Optimization method and device for dam inflow correction

Abstract

The present invention provides a method and apparatus for optimizing dam inflow correction. The method for optimizing dam inflow correction includes the steps of obtaining initial dam inflow values ​​for multiple time periods, inputting the initial dam inflow values ​​for each time period into a pre-established multi-objective optimization model, and solving the multi-objective optimization model to obtain a non-dominated solution set, wherein the optimization variables of the multi-objective optimization model include weights and weight distribution coefficients for each time period, and any non-dominated solution in the non-dominated solution set includes a weight value and weight distribution coefficient value for each time period and a dam inflow correction value for each time period, and the dam inflow correction value is calculated based on the initial values, weights, weight distribution coefficients, and initial values ​​for adjacent time periods, and selecting an optimized dam inflow correction value for each time period based on the non-dominated solution set of dam inflow correction values. According to the present invention, by introducing variable weights and distribution coefficients when correcting dam inflow, the correction process can be prevented from being too smooth, which would affect the flood peak correction results.

Description

[Technical Field]

[0001] FIELD OF THE INVENTION Embodiments of the present invention relate to the technical field of dam regulation and hydrological measurement, and more particularly to a method and apparatus for optimizing dam inflow correction. [Background technology]

[0002] Dam inflow is usually calculated from the difference in reservoir volume and the dam outflow. However, due to calculation errors in the difference in reservoir volume and the dam outflow, the calculated dam inflow can have sawtooth fluctuations or negative values. Therefore, correcting the dam inflow to conform to natural laws has been a long-standing research topic in the field of hydrology.

[0003] Currently, dam regulators always use the simple and easy-to-implement moving average method, combined with artificial experience, to correct dam inflow. The moving average method is calculated by summing the weighted values ​​of dam inflow at a target time and multiple times before and after the target time. However, the weights used in the current moving average method are fixed, and the moving average method does not consider the influence of different flow levels on the correction value. Since the weights are the same for the same flood process, the correction process becomes too smooth, which affects the correction results of flood peaks, such as excessive reduction in flood peak flow and delay in peak appearance time. Summary of the Invention [Problem to be solved by the invention]

[0004] In order to avoid the dam inflow correction process from being too smooth and to optimize the dam inflow correction effect, the present invention proposes a dam inflow correction optimization method and device. [Means for solving the problem]

[0005] In a first aspect, the present invention provides a method for producing a method of manufacturing a semiconductor device comprising: A step of obtaining initial dam inflow values ​​for a plurality of target periods; a step of inputting the initial dam inflow value for each target period into a multi-objective optimization model constructed in advance, and obtaining a non-dominated solution set by solving the multi-objective optimization model, wherein the optimization variables of the multi-objective optimization model include a weight and a weight division coefficient for each target period, and any non-dominated solution in the non-dominated solution set includes a weight value and a weight division coefficient value for each target period, and a dam inflow correction value for each target period, and the dam inflow correction value is calculated based on the initial dam inflow value for each target period, the weight value for the target period, the weight division coefficient for the target period, and the dam inflow initial value for an adjacent period; and selecting an optimized value of the dam inflow correction for each target period based on a non-dominated solution set of the dam inflow correction values.

[0006] Using the above method, dynamic weights that take time and flow rate into account are introduced into the traditional moving average method, and a multi-objective optimization model is constructed. The weights and division coefficients for each target period are used as optimization variables to calculate the optimized value of dam inflow correction for each target period. Different weight values ​​and weight division coefficient values ​​are used according to the dam flow rate, and the short-term flow rate sudden change characteristics of flood peak flow are maintained, so that the dam inflow correction effect is better and the correction process is prevented from becoming too smooth.

[0007] In combination with the first aspect, in a first embodiment of the first aspect, the multi-objective optimization model includes objective functions, and the objective functions include a function for minimizing a cumulative sum of fluctuation ranges of dam inflows over adjacent time periods, a function for minimizing a root mean square error of water level simulation, and a function for minimizing a water volume balance index.

[0008] In combination with the first aspect or the first embodiment of the first aspect, in a second embodiment of the first aspect, the multi-objective optimization model includes parameter constraints, and the parameter constraints include a water volume balance constraint, a dam capacity constraint, and a water level-capacity relationship.

[0009] In combination with the first aspect, in a third embodiment of the first aspect, the dam inflow correction value is calculated by weighted addition of the dam inflow initial value for each target period, the weight value for the target period, the dam inflow initial value for the adjacent period, and the provisional weight for the adjacent period, and the provisional weight for the adjacent period is determined based on the weight value for the target period and the weight division coefficient value.

[0010] In combination with the first example of the first aspect, in a fourth example of the first aspect, a function for minimizing the cumulative sum of the fluctuation range of the dam inflow for adjacent periods is constructed based on the difference between the dam inflow correction values ​​for each adjacent period, and the weight value and weight division coefficient value for each period are calculated based on the function for minimizing the cumulative sum of the fluctuation range of the dam inflow for adjacent periods so that the cumulative sum of the differences between the dam inflow correction values ​​for each adjacent period is minimized.

[0011] In combination with the first embodiment of the first aspect, in a fifth embodiment of the first aspect, a function for minimizing the root mean square error of the water level simulation is constructed based on the root mean square of the difference between the water level correction value and the actual water level measurement value for each period, and the weight value and weight division coefficient value for each period are calculated based on the function for minimizing the root mean square error of the water level simulation so that the root mean square of the difference between the water level correction value and the actual water level measurement value for each period is minimized.

[0012] In combination with the first embodiment of the first aspect, in the sixth embodiment of the first aspect, a minimization function for the water volume balance index is constructed based on the difference between the corrected dam inflow value and the actual measured dam outflow value for each period, and the weight value and weight division coefficient value for each period are calculated based on the minimization function for the water volume balance index so that the water volume balance index is minimized.

[0013] In combination with the first aspect, in a seventh example of the first aspect, after the step of acquiring initial dam inflow values ​​for a plurality of target periods, and before the step of inputting the initial dam inflow values ​​for each target period into a pre-constructed multi-objective optimization model, If the obtained initial dam inflow value for the target period is negative, the method further includes changing the negative initial dam inflow value to 0 to obtain a non-negative initial dam inflow value.

[0014] According to the above embodiment, by changing the negative initial value of the dam inflow to 0, the dam inflow conforms to the law of nature.

[0015] In combination with the seventh embodiment of the first aspect, in an eighth embodiment of the first aspect, after the step of obtaining a non-negative initial dam inflow value, In the case of non-negative dam inflow initial values, if the non-negative dam inflow initial values ​​of two adjacent periods are both 0, the method further includes replacing the corresponding target period having the larger absolute value of the dam inflow initial value with the historical average value of the corresponding target period, and leaving the value of the other target period at 0.

[0016] According to the above embodiment, it is guaranteed that consecutive zero values ​​do not appear in adjacent periods of the dam inflow, so that the dam inflow is closer to the actual situation.

[0017] In a second aspect, the present invention provides a method for producing a method of manufacturing a semiconductor device comprising: an acquisition module for acquiring initial dam inflow values ​​for multiple target periods; a model solving module for inputting the initial dam inflow value for each target period into a pre-constructed multi-objective optimization model, and solving the multi-objective optimization model to obtain a non-dominated solution set, wherein the optimization variables of the multi-objective optimization model include a weight and a weight division coefficient for each target period, and any non-dominated solution in the non-dominated solution set includes a weight value and a weight division coefficient value for each target period, and a dam inflow correction value for each target period, and the dam inflow correction value is calculated based on the initial dam inflow value for each target period, the weight value for the target period, the weight division coefficient for the target period, and the dam inflow initial value for an adjacent period; and a selection module for selecting an optimized value of the dam inflow correction for each target period based on a non-dominated solution set of the dam inflow correction values. [Effects of the Invention]

[0018] The above device introduces dynamic weights that take time and flow rate into account into the conventional moving average method, constructs a multi-objective optimization model, and uses the weights and division coefficients for each target period as optimization variables to calculate the optimized value of dam inflow correction for each target period. Different weight values ​​and weight division coefficient values ​​are used according to the dam flow rate, and the short-term flow rate sudden change characteristics of flood peak flow are maintained, so that the dam inflow correction effect is better and the correction process is prevented from becoming too smooth. [Brief explanation of the drawings]

[0019] In order to more clearly describe the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly describes the drawings that need to be described in the description of the specific embodiments or the prior art.

[0020] [Figure 1] 1 is a flowchart of a method for optimizing dam inflow correction in accordance with an example embodiment. [Figure 2] 1 is a specific flowchart of a method for optimizing dam inflow correction according to an exemplary embodiment; [Figure 3(a)] 10 is a process of dam inflow from January 1 to January 18, 2015, at hydroelectric power plant A according to an exemplary embodiment. [Figure 3(b)] 10 is a process of dam inflow from April 1 to April 18, 2015, at hydroelectric power plant A according to an exemplary embodiment. [Figure 3(c)] 10 is a process of dam inflow from July 1 to July 18, 2015, at hydroelectric power plant A according to an exemplary embodiment. [Figure 3(d)] 10 is a process of dam inflow from November 1 to November 18, 2015, at hydroelectric power plant A according to an exemplary embodiment. [Figure 4(a)] 10 is a process of the dam water level of hydroelectric power plant A from January 1 to January 18, 2015 according to an exemplary embodiment. [Figure 4(b)] 10 is a process of the dam water level of the hydroelectric power plant A from April 1 to April 18, 2015 according to an exemplary embodiment. [Figure 4(c)]1 is a process of the dam water level of hydroelectric power plant A from July 1 to July 18, 2015 according to an exemplary embodiment. [Figure 4(d)] 1 is a process of the dam water level of hydroelectric power plant A from November 1 to November 18, 2015 according to an exemplary embodiment. [Figure 5(a)] 10 is a process of dam inflow from January 1 to January 18, 2015, at hydroelectric power plant B according to an exemplary embodiment. [Figure 5(b)] 10 is a process of dam inflow from April 1 to April 18, 2015, at hydroelectric power plant B according to an exemplary embodiment. [Figure 5(c)] 10 is a process of dam inflow from July 1 to July 18, 2015, at hydroelectric power plant B according to an exemplary embodiment. [Figure 5(d)] 10 is a process of dam inflow from November 1 to November 18, 2015, at hydroelectric power plant B according to an exemplary embodiment. [Figure 6(a)] 10 illustrates the process of dam water level from January 1 to January 18, 2015, at hydroelectric power plant B according to an exemplary embodiment. [Figure 6(b)] 10 illustrates the process of dam water level from April 1 to April 18, 2015, at hydroelectric power plant B according to an exemplary embodiment. [Figure 6(c)] 10 is a process of the dam water level of hydroelectric power plant B from July 1 to July 18, 2015 according to an exemplary embodiment. [Figure 6(d)] 10 is a process of the dam water level of hydroelectric power plant B from November 1 to November 18, 2015 according to an exemplary embodiment. [Figure 7(a)] 10 is a process of dam inflow from January 1 to January 18, 2015, at hydroelectric power plant C according to an exemplary embodiment. [Figure 7(b)] 10 is a process of dam inflow from April 1 to April 18, 2015, at hydroelectric power plant C according to an exemplary embodiment. [Figure 7(c)] 10 is a process of dam inflow from July 1 to July 18, 2015, at hydroelectric power plant C according to an exemplary embodiment. [Figure 7(d)] 10 is a process of dam inflow from November 1 to November 18, 2015, at hydroelectric power plant C according to an exemplary embodiment. [Figure 8(a)] 10 illustrates the process of dam water level from January 1 to January 18, 2015, at hydroelectric power plant C according to an exemplary embodiment. [Figure 8(b)] 10 illustrates the process of dam water level from April 1 to April 18, 2015, at hydroelectric power plant C according to an exemplary embodiment. [Figure 8(c)] 10 is a process of the dam water level of hydroelectric power plant C from July 1 to July 18, 2015 according to an exemplary embodiment. [Figure 8(d)] 10 illustrates the process of the dam water level of hydroelectric power plant C from November 1 to November 18, 2015 according to an exemplary embodiment. [Figure 9] FIG. 1 is a structural block diagram of an optimization device for dam inflow correction according to an exemplary embodiment. [Figure 10] FIG. 2 is a schematic diagram of a hardware structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0021] The technical solutions of the present invention will be clearly and completely described below with reference to the drawings.

[0022] Furthermore, the technical features described in the different embodiments of the present invention described below can be combined with each other unless they are mutually inconsistent.

[0023] In order to avoid the dam inflow correction process from being too smooth and to optimize the dam inflow correction effect, the present invention proposes a dam inflow correction optimization method and device.

[0024] 1 is a flowchart of a method for optimizing dam inflow correction according to an exemplary embodiment. As shown in FIG. 1, the method for optimizing dam inflow correction includes the following steps S101 to S103.

[0025] In step S101, initial values ​​of inflow to the dam for a plurality of target periods are acquired.

[0026] Specifically, the initial dam inflow values ​​for multiple target periods are back-calculated from the difference in storage volume and the dam outflow. Due to calculation errors in the difference in storage volume and the dam outflow, the initial dam inflow values ​​obtained from the difference in storage volume and the dam outflow have the problem of sawtooth fluctuations or negative values. Therefore, it is necessary to perform correction optimization of the initial dam inflow values.

[0027] In step S102, the initial dam inflow value for each target period is input into a pre-constructed multi-objective optimization model, and a solution is obtained for the multi-objective optimization model to obtain a non-dominated solution set, the optimization variables of the multi-objective optimization model include a weight and a weight distribution coefficient for each target period, and any non-dominated solution in the non-dominated solution set includes a weight value and a weight distribution coefficient value for each target period, and a dam inflow correction value for each target period, and the dam inflow correction value is calculated based on the initial dam inflow value for each target period, the weight value for the target period, the weight distribution coefficient for the target period, and the dam inflow initial value for an adjacent period.

[0028] Specifically, a multi-objective optimization model is created according to the objective function and parameter constraints, and a non-dominated solution set including weight values, weight division coefficient values, and dam inflow correction values ​​for each target period is obtained by the multi-objective optimization model. Compared with the fixed weights used in the traditional moving average method, in the embodiment of the present invention, when dam inflow correction is performed for different flow rates, the corresponding weight values ​​for each target period are different, which makes the dam inflow correction effect better and more suited to actual conditions.

[0029] Specifically, the adjacent periods of each target period are the two adjacent periods before and after the target period. When a certain period is used to correct the initial dam inflow value of the adjacent target period, the weight value used for the period is calculated by the weight value of the target period and the weight division coefficient value. When the period is used as the target period and the initial dam inflow value needs to be corrected, the weight value used for the period is the weight value determined by the multi-objective optimization model.

[0030] In step S103, an optimized value of the dam inflow correction value for each target period is selected based on the non-dominated solution set of the dam inflow correction value.

[0031] Using the above method, dynamic weights that take time and flow rate into account are introduced into the traditional moving average method, and a multi-objective optimization model is constructed. The weights and division coefficients for each target period are used as optimization variables to calculate the optimized value of dam inflow correction for each target period. Different weight values ​​and weight division coefficient values ​​are used according to the dam flow rate, and the short-term flow rate sudden change characteristics of flood peak flow are maintained, so that the dam inflow correction effect is better and the correction process is prevented from becoming too smooth.

[0032] In one example, after performing the above step S101, before performing the above step S102, the optimization method for dam inflow correction according to the embodiment of the present invention further includes a step of pre-processing the obtained initial dam inflow values ​​for multiple target periods, and the pre-processing process of the initial dam inflow values ​​is specifically as follows: If the obtained initial value of the dam inflow for the target period is negative, the negative initial value of the dam inflow is changed to 0 to obtain a non-negative initial value of the dam inflow, thereby ensuring that all values ​​of the dam inflow sequence are non-negative and more in line with the laws of nature.

[0033] In another example, the pre-processing process for the initial dam inflow value is In the case of non-negative initial dam inflow values, if the non-negative initial dam inflow values ​​of two adjacent periods are both 0, the method further includes a step of replacing the corresponding target period with the larger absolute value of the initial dam inflow value by the historical average value of the corresponding target period, and leaving the value of the other target period at 0, thereby ensuring that consecutive 0 values ​​do not appear in adjacent periods of the dam inflow, making the dam inflow closer to the actual situation.

[0034] In one example, the multi-objective optimization model in step S102 includes objective functions. The objective functions include a function for minimizing the cumulative sum of the fluctuation range of dam inflow over adjacent periods, a function for minimizing the root mean square error of the water level simulation, and a function for minimizing the water volume balance index. According to an embodiment of the present invention, the root mean square error of the water level simulation and the water volume balance index can be minimized while ensuring the continuity of the dam inflow process. In other words, the water level correction value and the capacity fluctuation range correction value related to the dam inflow correction value can be made to approach the actual measured water level value and the actual measured capacity fluctuation range value while ensuring the continuity of the dam inflow process.

[0035] In one alternative embodiment, a function for minimizing the cumulative sum of the fluctuation range of reservoir dam inflow for adjacent periods is constructed based on the difference between the reservoir dam inflow correction values ​​for each adjacent period, and weight values ​​and weight division coefficient values ​​for each period are calculated based on the function for minimizing the cumulative sum of the fluctuation range of reservoir dam inflow for adjacent periods so that the cumulative sum of the differences between the reservoir dam inflow correction values ​​for each adjacent period is minimized.

[0036] TIFF2025532316000002.tif39164

[0037] In one alternative embodiment, a function for minimizing the root mean square error of the water level simulation is constructed based on the root mean square of the difference between the water level correction value and the actual water level measurement value for each period, and the weight value and weight division coefficient value for each period are calculated based on the function for minimizing the root mean square error of the water level simulation so that the root mean square of the difference between the water level correction value and the actual water level measurement value for each period is minimized.

[0038] TIFF2025532316000003.tif28164

[0039] In one alternative embodiment, a minimization function for the water volume balance index is constructed based on the difference between the corrected dam inflow value and the measured dam outflow value for each period, and weight values ​​and weight division coefficient values ​​for each period are calculated based on the minimization function for the water volume balance index so that the water volume balance index is minimized.

[0040] TIFF2025532316000004.tif57164

[0041] In one example, the multi-objective optimization model in step S102 includes parameter constraints. The parameter constraints include a water volume balance constraint, a dam capacity constraint, and a water level-capacity relationship. By determining the objective function and the parameter constraints, a multi-objective optimization model for dam inflow correction is constructed, and a non-dominated solution set is obtained using the model. Any non-dominated solution in the non-dominated solution set includes a weight value and a weight division coefficient value for each target period, and a dam inflow correction value for each target period. The dam inflow correction value for each target period is calculated based on the weight value and weight division coefficient for each target period, and the dam inflow initial value for an adjacent period.

[0042] JPEG2025532316000005.jpg25164

[0043] JPEG2025532316000006.jpg23164

[0044] JPEG2025532316000007.jpg29164

[0045] In one example, the input quantities of the multi-objective optimization model in step S102 above include the initial dam inflow value for each target period as well as the actual water level and dam outflow value for each target period, and the non-dominated solution set obtained by the model includes the weight value and weight division coefficient value for each target period, the dam inflow correction value for each target period as well as the water level correction value for each target period.

[0046] JPEG2025532316000008.jpg135164

[0047] In the embodiment of the present invention, in the multi-objective optimization model, the optimization variables are the weight value and weight division coefficient of each period, i.e., {ω1,...,ω t ,...,ω n} and {λ1,...,λ t ,...,λ n}, which is a total of 2n optimization variables. In order to avoid problems such as a decrease in calculation efficiency due to too many optimization variables and rapid descent into a local optimum, it is preferable that the total number of periods of dam inflow n is 100 or less. If the total number of periods of dam inflow n is greater than 100, they can be optimized and corrected one by one using the sliding window method.

[0048] When the objective functions selected are a function minimizing the cumulative sum of the fluctuation range of dam inflow between adjacent periods, a function minimizing the root mean square error of water level simulation, and a function minimizing the water volume balance index, a multi-objective heuristic intelligent optimization algorithm is used to find a solution for the constructed multi-objective optimization model through initialization, fitness evaluation and comparison, optimization variable adjustment, population iterative calculation, etc., to obtain a non-dominated solution set for the dam inflow correction value, a specific process of which is shown in Figure 2. In the embodiment of the present invention, the multi-objective heuristic intelligent optimization algorithm is not limited, and for example, algorithms such as NSGA-2 / 3, SCE-UA, PA-DDS, etc. can be used to find a solution for the model.

[0049] In one example, in step S103, an optimal solution for optimizing the dam inflow correction value is selected from the obtained non-dominated solution set of the dam inflow correction value using a multi-attribute decision-making method. The embodiment of the present invention does not specifically limit the multi-attribute decision-making method, and may be a fuzzy optimization method, a projection pursuit method, a technique for order preference by similarity to an ideal solution (TOPSIS), etc.

[0050] In another example, three hydropower stations A, B, and C in the Yangtze River basin were selected as examples to compare the optimization method for dam inflow correction according to the embodiment of the present invention with the three-point moving average method. Figures 3, 5, and 7 show the dam inflow process for Hydropower Station A, Hydropower Station B, and Hydropower Station C in 2015, respectively, where the abscissa represents the corresponding period number and the ordinate represents the dam inflow discharge, i.e., the dam inflow. Figures 4, 6, and 8 show the dam water level process for Hydropower Station A, Hydropower Station B, and Hydropower Station C in 2015, respectively, where the abscissa represents the corresponding period number and the ordinate represents the dam water level.

[0051] In Figures 3 to 8, Figures 3(a), 3(b), 3(c), and 3(d) show the dam inflow process for four periods of hydroelectric power plant A: January 1st to January 18th, April 1st to April 18th, July 1st to July 18th, and November 1st to November 18th, 2015, respectively. Figures 4(a), 4(b), 4(c), and 4(d) show the dam inflow process for hydroelectric power plant A: January 1st to January 18th, 2015, respectively. Figure 5(a), Figure 5(b), Figure 5(c), and Figure 5(d) show the dam water level process for four periods: January 18th, April 1st to April 18th, July 1st to July 18th, and November 1st to November 18th, respectively. Figure 5(a), Figure 5(b), Figure 5(c), and Figure 5(d) show the dam inflow process for four periods: January 1st to January 18th, April 1st to April 18th, July 1st to July 18th, and November 1st to November 18th, 2015, respectively. Figures 6(a), 6(b), 6(c), and 6(d) show the dam water level process for four periods of 2015 at Hydropower Plant B: January 1st to January 18th, April 1st to April 18th, July 1st to July 18th, and November 1st to November 18th. Figures 7(a), 7(b), 7(c), and 7(d) show the dam water level process for Hydropower Plant C: January 1st to January 18th, 2015. , the dam inflow process for four periods: April 1st to April 18th, July 1st to July 18th, and November 1st to November 18th. Figures 8(a), 8(b), 8(c), and 8(d) show the dam water level process for four periods: January 1st to January 18th, April 1st to April 18th, July 1st to July 18th, and November 1st to November 18th, 2015, respectively, for Hydropower Plant C.

[0052] When calculating the dam inflow discharge and dam water level for each of the periods of January 1st to January 18th, April 1st to April 18th, July 1st to July 18th, and November 1st to November 18th, the calculation period for each period is 3 hours. Optimization is performed using the PA-DDS algorithm to obtain a non-dominated solution set of the dam inflow discharge correction value for each target period, and then the final optimized value of the dam inflow discharge correction is determined using the fuzzy optimization method.

[0053] As can be seen from Figures 3 to 8, the dam inflow after correction is smoother than the actual measured process, and the continuity of the dam inflow process is higher. Compared with the three-point moving average, the dam inflow calculated by the method of the present invention is not too smooth, the flood peak flow is more in line with the actual situation, and is not excessively reduced, resulting in a better correction effect of the dam inflow.

[0054] Tables 1 to 3 show a comparison of the corresponding objective function values ​​for hydroelectric power plants A, B, and C in 2015, respectively, when correcting the dam inflow using the method according to the embodiment of the present invention and when correcting the dam inflow using the three-point moving average method.

[0055] TIFF2025532316000009.tif95163

[0056] TIFF2025532316000010.tif103163

[0057] TIFF2025532316000011.tif96163

[0058] As can be seen from Tables 1 to 3, compared with the three-point moving average, the method of the present invention can significantly reduce the root mean square error and water volume balance index of the water level simulation while ensuring the continuity of the dam inflow, so that the corrected dam inflow and dam water level can be maximally adapted to the actual situation.

[0059] Based on the same inventive concept, an embodiment of the present invention further provides an optimization apparatus for dam inflow correction, which, as shown in FIG. 9, includes an acquisition module 901, a model solving module 902, and a selection module 903.

[0060] The acquisition module 901 is used to acquire the initial value of the inflow into the reservoir for multiple target periods. For details, please refer to the description of step S101 in the above embodiment, and the description will not be repeated here.

[0061] The model solving module 902 inputs the initial dam inflow values ​​for each target period into a pre-established multi-objective optimization model, solves the multi-objective optimization model, and obtains a non-dominated solution set, where the optimization variables of the multi-objective optimization model include weights and weight distribution coefficients for each target period, and any non-dominated solution in the non-dominated solution set includes weights and weight distribution coefficients for each target period, and a dam inflow correction value for each target period, where the dam inflow correction value is calculated based on the initial dam inflow values ​​for each target period, the weights for the target period, the weight distribution coefficients for the target period, and the initial dam inflow values ​​for adjacent periods. For details, please refer to the description of step S102 in the above embodiment, and this will not be repeated here.

[0062] The selection module 903 selects an optimized value of the dam inflow correction value for each target period based on the non-dominated solution set of the dam inflow correction value. For details, please refer to the description of step S103 in the above embodiment, and the description will not be repeated here.

[0063] In one example, in the model solving module 902, the objective functions in the multi-objective optimization model include a function for minimizing the cumulative sum of fluctuation ranges of dam inflows over adjacent periods, a function for minimizing the root mean square error of water level simulation, and a function for minimizing the water volume balance index. For details, please refer to the description in the above example, and the description will not be repeated here.

[0064] In one alternative embodiment, in the model solving module 902, a function for minimizing the cumulative sum of the fluctuation range of the dam inflow for adjacent periods is constructed based on the difference between the dam inflow correction values ​​for each adjacent period, and the weight value and the weight division coefficient value for each period are calculated based on the function for minimizing the cumulative sum of the fluctuation range of the dam inflow for adjacent periods so that the cumulative sum of the difference between the dam inflow correction values ​​for each adjacent period is minimized. For details, please refer to the description in the above embodiment, and the description will not be repeated here.

[0065] In another alternative embodiment, in the model solving module 902, a function for minimizing the root mean square error of the water level simulation is constructed based on the root mean square of the difference between the water level correction value and the actual water level measurement value for each period, and the weight value and the weight division coefficient value for each period are calculated based on the function for minimizing the root mean square error of the water level simulation so that the root mean square of the difference between the water level correction value and the actual water level measurement value for each period is minimized. For details, please refer to the description in the above embodiment, and will not be repeated here.

[0066] In yet another alternative embodiment, in the model solving module 902, a minimization function of the water volume balance index is constructed based on the difference between the corrected dam inflow value and the measured dam outflow value for each period, and the weight value and the weight division coefficient value for each period are calculated based on the minimization function of the water volume balance index so that the water volume balance index is minimized. For details, please refer to the description in the above embodiment, and the description will not be repeated here.

[0067] In another example, the parameter constraints in the multi-objective optimization model in the model solving module 902 include a water volume balance constraint, a dam capacity constraint, and a water level-capacity relationship. For details, please refer to the description in the above embodiment, and the description will not be repeated here.

[0068] In one example, in the model solving module 902, the dam inflow correction value is calculated by weighted addition of the dam inflow initial value of each target period, the weight value of the target period, the dam inflow initial value of an adjacent period, and the provisional weight of the adjacent period, and the provisional weight of the adjacent period is determined based on the weight value of the target period and the weight division coefficient value. For details, please refer to the description in the above embodiment, and the description will not be repeated here.

[0069] In another example, the device is further used to change the negative initial dam inflow value to 0 when the obtained initial dam inflow value for the target period is negative, and obtain a non-negative initial dam inflow value. For details, please refer to the description in the above embodiment, and the description will not be repeated here.

[0070] In one example, the device is further used in the following manner: in the case of a non-negative initial dam inflow value, if the non-negative initial dam inflow values ​​of two adjacent periods are both 0, the corresponding target period having a larger absolute value of the initial dam inflow value is replaced with the historical average value of the corresponding target period, and the value of the other target period remains 0. For details, please refer to the description in the above embodiment, and the description will not be repeated here.

[0071] The specific limitations and beneficial effects of the above device can be referred to the limitations on the optimization method for dam inflow correction above, and will not be described again here. Each of the above modules can be realized in whole or in part by software, hardware, or a combination thereof. Each of the above modules can be incorporated in the processor of a computer device in the form of hardware, or can be located independently of the processor, or can be stored in the memory of a computer device in the form of software, allowing the processor to call and execute operations corresponding to each of the above modules.

[0072] Fig. 10 is a schematic diagram of the hardware structure of a computer device according to an exemplary embodiment. As shown in Fig. 10, the device includes one or more processors 1010 and a memory 1020, where the memory 1020 includes persistent memory, volatile memory, and a hard disk. Fig. 10 shows an example of one processor 1010. The device may further include an input device 1030 and an output device 1040.

[0073] The processor 1010, memory 1020, input device 1030, and output device 1040 can be connected by a bus or other methods, and FIG. 10 shows them connected by a bus as an example.

[0074] The processor 1010 may be a central processing unit (CPU). The processor 1010 may also be other general-purpose processors, chips such as digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware units, or a combination of the above chips. The general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.

[0075] The memory 1020 includes a persistent memory, a volatile memory, and a hard disk as a non-transitory computer-readable storage medium, and can be used to store non-transitory software programs, non-transitory computer-executable programs and modules, such as corresponding program instructions / modules of the dam inflow correction optimization method in the embodiments of the present application. The processor 1010 executes the non-transitory software programs, instructions, and modules stored in the memory 1020 to perform various functional applications and data processing of the server, i.e., to realize any of the above-mentioned dam inflow correction optimization methods.

[0076] The memory 1020 may include a program storage area and a data storage area. The program storage area may store an operating system and / or application programs required for at least one function, and the data storage area may store data required for use. Additionally, the memory 1020 may include high-speed random access memory and / or non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 1020 may optionally include memory located remotely from the processor 1010, and these remote memories may be connected to the data processing device via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0077] The input device 1030 can receive input digital or textual information and generate signal inputs related to user settings and function control. The output device 1040 can include a display device such as a display screen.

[0078] The one or more modules are stored in memory 1020 and, when executed by one or more processors 1010, perform the method illustrated in FIG.

[0079] The above-mentioned product can implement the method according to the embodiment of the present invention, and has corresponding functional modules and beneficial effects for implementing the method. For technical details not described in detail in this embodiment, please refer to the relevant description of the embodiment shown in FIG.

[0080] An embodiment of the present invention further provides a non-transitory computer storage medium, the computer storage medium having computer-executable instructions stored thereon, the computer-executable instructions being capable of performing the optimization method in any of the above method embodiments. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD), a solid-state drive (SSD), etc., and the storage medium may also include a combination of the above types of memory.

Claims

1. A method for optimizing dam inflow correction, comprising: A step of obtaining initial dam inflow values ​​for a plurality of target periods; a step of inputting the initial dam inflow value for each target period into a multi-objective optimization model constructed in advance, and obtaining a non-dominated solution set by solving the multi-objective optimization model, wherein the optimization variables of the multi-objective optimization model include a weight and a weight division coefficient for each target period, and any non-dominated solution in the non-dominated solution set includes a weight value and a weight division coefficient value for each target period, and a dam inflow correction value for each target period, and the dam inflow correction value is calculated based on the initial dam inflow value for each target period, the weight value for the target period, the weight division coefficient for the target period, and the dam inflow initial value for an adjacent period; and selecting an optimized value of the dam inflow correction for each target period based on a non-dominated solution set of the dam inflow correction values.

2. 2. The method of claim 1, wherein the multi-objective optimization model includes objective functions, which include a function for minimizing the cumulative sum of fluctuation ranges of dam inflows over adjacent periods, a function for minimizing the root mean square error of water level simulation, and a function for minimizing a water volume balance index.

3. The method according to claim 1 or 2, wherein the multi-objective optimization model includes parameter constraints, and the parameter constraints include a water balance constraint, a dam capacity constraint, and a water level-capacity relationship.

4. The method according to claim 1, characterized in that the dam inflow correction value is calculated by weighted addition of the initial dam inflow value for each target period, the weight value for the target period, the initial dam inflow value for an adjacent period, and the provisional weight for the adjacent period, and the provisional weight for the adjacent period is determined based on the weight value and weight division coefficient value for the target period.

5. The method according to claim 2, characterized in that the minimization function of the cumulative sum of the fluctuation range of the dam inflow between adjacent periods is constructed based on the difference between the dam inflow correction values ​​between each adjacent period, and the weight value and weight division coefficient value for each period are calculated based on the minimization function of the cumulative sum of the fluctuation range of the dam inflow between adjacent periods so that the cumulative sum of the differences between the dam inflow correction values ​​between each adjacent period is minimized.

6. The method according to claim 2, characterized in that a function for minimizing the root mean square error of the water level simulation is constructed based on the root mean square of the difference between the water level correction value and the actual water level measurement value for each period, and the weight value and weight division coefficient value for each period are calculated based on the function for minimizing the root mean square error of the water level simulation so that the root mean square of the difference between the water level correction value and the actual water level measurement value for each period is minimized.

7. The method according to claim 2, characterized in that a minimization function of the water volume balance index is constructed based on the difference between the corrected dam inflow value and the measured dam outflow value for each period, and the weight value and weight division coefficient value for each period are calculated based on the minimization function of the water volume balance index so that the water volume balance index is minimized.

8. After the step of acquiring initial dam inflow values ​​for a plurality of target periods, and before the step of inputting the initial dam inflow values ​​for each target period into a pre-constructed multi-objective optimization model, 2. The method of claim 1, further comprising the step of: if the obtained initial dam inflow value for the target period is negative, changing the negative initial dam inflow value to 0 to obtain a non-negative initial dam inflow value.

9. After obtaining the initial non-negative dam inflow value, the method further comprises:

9. The method of claim 8, further comprising the step of: in the case of the non-negative dam inflow initial values, if the non-negative dam inflow initial values ​​of two adjacent time periods are both 0, replacing the corresponding time period having the larger absolute value of the dam inflow initial value with the historical average value of the corresponding time period, and leaving the value of the other time period at 0.

10. An optimization device for dam inflow correction, comprising: an acquisition module for acquiring initial dam inflow values ​​for multiple target periods; a model solving module for inputting the initial dam inflow value for each target period into a pre-constructed multi-objective optimization model, and solving the multi-objective optimization model to obtain a non-dominated solution set, wherein the optimization variables of the multi-objective optimization model include a weight and a weight division coefficient for each target period, and any non-dominated solution in the non-dominated solution set includes a weight value and a weight division coefficient value for each target period, and a dam inflow correction value for each target period, and the dam inflow correction value is calculated based on the initial dam inflow value for each target period, the weight value for the target period, the weight division coefficient for the target period, and the dam inflow initial value for an adjacent period; and a selection module for selecting an optimized value of the dam inflow correction for each target period based on a non-dominated solution set of the dam inflow correction values.

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