Reservoir ecological regulation method and system based on IHA index and hydrological simulation

Abstract

This invention relates to the technical field and discloses a reservoir ecological scheduling method and system based on IHA indicators and hydrological simulation. The method constructs a fuzzy comprehensive evaluation index for flow and a runoff fluctuation change index; it also constructs a reservoir power generation index corresponding to second runoff data and reservoir storage capacity; using the maximization of the fuzzy comprehensive evaluation index for flow, the minimization of the runoff fluctuation change index, and the maximization of the reservoir power generation index as objective functions, and the reservoir discharge volume as the decision variable, a reservoir ecological scheduling model is constructed; based on the reservoir storage capacity corresponding to different reservoir discharge volumes obtained according to the pre-constructed target hydrological model, the first runoff data of the sub-basin where the ecological control section is located, and the second runoff data of the sub-basin where the reservoir is located, the reservoir ecological scheduling model is solved to obtain the optimal reservoir discharge volume for scheduling the reservoir in the target basin. This method achieves multi-objective regulation of ecological flow, runoff fluctuation, and reservoir power generation.

Description

Technical Field

[0001] This invention relates to the field of reservoir scheduling technology, and in particular to a reservoir ecological scheduling method and system based on IHA index and hydrological simulation. Background Technology

[0002] With rapid societal development and ongoing climate change, variations in river ecological flow, natural hydrological rhythms, and ecosystems have intensified, leading to risks to ecosystem health. Ensuring river ecological flow is a fundamental and strategic task for promoting river and lake ecological protection and restoration. It represents the minimum water volume necessary to maintain the structural integrity of river and lake ecosystems, the continuity of key ecological processes, and the normal functioning of their ecosystem services. However, against the backdrop of increasingly prominent water resource supply and demand contradictions, ensuring river ecological flow is facing severe challenges. On the one hand, upstream water conservancy project scheduling, excessive water extraction, and water pollution continue to weaken downstream ecological baseflow supply; on the other hand, the increasing demand for comprehensive watershed management and watershed ecological security construction places higher requirements on the rigid guarantee of ecological water use.

[0003] Reservoirs possess advantages such as strong regulation capacity, wide control range, and rapid response speed, making them an important technical means for ensuring river ecological flow, reconstructing natural hydrological rhythms, and restoring degraded ecosystems. Existing reservoir operation methods primarily focus on flood control, power generation, and water supply, paying less attention to hydrological fluctuations caused by climate change and human activities. This results in insufficient reservoir operation capacity to regulate hydrological variability and weak ecological regulation functions. Furthermore, when implementing downstream ecological regulation, existing reservoir operation methods do not consider the propagation and response mechanisms of hydrological processes at the watershed scale and lack dynamic simulation support for hydrological processes, thus leading to low accuracy in ecological response.

[0004] Therefore, improving the ecological regulation function and ecological response accuracy of reservoir scheduling has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] This invention provides a reservoir ecological scheduling method and system based on IHA index and hydrological simulation to solve the technical problem of how to improve the ecological regulation function and ecological response accuracy of reservoir scheduling, and to achieve multi-objective regulation of ecological flow, runoff fluctuation and reservoir power generation, thereby improving the ecological response accuracy.

[0006] In a first aspect, the present invention provides a reservoir ecological scheduling method based on IHA index and hydrological simulation, the method comprising:

[0007] Based on the pre-constructed target hydrological model of the target watershed, the reservoir storage capacity, the first runoff data of the sub-watershed where the ecological control section is located, and the second runoff data of the sub-watershed where the reservoir is located are obtained for different reservoir discharge volumes.

[0008] Based on the first runoff data, the second runoff data, and the reservoir storage capacity, a pre-constructed reservoir ecological scheduling model is solved to obtain the optimal reservoir discharge rate for scheduling the reservoirs in the target watershed. The construction of the reservoir ecological scheduling model includes:

[0009] A fuzzy comprehensive evaluation index for flow and a runoff fluctuation change index for the ecological control section are constructed. The fuzzy comprehensive evaluation index for flow is used to reflect the fuzzy evaluation level of the monthly average flow index of the hydrological situation index. The runoff fluctuation change index is used to reflect the change degree of the runoff fluctuation index of the hydrological situation index. The parameter values ​​corresponding to the monthly average flow index and the runoff fluctuation index are obtained through the first runoff data.

[0010] Construct the reservoir power generation index corresponding to the second runoff data and the reservoir storage capacity;

[0011] The reservoir ecological scheduling model is constructed with the objective functions of maximizing the fuzzy comprehensive evaluation index of flow, minimizing the runoff fluctuation change index, and maximizing the reservoir power generation index, and the reservoir discharge volume as the decision variable.

[0012] Preferably, the step of obtaining reservoir storage corresponding to different reservoir discharge volumes, first runoff data of the sub-basin where the ecological control section is located, and second runoff data of the sub-basin where the reservoir is located, based on the pre-constructed target hydrological model of the target watershed, includes:

[0013] Collect digital elevation model data, land use type data, and soil type data for the target watershed;

[0014] Based on the digital elevation model data, the land use type data, and the soil type data, soil and water resource assessment tools are used to sequentially divide sub-basins, adjust reservoir operation parameters, and calibrate hydrological parameters to obtain the target hydrological model of the target basin.

[0015] Based on the target hydrological model, hydrological simulations are performed on the target watershed to obtain the reservoir storage capacity corresponding to different reservoir discharge volumes, the first runoff data of the sub-watershed where the ecological control section is located, and the second runoff data of the sub-watershed where the reservoir is located.

[0016] Preferably, the fuzzy comprehensive evaluation index for flow and the runoff fluctuation change index for the ecological control section include:

[0017] Based on the historical average flow data of the ecological control section, the baseflow standard was set using the Tennessee method's baseflow standard classification criteria, resulting in the first baseflow standard for the general water use period and the second baseflow standard for the peak water use period.

[0018] The monthly average flow rate of the hydrological situation indicators during the general water use period is set as the first indicator set, and the monthly average flow rate of the indicators during the peak water use period is set as the second indicator set.

[0019] Based on the first base flow standard, a first fuzzy membership matrix of the first index set is constructed using a fuzzy algorithm; based on the second base flow standard, a second fuzzy membership matrix of the second index set is constructed using the same fuzzy algorithm.

[0020] Based on the first fuzzy membership matrix and the second fuzzy membership matrix, a comprehensive fuzzy membership matrix of the monthly average flow index is constructed, and a fuzzy comprehensive evaluation index of flow is obtained according to the comprehensive fuzzy membership matrix and the preset evaluation level score matrix.

[0021] The degree of change of the runoff fluctuation index of the hydrological situation index is expressed by the degree of change calculation method, and the degree of change of runoff fluctuation of the ecological control section is obtained.

[0022] Preferably, the step of constructing a first fuzzy membership matrix of the first index set based on the first base current standard using a fuzzy algorithm, and constructing a second fuzzy membership matrix of the second index set based on the second base current standard using the fuzzy algorithm, includes:

[0023] The first weight matrix of the first index set and the second weight matrix of the second index set are determined using the entropy weight method.

[0024] The first membership function of the first base current standard and the second membership function of the second base current standard are respectively set using trapezoidal membership functions;

[0025] Based on the first membership function, the first membership degree of the first index set to each evaluation level is obtained, and a first membership matrix is ​​constructed based on the first membership degree. Based on the second membership function, the second membership degree of the second index set to each evaluation level is obtained, and a second membership matrix is ​​constructed based on the second membership degree.

[0026] According to the preset fuzzy calculation rules, fuzzy calculation is performed on the first weight matrix and the first membership matrix to obtain the first fuzzy membership matrix, and fuzzy calculation is performed on the second weight matrix and the second membership matrix to obtain the second fuzzy membership matrix.

[0027] Preferably, the method of calculating the degree of change is used to represent the degree of change of the runoff fluctuation index of the hydrological situation index, thereby obtaining the degree of change index of runoff fluctuation of the ecological control section, including:

[0028] Based on the historical runoff sequence of the sub-basin where the ecological control section is located under natural, undisturbed conditions, the standard value and weight of the runoff fluctuation index are obtained.

[0029] Based on the first runoff data, the runoff fluctuation index control value is determined, and based on the runoff fluctuation index standard value, the runoff fluctuation index weight, and the runoff fluctuation index control value, the runoff fluctuation change index of the ecological control section is constructed.

[0030] Preferably, the step of constructing the reservoir power generation index corresponding to the second runoff data and the reservoir storage capacity includes:

[0031] Based on the second runoff data, the monthly outflow of the reservoir is obtained, and based on the reservoir storage capacity, the monthly average water level of the reservoir is obtained.

[0032] Based on the monthly outflow of the reservoir, the monthly average water level of the reservoir, and the obtained monthly average downstream water level, a reservoir power generation index is constructed.

[0033] Preferably, the step of solving the pre-constructed reservoir ecological scheduling model based on the first runoff data, the second runoff data, and the reservoir storage capacity to obtain the optimal reservoir discharge includes:

[0034] A set of reservoir discharge schemes is generated using a genetic algorithm, and the set of reservoir discharge schemes is input into the target hydrological model to obtain the first runoff data, the second runoff data, and the reservoir storage capacity corresponding to the set of reservoir discharge schemes.

[0035] Based on the first runoff data, the monthly average flow data corresponding to the monthly average flow index and the flow fluctuation data corresponding to the runoff fluctuation index are obtained.

[0036] Based on the monthly average flow data, the flow fluctuation data, the second runoff data, and the reservoir storage capacity, the first index result corresponding to the flow fuzzy comprehensive evaluation index, the second index result corresponding to the runoff fluctuation change index, and the third index result corresponding to the reservoir power generation index are obtained.

[0037] The suitability of the reservoir discharge scheme group is scored based on the results of the first indicator, the second indicator, and the third indicator. The population of the reservoir discharge scheme group is then updated based on the suitability score results, and the optimal reservoir discharge is obtained through iterative calculation.

[0038] Secondly, the present invention also provides a reservoir ecological scheduling system based on IHA index and hydrological simulation to realize the reservoir ecological scheduling method based on IHA index and hydrological simulation described above. The system includes: a hydrological data generation module and a model solving module.

[0039] The hydrological data generation module is used to obtain, based on the pre-constructed target hydrological model of the target watershed, the reservoir storage volume corresponding to different reservoir discharge volumes, the first runoff data of the sub-watershed where the ecological control section is located, and the second runoff data of the sub-watershed where the reservoir is located;

[0040] The model solving module is used to solve the pre-constructed reservoir ecological scheduling model based on the first runoff data, the second runoff data and the reservoir storage capacity to obtain the optimal reservoir discharge volume for scheduling the reservoirs in the target watershed.

[0041] The model solving module includes: a first index construction unit, a second index construction unit, and a model construction unit;

[0042] The first indicator construction unit is used to construct the fuzzy comprehensive evaluation index of flow and the runoff fluctuation change index of the ecological control section. The fuzzy comprehensive evaluation index of flow is used to reflect the fuzzy evaluation level of the monthly average flow index of the hydrological situation index. The runoff fluctuation change index is used to reflect the change degree of the runoff fluctuation index of the hydrological situation index. The parameter values ​​corresponding to the monthly average flow index and the runoff fluctuation index are obtained through the first runoff data.

[0043] The second indicator construction unit is used to construct the reservoir power generation indicator corresponding to the second runoff data and the reservoir storage capacity;

[0044] The model building unit is used to construct the reservoir ecological scheduling model with the objective functions of maximizing the fuzzy comprehensive evaluation index of flow, minimizing the runoff fluctuation change index, and maximizing the reservoir power generation index, and the reservoir discharge volume as the decision variable.

[0045] This application provides a reservoir ecological scheduling method and system based on IHA index and hydrological simulation. Compared with the prior art, the beneficial effects of the embodiments of this application are as follows:

[0046] This application discloses a reservoir ecological scheduling method based on IHA indicators and hydrological simulation. The method uses the maximization of the fuzzy comprehensive evaluation index of flow, the minimization of the runoff fluctuation change index, and the maximization of the reservoir power generation index as objective functions to achieve multi-objective regulation of ecological flow, runoff fluctuation, and reservoir power generation. The IHA indicator system is used to quantitatively characterize the variation characteristics of river hydrological conditions under environmental changes. Combined with the Tennant ecological water demand assessment method, fuzzy comprehensive evaluation method, and hydrological change index, it addresses the potential impacts of changes in water volume and runoff fluctuation in the target basin on the structure and function of the ecosystem. Furthermore, by combining reservoir power generation calculation, it quantifies reservoir capacity under different reservoir scheduling strategies. This method possesses strong computational efficiency and adaptability, a clear structure, and simple operation. It can quickly respond to changes in hydrological conditions and ecological flow assessment under different climate change scenarios, achieving high efficiency and timeliness in reservoir ecological scheduling. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the steps of a reservoir ecological scheduling method based on IHA index and hydrological simulation provided in a preferred embodiment of the present invention;

[0048] Figure 2 This is a schematic diagram of the construction method steps of a reservoir ecological scheduling model provided in a preferred embodiment of the present invention;

[0049] Figure 3 This is a schematic diagram of the first membership function of the first basic current standard provided in a preferred embodiment of the present invention;

[0050] Figure 4 This is a schematic diagram of the structure of a reservoir ecological scheduling system based on IHA index and hydrological simulation provided in a preferred embodiment of the present invention;

[0051] Figure label:

[0052] 1-Hydrological data generation module, 2-Model solving module, 21-First indicator construction unit, 22-Second indicator construction unit, 23-Model construction unit. Detailed Implementation

[0053] The embodiments of the present invention are described in detail below with reference to the accompanying drawings. The embodiments are provided for illustrative purposes only and should not be construed as limiting the invention. The accompanying drawings are for reference and illustration only and do not constitute a limitation on the scope of protection of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of this invention. In the description of this invention, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0054] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items. Those skilled in the art will understand the specific meaning of these terms in this invention based on the specific circumstances.

[0055] In the description of this invention, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0056] Please see Figure 1 The diagram illustrates the steps of a reservoir ecological scheduling method based on IHA indicators and hydrological simulation. In an embodiment of the present invention, a reservoir ecological scheduling method based on IHA indicators and hydrological simulation is provided, the method comprising:

[0057] S1. Based on the pre-constructed target hydrological model of the target watershed, the reservoir storage capacity, the first runoff data of the sub-watershed where the ecological control section is located, and the second runoff data of the sub-watershed where the reservoir is located are obtained corresponding to different reservoir outflow rates. In the preferred embodiment of this application, a target hydrological model constructed using SWAT (Soil and Water Assessment Tool) is introduced to simulate the impact of hydrological processes and land management measures on water resources in the target watershed under different reservoir outflow scheduling schemes. Given a determined reservoir outflow rate, the reservoir storage capacity, the first runoff data of the sub-watershed where the ecological control section is located, and the second runoff data of the sub-watershed where the reservoir is located are obtained. SWAT is a widely used distributed hydrological model at the watershed scale, simulating the complete hydrological cycle process including precipitation, evaporation, surface runoff, groundwater runoff, soil moisture content, and river confluence. It can quantify the impact of different climatic conditions and land use patterns on runoff, simulate the regulatory effects of different agricultural management measures and reservoir scheduling on water resources, and predict the long-term impact of climate change on watershed water supply and demand and the ecological environment. In a preferred embodiment of this application, digital elevation model (DEM) data, land use type data, and soil type data of the target watershed are collected. The DEM data is derived from SRTM DEM (90m resolution elevation dataset), a digital elevation database generated by the Space Shuttle Radar Topographic Mapping Digital Elevation Model. The land use type data is derived from CGLS-LC100 (100m resolution dynamic land cover map), and the soil type data is derived from HWSD (World Soil Database). Sub-watersheds are divided using ArcSWAT (a soil and water resource assessment tool integrated with Geographic Information System software), and reservoir operation parameters are adjusted. These parameters include the reservoir operation start time, the capacity corresponding to the normal spillway, the capacity corresponding to the emergency spillway, water surface area parameters, and measured reservoir flow data. The reservoir simulation uses the measured daily flow method and is driven by rainfall and temperature data.

[0058] Furthermore, the SUFI-2 algorithm (Sequential Uncertainty Fitting) in SWAT-CUP software (SWAT Calibration and Uncertainty Program) was used for hydrological parameter calibration. Parameter optimization was performed using runoff data monitored by hydrological stations, and the calibration results were fed back into the SWAT model for model performance verification, resulting in the target hydrological model for the target watershed. Model performance verification was performed using the Nash efficiency coefficient (NSE) and the coefficient of determination (R²). 2 ) is performed when NSE is greater than the first threshold, R 2When the value exceeds the second threshold, the model exhibits good simulation performance. The first threshold is 0.6, and the second threshold is 0.5. Simultaneously, measured runoff is obtained from the reservoir's actual flow data. The degree of agreement between simulated and measured runoff during peak flood periods and low-flow periods is compared. A higher degree of agreement indicates that the target hydrological model can better reflect the variation characteristics between high and low flow values.

[0059] The target hydrological model, whose hydrological parameters have been calibrated and whose performance has been verified, is used to simulate the hydrological processes of the target watershed during the reservoir's ecological regulation period. It reads the reservoir's water storage corresponding to the reservoir discharge, the first runoff data of the sub-watershed where the ecological control section is located, and the second runoff data of the sub-watershed where the reservoir is located. The first runoff data is used to calculate the ecological index values ​​of the ecological control section, which mainly include the fuzzy comprehensive evaluation index of flow and the runoff fluctuation change index. The second runoff data and the reservoir's water storage are used to calculate the reservoir's power generation index value.

[0060] In a preferred embodiment of this application, the SWAT hydrological model is introduced to overcome the lack of dynamic simulation of downstream ecological response processes in the prior art, simulate the hydrological processes of the target watershed under different reservoir scheduling schemes, and obtain the first runoff data of the sub-watershed where the downstream ecological control section is located.

[0061] S2. Based on the first runoff data, the second runoff data, and the reservoir storage capacity, solve the pre-constructed reservoir ecological scheduling model to obtain the optimal reservoir discharge rate for scheduling the reservoirs in the target watershed. Figure 2 The diagram illustrates the steps involved in constructing a reservoir ecological scheduling model, including:

[0062] S01. Construct the fuzzy comprehensive evaluation index of flow and the runoff fluctuation change index for the ecological control section. The fuzzy comprehensive evaluation index of flow is used to reflect the fuzzy evaluation level of the monthly average flow index of the hydrological situation index. The runoff fluctuation change index is used to reflect the change degree of the runoff fluctuation index of the hydrological situation index. The parameter values ​​corresponding to the monthly average flow index and the runoff fluctuation index are obtained through the first runoff data. In the preferred embodiment of this application, the Indicators of Hydrologic Alteration (IHA) system is introduced to quantitatively characterize the variation characteristics of the watershed hydrological situation under environmental changes. As an important tool for assessing the degree of hydrological change, the IHA index system can simultaneously quantify the change in flow magnitude and hydrological fluctuation. The IHA index system includes 33 indicators in 5 categories, covering 5 categories: monthly average flow, extreme flow, flow change rate, pulse event frequency and duration. It can comprehensively reflect the multidimensional characteristics of hydrological processes and is particularly suitable for assessing the "deviation degree" of river systems under human activity interference, as shown in the table below:

[0063]

[0064] The monthly average flow rate is the average daily flow rate from January to December of each year, describing the monthly flow rate variation. The calculation formula is as follows:

[0065]

[0066] Among them, Q M,i N represents the average daily flow in the i-th month of each year. i Q represents the number of days in the i-th month. d,i This represents the daily flow rate on day d of month i.

[0067] Extreme flow indicators include the annual minimum 1-day average flow, annual maximum 1-day average flow, annual minimum 3-day average flow, annual maximum 3-day average flow, annual minimum 7-day average flow, annual maximum 7-day average flow, annual minimum 30-day average flow, annual maximum 30-day average flow, annual minimum 90-day average flow, annual maximum 90-day average flow, as well as the number of days with zero flow and base flow, reflecting the magnitude and timing of extreme flow values. The formula for calculating the annual minimum 1-day average flow is:

[0068] Q Min1 =min(Q) t )

[0069] Among them, Q Min1 Q represents the minimum daily average flow rate in a given year. t This represents the daily flow rate on day t of a year.

[0070] The formula for calculating the maximum annual average daily flow is:

[0071] Q Max1 =max(Q t )

[0072] Among them, Q Max1 This represents the minimum daily average flow rate in a given year.

[0073] The minimum annual average flow over three consecutive days is the minimum value of the average flow over any three consecutive days in a year, calculated using the following formula:

[0074]

[0075] Among them, Q Min3 Let Y represent the minimum three-day average flow rate for the year, and Q represent the total number of days in the year. t:1 Q represents the daily flow rate on day t+1 of a year. t:2 This represents the daily flow rate on day t+2 of the year.

[0076] The calculation logic for the annual maximum continuous 3-day average flow, annual minimum continuous 7-day average flow, annual maximum continuous 7-day average flow, annual minimum continuous 30-day average flow, annual maximum continuous 30-day average flow, annual minimum continuous 90-day average flow, and annual maximum continuous 90-day average flow differs from that of the annual minimum continuous 3-day average flow only in the number of consecutive days, so it will not be elaborated here.

[0077] The rate of change in flow rate measures the drastic nature of flow rate changes, including the daily average rate of change, the daily average rate of increase, and the daily average rate of decrease. The daily average rate of increase is the average of all daily increases in flow rate, while the daily average rate of decrease is the average of all daily decreases in flow rate. The formulas are as follows:

[0078]

[0079] Among them, R m R is the daily average rate of change of flow rate. rise R represents the average daily increase rate of traffic flow. fall Y represents the average daily flow rate decrease. : Y represents the number of days in a year when daily traffic volume increases. ； This refers to the number of days in a year when daily traffic volume decreases.

[0080] High / low flow pulse events describe the frequency and duration of flood and drought events. The 25th percentile of all annual flow is used as the low flow threshold (L), and the 75th percentile is used as the high flow threshold (H). A low flow pulse event is defined as a period of continuous flow below L, and a high flow pulse event is defined as a period of continuous flow above H. The indicators include the number of low flow pulses, the average duration of each low flow pulse, the number of high flow pulses, and the average duration of each high flow pulse.

[0081] The time of extreme flow occurrence is expressed in cumulative days, which includes the cumulative days of the occurrence of the maximum and minimum average flow on the 1st of each year. The cumulative days refer to the number of consecutive days calculated from January 1st of the current year.

[0082] In a preferred embodiment of this application, the monthly average flow rate is estimated using the Tennant method and a fuzzy algorithm to construct a fuzzy comprehensive evaluation index for flow, reflecting the fuzzy evaluation level of the monthly average flow rate of hydrological conditions. The Tennant method is a simple and rapid method for evaluating river ecological flow based on historical flow data. Based on the baseflow standard classification criteria recommended by the Tennant method, the river ecological condition is divided into eight evaluation levels: severely degraded, poor, beginning to degrade, good, very good, excellent, optimal flow, and beginning to scour. In this application, the baseflow standard differs for different water use periods. Based on the historical average flow data of the obtained ecological control sections and the baseflow standard classification criteria recommended by the Tennant method, the first baseflow standard for the general water use period (October to March) is set as follows:

[0083]

[0084] The second baseflow standard for the peak water consumption period (April to September) is set as follows:

[0085]

[0086] Group 1 of the IHA indicators comprises 12 indicators, representing the monthly average flow from January to December, which is designated as the monthly average flow indicator set U. Based on the Tennant method, it is divided into two subsets: the normal water use period and the peak water use period. The normal water use period adopts the first baseflow standard V1, and the peak water use period adopts the second baseflow standard V2. A first-level fuzzy evaluation is performed for each subset, and a second-level fuzzy comprehensive evaluation is performed based on the subset results to obtain the final result.

[0087] The entropy weight method is used to perform weight analysis on the first and second indicator sets respectively. For the general water use period U1, its first weight matrix A1 = [a′1, a′2, ..., a′6] is determined, and for the peak water use period U2, its second weight matrix A2 = [a"1, a"2, ..., a"6]. Here, a′1 represents the weight of the first indicator in the first indicator set, a′2 represents the weight of the second indicator in the first indicator set, a′6 represents the weight of the sixth indicator in the first indicator set, a"1 represents the weight of the second indicator in the second indicator set, a"2 represents the weight of the third indicator in the second indicator set, and a"6 represents the weight of the sixth indicator in the second indicator set.

[0088] Based on the first fundamental current criterion, a first membership function is defined. In this application, a trapezoidal membership function is adopted, such as... Figure 3 The diagram shows the first membership function of the first basestream standard. For each evaluation level interval of the first basestream standard, it is divided into 3 subintervals, and the threshold value of the subinterval is represented by k. Then, the interval of evaluation level 1... It can be divided into three equal intervals: 0~k1, k1~k2, and but:

[0089]

[0090] For the remaining rating ranges, the same principle applies. Figure 3In this context, k1 represents the first critical value within a sub-interval of rating level 1, k2 represents the second critical value within a sub-interval of rating level 1, k3 represents the first critical value within a sub-interval of rating level 2, k4 represents the second critical value within a sub-interval of rating level 2, k5 represents the first critical value within a sub-interval of rating level 3, k6 represents the second critical value within a sub-interval of rating level 3, k7 represents the first critical value within a sub-interval of rating level 4, k8 represents the second critical value within a sub-interval of rating level 4, and k9 represents the first critical value within a sub-interval of rating level 5. 10 k represents the second critical value in the interval of rating level 5. 11 k represents the first critical value in the sub-interval of rating level 6. 12 k represents the second critical value in the sub-interval of rating level 6. 13 k represents the first critical value in the interval of rating level 7. 14 k represents the second critical value in the sub-interval of rating level 7. 15 The value represents the first critical value in the sub-interval of evaluation level 8, u represents the index value, and r represents the membership degree corresponding to the index value.

[0091] The membership degree of the first set of indicators is 1 if the indicator value falls in the middle of the evaluation level interval. The membership degree decreases linearly from the middle interval to both sides. Therefore, for evaluation level 1 (severe degradation), the membership degree calculation formula is as follows:

[0092]

[0093] Where r1 represents the membership degree of evaluation level 1.

[0094] For an evaluation level of 2 (poor), the membership degree calculation formula is as follows:

[0095]

[0096] Where r2 represents the membership degree of evaluation level 2, and the same applies to evaluation levels 3-7.

[0097] For evaluation level 8 (start of flushing), the membership degree calculation formula is:

[0098]

[0099] Where r8 represents the membership degree of evaluation level 8.

[0100] Based on the membership function, the membership degree r of each indicator of U1 to each evaluation level is calculated, and the first membership matrix R1 is constructed. The first membership matrix R1 is expressed as:

[0101]

[0102] Where r is the first subscript representing the index and the second subscript representing the rating level index, i.e., r 11 The degree of membership of index 1 (October average flow) of U1 to the river ecological condition assessment level 1 (severe degradation), r 18 The degree of membership of index 1 (average October flow) of U1 to the river channel ecological condition assessment level 8 (beginning of scouring), r 61 The degree of membership of index 6 (March average flow) of U1 to the river's ecological condition assessment level 1 (severe degradation), r 68 This indicates the degree of membership of index 6 (average flow in March) of U1 to the river ecological condition evaluation level 8 (beginning of scouring).

[0103] According to the preset fuzzy calculation rules, a first-level fuzzy calculation is performed on the first weight matrix and the first membership matrix to obtain the first fuzzy membership matrix, which is expressed as:

[0104]

[0105] Where B1 represents the first fuzzy membership matrix, The operation relationship between matrices A1 and R1 is represented by the first fuzzy membership matrix B1, which is obtained by the first weight matrix A1 and the first membership matrix R1 through the fuzzy calculation formula. b1′ represents the membership degree of the first index set to evaluation level 1, b2′ represents the membership degree of the first index set to evaluation level 2, and b′8 represents the membership degree of the first index set to evaluation level 8.

[0106] The fuzzy calculation formula is:

[0107]

[0108] Where m represents the indicator index, n represents the evaluation level index, and M represents the total number of indicators. Since the total number of indicators in the first indicator set is 6, M = 6. n This represents the membership degree of the first indicator set to the evaluation level n.

[0109] For the second index set, the same method as for the first index set is used to obtain the second fuzzy membership matrix, which is expressed as:

[0110]

[0111] Where B2 represents the second fuzzy membership matrix, A2 represents the second weight matrix, R2 represents the second membership matrix, and r′ 11 The degree of membership of index 2 (April average flow) of U2 to the river's ecological condition assessment level 1 (severe degradation), r′ 18The degree of membership of index 2 (April average flow) of U2 to the river channel ecological condition assessment level 8 (beginning of scouring), r′ 61 The degree of membership of index 6 (September average flow) of U2 to the river's ecological condition assessment level 1 (severe degradation), r′ 68 b″1 represents the membership degree of indicator 6 (average flow in September) of U2 to the evaluation level 8 (beginning of scouring) of the river ecological condition; b″2 represents the membership degree of the second indicator set to evaluation level 1; b″8 represents the membership degree of the second indicator set to evaluation level 2; and b″8 represents the membership degree of the second indicator set to evaluation level 8.

[0112] Furthermore, using B1 and B2 as the membership matrix R of the index set U, i.e., R = [B1; B2], a second-level fuzzy comprehensive evaluation is performed to obtain the comprehensive fuzzy membership matrix, which is expressed as:

[0113]

[0114] Where B represents the comprehensive fuzzy membership matrix, A represents the weight matrix of the first and second indicator sets, a1 represents the weight of the first indicator set, a2 represents the weight of the second indicator set, the first and second indicator sets are equally important, so a1 and a2 are both 0.5, b1 represents the membership degree of the monthly average flow indicator set to evaluation level 1, b2 represents the membership degree of the monthly average flow indicator set to evaluation level 2, and b8 represents the membership degree of the monthly average flow indicator set to evaluation level 8.

[0115] Each evaluation level is assigned a score, resulting in an evaluation level score matrix. Based on the comprehensive fuzzy membership matrix and the preset evaluation level score matrix, a fuzzy comprehensive evaluation index for traffic flow is obtained. The fuzzy comprehensive evaluation index for traffic flow is expressed as follows:

[0116]

[0117] Where ξ represents the fuzzy comprehensive evaluation index of traffic, n represents the evaluation level index, and c n This represents the score for the evaluation level n.

[0118] The extreme flow index, flow variation, high / low flow pulse events, and extreme flow occurrence time in the IHA index system reflect the flow fluctuations in the target watershed. Therefore, these factors are considered as a whole as runoff fluctuation indicators. It is generally believed that undisturbed hydrological conditions are more conducive to watershed ecology; therefore, in this application, a change-degree calculation method is used to measure runoff fluctuation indicators to assess the runoff fluctuation situation in the target watershed.

[0119] Historical runoff sequence data of the sub-basin where the ecological control section is located under undisturbed natural conditions were collected. Based on the historical runoff sequence data, the values ​​of runoff fluctuation indicators under natural conditions were calculated, and the mean value of each runoff fluctuation indicator was obtained. The mean value of each runoff fluctuation indicator was used as the standard value of the corresponding runoff fluctuation indicator. The values ​​of each runoff fluctuation indicator obtained from the first runoff data were used as the control values ​​of the runoff fluctuation indicators. Based on the standard values ​​of the runoff fluctuation indicators, the weights of the runoff fluctuation indicators, and the control values ​​of the runoff fluctuation indicators, a runoff fluctuation change index for the ecological control section was constructed. The calculation formula for the runoff fluctuation change index is expressed as follows:

[0120]

[0121] Where, δ i (Q) represents the degree of change in the runoff fluctuation index i, θ i θ' represents the standard value of the runoff fluctuation index i. i δ(Q) represents the control value of runoff fluctuation index i, δ(Q) represents the degree of change in runoff fluctuation at the ecological control section, and ω i The weight of runoff fluctuation index i is represented by Q, which represents the daily runoff sequence data of the sub-basin where the ecological control section is located during the reservoir scheduling period, i.e., the first runoff data.

[0122] In a preferred embodiment of this application, during the actual calculation of the fuzzy comprehensive evaluation index of flow and the runoff fluctuation change index, the specific values ​​of the monthly average flow index and the runoff fluctuation index are obtained based on the first runoff data of the sub-basin where the ecological control section corresponding to the reservoir discharge is located, generated through a hydrological model. Based on the specific value of the monthly average flow index, the entropy weight method is used to calculate the weights of the first and second index sets respectively, resulting in a first weight matrix and a second weight matrix. Based on the aforementioned fuzzy evaluation process of flow, the score value of the fuzzy comprehensive evaluation index of flow is obtained to evaluate the monthly average flow. Based on the specific value of the runoff fluctuation index, the entropy weight method is used to calculate the weights of each runoff fluctuation index, resulting in the weights of each runoff fluctuation index. The score value of the runoff fluctuation change index is calculated using the aforementioned calculation formula for the runoff fluctuation change index to evaluate flow fluctuation. This achieves precise regulation of ecological flow while improving the reservoir's adaptability to climate change and human disturbance.

[0123] S02. Construct a reservoir power generation index corresponding to the second runoff data and the reservoir storage capacity; In a preferred embodiment of this application, the total power generation of the target watershed during the reservoir scheduling period, which can be achieved by the second runoff data and the reservoir storage capacity, is used as the reservoir power generation index. Historical data of reservoir parameters for the target watershed are obtained. The reservoir parameters include at least the water level and the reservoir storage capacity. A water level-storage capacity relationship equation is constructed, which is expressed as:

[0124]

[0125] Among them, V jt H represents the water storage capacity of the j-th reservoir in month t. jt V represents jt The corresponding water level, α j and β j It is a constant, obtained by fitting historical data of reservoir parameters.

[0126] The formula for calculating the reservoir's power generation index is:

[0127]

[0128] N jt =k j q jt (ZU jt -ZD jt )

[0129] Where, N jt For the output of the j-th reservoir in month t, T t k represents the length of the reservoir's operation period. j Let q be the output coefficient of the j-th reservoir. jt ZU represents the outflow of the j-th reservoir in month t. jt Let ZD be the average water level of the j-th reservoir in month t. jt Let be the average downstream water level of the j-th reservoir in month t. The monthly outflow is obtained from the second runoff data. The average storage level is calculated using the water level-storage capacity relationship equation based on the reservoir's storage capacity. The monthly average downstream water level is approximated by the monthly average value of the measured water level at the downstream hydrological station.

[0130] S03. Using the maximization of the fuzzy comprehensive evaluation index of flow, the minimization of the runoff fluctuation change index, and the maximization of the reservoir power generation index as objective functions, and the reservoir discharge as a decision variable, the reservoir ecological scheduling model is constructed. In the preferred embodiment of this application, the objective function is constructed by comprehensively considering the fuzzy comprehensive evaluation index of flow, the runoff fluctuation change index, and the reservoir power generation index, thereby maximizing the fuzzy comprehensive evaluation index of flow, minimizing the runoff fluctuation change index, and maximizing the reservoir power generation index. Since the first runoff data, the second runoff data, and the reservoir storage are all related to the reservoir discharge, the reservoir discharge is used as the decision variable. This application uses the monthly reservoir discharge as the decision variable, which can effectively simulate the daily hydrological cycle of the target watershed while significantly improving the model optimization solution efficiency. Therefore, the objective function is expressed as:

[0131] f=[Maxξ(Q1(x jt)),Minδ(Q1(x jt Max E(Q2(x)) jt ),V(x jt ))]

[0132] Where, x jt Let Q1(x) represent the monthly reservoir discharge of the j-th reservoir in month t. jt ) represents x jt The first runoff data of the sub-basin where the ecological control section is located, Q2(x) jt ) represents x jt Second runoff data for the sub-basin where the reservoir is located, V(x) jt ξ(Q1(x)) represents the water storage capacity of the j-th reservoir in month t. jt )) represents x jt The corresponding fuzzy comprehensive evaluation index for traffic is δ(Q1(x) jt )) represents x jt The corresponding runoff fluctuation change index, E(Q2(x) jt ),V(x jt )) represents x jt The corresponding reservoir power generation indicators.

[0133] The constraints of the objective function are:

[0134] V(x j,t:1 )=V(x jt )+V jti -V jto (x jt )+V jtp -V jte -V jts

[0135] H j,min ≤H jt (x jt )≤H j,max

[0136] Q min ≤Q1(x jt )≤Q max

[0137] x jt ≥0

[0138] Where V(x) j,t:1 V represents the water storage capacity of the j-th reservoir in month t+1. jti V represents the inflow rate of the j-th reservoir in month t. jto (x jt V represents the outflow of the j-th reservoir in month t. jtp V represents the rainfall in the reservoir area.jte V represents the evaporation rate in the reservoir area. jts H represents the infiltration rate in the reservoir area. jt (x jt H represents the water level of the j-th reservoir in month t. j,min and H j,max Let Q represent the minimum and maximum allowable water levels of the j-th reservoir, respectively. min and Q max These represent the minimum and maximum allowable flow rates in the sub-basin where the ecological control section is located, respectively.

[0139] In a preferred embodiment of this application, a reservoir ecological scheduling model is constructed based on an objective function and using reservoir discharge as the decision variable. Furthermore, a genetic algorithm is used to solve the reservoir ecological scheduling model to obtain the optimal reservoir discharge. Specifically, the reservoir scheduling period is determined, and the reservoir discharge is used as an initial population to represent the reservoir discharge during different scheduling periods. Through operations such as replication, crossover, and mutation, a set of chromosomes, i.e., a reservoir discharge scheme group, is generated. The reservoir discharge represented by this set of chromosomes is input into the hydrological model, and the corresponding first runoff data, second runoff data, and reservoir storage are read. Based on the first runoff data, the monthly average flow data corresponding to the monthly average flow index and the flow fluctuation data corresponding to the runoff fluctuation index are obtained. Based on the monthly average flow data, flow fluctuation data, second runoff data, and reservoir storage, the first index result corresponding to the fuzzy comprehensive evaluation index of flow, the second index result corresponding to the runoff fluctuation change index, and the third index result corresponding to the reservoir power generation index are obtained. The suitability of the reservoir outflow scheduling schemes is scored based on the results of the first, second, and third indicators. Based on the suitability scores, the population of the reservoir discharge scheme group is updated through replication, crossover, and mutation until the number of iterations is reached to obtain the optimal reservoir discharge. The optimal reservoir outflow scheduling scheme is then obtained based on the optimal reservoir discharge.

[0140] In a preferred embodiment of the present invention, based on a pre-constructed target hydrological model of the target watershed, the reservoir storage corresponding to different reservoir discharge volumes, the first runoff data of the sub-watershed where the ecological control section is located, and the second runoff data of the sub-watershed where the reservoir is located are obtained. Based on the first runoff data, the second runoff data, and the reservoir storage volume, the pre-constructed reservoir ecological scheduling model is solved to obtain the optimal reservoir discharge volume for scheduling the reservoirs in the target watershed. The construction of the reservoir ecological scheduling model includes: constructing a fuzzy comprehensive evaluation index for the flow of the ecological control section and an index for the degree of runoff fluctuation. A fuzzy comprehensive evaluation index for flow is used to reflect the fuzzy evaluation level of the monthly average flow index, a hydrological situation indicator. A runoff fluctuation change index is used to reflect the change in the runoff fluctuation index, a hydrological situation indicator. The parameter values ​​corresponding to the monthly average flow index and the runoff fluctuation index are obtained from the first runoff data. A reservoir power generation index corresponding to the second runoff data and the reservoir storage capacity is constructed. A reservoir ecological scheduling model is constructed with the objective functions of maximizing the fuzzy comprehensive evaluation index for flow, minimizing the runoff fluctuation change index, and maximizing the reservoir power generation index, and the reservoir discharge volume as the decision variable. The reservoir ecological scheduling method based on the IHA index and hydrological simulation disclosed in this application achieves multi-objective regulation of ecological flow, runoff fluctuation, and reservoir power generation by maximizing the fuzzy comprehensive evaluation index for flow, minimizing the runoff fluctuation change index, and maximizing the reservoir power generation index. The IHA index system is used to quantitatively characterize the variation characteristics of river hydrological conditions under environmental changes. Combining the Tennant ecological water demand assessment method, fuzzy comprehensive evaluation method, and hydrological change index, it assesses the potential impact of water volume and runoff fluctuations in target watersheds on ecosystem structure and function. Furthermore, by combining reservoir power generation calculation, it quantifies reservoir capacity under different reservoir scheduling strategies. It has strong computational efficiency and adaptability, clear structure, and simple operation. It can quickly respond to changes in hydrological conditions and ecological flow assessment under different climate change scenarios, achieving high efficiency and timeliness of reservoir ecological scheduling.

[0141] Accordingly, such as Figure 4 The diagram shows the structure of a reservoir ecological scheduling system based on IHA index and hydrological simulation. Based on a reservoir ecological scheduling method based on IHA index and hydrological simulation, this embodiment of the invention also provides a reservoir ecological scheduling system based on IHA index and hydrological simulation, which implements the reservoir ecological scheduling method based on IHA index and hydrological simulation disclosed in this embodiment of the invention, including: a hydrological data generation module 1 and a model solving module 2.

[0142] The hydrological data generation module 1 is used to obtain, based on the pre-constructed target hydrological model of the target watershed, the reservoir storage volume corresponding to different reservoir discharge volumes, the first runoff data of the sub-watershed where the ecological control section is located, and the second runoff data of the sub-watershed where the reservoir is located;

[0143] The model solving module 2 is used to solve the pre-constructed reservoir ecological scheduling model based on the first runoff data, the second runoff data and the reservoir storage capacity to obtain the optimal reservoir discharge volume for scheduling the reservoirs in the target watershed.

[0144] The model solving module 2 includes: a first index construction unit 21, a second index construction unit 22, and a model construction unit 23;

[0145] The first index construction unit 21 is used to construct the fuzzy comprehensive evaluation index of flow and the runoff fluctuation change index of the ecological control section. The fuzzy comprehensive evaluation index of flow is used to reflect the fuzzy evaluation level of the monthly average flow index of the hydrological situation index. The runoff fluctuation change index is used to reflect the change degree of the runoff fluctuation index of the hydrological situation index. The parameter values ​​corresponding to the monthly average flow index and the runoff fluctuation index are obtained through the first runoff data.

[0146] The second index construction unit 22 is used to construct the reservoir power generation index corresponding to the second runoff data and the reservoir storage capacity;

[0147] The model building unit 23 is used to construct the reservoir ecological scheduling model with the objective functions of maximizing the fuzzy comprehensive evaluation index of flow, minimizing the runoff fluctuation change index, and maximizing the reservoir power generation index, and the reservoir discharge volume as the decision variable.

[0148] Specific limitations regarding a reservoir ecological scheduling system based on IHA indicators and hydrological simulation can be found in the above-described limitations regarding a reservoir ecological scheduling method based on IHA indicators and hydrological simulation, and will not be repeated here. Those skilled in the art will recognize that the various modules and steps described in conjunction with the embodiments disclosed in this invention can be implemented in hardware, software, or a combination of both. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention.

[0149] In summary, the reservoir ecological scheduling method and system based on IHA index and hydrological simulation provided in this application addresses the technical problem of improving the ecological regulation function and ecological response accuracy of reservoir scheduling. The method includes: obtaining, based on a pre-constructed target hydrological model of the target watershed, the reservoir storage corresponding to different reservoir discharge volumes, the first runoff data of the sub-watershed where the ecological control section is located, and the second runoff data of the sub-watershed where the reservoir is located; solving the pre-constructed reservoir ecological scheduling model based on the first runoff data, the second runoff data, and the reservoir storage volume to obtain the optimal reservoir discharge volume for scheduling the reservoir in the target watershed; and constructing the reservoir ecological scheduling model. This includes: constructing a fuzzy comprehensive evaluation index for flow and a runoff fluctuation change index for ecological control sections. The fuzzy comprehensive evaluation index for flow is used to reflect the fuzzy evaluation level of the monthly average flow index, which is a hydrological situation indicator. The runoff fluctuation change index is used to reflect the degree of change of the runoff fluctuation index, which is a hydrological situation indicator. The parameter values ​​corresponding to the monthly average flow index and the runoff fluctuation index are obtained through the first runoff data. A reservoir power generation index corresponding to the second runoff data and the reservoir storage capacity is constructed. A reservoir ecological scheduling model is constructed with the objective functions of maximizing the fuzzy comprehensive evaluation index for flow, minimizing the runoff fluctuation change index, and maximizing the reservoir power generation index, and the reservoir discharge volume as the decision variable. The reservoir ecological scheduling method based on IHA index and hydrological simulation disclosed in this application achieves multi-objective regulation of ecological flow, runoff fluctuation, and reservoir power generation by maximizing the fuzzy comprehensive evaluation index for flow, minimizing the runoff fluctuation change index, and maximizing the reservoir power generation index. The IHA index system is used to quantitatively characterize the variation characteristics of river hydrological conditions under environmental changes. Combining the Tennant ecological water demand assessment method, fuzzy comprehensive evaluation method, and hydrological change index, it assesses the potential impact of water volume and runoff fluctuations in target watersheds on ecosystem structure and function. Furthermore, by combining reservoir power generation calculation, it quantifies reservoir capacity under different reservoir scheduling strategies. It has strong computational efficiency and adaptability, clear structure, and simple operation. It can quickly respond to changes in hydrological conditions and ecological flow assessment under different climate change scenarios, achieving high efficiency and timeliness of reservoir ecological scheduling.

[0150] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0151] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the technical principles of this application, and these improvements and substitutions should also be considered within the scope of protection of this application. Therefore, the scope of protection of this patent application should be determined by the scope of the claims.

Claims

1. A reservoir ecological regulation method based on an IHA index and hydrological simulation, characterized in that, The method includes: Based on the pre-constructed target hydrological model of the target watershed, the reservoir storage capacity, the first runoff data of the sub-watershed where the ecological control section is located, and the second runoff data of the sub-watershed where the reservoir is located are obtained for different reservoir discharge volumes. Based on the first runoff data, the second runoff data, and the reservoir storage capacity, a pre-constructed reservoir ecological scheduling model is solved to obtain the optimal reservoir discharge rate for scheduling the reservoirs in the target watershed. The construction of the reservoir ecological scheduling model includes: The construction of the fuzzy comprehensive evaluation index and runoff fluctuation change index for the ecological control section includes: setting the baseflow standard based on the historical average flow data of the ecological control section using the Tennessee method's baseflow standard classification criterion, obtaining a first baseflow standard for the general water use period and a second baseflow standard for the peak water use period; setting the monthly average flow index of the hydrological situation indicators for the general water use period as a first index set, and setting the monthly average flow index of the peak water use period as a second index set; constructing a first fuzzy membership matrix of the first index set based on the first baseflow standard using a fuzzy algorithm, and constructing a second fuzzy membership matrix of the second index set based on the second baseflow standard using the same fuzzy algorithm; and constructing the comprehensive fuzzy membership degree of the monthly average flow index based on the first and second fuzzy membership matrices. The system generates a matrix and obtains a fuzzy comprehensive evaluation index for flow based on the comprehensive fuzzy membership matrix and a preset evaluation level score matrix. It also obtains the standard value and weight of the runoff fluctuation index based on the historical runoff sequence of the sub-basin where the ecological control section is located under undisturbed natural conditions. Based on the first runoff data, it determines the control value of the runoff fluctuation index and constructs a runoff fluctuation change index for the ecological control section based on the standard value, weight, and control value. The fuzzy comprehensive evaluation index for flow reflects the fuzzy evaluation level of the monthly average flow index of the hydrological situation index, and the runoff fluctuation change index reflects the degree of change of the runoff fluctuation index of the hydrological situation index. The parameter values ​​corresponding to the monthly average flow index and the runoff fluctuation index are obtained from the first runoff data. Construct the reservoir power generation index corresponding to the second runoff data and the reservoir storage capacity; The reservoir ecological scheduling model is constructed with the objective functions of maximizing the fuzzy comprehensive evaluation index of flow, minimizing the runoff fluctuation change index, and maximizing the reservoir power generation index, and the reservoir discharge volume as the decision variable.

2. The reservoir ecological operation method based on IHA index and hydrological simulation according to claim 1, characterized in that, The process involves obtaining, based on a pre-constructed target hydrological model for the target watershed, reservoir storage corresponding to different reservoir discharge volumes, first runoff data for the sub-watershed where the ecological control section is located, and second runoff data for the sub-watershed where the reservoir is located, including: Collect digital elevation model data, land use type data, and soil type data for the target watershed; Based on the digital elevation model data, the land use type data, and the soil type data, soil and water resource assessment tools are used to sequentially divide sub-basins, adjust reservoir operation parameters, and calibrate hydrological parameters to obtain the target hydrological model of the target basin. Based on the target hydrological model, hydrological simulations are performed on the target watershed to obtain the reservoir storage capacity corresponding to different reservoir discharge volumes, the first runoff data of the sub-watershed where the ecological control section is located, and the second runoff data of the sub-watershed where the reservoir is located.

3. The reservoir ecological scheduling method based on IHA index and hydrological simulation as described in claim 1, characterized in that, The step of constructing a first fuzzy membership matrix of the first index set based on the first base current standard using a fuzzy algorithm, and constructing a second fuzzy membership matrix of the second index set based on the second base current standard using the same fuzzy algorithm, includes: The first weight matrix of the first index set and the second weight matrix of the second index set are determined using the entropy weight method. The first membership function of the first base current standard and the second membership function of the second base current standard are respectively set using trapezoidal membership functions; Based on the first membership function, the first membership degree of the first index set to each evaluation level is obtained, and a first membership matrix is ​​constructed based on the first membership degree. Based on the second membership function, the second membership degree of the second index set to each evaluation level is obtained, and a second membership matrix is ​​constructed based on the second membership degree. According to the preset fuzzy calculation rules, fuzzy calculation is performed on the first weight matrix and the first membership matrix to obtain the first fuzzy membership matrix, and fuzzy calculation is performed on the second weight matrix and the second membership matrix to obtain the second fuzzy membership matrix.

4. The reservoir ecological scheduling method based on IHA index and hydrological simulation as described in claim 1, characterized in that, The construction of the reservoir power generation index corresponding to the second runoff data and the reservoir storage capacity includes: Based on the second runoff data, the monthly outflow of the reservoir is obtained, and based on the reservoir storage capacity, the monthly average water level of the reservoir is obtained. Based on the monthly outflow of the reservoir, the monthly average water level of the reservoir, and the obtained monthly average downstream water level, a reservoir power generation index is constructed.

5. The reservoir ecological scheduling method based on IHA index and hydrological simulation as described in claim 1, characterized in that, The process of solving the pre-constructed reservoir ecological scheduling model based on the first runoff data, the second runoff data, and the reservoir storage capacity to obtain the optimal reservoir discharge includes: A set of reservoir discharge schemes is generated using a genetic algorithm, and the set of reservoir discharge schemes is input into the target hydrological model to obtain the first runoff data, the second runoff data, and the reservoir storage capacity corresponding to the set of reservoir discharge schemes. Based on the first runoff data, the monthly average flow data corresponding to the monthly average flow index and the flow fluctuation data corresponding to the runoff fluctuation index are obtained. Based on the monthly average flow data, the flow fluctuation data, the second runoff data, and the reservoir storage capacity, the first index result corresponding to the flow fuzzy comprehensive evaluation index, the second index result corresponding to the runoff fluctuation change index, and the third index result corresponding to the reservoir power generation index are obtained. The suitability of the reservoir discharge scheme group is scored based on the results of the first indicator, the second indicator, and the third indicator. The population of the reservoir discharge scheme group is then updated based on the suitability score results, and the optimal reservoir discharge is obtained through iterative calculation.

6. A reservoir ecological scheduling system based on IHA index and hydrological simulation, used to implement the reservoir ecological scheduling method based on IHA index and hydrological simulation as described in any one of claims 1-5, characterized in that, The system includes: a hydrological data generation module and a model solving module; The hydrological data generation module is used to obtain, based on the pre-constructed target hydrological model of the target watershed, the reservoir storage capacity corresponding to different reservoir discharge volumes, the first runoff data of the sub-watershed where the ecological control section is located, and the second runoff data of the sub-watershed where the reservoir is located; The model solving module is used to solve the pre-constructed reservoir ecological scheduling model based on the first runoff data, the second runoff data and the reservoir storage capacity to obtain the optimal reservoir discharge volume for scheduling the reservoirs in the target watershed. The model solving module includes: a first index construction unit, a second index construction unit, and a model construction unit; The first index construction unit, used to construct the fuzzy comprehensive evaluation index of flow and the runoff fluctuation change index of the ecological control section, includes: setting the baseflow standard according to the obtained historical average flow data of the ecological control section using the Tennessee method's baseflow standard classification criterion, obtaining a first baseflow standard for the general water use period and a second baseflow standard for the peak water use period; setting the monthly average flow index of the hydrological situation index for the general water use period as a first index set, and setting the monthly average flow index for the peak water use period as a second index set; constructing a first fuzzy membership matrix of the first index set based on the first baseflow standard using a fuzzy algorithm, and constructing a second fuzzy membership matrix of the second index set based on the second baseflow standard using the fuzzy algorithm; and constructing the monthly average flow index based on the first fuzzy membership matrix and the second fuzzy membership matrix. A fuzzy comprehensive evaluation index for flow is obtained by integrating the fuzzy membership matrix and a preset evaluation level score matrix. Based on the historical runoff sequence of the sub-basin where the ecological control section is located under undisturbed natural conditions, standard values ​​and weights of the runoff fluctuation index are obtained. Based on the first runoff data, a control value for the runoff fluctuation index is determined, and based on the standard value, weight, and control value, a runoff fluctuation change index for the ecological control section is constructed. The fuzzy comprehensive evaluation index for flow reflects the fuzzy evaluation level of the monthly average flow index of the hydrological situation index, and the runoff fluctuation change index reflects the degree of change of the runoff fluctuation index of the hydrological situation index. The parameter values ​​corresponding to the monthly average flow index and the runoff fluctuation index are obtained from the first runoff data. The second indicator construction unit is used to construct the reservoir power generation indicator corresponding to the second runoff data and the reservoir storage capacity; The model building unit is used to construct the reservoir ecological scheduling model with the objective functions of maximizing the fuzzy comprehensive evaluation index of flow, minimizing the runoff fluctuation change index, and maximizing the reservoir power generation index, and the reservoir discharge volume as the decision variable.

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