Cascade reservoir drought resistance and disaster reduction effect evaluation method and system and storage medium

Abstract

The invention discloses a cascade reservoir drought resistance and disaster reduction effect evaluation method and system and a storage medium. The method comprises the following steps: S1, carrying out cascade reservoir dispatching simulation; s2, acquiring a drought scene data set; s3, constructing a three-level nine-index system; s4, performing index standardization processing; s5, weight distribution by an entropy weight method; and S6, TOPSIS evaluation model calculation is carried out. The method has the advantages that by constructing a scientific multi-index evaluation system, the regulation and storage capacity and the disaster reduction effect of the reservoir group under the drought scene are quantitatively evaluated, and technical support is provided for reservoir regulation optimization and drought resistance decision.

Description

Technical Field

[0001] This invention relates to the field of water conservancy project scheduling and water resources assessment technology, and in particular to a method, system and storage medium for assessing the drought resistance and disaster reduction effects of cascade reservoirs. Background Technology

[0002] Reservoirs are core hub projects in the national water network regulation and storage system. As important regulation and storage units at the watershed scale, cascade reservoir groups not only undertake multiple functions such as water supply, power generation, and ecological protection, but are also important strategic infrastructure for coping with extreme drought scenarios and enhancing regional drought resilience. With the increase in extreme climate events and the continuous growth in rigid water demand, the regulation and storage capacity of cascade reservoirs, their joint operation mechanisms, and their disaster reduction effects under drought scenarios are receiving increasing attention from all parties.

[0003] As a crucial component of the national water network and water resource regulation system, cascade reservoirs are no longer isolated water conservancy units, but rather integrated into a systemic interaction link between multi-source allocation and multi-user demands, undertaking a systematic role of regulation, buffering, and protection. Currently, there is a lack of systematic assessment methods and technologies for the drought mitigation effects of cascade reservoir groups, particularly in the context of multiple drought scenarios and supply and demand across various industries, lacking a quantifiable, traceable, and operable comprehensive assessment system and technical framework. Therefore, this invention constructs a scientific, systematic, and practical technical method for assessing drought mitigation effects to support the construction of an intelligent decision-making system platform for reservoir scheduling optimization and watershed drought risk management. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, and storage medium for evaluating the drought resistance and disaster reduction effects of cascade reservoirs, thereby solving the aforementioned problems existing in the prior art.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A method for assessing the drought resistance and disaster reduction effects of cascade reservoirs includes the following steps:

[0007] S1. Conduct cascade reservoir scheduling simulation: Collect basic data information of the study area and apply the cascade reservoir scheduling model to conduct drought response simulation of reservoir operation scheduling under different inflow series;

[0008] S2. Obtaining a drought scenario dataset: Simulate different drought scenarios based on the water resource allocation model, obtain the allocation results under different drought responses, and construct a drought scenario dataset for multi-index evaluation.

[0009] S3. Construct a three-level, nine-indicator system: Based on drought scenario datasets, construct a three-level, nine-indicator comprehensive evaluation system consisting of three primary evaluation indicators and nine secondary evaluation indicators;

[0010] S4. Indicator Standardization: Construct a data matrix based on the secondary evaluation indicators and perform standardization on the data matrix;

[0011] S5. Entropy weight method for weight allocation: Based on the information entropy method, weights are assigned to the standardized secondary evaluation indicators to determine the weights of each secondary evaluation indicator;

[0012] S6. TOPSIS Evaluation Model Calculation: Based on the weights of each secondary evaluation index, the TOPSIS method is used to calculate the comprehensive effect closeness of reservoir operation schemes under different drought scenarios, and the reservoir operation schemes under different drought scenarios are ranked based on the magnitude of the comprehensive effect closeness.

[0013] Preferably, step S1 specifically involves collecting historical drought year hydrological and meteorological data, socio-economic water use data, and reservoir capacity scheduling data within the study area; based on regional hydrological characteristics and water demand, calling the cascade reservoir scheduling model to simulate the cascade reservoir operation and scheduling process, and obtaining scheduling output data.

[0014] The scheduling simulation process considers key elements such as joint scheduling rules among reservoir groups, priority water supply order, minimum ecological flow constraints, and time-based water balance to reproduce the supply and demand process under drought response.

[0015] Preferably, step S2 specifically involves calling the water resource allocation model based on the simulation results of the cascade reservoir scheduling model to simulate and obtain the configuration results of reservoir operation scheduling under different drought responses, so as to construct a drought scenario dataset for multi-indicator evaluation.

[0016] Preferably, step S3 specifically involves, based on a drought scenario dataset, and with drought resistance and disaster reduction capacity as the objective, constructing a three-layer architecture consisting of three primary evaluation indicators: drought resistance capacity, drought resistance effect, and disaster reduction benefit; and establishing a comprehensive indicator system consisting of nine secondary evaluation indicators: reservoir diameter ratio during the dry season, reservoir full capacity rate, reservoir water supply during the dry season, agricultural water shortage rate during the dry season, domestic water shortage rate during the dry season, industrial water shortage rate during the dry season, agricultural loss reduction effect during the dry season, domestic loss reduction effect during the dry season, and industrial loss reduction effect during the dry season.

[0017] Preferably, the calculation of each secondary evaluation index in step S3 is as follows:

[0018] 1) Reservoir diameter ratio during dry season

[0019]

[0020] Among them, K R V represents the reservoir's diameter ratio; 总 Q represents the total beneficial storage capacity of the reservoir; 枯 This indicates the total runoff during the dry season at the cross-section under investigation;

[0021] 2) Reservoir full storage rate

[0022]

[0023] Among them, R 蓄 Indicates the reservoir's full storage rate; U 10月末，t Indicates whether the reservoir is full at the end of October in year t. 10月末，t =1 means fully charged, U 10月末，t =0 means not fully charged;

[0024] 3) Water supply capacity during the dry season of the reservoir

[0025] Q 供 =Q 蓄 +Q 来 -Q 基 -Q 死 -Q 耗 (3)

[0026] Among them, Q 供 Q represents the available water volume of the reservoir; 蓄 Q represents the water storage capacity of a reservoir during a given period; 来 Indicates the inflow volume of the reservoir during a given period; Q 基 Q represents the base discharge required by the reservoir; 死 Q represents the reservoir capacity corresponding to the dead water level. 耗 This indicates the amount of water lost through evaporation and infiltration during a given period in the reservoir.

[0027] 4) Dry season agricultural water shortage rate

[0028]

[0029] Among them, Q 枯，A D represents the rate of agricultural water shortage during the dry season; 枯，A S represents the total amount of water required for agricultural use during the dry season. 枯，A This indicates the total water supply for agricultural use during the dry season.

[0030] The calculation methods for the dry season domestic water shortage rate and the dry season industrial water shortage rate are the same as above;

[0031] 5) Agricultural loss reduction benefits during dry season

[0032] E 枯，A =L 枯，AS -L 枯，AR (5)

[0033] Among them, E 枯，A Indicates the agricultural loss reduction benefits during the dry season; L 枯，AS This indicates agricultural drought losses without reservoir regulation; L 枯，AR This indicates agricultural drought losses due to reservoir regulation; agricultural drought loss L A The calculation formula is as follows:

[0034] L A =ΔW A ·C A ·V A (6)

[0035] Among them, L A Indicates agricultural drought losses; ΔW A Indicates the agricultural water shortage; C A V represents the agricultural benefit sharing coefficient; A This represents the added value of the agricultural industry;

[0036] The calculation methods for the dry season loss reduction benefits and the dry season industrial loss reduction benefits are the same as above.

[0037] Preferably, step S4 specifically includes the following:

[0038] S41. Collect the raw data of the nine secondary evaluation indicators in the drought scenario dataset and construct a data matrix X; each row of the data matrix represents a sample, and different samples represent reservoir operation schemes under different drought scenarios; each column represents a secondary evaluation indicator.

[0039]

[0040] Where, x ij This represents the value of the i-th sample on the j-th secondary evaluation indicator; m is the number of samples, and n is the number of secondary evaluation indicators;

[0041] S42. Standardize the original data using the range standardization method;

[0042] For positive indicators,

[0043]

[0044] For negative indicators,

[0045]

[0046] in, and Let s represent the maximum and minimum values ​​of the j-th secondary evaluation indicator, respectively; ij The standardized matrix is ​​obtained by taking the standard value of the i-th sample on the j-th secondary evaluation index.

[0047] Preferably, step S5 specifically includes the following:

[0048] S51. Calculate the weight of each sample in each secondary evaluation index;

[0049]

[0050] Where, p ij This represents the weight of the i-th sample on the j-th secondary evaluation indicator;

[0051] S52. Calculate the entropy value of each secondary evaluation indicator;

[0052]

[0053] Among them, e j This represents the entropy value of the j-th secondary evaluation indicator; When p ij When p = 0, then ij ln(p ij ) = 0;

[0054] S53. Calculate the information utility value of each secondary evaluation indicator;

[0055] d j =1-e j (12)

[0056] Where, d j This represents the information utility value of the j-th secondary evaluation indicator;

[0057] S54. Determine the weight of each secondary evaluation indicator;

[0058]

[0059] Among them, w j This represents the weight of the j-th secondary evaluation indicator.

[0060] Preferably, step S6 specifically includes the following:

[0061] S61. Construct a weighted standardized matrix;

[0062] Y = w j s ij (14)

[0063] Where Y represents the weighted standardization matrix;

[0064] S62. Determine the positive ideal solution and the negative ideal solution;

[0065] Y j + =max(y 1j ,y 2j ,…,y mj (15)

[0066] Y j - =min(y 1j ,y 2j ,…,ymj (16)

[0067] Among them, Y j + and Y j - Let y represent the positive and negative ideal solutions for the i-th sample, respectively; ij This represents the ideal solution for the i-th sample on the j-th secondary evaluation index;

[0068] S63. Calculate the distance between the positive and negative ideal solutions;

[0069]

[0070] in, and Let represent the positive ideal solution distance and the negative ideal solution distance of the i-th sample, respectively;

[0071] S64. Calculate the closeness of the comprehensive effect;

[0072]

[0073] Among them, C i The overall effect closeness of the i-th sample;

[0074] S65. Sort the samples according to the degree of similarity of their comprehensive effects. The greater the similarity of the comprehensive effects, the stronger the comprehensive drought resistance and disaster reduction effect of the sample.

[0075] The present invention also aims to provide a system for evaluating the drought resistance and disaster reduction effects of cascade reservoirs, including a memory, a processor, a model calling interface, and a model output interface;

[0076] The memory module serves as the model database module, used to store and manage various types of input data;

[0077] The processor is the core platform for model operation, responsible for scheduling data reading, model program invocation, and computational execution to realize the drought resistance and disaster reduction effect assessment method of the cascade reservoirs. The processor includes...

[0078] Data preprocessing module: Cleans the raw data, handles missing values, and standardizes it;

[0079] Indicator Calculation Module: Completes the calculation and normalization of nine secondary evaluation indicators;

[0080] Entropy weight allocation module: Implements the objective assignment of weights for secondary evaluation indicators;

[0081] TOPSIS Calculation Module: Completes the construction of positive and negative ideal solutions and the calculation of the distance and the closeness of the comprehensive effect between the positive and negative ideal solutions;

[0082] The processor can flexibly load various modules according to the calling requirements, and is suitable for simulating different drought intensities or scheduling schemes;

[0083] The model call interface supports importing existing reservoir operation and scheduling schemes from external systems as input data sources for drought scenarios;

[0084] The model output interface is used to export evaluation results and supports the generation of data tables including the comprehensive effect closeness and scores of various secondary evaluation indicators.

[0085] A further objective of this invention is to provide a computer-readable storage medium storing program instructions executable by a processor, the program instructions being used to implement the aforementioned method for evaluating the drought resistance and disaster reduction effects of cascade reservoirs; the program instructions include,

[0086] Data loading command: Used to read raw inputs including drought scenario hydrological data, scheduling output data, and water demand structure data, and store them in system memory for later use;

[0087] Indicator Calculation and Standardization Instructions: Based on the loaded data, calculate the original values ​​of nine indicators at three levels, and normalize them according to the preset standardization method to form a standardized indicator matrix;

[0088] Weighting instruction: Automatically calculate the objective weights of each secondary evaluation indicator using the entropy weighting method and output the weight vector;

[0089] TOPSIS evaluation instructions: Invoke the TOPSIS method to complete the construction of positive and negative ideal solutions, the distance between positive and negative ideal solutions, and the calculation of the closeness of the comprehensive effect;

[0090] Output command: Output the evaluation results in an interactive format.

[0091] The beneficial effects of this invention are as follows: 1. This invention, through objective weighting and system integration, scientifically evaluates the comprehensive drought resistance and disaster reduction effects of cascade reservoirs under multiple scenarios, providing a scientific basis for optimizing watershed scheduling strategies, assessing reservoir group operation and scheduling, and judging drought resistance capabilities under drought conditions. 2. This invention constructs a scientific multi-index evaluation system to quantitatively assess the storage capacity and disaster reduction effectiveness of reservoir groups under drought conditions, providing technical support for reservoir scheduling optimization and drought resistance decision-making. Attached Figure Description

[0092] Figure 1 This is a flowchart of the evaluation method in an embodiment of the present invention;

[0093] Figure 2 This is a technical roadmap of the evaluation method in the embodiments of the present invention;

[0094] Figure 3This is a structural diagram of the evaluation system in an embodiment of the present invention. Detailed Implementation

[0095] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0096] like Figure 1 and Figure 2 As shown, this embodiment proposes a multi-index comprehensive effect evaluation method based on entropy weight-TOPSIS, establishes a three-level, nine-index evaluation system of "drought resistance capacity—drought resistance effect—disaster reduction benefit," and thus constructs a scientific evaluation method for the drought resistance and disaster reduction performance of cascade reservoirs, and develops a supporting evaluation system. This method objectively assigns weights, integrates systems, and scientifically evaluates the comprehensive drought resistance and disaster reduction effects of cascade reservoirs in multiple scenarios. It can provide a scientific basis for optimizing watershed scheduling strategies under drought scenarios, evaluating reservoir group operation scheduling, and judging drought resistance capacity. Specifically, it includes the following parts:

[0097] I. Conducting Cascade Reservoir Scheduling Simulation

[0098] We collected basic data on the study area and used a cascade reservoir scheduling model to simulate the drought response of reservoir operation scheduling under different inflow series.

[0099] Specifically: collect basic data information within the study area, and based on regional hydrological characteristics and water demand, call the cascade reservoir scheduling model to simulate the operation and scheduling process of cascade reservoirs and obtain scheduling output data; during the scheduling simulation, consider key elements such as joint scheduling rules among reservoir groups, priority water supply order, minimum ecological flow constraints, and time period water balance to reproduce the supply and demand process under drought response.

[0100] The basic data for the study area includes hydrological and meteorological data from historical drought years, socio-economic water use data, and reservoir capacity scheduling data.

[0101] II. Obtaining Drought Scenario Datasets

[0102] Based on the water resource allocation model, different drought scenarios are simulated to obtain the allocation results under different drought responses, and a drought scenario dataset for multi-index evaluation is constructed.

[0103] Specifically, based on the simulation results of the cascade reservoir scheduling model, a water resource allocation model is invoked to simulate and obtain the configuration results of reservoir operation and scheduling under different drought responses, in order to construct a drought scenario dataset for multi-indicator evaluation. This dataset serves as a unified input source throughout the entire indicator system calculation process.

[0104] III. Constructing a Three-Tier, Nine-Indicator System

[0105] This invention proposes a three-tiered primary evaluation index framework of "drought resistance capacity—drought resistance effect—disaster reduction benefit," and establishes a comprehensive index system composed of nine secondary evaluation indicators, reflecting the complete drought resistance logic chain from resource security to effect response and then to economic effectiveness. As shown in the table below:

[0106] Table 1. Three-level, nine-indicator system for comprehensive drought resistance and disaster reduction effects.

[0107]

[0108] The calculation of each secondary evaluation indicator is as follows:

[0109] 1) Reservoir diameter ratio during dry season

[0110] The dry season-to-drainage ratio is an important parameter for assessing a reservoir's storage capacity and optimizing water resource allocation. It is defined as the ratio of the reservoir's total storage capacity to the total runoff during the dry season.

[0111]

[0112] Among them, K R V represents the reservoir's diameter ratio; 总 This indicates the total usable storage capacity of the reservoir (100 million m³). 3 );Q 枯 This represents the total dry season runoff (100 million m³) at the cross-section under investigation. 3 ).

[0113] 2) Reservoir full storage rate

[0114] The reservoir fullness rate is used to reflect the reservoir's water storage capacity before the dry season, especially the reservoir's potential for regulating water levels during the dry season.

[0115]

[0116] Among them, R 蓄 Indicates the reservoir's full capacity rate (expressed as a percentage, unit: %); U 10月末，t Indicates whether the reservoir is full at the end of October in year t. 10月末，t =1 means fully charged, U 10月末，t =0 means not full.

[0117] 3) Water supply capacity during the dry season of the reservoir

[0118] The amount of water available during the dry season is the most direct manifestation of a reservoir's drought resistance capacity. Referring to the research of Zhang Zixian, Yang Fen, and others, this study proposes to define the available water volume of a reservoir as the amount of water that needs to be released to ensure ecological base flow, minimum power generation discharge, and minimum navigation flow.

[0119] Q供 =Q 蓄 +Q 来 -Q 基 -Q 死 -Q 耗 (3)

[0120] Among them, Q 供 This indicates the available water volume of the reservoir (100 million m³). 3 );Q 蓄 This indicates the reservoir's water storage capacity over a given period (in billions of cubic meters). 3 );Q 来 This indicates the inflow volume of the reservoir over a given period (in billions of cubic meters). 3 );Q 基 This indicates the required base discharge from the reservoir (in billions of cubic meters). 3 );Q 死 This indicates the reservoir capacity (in billions of cubic meters) corresponding to the dead water level. 3 );Q 耗 This represents the amount of water lost through evaporation and infiltration during a given period in the reservoir (in billions of cubic meters). 3 ).

[0121] 4) Dry season agricultural water shortage rate

[0122] The dry season agricultural water shortage rate is an important indicator for measuring whether agricultural production can obtain sufficient water during dry seasons or when water resources are scarce.

[0123]

[0124] Among them, Q 枯，A D represents the rate of agricultural water shortage during the dry season; 枯，A This represents the total amount of water required for agricultural use during the dry season (in 100 million m³). 3 );S 枯，A This represents the total water supply for agricultural use during the dry season (100 million m³). 3 ).

[0125] The calculation methods for the dry season domestic water shortage rate and the dry season industrial water shortage rate are the same as above.

[0126] 5) Agricultural loss reduction benefits during dry season

[0127] Dry season agricultural loss reduction benefits are an important indicator for measuring the extent to which measures such as reservoir regulation can reduce the economic losses to agricultural production caused by drought.

[0128] E 枯，A =L 枯，AS -L 枯，AR (5)

[0129] Among them, E 枯，A Indicates the agricultural loss reduction benefits during the dry season (in billions of yuan); L 枯，ASThis represents agricultural drought losses (in billions of yuan) in the scenario (without reservoir regulation); L 枯，AR This represents agricultural drought losses (in billions of yuan) due to reservoir regulation; agricultural drought losses (in L). A The calculation formula is as follows:

[0130] L A =ΔW A ·C A ·V A (6)

[0131] Among them, L A Indicates agricultural drought losses; ΔW A This represents the agricultural water shortage (the difference between water demand and water supply); C A V represents the agricultural benefit sharing coefficient; A This represents the added value of the agricultural industry.

[0132] The calculation methods for the dry season loss reduction benefits and the dry season industrial loss reduction benefits are the same as above.

[0133] IV. Standardization of Indicators

[0134] 4.1 Constructing the data matrix:

[0135] The raw data of nine secondary evaluation indicators in the drought scenario dataset were collected and a data matrix X was constructed. Each row of the data matrix represents a sample, and different samples represent reservoir operation schemes under different drought scenarios. Each column represents a secondary evaluation indicator.

[0136]

[0137] Where, x ij This represents the value of the i-th sample on the j-th secondary evaluation indicator; m is the number of samples, and n is the number of secondary evaluation indicators.

[0138] 4.2 Data Standardization Processing:

[0139] To eliminate the dimensional differences between different indicators and enable them to participate in unified calculations, the raw data needs to be standardized. The range standardization method is used, and the specific formula is as follows:

[0140] For positive indicators,

[0141]

[0142] For negative indicators,

[0143]

[0144] in, and Let s represent the maximum and minimum values ​​of the j-th secondary evaluation indicator, respectively; ij Let S be the standard value of the i-th sample on the j-th secondary evaluation index, and then we obtain the standardization matrix S.

[0145] V. Entropy Weight Method for Weight Allocation

[0146] The information entropy method is used to assign weights to the nine standardized indicators, ensuring the objectivity of the weight allocation. Specifically:

[0147] 5.1 Calculate the weight of each sample in each secondary evaluation indicator:

[0148]

[0149] Where, p ij This represents the weight of the i-th sample in the j-th secondary evaluation index.

[0150] 5.2 Calculate the entropy value of each secondary evaluation indicator:

[0151]

[0152] Among them, e j This represents the entropy value of the j-th secondary evaluation indicator; When p ij When p = 0, then ij ln(p ij ) = 0.

[0153] 5.3 Calculate the information utility value of each secondary evaluation indicator:

[0154] d j =1-e j (12)

[0155] Where, d j This represents the information utility value of the j-th secondary evaluation indicator.

[0156] 5.4 Determine the weight of each secondary evaluation indicator:

[0157]

[0158] Among them, w j This represents the weight of the j-th secondary evaluation indicator.

[0159] The entropy weight method reflects the discreteness of the information distribution of secondary evaluation indicators. If a certain secondary evaluation indicator has a higher degree of discrimination for the whole, its entropy value is smaller, its redundancy is greater, and its weight is higher, thus forming a scientific and reasonable weighting system.

[0160] VI. TOPSIS Evaluation Model Calculation

[0161] The TOPSIS method, based on the principle of finding the optimal ideal solution, calculates the degree of closeness between the proposed and unplanned solutions and the ideal state. Specifically,

[0162] 6.1 Constructing a weighted standardized matrix:

[0163] Y = w j s ij (14)

[0164] Where Y represents the weighted standardized matrix.

[0165] 6.2 Determine the positive and negative ideal solutions:

[0166]

[0167] Among them, Y j + and Y j - Let y represent the positive and negative ideal solutions for the i-th sample, respectively; ij This represents the ideal solution for the i-th sample on the j-th secondary evaluation index.

[0168] 6.3 Calculate the distance between the positive and negative ideal solutions:

[0169]

[0170] in, and Let represent the positive ideal solution distance and the negative ideal solution distance of the i-th sample, respectively.

[0171] 6.4 Calculate the closeness of the comprehensive effect:

[0172]

[0173] Among them, C i Let be the overall effect closeness of the i-th sample.

[0174] 6.5 Sample Ranking:

[0175] The samples (reservoir operation schemes under different drought scenarios) are ranked according to the degree of similarity of their comprehensive effects. The greater the similarity of the comprehensive effects, the stronger the comprehensive drought resistance and disaster reduction effect. The evaluation results can be used to optimize scheduling strategies and guide decision-making.

[0176] This embodiment also provides a cascade reservoir drought resistance and disaster reduction effect assessment system, including four major functional modules: memory, processor, model calling interface, and model output interface. The system structure is as follows: Figure 3 As shown.

[0177] The storage module serves as the model database, used to hold and manage various types of input data. This includes, but is not limited to: reservoir scheduling rules, storage capacity parameters, hydrological data for typical drought scenarios, water demand data for various industries (agriculture, industry, and domestic), and socio-economic background data. Storage files can be in callable database formats such as .db, .sqlite, and .dmp, or readable file formats such as .scv.

[0178] The processor, as the core platform for model execution, is responsible for scheduling data reading, model program invocation, and computation execution. The processor mainly comprises the following functional sub-modules:

[0179] 1) Data preprocessing module: Cleans the raw data, handles missing values, and standardizes the data;

[0180] 2) Indicator Calculation Module: Completes the calculation and normalization of nine indicators;

[0181] 3) Entropy weight allocation module: Implements objective assignment of weights for secondary evaluation indicators;

[0182] 4) TOPSIS Calculation Module: Completes the construction of positive and negative ideal solutions and the calculation of the distance and the closeness of the comprehensive effect between the positive and negative ideal solutions.

[0183] The processor can flexibly load various modules according to the calling requirements, and is suitable for simulating different drought intensities or scheduling schemes.

[0184] The model call interface supports importing existing reservoir operation and scheduling schemes from external systems (water resources allocation model platform) as input data sources for drought scenarios; the model output interface is used to export evaluation results and supports generating data tables including comprehensive effect proximity and scores of various secondary evaluation indicators.

[0185] In this embodiment, a computer-readable storage medium is also provided, on which program instructions executable by a processor are stored. These program instructions are used to implement a method for evaluating the drought resistance and disaster reduction effects of cascade reservoirs. The program instructions include the following:

[0186] 1) Data loading command: Used to read raw inputs including drought scenario hydrological data, scheduling output data, and water demand structure data, and store them in system memory for later use;

[0187] 2) Indicator Calculation and Standardization Instructions: Based on the loaded data, calculate the original values ​​of nine indicators at three levels, and normalize them according to the preset standardization method to form a standardized indicator matrix;

[0188] 3) Weight allocation instruction: Automatically calculate the objective weights of each secondary evaluation indicator using the entropy weight method and output the weight vector;

[0189] 4) TOPSIS evaluation command: Call the TOPSIS model to complete the construction of positive and negative ideal solutions, as well as the distance calculation of positive and negative ideal solutions and the closeness calculation of the comprehensive effect;

[0190] 5) Output command: Output the evaluation results in an interactive format (e.g., .xlsx, .png, .json).

[0191] Computer-readable storage media include, but are not limited to, hard disks, ROMs, RAMs, or other media capable of storing program code and being read and executed by a computer device. By writing the method of the present invention into the aforementioned storage media, it can be deployed on any compatible terminal device, server system, or embedded platform.

[0192] By adopting the above-disclosed technical solution of this invention, the following beneficial effects are obtained:

[0193] This invention provides a method, system, and storage medium for evaluating the drought resistance and disaster reduction effects of cascade reservoirs. Through objective weighting and system integration, this invention scientifically evaluates the comprehensive drought resistance and disaster reduction effects of cascade reservoirs under multiple scenarios, providing a scientific basis for optimizing watershed scheduling strategies, evaluating reservoir group operation and scheduling, and assessing drought resistance capabilities under drought conditions. This invention constructs a scientific multi-index evaluation system to quantitatively assess the storage capacity and disaster reduction effectiveness of reservoir groups under drought conditions, providing technical support for reservoir scheduling optimization and drought resistance decision-making.

[0194] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for evaluating the drought resistance and disaster reduction effects of cascade reservoirs, characterized in that: Includes the following steps, S1. Conduct cascade reservoir scheduling simulation: Collect basic data information of the study area and apply the cascade reservoir scheduling model to conduct drought response simulation of reservoir operation scheduling under different inflow series; S2. Obtaining a drought scenario dataset: Simulate different drought scenarios based on the water resource allocation model, obtain the allocation results under different drought responses, and construct a drought scenario dataset for multi-index evaluation. S3. Construct a three-level, nine-indicator system: Based on drought scenario datasets, construct a three-level, nine-indicator comprehensive evaluation system consisting of three primary evaluation indicators and nine secondary evaluation indicators; S4. Indicator Standardization: Construct a data matrix based on the secondary evaluation indicators and perform standardization on the data matrix; S5. Entropy weight method for weight allocation: Based on the information entropy method, weights are assigned to the standardized secondary evaluation indicators to determine the weights of each secondary evaluation indicator; S6. TOPSIS Evaluation Model Calculation: Based on the weights of each secondary evaluation index, the TOPSIS method is used to calculate the comprehensive effect closeness of reservoir operation schemes under different drought scenarios, and the reservoir operation schemes under different drought scenarios are ranked based on the magnitude of the comprehensive effect closeness.

2. The method for evaluating the drought resistance and disaster reduction effects of cascade reservoirs according to claim 1, characterized in that: Step S1 specifically involves collecting historical drought year hydrological and meteorological data, socio-economic water use data, and reservoir capacity scheduling data within the study area. Based on regional hydrological characteristics and water demand, a cascade reservoir scheduling model is invoked to simulate the operation and scheduling process of the cascade reservoirs, and scheduling output data is obtained. The scheduling simulation process considers key elements such as joint scheduling rules among reservoir groups, priority water supply order, minimum ecological flow constraints, and time-based water balance to reproduce the supply and demand process under drought response.

3. The method for evaluating the drought resistance and disaster reduction effects of cascade reservoirs according to claim 2, characterized in that: Step S2 specifically involves using the simulation results of the cascade reservoir scheduling model to call the water resource allocation model and simulate the configuration results of reservoir operation scheduling under different drought responses, in order to construct a drought scenario dataset for multi-indicator evaluation.

4. The method for evaluating the drought resistance and disaster reduction effects of cascade reservoirs according to claim 3, characterized in that: Step S3 specifically involves, based on a drought scenario dataset, and with drought resistance and disaster reduction capacity as the objective, constructing a three-layer architecture consisting of three primary evaluation indicators: drought resistance capacity, drought resistance effect, and disaster reduction benefit. It also involves establishing a comprehensive indicator system composed of nine secondary evaluation indicators: reservoir diameter ratio during the dry season, reservoir full capacity rate, reservoir water supply during the dry season, agricultural water shortage rate during the dry season, domestic water shortage rate during the dry season, industrial water shortage rate during the dry season, agricultural loss reduction effect during the dry season, domestic loss reduction effect during the dry season, and industrial loss reduction effect during the dry season.

5. The method for evaluating the drought resistance and disaster reduction effects of cascade reservoirs according to claim 4, characterized in that: The calculation of each secondary evaluation indicator in step S3 is as follows: 1) Reservoir diameter ratio during dry season Among them, K R V represents the reservoir's diameter ratio; 总 Q represents the total beneficial storage capacity of the reservoir; 枯 This indicates the total runoff during the dry season at the cross-section under investigation; 2) Reservoir full storage rate Among them, R 蓄 Indicates the reservoir's full storage rate; U 10月末，t Indicates whether the reservoir is full at the end of October in year t. 10月末，t =1 means fully charged, U 10月末，t =0 means not fully charged; 3) Water supply capacity during the dry season of the reservoir Q 供 =Q 蓄 +Q 来 -Q 基 -Q 死 -Q 耗 (3) Among them, Q 供 Q represents the available water volume of the reservoir; 蓄 Q represents the water storage capacity of a reservoir during a given period; 来 Q represents the inflow volume of the reservoir during a given time period; 基 Q represents the base discharge required by the reservoir; 死 Q represents the reservoir capacity corresponding to the dead water level. 耗 This indicates the amount of water lost through evaporation and infiltration during a given period in the reservoir. 4) Dry season agricultural water shortage rate Among them, Q 枯，A D represents the rate of agricultural water shortage during the dry season; 枯，A S represents the total amount of water required for agricultural use during the dry season. 枯，A This indicates the total water supply for agricultural use during the dry season. The calculation methods for the dry season domestic water shortage rate and the dry season industrial water shortage rate are the same as above; 5) Agricultural loss reduction benefits during dry season AND 枯，A =L 枯，AS -THE 枯，AR (5) Among them, E 枯，A Indicates the agricultural loss reduction benefits during the dry season; L 枯，AS This indicates agricultural drought losses without reservoir regulation; L 枯，AR This indicates agricultural drought losses due to reservoir regulation; agricultural drought loss L A The calculation formula is as follows: Lx=ΔW A ·C A ·V A (6) Among them, L A Indicates agricultural drought losses; ΔW A Indicates the agricultural water shortage; C A V represents the agricultural benefit sharing coefficient; A This represents the added value of the agricultural industry; The calculation methods for the dry season loss reduction benefits and the dry season industrial loss reduction benefits are the same as above.

6. The method for evaluating the drought resistance and disaster reduction effects of cascade reservoirs according to claim 5, characterized in that: Step S4 specifically includes the following: S41. Collect the raw data of the nine secondary evaluation indicators in the drought scenario dataset and construct a data matrix X; each row of the data matrix represents a sample, and different samples represent reservoir operation schemes under different drought scenarios; each column represents a secondary evaluation indicator. Where, x ij This represents the value of the i-th sample on the j-th secondary evaluation indicator; m is the number of samples, and n is the number of secondary evaluation indicators; S42. Standardize the original data using the range standardization method; For positive indicators, For negative indicators, in, and Let s represent the maximum and minimum values ​​of the j-th secondary evaluation indicator, respectively; ij The standardized matrix is ​​obtained by taking the standard value of the i-th sample on the j-th secondary evaluation index.

7. The method for evaluating the drought resistance and disaster reduction effects of cascade reservoirs according to claim 6, characterized in that: Step S5 specifically includes the following: S51. Calculate the weight of each sample in each secondary evaluation index; Where, p ij This represents the weight of the i-th sample on the j-th secondary evaluation indicator; S52. Calculate the entropy value of each secondary evaluation indicator; Among them, e j This represents the entropy value of the j-th secondary evaluation indicator; When p ij When p = 0, then ij ln(p ij ) = 0; S53. Calculate the information utility value of each secondary evaluation indicator; d j =1-e j (12) Where, d j This represents the information utility value of the j-th secondary evaluation indicator; S54. Determine the weight of each secondary evaluation indicator; Among them, w j This represents the weight of the j-th secondary evaluation indicator.

8. The method for evaluating the drought resistance and disaster reduction effects of cascade reservoirs according to claim 7, characterized in that: Step S6 specifically includes the following: S61. Construct a weighted standardized matrix; Y=w j s ij (14) Where Y represents the weighted standardization matrix; S62. Determine the positive ideal solution and the negative ideal solution; in, and Let y represent the positive and negative ideal solutions for the i-th sample, respectively; ij This represents the ideal solution for the i-th sample on the j-th secondary evaluation index; S63. Calculate the distance between the positive and negative ideal solutions; in, and Let represent the positive ideal solution distance and the negative ideal solution distance of the i-th sample, respectively; S64. Calculate the closeness of the comprehensive effect; Among them, C i The overall effect closeness of the i-th sample; S65. Sort the samples according to the degree of similarity of their comprehensive effects. The greater the similarity of the comprehensive effects, the stronger the comprehensive drought resistance and disaster reduction effect of the sample.

9. A system for evaluating the drought resistance and disaster reduction effects of cascade reservoirs, characterized in that: This includes memory, processor, model call interface, and model output interface; The memory module serves as the model database module, used to store and manage various types of input data; The processor is the core platform for model operation, responsible for scheduling data reading, model program invocation, and computation execution to implement the drought resistance and disaster reduction effect assessment method for cascade reservoirs as described in any one of claims 1 to 8. The processor includes... Data preprocessing module: Cleans the raw data, handles missing values, and standardizes it; Indicator Calculation Module: Completes the calculation and normalization of nine secondary evaluation indicators; Entropy weight allocation module: Implements the objective assignment of weights for secondary evaluation indicators; TOPSIS Calculation Module: Completes the construction of positive and negative ideal solutions and the calculation of the distance and the closeness of the comprehensive effect between the positive and negative ideal solutions; The processor can flexibly load various modules according to the calling requirements, and is suitable for simulating different drought intensities or scheduling schemes; The model call interface supports importing existing reservoir operation and scheduling schemes from external systems as input data sources for drought scenarios; The model output interface is used to export evaluation results and supports the generation of data tables including the comprehensive effect closeness and scores of various secondary evaluation indicators.

10. A computer-readable storage medium having stored thereon program instructions executable by a processor, characterized in that, This program instruction is used to implement the drought resistance and disaster reduction effect assessment method for cascade reservoirs as described in any one of claims 1 to 8; the program instruction includes, Data loading command: Used to read raw inputs including drought scenario hydrological data, scheduling output data, and water demand structure data, and store them in system memory for later use; Indicator Calculation and Standardization Instructions: Based on the loaded data, calculate the original values ​​of nine indicators at three levels, and normalize them according to the preset standardization method to form a standardized indicator matrix; Weighting instruction: Automatically calculate the objective weights of each secondary evaluation indicator using the entropy weighting method and output the weight vector; TOPSIS evaluation instructions: Invoke the TOPSIS method to complete the construction of positive and negative ideal solutions, the distance between positive and negative ideal solutions, and the calculation of the closeness of the comprehensive effect; Output command: Output the evaluation results in an interactive format.

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