A reservoir drought staging method, device, equipment and medium

Abstract

The present application discloses a reservoir drought staging method, device, equipment and medium, and relates to the field of hydrological staging. The method comprises determining the basic time step of reservoir analysis based on reservoir characteristics and drought staging requirements, screening representative characteristics in the reservoir's historical long-sequence hydrological elements that affect the reservoir drought staging; quantitatively calculating various reservoir drought staging indicators based on the representative characteristics and in combination with the long-term optimal scheduling of reservoir water supply; determining the positive effect indicators and negative effect indicators of the drought staging indicators based on the drought staging requirements, and standardizing the positive effect indicators and the negative effect indicators to generate standardized drought staging indicators; converting the standardized drought staging indicators into multidimensional time series data for each period within a year; performing cluster analysis on the multidimensional time series data for each period within a year to complete the reservoir drought staging. The present application can reduce the drought staging error and improve the robustness of the method.

Description

Technical Field

[0001] The present application relates to the field of hydrological staging, and in particular to a reservoir drought staging method, device, equipment and medium. Background Art

[0002] The staging of reservoir drought is the basic work for reasonably determining the static control of reservoir drought warning water levels. Compared with the fixed drought warning water level that is uniform throughout the year, the use of staged drought warning water levels can better consider the continuity of the drought process and the seasonal laws of low water flow.

[0003] At present, there are relatively few studies on reservoir drought period classification. Hydrological classification methods generally refer to flood season classification methods and can be mainly divided into three categories: cause analysis, mathematical statistics, and cluster analysis. The first two methods generally have the problem of focusing on qualitative analysis and low classification accuracy. Cluster analysis has a stronger mathematical and physical background and theoretical basis, and the method is more objective and clear. Such methods mainly include Fisher optimal segmentation, set pair analysis, and fractal analysis. However, these flood season classification methods generally only consider a single statistical feature of a single indicator (such as the maximum daily flood peak for many years). When implemented, the original multi-year continuous time series needs to be processed into a single element sequence for each period of the year using statistical features. In the reservoir drought classification problem, the cause of drought is the joint action of multiple aspects and multiple factors. Cluster analysis should not be simply based on the multi-year mean or extreme runoff index to carry out the classification work. This kind of classification work using a single statistical feature specified by humans, such as the average or extreme value, does not have the diversity and interannual variability of drought classification indicators, resulting in large errors and poor robustness in the reservoir drought classification results. Therefore, how to comprehensively consider the characteristics of multiple influencing factors and the uncertainty of factors in reservoir staging and propose a more robust and less error-prone reservoir drought staging method remains a difficulty in drought emergency response and management. Summary of the invention

[0004] The purpose of this application is to provide a reservoir drought staging method, device, equipment and medium to solve the problem of large error and low robustness in reservoir drought staging using only a single statistical feature for staging.

[0005] To achieve the above objectives, this application provides the following solutions.

[0006] In a first aspect, the present application provides a reservoir drought staging method, comprising the following steps.

[0007] Based on reservoir characteristics and drought staging requirements, the basic time step of reservoir analysis is determined, and based on the basic time step, representative characteristics of the reservoir's historical long-sequence hydrological elements that affect the reservoir's drought staging are screened; the reservoir's historical long-sequence hydrological elements for the reservoir's drought staging include precipitation in the reservoir-controlled basin, evaporation, air temperature, reservoir operating water level, inflow, water demand of water supply objects, and reservoir capacity characteristics.

[0008] Based on the representative characteristics and combined with the long-term optimal scheduling of reservoir water supply, the various drought stage indicators of the reservoir are quantitatively calculated; the drought stage indicators are multi-year series data of multiple influencing factors; the long-term optimal scheduling of reservoir water supply includes a single-period water supply benefit loss objective function, water balance constraints, flow upper and lower limit constraints, and reservoir storage capacity upper and lower limit constraints; the multi-year series data of multiple influencing factors includes the reservoir storage capacity difference indicator for each period, the water supply index for each period, and the reservoir meteorological drought index.

[0009] Based on the drought staging requirements, the positive effect index and the negative effect index of the drought staging index are determined, and the positive effect index and the negative effect index are standardized to generate a standardized drought staging index.

[0010] The standardized drought staging index is converted into multi-dimensional time series data for each period within a year.

[0011] Cluster analysis was performed on the multidimensional time series data for each period within the year to complete the reservoir drought staging.

[0012] In a second aspect, the present application provides a reservoir drought staging device, comprising the following modules.

[0013] The representative feature screening module is used to determine the basic time step of reservoir analysis based on reservoir characteristics and drought staging requirements, and based on the basic time step, screen the representative features of the long-sequence hydrological elements of the reservoir history that affect the reservoir drought staging.

[0014] The drought stage index calculation module is used to quantitatively calculate various drought stage indicators of the reservoir based on the representative characteristics and combined with the long-term optimal scheduling of reservoir water supply; the drought stage index is multi-year series data of multiple influencing factors; the multi-year series data of multiple influencing factors includes the reservoir capacity difference index for each period of the reservoir stage, the water supply index for each period of the reservoir stage, and the reservoir meteorological drought index.

[0015] The standardization processing module is used to determine the positive effect index and the negative effect index of the drought staging index based on the drought staging requirements, and perform standardization processing on the positive effect index and the negative effect index to generate a standardized drought staging index.

[0016] The conversion module is used to convert the standardized drought stage index into multi-dimensional time series data for each period of the year.

[0017] The reservoir drought staging module is used to perform cluster analysis on the multi-dimensional time series data for each period within the year to complete the reservoir drought staging.

[0018] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any one of the reservoir drought staging methods described above.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-described reservoir drought staging methods.

[0020] According to the specific embodiments provided by the present application, the present application discloses the following technical effects: the present application determines the basic time step for reservoir analysis based on reservoir characteristics and drought staging requirements, and based on the basic time step, screens representative characteristics of the reservoir's historical long-sequence hydrological elements that affect the reservoir's drought staging, comprehensively considering the reservoir's historical long-sequence hydrological elements of various drought characterization factors including precipitation in the reservoir-controlled basin, evaporation, temperature, reservoir operating water level, inflow, water demand of water supply objects, and reservoir storage capacity characteristics, thereby retaining many years of long-sequence data information and significantly improving the scale and quality of the reservoir hydrological staging data set; in addition, in response to the problem of low water level and low runoff time being out of sync due to flood control and water supply tasks in large reservoirs, the long-term optimal scheduling of reservoir water supply including changes in storage capacity constraints is integrated into the drought staging indicators, realizing the synchronous changes of storage capacity characteristics and reservoir inflow characteristics; finally, the drought staging indicators are standardized and the data structure is converted, and cluster analysis is performed in combination with the multidimensional time series clustering analysis method to determine the reservoir drought staging. This application can avoid to a large extent the errors caused by subjective human preferences and the use of multi-year average characteristics of a single factor for drought staging, and can ensure the objectivity and robustness of drought staging. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 This is a schematic flow chart of a reservoir drought staging method in one embodiment of the present application.

[0023] Figure 2 A schematic flow chart of a reservoir drought staging method provided in another embodiment of the present application.

[0024] Figure 3 A process diagram of long-sequence optimization scheduling of reservoirs provided in one embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0027] like Figure 1 As shown, a reservoir drought staging method provided in an embodiment of the present application is executed by a computer device, and specifically can be executed by a computer device such as a terminal or a server alone, or can be executed by a terminal and a server together. In an embodiment of the present application, the following steps 101 to 105 are included.

[0028] Step 101: Based on reservoir characteristics and drought staging requirements, determine the basic time step for reservoir analysis, and based on the basic time step, screen representative characteristics of the reservoir's historical long-sequence hydrological elements that affect the reservoir's drought staging; the reservoir's historical long-sequence hydrological elements for the reservoir's drought staging include precipitation in the reservoir-controlled basin, evaporation, air temperature, reservoir operating water level, inflow, water demand of water supply objects, and reservoir capacity characteristics.

[0029] Step 102: Based on the representative characteristics and combined with the long-term optimal scheduling of reservoir water supply, quantitatively calculate various drought stage indicators of the reservoir; the drought stage indicators are multi-year series data of multiple influencing factors; the long-term optimal scheduling of reservoir water supply includes a single-period water supply benefit loss objective function, water balance constraints, flow upper and lower limit constraints, and reservoir storage capacity upper and lower limit constraints; the multi-year series data of multiple influencing factors includes the reservoir storage capacity difference indicator for each period, the water supply index for each period, and the reservoir meteorological drought index.

[0030] Step 103: Based on the drought staging requirements, the positive effect indicators and negative effect indicators of the drought staging indicators are determined, and the positive effect indicators and the negative effect indicators are standardized to generate a standardized drought staging indicator.

[0031] Step 104: converting the standardized drought staging index into multi-dimensional time series data for each period of the year.

[0032] Step 105: cluster analysis is performed on the multi-dimensional time series data for each period within the year to complete the reservoir drought stage classification.

[0033] In an exemplary embodiment, step 101 includes the following steps.

[0034] Step 201: Based on the drought stage requirements, investigate and analyze the reservoir characteristics; the reservoir characteristics include scheduling tasks, water supply objects, inflow water volume, changes in storage capacity constraints during the flood season, and changes in water supply and demand during the dry season.

[0035] Step 202: Determine the basic time step for reservoir analysis according to the reservoir characteristics; the basic time step includes three basic time steps, which are month and ten days, or ten days in the flood season and month in the dry season; wherein the flood season month is divided into the first ten days, the middle ten days and the last ten days, with one value for each ten days, and the dry season month has one value for each month.

[0036] Furthermore, from the perspectives of the purpose of reservoir drought classification and reservoir dispatching objectives, and from the perspectives of the natural and human characteristics of water resource system elements such as meteorology, hydrology, topography, reservoir management, and socio-economics, the main influencing factors that determine reservoir drought classification are analyzed.

[0037] Combined with the basic time step of reservoir analysis and the availability of historical processes that do not affect factors, effective representative characteristics of the reservoir are extracted. Characteristics with inconsistent time steps are processed into data processes of the required basic time step, such as the process of adding daily precipitation to obtain the monthly (ten-day) total precipitation; characteristics that do not directly affect the water volume of the reservoir are processed into water volume processes that act on the reservoir, such as the irrigation area of ​​the reservoir, which needs to be processed into the process of water volume required for irrigation.

[0038] Step 203: within the basic time step, representative features of the long-sequence hydrological elements of the reservoir history that affect the reservoir drought staging are screened.

[0039] In an exemplary embodiment of the present application, step 102 includes the following steps.

[0040] Step 301: Based on the representative characteristics, a dynamic programming algorithm is used to perform long-term optimal scheduling of reservoir water supply to obtain the optimal operation process of the reservoir at each basic time step.

[0041] Step 302: Based on the optimal operation process of the reservoir at each basic time step, the storage capacity constraint upper limit of the reservoir at each basic time step is subtracted from the optimal operation storage capacity of the reservoir to obtain the storage capacity difference index of the reservoir in each period.

[0042] Step 303: subtract the water volume of the reservoir from the water demand of multiple water supply objects of the reservoir at each basic time step to obtain the water volume index of the reservoir to be supplied in each period.

[0043] Step 304: The precipitation data and area of ​​each meteorological station within the reservoir control basin are weighted averaged to calculate the standardized precipitation coefficient to obtain the reservoir meteorological drought index.

[0044] In another exemplary embodiment of the present application, step 102 includes the following steps.

[0045] Step 401: Assume that the reservoir has N water supply objects, any object n Different years y Different basic time steps The water requirement is , the single-period water supply benefit loss function is ;in, is the weighted target and , is the reservoir water supply decision variable, is the reservoir water storage decision variable.

[0046] The goal is to minimize the loss of the reservoir's cumulative water supply benefits over the years: .

[0047] Consider the water balance constraint in the constraints: .

[0048] Traffic upper and lower limit constraints: .

[0049] Reservoir capacity upper and lower limit constraints: .

[0050] in, For the year, Y is the number of years of historical data; t is the basic time step, T Divide the total number of steps within the year; and For the t The beginning of the period t The amount of water stored in the reservoir at the end of the period; is the average water inflow to the reservoir during the period; is the evaporation and leakage loss of the reservoir during the period; The maximum allowable operating storage capacity of the reservoir at each basic time step within a year. Generally, the dry season is the normal water level, and the flood season is the flood limit water level; It is the dead storage capacity of the reservoir.

[0051] The dynamic programming optimization algorithm is used to recursively calculate the optimal operation process of the reservoir at each basic time step. .

[0052] Step 402: Considering the characteristics of large reservoirs, such as storing water in high water and replenishing water in low water, and taking into account both flood control and drought resistance, the reservoir capacity difference index for each period is calculated. for: .

[0053] Step 403: Considering that the reservoir drought phase focuses on water shortage events and the water inflow variability during the flood season is large, the reservoir phase water supply index for each period is for: .

[0054] Step 404: Count the long-sequence precipitation process of each meteorological station within the reservoir control basin, and take the arithmetic mean of each station as the surface average precipitation. Refer to the meteorological industry standard "Meteorological Drought Grade (GB / T 20481-2017)", this application adopts Γ Distribution fitting of precipitation on a 3-month time scale , its cumulative probability is: ;in, e is the natural constant 2.718, is a differential operator, which represents the precipitation 's points.

[0055] After normal standardization, the standardized precipitation index ( Standardized precipitation index, SPI) : .

[0056] in, 0 and 0 represents The shape and scale parameters of the distribution were estimated by the maximum likelihood method; 、 、 、 、 、 All of them are constants, namely 2.515517, 0.80253, 0.010328, 1.432788, 0.189269, 0.001308; for , add consideration The actual cumulative probability function after zero probability is 0.5 hours ,when 0.5 hours .

[0057] by SPI Characterizing reservoir meteorological drought indicators : ;in, For different years y Different basic time steps Standardized precipitation index.

[0058] In an exemplary embodiment of the present application, the standardization process in step 103 specifically includes the following steps.

[0059] Step 601: For reservoir drought stages, determine the numerical values ​​of different indicators to characterize the degree of drought, and identify the positive and negative effects of the indicators. For the reservoir capacity difference indicator, the larger the value, the lower the current operating water level of the reservoir, and the stronger the ability to characterize drought, so it is a positive effect indicator; for the water supply indicator, the larger the value, the more difficult it is for the reservoir to guarantee water supply, the more serious the water shortage, and the stronger the ability to characterize drought, so it is a positive effect indicator; for the standardized precipitation index, the larger the value, the more abundant the water, and the weaker the ability to characterize drought, so it is a negative effect indicator.

[0060] Step 602: Considering the different physical dimensions of the indicators of different drought stages and reducing the impact of outliers, the positive effect indicators or negative effect indicators are standardized: .

[0061] in, is the standardized drought stage index; is a positive effect indicator or a negative effect indicator; j is the type number of drought stage indicators, , J is the total number of types; is the mean of drought stage indicators; is the variance of drought stage index.

[0062] In an exemplary embodiment of the present application, the multidimensional time series data of each period within a year for: ;in, To make the dimension Y×T OK J The standardized drought stage index of the column is converted into a dimension T OK Y×J Multidimensional time series data for each period of the year; Y is the number of years of historical data; T Divide the total number of steps within the year; t =1,2,.. T ; y =1,2,..Y .

[0063] In an exemplary embodiment of the present application, step 105 includes the following steps.

[0064] Step 601: for the multidimensional time series data of each period within the year, a cluster quantity optimization algorithm is used to determine the optimal number of reservoir drought stages; the cluster quantity optimization algorithm includes a gap statistics cluster quantity optimization algorithm and an average profile cluster quantity optimization algorithm.

[0065] Step 602: Based on the optimal number of reservoir drought stages, a clustering method is used to complete the reservoir drought stages; the clustering method includes a K-means clustering method and a hierarchical clustering method.

[0066] The present application also provides an application scenario, which applies the above-mentioned reservoir drought staging method.

[0067] This embodiment takes Reservoir A in the Yangtze River Basin as an example to illustrate the implementation process of the large reservoir drought staging method based on multidimensional time series clustering provided in the present application.

[0068] In this embodiment, a reservoir drought staging method is implemented as follows: Figure 2 As shown, the following steps are included, wherein S1 and S2 are indicator calculation steps, S3 and S4 are format conversion steps, and S5 is a cluster analysis step.

[0069] S1. Based on reservoir characteristics and drought staging requirements, determine the analysis time step and screen the influencing factors; the analysis time step is the basic time step for reservoir analysis, and the influencing factors are the representative characteristics of the long-series hydrological elements in the reservoir history that affect the reservoir drought staging.

[0070] Furthermore, the reservoir's scheduling tasks, water supply objects, inflow, changes in storage capacity constraints during the flood season, and changes in water supply and demand during the dry season are investigated and analyzed to determine the basic time step for reservoir analysis.

[0071] Screen the characteristics that may affect the reservoir drought stages, and determine the number of representative characteristic indicators and their calculation expressions.

[0072] For example, at present, Reservoir A is a multi-year regulation reservoir, the normal water level corresponds to a storage capacity of 29.05 billion cubic meters, and the summer flood limit water level corresponds to a storage capacity of 19.82 billion m 3 The autumn flood limit water level corresponds to a reservoir capacity of 22.858 billion m 3 The dead water level corresponds to a reservoir capacity of 10 billion m 3 The water supply of the reservoir includes water diversion from outside the basin and ecological water use in the lower reaches of the basin. The basic time step t of the reservoir drought stage is set to month, T=12.

[0073] Collect the monthly inflow and runoff process of Reservoir A from 1971 to 2018 (historical data years Y = 48 years), the characteristic relationship curve of reservoir water level-area-storage capacity, the evaporation coefficient of the reservoir in different seasons, and the monthly water diversion demand of the downstream of the reservoir and ecological flow demand There are 12 meteorological stations in the control basin, and the average monthly precipitation and temperature process of the reservoir surface are obtained by arithmetic mean addition.

[0074] The flood control capacity of the reservoir accounts for 48% of the total effective storage capacity of the reservoir. Considering that the storage capacity constraints during the flood season vary greatly compared with the normal water storage level, it is necessary to include the maximum storage capacity constraints in the reservoir drought staging indicators.

[0075] In addition, reservoir water inflow, water demand, and water level should also be included in the drought staging indicators. Since there are cascade reservoirs upstream of the reservoir, meteorological information cannot well reflect the reservoir's flood and drought conditions, so meteorological indicators are not included in the reservoir drought staging indicators.

[0076] S2. Combined with the long-term optimal scheduling of water supply in large reservoirs, quantitatively calculate various drought stage indicators of reservoirs.

[0077] Furthermore, a dynamic programming algorithm is used to optimize the long-term scheduling of reservoir water supply, obtain the optimal operation process of the reservoir at each basic time step, and calculate the various drought stage indicators of the reservoir in turn.

[0078] For example, currently, Reservoir A has two water supply objects, and the same weight target is set. , with the goal of minimizing the loss of the reservoir's cumulative water supply benefits over the years: .

[0079] Consider the water balance constraint in the constraints: .

[0080] Traffic upper and lower limit constraints: .

[0081] Reservoir capacity upper and lower limit constraints: .

[0082] The dynamic programming optimization algorithm is used for recursive calculation to obtain the optimal operation process of the reservoir in each period. .

[0083] The results of reservoir operation capacity optimization are as follows: Figure 3 As shown in the figure, the operating storage capacity of the reservoir fluctuates greatly and is more significantly constrained by the water level and storage capacity during the flood season. If only the reservoir water level is considered without considering the changes in storage capacity constraints, it is easy to overestimate the impact of water level fluctuations on drought stages.

[0084] Furthermore, the reservoir capacity difference index for each period is calculated according to the formula: .

[0085] The water supply indicators of the reservoir in different periods are as follows: .

[0086] S3. Determine the positive and negative effects of drought staging indicators based on drought staging requirements and perform standardization.

[0087] Furthermore, for reservoir drought stages, the numerical values ​​of different indicators are determined to characterize the degree of drought and identify the positive and negative effects of the indicators. Considering the different physical dimensions of indicators in different drought stages, and in order to reduce the impact of outliers, the indicators are standardized.

[0088] For example, currently, the reservoir capacity difference index of reservoir A in each period is The water supply indicators of reservoirs in different periods and phases There is a positive correlation with drought characteristics, so it is a positive effect indicator. The standardized processing satisfies the Gaussian distribution. The formula is as follows: .

[0089] S4. The multi-year series data of multiple influencing factors are converted into multi-dimensional time series data for each period within a year. The size of the reservoir drought stage index matrix after standardization is OK Columns, without changing values, convert the data structure to OK Matrix of columns ; .

[0090] For example, currently, for reservoir A, the multi-year series data matrix of multiple influencing factors is: .

[0091] The matrix after data structure conversion is: .

[0092] S5. Complete the drought staging of large reservoirs based on cluster analysis with optimized number of clusters.

[0093] For example, currently, for the matrix R , using gap statistics to determine the optimal number of clusters Specifically, first randomly generate the matrix R Having the same range and distribution characteristics B A random reference dataset, for each reference dataset and each number of clusters k , calculate the logarithmic mean of the reference data , standard error and gap statistics .

[0094] 。

[0095] 。

[0096] .

[0097] in, is the sum of squares of the within-cluster deviations of the original data; is the sum of squares of intra-cluster deviations of the reference data; For the b-th group of reference data sets, the number of clusters is k The sum of squares of intra-cluster deviations when ; b is the serial number of the reference dataset, b= 1,2,… B .

[0098] The number of clusters Set them to 2, 3, ..., 10, and calculate the Gap value and standard error accordingly. The number of clusters corresponding to the maximum gap statistic is the optimal number of clusters.

[0099] Right now: .

[0100] in, The number of clusters is The gap statistics when The number of clusters is When the standard error of the reference data is .

[0101] For Reservoir A, .

[0102] On this basis, the K-means clustering method is used to specify the number of clusters. , randomly select 5 data points from any reference data set as the initial centroid, and each point in the reference data set , calculate its Euclidean distance to each centroid, and Assign to the cluster represented by the nearest centroid; recalculate the centroid of each cluster based on all the points newly assigned to each cluster; repeat the above steps until the centroid converges.

[0103] The clustering results of drought stages of Reservoir A are shown in Table 1, that is, January to March, April to May, June to September, October to November, and December are each a stage. Obviously, the dry season from October to April of the following year is divided more finely, and the flood season is divided into only two stages because the difference between the water supply and the reservoir capacity is generally small. Compared with the drought stage division based solely on the average monthly water inflow of the reservoir combined with Fisher segmentation, since the water inflow of the reservoir is generally small during the dry season and the difference in the flood season is greater, Fisher segmentation divides the entire dry season into one stage, and on the contrary, the flood season is divided into more detailed stages, which is obviously contrary to the demand for reservoir drought stages. This application can correctly identify the flood season and the dry season, and according to the seasonal law of dry season, identify the early dry season (November and December), the main dry season (January to March) and the late dry season (April and May), thereby allowing the reservoir to set different drought warning water levels during the dry season and maximize the potential of the reservoir's drought resistance, as shown in Table 1.

[0104] Table 1 Comparison of the results of reservoir drought stages using this application and based only on the reservoir's multi-year average monthly water inflow

[0105]

[0106] Based on the same inventive concept, the embodiment of the present application also provides a reservoir drought staging device for implementing the reservoir drought staging method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more reservoir drought staging device embodiments provided below can refer to the limitations of the reservoir drought staging method above, and will not be repeated here.

[0107] In an exemplary embodiment, a reservoir drought staging device is provided, including the following modules.

[0108] The representative feature screening module is used to determine the basic time step of reservoir analysis based on reservoir characteristics and drought staging requirements, and based on the basic time step, screen the representative features of the long-sequence hydrological elements of the reservoir history that affect the reservoir drought staging.

[0109] The drought stage index calculation module is used to quantitatively calculate various drought stage indicators of the reservoir based on the representative characteristics and combined with the long-term optimal scheduling of reservoir water supply; the drought stage index is multi-year series data of multiple influencing factors; the multi-year series data of multiple influencing factors includes the reservoir capacity difference index for each period of the reservoir stage, the water supply index for each period of the reservoir stage, and the reservoir meteorological drought index.

[0110] The standardization processing module is used to determine the positive effect index and the negative effect index of the drought staging index based on the drought staging requirements, and perform standardization processing on the positive effect index and the negative effect index to generate a standardized drought staging index.

[0111] The conversion module is used to convert the standardized drought stage index into multi-dimensional time series data for each period of the year.

[0112] The reservoir drought staging module is used to perform cluster analysis on the multi-dimensional time series data for each period within the year to complete the reservoir drought staging.

[0113] In an exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store reservoir drought staging data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a reservoir drought staging method is implemented.

[0114] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0115] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0116] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0117] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0118] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0119] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0120] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A reservoir drought staging method, characterized in that: The reservoir drought staging method comprises: Based on reservoir characteristics and drought staging requirements, the basic time step for reservoir analysis is determined, and based on the basic time step, representative characteristics of the long-sequence hydrological elements of the reservoir history that affect the reservoir drought staging are screened, including: Based on the drought stage requirements, investigate and analyze the reservoir characteristics; the reservoir characteristics include scheduling tasks, water supply objects, water inflow, changes in storage capacity constraints during the flood season, and changes in water supply and demand during the dry season; Determine the basic time step of reservoir analysis according to the reservoir characteristics; the basic time step includes three basic time steps, which are month and ten days, or ten days in the flood season and month in the dry season; wherein the flood season month is divided into the first ten days, the middle ten days and the last ten days, with one value for each ten days, and one value for each month in the dry season; Within the basic time step, representative features of the long-sequence hydrological elements of the reservoir history that affect the reservoir drought stage are screened; the long-sequence hydrological elements of the reservoir history that affect the reservoir drought stage include precipitation, evaporation, temperature, reservoir operating water level, inflow water, water demand of water supply objects and reservoir storage capacity characteristics in the reservoir control basin; Based on the representative characteristics, combined with the long-term optimal scheduling of reservoir water supply, the various drought stage indicators of the reservoir are quantitatively calculated; the drought stage indicators are multi-year series data of multiple influencing factors; the long-term optimal scheduling of reservoir water supply includes a single-period water supply benefit loss objective function, water balance constraints, flow upper and lower limit constraints, and reservoir storage capacity upper and lower limit constraints; the multi-year series data of multiple influencing factors includes the reservoir storage capacity difference index for each period, the water supply index for each period, and the reservoir meteorological drought index; Based on the drought staging requirements, determining the positive effect index and the negative effect index of the drought staging index, and performing standardization processing on the positive effect index and the negative effect index to generate a standardized drought staging index; Converting the standardized drought stage index into multi-dimensional time series data for each period within a year; Cluster analysis was performed on the multidimensional time series data for each period within the year to complete the reservoir drought staging.

2. The reservoir drought staging method according to claim 1, characterized in that: Based on the representative characteristics and combined with the long-term optimal scheduling of reservoir water supply, the various drought stage indicators of reservoirs are quantitatively calculated, including: Based on the representative characteristics, a dynamic programming algorithm is used to perform long-term optimal scheduling of reservoir water supply to obtain the optimal operation process of the reservoir at each basic time step; Based on the optimal operation process of the reservoir at each basic time step, the storage capacity constraint upper limit of the reservoir at each basic time step is subtracted from the optimal operation storage capacity of the reservoir to obtain the storage capacity difference index of the reservoir in each period; The water quantity of the reservoir that should be supplied by the reservoir in each period is obtained by subtracting the water quantity of the reservoir from the water demand of multiple water supply objects in each basic time step; The precipitation data and area of ​​each meteorological station within the reservoir control basin are weighted averaged to calculate the standardized precipitation coefficient and obtain the reservoir meteorological drought index.

3. The reservoir drought staging method according to claim 2, characterized in that: The reservoir capacity difference index for each period of the reservoir for: ;in, The maximum allowable operating storage capacity of the reservoir at each basic time step within a year; It is the optimal operation process of the reservoir at each basic time step; y for the year; t is the basic time step; The water supply indicators of the reservoir in different periods for: ;in, N The number of objects supplied with water in the reservoir; n The serial number of the water supply object; For any object n Different years y Different time steps Water demand; is the average water inflow to the reservoir during the period; Reservoir meteorological drought index for: ;in, is the standardized precipitation coefficient.

4. The reservoir drought staging method according to claim 1, characterized in that: The positive effect index and the negative effect index are standardized to generate a standardized drought stage index, which specifically includes: Using formula The positive effect index and the negative effect index are standardized to generate a standardized drought stage index; wherein, is the standardized drought stage index; is a positive effect indicator or a negative effect indicator; j is the type number of drought stage indicators, , J is the total number of types; is the mean of drought stage indicators; is the variance of drought stage index; y is the year; t is the basic time step.

5. The reservoir drought staging method according to claim 4, characterized in that: Multidimensional time series data for each period of the year for: ;in, To make the dimension Y×T OK J The standardized drought stage index of the column is converted into a dimension of T OK Y×J Multidimensional time series data for each period of the year; Y is the number of years of historical data; T Divide the total number of steps into the year.

6. The reservoir drought staging method according to claim 1, characterized in that: Cluster analysis was performed on the multidimensional time series data for each period of the year to complete the reservoir drought stage, including: For the multi-dimensional time series data of each period in the year, a cluster quantity optimization algorithm is used to determine the optimal number of stages for reservoir drought; the cluster quantity optimization algorithm includes a gap statistics cluster quantity optimization algorithm and an average profile cluster quantity optimization algorithm; According to the optimal number of reservoir drought stages, a clustering method is used to complete the reservoir drought stages; the clustering method includes a K-means clustering method and a hierarchical clustering method.

7. A reservoir drought staging device, characterized in that: The reservoir drought staging device comprises: A representative feature screening module is used to determine the basic time step of reservoir analysis based on reservoir characteristics and drought staging requirements, and based on the basic time step, screen representative features of the long-sequence hydrological elements of the reservoir history that affect the reservoir drought staging; Based on reservoir characteristics and drought staging requirements, the basic time step for reservoir analysis is determined, and based on the basic time step, representative characteristics of the long-sequence hydrological elements of the reservoir history that affect the reservoir drought staging are screened, including: Based on the drought stage requirements, investigate and analyze the reservoir characteristics; the reservoir characteristics include scheduling tasks, water supply objects, water inflow, changes in storage capacity constraints during the flood season, and changes in water supply and demand during the dry season; Determine the basic time step of reservoir analysis according to the reservoir characteristics; the basic time step includes three basic time steps, which are month and ten days, or ten days in the flood season and month in the dry season; wherein the flood season month is divided into the first ten days, the middle ten days and the last ten days, with one value for each ten days, and one value for each month in the dry season; Within the basic time step, representative features of the long-series hydrological elements of the reservoir history that affect the reservoir drought stage are screened; A drought stage index calculation module is used to quantitatively calculate various drought stage indexes of the reservoir based on the representative characteristics and in combination with the long-term optimal scheduling of reservoir water supply; the drought stage index is multi-year series data of multiple influencing factors; the multi-year series data of multiple influencing factors includes reservoir capacity difference index for each period of ... A standardization processing module, used for determining the positive effect index and the negative effect index of the drought staging index based on the drought staging requirement, and performing standardization processing on the positive effect index and the negative effect index to generate a standardized drought staging index; A conversion module, used for converting the standardized drought stage index into multi-dimensional time series data for each period of the year; The reservoir drought staging module is used to perform cluster analysis on the multi-dimensional time series data for each period within the year to complete the reservoir drought staging.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the reservoir drought staging method according to any one of claims 1 to 6.

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

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