A reservoir water regulation rule optimization method, device, equipment and medium

Abstract

The application discloses a reservoir water regulation optimization method and device, equipment and medium, and relates to the field of reservoir water regulation optimization. The method comprises the following steps: obtaining reservoir characteristic data, including water thermal stratification data, water quality data, ecological characteristic data and external driving factor data; establishing a water quantity-water quality-water ecology model based on the reservoir characteristic data to simulate the water quality and ecological changes of the reservoir, and obtaining water quality change data; according to the water quality change data, taking the annual average water regulation quantity, the average eutrophication index and the minimum peak concentration of chlorophyll-a as the target, taking the water level threshold of water regulation starting in the flood season and the non-flood season, the monthly water regulation quantity in the flood season and the non-flood season as the decision variable, constructing a multi-objective optimization model and solving the optimal water regulation rule. The application combines water allocation, water quality safety and ecological protection, ensures sufficient water supply, significantly reduces the eutrophication risk and the growth of algae, and improves the adaptability and comprehensive benefits of the water regulation rule.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of reservoir water regulation optimization, and particularly relates to a reservoir water regulation rule optimization method, device, equipment and medium. BACKGROUND

[0002] The reservoir is an important water source for flood control, water supply and ecological protection. When additional water sources are adjusted to supplement, the rationality of the water regulation rule directly affects the water distribution, water quality safety and the stability of the ecological system. Therefore, reasonable water regulation rule evaluation and optimization is an important topic of discussion.

[0003] In existing research, empirical methods and methods based on hydrological analysis are often used to optimize water regulation rules. Empirical methods are widely used due to their simple operation, but their essence is to develop water regulation strategies based on the simple correspondence between reservoir water level and preset thresholds (such as dead water level and flood limit water level). For example, when the water level is lower than the dead water level, the pump station is started to full load water supply, and when the water level is higher than a certain upper limit water level, the water regulation is stopped. This mode completely ignores the water quality differences under different inflow situations - if the external water source is seasonally polluted, only relying on water level thresholds to regulate water may lead to a large amount of poor quality water entering the reservoir, disrupting the original water quality balance. At the same time, empirical methods do not consider the impact of the water regulation process on aquatic habitats, such as sudden large flow water regulation, which may cause sudden changes in water temperature and fluctuations in dissolved oxygen, threatening fish survival.

[0004] Methods based on hydrological analysis simulate water balance through long series of inflow data, which can optimize the distribution of water supply and abandoned water, but still have obvious shortcomings. On the one hand, this method seriously lacks consideration of water quality factors, as it does not include pollutant concentration indicators of external water sources, nor does it evaluate the impact of water regulation on eutrophication of reservoir water bodies, which may lead to the contradiction of "water regulation supplement but polluted water quality". On the other hand, it lacks overall attention to the water ecological system, only indirectly inferring ecological impact through water level fluctuations, without establishing a correlation model between water regulation flow, duration and key ecological indicators such as water vegetation coverage and benthic biodiversity, making it difficult to achieve "water quantity-water quality-ecology" coordinated protection.

[0005] In addition, existing research has limitations in the comprehensive evaluation of water regulation rules: most schemes only take a single benefit (such as water supply guarantee rate) as the optimization target, without building an evaluation system that includes multi-dimensional indicators such as water quality compliance rate and ecological system health index, making it difficult for water regulation rules to adapt to the management needs of complex water systems. Therefore, developing a water regulation rule design method that can coordinate the relationship between water quantity, water quality and water ecology has become an inevitable demand to break through the current technical bottleneck. SUMMARY

[0006] The purpose of the present application is to provide a reservoir water regulation rule optimization method, device, equipment and medium, which can improve the adaptability and comprehensive benefit of the water regulation rule.

[0007] To achieve the above object, the application provides the following scheme:

[0008] In a first aspect, the application provides a reservoir water regulation rule optimization method, comprising:

[0009] obtaining reservoir characteristic data; the reservoir characteristic data includes water body thermal stratification data, water quality data, ecological characteristic data and external driving factor data of the reservoir;

[0010] establishing a water quantity-water quality-water ecology model based on the reservoir characteristic data;

[0011] simulating water quality and ecological changes of the reservoir based on the water quantity-water quality-water ecology model to obtain water quality change data;

[0012] According to the water quality change data, the minimum of the average water regulation quantity, the average eutrophication index and the peak concentration of chlorophyll-a is taken as the target, and the water level threshold for starting water regulation in the flood season, the water level threshold for starting water regulation in the non-flood season, the monthly water regulation quantity in the flood season and the monthly water regulation quantity in the non-flood season are taken as the decision variables to construct a multi-objective optimization model;

[0013] solving the multi-objective optimization model to obtain the optimal water regulation rule.

[0014] In a second aspect, the application provides a reservoir water regulation rule optimization device, comprising:

[0015] a data acquisition module for acquiring reservoir characteristic data; the reservoir characteristic data includes water body thermal stratification data, water quality data, ecological characteristic data and external driving factor data of the reservoir;

[0016] a model establishment module for establishing a water quantity-water quality-water ecology model based on the reservoir characteristic data;

[0017] a water quality simulation module for simulating water quality and ecological changes of the reservoir based on the water quantity-water quality-water ecology model to obtain water quality change data;

[0018] an optimization module for constructing a multi-objective optimization model according to the water quality change data, taking the minimum of the average water regulation quantity, the average eutrophication index and the peak concentration of chlorophyll-a as the target, and taking the water level threshold for starting water regulation in the flood season, the water level threshold for starting water regulation in the non-flood season, the monthly water regulation quantity in the flood season and the monthly water regulation quantity in the non-flood season as the decision variables;

[0019] a model solving module for solving the multi-objective optimization model to obtain the optimal water regulation rule.

[0020] In a third aspect, the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the reservoir water regulation optimization method.

[0021] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the reservoir water regulation optimization method.

[0022] According to the specific embodiments provided by the present application, the present application has the following technical effects:

[0023] The present application provides a reservoir water regulation optimization method, device, equipment and medium, by coupling water quantity-water quality-water ecological model and multi-objective optimization model, considering hydrological conditions, water demand and ecological benefits, optimizing water regulation decision variables, including water level threshold and water regulation quantity of water regulation start in flood season and non-flood season. The optimization goal is to minimize the annual average water regulation quantity, the average eutrophication index and the peak concentration of chlorophyll-a, and the optimal water regulation rule suitable for different water quality scenarios is generated by solving the multi-objective optimization model. Combining water allocation, water quality safety and ecological protection, while ensuring sufficient water supply, significantly reducing the eutrophication risk and the growth of algae, supplementing the deficiencies of the existing artificial experience control of water regulation process, and improving the adaptability and comprehensive benefits of the water regulation rule. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0025] Figure 1 The application environment diagram of a reservoir water regulation optimization method in an embodiment of the present application.

[0026] Figure 2 The flowchart of a reservoir water regulation optimization method provided by an embodiment of the present application.

[0027] Figure 3 The architecture diagram of a reservoir water regulation optimization method provided by an embodiment of the present application.

[0028] Figure 4 The Pareto frontier diagram under different water regulation water quality in an embodiment of the present application.

[0029] Figure 5A Pareto frontier diagram of average eutrophication index and average annual water diversion volume in different water diversion water quality in an embodiment of the present application.

[0030] Figure 6 A Pareto frontier diagram of chlorophyll-a peak concentration and average annual water diversion volume in different water diversion water quality in an embodiment of the present application.

[0031] Figure 7 A Pareto frontier diagram of chlorophyll-a peak concentration and average eutrophication index in different water diversion water quality in an embodiment of the present application.

[0032] Figure 8 A functional module diagram of a reservoir water diversion rule optimization device provided in an embodiment of the present application.

[0033] Figure 9 A structural diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts fall within the scope of protection of the present application.

[0035] In recent years, the method based on simulation-optimization coupling model has gradually been widely recognized. The optimization idea thereof provides comprehensive basis for the design of water diversion rules by simulating the nutrient cycling, algal growth and ecological dynamic process of water body, combining hydrological, water quality and ecological coupling analysis. For example, the method based on water quality and ecological simulation evaluates the influence of water diversion by quantifying the change of nutrient concentration, and focuses on the response of chlorophyll-a, a key index of the ecological system, while the method based on simulation-optimization coupling balances multiple benefits by integrating hydrological forecast, water quality and ecological dynamic process, and optimizing the water diversion strategy.

[0036] The present application couples the process-based simulation model and the multi-objective optimization model and introduces them into the comprehensive benefit evaluation of reservoir water diversion. The water quantity, water quality and water ecological change process are simulated by constructing a coupled water quantity-water quality-water ecological model, and a multi-objective water diversion rule optimization method of minimizing the average annual water diversion volume, the average eutrophication index and the chlorophyll-a peak concentration is established.

[0037] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0038] The reservoir water regulation optimization method provided in the embodiments of the present application can be applied in an application environment as shown in Figure 1 The terminal 101 communicates with the server 102 through a network. The data storage system can store data required to be processed by the server 102. The data storage system can be separately arranged, integrated on the server 102, or placed on a cloud or other server. The terminal 101 can send reservoir characteristic data to the server 102. After receiving the reservoir characteristic data, the server 102 establishes a water quantity-water quality-water ecology model based on the reservoir characteristic data, simulates the water quality and ecological changes of the reservoir, and obtains water quality change data. According to the water quality change data, the minimum of the annual average water regulation quantity, the average eutrophication index, and the peak concentration of chlorophyll-a is taken as the target, the water level threshold for starting water regulation in the flood season, the water level threshold for starting water regulation in the non-flood season, the monthly water regulation quantity in the flood season, and the monthly water regulation quantity in the non-flood season are taken as the decision variables, a multi-objective optimization model is constructed, and the optimal water regulation rule is obtained by solving. The server 102 can feed back the optimal water regulation rule obtained to the terminal 101. In addition, in some embodiments, the reservoir water regulation optimization method can also be implemented by the server 102 or the terminal 101 alone.

[0039] The terminal 101 can be, but is not limited to, various desktop computers, notebook computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things device can be a smart speaker, a smart television, a smart air conditioner, a smart vehicle device, etc. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, etc. The server 102 can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0040] In an exemplary embodiment, as shown in Figure 2 and Figure 3 A reservoir water regulation optimization method is provided, which is executed by a computer device, specifically, can be executed by a terminal or a server, or both, in the embodiments of the present application, taken as an example of the method applied to the server 102 in Figure 1 The method includes the following steps 201 to 205.

[0041] Step 201, obtaining reservoir characteristic data. The reservoir characteristic data includes water body thermal stratification data, water quality data, ecological characteristic data, and external driving factor data of the reservoir.

[0042] The water body thermal stratification data includes monthly temperature monitoring data of the upper layer of the reservoir water body and monthly temperature monitoring data of the lower layer of the reservoir water body.

[0043] The water quality data is monthly water quality sampling monitoring data of the reservoir.

[0044] The ecological characteristic data is fish and benthic animal biomass survey data.

[0045] The external driving factor data includes precipitation, evaporation, runoff, reservoir discharge, water transfer amount, and river water quality data. The precipitation, evaporation, runoff, and reservoir discharge are monthly monitoring data of a reservoir hydrological station. The water transfer amount is daily monitoring data of a pumping station of a reservoir management department. The river water quality data are monthly water quality sampling monitoring data of a river entering the reservoir.

[0046] Step 202, based on the reservoir characteristic data, a water quantity-water quality-water ecology model is established.

[0047] In one specific application example, the external driving factor data, the water body thermal stratification data, and the ecological characteristic data are taken as inputs of the PCLAKE+ model, and the water quality data are used to calibrate and verify the PCLAKE+ model, so as to obtain the water quantity-water quality-water ecology model.

[0048] The calibration and verification of the PCLAKE+ model are to divide the obtained water quality data, such as total nitrogen, total phosphorus, and chlorophyll-a, into a training set and a verification set, use the training set to calibrate the parameters of the PCLAKE+ model, and use the verification set to verify the results of the PCLAKE+ model.

[0049] Step 203, based on the water quantity-water quality-water ecology model, the water quality and ecological changes of the reservoir are simulated to obtain water quality change data. The water quality change data include total nitrogen, total phosphorus, and chlorophyll-a concentration change data.

[0050] Step 204, according to the water quality change data, taking the average annual water transfer amount, the average eutrophication index, and the minimum peak concentration of chlorophyll-a as targets, taking the water level threshold value of the start of water transfer in the flood season , the water level threshold value of the start of water transfer in the non-flood season , the monthly water transfer amount in the flood season , and the monthly water transfer amount in the non-flood season as decision variables, a multi-objective optimization model is constructed. The range of the water level threshold value is from the dead water level to the highest water level.

[0051] Specifically, the average annual water transfer amount, the average eutrophication index, and the peak concentration of chlorophyll-a are calculated based on the total nitrogen, total phosphorus, and chlorophyll-a concentration change data output by the water quantity-water quality-water ecology model. The multi-objective optimization model is: ; wherein, is the average annual water transfer amount, is the average eutrophication index, is the peak concentration of chlorophyll-a, is a time period The amount of water diverted, that is, the amount of water diverted into the reservoir. This represents the total number of months in which water diversion occurred. For the total number of years in the simulation, For the upper layer of water in a certain time period The eutrophication index For the lower layer of water body in time period The eutrophication index and The calculation method is described in the Technical Specification for Surface Water Resources Quality Assessment (SL 395-2007). This represents the average value of the entire simulation process. For the upper layer of water in a certain time period chlorophyll-a concentration, For the lower layer of water body in time period chlorophyll-a concentration.

[0052] The constraints of the multi-objective optimization model include water balance constraints, water supply guarantee constraints, water level constraints, and water transfer pump station constraints.

[0053] The water balance constraint is: ;in, Time period Reservoir capacity, Time period Reservoir capacity, Time period The amount of water flowing into the reservoir Time period The overflow volume of the inner reservoir, Time period The amount of rainfall that falls directly onto the surface of the reservoir. Time period Evaporation from the surface of the inner reservoir Time period Irrigation water consumption within the area Time period The amount of water supplied to the water treatment plant.

[0054] The water supply guarantee constraints are: ; ;in, Time period Urban water demand Time period Agricultural water demand, Time period The actual urban water supply Time period The actual urban water supply.

[0055] Water level constraints are: ; wherein, is the water level of the reservoir in the time period , is the dead water level of the reservoir, is the highest water level of the reservoir.

[0056] Water transfer pump station constraints are: ; ; wherein, is the maximum water transfer amount that the pump station can provide.

[0057] Through the water balance constraint, the reservoir storage capacity in the time period is calculated When the reservoir storage capacity is lower than the storage capacity corresponding to the water level threshold value in the decision variable and , the monthly water transfer amount and is generated to update the model input, and the PCLAKE+ model is coupled to dynamically simulate the change process of total nitrogen, total phosphorus and chlorophyll-a concentration.

[0058] In step 205, the multi-objective optimization model is solved to obtain the optimal water transfer rule.

[0059] In one specific application example, the enumeration method and the weighted ideal point method are used to solve the multi-objective optimization model to obtain the optimal water transfer rule.

[0060] Specifically, the decision variable is discretized by the enumeration method to generate all feasible solutions, wherein the water level threshold value for starting water transfer in the flood season and the water level threshold value for starting water transfer in the non-flood season are taken as 0.5m as the step length, the monthly water transfer amount in the flood season and the monthly water transfer amount in the non-flood season are taken as 1x10 6 m 3 as the step length. The annual average water transfer amount, the average eutrophication index and the peak concentration of chlorophyll-a are calculated through the simulation results of all feasible solutions under the decision variable, and the Pareto solution set is generated through these target values. The Pareto solution set represents the target combination that cannot be further improved in the multi-objective space. Then the weighted ideal point method is used to calculate the weighted Euclidean distance between each Pareto solution and the ideal point (i.e. the combination point of the optimal values of each target), and the solution with the smallest Euclidean distance is selected as the optimal solution. The decision variable corresponding to the optimal solution is the optimal water transfer rule, achieving the coordinated optimization of water quantity, water quality and water ecology.

[0061] The expression of the weighted ideal point method is: ; ; wherein, is the value, MAWT, AEI and PCC, respectively; value of the normalized objective function value; optimal value of the ideal point; weight of the objective function; Euclidean distance from the solution to the ideal point.

[0062] The present application optimizes the water diversion decision variables, including the water level threshold and the water diversion amount for starting water diversion in the flood season and non-flood season, by coupling the water quantity-water quality-water ecological model with the multi-objective optimization model, comprehensively considering the hydrological conditions, water supply demand and ecological benefits. The optimization objectives are to minimize the annual average water diversion amount, the average eutrophication index and the peak concentration of chlorophyll-a. The multi-objective optimization problem is solved by the enumeration method and the weighted ideal point method to generate water diversion rules applicable to different water quality scenarios. The combination of water allocation, water quality safety and ecological protection ensures sufficient water supply while significantly reducing the eutrophication risk and algal growth, making up for the deficiencies of the existing empirical control of the water diversion process and proposing an adaptive and comprehensive benefit high water diversion rule optimization framework.

[0063] The present application takes a reservoir as an example to verify the rationality and effectiveness of the reservoir water diversion rule optimization method.

[0064] (1) Basin overview.

[0065] The research area has a basin area of 148.5 square kilometers, a total reservoir capacity of 1.086×10 8 m 3 , and an annual average runoff of 7.1×10 7 m 3 . The reservoir is a deep water thermal stratification reservoir, showing obvious thermal stratification characteristics from May to September. Its main functions include flood control, urban water supply, agricultural irrigation and tourism, providing drinking water for about 700,000 people. Due to the extreme drought since the flood season in 2022, the water level of the reservoir has dropped significantly, resulting in insufficient water supply capacity. Since the end of November 2023, the water diversion project has been started, and water is pumped from the river to supplement the reservoir. Due to water quality, the total phosphorus concentration of the diverted water body is 0.034~0.479mg / L, the total nitrogen concentration is 2.07~3.83mg / L, and there is a problem of seasonal rapid growth of algae in the reservoir, which poses a threat to water supply safety.

[0066] (2) Water quantity-water quality-water ecological model.

[0067] PCLAKE+ model was preheated for 10 years to reduce the impact of initial condition uncertainty. The Morris method was used for sensitivity analysis to determine 20 key parameters (e.g., stratification exchange rate, nutrient transformation rate, and algal growth parameters). Model calibration used monthly upper-layer water data sets (water depth, water temperature, dissolved oxygen, total nitrogen, total phosphorus, and chlorophyll-a) from 2020 to 2023, and parameters were adjusted by trial and error; validation used monthly data sets from 2024, and evaluation indicators included the coefficient of determination (R 2 ) and the mean relative absolute error (MRAE). Calibration and validation results showed that R² ranged from 0.44 to 0.69 (mean 0.57), MRAE ranged from 0.18 to 0.30 (mean 0.23), and the model effectively captured seasonal changes.

[0068] (3) Multi-objective optimization.

[0069] The reservoir water regulation rule was optimized according to three objective functions, with the optimization targets being the minimization of annual average water regulation, average eutrophication index, and peak concentration of chlorophyll-a. Historical hydrological data were used to simulate the reservoir water quantity-quality-ecology change process from 1967 to 2024, and the corresponding objective function values were calculated based on model output results. To analyze the impact of different water quality scenarios, six water quality scenarios were defined based on China's surface water environmental quality standards, as shown in Table 1.

[0070] Table 1. Different water quality conditions for water regulation scenarios

[0071]

[0072] The decision variables included the water level thresholds for water regulation start in flood season and non-flood season ( and , range 15.5-21.0 m, increment 0.5 m) and monthly water regulation ( and , range 0-6.42×10 6 m 3 , increment 1×10 6 m 3 ). The enumeration method was used to traverse the solution space, and the Pareto solution set was obtained. The optimization results showed that the water quality for water regulation significantly affected the correlation between MAWT and AEI, but PCC had no obvious linear relationship with both, as shown in Figures 4 to 7 .

[0073] The weighted ideal point method was used to calculate the Euclidean distance between the solution set and the ideal point, and the parameter settings included equal weights ( =1 / 3), the optimal water diversion rules under different water quality conditions are obtained, as shown in Table 2. The optimized water diversion rule of Scenario 1 uses low-flow water diversion (1.00 x 10 6 m 3 / month) when the flood season water level threshold is 19.0 m, and high-flow water diversion (5.00 x 10 6 m 3 / month) when the non-flood season water level threshold is 17.0 m. The optimized water diversion rule of Scenario 2 uses no water diversion in the flood season and maximum-flow water diversion (6.42 x 10 6 m 3 / month) when the non-flood season water level threshold is 16.5 m. The optimized water diversion rule of Scenario 3 uses low-flow water diversion (1.00 x 10 6 m 3 / month) when the flood season water level threshold is 20.5 m, and high-flow water diversion (5.00 x 10 6 m 3 / month) when the non-flood season water level threshold is 17.0 m. The optimized water diversion rule of Scenario 4 uses low-flow water diversion (1.00 x 10 6 m 3 / month) when the flood season water level threshold is 20.0 m, and maximum-flow water diversion (6.42 x 10 6 m 3 / month) when the non-flood season water level threshold is 16.0 m. The optimized water diversion rule of Scenario 5 uses low-flow water diversion (1.00 x 10 6 m 3 / month) when the flood season water level threshold is 20.0 m, and maximum-flow water diversion (6.42 x 10 6 m 3 / month) when the non-flood season water level threshold is 16.0 m. The optimized water diversion rule of Scenario 6 uses high-flow water diversion (6.42 x 10 6 m 3 / month) at a low water level of 17 m in the non-flood season, and no water diversion in the flood season, significantly reducing the risk of eutrophication and algal growth. Compared with the traditional fixed water diversion rule (starting with maximum-flow water diversion below the dead water level), the optimized rule reduces AEI from 48.52 to 44.36 and PCC from 51.99 μg / L to 35.66 μg / L (p < 0.05) under typical Scenario 6.

[0074] Table 2 Optimal water diversion rules and multi-objective results under different water quality conditions

[0075]

[0076] Comparing the average eutrophication and the peak value of chlorophyll-a simulation results under the two cases of the water reservoir water regulation rule optimization method considering water quantity, water quality and water ecology and the empirical water regulation rule, it can be known that the water reservoir water regulation rule optimization method considering water quantity, water quality and water ecology has better comprehensive benefits on average.

[0077] The present application couples the multi-objective optimization with the water quantity-water quality-water ecology model, and applies it to a certain reservoir, and the significant effects are as follows.

[0078] 1. The optimized water regulation rule adopts high flow water regulation in the non-flood season low water level, adopts low flow and takes water through the bottom layer to dilute nutrients in the flood season high water level, significantly reduces the annual average water regulation quantity (MAWT) to reduce the water supply cost, at the same time reduces the eutrophication index (AEI from 48.52 to 44.36) and the peak concentration of chlorophyll-a (PCC from 51.99 μg / L to 35.66 μg / L), and effectively avoids the occurrence of algal bloom.

[0079] 2. The optimized water regulation rule is applied to the reservoir operation and management, and from the comprehensive benefits of water quantity, water quality and water ecology, the deficiencies of the existing empirical water regulation rule are supplemented, and reliable technical support is provided for water supply safety, water quality guarantee and ecological protection.

[0080] In summary, the present application couples the multi-objective optimization with the water quantity-water quality-water ecology model, comprehensively optimizes the water quantity, water quality and water ecology, improves the comprehensiveness and scientificity of the water regulation rule design, provides guidance for the optimized management of the reservoir, and has rationality and effectiveness.

[0081] Based on the same inventive concept, the present application also provides a reservoir water regulation rule optimization device for implementing the above-mentioned reservoir water regulation rule optimization method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more reservoir water regulation rule optimization device embodiments provided below can be referred to the limitations of the reservoir water regulation rule optimization method in the foregoing, which will not be repeated here.

[0082] In an exemplary embodiment, as shown in Figure 8 a reservoir water regulation rule optimization device is provided, which includes a data acquisition module 801, a model establishment module 802, a water quality simulation module 803, an optimization module 804 and a model solution module 805.

[0083] The data acquisition module 801 is used to acquire reservoir characteristic data. The reservoir characteristic data includes water body thermal stratification data, water quality data, ecological characteristic data and external driving factor data of the reservoir.

[0084] The model establishment module 802 is used to establish a water quantity-water quality-water ecology model based on the reservoir characteristic data.

[0085] The water quality simulation module 803 is configured to simulate water quality and ecological changes of the reservoir based on the water quantity-water quality-water ecology model to obtain water quality change data.

[0086] The optimization module 804 is configured to construct a multi-objective optimization model according to the water quality change data, with the minimum annual average water regulating quantity, average eutrophication index and peak chlorophyll-a concentration as the target, and the water level threshold for starting water regulation in the flood season, the water level threshold for starting water regulation in the non-flood season, the monthly water regulating quantity in the flood season and the monthly water regulating quantity in the non-flood season as the decision variables.

[0087] The model solving module 805 is configured to solve the multi-objective optimization model to obtain an optimal water regulating rule.

[0088] In an exemplary embodiment, a computer device is provided, which can be a server or a terminal, and an internal structure diagram thereof can be as shown in Figure 9 The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is configured to store reservoir characteristic data. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals through network connection. The computer program is executed by the processor to implement a reservoir water regulating rule optimization method.

[0089] Those skilled in the art can understand that Figure 9 the structure shown in the above embodiment is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. Specifically, the computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0090] In an exemplary embodiment, a computer readable storage medium is provided, which stores a computer program. The computer program is executed by a processor to implement the steps in each of the above method embodiments.

[0091] In an exemplary embodiment, a computer program product is provided, which includes a computer program. The computer program is executed by a processor to implement the steps in each of the above method embodiments.

[0092] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0093] In the present application, all actions of obtaining signals, information or data are carried out in compliance with the data protection regulations and policies of the country where the device is located, and with the authorization given by the owner of the corresponding device.

[0094] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. Any reference to memory, database or other medium used in the embodiments provided by the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0095] The database involved in the embodiments provided by the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on blockchain, etc., without being limited thereto. The processor involved in the embodiments provided by the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0096] Any combination of the technical features in the above embodiments can be made. For the sake of brevity, the foregoing description has not described all possible combinations of the technical features in the above embodiments, however, it is understood that any combination of the technical features is within the scope of the present disclosure as long as there is no contradiction.

[0097] The principles and implementation manners of the present application are described herein by using specific examples, and the above embodiments are only used to help understand the method of the present application and its core idea; meanwhile, according to the idea of the present application, the specific implementation manners and application ranges will be changed by those skilled in the art. In conclusion, the content of the present description should not be understood as a limitation of the present application.

Claims

1. A method for optimizing reservoir water transfer rules, characterized in that, The method comprises: obtaining reservoir characteristic data; the reservoir characteristic data comprises water body thermal stratification data, water quality data, ecological characteristic data and external driving factor data of the reservoir; the water body thermal stratification data comprises monthly temperature monitoring data of an upper layer of the water body of the reservoir and monthly temperature monitoring data of a lower layer of the water body of the reservoir; the water quality data is monthly water quality sampling monitoring data of the reservoir; the ecological characteristic data is fish and benthic animal biomass investigation data; and the external driving factor data comprises precipitation, evaporation, runoff, reservoir discharge, water diversion and river water quality data; based on the reservoir characteristic data, a water quantity-water quality-water ecology model is established, specifically comprising: taking the external driving factor data, the water body thermal stratification data and the ecological characteristic data as inputs of a PCLAKE+ model, and using the water quality data to calibrate and verify the PCLAKE+ model to obtain the water quantity-water quality-water ecology model; based on the water quantity-water quality-water ecology model, water quality and ecological changes of the reservoir are simulated to obtain water quality change data; according to the water quality change data, a multi-objective optimization model is constructed with annual average water diversion amount, average eutrophication index and minimum chlorophyll-a peak concentration as targets, and with water level threshold values for starting water diversion in flood season and non-flood season, monthly water diversion amounts in the flood season and the non-flood season as decision variables; ； in, This represents the average annual water diversion volume. The average eutrophication index. This represents the peak concentration of chlorophyll-a. Time period The amount of water diverted, This represents the total number of months in which water diversion occurred. For the total number of years in the simulation, For the upper layer of water in a certain time period The eutrophication index For the lower layer of water body in time period The eutrophication index This represents the average value of the entire simulation process. For the upper layer of water in a certain time period chlorophyll-a concentration, For the lower layer of water body in time period chlorophyll-a concentration; constraint conditions of the multi-objective optimization model comprise water quantity balance constraint, water supply guarantee constraint, water level constraint and water diversion pump station constraint; The water balance constraint is: ; wherein, is the reservoir capacity for the time period , is the reservoir capacity for the time period , is the inflow to the reservoir for the time period , is the spillway flow from the reservoir for the time period , is the rainfall directly on the reservoir surface for the time period , is the evaporation from the reservoir surface for the time period , is the irrigation water use for the time period , is the water supply to the water treatment plant for the time period ; The water supply assurance constraint is: ; ; wherein, is the urban water demand for a time period , is the agricultural water demand for a time period , is the actual urban water supply for a time period , is the actual urban water supply for a time period ; The water level constraint is: ; wherein, is the water level of the reservoir for a time period , is the dead water level of the reservoir, is the highest water level of the reservoir; The water diversion pump station constraints are: ; ; wherein, is the maximum water diversion amount that the pump station can provide, is the water level threshold for starting water diversion during the flood season, is the water level threshold for starting water diversion during the non-flood season, is the monthly water diversion amount during the flood season, is the monthly water diversion amount during the non-flood season; the multi-objective optimization model is solved by using an enumeration method and a weighted ideal point method to obtain optimal water diversion rules; specifically, the enumeration method is used to discretize the decision variables to generate all feasible solutions; the annual average water diversion amount, the average eutrophication index and the chlorophyll-a peak concentration are calculated through simulation results of all feasible solutions under the decision variables, and a Pareto solution set is generated through these target values; then, the weighted ideal point method is used to calculate weighted Euclidean distances between each Pareto solution and an ideal point, and the solution with the smallest Euclidean distance is selected as the optimal solution; The expression of the weighted ideal point method is: ; ; wherein, is the value of the objective function, is MAWT, AEI, and PCC, respectively; is the value of the normalized objective function; is the optimal value of the ideal point; is the weight of the objective function; is the Euclidean distance from the solution to the ideal point.

2. The reservoir water regulation rule optimization method of claim 1, wherein, the water quality change data comprises total nitrogen, total phosphorus and chlorophyll-a concentration change data.

3. A reservoir water regulation rule optimization apparatus characterized by comprising: The device is applied to the reservoir water diversion rule optimization method in any one of claims 1-2, and the device comprises: a data acquisition module configured to acquire reservoir characteristic data; the reservoir characteristic data comprises water body thermal stratification data, water quality data, ecological characteristic data and external driving factor data of the reservoir; a model establishment module configured to establish a water quantity-water quality-water ecology model based on the reservoir characteristic data; a water quality simulation module configured to simulate water quality and ecological changes of the reservoir based on the water quantity-water quality-water ecology model to obtain water quality change data; an optimization module configured to construct a multi-objective optimization model with annual average water diversion amount, average eutrophication index and minimum chlorophyll-a peak concentration as targets, and with water level threshold values for starting water diversion in flood season and non-flood season, monthly water diversion amounts in the flood season and the non-flood season as decision variables according to the water quality change data; a model solving module configured to solve the multi-objective optimization model to obtain optimal water diversion rules.

4. A computer device comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that the processor executes the computer program to implement the reservoir water regulation rule optimization method of any one of claims 1-2.

5. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the reservoir water regulation rule optimization method of any one of claims 1-2.

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