A water project ecological regulation method, system, device and medium

Abstract

The application discloses a water project ecological scheduling method, system, device and medium, wherein the method acquires original scheduling rules of a water project and water flux data and carbon flux data of the water project; an ecological optimization scheduling model is constructed according to the water flux data and the carbon flux data; scheduling rule parameters in the original scheduling rules are subjected to parameter optimization through the ecological optimization scheduling model to obtain optimal solution parameters; and the scheduling rule parameters in the original scheduling rules are subjected to parameter updating according to the optimal solution parameters to obtain target scheduling rules. The method can effectively improve the objectivity and rationality of ecological scheduling and is beneficial to improving the health stability of a river ecological environment. The application relates to the technical field of hydrology.

Description

Technical Field

[0001] This invention relates to the field of hydrological science and technology, and in particular to a method, system, equipment and medium for ecological scheduling of water projects. Background Technology

[0002] The flow of water in river channels is the main carrier of carbon transport in rivers. The carbon flux of rivers is significantly correlated with the flow in the river channels. In the process of regulating the flow in river channels through water projects such as reservoirs, humans not only affect the aquatic ecological environment of rivers, but also inevitably change the amount of carbon transported in rivers, which in turn affects the carbon cycle from land to ocean, and affects the stability of the ecological environment system on a larger scale.

[0003] Currently, existing technologies typically consider rivers as an independent system, viewing river ecological flow as the amount of water required to maintain the healthy and stable development of the river's aquatic ecology and environment. Water projects are then scheduled based on this river ecological flow to maintain the ecological environment of the river and its surroundings. This approach can alter the natural rhythm of the river's water and carbon transport process to some extent, easily damaging the health and stability of the river's ecological environment. The objectivity and rationality of ecological scheduling are also poor.

[0004] Therefore, the problems existing in the current technology still need to be solved and optimized. Summary of the Invention

[0005] The purpose of this invention is to at least partially solve one of the technical problems existing in the related art.

[0006] Therefore, one objective of this invention is to provide a method, system, equipment, and medium for ecological scheduling of water projects, wherein the method can effectively improve the objectivity and rationality of ecological scheduling, and is conducive to improving the health and stability of the river ecological environment.

[0007] To achieve the above-mentioned technical objectives, the technical solutions adopted in the embodiments of this application include:

[0008] In a first aspect, embodiments of this application provide a water engineering ecological scheduling method, including:

[0009] Obtain the original scheduling rules of the water project and the water flux and carbon flux data of the water project;

[0010] Based on the water flux data and the carbon flux data, an ecological optimization scheduling model is constructed;

[0011] The ecological optimization scheduling model is used to optimize the scheduling rule parameters in the original scheduling rules to obtain the optimal demodulation parameters.

[0012] Based on the optimal demodulation parameters, the scheduling rule parameters in the original scheduling rule are updated to obtain the target scheduling rule.

[0013] In addition, the method according to the above embodiments of this application may also have the following additional technical features:

[0014] Furthermore, in one embodiment of this application, the step of constructing an ecological optimization scheduling model based on the water flux data and the carbon flux data includes:

[0015] Obtain the objective functions for water supply and power generation, as well as the constraints for water project scheduling, which include water balance constraints, water level constraints, power output constraints, and downstream flow constraints.

[0016] Based on the water flux data and carbon flux data, an ecological objective function is constructed;

[0017] The ecological optimization scheduling model is constructed based on the ecological objective function, the water supply objective function, the power generation objective function, and the water project scheduling constraints.

[0018] Furthermore, in one embodiment of this application, the step of constructing an ecological objective function based on the water flux data and carbon flux data includes:

[0019] Based on the water flux data, the joint probability distribution of the carbon flux data is calculated to obtain the joint probability distribution of water and carbon flux.

[0020] The distribution variation analysis of the joint probability distribution of water and carbon fluxes was performed to obtain water and carbon flux variation indicators.

[0021] The ecological objective function is constructed by minimizing the water and carbon flux change index.

[0022] Furthermore, in one embodiment of this application, the step of calculating the joint probability distribution of water and carbon fluxes based on the water flux data to obtain the joint probability distribution of water and carbon fluxes includes:

[0023] The water flux data are analyzed for water flux distribution to obtain the water flux probability distribution;

[0024] Carbon flux distribution analysis was performed on the carbon flux data to obtain the carbon flux probability distribution;

[0025] Based on the water flux probability distribution, the carbon flux probability distribution is subjected to distribution correlation processing to obtain the joint probability distribution of water and carbon flux.

[0026] Furthermore, in one embodiment of this application, the step of performing distribution correlation processing on the carbon flux probability distribution based on the water flux probability distribution to obtain the joint water-carbon flux probability distribution includes:

[0027] Obtain the first marginal distribution function of the water flux data and the second marginal distribution function of the carbon flux data;

[0028] Based on the first edge distribution function, the second edge distribution function is filtered by connection function to obtain the target connection function;

[0029] Based on the target connection function, the water flux probability distribution and the carbon flux probability distribution are correlated to obtain the joint water and carbon flux probability distribution.

[0030] Further, in this embodiment of the application, the step of filtering the second edge distribution function based on the first edge distribution function to obtain the target connection function includes:

[0031] Obtain several intermediate join functions;

[0032] Based on the first marginal distribution function and the second marginal distribution function, a fitting test is performed on all the intermediate connection functions to obtain the fitting test result corresponding to each intermediate connection function;

[0033] Based on the first marginal distribution function and the second marginal distribution function, a goodness-of-fit analysis is performed on all the intermediate connection functions to obtain the goodness-of-fit analysis results corresponding to each intermediate connection function;

[0034] Based on all the fitting test results and all the goodness-of-fact analysis results, the intermediate connection functions are screened to obtain the target connection function.

[0035] Furthermore, in one embodiment of this application, the step of performing distribution change analysis on the joint probability distribution of water and carbon flux to obtain water and carbon flux change indicators includes:

[0036] Obtain the preset joint distribution change index function;

[0037] Based on the joint probability distribution of water and carbon flux, a first joint probability density function and a second joint probability density function are obtained. The first joint probability density function is the joint probability density function of water and carbon flux of the water project before scheduling, and the second joint probability density function is the joint probability density function of water and carbon flux of the water project after scheduling.

[0038] Based on the joint distribution change index function, change index analysis is performed on the first joint probability density function and the second joint probability density function to obtain the water and carbon flux change index.

[0039] Secondly, embodiments of this application provide a water engineering ecological scheduling system, including:

[0040] The first processing unit is used to acquire the original scheduling rules of the water project and the water flux data and carbon flux data of the water project;

[0041] The second processing unit is used to construct an ecological optimization scheduling model based on the water flux data and the carbon flux data.

[0042] The third processing unit is used to optimize the scheduling rule parameters in the original scheduling rules through the ecological optimization scheduling model to obtain the optimal demodulation parameters.

[0043] The fourth processing unit is used to update the scheduling rule parameters in the original scheduling rule according to the optimal demodulation parameters to obtain the target scheduling rule.

[0044] Thirdly, embodiments of this application also provide an electronic device, including:

[0045] At least one processor;

[0046] At least one memory for storing at least one program;

[0047] When the at least one program is executed by the at least one processor, the at least one processor implements the method of the first aspect described above.

[0048] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a processor-executable program, which, when executed by the processor, is used to implement the method of the first aspect described above.

[0049] The advantages and beneficial effects of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application:

[0050] This application discloses a water project ecological scheduling method. The method involves acquiring the original scheduling rules of the water project and its water flux and carbon flux data; constructing an ecological optimization scheduling model based on the water flux and carbon flux data; optimizing the scheduling rule parameters in the original scheduling rules using the ecological optimization scheduling model to obtain optimal demodulation parameters; and updating the scheduling rule parameters in the original scheduling rules based on the optimal demodulation parameters to obtain the target scheduling rule. This method, based on the ecological optimization scheduling model constructed from water flux and carbon flux data, optimizes the parameters in the original scheduling rules to obtain a target scheduling rule that fully considers river water carbon transport. This effectively improves the objectivity and rationality of ecological scheduling, and is conducive to enhancing the health and stability of the river's ecological environment. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the following description is provided with accompanying drawings of the relevant technical solutions in the embodiments of this application or the prior art. It should be understood that the accompanying drawings described below are only for the purpose of clearly illustrating some embodiments of the technical solutions in this application. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0052] Figure 1 A schematic flowchart of a water engineering ecological scheduling method provided in an embodiment of this application;

[0053] Figure 2 A schematic diagram of the structure of a water engineering ecological scheduling system provided in this application embodiment;

[0054] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0055] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0057] Currently, existing technologies typically consider rivers as independent systems, viewing river ecological flow as the amount of water required to maintain the healthy and stable development of the river's aquatic ecosystem and environment. Water projects (such as reservoirs, water conservancy projects, and sluice gates) are then scheduled based on this ecological flow to maintain the ecological environment of the river and its surroundings. However, this approach can alter the natural rhythms of river water and carbon transport processes to some extent, easily damaging the health and stability of the river's ecological environment (such as the river's own ecosystem, the estuary, and the nearshore ecosystem), resulting in poor objectivity and rationality in ecological scheduling.

[0058] In view of this, embodiments of the present invention provide a water engineering ecological scheduling method, system, equipment, and medium. The method optimizes the parameters in the original scheduling rules based on an ecological optimization scheduling model constructed from water flux data and carbon flux data. This allows the obtained target scheduling rules to consider not only the impact of water flux on the river's ecological environment but also the impact of carbon flux on the river's ecological environment, effectively improving the objectivity and rationality of ecological scheduling based on the target scheduling rules, and contributing to improving the health and stability of the river's ecological environment.

[0059] Reference Figure 1 In this embodiment of the application, a water engineering ecological scheduling method includes:

[0060] Step 110: Obtain the original scheduling rules of the water project and the water flux data and carbon flux data of the water project;

[0061] In the embodiments of this application, the water project may be a reservoir, a water conservancy hub, a sluice gate, etc., and the original scheduling rule may be the initial ecological scheduling rule of the water project, which may include the outflow of the water project at each time point in a certain time series; or, the original scheduling rule may be a scheduling rule that does not consider the ecological flow demand.

[0062] It is understood that water flux data can be the river flow at a certain river cross-section of a water project, while carbon flux data can be the amount of carbon transported per unit time at a certain river cross-section of a water project. In this embodiment of the application, the river cross-section corresponding to the carbon flux data and water flux data is the same as an example.

[0063] For example, for water flux data, the first method of acquisition can be based on long-series flow observation data collected from hydrological observation stations; or, in the second method, long-series river flow data can be generated based on hydrological models, such as the Xin'anjiang hydrological model, the SWAT (Soil and Water Assessment Tool) model, the VIC (Variable Infiltration Capacity) model, etc. Specifically, data on river meteorology, topography, land cover, soil, flow, etc. can be acquired first, and the acquired data can be simulated and calculated using a configured hydrological model to obtain long-series flow data for a specified river section.

[0064] It should be noted that the carbon flux data is obtained in a similar way to the above. The carbon flux data can be obtained by simulating and calculating the carbon flux of the river using any of the MIKE hydrodynamic model, RAP (River Aquatic Productivity) model, etc. This application will not elaborate further here.

[0065] Step 120: Construct an ecological optimization scheduling model based on the water flux data and the carbon flux data;

[0066] In this embodiment of the application, ecological objectives can be constructed based on water flux data and carbon flux data. These ecological objectives are used to maintain the ecological environment of the river. Then, an ecological optimization scheduling model is constructed based on these ecological objectives. This allows the scheduling rules output by the ecological optimization scheduling model to take into account the combined impact of water flux and carbon flux on the river's ecological environment, reduce the impact on the natural rhythm of the river's water and carbon transport process, improve the health and stability of the river's ecological environment, and thus improve the objectivity and rationality of ecological scheduling.

[0067] In some embodiments, step 120, constructing an ecological optimization scheduling model based on the water flux data and the carbon flux data, includes:

[0068] A1. Obtain the water supply objective function and the power generation objective function, as well as the water project scheduling constraints, which include water balance constraints, water level constraints, power output constraints, and downstream flow constraints.

[0069] In this embodiment, the water supply objective function is used to meet the water supply demand in the ecological scheduling of water projects. This objective function includes minimizing the maximum damage depth and maximizing the water supply guarantee rate. The maximum damage depth represents the proportion of the period with the greatest water shortage relative to the water supply target across all time periods, while the water supply guarantee rate represents the proportion of time periods in which the water supply target is met. Specifically, the objective function for minimizing the maximum damage depth can be expressed as:

[0070]

[0071] Where H2 is the objective function for minimizing the maximum damage depth; min(·) is the minimum value function; D max W represents the maximum depth of damage; W(t) represents the average water supply of the water project during time period t; W obj The target water supply volume for water projects;

[0072] The objective function for maximizing the water supply guarantee rate can be expressed as:

[0073]

[0074] Where H3 is the objective function for maximizing the water supply guarantee rate; max(·) is the maximum value function; R is the water supply guarantee rate; N s The number of time periods required to meet the water supply target; T is the total number of time periods.

[0075] It is understandable that the power generation objective function is used to meet the power generation requirements in the ecological scheduling of water projects. This power generation objective function includes maximizing power generation, and the objective function for maximizing power generation can be expressed as:

[0076]

[0077] Where H4 is the objective function for maximizing power generation; E is the total power generation of the water project in all time periods; is the output of the i-th water project in time period t; and n is the number of water projects.

[0078] It should be noted that the constraints of water project scheduling include water balance constraints, water level constraints, power output constraints, and downstream flow constraints. Specifically, the water balance constraint can be expressed as:

[0079] V i (t+1)=V i (t)+(Ii(t)-Q i (t))Δt

[0080] Among them, V i (t) represents the water storage capacity of the i-th water project at the beginning of time period t; V i (t+1) represents the water storage volume of the i-th water project at the beginning of time period t+1, which is also the water storage volume of the i-th water project at the end of time period t; i (t) represents the inflow rate of the i-th water project during time period t; Q i (t) represents the discharge flow of the i-th water project in time period t; Δt represents the time step of time period t.

[0081] The water level constraint can be expressed as:

[0082] Z i，min ≤Z i (t)≤Z i，max

[0083] Among them, Z i，min Z represents the minimum permissible water level for the i-th water project across all time periods; i，max Z represents the highest permissible water level for the i-th water project across all time periods; i (t) represents the water level of the i-th water project during time period t.

[0084] The output constraint can be expressed as:

[0085] P i，min ≤P i (t)≤P i，max

[0086] Among them, P i，min P represents the minimum allowable output of the i-th water project over all time periods; i，max P represents the maximum allowable output of the i-th water project over all time periods; i (t) represents the output of the i-th water project during time period t.

[0087] The discharge flow constraint can be expressed as:

[0088] Q i (t)≤q max (Z i (t))

[0089] Where, q max (Z i (t) represents the water level of the i-th water project at time t, where Z is the water level. i Maximum discharge capacity at (t).

[0090] A2. Based on the water flux data and carbon flux data, construct an ecological objective function;

[0091] Further, step A2, constructing an ecological objective function based on the water flux data and carbon flux data, includes:

[0092] B1. Based on the water flux data, perform a joint probability distribution calculation on the carbon flux data to obtain the joint probability distribution of water and carbon flux;

[0093] Further, step B1, calculating the joint probability distribution of the carbon flux based on the water flux data to obtain the joint probability distribution of water and carbon flux, includes:

[0094] C1. Perform water flux distribution analysis on the water flux data to obtain the water flux probability distribution;

[0095] C2. Perform carbon flux distribution analysis on the carbon flux data to obtain the carbon flux probability distribution;

[0096] In this embodiment, the ecological objective function of the ecological optimization scheduling model can be obtained based on the acquired water flux data and carbon flux data. Specifically, the water flux distribution analysis in step C1 can first involve statistical analysis of the long-sequence water flux data to obtain statistical parameters such as the mean, coefficient of variation, and skewness coefficient of the water flux data; then, based on the obtained statistical parameters, the water flux probability distribution of the water flux data is calculated using a Pearson type distribution, specifically a Pearson type III distribution.

[0097] Understandably, for carbon flux data, the first step is to perform statistical analysis on the long-sequence carbon flux data to determine its distribution characteristics such as mean, variance, skewness, and kurtosis. Then, a probability distribution model (such as a normal distribution model, a log-normal distribution model, or a gamma distribution model) is used to fit these distribution characteristics to obtain the carbon flux probability distribution of the carbon flux data.

[0098] C3. Based on the water flux probability distribution, perform distribution correlation processing on the carbon flux probability distribution to obtain the joint probability distribution of water and carbon flux.

[0099] Further, step C3, performing distribution correlation processing on the carbon flux probability distribution based on the water flux probability distribution to obtain the joint water-carbon flux probability distribution, includes:

[0100] D1. Obtain the first marginal distribution function of the water flux data and the second marginal distribution function of the carbon flux data;

[0101] In this embodiment of the application, the ecological flow provided by the ecological scheduling of water projects needs to take into account both water and carbon fluxes. Therefore, a joint probability distribution of water and carbon fluxes can be constructed based on the probability distributions of water fluxes and carbon fluxes, which is beneficial to fully consider the correlation between water fluxes and carbon fluxes.

[0102] It is understood that step D1 can be the acquisition of the first marginal distribution function of water flux data and the second marginal distribution function of carbon flux data. The first marginal distribution function can be obtained by statistical analysis of water flux data, or it can be obtained by a simple transformation of the above-mentioned water flux probability distribution. The second marginal distribution function is similar to the first marginal distribution function mentioned above and can be simply deduced by analogy. This application will not elaborate further here.

[0103] D2. Based on the first edge distribution function, perform connection function filtering on the second edge distribution function to obtain the target connection function;

[0104] Further, step D2, filtering the second edge distribution function based on the first edge distribution function to obtain the target connection function, includes:

[0105] E1. Obtain several intermediate join functions;

[0106] E2. Based on the first marginal distribution function and the second marginal distribution function, perform a fitting test on all the intermediate connection functions to obtain the fitting test result corresponding to each intermediate connection function;

[0107] E3. Based on the first marginal distribution function and the second marginal distribution function, perform goodness-of-fit analysis on all the intermediate connection functions to obtain the goodness-of-fit analysis results corresponding to each intermediate connection function;

[0108] E4. Based on all the fitting test results and all the goodness-of-fact analysis results, perform connection function screening on all the intermediate connection functions to obtain the target connection function.

[0109] In this embodiment, the intermediate join function can be a Copula function of different types. Taking a total of 3 intermediate join functions as an example, the first intermediate join function can be a Copula function of type Gumbell-Houggard(GH); the second intermediate join function can be a Copula function of type Clayton; and the third intermediate join function can be a Copula function of type Frank.

[0110] It is understood that the first intermediate linkage function, the second intermediate linkage function, and the third intermediate linkage function all contain a parameter θ. This parameter θ can be estimated by the correlation index method. For example, it can be calculated by the relationship between the Kendall rank correlation coefficient τ and the parameter θ. The Kendall rank correlation coefficient τ can be calculated based on the various water flux values ​​in the water flux data and the various carbon flux values ​​in the carbon flux data. This will not be elaborated further in this application.

[0111] It should be noted that after obtaining all intermediate connection functions, the first and second marginal distribution functions can be input into the theoretical function model corresponding to each intermediate connection function, and the fit test results for each intermediate connection function can be obtained through the Kolmogorov-Smirnov (KS) test of the Copula function. Simultaneously, the first and second marginal distribution functions are input into the theoretical function model corresponding to each intermediate connection function, and the effectiveness of each intermediate connection function is evaluated using the minimum sum of squared deviations (OLS) criterion. This completes the goodness-of-fit analysis, obtaining the goodness-of-fit analysis results for each intermediate connection function.

[0112] It is worth mentioning that after obtaining all fit test results and all goodness-of-fit analysis results, the target connection function can be selected from all intermediate connection functions. There are various specific screening methods. For example, the intermediate connection function with the smallest goodness-of-fit analysis result and the best fit test result can be determined as the target connection function. The example in this application is only for illustration.

[0113] D3. Based on the target connection function, perform water-carbon flux correlation on the water flux probability distribution and the carbon flux probability distribution to obtain the joint water-carbon flux probability distribution.

[0114] In this embodiment of the application, step D3 may involve inputting the water flux probability distribution and the carbon flux probability distribution into the target connection function for correlation calculation, thereby obtaining the joint probability distribution of water and carbon flux.

[0115] B2. Perform distribution change analysis on the joint probability distribution of water and carbon fluxes to obtain water and carbon flux change indicators;

[0116] Further, step B2 involves performing a distribution change analysis on the joint probability distribution of water and carbon fluxes to obtain water and carbon flux change indices, including:

[0117] F1. Obtain the preset joint distribution change index function;

[0118] F2. Based on the joint probability distribution of water and carbon flux, obtain a first joint probability density function and a second joint probability density function. The first joint probability density function is the joint probability density function of water and carbon flux of the water project before scheduling, and the second joint probability density function is the joint probability density function of water and carbon flux of the water project after scheduling.

[0119] F3. Based on the joint distribution change index function, perform change index analysis on the first joint probability density function and the second joint probability density function to obtain the water and carbon flux change index.

[0120] In this embodiment, the joint distribution change index function is used to evaluate the impact of water engineering on the change of water and carbon flux in the river channel during the scheduling of natural water flow processes. Step F2 can be based on the joint probability distribution of water and carbon flux to obtain the joint probability density function of water and carbon flux before and after scheduling. Specifically, it can be based on the functional form of the joint probability distribution of water and carbon flux before scheduling, and the first joint probability density function can be obtained through derivative transformation. As for the second joint probability density function, it can be obtained by simulating the joint probability distribution of water and carbon flux after scheduling, and then obtaining the second joint probability density function based on the functional form of the simulated joint probability distribution of water and carbon flux after scheduling.

[0121] It is understood that the first joint probability density function can also be calculated based on the target connection function corresponding to the joint probability distribution of water and carbon flux, as well as the inverse of the first marginal distribution function and the inverse of the second marginal distribution function. This application will not elaborate further here.

[0122] It should be noted that step F3 can involve performing a change index analysis on the joint distribution transformation index function of the input values ​​of the first and second joint probability density functions to obtain the water and carbon flux change index. Specifically, this water and carbon flux change index can be expressed as:

[0123] I=∫∫|f X，Y (x, y)-f′ X，Y (x, y)|dxdy

[0124] Where I represents the water and carbon flux variation index; f X，Y (x, y) is the first joint probability density function; f′ X，Y (x, y) is the second joint probability density function; X is water flux data; Y is carbon flux data; x is the water flux value in the water flux data; y is the carbon flux value in the water flux data.

[0125] B3. Construct the ecological objective function by minimizing the water and carbon flux change index.

[0126] In this embodiment, minimizing the water-carbon flux change index can be used as the ecological objective of water project scheduling, thereby obtaining the ecological objective function, which can be expressed as:

[0127] H1 = min(I)

[0128] Where H1 is the ecological objective function.

[0129] A3. Based on the ecological objective function, the water supply objective function, the power generation objective function, and the water project scheduling constraints, construct the ecological optimization scheduling model.

[0130] In the embodiments of this application, an optimization scheduling model with multiple objectives and multiple constraints can be constructed based on the ecological objective function, the water supply objective function, the power generation objective function, and the water project scheduling constraints, thereby obtaining an ecological optimization scheduling model.

[0131] Step 130: Optimize the scheduling rule parameters in the original scheduling rules using the ecological optimization scheduling model to obtain the optimal demodulation parameters;

[0132] In the embodiments of this application, a multi-objective optimization algorithm can be used, such as any one of the multi-objective particle swarm optimization algorithm (MOPSO) or non-dominated sorting genetic optimization algorithm (NSGA-II). The ecological objective function, water supply objective function, and power generation objective function are used as optimization objectives to iteratively optimize the scheduling rule parameters in the original scheduling rules, thereby obtaining a multi-objective non-dominated solution set. Then, combining the competitive relationship and guarantee requirements between different optimization objectives, the best solution is selected from the non-dominated solution set, and the best solution is determined as the best demodulation parameter.

[0133] It should be noted that the original scheduling rule can typically be in the form of a scheduling function or a scheduling graph. The functional form of the original scheduling rule can be expressed as:

[0134] Q i (t)=f(I i (t), V i (t), t; θ r )

[0135] Where, θ r These are the scheduling rule parameters in the original scheduling rules.

[0136] Step 140: Update the scheduling rule parameters in the original scheduling rule according to the optimal demodulation parameters to obtain the target scheduling rule.

[0137] In the embodiments of this application, after obtaining the optimal demodulation parameters, the optimal demodulation parameters can be substituted into the original scheduling rules to obtain the water engineering scheduling rules (i.e., the target scheduling rules) that take into account water and carbon transport.

[0138] The following describes in detail, with reference to the accompanying drawings, a water engineering ecological scheduling system proposed according to an embodiment of this application.

[0139] Reference Figure 2 The water engineering ecological scheduling system proposed in this application includes:

[0140] The first processing unit 101 is used to acquire the original scheduling rules of the water project and the water flux data and carbon flux data of the water project;

[0141] The second processing unit 102 is used to construct an ecological optimization scheduling model based on the water flux data and the carbon flux data.

[0142] The third processing unit 103 is used to optimize the scheduling rule parameters in the original scheduling rules through the ecological optimization scheduling model to obtain the optimal demodulation parameters.

[0143] The fourth processing unit 104 is used to update the scheduling rule parameters in the original scheduling rule according to the optimal demodulation parameters to obtain the target scheduling rule.

[0144] It is understood that the content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0145] Reference Figure 3 This application also provides an electronic device, including:

[0146] At least one processor 201;

[0147] At least one memory 202 is used to store at least one program;

[0148] When the at least one program is executed by the at least one processor 201, the at least one processor 201 implements the method embodiment described above.

[0149] Similarly, it can be understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0150] This application also provides a computer-readable storage medium storing a program executable by a processor 201, which, when executed by the processor 201, is used to implement the above-described method embodiments.

[0151] Similarly, the content of the above method embodiments is applicable to the present computer-readable storage medium embodiments. The specific functions implemented by the present computer-readable storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0152] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this application are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0153] Furthermore, although this application is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding this application. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional technology for an engineer. Therefore, those skilled in the art can implement the application set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of this application, which is determined by the full scope of the appended claims and their equivalents.

[0154] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods in the embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0155] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0156] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0157] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0158] In the foregoing description of this specification, the references to terms such as "one embodiment," "another embodiment," or "some embodiments," etc., indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0159] Although embodiments of this application have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the claims and their equivalents.

[0160] The above is a detailed description of the preferred embodiments of this application, but this application is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application, and these equivalent modifications or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A method for ecological scheduling of water projects, characterized in that, include: Obtain the original scheduling rules of the water project and the water flux and carbon flux data of the water project; Based on the water flux data and the carbon flux data, an ecological optimization scheduling model is constructed; The ecological optimization scheduling model is used to optimize the scheduling rule parameters in the original scheduling rules to obtain the optimal demodulation parameters. Based on the optimal demodulation parameters, the scheduling rule parameters in the original scheduling rule are updated to obtain the target scheduling rule; The step of constructing an ecological optimization scheduling model based on the water flux data and the carbon flux data includes: Obtain the objective functions for water supply and power generation, as well as the constraints for water project scheduling, which include water balance constraints, water level constraints, power output constraints, and downstream flow constraints. Based on the water flux data and carbon flux data, an ecological objective function is constructed; Based on the ecological objective function, the water supply objective function, the power generation objective function, and the water project scheduling constraints, the ecological optimization scheduling model is constructed. The step of constructing an ecological objective function based on the water flux data and carbon flux data includes: Based on the water flux data, the joint probability distribution of the carbon flux data is calculated to obtain the joint probability distribution of water and carbon flux. The distribution variation analysis of the joint probability distribution of water and carbon fluxes was performed to obtain water and carbon flux variation indicators. The ecological objective function is constructed by minimizing the water and carbon flux change index; The distribution change analysis of the joint probability distribution of water and carbon fluxes yields water and carbon flux change indices, including: Obtain the preset joint distribution change index function; Based on the joint probability distribution of water and carbon flux, a first joint probability density function and a second joint probability density function are obtained. The first joint probability density function is the joint probability density function of water and carbon flux of the water project before scheduling, and the second joint probability density function is the joint probability density function of water and carbon flux of the water project after scheduling. Based on the joint distribution change index function, change index analysis is performed on the first joint probability density function and the second joint probability density function to obtain the water and carbon flux change index.

2. The method according to claim 1, characterized in that, The step of calculating the joint probability distribution of water and carbon fluxes based on the water flux data to obtain the joint probability distribution of water and carbon fluxes includes: The water flux data are analyzed for water flux distribution to obtain the water flux probability distribution; Carbon flux distribution analysis was performed on the carbon flux data to obtain the carbon flux probability distribution; Based on the water flux probability distribution, the carbon flux probability distribution is subjected to distribution correlation processing to obtain the joint probability distribution of water and carbon flux.

3. The method according to claim 2, characterized in that, The step of performing distribution correlation processing on the carbon flux probability distribution based on the water flux probability distribution to obtain the joint water-carbon flux probability distribution includes: Obtain the first marginal distribution function of the water flux data and the second marginal distribution function of the carbon flux data; Based on the first edge distribution function, the second edge distribution function is filtered by connection function to obtain the target connection function; Based on the target connection function, the water flux probability distribution and the carbon flux probability distribution are correlated to obtain the joint water and carbon flux probability distribution.

4. The method according to claim 3, characterized in that, The step of filtering the second edge distribution function based on the first edge distribution function to obtain the target connection function includes: Obtain several intermediate join functions; Based on the first marginal distribution function and the second marginal distribution function, a fitting test is performed on all the intermediate connection functions to obtain the fitting test result corresponding to each intermediate connection function; Based on the first marginal distribution function and the second marginal distribution function, a goodness-of-fit analysis is performed on all the intermediate connection functions to obtain the goodness-of-fit analysis results corresponding to each intermediate connection function; Based on all the fitting test results and all the goodness-of-fact analysis results, the intermediate connection functions are screened to obtain the target connection function.

5. A water engineering ecological scheduling system, characterized in that, include: The first processing unit is used to acquire the original scheduling rules of the water project and the water flux data and carbon flux data of the water project; The second processing unit is used to construct an ecological optimization scheduling model based on the water flux data and the carbon flux data. The third processing unit is used to optimize the scheduling rule parameters in the original scheduling rules through the ecological optimization scheduling model to obtain the optimal demodulation parameters. The fourth processing unit is used to update the scheduling rule parameters in the original scheduling rule according to the optimal demodulation parameters to obtain the target scheduling rule; The step of constructing an ecological optimization scheduling model based on the water flux data and the carbon flux data includes: Obtain the objective functions for water supply and power generation, as well as the constraints for water project scheduling, which include water balance constraints, water level constraints, power output constraints, and downstream flow constraints. Based on the water flux data and carbon flux data, an ecological objective function is constructed; Based on the ecological objective function, the water supply objective function, the power generation objective function, and the water project scheduling constraints, the ecological optimization scheduling model is constructed. The step of constructing an ecological objective function based on the water flux data and carbon flux data includes: Based on the water flux data, the joint probability distribution of the carbon flux data is calculated to obtain the joint probability distribution of water and carbon flux. The distribution variation analysis of the joint probability distribution of water and carbon fluxes was performed to obtain water and carbon flux variation indicators. The ecological objective function is constructed by minimizing the water and carbon flux change index; The distribution change analysis of the joint probability distribution of water and carbon fluxes yields water and carbon flux change indices, including: Obtain the preset joint distribution change index function; Based on the joint probability distribution of water and carbon flux, a first joint probability density function and a second joint probability density function are obtained. The first joint probability density function is the joint probability density function of water and carbon flux of the water project before scheduling, and the second joint probability density function is the joint probability density function of water and carbon flux of the water project after scheduling. Based on the joint distribution change index function, change index analysis is performed on the first joint probability density function and the second joint probability density function to obtain the water and carbon flux change index.

6. An electronic device, characterized in that, include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method as described in any one of claims 1-4.

7. A computer-readable storage medium storing a processor-executable program, characterized in that, The processor-executable program, when executed by the processor, is used to implement the method as described in any one of claims 1-4.

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