A low-carbon-ecological collaborative scheduling method and system for a multi-lake system adapting to extreme climate

Abstract

This application discloses a low-carbon-ecological coordinated scheduling method and system for multi-lake systems adapted to extreme climates, comprising: constructing a set of future extreme climate scenarios; constructing an urban-natural watershed coupled hydrological model, and then obtaining multiple sets of inflow runoff sequences driven by various future extreme climate scenarios based on the urban-natural watershed coupled hydrological model; constructing a low-carbon-ecological coordinated optimization model with minimizing the annual total carbon emissions from water diversion as the single objective, based on the set of future extreme climate scenarios and the urban-natural watershed coupled hydrological model; inputting each set of inflow runoff sequences into the low-carbon-ecological coordinated optimization model for solution, obtaining multiple sets of candidate scheduling schemes; and screening the candidate scheduling schemes to obtain the optimal scheduling scheme. This application can fully consider the uncertainty of future climate, improve the reliability of scheduling schemes, and solve the problem of decision-making difficulties in multi-objective models, and can be widely applied in the field of water resource scheduling technology.

Description

Technical Field

[0001] This application relates to the field of water resource scheduling technology, and in particular to a low-carbon-ecological coordinated scheduling method and system for multi-lake systems adapted to extreme climates. Background Technology

[0002] Currently, the regulation model of urban lake water systems is undergoing a profound transformation from the traditional single objective of water supply and flood control to a synergistic regulation model that integrates ecology, low carbon emissions, and intelligence. As a crucial carrier of the urban water environment and ecosystem, the water renewal capacity of urban lakes directly determines water purification efficiency and ecosystem stability. Hydraulic retention time (HRT), as a key indicator for quantitatively characterizing water renewal efficiency, ecological response timeliness, and system metabolic intensity, directly reflects the self-purification capacity and ecological stability of lakes and has become a core variable for assessing the ecological health status and regulation effectiveness of lakes.

[0003] However, against the backdrop of frequent extreme hydrological events caused by climate change, lake hydrological conditions and hydrodynamic processes exhibit high non-stationarity, leading to a series of problems such as water retention, prolonged hydraulic retention time, and delayed pollutant migration, thus restricting the self-purification and recovery functions of aquatic ecosystems. At the same time, ecological water diversion and pump-gate control projects aimed at improving hydrodynamics involve significant energy consumption and carbon emissions during operation, posing new constraints to water resource management.

[0004] Currently, the scheduling methods applied to lakes or reservoir groups have problems such as insufficient consideration of future climate uncertainties, subjective and rigid scheduling schemes, and difficulties in decision-making for multi-objective models. Summary of the Invention

[0005] The main objective of this application is to propose a low-carbon-ecological coordinated scheduling method and system for multi-lake systems adapted to extreme climates. This method can fully consider the uncertainty of future climate, improve the reliability of scheduling schemes, and solve the problem of decision-making difficulties in multi-objective models.

[0006] To achieve the above objectives, one aspect of this application proposes a low-carbon-ecological coordinated scheduling method for multi-lake systems adapted to extreme climates, comprising the following steps:

[0007] Construct a set of future extreme climate scenarios, which includes several future extreme climate scenarios;

[0008] A coupled urban-natural watershed hydrological model is constructed, and then, based on the coupled urban-natural watershed hydrological model, multiple sets of inflow runoff sequences driven by various future extreme climate scenarios are obtained;

[0009] With the single objective of minimizing the total annual carbon emissions from water diversion, and based on the set of future extreme climate scenarios and the urban-natural watershed coupled hydrological model, a low-carbon-ecological synergistic optimization model is constructed.

[0010] The inflow sequence of each group is input into the low-carbon-ecological synergistic optimization model for solution, resulting in multiple candidate scheduling schemes.

[0011] The candidate scheduling schemes are screened to obtain the optimal scheduling scheme.

[0012] In some embodiments, constructing a set of future extreme climate scenarios specifically includes:

[0013] Obtain the first multi-model global climate prediction data;

[0014] Bias corrections are applied to the multi-model global climate prediction data to obtain the second multi-model global climate prediction data.

[0015] Define an extreme event index, and perform extreme event detection and screening on the second multi-model global climate prediction data based on the extreme event index to obtain several future extreme climate scenarios;

[0016] A random weather generator is used to generate corresponding monthly meteorological sequences for various future extreme climate scenarios, thereby obtaining the set of future extreme climate scenarios.

[0017] The extreme event indicators include persistent drought indicators and extreme rainstorm indicators.

[0018] In some embodiments, the construction of a coupled urban-natural watershed hydrological model, and the subsequent acquisition of multiple inflow sequences driven by various future extreme climate scenarios based on the coupled urban-natural watershed hydrological model, specifically includes:

[0019] In natural regions, runoff is calculated using empirical hydrological models to obtain natural watershed hydrological models;

[0020] In urban areas, the flow generation is calculated based on the impermeable area to obtain the urban watershed hydrological model;

[0021] The natural watershed hydrological model and the urban watershed hydrological model are coupled to obtain the urban-natural watershed coupled hydrological model.

[0022] By inputting various future extreme climate scenarios into the urban-natural watershed coupled hydrological model, multiple sets of inflow runoff sequences into the lake are obtained.

[0023] In some embodiments, the step of minimizing the total annual carbon emissions from water diversion as the single objective, and constructing a low-carbon-ecological synergistic optimization model based on the set of future extreme climate scenarios and the urban-natural watershed coupled hydrological model, specifically includes:

[0024] An objective function is constructed with minimizing the total annual carbon emissions from water diversion as the sole objective.

[0025] Decision variables and auxiliary variables are defined, and various constraints are determined based on the set of future extreme climate scenarios and the urban-natural watershed coupled hydrological model to obtain the low-carbon-ecological synergistic optimization model.

[0026] The constraints include water balance constraints, water level constraints, pump station capacity constraints, annual total water diversion constraints, gate pump flow constraints, and fuzzy reliability ecological constraints, with the improvement rate of hydraulic residence time as the fuzzy reliability ecological constraint.

[0027] In some embodiments, the step of filtering the candidate scheduling schemes to obtain the optimal scheduling scheme specifically includes:

[0028] Obtain the performance indicators of each candidate scheduling scheme under each group of inflow runoff sequences, and construct a cross-scenario evaluation matrix;

[0029] Define robustness evaluation metrics;

[0030] Based on the multi-attribute decision-making method, the candidate scheduling schemes are screened according to the cross-scenario evaluation matrix and the robustness evaluation index to obtain the optimal scheduling scheme.

[0031] In some embodiments, the objective function is:

[0032] ;

[0033] in, Describe the objective function. This indicates the density of water. Represents gravitational acceleration. Indicates the first The total monthly flow rate that needs to be increased by the pumping station Indicates the first The average monthly head of the month, Indicates the first Total length of the month Indicates the overall efficiency of the gate pump. This indicates the carbon emission factor of the power grid in the region.

[0034] In some embodiments, the fuzzy credibility ecological constraint is:

[0035] ;

[0036] in, Indicates a measure of credibility. This represents the hydraulic residence time under the baseline scenario. Indicate candidate scheduling schemes Hydraulic residence time Indicates the improvement rate target. This indicates the preset minimum confidence level.

[0037] To achieve the above objectives, another aspect of this application proposes a low-carbon-ecological coordinated scheduling system for multi-lake systems adapted to extreme climates, comprising:

[0038] An extreme climate scenario construction module is used to construct a set of future extreme climate scenarios, which includes several future extreme climate scenarios.

[0039] An extreme hydrological scenario generation module is used to construct a city-natural watershed coupled hydrological model, and then obtain multiple sets of inflow runoff sequences driven by various future extreme climate scenarios based on the city-natural watershed coupled hydrological model.

[0040] The optimization scheduling model construction module is used to construct a low-carbon-ecological collaborative optimization model with the single objective of minimizing the total annual carbon emissions from water diversion, based on the set of future extreme climate scenarios and the urban-natural watershed coupled hydrological model.

[0041] The candidate scheduling scheme generation module is used to input the inflow runoff sequences of each group into the low-carbon-ecological synergistic optimization model for solving, and obtain multiple candidate scheduling schemes.

[0042] The optimal scheduling scheme generation module is used to filter the candidate scheduling schemes to obtain the optimal scheduling scheme.

[0043] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0044] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method.

[0045] The embodiments of this application include at least the following beneficial effects: The low-carbon-ecological coordinated scheduling method and system for multi-lake systems adapted to extreme climates of this application, on the one hand, combines the constructed set of future extreme climate scenarios with the urban-natural watershed coupled hydrological model to obtain multiple sets of inflow runoff sequences driven by various future extreme climate scenarios. Then, the multiple sets of lake runoff sequences are input into the low-carbon-ecological coordinated optimization model to generate candidate scheduling schemes with risk resistance capabilities, which can fully consider the uncertainty of future climate. On the other hand, the low-carbon-ecological coordinated optimization model is constructed with minimizing the annual total carbon emissions from water diversion as the single objective. After generating multiple sets of candidate scheduling schemes, the candidate scheduling schemes are further screened to obtain the final optimal scheduling scheme, which can improve the reliability of the scheduling scheme and solve the problem of decision-making difficulties in multi-objective models. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the embodiments of this application are described below. It should be understood that the 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 from these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating the steps of a low-carbon-ecological coordinated scheduling method for a multi-lake system adapted to extreme climates, as provided in one embodiment of this application.

[0048] Figure 2 This is a schematic diagram of water resource scheduling in a simulated scenic area provided in one embodiment of this application;

[0049] Figure 3 This is a flowchart illustrating a low-carbon-ecological coordinated scheduling method for a multi-lake system adapted to extreme climates, provided in one embodiment of this application.

[0050] Figure 4 This is a schematic diagram of the structure of a low-carbon-ecological coordinated scheduling system for a multi-lake system adapted to extreme climates, provided in one embodiment of this application.

[0051] Figure 5 This is a schematic diagram of the hardware structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0053] 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.

[0054] Currently, the regulation model of urban lake water systems is undergoing a profound transformation from the traditional single objective of water supply and flood control to a synergistic regulation model that integrates ecology, low carbon emissions, and intelligence. As a crucial carrier of the urban water environment and ecosystem, the water renewal capacity of urban lakes directly determines water purification efficiency and ecosystem stability. Hydraulic retention time (HRT), as a key indicator for quantitatively characterizing water renewal efficiency, ecological response timeliness, and system metabolic intensity, directly reflects the self-purification capacity and ecological stability of lakes and has become a core variable for assessing the ecological health status and regulation effectiveness of lakes.

[0055] However, against the backdrop of frequent extreme hydrological events caused by climate change, lake hydrological conditions and hydrodynamic processes exhibit high non-stationarity, leading to a series of problems such as water retention, prolonged hydraulic retention time, and delayed pollutant migration, severely restricting the self-purification and recovery functions of aquatic ecosystems. Simultaneously, ecological water diversion and pump-gate control projects aimed at improving hydrodynamics involve significant energy consumption and carbon emissions during operation, posing new constraints to water resource management. Therefore, how to minimize the carbon footprint during regulation while ensuring water environmental safety and aquatic ecological health, and how to construct an adaptive scheduling mechanism that can synergistically optimize hydraulic retention time and carbon emissions, has become a critical scientific and technological problem urgently needing to be solved in current urban water resource system governance.

[0056] Currently, the scheduling methods applied to lakes or reservoir groups mainly include:

[0057] 1) Traditional Experience-Based Scheduling and Static Scheduling Diagrams: Traditional experience-based scheduling and static scheduling diagrams mainly rely on historical experience and preset scheduling rules (such as charts and rule manuals). Operators determine the start / stop of gate pumps and the flow rate based on current water levels, water demand, and other information, according to fixed rules. The core objective is to ensure basic flood control and water supply security. However, this method relies excessively on historical experience, and its scheduling scheme lacks scientific verification, failing to guarantee optimality. More importantly, it has poor adaptability. When faced with extreme weather events that exceed historical experience (such as prolonged droughts), it is prone to decision-making failure, threatening the safety of the water system.

[0058] 2) Single-objective optimization scheduling based on mathematical programming: This approach abstracts the scheduling problem into a mathematical optimization model and uses operations research algorithms such as linear programming, nonlinear programming, or dynamic programming to seek the optimal solution for a single objective, such as maximizing power generation or water supply, while satisfying physical constraints like water balance and water level limits. However, this method oversimplifies complex scheduling problems, often focusing only on single economic benefits while ignoring the ecological and environmental consequences of decisions. This "optimal" approach, achieved at the expense of ecological health, does not meet the requirements of sustainable development and may lead to long-term ecological deficits.

[0059] 3) Multi-objective optimization scheduling based on intelligent algorithms: This approach employs multi-objective evolutionary algorithms (such as NSGA-II and MOPSO) to simultaneously optimize multiple conflicting objectives (e.g., minimizing water shortage and maximizing ecological flow), yielding a set of Pareto optimal solutions for decision-makers to weigh. For example, previous studies have used this method to balance "weighted hydraulic residence time" with "annual total carbon emissions from water diversion." While this method can reveal the trade-offs between different objectives, it produces a large set of Pareto optimal solutions rather than a single executable solution, thus returning the complexity and subjectivity of the final decision to the managers. To address this issue, modern research often introduces additional decision analysis methods to assist in selecting a single solution. Furthermore, its constraints are often "hard constraints," failing to flexibly handle the inherently flexible and ambiguous ecological management objectives.

[0060] 4) Scheduling models based on deterministic hydrological inputs: Whether for single-objective or multi-objective optimization, the hydrological inputs (such as natural inflow and rainfall) are typically based on historical multi-year averages, typical year (abundant, normal, or dry) runoff processes, or single short-term weather forecasts. These models assume the inputs are known and then solve for the optimal scheduling strategy based on this assumption. However, such models fail to adequately account for future uncertainties. The "optimal" scheduling schemes derived from historical averages or single typical year data may perform extremely poorly or even lead to system collapse under actual abundant or dry year scenarios, lacking the necessary robustness and reliability.

[0061] 5) Stochastic or Fuzzy Scheduling Methods Considering Uncertainty: To address the uncertainty in hydrological forecasting, methods such as stochastic dynamic programming (SDP) and fuzzy programming (FP) have been introduced into scheduling models. Stochastic programming handles randomness by assuming that uncertain variables follow a known probability distribution. Fuzzy programming utilizes fuzzy mathematics theory to describe the fuzziness of hydrological variables or scheduling objectives (such as "high water level," "severe water shortage," etc.), and can handle uncertainties caused by inaccurate data or subjective judgment. However, these methods have the following problems: ① Stochastic programming: requires the probability distribution of uncertain variables to be known and stable. However, under the influence of climate change, future hydrological scenarios exhibit non-stationary characteristics, and the probability distribution of historical data is no longer applicable to the future, causing the premise of this method to fail. ② Traditional fuzzy programming: although it can handle cognitive fuzziness, it has significant shortcomings in how to systematically couple multiple scenarios of future climate change and how to reliably quantify the satisfaction of constraints (i.e., reliability assessment). ③ Data-driven machine learning methods: their performance is highly dependent on the quality and coverage of historical training data. For extreme scenarios that may occur in the future and exceed the historical data range, the generalization ability and reliability of the model are questionable. At the same time, its "black box" nature makes the decision-making process lack physical explanation.

[0062] 6) Applying machine learning or artificial intelligence to scheduling decisions: Attempts can be made to apply artificial neural networks, support vector machines, or reinforcement learning to learn from historical or simulated data and construct a mapping relationship between inputs and scheduling decisions to achieve rapid or adaptive decision-making. However, existing research rarely offers a complete technical solution that systematically couples multiple climate scenarios representing deep future uncertainty generated by GCMs with advanced optimization frameworks (such as fuzzy credibility-constrained programming) capable of handling fuzzy ecological objectives. Current technologies fail to form an end-to-end closed loop from "quantifying future uncertainty" to "generating robust adaptive decisions," thus limiting the practical ability of scheduling schemes to address future climate challenges.

[0063] In summary, existing technologies suffer from problems such as subjective and rigid scheduling rules, single-objective models ignoring ecological value, multi-objective models facing decision-making difficulties, insufficient consideration of future climate uncertainties, and overly rigid descriptions of ecological objectives.

[0064] In view of this, this application proposes a low-carbon-ecological coordinated scheduling method for multi-lake systems adapted to extreme climates. On the one hand, it combines a set of future extreme climate scenarios with a coupled urban-natural watershed hydrological model to obtain multiple sets of inflow runoff sequences driven by various future extreme climate scenarios. Then, it inputs these multiple sets of lake runoff sequences into a low-carbon-ecological coordinated optimization model to generate candidate scheduling schemes with resilience to risks, which can fully consider the uncertainty of future climate. On the other hand, it constructs a low-carbon-ecological coordinated optimization model with minimizing the annual total carbon emissions from water diversion as the single objective. After generating multiple sets of candidate scheduling schemes, it further screens the candidate scheduling schemes to obtain the final optimal scheduling scheme, which can improve the reliability of the scheduling scheme and solve the problem of decision-making difficulties in multi-objective models.

[0065] Reference Figure 1 , Figure 1 This is a flowchart illustrating the steps of a low-carbon-ecological coordinated scheduling method for a multi-lake system adapted to extreme climates, as provided in one embodiment of this application. This embodiment proposes a low-carbon-ecological coordinated scheduling method for a multi-lake system adapted to extreme climates, which may include, but is not limited to, the following steps S101 to S105:

[0066] Step S101: Construct a set of future extreme climate scenarios, which includes several future extreme climate scenarios.

[0067] Understandably, the collection of several future extreme climate scenarios together constitutes a comprehensive and probabilistic "future extreme climate scenario set," laying a solid data foundation for the next stage of robust optimization and evaluation of scheduling schemes under various severe and possible climate conditions.

[0068] As an optional implementation, step S101 can be further divided into the following steps S1011 to S1014:

[0069] Step S1011: Obtain the first multi-model global climate prediction data;

[0070] In some optional embodiments, future meteorological data are derived from the outputs of multiple global climate models (GCMs) from the Sixth Coupled Model Intercomparison Project (CMIP6). To systematically cover uncertainties in future climate, this application's embodiments select typical scenario combinations covering low, medium, and high radiative forcing levels, specifically including: SSP1-2.6 (sustainable development low-emission pathway), SSP2-4.5 (medium-emission pathway), and SSP5-8.5 (high-emission pathway). Multiple global climate models (GCMs) that have performed well in historical climate simulations of the study area are selected to cover uncertainties arising from differences in model structure. The acquired meteorological variables include daily precipitation, as well as daily maximum / minimum temperatures, solar radiation, wind speed, and relative humidity required to calculate potential evapotranspiration.

[0071] Step S1012: Perform bias correction on the multi-model global climate prediction data to obtain the second multi-model global climate prediction data;

[0072] Specifically, to eliminate biases in multiple global climate models (GCMs) and retain their predicted future climate change signals, this application employs the quantile increment mapping (QDM) method to correct the original model data. This method first corrects the probability distribution shape of the model data through quantile mapping, and then superimposes the future changes predicted by the global climate model (GCM) relative to the historical baseline period, thereby preserving climate trends while matching the observed distribution. Defined variables include: historical observation data. Historical GCM simulation data Future GCM simulation data Corrected future data , Cumulative Distribution Function (CDF) It is the inverse function of the cumulative distribution function, i.e., the quantile function; These are quantile values. .

[0073] First, calculate the model prediction value for each future time point. This variation factor is relative to its value at the same quantile in historical climate states. It is divided into additive type (applicable to variables such as temperature) and multiplicative type (applicable to proportional variables such as rainfall), and the specific formulas are as follows:

[0074] ;

[0075] ;

[0076] Among them, the formula applicable to temperature (additive type) is... For rainfall (multiplicative type), the formula is applicable. .

[0077] Then, the original model data for future periods Treat it as an independent sequence, using historical observation data and historical model data The quantile relationship is used to correct it, and the specific formula is as follows:

[0078]

[0079] This step corrected The probability distribution is made consistent with the distribution of the observed data, but the future trend predicted by GCM is temporarily removed.

[0080] Finally, the aforementioned climate change factors Apply to the obtained sequence This allows us to recover future climate change signals and obtain the final correction results. (That is, the second multi-model global climate prediction data), the specific formula is as follows:

[0081] ;

[0082] ;

[0083] Among them, the formula applicable to temperature (additive type) is... For rainfall (multiplicative type), the formula is applicable. .

[0084] Through the above steps, the quantile increment mapping (QDM) method ensures that the final generated future meteorological sequence not only matches the local observation data in statistical distribution, but also accurately preserves the long-term climate evolution trends predicted by multiple global climate models (GCMs).

[0085] Step S1013: Define extreme event indicators. Based on the extreme event indicators, perform extreme event detection and screening on the second multi-model global climate prediction data to obtain several future extreme climate scenarios.

[0086] Among them, the extreme event indicators include the persistent drought indicator and the extreme rainstorm indicator.

[0087] Specifically, based on bias-corrected second-multi-model global climate prediction data, a representative "future extreme climate scenario set" is systematically identified, screened, and constructed from a vast array of future climate predictions. This scenario set does not simply compile data from all future periods, but focuses on typical extreme weather patterns that pose severe challenges to water resource systems.

[0088] First, we define extreme event indicators for identifying extreme climate events. For the lake water environment and management issues addressed in this application's embodiments, we mainly define the following two types of indicators: Persistent drought indicators: based on the Standardized Precipitation Evapotranspiration Index (SPEI), these identify medium- to long-term drought events lasting for months or even seasons, focusing on drought duration, cumulative intensity, and frequency; Extreme rainfall indicators: based on daily precipitation, these identify short-duration heavy rainfall events exceeding a specific return period (e.g., a 50-year return period), or periods of extremely high cumulative rainfall over several consecutive days.

[0089] Next, extreme events were detected under multiple models and scenarios. In the future simulation data of each emission scenario (SSP1-2.6, SSP2-4.5, SSP5-8.5) for each future extreme climate scenario, the above-mentioned extreme event detection algorithm was run to mark all years or periods in which extreme events occurred.

[0090] Next, scenario screening and classification are performed. All detected extreme event periods are analyzed and screened based on their intensity, duration, spatial extent, and potential impact on the hydrological system, selecting the most representative extreme climate scenarios. For example, the most severe persistent drought scenario is selected from the SSP5-8.5 scenario; and the most frequent and representative extreme rainfall scenarios are selected from the SSP2-4.5 scenario. This ensures that the final "future extreme climate scenario set" covers different types of extreme weather models under different emission pathways, providing a comprehensive and rigorous testing environment for evaluating the robustness of dispatching schemes.

[0091] Step S1014: Using a random weather generator, generate corresponding monthly meteorological sequences for various future extreme climate scenarios to obtain a set of future extreme climate scenarios;

[0092] It should be noted that, in order to transform the selected discrete "future extreme climate scenarios" into a large number of continuous meteorological input sequences that can directly drive the low-carbon-ecological synergistic optimization model, the embodiments of this application adopt a physical statistics-based random weather generator. While retaining the core statistical characteristics of the original extreme scenarios, it generates a large number of statistically reasonable and internally consistent monthly meteorological sequences, thereby quantifying the uncertainty of future climate prediction and providing rich external drivers for the optimization model.

[0093] Specifically, the process begins with parameterization and model calibration: features are extracted for each selected future extreme climate scenario period to obtain monthly meteorological variable sequences; then, key statistical parameters are estimated, including the shape and scale parameters of the Gamma distribution of monthly precipitation, marginal distribution parameters such as the mean and standard deviation of the normal distribution of monthly temperature, and spatiotemporal dependent structural parameters such as the inter-monthly autocorrelation coefficient and the cross-correlation coefficient between precipitation and temperature; finally, the weather generator is calibrated using this parameter set as the target to ensure that its output can reproduce the target statistical characteristics.

[0094] Next, the Monte Carlo-based stochastic simulation phase begins: using a calibrated generator, large-scale random sampling is performed to independently generate hundreds to thousands of monthly meteorological sequences with consistent statistical characteristics for each future extreme climate scenario. This process is achieved through multivariate time series methods such as vector autoregression models to simultaneously maintain the persistence of multiple meteorological variables and their synergistic relationship, ensuring the physical consistency of the generated sequences.

[0095] Finally, the extreme event preservation and quality verification phase begins: multiple tests ensure the reliability of the generated data, including: statistical consistency test (verifying the goodness of fit between the generated sequence's mean, variance, quantiles, etc., and the target parameters); extreme event reproducibility test (assessing whether the frequency, intensity, and duration of extreme events in the generated sequence conform to the characteristics of the original scenario); and physical rationality test (checking for any combinations of variables that violate physical laws). Through this process, a meteorological sequence set containing a large number of members is generated for each selected future extreme climate scenario. This ultimately forms a set of future extreme climate scenarios, providing a solid data foundation for the robustness assessment of subsequent scheduling schemes.

[0096] Step S102: Construct a city-natural watershed coupled hydrological model, and then obtain multiple sets of inflow runoff sequences driven by various future extreme climate scenarios based on the city-natural watershed coupled hydrological model.

[0097] Specifically, based on multi-source data such as Digital Elevation Model (DEM), land use, soil type, and urban drainage system structure, refined sub-basins are divided to accurately depict the complex watershed characteristics where urban areas and natural underlying surfaces intertwine.

[0098] As an optional implementation, step S102 can be further divided into the following steps S1021 to S1024:

[0099] Step S1021: In natural areas, runoff is calculated using empirical hydrological models to obtain natural watershed hydrological models;

[0100] Specifically, in natural regions, an empirical hydrological model (Soil Conservation Service-Curve Number model, SCS-CN model) is used to calculate runoff. This model comprehensively reflects the impact of watershed characteristics such as soil type and land use on the runoff generation process through the curve number (CN). Its core calculation formula is as follows:

[0101] ;

[0102] ;

[0103] in, P represents runoff (mm), S represents precipitation (mm), S represents the maximum possible water storage capacity of the basin (mm), and CN represents the number of curves that comprehensively reflect the characteristics of the underlying surface.

[0104] Step S1022: In urban areas, the runoff is calculated based on the impermeable area to obtain the urban watershed hydrological model;

[0105] Specifically, in urban areas, considering their unique impermeable surface characteristics, a runoff calculation method based on impermeable area is adopted, and the calculation formula is as follows:

[0106] ;

[0107] in, This represents the runoff volume per unit time in the urban area. Indicates the runoff coefficient of the impermeable zone. This represents the amount of rainfall per unit time. This indicates the area of ​​the impermeable zone.

[0108] Step S1023: Couple the natural watershed hydrological model and the urban watershed hydrological model to obtain the urban-natural watershed coupled hydrological model;

[0109] Specifically, by integrating the runoff generation and runoff processes of natural and urban areas, the total inflow into the lake is calculated, i.e., the urban-natural watershed coupled hydrological model:

[0110] ;

[0111] This coupled simulation framework achieves accurate simulation of complex watershed hydrological processes by simultaneously considering the soil saturation runoff generation mechanism in natural regions and the rapid runoff generation characteristics of impervious surfaces in urban areas. Specifically, the SCS-CN model for natural regions reasonably reflects the runoff generation capacity under different soil types and land use conditions, while the impervious area rule for urban areas accurately characterizes the significant impact of urbanization on hydrological processes. The organic combination of these two models provides reliable boundary condition inputs for subsequent lake hydrodynamic simulations and scheduling optimization, ensuring the accuracy and reliability of the entire model system.

[0112] Step S1024: Input various future extreme climate scenarios into the urban-natural watershed coupled hydrological model to obtain multiple sets of inflow runoff sequences into the lake.

[0113] Specifically, rainfall data under various future extreme climate scenarios can be obtained through a set of future extreme climate scenarios. Then, the rainfall data can be input into the aforementioned urban-natural watershed coupled hydrological model to obtain multiple sets of inflow runoff sequences into the lake.

[0114] Step S103: With minimizing the total annual carbon emissions from water diversion as the single objective, and based on the set of future extreme climate scenarios and the urban-natural watershed coupled hydrological model, construct a low-carbon-ecological synergistic optimization model.

[0115] It should be noted that, in order to find the optimal scheduling scheme under any given future extreme hydrological scenario, this application embodiment constructs and solves a low-carbon-ecological synergistic optimization model. The overall design idea of ​​this model adopts a single-objective optimization form, taking "low-carbon operation" with clear economic and environmental benefits as the sole and direct optimization objective; at the same time, the relatively complex and flexible objective of "ecological improvement" is transformed into a flexible constraint condition with reliability guarantee by introducing fuzzy credibility-constrained programming (Fuzzy CCP) theory.

[0116] As an optional implementation, step S103 can be further divided into the following steps S1031 and S1032:

[0117] Step S1031: Construct an objective function with minimizing the total annual carbon emissions from water diversion as the single objective;

[0118] As an optional implementation, the objective function is:

[0119] ;

[0120] in, Describe the objective function. This indicates the density of water. Represents gravitational acceleration. Indicates the first The total monthly flow rate that needs to be increased by the pumping station Indicates the first The average monthly head of the month, Indicates the first Total length of the month Indicates the overall efficiency of the gate pump. This indicates the carbon emission factor of the power grid in the region.

[0121] Specifically, the low-carbon-ecological synergistic optimization model proposed in this application sets minimizing the total annual carbon emissions from water diversion as the sole objective function. This function aims to reduce the energy consumption generated by pumping water from external water sources through optimized scheduling, thereby reducing the system's carbon footprint.

[0122] For example, such as Figure 2 The diagram shown is a simulation of water resource allocation in a scenic area. Figure 2 middle, This indicates the inflow rate to the lake at gate 1. This indicates the inflow rate to the lake at gate 2. This indicates the outflow rate from gate 3. This indicates the outflow rate from gate 4. This indicates the outflow rate from gate 5. This indicates the outflow rate from gate 6. This represents the outflow from gate 7. The set of indicators for this simulated scenic area includes: gate set. , Monthly Set , , It is the initial state; a collection of lakes , These correspond to Bohai Lake, Central Lake, Qinglian Lake, Fairy Lake, and Inner Lake, respectively; lake parameters include and :

[0123] ;

[0124] .

[0125] For this simulated scenic area, the objective function is constructed as follows:

[0126] ;

[0127] in, This represents the density of water (taken as 1000 kg / m³). This represents the acceleration due to gravity (taken as 9.8 m / s²). This indicates the amount of water diverted to the Xijiang River via the pumping station. Equal to the total amount flowing into the lake Subtract natural inflow into the lake and the amount of water flowing into the lake ,Right now , Indicates the first The average monthly head (m) of the month, assuming m, Indicates the first Total duration of the month (s). This represents the overall efficiency of the gate pump, which is related to the gate pump flow rate and the difference in water surface elevation; we assume it to be 0.7. This indicates the carbon emission factor of the power grid in the region (0.85 kg CO2 / kW). h), This represents the conversion factor from joules to kilowatt-hours (kWh).

[0128] Step S1032: Define decision variables and auxiliary variables, and determine various constraints based on the set of future extreme climate scenarios and the urban-natural watershed coupled hydrological model to obtain a low-carbon-ecological synergistic optimization model;

[0129] The constraints include water balance constraints, water level constraints, pump station capacity constraints, annual total water diversion constraints, gate pump flow constraints, and fuzzy reliability ecological constraints, with the improvement rate of hydraulic residence time as the fuzzy reliability ecological constraint.

[0130] Specifically, the decision variables of the model are defined as follows: , indicating the first Monthly passage through gates / pumps The average flow rate (m³ / s). This set of variables... This is the core of the model solution. Auxiliary variables are defined, including monthly pump priming volume. Hydraulic residence time And the target for improvement in hydraulic retention time Among them, the monthly pumped water volume Connecting the decision variable and the objective function, representing the first... The total monthly flow rate that needs to be pumped from the Xijiang River through the pumping station; hydraulic retention time. Hydraulic retention time is a key indicator for assessing the ecological status of lakes. During a single model run, its input (such as natural inflow) is deterministic. Therefore, for a given scheduling scheme X, hydraulic retention time is a deterministic value that can be calculated using a hydraulic-hydrological model. The target for improving fuzzy hydraulic retention time is... The fuzzy variable introduced in this application to describe resilient ecological objectives is a fuzzy number (e.g., a triangular fuzzy number with a center value of 0.2) set by the decision-maker, representing the expected HRT improvement rate for the k-th lake, such as "the improvement rate should be around 20%", thus making management objectives more flexible.

[0131] For example, for the aforementioned simulated scenic area, the decision variables of the model are defined. include: Flow rate of water entering Lake Balhae from external sources ( ); Flow rate of water entering Fairy Lake from external water sources ( ); : The flow rate of water discharged from the central lake to the outside ( ); : The flow rate of water discharged from Qinglian Lake to the outside ( ); : The flow rate of water discharged from Fairy Lake to the outside ( ); The flow rate from the central lake to the inner lake ( ); : The flow rate of water discharged from Lihu to the outside ( ).

[0132] The low-carbon-ecological synergistic optimization model also needs to meet a series of constraints to ensure the physical feasibility, operational safety, and achievement of ecological goals of the scheduling scheme. In the embodiments of this application, the determined constraints include water balance conditions, water level constraints, pump station capacity constraints, annual total water diversion constraints, gate pump flow constraints, and fuzzy confidence ecological constraints.

[0133] Specifically, the water balance constraint is as follows:

[0134] ;

[0135] in, Indicates the first Monthly rainfall (generated from a set of future extreme climate scenarios). Indicates the first Monthly water evaporation (generated from a set of future extreme climate scenarios). Indicates the first Total monthly inflow into the lake, Indicates the first Total monthly outflow from the lake Indicates the first Total length of the month Indicates the first The water level of the month, Indicates the first The water level of the month.

[0136] The simulated scenic area's lakes are divided into two systems: one with lakes composed of k=1, 2, 3, 4, and the other with k=5. The water balance of each system is calculated separately. The area of ​​lake system 1 is 5.98 km². 2 The area of ​​lake system 2 is 0.26 km². 2 The water balance of lake system 1 is then: total inflow. That is, the inflow rate of the lake through gates 1 and 2. Water diverted from the Xijiang River Natural inflow into the lake and the composition of reservoir water diversion Total outflow from the lake This refers to the outflow from gates 3, 4, 5, and 6. The water balance of lake system 2 is: total inflow. Total outflow from the lake .

[0137] Water level constraints are:

[0138] ;

[0139] in, This indicates the predicted water level before heavy rainfall, assumed to be 4.3m. This represents the designed flood level, assumed to be 6.2m.

[0140] The capacity constraints of the pumping station are:

[0141] ;

[0142] in, This indicates that the amount of water diverted to the Xijiang River through the pumping station is equal to the total amount flowing into the lake. Subtract natural inflow into the lake (Generated from a set of future extreme climate scenarios and a coupled urban-natural watershed hydrological model) and reservoir inflow. (The original plan) ; This indicates the maximum transport capacity of the water diversion pumping station.

[0143] The annual total water diversion volume constraint is:

[0144] ;

[0145] in, This indicates the amount of water diverted to the Xijiang River via the pumping station. This represents the total annual water diversion quota for the Xijiang River, assumed to be... .

[0146] The flow constraint for the gate pump is:

[0147] ;

[0148] in, Indicates the first The maximum flow rate of a single gate pump, assuming a single 0.5 .

[0149] As a further optional implementation, the fuzzy credibility ecological constraint is:

[0150] ;

[0151] in, Indicates a measure of credibility. This represents the hydraulic residence time under the baseline scenario. Indicate candidate scheduling schemes Hydraulic residence time Indicates the improvement rate target. This indicates the preset minimum confidence level.

[0152] Specifically, the fuzzy reliability ecological constraint is the core technical feature of the embodiments of this application. It stipulates that for scheduling scheme X, the calculated relative reduction rate of deterministic hydraulic residence time (left side of the formula) satisfies the preset improvement rate target. The credibility of this event should not be lower than the preset minimum confidence level. The specific formula is as follows.

[0153] ;

[0154] in, Indicates a measure of credibility; This represents the hydraulic residence time under the baseline scenario; Indicate candidate scheduling schemes The hydraulic residence time, for the aforementioned simulated scenic area, 、 , , , ; This indicates the lowest confidence level. Assume the improvement rate target... The desired HRT improvement rate is approximately 10%–20%, with a most likely value of 15. Assume a confidence level of [missing information]. Using the α-truncation method to find the lower bound, the above formula is equivalent to:

[0155] ;

[0156] That is, the deterministic constraint becomes:

[0157] .

[0158] Step S104: Input the runoff sequences of each group into the low-carbon-ecological synergistic optimization model for solution to obtain multiple candidate scheduling schemes;

[0159] It should be noted that the low-carbon-ecological synergistic optimization model constructed in this application embodiment is highly complex, characterized by: nonlinear relationships between the objective function and some constraints and decision variables; high dimensionality of decision variables (e.g., n sluice gates × 12 months); and its core innovation lies in the use of fuzzy mathematics theory (such as analytical methods based on triangular fuzzy numbers) to transform the formally complex fuzzy confidence ecological constraints into an equivalent and much simpler deterministic nonlinear constraint. Nevertheless, the transformed model remains nonlinear overall, making it difficult to effectively obtain the global optimum using traditional mathematical programming solvers. Therefore, this application embodiment employs a genetic algorithm (GA) as the core solution technique. A genetic algorithm is a global probabilistic search algorithm that simulates natural selection and genetic mechanisms, particularly suitable for handling such complex nonlinear optimization problems, exhibiting good robustness and global optimization capabilities.

[0160] The specific implementation steps of this solution technique are as follows:

[0161] 1) Encoding and Population Initialization: Encoding a complete scheduling scheme (i.e., all decision variables) The set of variables is encoded as a "chromosome". This chromosome is a long vector containing multiple real-valued genes, each representing the flow rate of a specific gate pump in a specific month. The algorithm first randomly generates a large number of chromosomes within the feasible region of the decision variables to form an initial "population".

[0162] 2) Fitness Function Design: The fitness function is a benchmark for evaluating the performance of each scheduling scheme (chromosome). Since this model is a single-objective optimization, the fitness function and the objective function... Direct correlation. To handle multiple complex constraints in the model, this application employs a penalty function method. Specifically, an individual's fitness value is determined by its objective function value and the degree to which it violates each constraint. If a candidate solution violates any constraint (including the transformed deterministic ecological constraint), its fitness value will be subject to a large penalty term. In this way, infeasible solutions will obtain extremely poor fitness and will be naturally eliminated during the evolutionary process.

[0163] 3) Genetic operator operations:

[0164] Selection: Using strategies such as roulette wheel selection or tournament selection, "superior" individuals are selected for the next generation based on their fitness values.

[0165] Crossover: Selected individuals serve as "parents" and undergo crossover with a certain probability, exchanging some gene segments to produce new "offspring" chromosomes.

[0166] Mutation: Certain genes in the offspring chromosomes undergo random changes with a small probability to maintain population diversity and prevent the algorithm from prematurely converging to a local optimum.

[0167] 4) Iteration and Termination: By repeatedly performing the genetic operations of "selection, crossover, and mutation," the overall fitness of the population will continuously improve. This iterative process continues until the preset maximum number of generations is reached, or the fitness of the best individual no longer shows significant improvement over multiple generations, at which point the algorithm converges and terminates.

[0168] Ultimately, the chromosome with the highest fitness in the population at the time of algorithm termination is the candidate scheduling scheme solved by the low-carbon-ecological synergistic optimization model constructed in this application embodiment under the current given future extreme hydrological scenario.

[0169] Step S105: Filter the candidate scheduling schemes to obtain the optimal scheduling scheme.

[0170] It should be noted that the aforementioned steps have yielded a corresponding candidate scheduling scheme for each given future extreme hydrological scenario. This results in a scenario-optimal solution set containing multiple candidate scheduling schemes. However, a scheme that performs optimally under a single extreme scenario may not perform ideally when dealing with other types of extreme events. Therefore, this application introduces a robustness evaluation and selection mechanism to identify and determine the optimal scheduling scheme from the candidate scheme set that exhibits the most robust and reliable overall performance under various potential future extreme challenges.

[0171] As an optional implementation, step S105 can be further divided into the following steps S1051 to S1053:

[0172] Step S1051: Obtain the performance indicators of each candidate scheduling scheme under each group of inflow runoff sequences, and construct a cross-scenario evaluation matrix;

[0173] Specifically, for each candidate scheduling scheme in the candidate scheme set (e.g., the candidate scheduling scheme obtained under the "extreme drought" scenario), simulations are performed under all preset extreme hydrological scenarios (such as "extreme drought", "extreme high temperature", etc.), and its performance indicators are calculated. This process forms an evaluation matrix, where the rows of the matrix represent candidate scheduling schemes, and the columns represent their specific performance under different extreme scenarios.

[0174] Step S1052: Define robustness evaluation metrics;

[0175] Specifically, to comprehensively evaluate the overall performance of each candidate scheme, this application defines three evaluation metrics: Efficiency, which refers to the average performance level of the scheduling scheme in all scenarios of the objective function (i.e., total annual carbon emissions). The lower the average carbon emissions, the higher the efficiency; Reliability, which refers to the stability of the scheduling scheme in meeting key constraints (especially fuzzy reliability ecological constraints) in all scenarios; and Stability, which refers to the degree of fluctuation of various performance indicators of the scheduling scheme in different scenarios, usually measured by the variance or standard deviation of key performance indicators (such as carbon emissions and HRT improvement rate) in all scenarios.

[0176] Step S1053: Based on the multi-attribute decision-making method, candidate scheduling schemes are screened according to the cross-scenario evaluation matrix and robustness evaluation index to obtain the optimal scheduling scheme.

[0177] Specifically, based on the constructed cross-scenario evaluation matrix and the aforementioned three-dimensional indicators, this embodiment employs a decision-making method based on multi-attribute utility theory (such as TOPSIS, VIKOR, etc.) for final selection. First, each indicator is dimensionless, and different indicators are assigned corresponding weights according to the decision-maker's preferences. Then, all options are ranked by calculating the comprehensive distance between each candidate solution and the "ideal optimal solution" and the "ideal worst solution." The candidate solution with the optimal comprehensive distance, that is, the one that achieves the best balance in efficiency, reliability, and stability, will be determined as the final optimal scheduling solution.

[0178] Through the above steps, the embodiments of this application ensure that the final output scheduling strategy is not only theoretically optimizable, but also possesses strong resilience and reliability in dealing with deep uncertainties in the future, realizing a decision-making upgrade from "scenario-optimal" to "panoramic robustness".

[0179] In summary, the process of the low-carbon-ecological coordinated scheduling method for multi-lake systems adapted to extreme climates in this application embodiment is as follows: Figure 3 As shown.

[0180] The first step is to construct extreme climate scenarios, including the screening of global climate models and the generation of monthly meteorological sequences using random weather generators.

[0181] The second step is to simulate the hydrological processes in natural and urban watersheds and construct a coupled urban-natural watershed hydrological model.

[0182] The third step is to construct a low-carbon-ecological collaborative optimization model, which introduces fuzzy credibility constraint programming theory to construct fuzzy credibility ecological constraints, and adopts low-carbon goal-oriented single-objective optimization, and then solves the problem through a genetic algorithm to obtain a variety of candidate scheduling schemes.

[0183] The fourth step is to introduce a robustness evaluation and selection mechanism to determine the optimal scheduling scheme from the candidate scheduling schemes. This includes constructing a cross-scenario evaluation matrix, defining robustness evaluation indicators (including efficiency, reliability, and stability), and using a multi-attribute decision-making method for selection.

[0184] The above describes the low-carbon-ecological coordinated scheduling method for multi-lake systems adapted to extreme climates, as described in the embodiments of this application. It can be recognized that the embodiments of this application have the following advantages:

[0185] First, existing methods are mostly based on historical data, which makes it difficult to cope with extreme climate scenarios that exceed historical experience, resulting in insufficient robustness of scheduling schemes. This application combines a constructed set of future extreme climate scenarios with a coupled urban-natural watershed hydrological model to obtain multiple sets of inflow runoff sequences driven by various future extreme climate scenarios. These multiple sets of lake runoff sequences are then input into a low-carbon-ecological co-optimization model to generate candidate scheduling schemes with resilience, fully considering the uncertainty of future climate and improving the adaptability of scheduling schemes to future climate uncertainties.

[0186] Second, existing methods often neglect ecological objectives or only provide Pareto solutions that require manual trade-offs, lacking directly executable optimization schemes. This application's embodiments innovatively and deeply couple future extreme climate scenarios, hydrological process simulation, low-carbon goal-oriented single-objective optimization, ecological constraints based on fuzzy confidence planning, and scheme robustness assessment to construct a forward-looking intelligent scheduling framework. This framework overcomes the limitations of traditional scheduling models that rely on historical data, overcomes the decision-making dilemma of Pareto solutions in multi-objective optimization, achieves a technological leap from "predicting future uncertainties" to "generating proactive adaptive strategies," and effectively integrates low-carbon objectives with water ecological improvement.

[0187] Third, traditional "hard constraints" are difficult to accurately characterize non-absolute ecological requirements such as "effective improvement of hydraulic residence time." This application's embodiments, based on fuzzy mathematics theory, transform complex ecological objectives into flexible constraints with clear confidence levels, and internalize multi-objective trade-offs into a single-objective optimization model. This model can generate optimal solutions for each future extreme climate scenario, and ultimately output a unique, executable, and robust scheduling scheme through a scientific selection mechanism. This enhances the model's practicality and fit, and reasonably expresses fuzzy and flexible ecological management objectives within the mathematical model.

[0188] Reference Figure 4This application also provides a low-carbon-ecological coordinated scheduling system for multi-lake systems adapted to extreme climates, including:

[0189] The extreme climate scenario construction module is used to construct a set of future extreme climate scenarios, which includes several future extreme climate scenarios.

[0190] The extreme hydrological scenario generation module is used to construct a coupled urban-natural watershed hydrological model, and then obtain multiple sets of inflow runoff sequences driven by various future extreme climate scenarios based on the coupled urban-natural watershed hydrological model.

[0191] The optimization scheduling model construction module is used to construct a low-carbon-ecological collaborative optimization model with the single objective of minimizing the total annual carbon emissions from water diversion, based on a set of future extreme climate scenarios and a coupled urban-natural watershed hydrological model.

[0192] The candidate scheduling scheme generation module is used to input the runoff sequences of each group into the lake into the low-carbon-ecological synergistic optimization model for solution, and obtain multiple candidate scheduling schemes.

[0193] The optimal scheduling scheme generation module is used to filter candidate scheduling schemes and obtain the optimal scheduling scheme.

[0194] 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.

[0195] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0196] It is 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.

[0197] Please see Figure 5 , Figure 5 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0198] The processor 1001 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0199] The memory 1002 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1002 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called and executed by the processor 1001 using the methods described in the embodiments of this application.

[0200] Input / output interface 1003 is used to implement information input and output;

[0201] The communication interface 1004 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0202] Bus 1005 transmits information between various components of the device (e.g., processor 1001, memory 1002, input / output interface 1003, and communication interface 1004);

[0203] The processor 1001, memory 1002, input / output interface 1003 and communication interface 1004 are connected to each other within the device via bus 1005.

[0204] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0205] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium 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.

[0206] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0207] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product 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.

[0208] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0209] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0210] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0211] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0212] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0213] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0214] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0215] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0216] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0217] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0218] If the integrated unit 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 all or 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 multiple 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 of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0219] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A low-carbon-ecological coordinated scheduling method for multi-lake systems adapted to extreme climates, characterized in that, Includes the following steps: Construct a set of future extreme climate scenarios, which includes several future extreme climate scenarios; A coupled urban-natural watershed hydrological model is constructed, and then, based on the coupled urban-natural watershed hydrological model, multiple sets of inflow runoff sequences driven by various future extreme climate scenarios are obtained, specifically including: In natural regions, runoff is calculated using empirical hydrological models to obtain natural watershed hydrological models; In urban areas, the flow generation is calculated based on the impermeable area to obtain the urban watershed hydrological model; The natural watershed hydrological model and the urban watershed hydrological model are coupled to obtain the urban-natural watershed coupled hydrological model. By inputting various future extreme climate scenarios into the urban-natural watershed coupled hydrological model, multiple sets of inflow runoff sequences into the lake are obtained. With minimizing the total annual carbon emissions from water diversion as the sole objective, and based on the aforementioned set of future extreme climate scenarios and the aforementioned urban-natural watershed coupled hydrological model, a low-carbon-ecological synergistic optimization model is constructed, specifically including: An objective function is constructed with minimizing the total annual carbon emissions from water diversion as the sole objective. Decision variables and auxiliary variables are defined, and various constraints are determined based on the set of future extreme climate scenarios and the urban-natural watershed coupled hydrological model to obtain the low-carbon-ecological synergistic optimization model. The constraints include water balance constraints, water level constraints, pump station capacity constraints, annual total water diversion constraints, gate pump flow constraints, and fuzzy confidence ecological constraints, with the improvement rate of hydraulic retention time as the fuzzy confidence ecological constraint. The inflow sequence of each group is input into the low-carbon-ecological synergistic optimization model for solution, resulting in multiple candidate scheduling schemes. The candidate scheduling schemes are screened to obtain the optimal scheduling scheme.

2. The method according to claim 1, characterized in that, The construction of the future extreme climate scenario set specifically includes: Obtain the first multi-model global climate prediction data; Bias corrections are applied to the multi-model global climate prediction data to obtain the second multi-model global climate prediction data. Define an extreme event index, and perform extreme event detection and screening on the second multi-model global climate prediction data based on the extreme event index to obtain several future extreme climate scenarios; A random weather generator is used to generate corresponding monthly meteorological sequences for various future extreme climate scenarios, thereby obtaining the set of future extreme climate scenarios. The extreme event indicators include persistent drought indicators and extreme rainstorm indicators.

3. The method according to claim 1, characterized in that, The step of filtering the candidate scheduling schemes to obtain the optimal scheduling scheme specifically includes: Obtain the performance indicators of each candidate scheduling scheme under each group of inflow runoff sequences, and construct a cross-scenario evaluation matrix; Define robustness evaluation metrics; Based on the multi-attribute decision-making method, the candidate scheduling schemes are screened according to the cross-scenario evaluation matrix and the robustness evaluation index to obtain the optimal scheduling scheme.

4. The method according to claim 1, characterized in that, The objective function is: ； in, Describe the objective function. This indicates the density of water. Represents gravitational acceleration. Indicates the first The total monthly flow rate that needs to be increased by the pumping station Indicates the first The average monthly head of the month, Indicates the first Total length of the month Indicates the overall efficiency of the gate pump. This indicates the carbon emission factor of the power grid in the region.

5. The method according to claim 1, characterized in that, The fuzzy credibility ecological constraint is: ； in, Indicates a measure of credibility. This represents the hydraulic residence time under the baseline scenario. Indicate candidate scheduling schemes Hydraulic residence time Indicates the improvement rate target. This indicates the preset minimum confidence level.

6. A low-carbon-ecological coordinated scheduling system for multi-lake systems adapted to extreme climates, characterized in that, include: An extreme climate scenario construction module is used to construct a set of future extreme climate scenarios, which includes several future extreme climate scenarios. An extreme hydrological scenario generation module is used to construct a coupled urban-natural watershed hydrological model, and then, based on the coupled urban-natural watershed hydrological model, obtain multiple sets of inflow runoff sequences driven by various future extreme climate scenarios, specifically including: In natural regions, runoff is calculated using empirical hydrological models to obtain natural watershed hydrological models; In urban areas, the flow generation is calculated based on the impermeable area to obtain the urban watershed hydrological model; The natural watershed hydrological model and the urban watershed hydrological model are coupled to obtain the urban-natural watershed coupled hydrological model. By inputting various future extreme climate scenarios into the urban-natural watershed coupled hydrological model, multiple sets of inflow runoff sequences into the lake are obtained. The optimized scheduling model construction module is used to construct a low-carbon-ecological synergistic optimization model with the single objective of minimizing the total annual carbon emissions from water diversion, based on the set of future extreme climate scenarios and the urban-natural watershed coupled hydrological model. Specifically, it includes: An objective function is constructed with minimizing the total annual carbon emissions from water diversion as the sole objective. Decision variables and auxiliary variables are defined, and various constraints are determined based on the set of future extreme climate scenarios and the urban-natural watershed coupled hydrological model to obtain the low-carbon-ecological synergistic optimization model. The constraints include water balance constraints, water level constraints, pump station capacity constraints, annual total water diversion constraints, gate pump flow constraints, and fuzzy confidence ecological constraints, with the improvement rate of hydraulic retention time as the fuzzy confidence ecological constraint. The candidate scheduling scheme generation module is used to input the inflow runoff sequences of each group into the low-carbon-ecological synergistic optimization model for solving, and obtain multiple candidate scheduling schemes. The optimal scheduling scheme generation module is used to filter the candidate scheduling schemes to obtain the optimal scheduling scheme.

7. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method of any one of claims 1 to 5.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 5.

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