Water system multi-model coupling simulation method, optimization method and computer equipment

Abstract

The application relates to the technical field of water system simulation, in particular to a water system multi-model coupling simulation method, a water system multi-objective optimization method and a computer device. The water system multi-model coupling simulation method obtains water use data of a first simulation time scale output by a water use process model of a target water system and input data format of a second simulation time scale required by a water network dynamics model, performs time scale conversion on the water use data, obtains water intake time series and drainage time series matched with the second simulation time scale, and performs physical constraint consistency verification. After the verification, the water intake time series and the drainage time series are configured as input data of the water network dynamics model, the model is driven to perform simulation, and water safety indexes, water environment indexes and water ecological indexes are calculated. The scheme solves the technical problem of bidirectional fragmentation of the water supply and drainage system and the engineering water network system model, and realizes multi-model deep coupling simulation.

Description

Technical Field

[0001] This application relates to the field of water system simulation technology, and in particular to a multi-model coupled simulation method for water systems, a multi-objective optimization method for water systems, and computer equipment. Background Technology

[0002] Simulations of hydrodynamic and water use processes in water systems typically consist of two models: a water use process model and a water network dynamics model. The water use process model simulates the water intake, use, and drainage activities of various water users (such as agriculture, industry, and urban areas) within a region on an annual or monthly timescale. The water network dynamics model, on the other hand, typically simulates the migration and transformation of water in rivers, reservoirs, and soil on a daily or even hourly timescale. When constructing and optimizing the overall simulation of water systems, the coupling process of these two types of models faces at least the following technical challenges: First, the models differ in both spatiotemporal scale and data format. Specifically, the output of water use process models includes, but is not limited to, the total annual water consumption of a region and its spatial distribution, while the input of water network dynamics models includes, but is not limited to, the daily water intake time series of a specific water intake. These two models are not directly compatible in terms of temporal and spatial scale, data dimensions, and formats. In practice, technicians often need to manually perform complex data extraction, summarization, downscaling, and format conversion and rewriting of the model input files. This process is inefficient, time-consuming, and inaccurate, leading to distorted simulation results.

[0003] Secondly, there is currently a lack of automated verification mechanisms for water system simulation models. For example, the water demand simulated by the water use process model should not exceed the actual water supply capacity of the water network system. However, currently, the two types of models often run independently or after simple splicing. Constraints such as water supply capacity of the water network model cannot be fed back to the water use process simulation in real time and automatically, which can easily lead to ineffective simulations and result in the consumption and waste of a large amount of simulation computing resources.

[0004] Finally, in the water system, water usage affects the state of the water network, while changes in the state of the water network, in turn, constrain and affect water usage. Currently, this type of coupling between the two models is mainly manual, which makes it difficult to support real-time bidirectional interactive simulation, resulting in insufficient dynamic response of the simulation system.

[0005] Therefore, there is an urgent need for a technical solution that deeply couples water use process models with water network dynamics models to solve the technical problems of difficult data integration, low simulation efficiency, lack of constraint real-time verification and bidirectional feedback mechanism caused by the heterogeneity of multiple models. Summary of the Invention

[0006] Therefore, it is necessary to provide a multi-model coupled simulation method for water systems, a multi-objective optimization method for water systems, and computer equipment to address the above-mentioned technical problems.

[0007] Firstly, this application provides a multi-model coupled simulation method for water systems, the method comprising: The water use data at the first simulation time scale output by the water use process model of the target water system, and the input data format at the second simulation time scale required by the water network dynamics model of the target water system, wherein the second simulation time scale is smaller than the first simulation time scale; The water usage data is transformed by time scale to obtain water intake time series and drainage time series that match the second simulation time scale; Perform a physical constraint consistency check between the water intake time series and the water supply capacity data of the target water system; If the verification passes, the water intake time series and drainage time series are configured as the input data for the water network dynamics model according to the input data format. The dynamic model of the water network after the driver configuration is simulated to obtain the state variable data of the water network; Based on the water network state variable data, calculate at least one of the following: water security index, water environment index, and water ecology index.

[0008] In some embodiments, when the first simulation timescale is an annual scale and the second simulation timescale is a daily scale, the timescale conversion includes: Annual water withdrawal Annual scale total drainage Based on the daily distribution coefficient sequence Decomposition yields daily-scale water intake time series. and diurnal scale drainage time series ; The decomposition is achieved using the following formula:

[0009]

[0010] The daily allocation coefficient sequence satisfies .

[0011] In some embodiments, the physical constraint consistency verification is a water resource supply and demand balance verification, which is achieved through the following formula: ; in, For the first Daily water intake of each water-using unit; For the first Daily water supply capacity of each water supply unit; This represents the total number of water-using units. This represents the total number of water supply units.

[0012] In some embodiments, the water security indicators include a flood risk index. It is calculated according to the following formula: ; in For the first t Daily key section river channel liquid level; The safety threshold for the critical section; For indicator functions, it is true if and only if The value is 1 if the condition is met, and 0 otherwise. N Total number of days; And / or, the water environment indicators include the comprehensive pollution index. It is calculated according to the following formula: ; in For the first i Measured concentrations of various pollutants; For the first i Evaluation standard values ​​for various pollutants; Number of pollutant types; And / or, the water ecological indicators include the minimum ecological flow guarantee rate. It is calculated according to the following formula: ; in For the first t Average daily flow; To preset the minimum ecological flow; For indicator functions, it is true if and only if The value is 1 if the condition is met, and 0 otherwise. T This represents the total number of days.

[0013] Secondly, this application also provides a multi-objective optimization method for a water system, the method comprising: S201. Define a decision variable vector; where each decision variable represents the adjustment amount of a controllable element in the target water system, and the controllable element includes the operating parameters of the water network project and / or the water use parameters of the water user. S202. For each set of given values ​​of the decision variable vector, perform the following sub-steps: Based on the given values, generate water use configuration data and water network engineering configuration data; The water use configuration data and water network engineering configuration data are input into the above-mentioned water system multi-model coupled simulation method to obtain the corresponding water security indicators, water environment indicators and water ecology indicators. S203. Based on the water security index, the water environment index, and the water ecology index, generate optimization objectives, and construct a multi-objective optimization problem with the decision variable vector as the optimization variables; use a multi-objective optimization algorithm to iteratively execute S202 to evaluate different combinations of decision variables, optimize the decision variable vector, and obtain the Pareto optimal solution set. S204. Select the optimal configuration parameters from the Pareto optimal solution set based on the entropy method.

[0014] In some embodiments, the water network engineering operation parameters include: at least one of the reservoir's flood control limit water level, beneficial water level, and outflow coefficient; the gate's opening coefficient or opening-time relationship function parameter; and the target flow value of the channel; And / or, the water usage parameters of the water users include: activity level adjustment coefficients and / or water usage efficiency adjustment coefficients for each water user.

[0015] In some embodiments, the optimization objective of the multi-objective optimization problem includes: Minimize the flood risk index, minimize the comprehensive pollution index, and maximize the ecological flow guarantee rate.

[0016] In some embodiments, the multi-objective optimization algorithm is used for optimization, including the following steps: S301, randomly generate multiple candidate decision variable vectors to form the initial population; S302, For each candidate decision variable vector in the current population, the corresponding water security index, water environment index and water ecology index are calculated by the water system multi-model coupling simulation method to form its objective function value vector; S303, based on the objective function value vector of all candidate decision variable vectors, performs non-dominated sorting to divide the current population individuals into different levels of non-dominated frontiers; S304, calculate the crowding distance between individuals within the same non-dominated front; S305, Based on the non-dominated ranking and crowding distance, an elite selection strategy is used to select an individual from the current population as the parent generation; S306 involves crossover and mutation operations on parent individuals to generate offspring populations; S307, merge the parent population with the offspring population, and select individuals of equal size to the initial population to form a new generation population based on non-dominated sorting and crowding distance; S308, repeat S302 to S307 until the preset termination condition is met, and take the non-dominated solution set in the final population as the Pareto optimal solution set.

[0017] In some embodiments, the selection of optimal parameter configuration based on the entropy method includes the following steps: S401, Standardize the performance index values ​​of each scheme in the Pareto optimal solution set: ; in, Let i be the performance index value of the j-th scheme; and These are the maximum and minimum values ​​of the i-th performance metric among all schemes, respectively; Let be the standardized performance index value of the j-th scheme, i.e.

[0018] S402, calculate the information entropy of each performance index: ; in, The information entropy of the i-th performance index value; The number of schemes; S403, calculate the weights of each performance metric: ; in, The weight of the i-th performance metric value; The number of performance indicators; S404, Calculate the comprehensive evaluation value of each scheme: ; in, Let be the comprehensive evaluation value of the j-th scheme; S405, select the scheme with the best comprehensive evaluation value as the optimal parameter configuration.

[0019] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a multi-model coupled simulation method for a water system or a multi-objective optimization method for a water system.

[0020] One of the above technical solutions has the following advantages or beneficial effects: First, addressing the inefficiency and accuracy loss caused by the mismatch between the spatiotemporal scales and data formats of the models, this application automatically converts the annual / monthly water use data output by the water use process model into the daily / hourly scale sequence required by the water network dynamics model through time scale conversion, achieving automatic matching of data format and scale, replacing inefficient and error-prone manual operations. Second, addressing the problem of invalid simulations due to the lack of an automated verification mechanism, this application compares the converted water demand with the system's water supply capacity in real time through physical constraint consistency verification, automatically intercepting infeasible solutions to avoid wasting computational resources. Finally, addressing the problem of the lack of a two-way feedback mechanism, the method proposed in this application automatically configures the verified data as the input to the water network model and drives the simulation, and then uses the simulated water network state variables to calculate various performance indicators, forming a complete automatic closed-loop coupled simulation process in which the water use process affects the water network state and the water network capacity constrains the water demand, which helps to achieve efficient, accurate, and dynamically responsive overall simulation of the water system. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating a multi-model coupled simulation method for a water system in one embodiment; Figure 2 This is a flowchart illustrating a multi-objective optimization method for a water system in one embodiment; Figure 3 This is a flowchart illustrating the steps for obtaining the Pareto optimal solution set in one embodiment; Figure 4 This is a flowchart illustrating the steps for obtaining the optimal parameter configuration in one embodiment; Figure 5 This is a schematic diagram of the structure of a computer device in one embodiment. Detailed Implementation

[0022] 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 and not intended to limit the scope of this application.

[0023] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0024] The multi-model coupled simulation method for water systems provided in this application can be deployed on computing devices such as servers, workstations, or computer clusters with computing capabilities, and implemented by running corresponding simulation and optimization programs. Combined with... Figure 1 The method may specifically include the following steps: S101, Obtain the water usage data at the first simulation time scale output by the water usage process model of the target water system, and the input data format at the second simulation time scale required by the water network dynamics model of the target water system, wherein the second simulation time scale is smaller than the first simulation time scale.

[0025] The target water system refers to the regional water system to be simulated or optimized, including its natural water bodies, water network projects (such as water diversion projects, reservoirs, dams, and canals), and various water users (such as industrial areas, agricultural irrigation areas, and urban residential areas). The spatial scope of the target water system can cover one or more administrative regions, and it has relatively clear water resource input and output boundaries and engineering topology relationships.

[0026] A water use process model can be understood as a computational model used to simulate the entire process of water intake, use, and drainage within a region over a certain time scale (such as year or month). It is a macroscopic process model that can characterize the social side's water resource intake and discharge behavior; the certain time scale here can be understood as the first simulation time scale mentioned above. Water use process models are usually constructed based on material flow analysis, water balance principles, or statistical regression methods. Input parameters may include the activity level of each water-using unit (such as the designed irrigation area of ​​the irrigation district, the designed production capacity of the factory, etc.), the average water use coefficient (water consumption quota per unit of product or service), the level of water-saving technology (the application ratio coefficient of water-saving technology), and the drainage impact rate (the reduction effect of water saving on wastewater generation). The output data of the water use process model, i.e., the water use data mentioned above, may include the total water intake, total drainage, and spatial distribution information of each water intake and drainage outlet in the region.

[0027] One specific implementation of constructing a water use process model can be based on a simplified material flow analysis method to simulate the entire process of water intake, supply, use, drainage, and treatment within a region. A spatial transformation matrix is ​​used to quantify the actual water resource intake and discharge at each intake and drainage point. This model can consist of a main intake and drainage prediction module, a water supply system simulation module, and a drainage system simulation module. Its input data mainly includes the basic activity parameters of each water use unit, the topology and design capacity of the water supply network and facilities, and the layout and process parameters of the drainage network and wastewater treatment facilities.

[0028] In the main water intake and drainage prediction module, the input parameter is the activity level of each water-using unit (such as a plant or irrigation area). Average water consumption coefficient under current process Water-saving technology level And the impact rate of this water-saving technology on drainage volume. ,in and This will serve as the decision variable for subsequent multi-objective optimization. This module calculates the water intake of various entities. Wastewater generation The calculation formula is as follows: ; ; The water supply system simulation module first determines the distribution ratio of the main water consumption in each water supply system service area. Calculate the total water consumption in each service area. . Composition of service area water supply vector series The physical connections between water sources, pumping stations, and water pipelines are generalized into a spatial transformation matrix for the water intake system. The water intake vector sequence is obtained by simulating the water supply project using the following formula. .

[0029] ; ; Among them, matrix The number of rows equals the number of water intake points, and the number of columns equals the total number of water supply service areas. , of which elements Indicates the first The water consumption of each service area comes from the first The proportion of water supply from each source.

[0030] The drainage system simulation module first distributes the wastewater generated by the main body to the corresponding drainage system service areas, thus obtaining the total amount of wastewater in each service area. . Constructing a drainage vector column By generalizing the drainage network topology into a drainage spatial transformation matrix. This simulation demonstrates the entire process of wastewater flowing from its source, through collection and transportation to a treatment plant, and finally to compliant discharge, yielding a wastewater discharge vector at each discharge point. Based on the wastewater treatment plant's design capacity and actual monitoring data, the annual average concentrations of major pollutants in the discharged effluent, including total phosphorus (TP), total nitrogen (TN), ammonia nitrogen (NH3-N), and chemical oxygen demand (COD), were determined and denoted as [missing information]. .

[0031] ; ; Among them, matrix The number of rows is the same as the total number of tailwater discharge points, and the number of columns is the same as the total number of drainage service areas. Same, where elements Indicates the first The sewage from the service area flows to the first The proportion of each emission location.

[0032] A hydrodynamic model can be understood as a model that simulates the movement and transformation of water in rivers, reservoirs, and soil based on physical mechanisms, such as SWAT, MIKE, and HEC-RAS. Its simulation timescale, or second simulation timescale, is typically daily or hourly. The input data format required by a hydrodynamic model can be understood as the specific data structure required by the model, such as the number of columns, units, and time step of a time series file.

[0033] S102, the water usage data is transformed to obtain the water intake time series and drainage time series that match the second simulation time scale.

[0034] Because water use process models often output aggregated data with annual or monthly steps, such as crop planting seasons and industrial production plans which have annual cycles, while water network dynamics models require input with finer daily or hourly steps, such as the evolution of rainstorm peaks and the lag effect of gate scheduling which have rapid intraday changes, there is an inherent time scale gap between the two. Therefore, time scale downscaling is necessary. Specific downscaling methods include, but are not limited to, uniform allocation, weighted allocation, and model-driven methods. Specifically, uniform allocation refers to distributing the annual / monthly total amount equally over the number of days; weighted allocation refers to allocating based on the daily variation coefficients of historical water use / drainage patterns; and model-driven methods refer to constructing an allocation model by incorporating factors such as meteorology and holidays.

[0035] After time scale transformation, the water intake time series and the drainage time series can be obtained. The water intake time series can be the daily water intake volume of each water intake or water user, while the drainage time series can be the daily pollutant load of each discharge outlet or water user.

[0036] S103, perform physical constraint consistency verification between the water intake time series and the water supply capacity data of the target water system.

[0037] To avoid physically infeasible scenarios such as water intake exceeding supply capacity during simulations, and to verify whether the social water demand falls within the feasible region of the engineering water network's physical supply capacity, automated physical constraint consistency verification is required. The supply capacity data can be obtained from the water network dynamics model or monitoring system, and may include reservoir available water volume, river base flow, and groundwater exploitability. Specific methods for physical constraint consistency verification include, but are not limited to, total quantity verification, node verification, and time-series verification. Total quantity verification determines whether the total water intake is less than or equal to the total available water volume, while node verification determines whether the water intake at each node is less than or equal to the node's supply capacity. Time-series verification determines whether the hourly or daily water intake process is within the dynamic range of the supply capacity. Specific physical constraint consistency verification schemes can be configured according to actual needs and are not limited here.

[0038] S104. If the verification passes, configure the water intake time series and drainage time series as the input data for the water network dynamics model according to the input data format.

[0039] The input data format can refer to the standard data structure defined by the water network dynamics model, such as timestamp format (e.g., ISO8601 standard string), spatial coordinate system (e.g., WGS84 geographic coordinates or local plane rectangular coordinates), attribute fields (e.g., water intake point ID, drainage outlet ID, physical quantity identifiers such as flow rate / concentration / temperature), file encapsulation method (e.g., NetCDF, TXT, HDF5 or CSV table), and metadata specifications (e.g., units, precision, source description).

[0040] In this step, once the physical constraint consistency check passes, the system can automatically encapsulate the converted time series data according to the format required by the water network dynamics model and write it into the specified input file or database table, thus completing the automated configuration of the model input.

[0041] S105, drive the configured water network dynamics model to simulate and obtain water network state variable data.

[0042] This step, by calling the execution program or API of the water network dynamics model, drives the model to run, thereby simulating the dynamic response of the water system under a given intake and discharge scenario. The simulated output water network state variable data includes, but is not limited to, time-series data such as flow rate, water level, flow velocity, water temperature, and pollutant concentrations (e.g., COD, ammonia nitrogen, total phosphorus) at key sections. In a specific implementation, the water network state variable data includes, but is not limited to, daily-scale river channel liquid level (unit: m) and daily-scale average flow rate (unit: m³) at key sections. 3 / s), pressure head along the canal (unit: m), reservoir capacity (unit: m³) 3Pollutant concentration (unit: mg / L, where pollutants may include specific pollutants such as TN, TP, COD, etc.) etc.

[0043] S106, Calculate at least one of the following indicators: water security, water environment, and water ecology, based on water network state variable data.

[0044] This step utilizes the simulated water network state variable data to further calculate various performance indicators. Among them, water security indicators can be used to reflect the system's flood control capacity, such as the flood risk index; water environment indicators can be used to reflect the degree of water pollution, such as the comprehensive pollution index; and water ecology indicators can be used to reflect the ecological health level of rivers, such as the ecological flow guarantee rate.

[0045] The above method solves the coupling problem caused by the heterogeneity of spatiotemporal scale and data format between the water use process model and the water network dynamics model. Specifically, through the automated time scale conversion shown in S102, the physical constraint verification shown in S103, and the data format adaptation shown in S104, the automatic closed-loop coupling of the two types of models, the water use process model and the water network dynamics model, is achieved, replacing the inefficient and error-prone manual operation and significantly improving the simulation efficiency and reliability.

[0046] In some embodiments, when the first simulation timescale is an annual scale and the second simulation timescale is a daily scale, the timescale conversion includes: converting the annual total water withdrawal amount... Annual scale total drainage Based on the daily distribution coefficient sequence Decomposition yields daily-scale water intake time series. and diurnal scale drainage time series The decomposition is achieved using the following formula:

[0047] Among them, the daily allocation coefficient sequence satisfies , =365 or 366.

[0048] The daily allocation coefficient sequence is of length [missing information]. A nonnegative real vector, each element Characterizing the first The daily proportion of total annual water consumption / discharge. The daily allocation coefficient sequence can be determined from various sources, such as historical monitoring and statistical methods. Specifically, it can be calculated based on the moving average proportion calculated from daily water intake / discharge measurement data for the region over the past 10 years. Alternatively, it can be determined using industry-specific patterns, such as using crop growth cycle water requirement curves for agricultural irrigation (e.g., rice tillering stage). =0.008, heading stage =0.012, etc.), urban domestic water use adopts the weekday / weekend difference coefficient (e.g., Monday to Friday). =0.0029, Saturday =0.0032, Sunday =0.0027); or it can be determined using meteorological driving methods, piecewise constant forms, trigonometric function fitting forms, etc., to adapt to different accuracy requirements. This implementation method simplifies the complex time series allocation problem into coefficient matrix operations, which is easy to implement in a program and can better preserve the seasonal and periodic characteristics of water use / drainage.

[0049] In some embodiments, the physical constraint consistency check is a water resource supply and demand balance check, which is implemented by the following formula: ; in, For the first Daily water intake of each water-using unit; For the first Daily water supply capacity of each water supply unit; This represents the total number of water-using units. This represents the total number of water supply units.

[0050] in, This can be understood as the daily water intake time series obtained after the above time scale transformation, corresponding to the first... The daily water intake value of a water user unit, in physical terms, represents the net water demand that the water user unit actually requests within a single simulated daily timeframe, which must be met by the water network system. The unit can be m³. 3 / day; This value is derived from the annual total water withdrawal output by the water use process model, and is obtained by weighted decomposition of the daily allocation coefficient sequence.

[0051] This can be understood as the first in the water network dynamics model The maximum stable water supply that a single water supply unit can provide within a single day's simulation step, in cubic meters (m³). 3 / day; its value can be determined by the physical properties and operating status of the water supply unit, for example, for reservoir-type water supply units, ,in This can be the reservoir capacity per day (t). The water level can be found by looking up the corresponding reservoir capacity-water level relationship curve. For inbound flow, This refers to the upper limit of outflow determined according to the scheduling rules; for water supply units of water diversion projects, The flow capacity can be determined by the channel cross-sectional dimensions, roughness, gradient, and the constraint of maximum allowable flow velocity; for groundwater well groups, then... Limited by the exploitable aquifer volume and the pumping capacity of a single well; the source of this value includes, but is not limited to, the calculation based on the superposition of natural baseflow and reservoir discharge output from the SWAT model, the extraction based on the real-time hydraulic calculation results of the MIKE HYDRO River, or the lookup of a table through a pre-calibrated water supply capacity response surface (with rainfall, previous soil moisture content, and reservoir storage status as input variables), and there are no restrictions here.

[0052] It represents the total water demand of all water-using units in the system on a daily scale, reflecting the instantaneous pressure of social water use behavior on the water resource system; This represents the total water supply capacity of all water supply units in the system on the same daily scale, reflecting the spatiotemporal reallocation capability of the engineering water network under current operating conditions; both are scalars and have the same dimensions, and this verification occurs within the same calendar day. and All simulations have been aligned to a unified daily step size through time scale conversion and a second simulation time scale. This verification can be performed daily or during critical periods. If the inequality holds true, the simulation passes; otherwise, it is deemed physically infeasible, and the simulation path is terminated.

[0053] This approach abstracts the complex water resource system balance problem into simple mathematical constraints, making it easy to integrate into automated simulation processes and ensuring the physical rationality of simulation results.

[0054] In some embodiments, water security indicators include flood risk indices. It is calculated according to the following formula: ; in For the first t Daily key section river channel liquid level; The safety threshold for critical sections; For indicator functions, it is true if and only if The value is 1 if the condition is met, and 0 otherwise. N This represents the total number of days.

[0055] It should be explained that the flood risk index is a quantitative indicator that addresses the dual management needs of flood control and urban waterlogging. Physically, it represents the sum of the cumulative excess water depth on all days exceeding the limit during the assessment period, measured in meters per day (m·d). This index reflects not only the frequency of flood events but also the cumulative effect of their intensity and duration. In other words, the greater the daily excess water depth and the more days exceeding the limit, the higher the flood risk index value, and the correspondingly higher the system's flood risk level.

[0056] In practice, the selection of key sections can be determined based on the importance of hydrological nodes in the basin, including but not limited to downstream control sections of reservoirs, outlets of main drainage channels in urban flood-prone areas, and water diversion points of inter-basin water transfer projects; safety thresholds can be comprehensively verified based on historical defense standards (such as water levels corresponding to a 50-year flood), permissible water levels for engineering structure safety, or urban road elevations, among other multi-source information.

[0057] In some embodiments, water environment indicators include a comprehensive pollution index. It is calculated according to the following formula: ; in For the first i Measured concentrations of various pollutants; For the first i Evaluation standard values ​​for various pollutants; This represents the number of pollutant types.

[0058] The comprehensive pollution index can be understood as a composite water quality evaluation index, which consists of two components, the first being the first term in the numerator. The first characterizes the ratio of the single pollutant with the most severe exceedance among all monitored pollutants, i.e., the maximum single exceedance rate, reflecting the weakest link effect in the system's water quality risk; the second is... This is the arithmetic mean of the degree to which each pollutant exceeds the standard, reflecting the overall pollution load level of the area. The geometric mean of both is obtained by squaring them, which avoids a single extremely high value dominating the evaluation result and prevents the average value from masking prominent pollution problems.

[0059] Furthermore, in specific implementation, the selection of pollutant types can be determined based on the "Surface Water Environmental Quality Standard" or local water function zoning requirements, including but not limited to total nitrogen (TN), total phosphorus (TP), and ammonia nitrogen (NH3). The evaluation criteria are categorized into four types: nitrogen oxides (NOx), chemical oxygen demand (COD), and nitrogen oxides (NOx). Values ​​can be selected according to the water function zone category. The number of pollutant types is usually 4, but it can be expanded to include emerging pollutants such as heavy metals and microplastics based on actual monitoring capabilities. In this case, the formula structure remains unchanged. The values ​​can be updated accordingly.

[0060] In some embodiments, water ecological indicators include the minimum ecological flow guarantee rate. It is calculated according to the following formula: ; in For the first t Average daily flow; To preset the minimum ecological flow; For indicator functions, it is true if and only if The value is 1 if the condition is met, and 0 otherwise. T This represents the total number of days.

[0061] In this embodiment, the minimum ecological flow guarantee rate is an eco-hydrological indicator directly related to the river's ability to maintain health. Essentially, it represents the percentage of days during the assessment period that meet basic ecological water requirements, output as a percentage. Within this indicator, average flow... The daily flow sequence of key sections can be obtained from the output of the hydrodynamic model, which can be the daily average value, time-period average value, or measured value of the instantaneous flow at the section. A minimum ecological flow is preset. The setting can follow the relevant technical specifications for determining river and lake ecological flow, and can be derived using methods such as the Tennant method, wetted perimeter method, and R2Cross method. Alternatively, it can be dynamically set based on ecologically sensitive periods such as fish spawning season and wetland vegetation growth period.

[0062] It should be explained that in areas with abundant water resources and sufficient ecological base flow guarantee but significant non-point source pollution, only [the following] may be activated. Conduct optimization effect evaluation; in river basins where floods are frequent during the flood season and engineering scheduling pressure is high, focus can be placed on Optimization; in ecologically sensitive areas or national parks, joint efforts are possible. and Construct a dual-objective constraint of flood control and ecology; all three can also be fully utilized to form a three-dimensional assessment system of water security, water environment, and water ecology.

[0063] It should be noted that in some alternative or scalable embodiments, water efficiency can also be considered, measured in terms of water consumption per unit of GDP ( The economic output efficiency per unit area of ​​land is an important indicator for measuring the comprehensive utilization efficiency and development intensity of land. The calculation method is shown in the formula: ; It refers to the gross domestic product of a cross-basin region; It refers to the total water consumption within the region.

[0064] In some embodiments, this application also provides a multi-objective optimization method for water systems, such as... Figure 2 As shown, it includes: S201. Define a decision variable vector; where each decision variable represents the adjustment amount of controllable elements in the target water system, including water network engineering operation parameters and / or water use parameters of water users.

[0065] It should be explained that the decision variable vector in S201 is a high-dimensional vector, where each element represents the adjustment amount to a certain controllable element in the target water system. Specifically, controllable elements include, but are not limited to, the operating parameters of the water network project and the water usage parameters of the water users.

[0066] Among them, the water network project operation parameters are used to characterize the dynamic optimization and control capabilities of the project facilities in real-time operation. Optional parameters include the flood control limit water level, beneficial water level, and outflow coefficient of the reservoir, the opening coefficient or opening-time relationship function parameter of the gate, and the target flow value of the channel.

[0067] The flood control limit water level of a reservoir refers to the upper limit of water level that the reservoir is allowed to store during the flood season to ensure flood control safety. Its value is determined by the characteristics of the basin's rainstorm and flood, the importance of downstream flood control targets, and the reservoir's flood regulation capacity. The typical range is 0.5m–3.0m below the normal storage water level, which can be obtained through reservoir operation regulations or hydrological calculations. As a key constraint variable of the reservoir boundary conditions in the water network dynamics model, this water level dynamically limits the increase in reservoir capacity in the simulation, affecting the adjustable storage space and discharge decisions in subsequent periods.

[0068] The beneficial water level refers to the lower limit of the normal operating water level range set by a reservoir to meet the beneficial tasks of water supply, irrigation, and power generation. It usually corresponds to the top elevation of the dead storage capacity. Its value depends on the water demand process of the irrigation area, the water supply guarantee rate requirements, and the prediction of sediment deposition. In the water network dynamics model, this water level is used to define the effective regulating capacity and participates in the interpolation calculation of the reservoir water level-storage capacity-outflow relationship curve.

[0069] The outflow coefficient is a dimensionless parameter characterizing the water release capacity of a reservoir. It is defined as the ratio of the actual outflow to the theoretical maximum discharge capacity. Its physical connotation reflects comprehensive hydraulic conditions such as gate opening degree, roughness of discharge facilities, and water level difference between upstream and downstream. In the model, this coefficient is coupled with the current water level and the target water level difference. The instantaneous outflow is calculated in real time using the Manning formula or empirical discharge equation. The typical value range is 0.1–1.0.

[0070] The gate opening coefficient is the ratio of the actual opening height of the gate to the maximum opening height. Its value ranges from 0 to 1. Its value directly determines the cross-sectional area of ​​the flow. In the water network dynamics model, it is used as a boundary driving variable and participates in the boundary condition assignment of the unsteady flow equation of the open channel.

[0071] The parameters of the gate opening-time relationship function refer to the adjustable parameters in the mathematical function used to describe the change of gate opening over time, such as linear functions. slope in With intercept ; sine function amplitude in angular frequency Initial phase and offset ; or the coefficients of each segment of a piecewise polynomial function; these parameters participate in the optimization process as decision variables, enabling the gate scheduling strategy to have temporal flexibility.

[0072] The target flow value of a channel refers to the average cross-sectional flow that is expected to be maintained under the design conditions of the water transmission and distribution channel, with the unit being m³ / s. Its value is determined by the total water demand of the service area, the water transmission loss rate, and the safety margin. In the water network dynamics model, this value serves as the downstream boundary condition or node constraint condition, driving the channel hydraulic calculation module to invert the upstream water level and gate opening.

[0073] Furthermore, the water use parameters of the water users are used to characterize the plasticity and responsiveness of water use behavior on the social side. Specifically, the activity level adjustment coefficient and water use efficiency adjustment coefficient of each water user can be selected.

[0074] Specifically, the activity level adjustment coefficient for each water user can refer to a dimensionless factor that scales the baseline load of the water user, used to quantify the impact of changes in its scale on the total water consumption of the system. Examples include the irrigation area adjustment coefficient for irrigation districts (values ​​can be selected from 0.6 to 1.4), the capacity utilization rate coefficient for industrial parks (values ​​can be selected from 0.5 to 1.2), and the urban population growth coefficient (values ​​can be selected from 0.9 to 1.3). This coefficient is directly multiplied into the baseline water consumption in the water use process model. This will generate the optimized water intake.

[0075] The water efficiency adjustment coefficient is a correction factor that reflects the impact of the degree of application of water-saving technology on the water consumption per unit of product / service. Its essence is the rate of reduction in water consumption quota brought about by water-saving measures. The value range can be 0.3–1.0. The smaller the value, the higher the level of water saving. This coefficient is coupled with the water consumption quota and can jointly determine the actual water intensity. In the model, its change simultaneously affects the water intake and drainage.

[0076] Both types of parameters can be uniformly represented as a real number vector, that is Among them, among them Characterizing the first Basic load capacity of each water-using unit Characterizing the differences in their water-saving technology levels, Characterizing the first The intensity of regulation or the construction status of individual water network engineering facilities can be used This indicates that the project has not been started or is remaining unchanged. Characterizing the total number of water-using entities, From 1 to integers, Characterizing the total number of water network construction projects, From 1 to The vector structure supports linear and nonlinear combinations for expansion, such as the introduction of cross terms. This characterizes the synergistic amplification relationship between scale effects and efficiency improvements; alternatively, some parameters can be limited to integers, such as... This indicates whether a project should be constructed, to adapt to discrete decision-making scenarios.

[0077] S202. For each set of given values ​​of the decision variable vector, perform the following sub-steps: Based on the set of given values, generate water use configuration data and water network engineering configuration data; input the water use configuration data and water network engineering configuration data into the water system multi-model coupling simulation method provided in any of the above embodiments to obtain the corresponding water security indicators, water environment indicators and water ecology indicators.

[0078] In S202, the decision variable vector defined in S201 is assigned specific values, which triggers the configuration data generation process. This process can be divided into two parallel branches: one is the generation of water configuration data, which can be based on the above... and The calculations involve two aspects: first, calculating the total daily water intake and discharge for each water-using unit; and second, generating water network engineering configuration data, which is based on the above. The boundary conditions and intrinsic parameters of the water network dynamics model are updated, such as reconfiguring the reservoir flood control level or gate opening coefficient. The data from both branches, after being encapsulated in a standardized format, can be used as input to the multi-model coupled simulation method of the water system in any of the above embodiments. Specifically, this method first performs time scale transformation, then conducts physical constraint consistency verification, subsequently drives the water network dynamics model simulation, and finally outputs a flood risk index. Comprehensive pollution index and minimum ecological flow guarantee rate .

[0079] S203. Based on water security indicators, water environment indicators, and water ecology indicators, generate optimization objectives and construct a multi-objective optimization problem with the decision variable vector as the optimization variable. Use a multi-objective optimization algorithm to iteratively execute S202 to evaluate different combinations of decision variables, optimize the decision variable vector, and obtain the Pareto optimal solution set.

[0080] This step aims to construct a multi-objective optimization problem using the aforementioned decision variable vector as the optimization variable and water security, water environment, and water ecology indicators as optimization objectives. It then employs multi-objective optimization algorithms (such as NSGA-II, MOEA / D, etc.) for iterative optimization. By repeatedly calling S202 to evaluate different decision combinations, the Pareto optimal solution set is finally obtained, which is a set of compromise solutions that cannot be improved on any objective.

[0081] Specifically, the multidimensional indices output by S202 are organized into a set of objective functions in S203, and together with the decision variable vector, they form a formalized multi-objective optimization problem. The mathematical expression of this problem is: .

[0082] In a specific embodiment of this application, a quantitative assessment of the socio-economic impacts of water network projects and water-saving measures can be further integrated and used as one of the optimization objectives, thereby achieving multi-dimensional synergistic optimization of water security, water environment, water ecology, and economic benefits. In this embodiment, when constructing the multi-objective optimization problem in S203, it not only includes the aforementioned water security indicators... Water environment indicators and water ecological indicators It will also include water consumption per unit of GDP Incorporating it into the optimization objective system, the optimization problem can be expressed as: .

[0083] Among these constraints, hard constraints on the balance between water supply and demand may be included. Engineering physical feasibility constraints (such as the reservoir water level must not be lower than the dead water level, and the gate opening degree must be between 0 and 100%) and socio-economic rationality constraints (such as restrictions on the activity level adjustment coefficient).

[0084] Furthermore, multi-objective optimization algorithms (such as NSGA-II) act as search engines in this process. A specific iterative optimization step could be to construct an initial population, which can be randomly generated. The decision variables are composed of groups. In each generation of evolution, the frontier level is first identified by non-dominated ranking, and then the crowding distance of individuals within the same frontier is calculated to maintain the diversity of the solution set. Subsequently, an elite selection strategy is adopted to retain high-quality individuals, and offspring are generated through simulated binary crossover and polynomial mutation. After merging the parent and offspring, a new generation of population is selected based on the non-dominated level and crowding. This process continues to iterate until the maximum number of generations or the convergence threshold is met. The final Pareto optimal solution set shows the essential trade-off relationship between the three types of indicators and is a set of non-dominated solutions rather than a unique optimal solution.

[0085] S204. Select the optimal configuration parameters from the Pareto optimal solution set based on the entropy method.

[0086] This step uses the entropy method to objectively determine the weights of each indicator from the Pareto optimal solution set, calculates the comprehensive evaluation value of each scheme, and selects the comprehensive best as the final optimized scheme. Thus, the Pareto optimal solution set obtained in S203 can achieve objective optimization in this step through the entropy method. This process strictly follows the data-driven principle, does not introduce any prior weights or subjective preferences, and ensures the objectivity of the scheme selection.

[0087] Through the above-described steps, this application achieves a leap from single-point engineering optimization to collaborative governance of social-engineering systems in multi-objective optimization of water systems. This application defines a high-dimensional decision variable vector encompassing both water network engineering operation parameters and water user parameters, solving the technical problems of traditional methods' single-dimensional decision variables and inability to respond to changes in social water use behavior. This enables the optimization scheme to simultaneously coordinate physical water network construction and the transformation of social water use structure. By embedding the multi-model coupled simulation method constructed above as the core evaluation engine in S202, the application solves the current technical problems of lacking dynamic closed-loop feedback and index calculations deviating from the actual system response, ensuring that all optimization results are based on coupled simulation of multiple processes involving water quantity, water quality, and water ecology. Furthermore, by constructing a multi-objective optimization problem with water security, water environment, and water ecology as three-dimensional objectives and solving it using the NSGA-II algorithm, the application addresses the technical problems of one-sided optimization objectives and difficulty in revealing the inherent trade-offs between indicators, providing a quantifiable scheme selection space for cross-basin regional water resource management. Finally, by introducing the entropy method to objectively weight and optimize the Pareto solution set, the technical problems of strong subjectivity and poor repeatability of multi-objective solution set evaluation and selection are solved, and the scientific nature of the optimization results is significantly improved.

[0088] In some embodiments, the optimization objectives of the multi-objective optimization problem include: minimizing the flood risk index, minimizing the comprehensive pollution index, and maximizing the ecological flow guarantee rate.

[0089] Minimizing the flood risk index: As shown in the aforementioned formula, the flood risk index is a quantitative indicator representing the probability and potential loss of flood disasters caused by water levels exceeding the standard in key sections of a region. This index represents the total risk by weighting the daily water level exceedance magnitude and duration; a higher value indicates stronger flood pressure. After inputting the time-scale-transformed water intake / drainage time series and the water supply capacity constraint verification results into the water network dynamics model, the output is the daily liquid level sequence of key sections. Substituting into the above formula yields... As one of the optimization objectives, minimizing the flood risk index means that, under the premise of meeting the hard constraints of water resource supply and demand, we can actively reduce the peak water level during high-risk periods and shorten the duration of exceeding the warning level by optimizing decision variables (such as reservoir flood control level, gate opening, and the activity level of water users), thereby reducing the overall flood exposure of the system.

[0090] Minimizing the overall pollution index: As can be seen from the aforementioned formula, the comprehensive pollution index is a comprehensive evaluation index of water environmental quality that integrates the degree of pollutant concentration exceeding standards and the average pollution load. In this embodiment, this index is output by the coupled simulation method described in the above embodiment. After incorporating the drainage time series and tailwater pollutant concentration, the water network dynamics model simulates and calculates the daily pollutant concentration series of key sections. Substituting into the aforementioned formula yields the result. As an optimization objective, minimization aims to achieve this through coordinated regulation of the intensity of water-saving technology application (such as reducing wastewater generation) and the upgrading and renovation of wastewater treatment plants (such as improving...). Measures such as improving the removal rate of various components and enhancing the dilution capacity of the water network (e.g., accelerating the transport and diffusion of pollutants) are used to simultaneously suppress the peak and average pollution levels.

[0091] To maximize the ecological flow guarantee rate: As shown in the aforementioned formula, the ecological flow guarantee rate refers to the percentage of time during which the minimum downstream flow required to ensure the basic structure and function of the river ecosystem is met. This indicator directly reflects the level of ecological base flow guarantee as a percentage; a higher value indicates stronger stability of the aquatic ecosystem. After configuring the water intake / discharge sequence and completing physical constraint verification, the hydrodynamic model simulates and generates daily flow sequences for key sections. Substituting into the formula yields... As an optimization goal, the maximum requirement is to prioritize ensuring the rigid demand for ecological water under the constraint of total water resources. This can be achieved by optimizing the water level setting for the benefit of reservoirs (such as reserving more space for ecological water storage), adjusting the water allocation ratio of inter-basin water transfer projects (such as increasing the share of ecological water replenishment), and regulating the reuse path of agricultural irrigation runoff (such as reducing reliance on river water intake).

[0092] Through the above-described steps, this application achieves synergistic optimization of multiple objectives in the water system. Minimizing the flood risk index, minimizing the comprehensive pollution index, and maximizing the ecological flow guarantee rate constitute a logically consistent and dimensionally complementary objective system: the former two focus on suppressing negative effects, i.e., mitigating disasters and pollution, while the latter aims to enhance positive effects, i.e., improving ecological health. This objective combination is deeply integrated with the coupled simulation model constructed in the above embodiments. All indicators are directly and rigorously calculated from the state variables output by the water network dynamics model, ensuring that the optimization process is entirely driven by physical mechanisms and avoiding empirical parameter drift and subjective weighting bias. Simultaneously, all three objectives can be effectively responded to by the decision variable vector defined in this application, enabling multi-objective optimization algorithms such as NSGA-II to efficiently search for the Pareto front within a unified solution space.

[0093] In some embodiments, such as Figure 3 As shown, a multi-objective optimization algorithm is used to solve the problem, including the following steps: S301, randomly generate multiple candidate decision variable vectors to form the initial population; S302, For each candidate decision variable vector in the current population, the corresponding water security index, water environment index and water ecology index are calculated by the multi-model coupled simulation method of water system to form its objective function value vector; S303, based on the objective function value vector of all candidate decision variable vectors, performs non-dominated sorting to divide the current population individuals into different levels of non-dominated frontiers; S304, calculate the crowding distance between individuals within the same non-dominated front; S305, based on non-dominated ranking and crowding distance, uses an elite selection strategy to select individuals from the current population as parents; S306 involves crossover and mutation operations on parent individuals to generate offspring populations; S307, merge the parent population with the offspring population, and select individuals of equal size to the initial population to form a new generation population based on non-dominated ranking and crowding distance; S308, repeat S302 to S307 until the preset termination condition is met, and take the non-dominated solution set in the final population as the Pareto optimal solution set.

[0094] Through the above steps, this application achieves a deep embedding of the multi-model coupled simulation method for water systems into its core evaluation loop, using the NSGA-II algorithm as the framework. This ensures that each evolutionary operation is based on a real and physically consistent system response. Through diversified initialization in S301, dual-criteria sorting and distance measurement in S303-S304, elite preservation in S305, directional perturbation in S306, mixed population environment selection in S307, and iterative convergence control in S308, a multi-objective optimization execution process is formed, providing a standardized solution paradigm with engineering implementation capabilities for the coordinated regulation of cross-basin water networks and water-related industries.

[0095] In some embodiments, such as Figure 4 As shown, selecting the optimal parameter configuration based on the entropy method includes the following steps: S401, standardizes the performance index values ​​of each scheme in the Pareto optimal solution set: ; in, Let i be the performance index value of the j-th scheme; and These are the maximum and minimum values ​​of the i-th performance metric among all schemes, respectively; Let be the standardized performance index value of the j-th scheme, i.e.

[0096] S402, calculate the information entropy of each performance index: ; in, The information entropy of the i-th performance index value; The number of schemes; S403, calculate the weights of each performance metric: ; in, The weight of the i-th performance metric value; The number of performance indicators; S404, Calculate the comprehensive evaluation value of each scheme: ; in, Let be the comprehensive evaluation value of the j-th scheme; S405, select the scheme with the best comprehensive evaluation value as the optimal parameter configuration.

[0097] The process first performs min-max standardization on the index values ​​of each scheme in the solution set to eliminate dimensional differences; secondly, it calculates the information entropy of each index, with smaller entropy values ​​indicating greater variability and richer discriminative information provided; then, it derives the index weights to ensure that weight allocation is entirely determined by the data itself; finally, it calculates the comprehensive evaluation value of each scheme and selects... The maximum value represents the optimal parameter configuration. This mechanism effectively avoids cognitive biases and vested interests commonly found in expert scoring, and possesses good robustness. When a certain indicator's value approaches consistency across all options (i.e., ...), ... Its weight automatically approaches zero, thereby achieving adaptive identification of the importance of the indicator.

[0098] This application achieves a closed loop from simulation evaluation to optimization decision-making by closely integrating multi-model coupled simulation methods for water systems with multi-objective water system optimization and control methods. Specifically, it expands the control dimensions by simultaneously using engineering parameters and water use parameters as decision variables, and achieves synergistic improvement of multiple objectives by incorporating various indicators such as water security, water environment, and water ecology into the optimization objectives. Finally, it provides decision-makers with a scientific and operable control scheme through objective optimization using the entropy method.

[0099] In some embodiments, such as Figure 5 As shown, this application also provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the water system multi-model coupled simulation method or the water system multi-objective optimization method of any of the above embodiments.

[0100] The internal structure diagram of this computer device can be as follows: Figure 5As shown, the computer device includes a processor, memory, input / output interfaces, and a communication interface. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the input / output interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a multi-model coupled simulation method or a multi-objective optimization method for a water system.

[0101] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0102] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0103] It should be noted that, in the embodiments of this application, certain existing solutions in the industry, such as software, components, and models, may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions. All methods described in this application are entirely independently developed executable software algorithms. All software algorithms are implemented using general-purpose high-level languages, such as C++ and Python. The development environments, such as Visual Studio Community Edition and PyCharm Community Edition, are free and publicly available software, and do not involve software licensing or other intellectual property issues.

[0104] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification. In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0105] The terms “comprising” and “having”, and any variations thereof, in the embodiments herein are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or (module) units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0106] In this article, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0107] The terms "first" and "second" used herein are merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first" and "second" can be interchanged in a specific order or sequence where permissible. It should be understood that the objects distinguished by "first" and "second" can be interchanged where appropriate so that the embodiments described herein can be implemented in an order other than those illustrated or described herein.

[0108] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A multi-model coupled simulation method for water systems, characterized in that, include: The water use data at the first simulation time scale output by the water use process model of the target water system, and the input data format at the second simulation time scale required by the water network dynamics model of the target water system, wherein the second simulation time scale is smaller than the first simulation time scale; The water usage data is transformed by time scale to obtain water intake time series and drainage time series that match the second simulation time scale; Perform a physical constraint consistency check between the water intake time series and the water supply capacity data of the target water system; If the verification passes, the water intake time series and drainage time series are configured as the input data for the water network dynamics model according to the input data format. The dynamic model of the water network after the driver configuration is simulated to obtain the state variable data of the water network; Based on the water network state variable data, calculate at least one of the water security index, water environment index, and water ecology index; The water security indicators include the flood risk index. It is calculated according to the following formula: ； in, For the first t Daily key section river channel liquid level; The safety threshold for the critical section; For indicator functions, it is true if and only if The value is 1 if the condition is met, and 0 otherwise. N Total number of days; And / or, the water environment indicators include the comprehensive pollution index. It is calculated according to the following formula: ； in, For the first i Measured concentrations of various pollutants; For the first i Evaluation standard values ​​for various pollutants; Number of pollutant types; And / or, the water ecological indicators include the minimum ecological flow guarantee rate. It is calculated according to the following formula: ； in, For the first t Average daily flow; To preset the minimum ecological flow; For indicator functions, it is true if and only if The value is 1 if the condition is met, and 0 otherwise. T This represents the total number of days.

2. The method according to claim 1, characterized in that, When the first simulation timescale is an annual scale and the second simulation timescale is a daily scale, the timescale conversion includes: Annual water withdrawal Annual scale total drainage Based on the daily distribution coefficient sequence Decomposition yields daily-scale water intake time series. and diurnal scale drainage time series ; The decomposition is achieved using the following formula: The daily allocation coefficient sequence satisfies .

3. The method according to claim 1, characterized in that, The physical constraint consistency verification is a water resource supply and demand balance verification, which is achieved through the following formula: ； in, For the first Daily water intake of each water-using unit; For the first Daily water supply capacity of each water supply unit; This represents the total number of water-using units. This represents the total number of water supply units.

4. A multi-objective optimization method for a water system, characterized in that, include: S201. Define the decision variable vector; Each decision variable represents the adjustment amount to controllable elements in the target water system, including water network engineering operation parameters and / or water use parameters of water users. S202. For each set of given values ​​of the decision variable vector, perform the following sub-steps: Based on the given values, generate water use configuration data and water network engineering configuration data; The water use configuration data and water network engineering configuration data are input into the water system multi-model coupled simulation method as described in any one of claims 1 to 3 to obtain the corresponding water security indicators, water environment indicators and water ecological indicators. S203. Based on the water security index, the water environment index, and the water ecology index, generate optimization objectives, and construct a multi-objective optimization problem with the decision variable vector as the optimization variables; use a multi-objective optimization algorithm to iteratively execute S202 to evaluate different combinations of decision variables, optimize the decision variable vector, and obtain the Pareto optimal solution set. S204. Select the optimal configuration parameters from the Pareto optimal solution set based on the entropy method.

5. The method according to claim 4, characterized in that, The operating parameters of the water network project include: at least one of the following: the flood control limit water level, the beneficial water level, and the outflow coefficient of the reservoir; the opening coefficient or the opening-time relationship function parameter of the gate; and the target flow value of the channel. And / or, the water usage parameters of the water users include: activity level adjustment coefficients and / or water usage efficiency adjustment coefficients for each water user.

6. The method according to claim 4, characterized in that, The optimization objectives of the multi-objective optimization problem include: Minimize the flood risk index, minimize the comprehensive pollution index, and maximize the ecological flow guarantee rate.

7. The method according to claim 4, characterized in that, The optimization solution using the aforementioned multi-objective optimization algorithm includes the following steps: S301, randomly generate multiple candidate decision variable vectors to form the initial population; S302, For each candidate decision variable vector in the current population, the corresponding water security index, water environment index and water ecology index are calculated by the water system multi-model coupling simulation method to form its objective function value vector; S303, based on the objective function value vector of all candidate decision variable vectors, performs non-dominated sorting to divide the current population individuals into different levels of non-dominated frontiers; S304, calculate the crowding distance between individuals within the same non-dominated front; S305, Based on the non-dominated ranking and crowding distance, an elite selection strategy is used to select an individual from the current population as the parent generation; S306 involves crossover and mutation operations on parent individuals to generate offspring populations; S307, merge the parent population with the offspring population, and select individuals of equal size to the initial population to form a new generation population based on non-dominated sorting and crowding distance; S308, repeat S302 to S307 until the preset termination condition is met, and take the non-dominated solution set in the final population as the Pareto optimal solution set.

8. The method according to claim 4, characterized in that, The method of selecting the optimal parameter configuration based on entropy value includes the following steps: S401, Standardize the performance index values ​​of each scheme in the Pareto optimal solution set: ； in, Let i be the performance index value of the j-th scheme; and These are the maximum and minimum values ​​of the i-th performance metric among all schemes, respectively; Let be the standardized performance index value of the j-th scheme; S402, calculate the information entropy of each performance index: ； in, The information entropy of the i-th performance index value; The number of schemes; S403, calculate the weights of each performance metric: ； in, The weight of the i-th performance metric value; The number of performance indicators; S404, Calculate the comprehensive evaluation value of each scheme: ； in, Let be the comprehensive evaluation value of the j-th scheme; S405, select the scheme with the best comprehensive evaluation value as the optimal parameter configuration.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 3 or claims 4 to 8.

Images