Cascade reservoir sediment regulation and control method and device, computer equipment and storage medium

Abstract

The invention provides a cascade reservoir sediment regulation and control method and device, computer equipment and a storage medium. The method comprises the steps that the sediment deposition process of a cascade reservoir in a prediction period is obtained; according to the sediment deposition process of the cascade reservoir in the prediction period, multiple groups of water and sediment amounts of the cascade reservoir in the prediction period are obtained; adopting a preset optimal segmentation method to select the combination of the optimal classification number of water and sediment volumes from the multiple groups of water and sediment volumes of the cascade reservoir in the prediction period to obtain a feature combination; and on the basis of a cascade reservoir sediment regulation and control model, solving each feature combination, and selecting an optimal sediment regulation and control scheme according to a solving result. The characteristic reservoir water amount, the characteristic reservoir sediment amount and the characteristic effective reservoir capacity combination are selected through a preset optimal segmentation method, the variation characteristic of the sediment regulation and control constraint condition of the cascade reservoir can be effectively solved, and the accuracy of an optimization algorithm is improved; the optimal sediment regulation and control scheme is obtained by solving the characteristic conditions based on the cascade reservoir sediment regulation and control model, and the scheduling optimization process is effectively simplified.

Description

Technical Field

[0001] The present invention relates to the technical field of sediment control in reservoirs, and in particular to a sediment control method, device, computer equipment and storage medium for cascade reservoirs. Background Art

[0002] The sediment control model of cascade reservoirs is the main technical means to slow down the sedimentation process of cascade reservoirs, and it has engineering significance for ensuring the long-term and efficient operation of cascade reservoirs. The sediment control of cascade reservoirs includes four basic steps: ① Establishing the objective function, and proposing the joint dispatching criteria of the reservoir group based on various needs; ② Combining the operation indicators of the reservoirs and the water flow connection between the upstream and downstream reservoirs, respectively propose the long-term conditions and short-term conditions that the reservoir operation needs to meet; ③ Selecting the optimization algorithm, calculating the optimal scheme for the joint dispatching of the reservoir group; ④ Evaluating the comprehensive benefits of different dispatching schemes and establishing the optimal dispatching strategy. There are two types of indicators for sediment control in cascade reservoirs. For a single reservoir, the sediment control indicator is the reservoir sediment discharge ratio, that is, the ratio of the amount of sediment discharged to the amount of sediment in the same period. This indicator is a dimensionless parameter and is suitable for multi-scale sediment control research. However, the sediment discharge ratios between cascade reservoirs have mutual influences. The amount of water and sediment discharged from the upper reservoir directly changes the sediment discharge ratio of the adjacent lower reservoir. For cascade reservoirs, the control indicator is the sum of the sediment discharged from multiple reservoirs. This indicator can characterize the cumulative sediment discharge effect of multiple reservoirs, but ignores the impact of the amount of sediment in the reservoir on the sediment discharge of the reservoir. Both of the above indicators are based on the perspective of sediment discharge and do not take into account the spatial equilibrium relationship of siltation in cascade reservoirs. Summary of the invention

[0003] Based on this, it is necessary to provide a cascade reservoir sediment control method, device, computer equipment and storage medium to address the above technical problems.

[0004] In a first aspect, the present disclosure provides a method for regulating sediment in cascade reservoirs, comprising:

[0005] Obtain the sedimentation progress of cascade reservoirs during the forecast period;

[0006] According to the sedimentation process of the cascade reservoirs in the forecast period, multiple groups of water and sediment volumes of the cascade reservoirs in the forecast period are obtained, each group of water and sediment volumes respectively including the inflow water volume, the inflow sediment volume and the effective storage capacity;

[0007] Using a preset optimal segmentation method, a combination of water and sediment volumes with an optimal number of classifications is selected from a plurality of groups of water and sediment volumes of the cascade reservoirs within a forecast period to obtain a feature combination;

[0008] Based on the sediment control model of cascade reservoirs, each characteristic combination is solved, and the optimal sediment control scheme is selected according to the solution results.

[0009] In one embodiment, the step of solving each of the feature combinations based on the cascade reservoir sediment control model and selecting the optimal sediment control scheme according to the solution results includes:

[0010] Based on the sediment control model of cascade reservoirs, each characteristic combination is solved to obtain a solution result;

[0011] The multi-objective particle swarm algorithm is used to select the optimal sediment control scheme from the solution results.

[0012] In one embodiment, the step of obtaining the sedimentation progress of the cascade reservoirs during the prediction period includes:

[0013] Obtain the annual inflow of water, annual inflow of sediment and total storage capacity of the cascade reservoirs;

[0014] According to the annual inflow of water, the annual inflow of sediment and the total storage capacity of the reservoir, the siltation process of the cascade reservoirs during the forecast period is calculated using the Siyam formula.

[0015] In one embodiment, the step of using a preset optimal segmentation method to select a combination of water and sediment volumes with the optimal classification quantity from multiple groups of water and sediment volumes of the cascade reservoirs within the forecast period to obtain a feature combination comprises:

[0016] Calculate the sum of squares of distances between various water and sediment quantities in the corresponding preset numerical interval and the mean of the values ​​in the preset numerical interval to obtain the interval diameter;

[0017] Calculate the sum of the diameters of the intervals of the same type to obtain the total sum of squares of deviations;

[0018] Generate a trend curve of the total deviation square sum of each type versus the number of categories, and determine the optimal number of categories based on the inflection point position of the trend curve;

[0019] A combination of water and sediment amounts with an optimal number of classifications is selected from a plurality of groups of water and sediment amounts of the cascade reservoirs within a forecast period to obtain a feature combination.

[0020] In one embodiment, it also includes:

[0021] The ratio of total sediment discharge ratio of cascade reservoirs to sedimentation balance index is taken as the comprehensive evaluation index of sediment regulation of cascade reservoirs, and a sediment regulation model for cascade reservoirs is constructed.

[0022] In one embodiment, the cascade reservoir sediment control model is:

[0023] η(t)=c1(t)×η'(t)+c2(t)×η”(t)

[0024]

[0025]

[0026] Among them, η(t) is the annual sediment discharge ratio of the reservoir in the tth year, η'(t), η" (t) are the sediment discharge ratios in the flood season and non-flood season in the tth year, c1(t), c2(t) are the proportions of sediment inflow in the flood season and non-flood season in the tth year to the annual sediment inflow, Q in '(t),Q in ” (t) is the amount of water entering the reservoir during the flood season and non-flood season in the tth year.

[0027] In a second aspect, the present disclosure provides a cascade reservoir sediment control device, comprising:

[0028] The sedimentation process acquisition module is used to obtain the sedimentation process of the cascade reservoirs during the forecast period;

[0029] A water and sediment volume acquisition module is used to acquire multiple groups of water and sediment volumes of the cascade reservoirs during the forecast period according to the sediment deposition process of the cascade reservoirs during the forecast period, each group of water and sediment volumes including the inflow water volume, the inflow sediment volume and the effective storage capacity;

[0030] A feature combination acquisition module is used to select a combination of water and sediment with the best classification quantity from multiple groups of water and sediment quantities of the cascade reservoirs within the forecast period by using a preset optimal segmentation method to obtain a feature combination;

[0031] The optimal solution solving module is used to solve each of the feature combinations based on the cascade reservoir sediment control model, and select the optimal sediment control solution according to the solution results.

[0032] In a third aspect, the present disclosure provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above aspects.

[0033] In a fourth aspect, the present disclosure provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method described in the above aspects when executed by a processor.

[0034] In a fifth aspect, the present disclosure provides a computer program product, including a computer program, which implements the steps of the method described in the above aspects when executed by a processor.

[0035] The above-mentioned cascade reservoir sediment control method, device, computer equipment and storage medium can effectively solve the variable characteristics of the sediment control constraint conditions of the cascade reservoirs and improve the accuracy of the optimization algorithm by selecting the characteristic inflow water volume, characteristic inflow sediment volume and characteristic effective storage capacity combination through a preset optimal segmentation method; and by solving the characteristic conditions based on the cascade reservoir sediment control model to obtain the optimal sediment control plan, it can effectively simplify the scheduling optimization process and improve the model optimization capability. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A schematic diagram of a flow chart of a cascade reservoir sediment control method in one embodiment;

[0037] Figure 2 A schematic diagram of a flow chart of a sediment control method for cascade reservoirs in another embodiment;

[0038] Figure 3 The process of water and sediment entering the cascade reservoirs on the lower reaches of the Jinsha River in the next 100 years in an embodiment;

[0039] Figure 4 The siltation process of the cascade reservoirs in the lower reaches of the Jinsha River in the next 100 years in one embodiment;

[0040] Figure 5 This is an example of sediment control effect of the cascade reservoirs in the lower reaches of the Jinsha River in the next 100 years. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0042] It is worth mentioning that the constraints for sediment control of cascade reservoirs in existing studies are mainly the balance relationship between inflow and outflow of water, as well as the hydraulic connection between cascade reservoirs, but the effective storage capacity loss caused by the long-term operation of cascade reservoirs is less considered. Based on the constraints of sediment control of long-water-sand sequence cascade reservoirs, an optimization algorithm is selected to solve the optimal control scheme. At present, there are many heuristic algorithms with strong optimization ability for deterministic constraints, among which multi-objective particle swarm optimization (MOPSO), genetic algorithm second generation (NSGA-II), genetic algorithm third generation (NSGA-III) are widely used. For the long-water-sand sequence conditions, the dynamic siltation of cascade reservoirs causes the effective storage capacity to change continuously, and the optimization ability of heuristic algorithms for dynamic boundary conditions needs to be demonstrated. In addition, the long-term sediment control strategy for cascade reservoirs proposed based on the existing algorithm lacks dynamic adjustment potential in the face of changes in control targets and large calculation errors in sediment control models (reservoir sediment discharge ratio formula).

[0043] Therefore, the current sediment control of cascade reservoirs has the following problems: There is a lack of dimensionless evaluation indicators for sediment control of cascade reservoirs. It is necessary to consider not only the comprehensive sediment discharge effect of cascade reservoirs, but also the spatial equilibrium relationship of siltation of cascade reservoirs. For long water-sediment sequences, the effective storage capacity of cascade reservoirs continues to decrease with the increase of operation time, and the sediment control constraints are constantly changing, resulting in poor optimization effects of existing optimization algorithms for sediment control of cascade reservoirs, and lack of corresponding dynamic adjustment capabilities.

[0044] Therefore, in order to solve the above problems, this application adopts the following technical solutions:

[0045] Embodiment 1

[0046] In this embodiment, Figure 1 As shown, a cascade reservoir sediment control method is provided, which includes:

[0047] Step 110, obtaining the sedimentation process of the cascade reservoirs during the prediction period.

[0048] In this embodiment, the forecast period refers to a period of time in the future, for example, the next 100 years. The sedimentation process records the change of sedimentation amount of the cascade reservoirs in the forecast period, that is, the sedimentation amount of the cascade reservoirs in each year in the forecast period.

[0049] Step 120, according to the sedimentation process of the cascade reservoirs during the prediction period, multiple groups of water and sediment volumes of the cascade reservoirs during the prediction period are obtained, each group of the water and sediment volumes respectively including the inflow water volume, the inflow sediment volume and the effective storage capacity.

[0050] In this embodiment, the effective storage capacity of each reservoir of the cascade reservoir can be calculated based on the amount of sediment deposition, where the effective storage capacity is the total storage capacity of the reservoir minus the volume of sediment deposition. In addition, the sediment deposition process also records the amount of water and sediment entering the reservoir in each year during the forecast period. The amount of water entering the reservoir, the amount of sediment entering the reservoir, and the effective storage capacity are different types of water and sediment.

[0051] Step 130, using a preset optimal segmentation method, select a combination of water and sediment amounts with an optimal number of classifications from multiple groups of water and sediment amounts of the cascade reservoirs within a forecast period to obtain a feature combination.

[0052] In this embodiment, the preset optimal segmentation method can determine the optimal number of classifications for each type of water and sediment volume. The optimal number of classifications is used to classify the numerical value of a water and sediment volume, so that the classification values ​​of the water and sediment volume with the smallest internal differences among each sub-interval and the largest differences between different categories can be obtained. In this way, a combination of characteristic inflow water volume, characteristic inflow sediment volume and characteristic effective storage capacity can be selected, which can more accurately reflect the changes in water and sediment volume, effectively solve the changing characteristics of the sediment control constraints of cascade reservoirs, and improve the accuracy of the optimization algorithm.

[0053] Step 140, based on the cascade reservoir sediment control model, solve each of the feature combinations, and select the optimal sediment control scheme according to the solution results.

[0054] In this embodiment, the selected feature combination is input into the cascade reservoir sediment control model for solving, and the solution result is calculated. Among the multiple solution results obtained, the optimal sediment control scheme is selected.

[0055] In the above embodiment, by selecting the characteristic inflow water volume, characteristic inflow sand volume, and characteristic effective storage capacity combination through the preset optimal segmentation method, the changing characteristics of the sediment control constraint conditions of the cascade reservoirs can be effectively solved, and the accuracy of the optimization algorithm can be improved; and by solving the characteristic conditions based on the cascade reservoir sediment control model to obtain the optimal sediment control plan, the scheduling optimization process can be effectively simplified and the model optimization capability can be improved.

[0056] Embodiment 2

[0057] On the basis of the above embodiments, in this embodiment, the steps of solving each of the feature combinations based on the cascade reservoir sediment control model and selecting the optimal sediment control scheme according to the solution results include: solving each of the feature combinations based on the cascade reservoir sediment control model to obtain solution results; and selecting the optimal sediment control scheme from the solution results using a multi-objective particle swarm algorithm.

[0058] In this embodiment, based on the sediment control model of cascade reservoirs, each of the feature combinations is solved to obtain multiple solution results. Due to the excessive number of solution results, it is necessary to select the solution results as the optimal sediment control solution. Therefore, a multi-objective particle swarm optimization (MOPSO) is used to solve the optimization problem of multiple solution results, and the optimal solution set, namely the Pareto optimal solution set, is found through iterative search.

[0059] The calculation principle and steps of the multi-objective particle swarm algorithm are as follows: 1) Randomly generate the initial solution, that is, initialize the population, determine the particle swarm size N = 1000, give the initial position x and velocity v of each particle in the population, and create an external archive space as an empty set to store the optimal solution in the iterative process; 2) Calculate the objective function value of each particle, and store the non-dominated solution of each particle in the external archive space; 3) Update the position x and velocity v of each particle, store the non-dominated solution in the external archive space, and determine whether the number of particles in the external archive space exceeds the threshold value N' = 500; 4) If the number of particles in the external archive space exceeds the threshold value N' = 500, then according to the minimum habitat sharing mechanism, calculate the particle fitness value F of the external archive space, select the global optimal position Gbest, and maintain the size of the external archive space:

[0060] F(i)=A(i) -1

[0061] F(i) is the fitness of the i-th particle in the external archive space, and A(i) is the number of particles contained in the niche of the external archive space where the i-th particle is located.

[0062] 5) According to the Pareto optimal principle, select the historical optimal position Pbest of each individual particle.

[0063] 6) According to the global optimal position Gbest and individual optimal position Pbest of the particle swarm, update the position and velocity of each particle in the swarm until the number of iterations reaches the maximum value.

[0064] x C+1 (i,j)=x C (i,j)+v C+1 (i,j)

[0065] v C+1 (i,j)=ω·v C (i,j)+k1·r1·(Pbest C (i,j)-x C (i,j))+k2·r2·(Gbest C (j)-x C (i,j))

[0066] x C+1 (i,j) is the position of the jth control variable of the i-th particle at the C+1th iteration, x C (i,j) is the position of the jth control variable of the i-th particle at the Cth iteration, v C+1(i,j) is the speed of the jth control variable of the i-th particle at the C+1th iteration, ω=0.5 is the inertia weight coefficient, k1=1.5 and k2=2.0 are cognitive learning rates (representing individuals) and social learning rates (representing populations), respectively; r1 and r2 are random factors ranging from [0 to 1], Pbest C (i,j) is the jth control variable value corresponding to the optimal individual position of the i-th particle in the C-th iteration, Gbest C (i,j) is the j-th control variable value corresponding to the global position of the C-th iteration.

[0067] Embodiment 3

[0068] Based on the above embodiment, in this embodiment, the step of obtaining the sedimentation progress of the cascade reservoirs in the prediction period includes:

[0069] Obtain the annual inflow of water, annual inflow of sediment and total storage capacity of the cascade reservoirs;

[0070] According to the annual inflow of water, the annual inflow of sediment and the total storage capacity of the reservoir, the siltation process of the cascade reservoirs during the forecast period is calculated using the Siyam formula.

[0071] In this embodiment, the total storage capacity of the reservoir is the storage capacity of the reservoir when no siltation occurs, or the storage capacity of the reservoir with a known amount of siltation. The annual inflow of water and annual inflow of sediment are the annual inflow of water and annual inflow of sediment in the non-prediction period, and the annual inflow of water and annual inflow of sediment are known inflow of water and annual inflow of sediment, which can be used as the inflow of water and sediment conditions for calculation.

[0072] Specifically, in this embodiment, according to the water and sediment conditions of the cascade reservoirs, the Siyam formula is used to calculate the siltation process of the cascade reservoirs:

[0073]

[0074] V e (t) = V e (t-1)-P(t-1) / ρ' / 10 (2)

[0075] P(t) and P(t-1) are the reservoir sedimentation in year t and year t-1 respectively (10,000 tons), Qin(t) is the reservoir water inflow in year t (100 million m 3 ), Sin(t) is the amount of sediment entering the reservoir in year t (billion tons), Ve(t) and Ve(t-1) are the effective reservoir capacities in year t and year t-1 (billion m 3 ), ρ' is the density of sediment (taken as 1.1×103kg / m 3 )

[0076] In this embodiment, the siltation process of the reservoir can be calculated using the Siyam formula based on the known annual inflow of water, inflow of sediment and total storage capacity of the reservoir.

[0077] Embodiment 4

[0078] On the basis of the above embodiment, in this embodiment, the step of adopting a preset optimal segmentation method to select a combination of water and sediment volumes with the optimal classification quantity from multiple groups of water and sediment volumes of the cascade reservoirs within the forecast period to obtain a feature combination includes:

[0079] Calculate the sum of squares of distances between various water and sediment quantities in the corresponding preset numerical interval and the mean of the values ​​in the preset numerical interval to obtain the interval diameter;

[0080] Calculate the sum of the diameters of the intervals of the same type to obtain the total sum of squares of deviations;

[0081] Generate a trend curve of the total deviation square sum of each type versus the number of categories, and determine the optimal number of categories based on the inflection point position of the trend curve;

[0082] A combination of water and sediment amounts with an optimal number of classifications is selected from a plurality of groups of water and sediment amounts of the cascade reservoirs within a forecast period to obtain a feature combination.

[0083] In one embodiment, before the step of calculating the sum of squares of the distances between various water and sand amounts in the corresponding preset numerical interval and the mean of the values ​​in the preset numerical interval to obtain the interval diameter, the step further includes: calculating the ratio of various water and sand amounts to the maximum value of the water and sand amounts of the corresponding types, and normalizing the water and sand amounts according to each of the ratios to obtain the normalized water and sand amounts. By calculating the normalized values ​​of various water and sand amounts, the complexity of the values ​​can be reduced and the subsequent calculation efficiency can be improved.

[0084] In this embodiment, the preset optimal segmentation method is the Fisher optimal segmentation method. In this embodiment, the water and sediment volume is first normalized, and the ratio of the water and sediment volume to the maximum value of the sequence is calculated as the normalized water and sediment value:

[0085] x i '=x i / x max

[0086] x i is the i-th value in the water-sand sequence, x max is the maximum value in the water-sand sequence, x i ' is the normalized value of the i-th value of the water-sediment sequence.

[0087] Subsequently, the sum of squares of distances between each water and sand value in the preset numerical interval and the interval mean is calculated. The sum of squares of distances is used to reflect the internal differences within the sub-intervals. The sum of squares of distances is also called the interval diameter. The larger the interval diameter, the greater the internal differences within the interval.

[0088]

[0089]

[0090] Among them, D k is the diameter of the k-th subinterval, x' ki is the normalized value of water and sediment in the k-th subinterval, is the average value of the normalized samples in the k-th subinterval, n k is the number of samples in the k-th subinterval.

[0091] According to the diameter of each interval of the same type of water and sediment volume, the sum of the sum of squares of deviations is calculated, that is, the total sum of squares of deviations. The total sum of squares of deviations is the criterion for judging the classification results. When the total sum of squares of deviations is the smallest, the internal differences of each subclass are smaller, and the classification effect is optimal:

[0092]

[0093] Among them, e j is the total sum of squares of deviations when the total number of categories is j.

[0094] Finally, the optimal number of segmentations is determined based on the total deviation square sum. Specifically, starting from j=2, the total number of categories is gradually increased. When the curve of the total deviation square sum versus the total number of categories has an inflection point, the corresponding total number of categories is the optimal number of categories.

[0095] After determining the optimal number of classifications, a combination of water and sediment quantities with the optimal number of classifications is selected from the multiple groups of water and sediment quantities to obtain a feature combination. This feature combination can more accurately reflect the changes in water and sediment quantities, thereby effectively solving the changing characteristics of the sediment control constraints of cascade reservoirs and improving the accuracy of the optimization algorithm.

[0096] Embodiment 5

[0097] On the basis of the above embodiment, this embodiment further includes: using the ratio of the total sediment discharge ratio of the cascade reservoirs to the sedimentation balance index as a comprehensive evaluation index for sediment control of the cascade reservoirs, and constructing a sediment control model for the cascade reservoirs.

[0098] In this embodiment, the objective function of sediment control of cascade reservoirs is first constructed, and the total sediment discharge ratio and sedimentation balance index of the cascade reservoirs are calculated respectively. The total sediment discharge ratio and sedimentation balance index of the cascade reservoirs respectively characterize the sediment discharge effect and spatial sedimentation balance characteristics of sediment control of the cascade reservoirs:

[0099]

[0100]

[0101] Among them, η' is the total sediment discharge ratio of the cascade reservoirs, Sout' is the sediment discharge of the last reservoir (10,000 tons), Sin' is the total sediment inflow of the cascade reservoirs (10,000 tons), σ is the sedimentation equilibrium index of the cascade reservoirs, R is the reservoir capacity siltation loss rate, maxR and minR are the maximum and minimum values ​​of the cascade reservoir capacity siltation loss rate, is the average siltation rate of cascade reservoirs. Combining the physical meaning of the total sediment discharge ratio and the sedimentation balance index of cascade reservoirs, it can be seen that when the total sediment discharge ratio of the reservoir is larger and the sedimentation balance index is smaller, the sediment discharge effect of the reservoir is higher and the spatial difference of reservoir sedimentation is smaller. Therefore, the ratio of the two is selected as the comprehensive evaluation index of sediment control of cascade reservoirs:

[0102]

[0103] Among them, γ is the comprehensive evaluation index of sediment control in cascade reservoirs.

[0104] Embodiment 6

[0105] On the basis of the above embodiments, a sediment control model for cascade reservoirs can be constructed: the sediment control model for cascade reservoirs mainly constructs the quantitative relationship between the annual sediment discharge ratio of the reservoir and the sediment discharge ratios during the flood season and the non-flood season, and the annual sediment discharge ratio is increased by adjusting the flood season outflow of the cascade reservoirs. Therefore, the sediment control index of the cascade reservoirs is the ratio of the flood season outflow to the annual inflow. Combined with the inter-annual, flood season and non-flood season water and sediment conservation relationship of the reservoir, a weighted formula for the annual sediment discharge ratio and the flood season and non-flood season sediment discharge ratio is established. In this embodiment, the sediment control model for the cascade reservoirs is:

[0106] η(t)=c1(t)×η'(t)+c2(t)×η”(t)

[0107]

[0108]

[0109] Among them, η(t) is the annual sediment discharge ratio of the reservoir in the tth year, η'(t), η" (t) are the sediment discharge ratios in the flood season and non-flood season in the tth year, c1(t), c2(t) are the proportions of sediment inflow in the flood season and non-flood season in the tth year to the annual sediment inflow, Q in '(t),Q in ” (t) is the amount of water entering the reservoir during the flood season and non-flood season in the tth year.

[0110] It should be understood that although Figure 1The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0111] Embodiment 7

[0112] Based on the above embodiments, this embodiment provides an application example.

[0113] In response to the above-mentioned problems of this application, this application proposes a medium- and long-term sediment control method for cascade reservoirs in the form of clustering-optimization. By establishing a comprehensive evaluation index for sediment control of cascade reservoirs, the spatial sedimentation equilibrium relationship of the total sediment discharge effect of cascade reservoirs and reservoir sedimentation is characterized at the same time. The composite scheduling model in the form of clustering-optimization is used to solve the sediment control strategy of the long water-sediment sequence, which effectively solves the sediment scheduling optimization problem under dynamic constraints and can provide dynamic control standards for the operation of cascade reservoirs. The present invention uses the Siyam formula to calculate the sedimentation process of cascade reservoirs under the condition of a long water-sediment sequence, selects the characteristic inflow water volume-sediment volume-effective storage capacity combination through the Fisher optimal segmentation method, and combines the multi-objective particle swarm algorithm (MOPSO) to solve the optimal sediment control scheme under different characteristic conditions, and selects the optimal sediment control scheme corresponding to each year in turn according to the change process of the characteristic conditions of the cascade reservoirs to form a sediment control strategy for the long water-sediment sequence.

[0114] like Figure 2 As shown in the figure, the specific process of calculating the long-term sediment control method of cascade reservoirs in the form of clustering-optimization is as follows:

[0115] (1) Siyam formula for calculating the sedimentation process of cascade reservoirs

[0116] According to the inflow water and sediment conditions of cascade reservoirs, the Siyam formula is used to calculate the sedimentation process of cascade reservoirs.

[0117]

[0118] V e (t) = V e (t-1)-P(t-1) / ρ' / 10 (2)

[0119] P(t) and P(t-1) are the reservoir sedimentation in year t and year t-1 respectively (10,000 tons), Qin (t) is the amount of water entering the reservoir in year t (billion m 3 ), S in (t) is the amount of sand entering the reservoir in the tth year (billion tons), V e (t), V e (t-1) is the effective storage capacity in year t and year t-1 (billion m 3 ), ρ' is the density of sediment (taken as 1.1×10 3 kg / m 3 ).

[0120] (2) Fisher optimal segmentation method to select characteristic inflow water volume-sediment volume-effective storage capacity combination

[0121] Fisher's optimal segmentation method is a key technology for dividing the annual water and sediment characteristic values. Its principle is to minimize the total sum of squares of deviations within the water and sediment sequence. It not only requires the minimum internal differences of each sub-interval, but also the maximum differences between different categories. Fisher's optimal segmentation method includes 4 steps:

[0122] ① Normalization of water-sediment sequence

[0123] The ratio of the water-sediment volume to the maximum value of the sequence is calculated as the normalized water-sediment value.

[0124] x i '=x i / x max (3)

[0125] x i is the i-th value in the water-sand sequence, x max is the maximum value in the water-sand sequence, x i ' is the normalized value of the i-th value of the water-sediment sequence.

[0126] ②Define the subinterval diameter

[0127] The sum of the squares of the distances between each water and sediment value in the interval and the interval mean is calculated to reflect the internal differences within the sub-interval. The larger the diameter, the greater the internal differences within the interval.

[0128]

[0129]

[0130] D k is the diameter of the k-th subinterval, x' ki is the normalized value of water and sediment in the k-th subinterval, is the average value of the normalized samples in the k-th subinterval, n k is the number of samples in the k-th subinterval.

[0131] ③Calculate the total sum of squares of deviations

[0132] The total sum of square deviations is the criterion for judging the classification results. When the total sum of square deviations is the smallest, the internal differences of each subclass are smaller and the classification effect is optimal.

[0133]

[0134] e j is the total sum of squares of deviations when the total number of categories is j.

[0135] ④Determine the optimal number of splits

[0136] Starting from j=2, the total number of categories is gradually increased. When the curve of the total sum of squared deviations versus the total number of categories reaches an inflection point, the corresponding total number of categories is the optimal number.

[0137] (3) Multi-objective particle swarm optimization (MOPSO) to solve the optimal sediment control scheme

[0138] ① Objective function of sediment control in cascade reservoirs

[0139] The total sediment discharge ratio and sedimentation balance index of the cascade reservoirs were calculated respectively to characterize the sediment discharge effect and spatial sedimentation balance characteristics of the cascade reservoir sediment regulation.

[0140]

[0141] η' is the total sediment discharge ratio of the cascade reservoirs, S out ' is the sediment discharge from the last reservoir (10,000 tons), S in ' is the total sediment inflow of the cascade reservoirs (10,000 tons), σ is the sedimentation equilibrium index of the cascade reservoirs, R is the reservoir capacity siltation loss rate, maxR and minR are the maximum and minimum values ​​of the cascade reservoir capacity siltation loss rate, is the average siltation rate of cascade reservoirs. Combining the physical meaning of the total sediment discharge ratio and the sedimentation balance index of cascade reservoirs, it can be seen that when the total sediment discharge ratio of the reservoir is larger and the sedimentation balance index is smaller, the sediment discharge effect of the reservoir is higher and the spatial difference of reservoir sedimentation is smaller. Therefore, the ratio of the two is selected as the comprehensive evaluation index of sediment control of cascade reservoirs.

[0142]

[0143] γ is the comprehensive evaluation index of sediment control in cascade reservoirs.

[0144] ② Sediment control model of cascade reservoirs

[0145] The sediment control model of cascade reservoirs mainly constructs the quantitative relationship between the annual sediment discharge ratio of the reservoir and the sediment discharge ratio during the flood season and the non-flood season. The annual sediment discharge ratio is increased by adjusting the flood season outflow of the cascade reservoirs. Therefore, the sediment control index of the cascade reservoirs is the ratio of flood season outflow to annual inflow. Combined with the inter-annual, flood season and non-flood season water and sediment conservation relationship of the reservoir, a weighted formula for the annual sediment discharge ratio and the flood season and non-flood season sediment discharge ratio is established.

[0146] η(t)=c1(t)×η'(t)+c2(t)×η”(t) (10)

[0147]

[0148] η(t) is the annual sediment discharge ratio of the reservoir in the tth year, η'(t), η"(t) are the sediment discharge ratios in the flood season and non-flood season in the tth year, c1(t), c2(t) are the proportions of sediment inflow in the flood season and non-flood season in the tth year to the annual sediment inflow, Q in '(t),Q in "(t) is the amount of water entering the reservoir during the flood season and non-flood season in the tth year (billion m3).

[0149] ③ Constraints on sediment control in cascade reservoirs

[0150] The constraints for sediment control in cascade reservoirs include: conservation of water and sediment volume in and out of the reservoir, and water and sediment transfer relationships among cascade reservoirs. The conservation relationship of annual water and sediment volume in and out of the reservoir is as follows:

[0151] Q out =Q in -V S (13)

[0152] S out =S in -P (14)

[0153] Q out , Q in is the amount of water flowing in and out of the reservoir (billion m 3 ), S out , S in is the amount of sand entering and leaving the reservoir (10,000 tons), V S is the reservoir water storage capacity (billion m 3 ), P is the reservoir sedimentation volume (10,000 tons).

[0154] The conservation relationship between annual and flood season-non-flood season water and sediment is:

[0155] Q in (n,t)=Q out (n-1,t)+Q E (n,t) (15)

[0156] S in (n,t)=S out(n-1,t)+S E (n,t) (16)

[0157] Q in (n,t),S in (n,t) is the amount of water inflow into the nth reservoir in year t (billion m 3 ), sand volume entering the warehouse (10,000 tons), Q in (n-1,t),S in (n-1,t) is the water discharge of the n-1 reservoir in year t (billion m 3 ), sand volume discharged from the warehouse (10,000 tons), Q E (n,t),S E (n,t) is the interval water replenishment of the nth reservoir in year t (billion m 3 ) and sand replenishment amount (10,000 t).

[0158] ④Multi-objective particle swarm algorithm

[0159] The calculation principle and specific steps of the multi-objective particle swarm algorithm are as follows: 1) Randomly generate the initial solution, that is, initialize the population, determine the particle swarm size N = 1000, give the initial position x and velocity v of each particle in the population, and create an external archive space as an empty set to store the better solution in the iterative process; 2) Calculate the objective function value of each particle, and store the non-dominated solution of each particle in the external archive space; 3) Update the position x and velocity v of each particle, store the non-dominated solution in the external archive space, and determine whether the number of particles in the external archive space exceeds the threshold value N' = 500; 4) If the number of particles in the external archive space exceeds the threshold value N' = 500, then according to the minimum habitat sharing mechanism, calculate the particle fitness value F of the external archive space, select the global optimal position Gbest, and maintain the scale of the external archive space.

[0160] F(i)=A(i) -1 (17)

[0161] F(i) is the fitness of the i-th particle in the external archive space, and A(i) is the number of particles contained in the niche of the external archive space where the i-th particle is located.

[0162] 5) According to the Pareto optimal principle, select the historical optimal position Pbest of each individual particle. 6. According to the global optimal position Gbest and the individual optimal position Pbest of the particle swarm, update the position and speed of each particle in the swarm until the number of iterations reaches the maximum value.

[0163] x C+1 (i,j)=x C (i,j)+v C+1 (i,j) (18)

[0164] vC+1 (i,j)=ω·v C (i,j)+k1·r1·(Pbest C (i,j)-x C (i,j))+k2·r2·(Gbest C (j)-x C (i,j)) (19)

[0165] x C+1 (i,j) is the position of the jth control variable of the i-th particle at the C+1th iteration, x C (i,j) is the position of the jth control variable of the i-th particle at the Cth iteration, v C+1 (i,j) is the speed of the jth control variable of the i-th particle at the C+1th iteration, ω=0.5 is the inertia weight coefficient, k1=1.5 and k2=2.0 are cognitive learning rates (representing individuals) and social learning rates (representing populations), respectively; r1 and r2 are random factors ranging from [0 to 1], Pbest C (i,j) is the jth control variable value corresponding to the optimal individual position of the i-th particle in the C-th iteration, Gbest C (i,j) is the j-th control variable value corresponding to the global position of the C-th iteration.

[0166] (4) Optimal control scheme for the configuration characteristics of Changshui sand sequence

[0167] Based on the characteristic inflow water volume, sediment volume, and effective storage capacity combination selected by Fisher's optimal segmentation method, the MOSPO algorithm is used to solve the optimal control scheme. According to the annual water and sediment changes in the basin, the corresponding optimal control scheme is selected for the characteristic conditions of the cascade each year. The optimal schemes of each year are sequentially spliced ​​together to form the optimal control strategy for the long water and sediment sequence.

[0168] (5) Evaluation of the effectiveness of sediment control strategies in Changshui Sediment Sequence

[0169] The evaluation indicators for the effectiveness of sediment control strategies for the Changshuisha cascade reservoirs include: the total sediment discharge ratio of the cascade reservoirs over many years and the cumulative sedimentation balance index.

[0170]

[0171] η T ' is the total sediment discharge ratio of cascade reservoirs over many years, σ T is the cumulative sedimentation equilibrium index of cascade reservoirs, R T is the cumulative siltation rate of reservoir capacity, maxR T 、minR T is the maximum and minimum value of the cumulative siltation rate of the cascade reservoir capacity, It is the average value of the cumulative storage capacity siltation rate of cascade reservoirs.

[0172] The following is a specific example, taking the calculation of sediment control strategy for the cascade reservoirs in the lower reaches of the Jinsha River (Wudongde-Baihetan-Xiluodu-Xiangjiaba Reservoir) in the next 100 years as an example, the next 100 years is the prediction period:

[0173] First, the water and sediment inflow process of the cascade reservoirs in the lower reaches of the Jinsha River in the next 100 years is given, such as Figure 3 The average annual water inflow in the Wudongde Reservoir area is 119.858 billion m 3 The average annual sediment inflow is 39.6171 million tons; the average annual water inflow in the Baihetan reservoir area is 7.594 billion m 3 The average annual sediment inflow is 45.2403 million tons; the average annual water inflow in the Xiluodu reservoir area is 4.733 billion m 3 The average annual sediment inflow is 32.0451 million tons; the average annual water inflow in the Xiangjiaba reservoir area is 2.15 billion m 3 The average annual sand inflow is 1.1961 million tons.

[0174] The Siyam formula is used to calculate the reservoir sedimentation process of the cascade reservoirs in the next 100 years under the design standard operation conditions. The results are as follows: Figure 4 As shown. In the next 100 years, the cumulative sedimentation of the cascade reservoirs in the lower reaches of the Jinsha River will be 13.014 billion tons, and the total storage capacity sedimentation rate will be 25.79%. Among them, the average annual sedimentation of the Wudongde Reservoir is 37.123 million tons, the average annual sedimentation of the Baihetan Reservoir is 54.3448 million tons, the average annual sedimentation of the Xiluodu Reservoir is 36.3076 million tons, and the average annual sedimentation of the Xiangjiaba Reservoir is 3.5683 million tons. According to the sedimentation process of the cascade reservoirs, the change process of the reservoir sedimentation rate is calculated. The ranking of the cascade reservoir storage capacity sedimentation rate speed in the next 100 years is: Wudongde Reservoir>Xiluodu Reservoir>Baihetan Reservoir>Xiangjiaba Reservoir. As of 2120, the cumulative siltation rate of Wudongde Reservoir was 45.29%, the cumulative siltation rate of Baihetan Reservoir was 23.66%, the cumulative siltation rate of Xiluodu Reservoir was 25.86%, and the cumulative siltation rate of Xiangjiaba Reservoir was 6.18%.

[0175] Formula (7-9) is used to calculate the total sediment discharge ratio, sedimentation balance index, and comprehensive evaluation index of the cascade reservoirs in the lower reaches of the Jinsha River in the next 100 years. The total sediment discharge ratio of the cascade reservoirs has an overall increasing trend over time, the sedimentation balance index has a decreasing trend, and the comprehensive evaluation index has an increasing trend. The annual average total sediment discharge ratio of the cascade reservoirs in the lower reaches of the Jinsha River from 2021 to 2120 is 7.47×10 -3 The average annual growth rate is 5.94×10 -5 The annual average value of sedimentation equilibrium index is 1.67, and the annual average decline rate is 2.19×10 -3 ; The annual average value of comprehensive evaluation index is 4.51×10 -3 The average annual growth rate is 4.14×10-5 .

[0176] Based on the changes in water volume, sediment volume, and effective storage capacity of the lower reaches of the Jinsha River in the next 100 years, 20 eigenvalue combinations were selected using the Fisher optimal segmentation method. The results are shown in Table 1.

[0177] Table 1 Combinations of characteristic inflow, sediment volume and effective storage capacity of the cascade reservoirs in the lower reaches of the Jinsha River

[0178]

[0179] Using the multi-objective particle swarm algorithm, the optimal sediment control scheme for cascade reservoirs under 20 characteristic conditions is discussed in turn, and the results are shown in Table 2.

[0180] Table 2 Optimal sediment control scheme for the cascade reservoirs in the lower reaches of the Jinsha River under different characteristic conditions

[0181]

[0182]

[0183] Based on the water and sediment process of the lower reaches of the Jinsha River in the next 100 years, the characteristic water and sediment combinations corresponding to the annual inflow of water and sediment and the corresponding annual regulation strategies are clarified, and the optimal scheme for multi-year sediment regulation of cascade reservoirs is obtained. Figure 5 As shown. Comparing the differences in reservoir sedimentation before and after sediment regulation in the cascade reservoirs of the lower reaches of the Jinsha River, the total sediment discharge ratio of the cascade reservoirs increased by 76.63% compared with before regulation, the sedimentation equilibrium index increased by 16.23%, and the comprehensive evaluation index of sediment regulation increased by 50.92% year-on-year. After sediment regulation, the cumulative sedimentation of the cascade reservoirs in the lower reaches of the Jinsha River from 2021 to 2120 was 13.04 billion tons, a reduction of 1.105 billion tons compared with before regulation.

[0184] like Figure 5 As shown, in the next 100 years, the average annual sediment interception capacity of Wudongde Reservoir will be 32.8998 million tons, and the cumulative siltation rate of reservoir capacity will be 40.79%, which is 4.50% lower than that before regulation; the average annual sediment interception capacity of Baihetan Reservoir will be 54.1782 million tons, and the cumulative siltation rate of reservoir capacity will be 20.55%, which is 3.10% lower than that before regulation; the average annual sediment interception capacity of Xiluodu Reservoir will be 28.6218 million tons, and the cumulative siltation rate of reservoir capacity will be 21.20%, which is 4.66% lower than that before regulation; the average annual sediment interception capacity of Xiangjiaba Reservoir will be 3.6546 million tons, and the cumulative siltation rate of reservoir capacity will be 1.49%, which is 4.69% lower than that before regulation.

[0185] The optimization and dispatching of sediment control in cascade reservoirs by using this embodiment has the following advantages:

[0186] (1) The ratio of the total sediment discharge ratio of cascade reservoirs to the sedimentation balance index is used as a comprehensive evaluation index for sediment control of cascade reservoirs, which can simultaneously characterize the total sediment discharge effect of cascade reservoirs and the spatial balance relationship of reservoir sedimentation;

[0187] (2) The combination of characteristic inflow water, inflow sediment, and effective storage capacity is selected by the Fisher optimal segmentation method, which can effectively solve the variable characteristics of the sediment control constraints of cascade reservoirs and improve the accuracy of the optimization algorithm;

[0188] (3) The MOPSO algorithm is used to solve the optimal sediment control scheme for characteristic conditions in turn, and a long-term control strategy is formed according to the dynamics of the water-sediment process, which can effectively simplify the scheduling optimization process and improve the model optimization capability.

[0189] Embodiment 8

[0190] In this embodiment, a cascade reservoir sediment control device is provided, comprising:

[0191] The sedimentation process acquisition module is used to obtain the sedimentation process of the cascade reservoirs during the forecast period;

[0192] A water and sediment volume acquisition module is used to acquire multiple groups of water and sediment volumes of the cascade reservoirs during the forecast period according to the sediment deposition process of the cascade reservoirs during the forecast period, each group of water and sediment volumes including the inflow water volume, the inflow sediment volume and the effective storage capacity;

[0193] A feature combination acquisition module is used to select a combination of water and sediment with the best classification quantity from multiple groups of water and sediment quantities of the cascade reservoirs within the forecast period by using a preset optimal segmentation method to obtain a feature combination;

[0194] The optimal solution solving module is used to solve each of the feature combinations based on the cascade reservoir sediment control model, and select the optimal sediment control solution according to the solution results.

[0195] In one embodiment, the optimal solution solving module includes:

[0196] A solving unit, used for solving each of the characteristic combinations based on the cascade reservoir sediment control model to obtain a solution result;

[0197] The optimal solution selection unit is used to select the optimal sediment control solution from the solution results using a multi-objective particle swarm algorithm.

[0198] In one embodiment, the silt deposition process acquisition module includes:

[0199] A water and sediment volume acquisition unit is used to obtain the annual inflow of water, annual inflow of sediment and total storage capacity of the cascade reservoirs;

[0200] The sedimentation process calculation unit is used to calculate the sedimentation process of the cascade reservoirs within the forecast period using the Siyam formula according to the annual inflow of water, the annual inflow of sediment and the total storage capacity of the reservoir.

[0201] In one embodiment, the feature combination acquisition module includes:

[0202] An interval diameter calculation unit, used to calculate the sum of squares of distances between various water and sediment quantities in a corresponding preset numerical interval and the mean of the values ​​in the preset numerical interval, to obtain an interval diameter;

[0203] A total deviation square sum calculation unit, used for calculating the sum of the same type of interval diameters to obtain a total deviation square sum;

[0204] An optimal classification number determination unit is used to generate a trend curve of the total deviation square sum of each type versus the classification number, and determine the optimal classification number according to the inflection point position of the change trend curve;

[0205] The feature combination obtaining unit is used to select a combination of water and sediment amounts with the best classification quantity from multiple groups of water and sediment amounts of the cascade reservoirs within the forecast period to obtain a feature combination.

[0206] In one embodiment, it also includes:

[0207] The model building unit is used to construct a cascade reservoir sediment control model by taking the ratio of the total sediment discharge ratio of the cascade reservoirs to the sedimentation balance index as a comprehensive evaluation indicator for the sediment control of the cascade reservoirs.

[0208] In one embodiment, the cascade reservoir sediment control model is:

[0209] η(t)=c1(t)×η'(t)+c2(t)×η”(t)

[0210]

[0211]

[0212] Among them, η(t) is the annual sediment discharge ratio of the reservoir in the tth year, η'(t), η" (t) are the sediment discharge ratios in the flood season and non-flood season in the tth year, c1(t), c2(t) are the proportions of sediment inflow in the flood season and non-flood season in the tth year to the annual sediment inflow, Q in '(t),Q in ” (t) is the amount of water entering the reservoir during the flood season and non-flood season in the tth year.

[0213] For the specific definition of the cascade reservoir sediment control device, please refer to the definition of the cascade reservoir sediment control method above, which will not be repeated here. Each unit in the above-mentioned cascade reservoir sediment control device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned units can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned units.

[0214] Embodiment 9

[0215] On the basis of the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above embodiments.

[0216] In some implementations of this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in the above embodiment are implemented.

[0217] In some implementations of this embodiment, a computer program product is provided, including a computer program, which implements the steps of the method described in the above embodiment when executed by a processor.

[0218] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components to execute the method in the above embodiments.

[0219] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof. Computer-readable storage media may include, but are not limited to, for example, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, computer storage media (such as hard disks, floppy disks, solid-state drives, removable disks, Blu-ray disks, etc.).

[0220] The computer-readable storage medium may also store at least one computer executable program, such as a computer-readable instruction. The computer-readable storage medium includes, but is not limited to, for example, a volatile memory and / or a non-volatile memory. The volatile memory may include, for example, a random access memory (RAM) and / or a cache memory (cache), etc. The computer-readable storage medium may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device runs the computer-readable instructions stored on the computer-readable storage medium, the various methods described above may be performed.

[0221] In addition, the computer device may also include (but not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (eg, keyboard, mouse, speaker, etc.), etc.

[0222] The processor may communicate with external devices via an I / O bus via a wired or wireless network.

[0223] In one embodiment, the at least one computer executable instruction may also be compiled into or constitute a software product / computer program product, wherein one or more computer executable instructions are executed by a processor to perform the various functions and / or method steps in the embodiments described in the present technology.

[0224] In the embodiments provided in the present disclosure, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the above-mentioned module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0225] It should be noted that in the present disclosure, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element limited by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0226] Although the embodiments disclosed in the present disclosure are as above, the above contents are only embodiments adopted for facilitating the understanding of the present disclosure and are not intended to limit the present disclosure. Any technician in the technical field to which the present disclosure belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present disclosure, but the scope of patent protection of the present disclosure shall still be subject to the scope defined in the attached claims.

Claims

1. A method for regulating sediment in cascade reservoirs, characterized in that: include: Obtain the sedimentation progress of cascade reservoirs during the forecast period; According to the sedimentation process of the cascade reservoirs in the forecast period, multiple groups of water and sediment volumes of the cascade reservoirs in the forecast period are obtained, each group of water and sediment volumes respectively including the inflow water volume, the inflow sediment volume and the effective storage capacity; Using a preset optimal segmentation method, a combination of water and sediment volumes with an optimal number of classifications is selected from a plurality of groups of water and sediment volumes of the cascade reservoirs within a forecast period to obtain a feature combination; Based on the sediment control model of cascade reservoirs, each characteristic combination is solved, and the optimal sediment control scheme is selected according to the solution results.

2. The method according to claim 1, characterized in that The step of solving each of the characteristic combinations based on the cascade reservoir sediment control model and selecting the optimal sediment control scheme according to the solution results comprises: Based on the sediment control model of cascade reservoirs, each characteristic combination is solved to obtain a solution result; The multi-objective particle swarm algorithm is used to select the optimal sediment control scheme from the solution results.

3. The method according to claim 1, characterized in that The step of obtaining the sedimentation process of the cascade reservoirs during the forecast period includes: Obtain the annual inflow of water, annual inflow of sediment and total storage capacity of the cascade reservoirs; According to the annual inflow of water, the annual inflow of sediment and the total storage capacity of the reservoir, the siltation process of the cascade reservoirs during the forecast period is calculated using the Siyam formula.

4. The method according to claim 1, characterized in that: The step of adopting a preset optimal segmentation method to select a combination of water and sediment volumes with the optimal classification quantity from multiple groups of water and sediment volumes of the cascade reservoirs within the forecast period to obtain a feature combination comprises: Calculate the sum of squares of distances between various water and sediment quantities in the corresponding preset numerical interval and the mean of the values ​​in the preset numerical interval to obtain the interval diameter; Calculate the sum of the diameters of the intervals of the same type to obtain the total sum of squares of deviations; Generate a trend curve of the total deviation square sum of each type versus the number of categories, and determine the optimal number of categories based on the inflection point position of the trend curve; A combination of water and sediment amounts with an optimal number of classifications is selected from a plurality of groups of water and sediment amounts of the cascade reservoirs within a forecast period to obtain a feature combination.

5. The method according to claim 1, characterized in that Also includes: The ratio of total sediment discharge ratio of cascade reservoirs to sedimentation balance index is taken as the comprehensive evaluation index of sediment regulation of cascade reservoirs, and a sediment regulation model for cascade reservoirs is constructed.

6. The method according to claim 5, characterized in that The sediment control model of the cascade reservoirs is: η(t)=c1(t)×η'(t)+c2(t)×η”(t) Among them, η(t) is the annual sediment discharge ratio of the reservoir in the tth year, η'(t), η" (t) are the sediment discharge ratios in the flood season and non-flood season in the tth year, c1(t), c2(t) are the proportions of sediment inflow in the flood season and non-flood season in the tth year to the annual sediment inflow, Q in '(t), Q in ” (t) is the amount of water entering the reservoir during the flood season and non-flood season in the tth year.

7. A cascade reservoir sediment control device, characterized in that: include: The sedimentation process acquisition module is used to obtain the sedimentation process of the cascade reservoirs during the forecast period; A water and sediment volume acquisition module is used to acquire multiple groups of water and sediment volumes of the cascade reservoirs during the forecast period according to the sediment deposition process of the cascade reservoirs during the forecast period, each group of water and sediment volumes including the inflow water volume, the inflow sediment volume and the effective storage capacity; A feature combination acquisition module is used to select a combination of water and sediment with the best classification quantity from multiple groups of water and sediment quantities of the cascade reservoirs within the forecast period by using a preset optimal segmentation method to obtain a feature combination; The optimal solution solving module is used to solve each of the feature combinations based on the cascade reservoir sediment control model, and select the optimal sediment control solution according to the solution results.

8. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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