Reservoir flood control forecast scheduling method, device and equipment and storage medium

Abstract

The invention relates to the technical field of reservoir scheduling, and discloses a reservoir flood control forecast scheduling method, device and equipment and a storage medium, and the method comprises the steps: obtaining forecast hydrological information; performing error analysis on the forecast hydrological information, and determining hydrological error probability distribution based on an error analysis result; determining a first reservoir scheduling strategy, wherein the first reservoir scheduling strategy is used for determining reservoir discharge based on the forecast hydrological information and the real-time hydrological information; the applicability of the first reservoir dispatching strategy is determined based on the hydrological error probability distribution, the target reservoir dispatching strategy is determined based on the applicability, the reservoir dispatching strategy is generated in combination with the forecast hydrological information, the applicability of the reservoir dispatching strategy is determined by considering the error condition of the forecast hydrological information, and therefore the target reservoir dispatching strategy is determined. According to the target reservoir dispatching strategy, pre-discharge can be achieved in advance, the required flood control reservoir capacity is reduced, the flood control water level is raised, and the water resource utilization rate is increased.

Description

Technical Field

[0001] The present invention relates to the technical field of reservoir dispatching, and in particular to a reservoir flood control forecast dispatching method, device, equipment and storage medium. Background Art

[0002] At present, with the rapid development of economy and society, the consumption of electricity is increasing. In the process of responding to and adapting to the growing demand for electricity, many large reservoirs mainly for power generation have played an important role. Hydropower station reservoirs also bear the arduous task of flood control, and need to reserve a large amount of flood control storage capacity during the flood season, resulting in a more prominent contradiction between flood control and power generation during the flood season. At the same time, with the development of science and technology, the accuracy of forecast information is constantly improving, which provides a basis for reservoirs to consider forecast information for scheduling. Therefore, in order to coordinate and ease the contradiction between the two, it is necessary and feasible to carry out flood forecast scheduling research on these reservoirs.

[0003] Conventional reservoir operation is based on a combination of historical data and theory, using empirical charts to guide operation. It is simple and reliable but more suitable for small and medium-sized reservoirs. In order to deal with complex problems, related technologies use modern technology and optimization methods to explore optimal operation, but the research focuses on theory and the model is complex, making it difficult to be widely used in practice. Summary of the invention

[0004] In view of this, the present invention provides a reservoir flood control forecasting and dispatching method, device, equipment and storage medium to solve the problem of difficulties in reservoir flood control dispatching prediction.

[0005] In a first aspect, the present invention provides a reservoir flood control forecasting and dispatching method, the method comprising:

[0006] Obtain forecast hydrological information;

[0007] Performing error analysis on the forecast hydrological information, and determining hydrological error probability distribution based on the error analysis results;

[0008] Determining a first reservoir dispatching strategy, wherein the first reservoir dispatching strategy is used to determine a reservoir discharge based on the forecast hydrological information and the real-time hydrological information;

[0009] The applicability of the first reservoir operation strategy is determined based on the hydrological error probability distribution, so as to determine a target reservoir operation strategy based on the applicability.

[0010] In an optional implementation, the forecast hydrological information includes at least an evaluation parameter, and the error analysis of the forecast hydrological information and determining the hydrological error probability distribution based on the error analysis result includes:

[0011] Perform error analysis based on the evaluation parameters to obtain error analysis results;

[0012] The hydrological error probability distribution is calculated based on the error analysis result to obtain the hydrological error probability distribution.

[0013] In an optional implementation, when the forecast hydrological information includes forecast rainfall information, the evaluation parameters include at least the total number of published forecast samples, the number of published accurate forecasts, and the number of published forecast omissions, and the error analysis is performed based on the evaluation parameters to obtain the error analysis results, including:

[0014] Determining the accuracy of the forecast rainfall information based on the number of accurate forecast releases and the total number of forecast samples released;

[0015] The forecast rainfall information omission rate is calculated based on the number of omissions in the published forecast and the total number of published forecast samples;

[0016] The false alarm rate of the forecast rainfall information is determined based on the forecast rainfall information accuracy rate and the forecast rainfall information omission rate, and the error analysis result at least includes the forecast rainfall information accuracy rate, the forecast rainfall information omission rate, and the forecast rainfall information omission rate.

[0017] In an optional implementation manner, the false alarm rate of the rainfall forecast information is determined according to the following formula:

[0018] k = [1-(η+β)] × 100%

[0019] Among them, η represents the accuracy rate of the forecast rainfall information, β represents the underreporting rate of the forecast rainfall information, and k represents the false reporting rate of the forecast rainfall information.

[0020] In an optional implementation, the calculating the hydrological error probability distribution based on the error analysis result to obtain the hydrological error probability distribution includes:

[0021] Determine whether the error analysis result meets the preset accuracy index;

[0022] When the error analysis result meets the preset accuracy, historical hydrological data is obtained;

[0023] Based on the forecast hydrological information and historical hydrological data, an error probability distribution simulation is performed to determine an error probability distribution curve.

[0024] In an optional implementation, determining the first reservoir scheduling strategy includes:

[0025] Acquire a reservoir dispatching strategy form, wherein the reservoir dispatching strategy form is generated based on a decision tree structure, wherein the decision tree structure includes a plurality of decision branches, wherein the decision branches are set based on forecast hydrological information and real-time hydrological information, and wherein the branches correspond to the dispatching strategies one by one;

[0026] The judgment index in the reservoir dispatching strategy form is adjusted based on the preset start-up water level to determine the first reservoir dispatching strategy.

[0027] In an optional implementation, the step of adjusting the judgment index in the reservoir dispatching strategy form based on the preset start-up water level to determine the first reservoir dispatching strategy includes:

[0028] Get the preset starting water level;

[0029] Taking the preset water level as the initial condition, simulating the reservoir state under each magnitude of flood step by step to obtain simulation results;

[0030] The simulation results are analyzed to determine target judgment indicators corresponding to floods of different magnitudes, so as to determine the first reservoir scheduling strategy based on the target judgment indicators.

[0031] The reservoir flood control forecasting and dispatching method provided in this embodiment includes: obtaining forecast hydrological information; performing error analysis on the forecast hydrological information, and determining the hydrological error probability distribution based on the error analysis results; determining a first reservoir dispatching strategy, the first reservoir dispatching strategy is used to determine the reservoir discharge based on the forecast hydrological information and the real-time hydrological information; determining the applicability of the first reservoir dispatching strategy based on the hydrological error probability distribution, and determining the target reservoir dispatching strategy based on the applicability. This method generates a reservoir dispatching strategy in combination with forecast hydrological information, and considers the error of the forecast hydrological information to determine the applicability of the reservoir dispatching strategy, thereby determining the target reservoir dispatching strategy. According to the target reservoir dispatching strategy, early discharge can be achieved, so that the required flood control storage capacity is reduced, thereby raising the flood limit water level and increasing the utilization rate of water resources.

[0032] In a second aspect, the present invention provides a reservoir flood control forecasting and dispatching device, the device comprising:

[0033] Information acquisition module, used to obtain forecast hydrological information;

[0034] An error analysis module, used to perform error analysis on the forecast hydrological information and determine the hydrological error probability distribution based on the error analysis results;

[0035] A strategy acquisition module, used to determine a first reservoir dispatching strategy, wherein the first reservoir dispatching strategy is used to determine the reservoir discharge based on the forecast hydrological information and the real-time hydrological information;

[0036] A target strategy determination module is used to determine the applicability of the first reservoir scheduling strategy based on the hydrological error probability distribution, so as to determine a target reservoir scheduling strategy based on the applicability.

[0037] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the reservoir flood forecasting and scheduling method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0038] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the reservoir flood forecasting and scheduling method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 is a schematic flow chart of a reservoir flood control forecasting and dispatching method according to an embodiment of the present invention;

[0041] Figure 2 is a structural block diagram of a reservoir flood control forecasting and dispatching device according to an embodiment of the present invention;

[0042] Figure 3 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0044] Hydropower station reservoirs often shoulder the dual tasks of flood control and economic development. The conventional dispatching method of reservoirs always guards against the occurrence of floods with a small probability. During the flood season, the water level of the reservoir is controlled at the planned flood control limit water level, resulting in a relatively small power generation head of the hydropower station, which seriously affects the power generation efficiency of the hydropower station. With the rapid development of the economy and society, the consumption of electricity is becoming more and more huge. In the process of responding to and adapting to the growing demand for electricity, many large reservoirs mainly for power generation have played an important role. Reservoirs also bear the arduous task of flood control, and a large amount of flood control storage capacity needs to be reserved during the flood season, resulting in a more prominent contradiction between flood control and power generation during the flood season.

[0045] The conventional dispatching method of reservoirs is based on historical measured data and combined with the theories of classical hydrology, hydraulics and runoff regulation. This method is a combination of semi-empirical and semi-theoretical, and mainly guides reservoir operation through the study of dispatching methods, dispatching rules, drawing dispatching diagrams and compiling dispatching procedures through a long series of regulation. Conventional dispatching is implemented by using empirical charts such as benefit dispatching charts, flood resistance charts, and flood control dispatching charts. However, the specific flood control operations of this method rely on graphical methods, and there are certain empirical and deviation phenomena, so it is more suitable for the dispatching of small and medium-sized reservoir groups. In order to deal with more complex multi-objective and multi-constrained water conservancy system dispatching problems, domestic and foreign scholars have sought more optimized dispatching methods or solutions based on the theory of operations research (system engineering) and using modern computing technology and optimization methods. The research on reservoir optimization dispatching focuses more on theoretical and model solution methods, and its optimization goals are mainly concentrated on maximizing the multi-year power of the reservoir group or minimizing the system computing cost. The model input may be deterministic, random or fuzzy. The research covers a wide range of fields from single objectives to multiple objectives, and the objects have also expanded from a single reservoir to multiple reservoirs and even the entire basin or system reservoir group. The solution methods have developed from traditional linear, nonlinear and dynamic programming to network flow methods, stepwise optimization algorithms, large system decomposition and coordination algorithms, and emerging intelligent algorithms such as genetic algorithms, ant colony algorithms, artificial neural networks, fuzzy system multi-objective decision-making theory, etc. Although certain achievements have been made in theory and methods, the application of these studies in the actual operation of hydropower station groups is relatively limited. The main reason is that the research focuses on theoretical discussion, the model is complex, and it is difficult to promote and apply it in practice due to the business quality, theoretical level and current management system of production users. In addition, due to too many assumptions and simplifications in the dispatching model, the model cannot accurately describe the actual working conditions of the reservoir group, and the optimization results deviate from the actual situation. In addition, some methods ignore the complexity, variability, dynamics and uncertainty of the hydropower station reservoir group system in the process of pursuing the optimal solution. For a basin with a large number of complex reservoirs, the river runoff process is strongly disturbed by engineering regulation, and it is difficult to use net rain as a discriminant indicator to study forecasting and scheduling rules. Based on this, an embodiment of the present invention provides a reservoir flood control forecasting and scheduling method.

[0046] According to an embodiment of the present invention, an embodiment of a reservoir flood forecasting and scheduling method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0047] In this embodiment, a reservoir flood control forecasting and dispatching method is provided, which is applied to terminals such as desktop computers, tablet computers, and mobile phones. Figure 1 is a flow chart of a reservoir flood control forecasting and dispatching method according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0048] Step S101, obtaining forecast hydrological information.

[0049] The reservoir dispatching strategy is generated by adjusting different design standards using a variety of discrimination indicators on the premise of meeting flood control safety requirements. In the process of reservoir flood forecasting and dispatching, different stages focus on different issues and use different discrimination indicators. The required discrimination indicators are adopted according to the focus of the reservoir flood forecasting and dispatching process. The forecast hydrological information is obtained based on the preset discrimination indicators. The discrimination indicators may include the water level in front of the reservoir dam, the forecast flood flow, the accumulated rainfall, the forecast rainfall, etc. The corresponding forecast hydrological information may include the forecast flood information, the forecast rainfall information, etc. The forecast hydrological information is obtained based on the predictions of relevant agencies.

[0050] The following principles should be followed when selecting the discrimination indicators: (1) the principle of flood control safety control factors, that is, the selected indicators are the key influencing factors that control the safety of reservoirs during flood control and scheduling, such as water level, flood volume, peak flow, etc.; (2) the principle of multi-indicator control, that is, the selected indicators should be of various types, mainly considering real-time water and rainfall information, forecast water and rainfall information and its errors, etc.; (3) the principle of representativeness and outsourcing, that is, the selected indicators are summarized from the design floods of various typical flood forecasts, or are proven to be safe and reasonable through their verification.

[0051] According to the above-mentioned principle of selecting discriminant indicators, the most reliable and last-appearing discriminant indicator is placed in the outermost layer, and the discriminant indicator with the strongest uncertainty and the longest forecast period is placed in the innermost layer, forming a multi-layer indicator nesting system to guide the real-time scheduling of reservoirs and ensure the flood control safety of reservoirs and their protection objects. As an implementation method, the water level in front of the reservoir dam can be used as the first discriminant indicator, the forecast flood information or the forecast net rain information can be used as the second discriminant indicator, the accumulated falling rain can be used as the third discriminant indicator, and the forecast rainfall information can be used as the fourth discriminant indicator. Among them, the water level in front of the reservoir dam is the elevation from a certain point on the reservoir water storage surface to the fixed base surface, which refers to the water level in front of the reservoir dam.

[0052] Step S102: performing error analysis on the forecast hydrological information, and determining the hydrological error probability distribution based on the error analysis result.

[0053] Hydrological information forecast includes flood forecast information, rainfall forecast information, etc. There may be certain errors in the forecast information, so error analysis is needed. Specifically, the accuracy, underreporting rate, and false alarm rate can be analyzed during the error analysis. The error probability distribution is performed based on the error analysis results, and then the error distribution law of the hydrological information forecast is determined, so that the degree of influence of the error on the reservoir operation strategy can be determined as a criterion for judging whether the hydrological information forecast is usable. When simulating the forecast error probability distribution, the Pearson III (P-III) curve and the normal distribution curve can be used for analysis to obtain the hydrological error probability distribution.

[0054] Step S103, determining the first reservoir scheduling strategy.

[0055] Among them, the first reservoir scheduling strategy is generated based on the forecast hydrological information and the real-time hydrological information, and the first reservoir scheduling strategy is used to determine the reservoir discharge based on the forecast hydrological information and the real-time hydrological information.

[0056] The first reservoir dispatching strategy may adopt a strategy tree structure, in which the correspondence between the forecast hydrological information, the real-time hydrological information and the reservoir discharge is stored. Specifically, in the first reservoir strategy, different forecast flood information, accumulated rainfall, forecast rainfall information, etc. are segmented, the forecast hydrological information includes forecast flood information, forecast rainfall information, etc., and the real-time hydrological information includes actual water level, accumulated rainfall, etc. The corresponding reservoir discharge is determined according to the above information falling in different intervals.

[0057] In the flooding stage, if the reservoir water level exceeds the flood control high water level, the first reservoir dispatching strategy implements flood control dispatching to ensure the safety of the hub, that is, when the inflow is less than or equal to the flood discharge capacity corresponding to the reservoir water level, the inflow is discharged according to the inflow, and when the inflow is greater than the flood discharge capacity corresponding to the reservoir water level, the discharge is discharged according to the flood discharge capacity. In the real-time dispatching of the reservoir, if it is predicted that a preset first magnitude (for example, magnitude IV) or above rainfall will occur or has occurred in the long-term interval in the future, the discharge will be increased by one level in advance; if a preset second magnitude (for example, magnitude III) or below rainfall will occur or has occurred, the original discharge will be maintained unchanged.

[0058] During the water withdrawal stage, when the reservoir water level is close to the starting water level, if it is predicted that rainfall of the preset third magnitude (for example, magnitude III) or above will occur or has occurred within a long period of time in the future, the original discharge volume will be maintained unchanged; if rainfall of the preset fourth magnitude (for example, magnitude II) or below will occur or has occurred, the reservoir water level will be maintained unchanged.

[0059] The first reservoir dispatching strategy is adjusted according to its form. The adjustment can be made in a step-by-step manner, for example, the judgment index value of a smaller flood is adjusted first, and then the judgment index value of a larger flood is adjusted in sequence. The judgment index value is a threshold used to segment the real-time hydrological information and the forecast hydrological information. The first reservoir dispatching strategy is generated after the above adjustment.

[0060] Step S104, determining the applicability of the first reservoir scheduling strategy based on the hydrological error probability distribution, so as to determine the target reservoir scheduling strategy based on the applicability.

[0061] The first reservoir dispatching strategy is determined under the condition that the forecast hydrological information is completely accurate, without considering the forecast error. However, there may be forecast errors in the actual forecast process. If the forecast error is not considered, when the forecast hydrological information is too large, the reservoir discharge will be too large; if the forecast is too small, the reservoir discharge will be too small, and the reservoir will face certain flood control safety issues.

[0062] Therefore, it is necessary to adjust the first reservoir scheduling strategy based on the probability of hydrological errors, and obtain the flood control characteristic value by flood calculation. The flood control characteristic value includes the highest flood control water level, safe discharge, maximum discharge, etc. The flood control characteristic values ​​are different for reservoirs with different functions. In fact, the corresponding flood control characteristic value can be selected according to the type of reservoir for calculation. If the flood control characteristic value meets the preset constraints, it indicates that the first reservoir scheduling strategy is applicable to the current reservoir, and the first reservoir scheduling strategy is determined as the target reservoir scheduling strategy. If the flood control characteristic value does not meet the preset constraints, it indicates that the first reservoir scheduling strategy is not applicable to the current reservoir, and the first reservoir scheduling strategy needs to be adjusted again. Among them, the preset constraints are set according to the characteristic water level corresponding to each design standard flood approved by the plan, the upstream and downstream flood control standards of the reservoir, and the allowable backwater level and safe discharge, discharge capacity, etc.

[0063] The reservoir flood control forecasting and dispatching method provided in this embodiment includes: obtaining forecast hydrological information; performing error analysis on the forecast hydrological information, and determining the hydrological error probability distribution based on the error analysis results; determining a first reservoir dispatching strategy, the first reservoir dispatching strategy is used to determine the reservoir discharge based on the forecast hydrological information and the real-time hydrological information; determining the applicability of the first reservoir dispatching strategy based on the hydrological error probability distribution, and determining the target reservoir dispatching strategy based on the applicability. This method generates a reservoir dispatching strategy in combination with forecast hydrological information, and considers the error of the forecast hydrological information to determine the applicability of the reservoir dispatching strategy, thereby determining the target reservoir dispatching strategy. According to the target reservoir dispatching strategy, early discharge can be achieved, so that the required flood control storage capacity is reduced, thereby raising the flood limit water level and increasing the utilization rate of water resources.

[0064] In some optional implementations, the forecast hydrological information includes at least evaluation parameters, and step S102 includes the following steps:

[0065] Step S201, performing error analysis based on evaluation parameters to obtain error analysis results.

[0066] Evaluation parameters are parameters in the forecast hydrological information that can be used for error analysis. Corresponding evaluation parameters are used for different forecast hydrological information. Each forecast hydrological information may include one or more evaluation parameters. Specifically, during error analysis, the accuracy, missed reporting rate, and false reporting rate of forecast hydrological information in different future periods can be calculated.

[0067] Further, when the forecast hydrological information includes forecast rainfall information, the evaluation parameters include at least the total number of published forecast samples, the number of published accurate forecasts, and the number of published forecast omissions. The above step S201 includes the following steps:

[0068] Step S301, determining the accuracy of the forecast rainfall information based on the number of accurate forecast releases and the total number of forecast samples released.

[0069] Specifically, the accuracy of the forecast rainfall information is determined according to the following formula:

[0070]

[0071] Among them, η represents the accuracy of the forecast rainfall information, n represents the number of accurate forecasts released, and m represents the total number of forecast samples released.

[0072] Step S302, calculating the missed reporting rate of the forecast rainfall information based on the missed reporting number of the published forecasts and the total number of published forecast samples.

[0073] Specifically, the underreporting rate of rainfall forecast information is determined according to the following formula:

[0074]

[0075] Among them, β represents the underreporting rate of forecast rainfall information, μ represents the number of underreporting of published forecasts, and m represents the total number of published forecast samples.

[0076] Step S303, determining the false alarm rate of the forecast rainfall information based on the accuracy rate of the forecast rainfall information and the false alarm rate of the forecast rainfall information.

[0077] The error analysis results at least include the accuracy of the forecast rainfall information, the underreporting of the forecast rainfall information, and the underreporting rate of the forecast rainfall information.

[0078] Specifically, the false alarm rate of rainfall forecast information is determined according to the following formula:

[0079] k = [1-(η+β)] × 100%

[0080] Among them, η represents the accuracy rate of rainfall forecast information, β represents the underreporting rate of rainfall forecast information, and k represents the false reporting rate of rainfall forecast information.

[0081] In some optional implementations, when the forecast hydrological information includes forecast flood information, error analysis of the forecast flood information may be performed based on evaluation parameters such as peak error, peak flow error, flow error, and certainty coefficient.

[0082] Step S202: Calculate the hydrological error probability distribution based on the error analysis result to obtain the hydrological error probability distribution.

[0083] The Pearson type III curve and normal distribution curve can be used to simulate the forecast error probability distribution, thereby obtaining the hydrological error probability distribution.

[0084] Furthermore, the above step S202 includes the following steps:

[0085] Step S2021, determine whether the error analysis result meets the preset accuracy index.

[0086] The preset accuracy index is used as the accuracy judgment threshold to judge the error analysis results. Taking the forecast rainfall information as an example, the forecast rainfall information accuracy rate, forecast rainfall information omission rate, and forecast rainfall information omission rate in the error analysis results have corresponding preset accuracy indicators.

[0087] Step S2022: when the error analysis result meets the preset accuracy, historical hydrological data is obtained.

[0088] The error analysis result meets the preset accuracy, which means that the specific parameters in the error analysis result meet the requirements of the preset accuracy threshold. For different parameters, it can be either exceeding the preset accuracy threshold or not exceeding the preset accuracy threshold. Historical hydrological data is the recorded hydrological data that has occurred.

[0089] Step S2023, performing error probability distribution simulation based on the forecast hydrological information and historical hydrological data to determine an error probability distribution curve.

[0090] Parameters are calculated based on the forecast hydrological information, including coefficient of variation, coefficient of skewness and mean. These parameters are used as the basic parameters for simulating the probability distribution of errors. Simulation is performed based on the parameters and the set distribution type. The theoretical distribution curve is drawn based on the statistical actual rainfall data and the calculated parameters (coefficient of variation, coefficient of skewness, mean) using the eye-estimation fitting method. The theoretical curve is then adjusted to best fit the actual data points. Based on the theoretical distribution curve, the error probability distribution curves of rainfall and flood forecasts of different magnitudes are simulated.

[0091] After simulating the error probability distribution curve, it is necessary to evaluate whether its accuracy meets the scheduling requirements. This can be achieved by comparing it with historical hydrological information and calculating indicators such as the accuracy, underreporting rate, and false alarm rate of the simulation error. If the accuracy of the simulation result does not meet the requirements, it is necessary to return to the previous steps for adjustment. For example, you can re-estimate the parameters, select a more appropriate distribution type, or adjust the fitting process of the eye-fitting method.

[0092] In some optional implementations, the above step S103 includes:

[0093] Step S401, obtaining the reservoir dispatching strategy form.

[0094] Among them, the reservoir operation strategy form is generated based on the decision tree structure, which includes multiple decision branches. The decision branches are set based on forecast hydrological information and real-time hydrological information, and the branches correspond to the operation strategies one by one.

[0095] Forecasted hydrological information includes forecasted flood information and forecasted rainfall information, and real-time hydrological information includes real-time reservoir water level information and accumulated rainfall information. According to forecasted flood information, forecasted rainfall information, real-time reservoir water level information and accumulated rainfall information of different scales and magnitudes, the decision tree idea is used to determine the form of reservoir dispatching strategy. In the decision tree, decision nodes are set according to forecasted hydrological information and real-time hydrological information. Each decision node can be used as a conditional judgment, and each branch ultimately corresponds to a decision result. In the reservoir dispatching strategy, the decision result is the reservoir discharge. Among them, different scales refer to the length of the analysis and statistics period (for example, 3 hours, 6 hours, 12 hours, etc.), and different magnitudes refer to the magnitude of the forecasted rainfall (for example, heavy rain, moderate rain, light rain, etc.)

[0096] Taking into account the different characteristics of the flood stage and the receding stage, the reservoir operation strategy needs to be formulated in stages. In the flood stage, more attention may be paid to the water storage and flood discharge capacity of the reservoir to prevent floods from overflowing the dam; in the receding stage, more attention may be paid to the rational use of water resources and the restoration of reservoir capacity.

[0097] Step S402: adjusting the judgment index in the reservoir dispatching strategy form based on the preset water level to determine the first reservoir dispatching strategy.

[0098] Set the corresponding preset starting water level according to different stages, and adjust the judgment index through the step-by-step adjustment method.

[0099] For example, the reservoir dispatching strategy is as shown in the table below:

[0100]

[0101]

[0102] Among them, q i It is a parameter determined by simulation of all standard flood regulation of the reservoir; Q 入 represents the inflow of the reservoir at the current moment; Q i represents the flood flow forecast threshold; Q represents the actual flood flow forecast; P represents the actual accumulated rainfall; P i represents the cumulative rainfall threshold; T1, T2, and T3 represent different levels of forecast rainfall (heavy rain, moderate rain, and light rain, respectively); Z 防 represents the flood control high water level; Z represents the reservoir water level, Z 起调 Indicates the preset water level; PF means forecast rainfall.

[0103] During the flood stage, if the reservoir water level Z exceeds the flood control high water level Z 防 , the reservoir implements flood control dispatching to ensure the safety of the hub, that is, when the inflow Q 入 When the water level is less than or equal to the corresponding flood discharge capacity of the reservoir water level, the water will be discharged according to the inflow. When the inflow is greater than the corresponding flood discharge capacity of the reservoir water level, the water will be discharged according to the flood discharge capacity. If it is predicted that a rainfall of magnitude IV or above will occur or has occurred in the long term in the future, the discharge will be increased by one level in advance; if a rainfall of magnitude III or below will occur or has occurred, the original discharge will remain unchanged.

[0104] In the water withdrawal stage, when the reservoir water level Z approaches the start water level Z 起调 If it is predicted that rainfall of magnitude III or above will occur or has occurred in the long term in the future, the original discharge volume will be maintained unchanged; if rainfall of magnitude II or below will occur or has occurred, the reservoir water level will be maintained unchanged.

[0105] Furthermore, the above step S402 includes the following steps:

[0106] Step S4021, obtaining a preset start water level;

[0107] Step S4022, taking the preset water level as the initial condition, simulating the reservoir state under each flood level step by step to obtain the simulation result;

[0108] Step S4023, analyzing the simulation results, determining target judgment indicators corresponding to floods of different magnitudes, and determining the first reservoir dispatching strategy based on the target judgment indicators.

[0109] The preset water level is set according to the actual situation of the reservoir and the need for flood control. There can be one or more water levels. For each water level, the Runge-Kutta numerical solution is used to simulate the changes in the water level, storage capacity and discharge of the reservoir under different frequency design floods to obtain the simulation results. The target judgment indicators corresponding to floods of different magnitudes are adjusted step by step. The target judgment indicators include thresholds such as predicted flood flow, reservoir water level, predicted rainfall, and accumulated rainfall.

[0110] For example, the Runge-Kutta numerical solution is used in the process of determining the leakage. The specific solution steps of the Runge-Kutta numerical solution are as follows:

[0111] If it is assumed that the reservoir water level rises and falls horizontally, the essence of reservoir regulation calculation is to solve the differential equation, that is, dV(Z) / dt=Q(t)-S(Z)

[0112] Among them, Q(t) represents the inflow at time t, S(Z) represents the discharge through the discharge structure when the reservoir water level is Z, Z=Z(t), which represents the function of time t, and V(Z) represents the storage capacity when the reservoir water level is Z.

[0113] If the average predicted inflow in the n-period is Qn, the water level Zn-1 and reservoir capacity Vn-1 at the beginning of the n-period, the discharge flow S(Zn-1) at the beginning of the period, and the opening state of the discharge device are known, the Runge-Kutta method can be used to solve the above differential equation to obtain the reservoir capacity V at the end of the n-period. n ,Right now

[0114]

[0115] in,

[0116] k 1 =h n [Q n -S(Z(V n-1 ))]

[0117] k 2 =h n [Q n -S(Z(V n-1 +k 1 / 2))]

[0118] k 3 =h n [Q n -S(Z(V n-1 +k 2 / 2))]

[0119] k 4 =h n [Q n -S(Z(V n-1 +k 3 ))]

[0120] Among them, Z(·) is obtained by the reservoir capacity on the reservoir water level-reservoir capacity relationship curve using the three-point interpolation method, S(·) is obtained by the reservoir water level on the water level-discharge relationship curve, usually using the three-point interpolation method, h nIndicates the length of the n-period period, n = 1, 2, ..., N; N represents the total number of periods for flood control calculation; k 1 , k 2 , k 3 , k 4 Represent different coefficients respectively.

[0121] In the above formula, the unit of flow is m 3 / s; the unit of water level is m; the unit of water volume is m 3 ; The unit of time period length is s.

[0122] After obtaining Vn, Zn can be obtained, and thus the change process of reservoir water level, storage capacity and discharge flow over time can be obtained.

[0123] The reservoir flood control forecasting and dispatching method provided by the embodiment of the present invention proposes a multi-stage reservoir flood control forecasting and dispatching rule form considering multivariate information, and then evaluates the rainfall and flood forecasting accuracy and the forecast error according to the proposed reservoir flood control forecasting and dispatching rule form to ensure the availability of rainfall and flood forecasting information, and then simulates the rainfall error distribution and flood forecasting error probability distribution of different magnitudes according to the comparison between the actual rainfall data and the forecast rainfall data, and then adopts the Runge-Kutta numerical solution to adjust the design floods of various frequencies, formulates feasible forecasting and dispatching rules without considering the forecast error, and finally analyzes the applicability of the flood control forecasting and dispatching rules according to the simulated rainfall error distribution and flood forecasting error probability distribution of different magnitudes, and finally generates a target reservoir dispatching strategy considering multivariate information. Make full use of rainfall forecast and flood forecasting information to realize reservoir flood control forecasting and dispatching, and form a basin flood control and benefit integrated dispatching system, so as to not only ensure the safety of the reservoir and the important protection objects upstream and downstream, but also raise the reservoir operating water level during the flood season and increase the power generation efficiency.

[0124] In this embodiment, a reservoir flood control forecast dispatching device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made are not repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0125] This embodiment provides a reservoir flood control forecasting and dispatching device, such as Figure 3 As shown, including:

[0126] Information acquisition module 501, used to obtain forecast hydrological information;

[0127] The error analysis module 502 is used to perform error analysis on the forecast hydrological information and determine the hydrological error probability distribution based on the error analysis results;

[0128] A strategy acquisition module 503, used to determine a first reservoir dispatching strategy, wherein the first reservoir dispatching strategy is used to determine the reservoir discharge based on the forecast hydrological information and the real-time hydrological information;

[0129] The target strategy determination module 504 is used to determine the applicability of the first reservoir scheduling strategy based on the hydrological error probability distribution, so as to determine the target reservoir scheduling strategy based on the applicability.

[0130] In some optional implementations, the error analysis module 502 includes:

[0131] An error analysis unit, used to perform error analysis based on the evaluation parameters to obtain an error analysis result;

[0132] The probability distribution calculation unit is used to calculate the hydrological error probability distribution based on the error analysis result to obtain the hydrological error probability distribution.

[0133] In some optional implementations, when the forecast hydrological information includes forecast rainfall information, the evaluation parameters include at least the total number of published forecast samples, the number of published accurate forecasts, and the number of published forecast omissions, and the error analysis unit includes:

[0134] An accuracy determination subunit, used to determine the accuracy of the forecast rainfall information based on the number of accurate forecast releases and the total number of forecast samples released;

[0135] An underreporting rate determination subunit, configured to calculate the underreporting rate of the forecast rainfall information based on the number of underreporting times of the published forecast and the total number of published forecast samples;

[0136] The false alarm rate determination subunit is used to determine the false alarm rate of the forecast rainfall information based on the forecast rainfall information accuracy rate and the forecast rainfall information omission rate, and the error analysis result at least includes the forecast rainfall information accuracy rate, the forecast rainfall information omission rate, and the forecast rainfall information omission rate.

[0137] In some optional implementations, the false alarm rate of the forecast rainfall information is determined according to the following formula:

[0138] k = [1-(η+β)] × 100%

[0139] Among them, η represents the accuracy rate of the forecast rainfall information, β represents the underreporting rate of the forecast rainfall information, and k represents the false reporting rate of the forecast rainfall information.

[0140] In some optional implementations, the probability distribution calculation unit includes:

[0141] An index judgment subunit, used to judge whether the error analysis result meets the preset accuracy index;

[0142] A hydrological data acquisition subunit, used for acquiring historical hydrological data when the error analysis result meets the preset accuracy;

[0143] The curve determination subunit is used to perform error probability distribution simulation based on the forecast hydrological information and historical hydrological data to determine the error probability distribution curve.

[0144] In some optional implementations, the policy acquisition module 503 includes:

[0145] A form acquisition unit, used for acquiring a reservoir dispatching strategy form, wherein the reservoir dispatching strategy form is generated based on a decision tree structure, wherein the decision tree structure includes a plurality of decision branches, wherein the decision branches are set based on forecast hydrological information and real-time hydrological information, and wherein the branches correspond to the dispatching strategy one by one;

[0146] An adjustment unit is used to adjust the judgment index in the reservoir scheduling strategy form based on a preset start-up water level to determine the first reservoir scheduling strategy.

[0147] In some optional implementations, the adjustment unit includes:

[0148] A starting water level acquisition subunit is used to acquire a preset starting water level;

[0149] The simulation subunit is used to simulate the reservoir state under each magnitude of flood step by step with the preset start water level as the initial condition to obtain the simulation result;

[0150] The strategy determination subunit is used to analyze the simulation results, determine the target judgment indicators corresponding to floods of different magnitudes, and determine the first reservoir scheduling strategy based on the target judgment indicators.

[0151] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0152] The device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0153] The embodiment of the present invention also provides a computer device having the above Figure 2 The device shown.

[0154] See also Figure 2 , Figure 2 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 2As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 2 A processor 10 is taken as an example.

[0155] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0156] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0157] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer 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.

[0158] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0159] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0160] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0161] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the present invention.

Claims

1. A reservoir flood control forecasting and dispatching method, characterized in that: The method comprises: Obtain forecast hydrological information; Performing error analysis on the forecast hydrological information, and determining hydrological error probability distribution based on the error analysis results; Determining a first reservoir dispatching strategy, wherein the first reservoir dispatching strategy is used to determine a reservoir discharge based on the forecast hydrological information and the real-time hydrological information; The applicability of the first reservoir operation strategy is determined based on the hydrological error probability distribution, so as to determine a target reservoir operation strategy based on the applicability.

2. The method according to claim 1, characterized in that The forecast hydrological information at least includes evaluation parameters, and the error analysis of the forecast hydrological information and determining the hydrological error probability distribution based on the error analysis result include: Perform error analysis based on the evaluation parameters to obtain error analysis results; The hydrological error probability distribution is calculated based on the error analysis result to obtain the hydrological error probability distribution.

3. The method according to claim 2, characterized in that When the forecast hydrological information includes forecast rainfall information, the evaluation parameters include at least the total number of published forecast samples, the number of published accurate forecasts, and the number of published forecast omissions, and the error analysis is performed based on the evaluation parameters to obtain the error analysis results, including: Determining the accuracy of the forecast rainfall information based on the number of accurate forecast releases and the total number of forecast samples released; The forecast rainfall information omission rate is calculated based on the number of omissions in the published forecast and the total number of published forecast samples; The false alarm rate of the forecast rainfall information is determined based on the forecast rainfall information accuracy rate and the forecast rainfall information omission rate, and the error analysis result at least includes the forecast rainfall information accuracy rate, the forecast rainfall information omission rate, and the forecast rainfall information omission rate.

4. The method according to claim 3, characterized in that The false alarm rate of the rainfall forecast information is determined according to the following formula: k = [1-(η+β)] × 100% Among them, η represents the accuracy rate of the forecast rainfall information, β represents the underreporting rate of the forecast rainfall information, and k represents the false reporting rate of the forecast rainfall information.

5. The method according to claim 2, characterized in that: The calculating of hydrological error probability distribution based on the error analysis result to obtain the hydrological error probability distribution includes: Determine whether the error analysis result meets the preset accuracy index; When the error analysis result meets the preset accuracy, historical hydrological data is obtained; Based on the forecast hydrological information and historical hydrological data, an error probability distribution simulation is performed to determine an error probability distribution curve.

6. The method according to claim 1, characterized in that Determining the first reservoir dispatching strategy includes: Acquire a reservoir dispatching strategy form, wherein the reservoir dispatching strategy form is generated based on a decision tree structure, wherein the decision tree structure includes a plurality of decision branches, wherein the decision branches are set based on forecast hydrological information and real-time hydrological information, and wherein the branches correspond to the dispatching strategies one by one; The judgment index in the reservoir dispatching strategy form is adjusted based on the preset start-up water level to determine the first reservoir dispatching strategy.

7. The method according to claim 6, characterized in that The step of adjusting the judgment index in the reservoir dispatching strategy form based on the preset water level to determine the first reservoir dispatching strategy includes: Get the preset starting water level; Taking the preset water level as the initial condition, simulating the reservoir state under each magnitude of flood step by step to obtain simulation results; The simulation results are analyzed to determine target judgment indicators corresponding to floods of different magnitudes, so as to determine the first reservoir scheduling strategy based on the target judgment indicators.

8. A reservoir flood control forecasting and dispatching device, characterized in that: The device comprises: Information acquisition module, used to obtain forecast hydrological information; An error analysis module, used to perform error analysis on the forecast hydrological information and determine the hydrological error probability distribution based on the error analysis results; A strategy acquisition module, used to determine a first reservoir dispatching strategy, wherein the first reservoir dispatching strategy is used to determine the reservoir discharge based on the forecast hydrological information and the real-time hydrological information; A target strategy determination module is used to determine the applicability of the first reservoir scheduling strategy based on the hydrological error probability distribution, so as to determine a target reservoir scheduling strategy based on the applicability.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the reservoir flood forecasting and dispatching according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the reservoir flood control forecast dispatching according to any one of claims 1 to 7.

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