flow (m3 / s) and sediment concentration (kg / m3) of the hydrological station in the flood season of the middle and lower reaches of the Yangtze River, respectively; F is the flow erosion intensity (m9 / (kg·s2)) in the flood season, which is the effect of water flow on riverbed evolution.

[0049] The calculated sediment arrival coefficient and flow intensity of the Yichang-Zhicheng river section entrance station in the flood season are plotted to show the multi-year changes of the two water and sediment indicators from 2003 to 2023 as shown in Figure 2 and Figure 3 . The variation trend of the sediment arrival coefficient at the entrance Yichang station is similar, with a general downward trend, a significant decrease from 2003 to 2017, and an increase in the sediment arrival coefficient from 2017 to 2023. Figure 2 and Figure 3 The multi-year variation trend of the flow intensity at the Zhicheng station is similar to that at the Yichang station. The general trend of the flow intensity at the Yichang station is increasing, with local fluctuations.

[0050] S3. In the process of continuous erosion in the downstream of the Three Gorges Dam, the shape, roughness, and material composition of the riverbed in the erosion and deposition process will continuously adjust and change, which will affect the changes of water flow and sediment. Due to the sand blocking effect of the Three Gorges Dam, the upstream sediment arrival is sharply reduced, and the downstream river channel is in the process of clear water erosion. The nonlinear relationship between the cumulative erosion and deposition ( ) and the sediment arrival coefficient ( ), the cumulative erosion and deposition ( ) and the flow erosion intensity ( ), and the bed sediment median ( ) the logarithmic relationship between the two, the time effect of ( ) Four factors are regressed in polynomial form as follows:

[0051] In the formula: is the cumulative erosion and deposition amount of the river section, with the unit of 108m3; lowercase letters (a, b, c, d, e, ) are coefficients; is the median of the bed sand after the flood, with the unit of mm; is the flow erosion intensity in the flood season every year; is the sediment coefficient every year; the year T is (1, 2, 3…) instead of (2003, 2004, 2005…) year sequence. According to the actual erosion and deposition of several river channels downstream of the Three Gorges Reservoir after the operation of the Three Gorges Reservoir, the calibrated coefficients (a, b, c, d, e, ) are seen in Figure 4 and Figure 5 .

[0052] The above examples explore the 20-year erosion and deposition changes of the Three Gorges Reservoir downstream river channel by the dynamic prediction method of the cumulative erosion and deposition amount of the reservoir downstream based on multi-parameter coupling, have good fitting and prediction effects, and the change formula given can well reflect the erosion and deposition evolution of different river sections downstream.

[0053] The above examples fully illustrate that the dynamic prediction method of the cumulative erosion and deposition amount of the reservoir downstream based on multi-parameter coupling takes the sediment coefficient, the flow erosion intensity, the median particle size of the bed sand and the time coefficient as core parameters, quantifies the space-time evolution of the erosion and deposition amount through the empirical formula method, breaks through the limitation of the traditional single parameter model, can significantly improve the prediction accuracy, and can provide a scientific basis for reservoir optimization scheduling and river channel management.

[0054] Embodiment three Another embodiment of the present application relates to an electronic device, which comprises at least one processor; and a memory connected in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the dynamic prediction method of the cumulative erosion and deposition amount of the reservoir downstream river channel as described above.

[0055] It should be noted that the control unit and the processing unit in the above device each comprise at least one processor; and a memory connected in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the dynamic prediction method of the cumulative erosion and deposition amount of the reservoir downstream river channel in each of the above embodiments.

[0056] The memory and the processor are connected via a bus. The bus can include any number of interconnecting buses and bridges depending on the specific application of the mobile terminal. The bus connects the various circuits of the memory and the processor together and mediates data communication among different components internal or external to the mobile terminal. The bus also connects with various other circuits such as peripheral devices, voltage regulators, power management circuits, and the like, which are well known in the art, therefore, the details of the bus have been omitted. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single component or can include a plurality of components, such as a plurality of receivers and transmitters, which provide means for communicating with various other apparatuses over a transmission medium. The data processed by the processor is transmitted over the wireless medium via the antenna, and further, the antenna also receives data and transmits the data to the processor.

[0057] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. The memory can be used to store data used by the processor during execution of operations.

[0058] Embodiment Four Another embodiment of the present application relates to a computer readable storage medium storing a computer program. The computer program, when executed by a processor, implements the steps of the method for dynamically predicting cumulative erosion and deposition of a river channel downstream of a reservoir according to the above embodiment.

[0059] That is, those skilled in the art can understand that all or part of the steps of the method according to the above embodiment can be completed by programs instructing relevant hardware, the programs are stored in a storage medium, and include a plurality of instructions for causing an apparatus (which can be a single-chip microcomputer, a chip, or the like) or a processor to execute all or part of the steps of the method according to the embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0060] Embodiment Five Another embodiment of the present application relates to a computer program product including a computer program, which, when executed by a processor, implements the steps of the method for dynamically predicting cumulative erosion and deposition of a river channel downstream of a reservoir according to the above embodiment.

[0061] Those skilled in the art can understand that the above embodiments are specific embodiments for implementing the present application, and in actual applications, various changes can be made in form and details without departing from the spirit and scope of the present application.

Claims

1. A method for dynamically predicting the cumulative scouring and sedimentation volume in a downstream river channel of a reservoir, characterized in that, Includes the following steps: Collect hydrological data of the target river section and preprocess the collected data to meet preset requirements; wherein the hydrological data includes flow data, sediment concentration data and bed sediment particle size. Based on the analysis of the pre-processed hydrological data, the water and sediment conditions of the target river section during the flood season are determined, and the sediment inflow coefficient and water scouring intensity during the flood season are determined. The amount of scouring and deposition caused by the combined action of river water and sediment and the riverbed boundary is determined based on the sediment inflow coefficient. The amount of scouring and deposition caused by the influence of upstream sediment inflow is determined based on the scouring intensity of the water flow and the particle size of the bed sand. The cumulative scouring and deposition volume of the target river section is determined based on the combined effects of river inflow and sediment flow with the riverbed boundary, the scouring and deposition volume influenced by upstream sediment flow, and the time effect.

2. The method for dynamically predicting the cumulative scouring and sedimentation volume of the downstream river channel of a reservoir according to claim 1, characterized in that, The cumulative scouring and deposition volume of the target river section is determined according to the following formula, based on the combined effects of river inflow and sediment flow with the riverbed boundary, the scouring and deposition volume influenced by upstream sediment flow, and the time effect: In the formula, The cumulative scouring and silting volume of the target river section; The amount of scouring and deposition resulting from the combined effects of river water and sediment with the riverbed boundary; This refers to the amount of scouring and deposition caused by the amount of sediment flowing from upstream. is the coefficient, and T is the year sequence number.

3. The method for dynamically predicting the cumulative scouring and silting volume of the downstream river channel of a reservoir according to claim 2, characterized in that, The scouring and deposition volume caused by the combined effects of river inflow and sediment with the riverbed boundary is determined using the following formula: In the formula, The scouring and deposition volume is caused by the combined effects of river water and sediment with the riverbed boundary; a, b, and c are coefficients. The median particle size of bed sand after the flood season; This refers to the intensity of water scouring during the annual flood season.

4. The method for dynamically predicting the cumulative scouring and sedimentation volume of the downstream river channel of a reservoir according to claim 2, characterized in that, The amount of scouring and deposition caused by the amount of sediment from upstream is determined using the following formula: In the formula: 2 represents the scouring and deposition volume caused by the amount of sediment from upstream; d For coefficients; The sediment coefficient for each year's flood season is multiplied by 10,000 to achieve a balance.

5. The method for dynamically predicting the cumulative scouring and sedimentation volume of the downstream river channel of a reservoir according to claim 2, characterized in that, The sediment inflow coefficient is determined according to the following formula: In the formula, It is the sediment coefficient. It is the average sand content. .

6. The method for dynamically predicting the cumulative scouring and sedimentation volume of the downstream river channel of a reservoir according to claim 2, characterized in that, The water flow scouring intensity is determined according to the following formula: In the formula, F is the water flow scouring intensity. It is the average sand content. .

7. The method for dynamically predicting the cumulative scouring and sedimentation volume of the downstream river channel of a reservoir according to claim 1, characterized in that, The preprocessing of the collected data includes data cleaning, data standardization, and data integration.

8. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the steps of the dynamic prediction method for cumulative scour and sedimentation in the downstream channel of a reservoir as described in any one of claims 1 to 7.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the dynamic prediction method for cumulative scour and siltation in the downstream channel of the reservoir as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the dynamic prediction method for cumulative scour and sedimentation in the downstream channel of the reservoir as described in any one of claims 1 to 7.

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