WO2024066902A1

WO2024066902A1 - Reservoir-inflow runoff correction optimization method and apparatus

Info

Publication number: WO2024066902A1
Application number: PCT/CN2023/116164
Authority: WO
Inventors: 张玮; 刘瑞阔; 刘志武; 黄康迪; 张璐; 李梦杰
Original assignee: 中国长江三峡集团有限公司
Priority date: 2022-09-28
Filing date: 2023-08-31
Publication date: 2024-04-04
Also published as: CN115659602A

Abstract

Provided in the present invention are a reservoir-inflow runoff correction optimization method and apparatus. The reservoir-inflow runoff correction optimization method comprises: acquiring reservoir-inflow runoff initial values of a plurality of target time periods; inputting a reservoir-inflow runoff initial value of each target time period into a pre-established multi-objective optimization model, and solving the multi-objective optimization model to obtain a non-inferior solution set, wherein optimization variables of the multi-objective optimization model comprise a weight of each target time period and a weight segmentation coefficient thereof, any non-inferior solution in the non-inferior solution set comprises a weight value of each target time period and a weight segmentation coefficient value thereof, and a reservoir-inflow runoff correction value of each target time period, the reservoir-inflow runoff correction value being obtained, by means of calculation, according to the initial value, the weight value, the weight segmentation coefficient and an initial value of an adjacent time period; and selecting an optimized reservoir-inflow runoff correction value of each target time period on the basis of a non-inferior solution set of the reservoir-inflow runoff correction value. By means of the present invention, varying weights and segmentation coefficients are introduced during reservoir-inflow runoff correction, thereby preventing a correction process from being too smooth to affect a correction result of a flood peak.

Description

Inflow runoff correction optimization method and device

Technical Field

The embodiments of the present invention relate to the technical field of reservoir scheduling and hydrological measurement, and in particular to a method and device for optimizing reservoir runoff correction.

Background technique

Reservoir inflow is usually calculated by reverse calculation of reservoir tolerance and outflow. Due to the calculation errors of reservoir tolerance and outflow, the inflow runoff obtained faces the problem of sawtooth fluctuation or even negative value. Therefore, it has always been a research topic in the field of hydrological technology to correct the inflow runoff to make it conform to the laws of nature.

At present, reservoir dispatching personnel often use the simple sliding average method and combine it with manual experience to correct the inflow runoff. The sliding average method is obtained by summing the weighted values of the inflow runoff at the target time and multiple times before and after the target time. However, in the current sliding average method, the weights used are fixed and unchanged. The sliding average method does not consider the impact of different flow levels on the correction value. For the same flood process, the weights are the same, which will cause the correction process to be too smooth and affect the correction results of the flood peak, including excessive reduction of the flood peak flow and delayed peak time.

Summary of the invention

In order to avoid the correction process of the inflow runoff being too smooth and optimize the correction effect of the inflow runoff, the present invention proposes an inflow runoff correction optimization method and device.

In a first aspect, the present invention provides a method for optimizing runoff correction, the method comprising:

Obtain the initial values of inflow runoff for multiple target periods;

Inputting the initial value of the inflow runoff of each target period into a pre-established multi-objective optimization model, solving the multi-objective optimization model to obtain a non-inferior solution set, wherein the optimization variables of the multi-objective optimization model include the weight and weight division coefficient of each target period, and any non-inferior solution in the non-inferior solution set includes the weight value and weight division coefficient value of each target period, and the inflow runoff correction value of each target period, and the inflow runoff correction value is calculated based on the inflow runoff initial value of each target period, the weight value of the target period, the weight division coefficient of the target period, and the inflow runoff initial value of adjacent periods;

Based on the non-inferior solution set of the inflow runoff correction value, the optimal value of the inflow runoff correction for each target period is selected.

Through the above method, the dynamic weights considering time and flow are introduced into the traditional sliding average method. By constructing a multi-objective optimization model, the weight and division coefficient of each target period are used as optimization variables, and the optimized value of the inflow runoff correction for each target period is calculated. Different weight values and weight division coefficient values are used for different flows of the reservoir, and the short-term flow mutation characteristics of the peak flow are retained, so that the inflow runoff correction effect is better and the correction process is avoided from being too smooth.

In combination with the first aspect, in a first embodiment of the first aspect, the multi-objective optimization model includes an objective function, which includes a cumulative sum minimization function of runoff fluctuations in adjacent time periods, a water level simulation root mean square error minimization function, and a water balance index minimization function.

In combination with the first aspect or the first embodiment of the first aspect, in the second embodiment of the first aspect, the multi-objective optimization model includes parameter constraints, and the parameter constraints include water balance constraints, reservoir capacity constraints and water level-reservoir capacity relationship.

In combination with the first aspect, in the third embodiment of the first aspect, the correction value of the inflow runoff is calculated by weighted summing the initial value of the inflow runoff in each target time period, the weight value of the target time period, the initial value of the inflow runoff in the adjacent time period and the temporary weight of the adjacent time period. The temporary weight of the adjacent time period is determined based on the weight value of the target time period and the weight division coefficient value.

In combination with the first embodiment of the first aspect, in the fourth embodiment of the first aspect, a function for minimizing the cumulative sum of variations of inflow runoff in adjacent time periods is constructed based on the difference in the corrected values of inflow runoff in each adjacent time period, and the weight value and weight division coefficient value of each time period are calculated based on the function for minimizing the cumulative sum of variations of inflow runoff in adjacent time periods, so that the cumulative sum of the differences in the corrected values of inflow runoff in each adjacent time period is minimized.

In combination with the first embodiment of the first aspect, in the fifth embodiment of the first aspect, a water level simulation root mean square error minimization function is constructed according to the root mean square of the difference between the corrected water level value and the actual measured water level value in each time period, and the weight value and weight division coefficient value of each time period are calculated according to the water level simulation root mean square error minimization function, so that the root mean square of the difference between the corrected water level value and the actual measured water level value in each time period is minimized.

In combination with the first embodiment of the first aspect, in the sixth embodiment of the first aspect, a water balance index minimization function is constructed based on the difference between the corrected value of the inflow runoff in each time period and the actual measured value of the reservoir outflow, and the weight value and weight division coefficient value of each time period are calculated according to the water balance index minimization function to minimize the water balance index.

In combination with the first aspect, in a seventh embodiment of the first aspect, after the step of obtaining the initial values of the inflow runoff in the plurality of target time periods and before the step of inputting the initial values of the inflow runoff in each target time period into the pre-established multi-objective optimization model, the method further includes:

If the obtained initial value of the inflow runoff in the target period is a negative value, the negative initial value of the inflow runoff is changed to 0 to obtain a non-negative initial value of the inflow runoff.

Through the above embodiment, the negative initial value of the inflow runoff is changed to 0, so that the obtained inflow runoff is more in line with the law of nature.

In combination with the seventh embodiment of the first aspect, in an eighth embodiment of the first aspect, after the step of obtaining the non-negative initial value of the inflow runoff, the method further includes:

For non-negative initial values of inflow runoff, if there are two adjacent time periods whose initial values are both 0, the target period corresponding to the one with the larger absolute value of the initial value of inflow runoff will be replaced by the historical average value of the corresponding target period, and the value of the other target period will remain 0.

Through the above embodiment, it is ensured that there are no consecutive 0 values in adjacent time periods of the inflow runoff, so that the inflow runoff is closer to the actual situation.

In a second aspect, the present invention provides a device for optimizing runoff correction in a reservoir, the device comprising:

An acquisition module is used to obtain the initial values of inflow runoff in multiple target periods;

A model solving module, used for inputting the initial value of the inflow runoff of each target period into a pre-established multi-objective optimization model, solving the multi-objective optimization model, and obtaining a non-inferior solution set, wherein the optimization variables of the multi-objective optimization model include the weight and weight division coefficient of each target period, and any non-inferior solution in the non-inferior solution set includes the weight value and weight division coefficient value of each target period, and the inflow runoff correction value of each target period, and the inflow runoff correction value is calculated based on the inflow runoff initial value of each target period, the weight value of the target period, the weight division coefficient of the target period, and the inflow runoff initial value of adjacent periods;

The selection module is used to select the optimized value of the inflow runoff correction for each target period based on the non-inferior solution set of the inflow runoff correction value.

Through the above device, the dynamic weights considering time and flow are introduced into the traditional sliding average method. By constructing a multi-objective optimization model, the weight and division coefficient of each target period are used as optimization variables, and the optimized value of the inflow runoff correction for each target period is calculated. Different weight values and weight division coefficient values are used for different flows of the reservoir, and the short-term flow mutation characteristics of the peak flow are retained, so that the inflow runoff correction effect is better and the correction process is avoided from being too smooth.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the specific implementation of the present invention or the technical solution in the prior art, the drawings required for use in the specific implementation or the description of the prior art will be briefly introduced below.

FIG1 is a flow chart of a method for optimizing runoff correction according to an exemplary embodiment;

FIG2 is a specific flow chart of a method for optimizing runoff correction according to an exemplary embodiment;

FIG3( a ) is a diagram showing the inflow runoff process of hydropower station A during the period from January 1 to January 18 in 2015 according to an exemplary embodiment;

FIG3( b ) is a diagram showing the inflow runoff process of the hydropower station A during the period from April 1 to April 18 in 2015 according to an exemplary embodiment;

FIG3( c ) is a diagram showing the inflow runoff process of the hydropower station A during the period from July 1 to July 18 in 2015 according to an exemplary embodiment;

FIG3( d ) is a diagram showing the inflow runoff process of the hydropower station A during the period from November 1 to November 18 in 2015 according to an exemplary embodiment;

FIG4( a ) is a diagram showing a reservoir water level process of a hydropower station A during the period from January 1 to January 18 in 2015 according to an exemplary embodiment;

FIG4( b ) is a diagram showing the reservoir water level process of hydropower station A during the period from April 1 to April 18 in 2015 according to an exemplary embodiment;

FIG4( c ) is a diagram showing the reservoir water level process of the hydropower station A during the period from July 1 to July 18 in 2015 according to an exemplary embodiment;

FIG4( d ) is a diagram showing the reservoir water level process of the hydropower station A during the period from November 1 to November 18 in 2015 according to an exemplary embodiment;

FIG5(a) is a diagram showing the inflow runoff process of hydropower station B during the period from January 1 to January 18 in 2015 according to an exemplary embodiment;

FIG5( b ) is a diagram showing the inflow runoff process of the hydropower station B during the period from April 1 to April 18 in 2015 according to an exemplary embodiment;

FIG5(c) is a diagram showing the inflow runoff process of the hydropower station B during the period from July 1 to July 18 in 2015 according to an exemplary embodiment;

FIG5(d) is a diagram showing the inflow runoff process of the hydropower station B during the period from November 1 to November 18 in 2015 according to an exemplary embodiment;

FIG6( a ) is a diagram showing the reservoir water level process of a hydropower station B during the period from January 1 to January 18 in 2015 according to an exemplary embodiment;

FIG6( b ) is a diagram showing the reservoir water level process of the hydropower station B during the period from April 1 to April 18 in 2015 according to an exemplary embodiment;

FIG6( c ) is a diagram showing the reservoir water level process of the hydropower station B during the period from July 1 to July 18 in 2015 according to an exemplary embodiment;

FIG6( d ) is a diagram showing the reservoir water level process of the hydropower station B during the period from November 1 to November 18 in 2015 according to an exemplary embodiment;

FIG. 7( a ) is a diagram showing the inflow runoff process of the hydropower station C during the period from January 1 to January 18 in 2015 according to an exemplary embodiment;

FIG. 7( b ) is a diagram showing the inflow runoff process of the hydropower station C during the period from April 1 to April 18 in 2015 according to an exemplary embodiment;

FIG. 7( c ) is a diagram showing the inflow runoff process of the hydropower station C during the period from July 1 to July 18 in 2015 according to an exemplary embodiment;

FIG7( d ) is a diagram showing the inflow runoff process of the hydropower station C during the period from November 1 to November 18 in 2015 according to an exemplary embodiment;

FIG8( a ) is a diagram showing the reservoir water level process of a hydropower station C during the period from January 1 to January 18 in 2015 according to an exemplary embodiment;

FIG8( b ) is a diagram showing the reservoir water level process of the hydropower station C during the period from April 1 to April 18 in 2015 according to an exemplary embodiment;

FIG8( c ) is a diagram showing the reservoir water level process of the hydropower station C during the period from July 1 to July 18 in 2015 according to an exemplary embodiment;

FIG8( d ) is a diagram showing the reservoir water level process of the hydropower station C during the period from November 1 to November 18 in 2015 according to an exemplary embodiment;

FIG9 is a structural block diagram of an inflow runoff correction and optimization device according to an exemplary embodiment;

FIG. 10 is a schematic diagram of a hardware structure of a computer device according to an exemplary embodiment.

Detailed ways

The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings.

In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

Fig. 1 is a flow chart of a method for optimizing the correction of inflow runoff according to an exemplary embodiment. As shown in Fig. 1, the method for optimizing the correction of inflow runoff includes the following steps S101 to S103.

In step S101, initial values of inflow runoff in multiple target time periods are obtained.

Specifically, the initial values of inflow runoff in multiple target periods are calculated by reverse calculation based on the reservoir tolerance and outflow flow. Due to the calculation errors of the reservoir tolerance and outflow flow, the initial values of inflow runoff obtained based on the reservoir tolerance and outflow flow face the problem of sawtooth fluctuations or even negative values. Therefore, it is necessary to correct and optimize the initial values of inflow runoff.

In step S102, the initial value of the inflow runoff in each target time period is input into a pre-established multi-objective optimization model, and the multi-objective optimization model is solved to obtain a non-inferior solution set, wherein the optimization variables of the multi-objective optimization model include the weight and weight division coefficient of each target time period, and any non-inferior solution in the non-inferior solution set includes the weight value and weight division coefficient value of each target time period, and the inflow runoff correction value of each target time period, and the inflow runoff correction value is calculated based on the initial value of the inflow runoff in each target time period, the weight value of the target time period, the weight division coefficient of the target time period, and the initial value of the inflow runoff in adjacent time periods.

Specifically, the multi-objective optimization model is established through the objective function and parameter constraints, and a non-inferior solution set including the weight value of each target time period, the weight division coefficient value and the inflow runoff correction value is obtained through the multi-objective optimization model. Compared with the fixed weight of the traditional sliding average method, in the embodiment of the present invention, when correcting the inflow runoff for different flow rates, the weight value corresponding to each target time period is different, which can make the correction effect of the inflow runoff better and more in line with the actual situation.

Specifically, the adjacent time periods of each target time period refer to the two adjacent time periods before and after each target time period. When a time period is used to correct the initial value of the inflow runoff of the adjacent target time period, the weight value used in the time period is calculated by the weight value of the target time period and the weight division coefficient value. When the time period is used as the target time period and the initial value of the inflow runoff needs to be corrected, the weight value used in the time period is the weight value determined by the multi-objective optimization model.

In step S103, based on the non-inferior solution set of the inflow runoff correction value, the optimized value of the inflow runoff correction for each target period is selected.

In one example, after executing the above step S101 and before executing the above step S102, the inflow runoff correction optimization method provided by the embodiment of the present invention further includes: preprocessing the acquired inflow runoff initial values of the multiple target time periods, and the process of preprocessing the inflow runoff initial values specifically includes:

If the obtained initial value of the inflow runoff in the target period is a negative value, the negative initial value of the inflow runoff will be changed to 0 to obtain a non-negative initial value of the inflow runoff, ensuring that all values in the inflow runoff sequence are non-negative, making it more in line with natural laws.

In another example, the process of preprocessing the initial value of the inflow runoff further includes:

For the non-negative initial value of inflow runoff, if there are two adjacent time periods with non-negative initial values of inflow runoff both being 0, the target period corresponding to the larger absolute value of the initial value of inflow runoff will be replaced by the historical average value of the corresponding target period, and the value of the other target period will remain at 0, ensuring that there are no consecutive 0 values in adjacent time periods of inflow runoff, making the inflow runoff closer to the actual situation.

In one example, the multi-objective optimization model in the above step S102 includes an objective function. The objective function includes a function for minimizing the cumulative sum of runoff amplitudes in adjacent time periods, a function for minimizing the root mean square error of water level simulation, and a function for minimizing the water balance index. Through the embodiment of the present invention, the root mean square error of water level simulation and the water balance index can be minimized as much as possible while ensuring the continuity of the runoff process, that is, while ensuring the continuity of the runoff process, the water level correction value related to the runoff correction value and the reservoir capacity amplitude correction value are made closer to the actual water level value and the actual reservoir capacity amplitude measurement value.

In an optional embodiment, a function for minimizing the cumulative sum of inflow runoff variations in adjacent time periods is constructed based on the difference in the inflow runoff correction values of each adjacent time period, and the weight value and weight division coefficient value of each time period are calculated based on the function for minimizing the cumulative sum of inflow runoff variations in adjacent time periods, so that the cumulative sum of the differences in the inflow runoff correction values of each adjacent time period is minimized.

For example, the cumulative sum of runoff variations in adjacent periods is minimized, that is, the continuity of runoff in the reservoir is maximized, and the function for minimizing the cumulative sum of runoff variations in adjacent periods is:

Among them, QM _t+1 and QM _t are the correction values of inflow runoff in the t+1th period and the tth period respectively; n is the total number of periods.

In an optional embodiment, a water level simulation root mean square error minimization function is constructed based on the root mean square of the difference between the corrected water level value and the actual measured water level value in each time period, and the weight value and weight division coefficient value of each time period are calculated based on the water level simulation root mean square error minimization function, so that the root mean square of the difference between the corrected water level value and the actual measured water level value in each time period is minimized.

Exemplarily, the water level simulation root mean square error minimization function is:

Among them, ZM _t and Z _t are the water level correction value and the measured water level value in the tth period respectively.

In an optional embodiment, a water balance index minimization function is constructed based on the difference between the corrected value of the inflow runoff in each time period and the actual measured value of the reservoir outflow. The weight value and weight division coefficient value of each time period are calculated based on the water balance index minimization function to minimize the water balance index.

Exemplarily, the water balance index minimization function is:

Where R _t is the measured value of reservoir outflow at time period t; is the measured value of the cumulative reservoir capacity variation; ΔT is the time period length.

In one example, the multi-objective optimization model in the above step S102 includes parameter constraints. The parameter constraints include water balance constraints, reservoir capacity constraints, and water level-capacity relationships. By determining the objective function and parameter constraints, a multi-objective optimization model for inflow runoff correction is constructed, and a non-inferior solution set is obtained using the model. Any non-inferior solution in the non-inferior solution set includes the weight value and weight division coefficient of each target period, and the inflow runoff correction value of each target period. The inflow runoff correction value of each target period is calculated based on the weight value of each target period, the weight division coefficient, and the initial value of the inflow runoff of the adjacent period.

In an optional embodiment, the water balance constraint is expressed as follows:
VM _t+1 =VM _t +(QM _t −R _t )×ΔT

Among them, VM _t+1 and VM _t are the storage capacity correction values of the t+1th period and the tth period respectively.

In an optional embodiment, the reservoir capacity constraint is expressed as follows:
VL _t ≤VM _t ≤VU _t

Among them, VL _t and VU _t are the lower limit and upper limit of storage capacity in the tth period respectively.

In an optional embodiment, the water level-storage capacity relationship is expressed as follows:
V _t = f(Z _t ) _, VM _t = f(ZM _t )

Among them, f(□) is the water level-reservoir capacity relationship curve, and both the measured values and corrected values of water level and reservoir capacity must satisfy this relationship curve.

In one example, the input of the multi-objective optimization model in the above step S102 includes not only the initial value of the inflow runoff in each target period, but also the measured water level value and the measured reservoir outflow value in each target period. The non-inferior solution set obtained by the model includes not only the weight value and weight division coefficient value of each target period, the inflow runoff correction value of each target period, but also the water level correction value of each target period.

In one example, in the above step S102, the correction value of the inflow runoff is calculated by weighted summing the initial value of the inflow runoff in each target period, the weight value of the target period, the initial value of the inflow runoff in the adjacent period, and the temporary weight of the adjacent period. The temporary weight of the adjacent period is determined according to the weight value of the target period and the weight division coefficient value. The calculation formula is as follows:

Where: _QMt is the correction value of the inflow runoff in period t; Qt _-1 , _Qt , Qt ₊₁ are the initial values of the inflow runoff after pretreatment in periods t-1, t, and t+1 respectively; _ωt represents the weight value of the inflow runoff in period t, and _λt is the weight division coefficient of the runoff in the adjacent periods before and after period t, and

In the embodiment of the present invention, in the multi-objective optimization model, the optimization variables are the weight values and weight division coefficients of each time period, namely {ω ₁ ,...,ω _t ,...,ω _n } and {λ ₁ ,...,λ _t ,...,λ _n }, a total of 2n optimization variables. In order to avoid problems such as too many optimization variables reducing the calculation efficiency and quickly falling into the local optimal solution, it is recommended that the total number of time periods for inflow runoff n≤100; for the total number of time periods for inflow runoff n>100, the sliding window method can be used to optimize and correct them one by one.

When the cumulative sum minimization function of runoff fluctuations in adjacent periods, the water level simulation root mean square error minimization function and the water balance index minimization function are selected as the objective function, a multi-objective heuristic intelligent optimization algorithm is used to solve the constructed multi-objective optimization model through initialization, fitness evaluation and comparison, optimization variable adjustment, population iterative calculation, etc., to obtain a non-inferior solution set of the runoff correction value. The specific process is shown in Figure 2. In the embodiment of the present invention, there is no limitation on the multi-objective heuristic intelligent optimization algorithm. For example, algorithms such as NSGA-2/3, SCE-UA, and PA-DDS can be used to solve the model.

In one example, in the above step S103, for the obtained non-inferior solution set of the inflow runoff correction value, the best solution for optimizing the inflow runoff correction is selected by a multi-attribute decision-making method. The embodiment of the present invention does not specifically limit the multi-attribute decision-making method, which can be a fuzzy optimization method, a projection pursuit method, a technique for ordering preference by similarity to an ideal solution (TOPSIS), etc.

In another example, three hydropower stations A, B, and C in the Yangtze River Basin are selected as embodiments, and the correction optimization method for inflow runoff proposed in the embodiment of the present invention is compared with the three-point moving average method for verification. Figures 3, 5, and 7 are the inflow runoff processes of Hydropower Station A, Hydropower Station B, and Hydropower Station C in 2015, respectively, where the horizontal axis is the corresponding time period number, and the vertical axis is the inflow flow, i.e., the inflow runoff. Figures 4, 6, and 8 are the reservoir water level processes of Hydropower Station A, Hydropower Station B, and Hydropower Station C in 2015, respectively, where the horizontal axis is the corresponding time period number, and the vertical axis is the reservoir water level.

In Figures 3 to 8, Figures 3(a), 3(b), 3(c), and 3(d) are the runoff processes of the hydropower station A during the four periods of January 1 to January 18, April 1 to April 18, July 1 to July 18, and November 1 to November 18, 2015; Figures 4(a), 4(b), 4(c), and 4(d) are the runoff processes of the hydropower station A during the four periods of January 1 to January 18, April 1 to April 18, 2015, respectively. Figure 5(a), Figure 5(b), Figure 5(c), and Figure 5(d) are the reservoir water level processes during the four periods of January 1 to January 18, April 1 to April 18, July 1 to July 18, and November 1 to November 18, 2015; Figure 6(a), Figure 6(b), Figure 6(c), and Figure 6(d) are the reservoir runoff processes during the four periods of January 1 to January 18, April 1 to April 18, 2015; The reservoir water level process of hydropower station B in the four periods from January 1 to January 18, April 1 to April 18, July 1 to July 18, and November 1 to November 18, 2015; Figures 7(a), 7(b), 7(c), and 7(d) are the reservoir runoff processes of hydropower station C in the four periods from January 1 to January 18, April 1 to April 18, July 1 to July 18, and November 1 to November 18, 2015; Figures 8(a), 8(b), 8(c), and 8(d) are the reservoir water level processes of hydropower station C in the four periods from January 1 to January 18, April 1 to April 18, July 1 to July 18, and November 1 to November 18, 2015.

When calculating the inflow and water level of each period from January 1 to January 18, April 1 to April 18, July 1 to July 18, and November 1 to November 18, the calculation period of each period is 3 hours. The PA-DDS algorithm is used to optimize the non-inferior solution set of the inflow runoff correction value of each target period, and then the fuzzy optimization method is used to determine the final inflow runoff correction optimization value.

It can be seen from Figures 3 to 8 that the corrected inflow runoff is smoother than the measured process, and the inflow runoff process has better continuity. Compared with the three-point sliding average, the inflow runoff calculated by the method of the present invention is not too smooth, the peak flow is in line with the actual situation, and will not be excessively reduced, and the inflow runoff correction effect is better.

Tables 1 to 3 respectively show the comparison of the corresponding objective function values when Hydropower Station A, Hydropower Station B and Hydropower Station C adopt the method proposed in the embodiment of the present invention to correct the inflow runoff and adopt the three-point sliding average method to correct the inflow runoff in 2015.

Table 1 Objective function values of two methods for hydropower station A in 2015

Table 2 Objective function values of two methods for hydropower station B in 2015

Table 3 Objective function values of two methods for hydropower station C in 2015

It can be seen from Tables 1 to 3 that, compared with the three-point moving average, the method of the present invention can significantly reduce the root mean square error of water level simulation and the water balance index while ensuring the continuity of the inflow runoff, so that the corrected inflow runoff and reservoir water level can be as consistent as possible with the actual situation.

Based on the same inventive concept, an embodiment of the present invention further provides an inflow runoff correction and optimization device, as shown in FIG9 , the device comprises:

The acquisition module 901 is used to acquire the initial values of the inflow runoff in multiple target time periods. For details, please refer to the description of step S101 in the above embodiment, which will not be repeated here.

The model solving module 902 is used to input the initial value of the inflow runoff of each target period into a pre-established multi-objective optimization model, solve the multi-objective optimization model, and obtain a non-inferior solution set, wherein the optimization variables of the multi-objective optimization model include the weight and weight division coefficient of each target period, and any non-inferior solution in the non-inferior solution set includes the weight value and weight division coefficient value of each target period, and the inflow runoff correction value of each target period, and the inflow runoff correction value is calculated based on the inflow runoff initial value of each target period, the weight value of the target period, the weight division coefficient of the target period, and the inflow runoff initial value of the adjacent period. For details, please refer to the description of step S102 in the above embodiment, which will not be repeated here.

The selection module 903 is used to select the optimized value of the inflow runoff correction in each target period based on the non-inferior solution set of the inflow runoff correction value. For details, please refer to the description of step S103 in the above embodiment, which will not be repeated here.

In one example, in the model solving module 902, the objective function in the multi-objective optimization model includes the function of minimizing the cumulative sum of runoff fluctuations in adjacent periods, the function of minimizing the root mean square error of water level simulation, and the function of minimizing the water balance index. For details, please refer to the description in the above embodiment, which will not be repeated here.

In an optional embodiment, in the model solving module 902, the cumulative sum minimization function of the runoff variation in adjacent time periods is constructed according to the difference of the runoff correction value in each adjacent time period, and the weight value and weight division coefficient value of each time period are calculated according to the cumulative sum minimization function of the runoff variation in adjacent time periods, so that the cumulative sum of the difference of the runoff correction value in each adjacent time period is minimized. For details, please refer to the description in the above embodiment, which will not be repeated here.

In another optional embodiment, in the model solving module 902, the water level simulation root mean square error minimization function is constructed according to the root mean square of the difference between the water level correction value and the water level measured value in each time period, and the weight value and weight division coefficient value of each time period are calculated according to the water level simulation root mean square error minimization function, so that the root mean square of the difference between the water level correction value and the water level measured value in each time period is minimized. For details, please refer to the description in the above embodiment, which will not be repeated here.

In another optional embodiment, in the model solving module 902, the water balance index minimization function is constructed according to the difference between the corrected value of the inflow runoff in each period and the measured value of the reservoir outflow, and the weight value and the weight division coefficient value of each period are calculated according to the water balance index minimization function, so that the water balance index is minimized. For details, please refer to the description in the above embodiment, which will not be repeated here.

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

In one example, in the model solving module 902, the runoff correction value is the initial runoff value, target runoff value, and runoff correction value of each target period. The weight value of the segment, the initial value of the runoff in the adjacent period, and the temporary weight of the adjacent period are calculated by weighted summation. The temporary weight of the adjacent period is determined according to the weight value of the target period and the weight division coefficient value. For details, please refer to the description in the above embodiment, which will not be repeated here.

In another example, the device is also used to change the negative initial value of the inflow runoff to 0 if the initial value of the inflow runoff in the target period is negative, so as to obtain a non-negative initial value of the inflow runoff. For details, please refer to the description in the above embodiment, which will not be repeated here.

In one example, the device is also used for non-negative initial values of inflow runoff. If there are two adjacent time periods with non-negative initial values of inflow runoff being 0, the target time period corresponding to the target time period with the larger absolute value of the initial value of inflow runoff is replaced by the historical average value of the corresponding target time period, and the value of the other target time period remains 0. For details, please refer to the description in the above embodiment, which will not be repeated here.

The specific limitations and beneficial effects of the above-mentioned device can be found in the above-mentioned limitations on the optimization method for correction of inflow runoff, which will not be repeated here. The above-mentioned modules can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules 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 modules.

FIG10 is a schematic diagram of the hardware structure of a computer device according to an exemplary embodiment. As shown in FIG10 , the device includes one or more processors 1010 and a memory 1020, and the memory 1020 includes a persistent memory, a volatile memory, and a hard disk. FIG10 takes one processor 1010 as an example. The device may also include: an input device 1030 and an output device 1040.

The processor 1010, the memory 1020, the input device 1030 and the output device 1040 may be connected via a bus or other means, and FIG10 takes the connection via a bus as an example.

The processor 1010 may be a central processing unit (CPU). The processor 1010 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips. A general-purpose processor may be a microprocessor or the processor may be any conventional processor.

The memory 1020 is a non-transient computer-readable storage medium, including a persistent memory, a volatile memory, and a hard disk, and can be used to store non-transient software programs, non-transient computer executable programs, and modules, such as program instructions/modules corresponding to the inflow runoff correction optimization method in the embodiment of the present application. The processor 1010 executes various functional applications and data processing of the server by running the non-transient software programs, instructions, and modules stored in the memory 1020, that is, implementing any of the above-mentioned inflow runoff correction optimization methods.

The memory 1020 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required by at least one function; the data storage area may store data required for use, etc. In addition, the memory 1020 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 1020 may optionally include a memory remotely arranged relative to the processor 1010, and these remote memories may be connected to the data processing device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

The input device 1030 can receive input digital or character information and generate signal input related to user settings and function control. The output device 1040 can include display devices such as display screens.

One or more modules are stored in the memory 1020 , and when executed by one or more processors 1010 , the method shown in FIG. 1 is performed.

The above product can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the relevant description in the embodiment shown in Figure 1.

The embodiment of the present invention further provides a non-transitory computer storage medium, which stores computer executable instructions, and the computer executable instructions can execute the optimization method in any of the above method embodiments. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (Flash Memory), a hard disk (HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above types of memory.

Claims

A method for optimizing runoff correction, characterized in that the method comprises:

Obtain the initial values of inflow runoff for multiple target periods;

Inputting the initial value of the inflow runoff of each target period into a pre-established multi-objective optimization model, solving the multi-objective optimization model to obtain a non-inferior solution set, wherein the optimization variables of the multi-objective optimization model include the weight and weight division coefficient of each target period, and any non-inferior solution in the non-inferior solution set includes the weight value and weight division coefficient value of each target period, and the inflow runoff correction value of each target period, and the inflow runoff correction value is calculated based on the inflow runoff initial value of each target period, the weight value of the target period, the weight division coefficient of the target period, and the inflow runoff initial value of adjacent periods;

Based on the non-inferior solution set of the inflow runoff correction value, the optimal value of the inflow runoff correction for each target period is selected.
The method according to claim 1, characterized in that

The multi-objective optimization model includes objective functions, which include a function for minimizing the cumulative sum of runoff variations in adjacent time periods, a function for minimizing the root mean square error of water level simulation, and a function for minimizing the water balance index.
The method according to claim 1 or 2, characterized in that

The multi-objective optimization model includes parameter constraints, which include water balance constraints, reservoir capacity constraints and water level-reservoir capacity relationship.
The method according to claim 1, characterized in that

The inflow runoff correction value is calculated by weighted summing the inflow runoff initial value of each target period, the weight value of the target period, the inflow runoff initial value of the adjacent period and the temporary weight of the adjacent period. The temporary weight of the adjacent period is determined based on the weight value of the target period and the weight division coefficient value.
The method according to claim 2, characterized in that

The function of minimizing the cumulative sum of runoff variations in adjacent time periods is constructed based on the difference in the corrected values of runoff variations in each adjacent time period. The weight value and weight division coefficient value of each time period are calculated based on the function of minimizing the cumulative sum of runoff variations in adjacent time periods, so that the cumulative sum of the differences in the corrected values of runoff variations in each adjacent time period is minimized.
The method according to claim 2, characterized in that

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

The water balance index minimization function is constructed based on the difference between the corrected value of the inflow runoff in each period and the measured value of the reservoir outflow. The weight value and weight division coefficient value of each period are calculated based on the water balance index minimization function to make the water balance The index is minimal.
The method according to claim 1 is characterized in that after the step of obtaining the initial values of the inflow runoff in the multiple target time periods and before the step of inputting the initial values of the inflow runoff in each target time period into the pre-established multi-objective optimization model, it also includes:

If the obtained initial value of the inflow runoff in the target period is a negative value, the negative initial value of the inflow runoff is changed to 0 to obtain a non-negative initial value of the inflow runoff.
The method according to claim 8, characterized in that after the step of obtaining the non-negative initial value of inflow runoff, the method further comprises:

For the non-negative initial value of inflow runoff, if there are two adjacent time periods whose non-negative initial value of inflow runoff is 0, the target period corresponding to the target period with the larger absolute value of the initial value of inflow runoff is replaced by the historical average value of the corresponding target period, and the value of the other target period remains 0.
A device for correcting and optimizing inflow runoff, characterized in that the device comprises:

An acquisition module is used to obtain the initial values of inflow runoff in multiple target periods;

A model solving module, used for inputting the initial value of the inflow runoff of each target period into a pre-established multi-objective optimization model, solving the multi-objective optimization model, and obtaining a non-inferior solution set, wherein the optimization variables of the multi-objective optimization model include the weight and weight division coefficient of each target period, and any non-inferior solution in the non-inferior solution set includes the weight value and weight division coefficient value of each target period, and the inflow runoff correction value of each target period, and the inflow runoff correction value is calculated based on the inflow runoff initial value of each target period, the weight value of the target period, the weight division coefficient of the target period, and the inflow runoff initial value of adjacent periods;

The selection module is used to select the optimized value of the inflow runoff correction for each target period based on the non-inferior solution set of the inflow runoff correction value.