Urban lake group low-carbon regulation and control method and system

Abstract

The invention provides an urban lake group low-carbon regulation and control method and system. The method comprises the following steps: acquiring geographic space, terrain, weather, hydrological observation and drainage basin management data of a target lake; on the basis, lake inflow and pollutant concentration data are calculated; simulating and calculating flow field distribution and water quality index data of the target lake by combining topographic data of the target lake; constructing an optimization model aiming at minimizing gate pump energy consumption and maximizing lake water quality improvement; and solving the optimization model based on the lake inlet flow, the pollutant concentration, the flow field distribution, the water quality index, the gate pump power coefficient, the gate pump flow, the lift and the seasonal duration to obtain gate pump regulation and control parameter set data so as to realize low-carbon regulation and control of the urban lake group. According to the urban lake group low-carbon regulation and control method and system provided by the invention, seasonal change can be allocated to optimize water quantity, lake inlet flow can be adjusted, water quality can be improved, water fluidity can be enhanced, lake ecological environment restoration can be promoted, energy consumption can be reduced, and green low-carbon treatment can be realized.

Description

Technical Field

[0001] The present invention relates to the field of urban lake group water resource management and ecological environment improvement, and in particular to a low-carbon regulation method and system for urban lake groups. Background Art

[0002] Urban lakes are of great significance in the ecosystem, and they have functions such as landscape beautification, rain and sewage reception, and flood regulation. However, with the rapid advancement of urbanization, urban lakes are facing severe challenges. The overly dense urban population and complex surrounding environment have exacerbated the problems of water pollution and ecological degradation in lakes. Many lake water systems have poor connectivity, insufficient water flow exchange, and slow water body flow. Especially in summer when the temperature is high, dead water areas are easily formed, and a large number of algae multiply to cause water blooms, seriously damaging the water body health and ecological balance. Currently, the methods for improving urban lakes mainly include physical, chemical, and ecological measures. Physical measures such as trash dams and sedimentation tanks can remove some pollutants, but they cannot fundamentally solve the problems of water body fluidity and water quality deterioration; chemical measures such as adding flocculants and oxidants can quickly remove some pollutants, but they are prone to cause secondary pollution, and the operation is complex and the cost is high; ecological measures such as phytoremediation and microbial remediation rely on natural processes, have slow effects, and are sensitive to environmental conditions, and have limited effects in lakes with poor water quality and poor fluidity. And in terms of water resource scheduling, many urban lakes lack an effective scheduling mechanism and cannot flexibly adjust the water volume flowing into the lake inlet according to seasonal changes. The traditional single sluice and dam regulation method lacks a scientific basis, can neither accurately control the water body fluidity nor effectively improve the water quality. Moreover, this regulation method does not fully consider the energy consumption problem. Frequent starting and stopping of sluice pumps not only wastes energy but also may damage the ecological balance, violating the concepts of green low-carbon and sustainable development.

[0003] Under the background of the existing technology, the urban lake treatment measures relying on physical, chemical, and ecological measures have limited effects. At the same time, the traditional water resource scheduling mechanism lacks scientificity, mostly relies on single sluice and dam regulation, and does not fully consider the impact of energy consumption and lake ecological environment. Under the action of such comprehensive factors, problems such as water quality deterioration, a large number of algae multiplying to form water blooms, ecological system imbalance, and energy waste will occur in urban lakes, seriously affecting the ecological functions and sustainable development of urban lakes. Summary of the Invention

[0004] The present invention aims to provide a low-carbon regulation method and system for urban lake groups to solve the above technical problems, avoid being unable to effectively coordinate various factors in the face of complex lake hydrological characteristics and poor water body fluidity, optimize water volume allocation through seasonal changes, flexibly adjust the flow rate at the lake inlet, improve water quality, enhance water body fluidity, and promote the restoration of the lake ecological environment, further reduce energy consumption, and achieve the green low-carbon governance goal.

[0005] To solve the above technical problems, the present invention provides a low-carbon regulation method for urban lake groups, comprising the following steps:

[0006] Obtain the geospatial data, topographic data, meteorological data, hydrological observation data, and watershed management data of the target lake;

[0007] Based on the geospatial data, meteorological data, hydrological observation data, and watershed management data, calculate the inflow data and pollutant concentration data of the target lake;

[0008] Based on the topographic data, meteorological data, inflow data, and pollutant concentration data of the target lake, simulate and calculate the flow field distribution data and water quality index data of the target lake;

[0009] Construct an optimization model with the goal of minimizing the energy consumption of sluice pumps and maximizing the improvement of lake water quality;

[0010] Based on the inflow data, pollutant concentration data, flow field distribution data of the target lake, water quality index data, sluice pump power coefficient, sluice pump flow data, sluice pump head data, and season duration, solve the optimization model to obtain the sluice pump regulation parameter set data, and achieve the low-carbon regulation of urban lake groups.

[0011] In the above solution, based on the geospatial data, meteorological data, hydrological observation data, and watershed management data of the target lake, hydrological processes such as precipitation, runoff, and evaporation can be simulated, and then the inflow data and pollutant concentration data can be calculated to provide input data for the follow-up. Then, based on the topographic data, meteorological data, inflow data, and pollutant concentration data of the target lake, through numerical simulation of the lake water body flow, pollutant transport, and sedimentation processes, the flow field distribution data and water quality index data of the target lake are obtained. Then, an optimization model with the goal of minimizing the energy consumption of sluice pumps and maximizing the improvement of lake water quality is constructed as the basis for solving the sluice pump regulation parameter set data. Finally, combined with the inflow data, pollutant concentration data, flow field distribution data of the target lake, water quality index data, sluice pump power coefficient, sluice pump flow data, sluice pump head data, and season duration, the optimization model is solved to simulate the improvement effect of sluice pump control on lake water quality and ecological environment, generate the sluice pump regulation parameter set data, and achieve the low-carbon regulation of urban lake groups, which can achieve the optimization of water allocation through seasonal changes, flexibly adjust the inflow at the lake inlet, improve water quality, enhance water body fluidity, and promote the restoration of the lake ecological environment, further reduce energy consumption, and achieve the green and low-carbon governance goal.

[0012] Furthermore, it further includes:

[0013] Arrange multiple lake inlets based on the target lake, and adjust and quantify the flow of each lake inlet;

[0014] Based on the flow rate at each lake inlet, calculate the growth rate of algae to characterize the control effect on water blooms. The specific calculation process is as follows:

[0015]

[0016] In the formula, μ algae represents the growth rate of algae; μ max represents the maximum growth rate of algae; Q pump represents the pumping flow rate; Q crit represents the critical flow rate for inhibiting the growth of algae; f(T, N, P) represents a function of temperature and nutrients; where T represents water temperature, nutrient N represents nitrogen, and nutrient P represents phosphorus.

[0017] In the above solution, by calculating the growth rate of algae, the flow rate at each lake inlet can be quantified for its control effect on water blooms. With the help of this quantification index, the specific improvement effect of the lake ecological environment after simulating the low-carbon regulation of the urban lake group can be obtained, so as to more intuitively and scientifically evaluate the effectiveness of the provided low-carbon regulation strategy and provide strong support for the sustainable governance of the urban lake group.

[0018] Furthermore, the construction of the optimization model with the goal of minimizing the energy consumption of sluice pumps and maximizing the improvement of lake water quality includes:

[0019] Construct the HSI index model during the fish spawning period, and solve the HSI index model based on the flow field distribution data, water quality index data, and water temperature to calculate HSI spawn ;

[0020] The general form of the optimization model is:

[0021] Minimize Z = z1(x, y) - z2(x, y)

[0022] Where:

[0023]

[0024] In the formula: z1 is the total energy consumption objective function; z2 is the water quality comprehensive index objective function; x = [x1, x2, x3, x4] represents the total inflow to the lake in each season; Ts represents the total number of seasons; y = [y1, y2, y3, y4] represents the proportion of the flow rate at each lake inlet, satisfying P i represents the power coefficient of the i-th sluice pump; Q i,t represents the flow rate of the i-th sluice pump in season t; H i,tDenote the head of the i-th sluice pump in season t; Δt represents the duration of season t; α, β, γ, δ, η denote weight coefficients, where α + β + γ + δ + η = 1; DO represents dissolved oxygen; SD represents transparency; TN represents total nitrogen; TP represents total phosphorus;

[0025] The constraint conditions of the optimization model are as follows:

[0026] a. Water volume balance constraint: x t = Q t

[0027] In the formula: Q t is the inflow data of the target lake;

[0028] b. Equipment physical limit of the i-th sluice pump:

[0029] c. Water quality compliance constraint:

[0030] d. Ecological flow constraint: x winter ≥ Q eco = 0.3·Q average annual

[0031] In the formula, Q eco represents the minimum ecological base flow; Q average annual represents the average annual runoff of the basin obtained from the inflow data of the target lake;

[0032] e. Dynamic rainstorm response constraint: x summer ≤ 1.2·Q summer

[0033] Q summer represents the summer inflow data of the target lake.

[0034] In the above solution, a multi-objective optimization model considering both total energy consumption and water quality comprehensive index is proposed. The model adjusts the total inflow into the lake and the proportion of the inflow at each inlet in different seasons, and combines the water volume balance constraint, the equipment physical limit of the sluice pump, the water quality compliance constraint, the ecological flow constraint, and the dynamic rainstorm response constraint to conduct multi-objective optimization. By introducing the multi-objective optimization model and each constraint condition, the balance between each objective is ensured, and the situation where one objective is overly favored during the optimization process, resulting in the ineffective realization of other objectives, is avoided. Finally, the optimization model is obtained, providing support for subsequent model solving and obtaining the data of the sluice pump regulation parameter set.

[0035] Further, the HSI index model for the fish spawning period is constructed, and the HSI index model is solved based on the flow field distribution data, water quality index data, and water temperature to calculate the HSI spawn , including:

[0036] Construct the HSI index model for the fish spawning period;

[0037] Solve the HSI index based on the flow field distribution data, water quality index data, and water temperature. The specific calculation process is as follows:

[0038]

[0039] In the formula, HSI spawn represents the suitability index for the fish spawning period, and DO represents the dissolved oxygen concentration in water; DO opt represents the optimal dissolved oxygen concentration for fish spawning; Q represents the water flow rate in the fish spawning area of the lake; DQ opt represents the optimal flow rate for fish spawning; T represents the water temperature; T opt represents the optimal temperature for fish spawning; The weights ω1, ω2, and ω3 are used to represent the relative importance of the three factors of dissolved oxygen, flow rate, and water temperature to the fish spawning suitability.

[0040] In the above solution, the HSI index model for the fish spawning period is constructed, which can quantify the comprehensive impact of environmental factors on the suitability of fish spawning in the fish spawning area. And the model takes into account the dissolved oxygen concentration, water flow rate, and water temperature, and reflects the relative importance of each factor through weight distribution. The calculated HSI spawn index can intuitively reflect the suitability degree of the lake ecological environment for fish spawning. When the index is relatively high, it indicates that the area is more favorable for fish spawning under the current environmental conditions; when the index is relatively low, it indicates that relevant environmental factors need to be adjusted or improved. Therefore, the HSI index model can assist in optimizing the formulation of the model. Considering the fish spawning suitability, when adjusting the total inflow into the lake and the proportion of the flow rate at each inlet to the lake, the negative impact on fish spawning caused by improper flow regulation can be avoided.

[0041] Further, based on the inflow data into the lake, pollutant concentration data, flow field distribution data of the target lake, water quality index data, gate-pump power coefficient, gate-pump flow rate data, gate-pump head data, and season duration, the optimization model is solved to obtain the gate-pump regulation parameter set data, realizing the low-carbon regulation of the urban lake group, including:

[0042] Solve the optimization model based on the non-dominated sorting genetic algorithm to obtain the gate-pump regulation parameter set data, realizing the low-carbon regulation of the urban lake group. Specifically:

[0043] Encode the total inflow into the lake in each season and the proportion of the inflow at each inlet as chromosomes;

[0044] Generate an initial solution that satisfies the constraints of the optimization model based on the chromosomes as the initial population;

[0045] Calculate the total energy consumption target and the comprehensive water quality index for each initial solution;

[0046] Perform Pareto non-dominated sorting on the initial population according to the total energy consumption target and the comprehensive water quality index of each initial solution to obtain the sorted population;

[0047] Generate an offspring population based on the sorted population and genetic operations;

[0048] Loop and execute the above operations. When the change rate of the Pareto front satisfies the preset threshold or reaches the maximum number of iterations, stop the iteration and return the optimal solution on the approximate Pareto front;

[0049] Decode based on the chromosomes of the final offspring population corresponding to the optimal solution to obtain the data of the gate-pump regulation parameter set, and realize the low-carbon regulation of the urban lake group.

[0050] In the above solution, the non-dominated sorting genetic algorithm is used as the core of multi-objective optimization to solve the optimization model. The total inflow into the lake in each season and the proportion of the inflow at each inlet are encoded into chromosomes, which facilitates the subsequent operations and optimization of these variables by the non-dominated sorting genetic algorithm. The numerical values of the total inflow into the lake in each season and the inflow at each inlet are mapped to the gene space of the genetic algorithm, enabling the algorithm to search for the optimal solution in this abstract space, thus improving the efficiency. Then, it is ensured that the solutions in the initial population meet the constraint conditions of the optimization model at the beginning, avoiding the generation of invalid solutions in subsequent calculations and reducing unnecessary waste of computing resources. Next, the performance of each initial solution in terms of energy consumption and water quality improvement is quantified, and the total energy consumption target and the comprehensive water quality index of each initial solution are obtained, providing an objective evaluation index for the subsequent Pareto non-dominated sorting. Further, the initial population is divided into different Pareto levels according to the non-dominated relationship in the total energy consumption target and the comprehensive water quality index of each initial solution. Through this sorting method, solutions with better performance in both the total energy consumption target and the comprehensive water quality index can be quickly screened out, providing a high-quality parental population for subsequent genetic operations and accelerating the convergence of the algorithm to the optimal solution. Then, the above operations are repeatedly executed to continuously optimize the population, making the algorithm gradually approach the optimal solution. By monitoring the change rate of the Pareto front, when the change rate is less than the preset threshold, it indicates that the algorithm has approached convergence, and the optimal solution on the approximate Pareto front is returned. The maximum number of iterations is used as a forced stop condition to prevent the algorithm from running infinitely. Finally, the chromosomes of the final offspring population optimized by the non-dominated sorting genetic algorithm are decoded into the actual gate-pump regulation parameter set, and these parameters can be directly applied to the actual regulation project of the urban lake group to achieve precise control of the gate-pump, and further achieve low-carbon regulation of the urban lake group. It can optimize the water volume allocation through seasonal changes, flexibly adjust the inflow at the inlet, improve the water quality, enhance the water body fluidity and promote the restoration of the lake ecological environment, further reduce the energy consumption, and achieve the green and low-carbon governance goal.

[0051] Further, generating the initial solution that meets the constraint conditions of the optimization model based on the chromosome as the initial population includes:

[0052] Generating a uniformly distributed original initial solution based on the chromosome, and checking the generated original initial solution. The original initial solution that meets the constraint conditions of the optimization model is set as the directly qualified solution, and the original initial solution that does not meet the constraint conditions of the optimization model is set as the solution to be repaired;

[0053] Repairing the solution to be repaired to make it meet the constraint conditions of the optimization model, and obtaining the repaired qualified solution;

[0054] The directly qualified solution and the repaired qualified solution together constitute the initial solution as the initial population.

[0055] In the above solution, generating uniformly distributed original initial solutions helps reduce the possibility of the algorithm falling into local optimal solutions. By checking and classifying the original initial solutions, solutions that meet and do not meet the constraints of the optimization model can be quickly identified. This ensures that subsequent calculations focus on valuable solutions, avoiding wasting computing resources on invalid solutions, and at the same time laying a foundation for screening out high-quality initial populations. Then, the solution to be repaired becomes a qualified solution after repair, which can meet the constraints of the optimization model, increasing the number of valid solutions in the initial population, thereby improving the overall quality of the initial population, ensuring that the subsequent genetic algorithm searches within a feasible solution space, making the algorithm more reliable and stable, and increasing the probability of the algorithm finding the global optimal solution. Integrating directly qualified solutions and qualified solutions after repair to form an initial population ensures that the initial population contains both solutions that meet the conditions without treatment and feasible solutions that have been repaired, enriching the diversity of the initial population.

[0056] Further, the Pareto non-dominated sorting of the initial population according to the total energy consumption target and water quality comprehensive index of each initial solution to obtain the sorted population includes:

[0057] Based on the total energy consumption target and water quality comprehensive index of each initial solution, perform hierarchical sorting on each initial solution in the initial population and divide the Pareto rank of each initial solution;

[0058] Sort each initial solution according to the divided Pareto rank to obtain the first sorted population;

[0059] Calculate the crowding degree of each initial solution in the first sorted population;

[0060] Sort the initial solutions according to the crowding degree in the same Pareto rank to obtain the second sorted population;

[0061] Obtain the sorted population according to the first sorted population and the second sorted population.

[0062] In the above solution, Pareto ranks are assigned to each initial solution, such that the initial solutions that are relatively superior in terms of both the total energy consumption objective and the comprehensive water quality index are at a higher rank. The initial solutions are sorted according to the Pareto rank to construct the first sorted population. The solutions at the front are more competitive in the multi-objective optimization process and are more likely to be selected as parents to participate in the evolutionary steps of the genetic algorithm in subsequent selection operations. The crowding degree of each initial solution in the first sorted population is calculated, which reflects the density of each initial solution in the solution space and can measure the diversity of the region where each initial solution is located, avoiding the algorithm from being overly concentrated in some local regions and falling into local optima. Within the same Pareto rank, the initial solutions are sorted again according to the crowding degree to obtain the second sorted population. This operation preferentially selects those initial solutions with relatively fewer surrounding initial solutions and greater exploration value on the premise of ensuring that the quality of the initial solutions is not reduced. It not only maintains the advancement of the population in the direction of multi-objective optimization but also fully explores the diversity of solutions within the same rank, facilitating the discovery of new and better solution combinations and promoting the continuous optimization of the algorithm. By integrating the Pareto rank advantage emphasized by the first sorted population and the crowding degree advantage within the same rank mined by the second sorted population, the finally sorted population is obtained, which contains both the solutions that performed outstandingly in the early stage of multi-objective optimization and the solutions with potential in maintaining diversity, providing a high-quality and diversified basis for subsequent genetic operations based on the sorted population.

[0063] Furthermore, generating an offspring population based on the sorted population and genetic operations includes:

[0064] Performing selection, crossover, and mutation operations on the sorted population to generate an offspring population.

[0065] In the above solution, selection is performed based on the sorted population, and relatively superior individuals are preferentially selected according to indicators such as the Pareto rank and crowding degree. The selected individuals are subjected to crossover and mutation operations to generate an offspring population.

[0066] Furthermore, based on the inflow data into the lake, pollutant concentration data, flow field distribution data of the target lake, water quality index data, gate-pump power coefficient, gate-pump flow rate data, gate-pump head data, and season duration, solving the optimization model to obtain the gate-pump regulation parameter set data to achieve low-carbon regulation of urban lake groups also includes:

[0067] Ensuring that the initial solutions in the offspring population satisfy all hard constraints and soft constraints during coding;

[0068] The hard constraints include water balance constraints, equipment physical limitations of gate-pumps, water quality compliance constraints, ecological flow constraints, and dynamic rainstorm response constraints, which are used to directly limit the initial solution space during coding;

[0069] Let the penalty function P = max(0, excess amount) 2As a soft constraint, it is added to the total energy consumption objective function and the comprehensive water quality index objective function.

[0070] In the above solution, through strict hard constraints and soft constraints, the spread of invalid solutions in the offspring population is avoided. For the initial solutions that slightly violate the soft constraints (such as the comprehensive water quality index approaching the critical value of exceeding the standard), by moderately increasing their objective function values, their competitiveness in the Pareto non-dominated sorting and the optimization process of the genetic algorithm decreases, guiding the algorithm to gradually optimize in the direction of meeting both low-carbon energy consumption and ensuring excellent water quality.

[0071] The present invention provides a low-carbon regulation system for urban lake groups, including a data acquisition module, a data calculation module, a flow field and water quality simulation module, a model construction module, and a parameter solution module, wherein:

[0072] The data acquisition module is used to obtain the geospatial data, topographic data, meteorological data, hydrological observation data, and watershed management data of the target lake;

[0073] The data calculation module is used to calculate the inflow data and pollutant concentration data of the target lake based on the geospatial data, meteorological data, hydrological observation data, and watershed management data;

[0074] The flow field and water quality simulation module is used to simulate and calculate the flow field distribution data and water quality index data of the target lake based on the topographic data, meteorological data, inflow data, and pollutant concentration data of the target lake;

[0075] The model construction module is used to construct an optimization model with the goal of minimizing the energy consumption of sluice pumps and maximizing the improvement of lake water quality;

[0076] The parameter solution module is used to solve the optimization model based on the inflow data, pollutant concentration data, flow field distribution data of the target lake, water quality index data, sluice pump power coefficient, sluice pump flow data, sluice pump head data, and season duration, and obtain the sluice pump regulation parameter set data to achieve the low-carbon regulation of urban lake groups.

[0077] The low-carbon regulation system for urban lake groups provided by the above solution has a simple structure. In practical applications, only the geospatial data, terrain data, meteorological data, hydrological observation data, and watershed management data of the target lake need to be obtained through the data acquisition module. Then, based on the geospatial data, meteorological data, hydrological observation data, and watershed management data of the target lake, the data calculation module simulates hydrological processes such as precipitation, runoff, and evaporation, and then calculates the inflow data and pollutant concentration data of the lake to provide input data for the subsequent steps. Next, based on the terrain data, meteorological data, inflow data, and pollutant concentration data of the target lake, the flow field and water quality simulation module numerically simulates the water body flow, pollutant transport, and sedimentation processes of the lake to obtain the flow field distribution data and water quality index data of the target lake. Then, through the model construction module, an optimization model with the goal of minimizing the energy consumption of sluice pumps and maximizing the improvement of lake water quality is constructed as the basis for solving the sluice pump regulation parameter set data. Finally, by combining the inflow data, pollutant concentration data, flow field distribution data, water quality index data of the target lake, sluice pump power coefficient, sluice pump flow data, sluice pump head data, and season duration, the parameter solving module solves the optimization model, simulates the improvement effect of sluice pump control on lake water quality and ecological environment, generates the sluice pump regulation parameter set data, realizes the low-carbon regulation of urban lake groups, and can achieve the goal of optimizing water allocation through seasonal changes, flexibly adjusting the inflow at the lake inlet, improving water quality, enhancing water body fluidity, promoting the restoration of the lake ecological environment, further reducing energy consumption, and realizing the green and low-carbon governance goal. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 It is a schematic flowchart of a low-carbon regulation method for urban lake groups provided by an embodiment of the present invention;

[0079] Figure 2 It is an architecture diagram of a low-carbon regulation system for urban lake groups provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0080] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0081] This embodiment provides a low-carbon regulation method for urban lake groups. For the specific steps, please refer to Figure 1 , including:

[0082] Obtain the geospatial data, terrain data, meteorological data, hydrological observation data, and watershed management data of the target lake;

[0083] Based on the geospatial data, meteorological data, hydrological observation data, and watershed management data, calculate the inflow data and pollutant concentration data of the target lake;

[0084] Based on the terrain data, meteorological data, inflow data, and pollutant concentration data of the target lake, simulate and calculate the flow field distribution data and water quality index data of the target lake;

[0085] Construct an optimization model with the goals of minimizing the energy consumption of sluice pumps and maximizing the improvement of lake water quality;

[0086] Based on the inflow data, pollutant concentration data, flow field distribution data of the target lake, water quality index data, sluice pump power coefficient, sluice pump flow data, sluice pump head data, and season duration, solve the optimization model to obtain the sluice pump regulation parameter set data, and realize the low-carbon regulation of urban lake groups.

[0087] In this embodiment, to improve the water quality of Xinghu Lake, Xinghu Lake is taken as the target lake for low-carbon regulation. An optimized plan for sluice and dam regulation is proposed. By adjusting the flow ratio of multiple lake inlets of Xinghu Lake, the improvement of water body fluidity and water quality is achieved. Based on the geospatial data, meteorological data, hydrological observation data and watershed management data of the target lake, the SWAT model can simulate hydrological processes such as precipitation, runoff and evaporation, so as to calculate the seasonal runoff of the watershed, groundwater recharge and pollutant load at the watershed outlet, and further obtain the in-lake flow data and pollutant concentration data, providing input data for the follow-up and basic boundary conditions for the in-lake water volume of the lake. The geospatial data includes DEM, land use type map and soil type map. The meteorological data includes precipitation, temperature and relative humidity. Then, based on the topographic data, meteorological data, in-lake flow data and pollutant concentration data of the target lake, the EFDC model numerically simulates the lake water body flow, pollutant transport and deposition processes through the three-dimensional finite difference method to obtain the flow field distribution data and water quality index data of the target lake. The model arranges multiple lake inlets based on the actual situation of Xinghu Lake, and by adjusting the flow of each inlet, the improvement effect of sluice and pump control on lake water quality and ecological environment is simulated. The lake topographic data includes high-resolution topographic maps. Then an optimization model with the goal of minimizing the sluice and pump energy consumption and maximizing the improvement of lake water quality is constructed as the basis for solving the sluice and pump regulation parameter set data. Finally, the optimization model is solved by combining the in-lake flow data, pollutant concentration data, flow field distribution data of the target lake, water quality index data, sluice and pump power coefficient, sluice and pump flow data, sluice and pump head data and season duration, and the improvement effect of sluice and pump control on lake water quality and ecological environment is simulated to generate the sluice and pump regulation parameter set data, realizing the low-carbon regulation of urban lake groups, being able to achieve the optimization of water volume allocation through seasonal changes, flexibly adjusting the flow of lake inlets, improving water quality, enhancing water body fluidity and promoting the restoration of the lake ecological environment, further reducing energy consumption and achieving the green and low-carbon governance goal. The above process is achieved by designing an interface program to seamlessly connect the in-lake flow data and pollutant concentration data output by the SWAT model to the boundary conditions of the EFDC model. First, the SWAT model is run to simulate the in-lake flow data and pollutant concentration data of the watershed; then the results are used as the input of the EFDC model to simulate the hydrodynamic and water quality changes in the lake. By combining these two models, the interaction of factors such as water flow, pollutant propagation and ecological environment in the lake can be comprehensively reflected, providing accurate data support for optimizing lake management and resource allocation. During the dynamic simulation process of the model, by adjusting the flow ratio of each lake inlet, the impact of different flow distribution schemes on the lake water exchange frequency, water quality improvement and ecosystem health is analyzed. In summer and autumn, due to the rich water resources in the watershed, the flow ratio can be appropriately increased to improve the lake water exchange efficiency; in winter and spring, the flow is optimized according to resource constraints to maintain water quality stability.The model output includes the flow field distribution data (such as flow velocity, residence time) of the target lake and water quality index data (such as transparency, nutrient concentration), providing a quantitative basis for flow regulation. Finally, by designing different inflow allocation schemes and regulation strategies, the impacts of gate-pump regulation on lake water quality improvement and energy consumption reduction are analyzed, thus providing a scientific basis for the optimal management of the lake. In this embodiment, by combining the SWAT hydrological model and the EFDC hydrodynamic model, the basin water resources and lake hydrodynamic processes are comprehensively simulated to construct a comprehensive model integrating hydrology, water quality and ecological optimization, thereby providing accurate data support and decision-making basis for the scientific treatment of the lake. In this embodiment, by combining the basin water resources volume and the seasonal changes of the lake, the water volume of each season is accurately allocated to optimize the water quality improvement and the restoration effect of the lake ecological environment. In addition, by combining the regulation schemes of the flow rates at multiple lake inlets, the water flow at the lake inlets can be flexibly adjusted to further improve the water body fluidity and water quality transparency of the lake.

[0088] Further, it also includes:

[0089] Arranging multiple lake inlets based on the target lake, and adjusting and quantifying the flow rates of each lake inlet;

[0090] Based on the flow rates of each lake inlet, calculating the growth rate of algae to characterize the effect of controlling algal blooms. The specific calculation process is as follows:

[0091]

[0092] In the formula, μ algae represents the growth rate of algae; μ max represents the maximum growth rate of algae; Q pump represents the pumping flow rate; Q crit represents the critical flow rate for inhibiting algal growth; f(T, N, P) represents a function of temperature and nutrients; where T represents water temperature, nutrient N represents nitrogen, and nutrient P represents phosphorus.

[0093] In this embodiment, by calculating the growth rate of algae, the flow rates of each lake inlet on the effect of controlling algal blooms can be quantified. With the help of this quantitative index, the specific improvement effect of the lake ecological environment after simulating the low-carbon regulation of the urban lake group is simulated, so as to more intuitively and scientifically evaluate the effectiveness of the provided low-carbon regulation strategy and provide strong support for the sustainable treatment of the urban lake group.

[0094] Further, the construction of the optimization model with the goal of minimizing the gate-pump energy consumption and maximizing the lake water quality improvement includes:

[0095] Constructing an HSI index model for the fish spawning period, and solving the HSI index model based on the flow field distribution data, water quality index data and water temperature to calculate HSI spawn ;

[0096] The general form of the optimization model is as follows:

[0097] Minimize Z=z1(x,y)-z2(x,y)

[0098] Where:

[0099]

[0100] In the formula: z1 is the total energy consumption objective function; z2 is the comprehensive water quality index objective function; x = [x1, x2, x3, x4] represents the total inflow into the lake in each season; Ts represents the total number of seasons; y = [y1, y2, y3, y4] represents the proportion of the flow at each lake inlet, satisfying P i represents the power coefficient of the i-th sluice pump; Q i,t represents the flow rate of the i-th sluice pump in season t; H i,t represents the head of the i-th sluice pump in season t; Δt represents the duration of season t; α, β, γ, γ, η represent weight coefficients, and α + β + γ + δ + η = 1; DO represents dissolved oxygen; SD represents transparency; TN represents total nitrogen; TP represents total phosphorus;

[0101] The constraint conditions of the optimization model are:

[0102] a. Water balance constraint: x t = Q t

[0103] In the formula: Q t is the inflow data of the target lake;

[0104] b. Equipment physical limit of the i-th sluice pump:

[0105] c. Water quality compliance constraint:

[0106] d. Ecological flow constraint: x winter ≥ Q eco = 0.3·Q average annual

[0107] In the formula, Q eco represents the minimum ecological base flow; Q average annual represents the average annual runoff of the basin obtained from the inflow data of the target lake;

[0108] e. Dynamic rainstorm response constraint: x summer ≤ 1.2·Q summer

[0109] Q summer represents the summer inflow data into the target lake.

[0110] In this embodiment, a multi-objective optimization model that comprehensively considers total energy consumption and water quality comprehensive index is proposed. The model conducts multi-objective optimization by adjusting the total inflow into the lake in different seasons and the proportion of the inflow at each inlet, combined with water balance constraints, physical limitations of gate pumps, water quality compliance constraints, ecological flow constraints, and dynamic rainstorm response constraints. Among them, z2 is the water quality comprehensive index (dimensionless), calculated by weighting key indicators, Δt represents the duration of season t, such as Δt = 92×86400s in summer, α, β, γ, δ, η need to be set according to ecological priorities. The water balance constraint needs to ensure that the total inflow into the lake in each season is consistent with the inflow data of the target lake simulated by SWAT. The ecological flow constraint means that the minimum ecological base flow needs to be guaranteed during the dry season, and the dynamic rainstorm response constraint means the upper limit of the flow during the rainy season to prevent flood overflow. By introducing the multi-objective optimization model and various constraint conditions, the balance between various objectives is ensured, and the situation where one objective is overly favored during the optimization process, resulting in the ineffective achievement of other objectives, is avoided. Finally, the optimization model is obtained, providing support for subsequent solving of the model and obtaining the data set of gate pump regulation parameters. This model not only optimizes the utilization efficiency of water resources but also balances the economic benefits in water resource management and the sustainable development of the ecological environment. This not only helps to reduce the treatment cost but also helps to achieve the green and low-carbon treatment goal. In this embodiment, by introducing the multi-objective optimization model, the synergistic effects of multiple decision-making factors such as water quality improvement, energy consumption minimization, water resource balance, and optimization of gate pump regulation strategies are considered. Ensure that while achieving water quality and ecological improvement, the energy consumption is minimized to the greatest extent, ensure the balance between various objectives, and avoid the situation where one objective is overly favored during the optimization process, resulting in the ineffective achievement of other objectives.

[0111] Furthermore, the HSI index model for the fish spawning period is constructed, and the HSI index model is solved based on the flow field distribution data, water quality index data, and water temperature to calculate HSI spawn , including:

[0112] Construct the HSI index model for the fish spawning period;

[0113] Solve the HSI index based on the flow field distribution data, water quality index data, and water temperature. The specific calculation process is as follows:

[0114]

[0115] In the formula, HSI spawn represents the suitability index during the fish spawning period, and DO represents the dissolved oxygen concentration in the water; DO optrepresents the optimal dissolved oxygen concentration for fish spawning; Q represents the water flow rate in the fish spawning area of the lake; DQ opt represents the optimal flow rate for fish spawning; T represents the water temperature; T opt represents the optimal temperature for fish spawning; ω1, ω2, ω3 are weights used to represent the relative importance of the three factors of dissolved oxygen, flow rate, and water temperature on the suitability of fish spawning.

[0116] In this embodiment, an HSI index model for the fish spawning period is constructed, which can quantify the comprehensive impact of environmental factors on the suitability of fish spawning in the fish spawning area. And the model takes into account the dissolved oxygen concentration, water flow rate, and water temperature, and reflects the relative importance of each factor through weight allocation. The calculated HSI spawn index can intuitively reflect the suitability of the lake ecological environment for fish spawning. When the index is relatively high, it indicates that the area is more favorable for fish spawning under the current environmental conditions; when the index is relatively low, it indicates that relevant environmental factors need to be adjusted or improved. Therefore, this HSI index model can assist in the formulation of the optimization model. Considering the suitability of fish spawning, when adjusting the total inflow into the lake and the proportion of the inflow at each inlet, the negative impact on fish spawning caused by improper flow regulation can be avoided.

[0117] Furthermore, based on the inflow data into the lake, pollutant concentration data, flow field distribution data of the target lake, water quality index data, gate-pump power coefficient, gate-pump flow rate data, gate-pump head data, and season duration, the optimization model is solved to obtain the gate-pump regulation parameter set data, realizing the low-carbon regulation of urban lake groups, including:

[0118] Based on the non-dominated sorting genetic algorithm, the optimization model is solved to obtain the gate-pump regulation parameter set data, realizing the low-carbon regulation of urban lake groups, specifically:

[0119] Encode the total inflow into the lake and the proportion of the inflow at each inlet in each season as chromosomes;

[0120] Generate an initial solution that satisfies the constraint conditions of the optimization model based on the chromosomes as the initial population;

[0121] Calculate the total energy consumption target and water quality comprehensive index of each initial solution;

[0122] Perform Pareto non-dominated sorting on the initial population according to the total energy consumption target and water quality comprehensive index of each initial solution to obtain the sorted population;

[0123] Generate an offspring population based on the sorted population and genetic operations;

[0124] Loop and execute the above operations. When the change rate of the Pareto front meets the preset threshold or reaches the maximum number of iterations, stop the iteration and return the optimal solution on the approximate Pareto front;

[0125] Decode based on the chromosomes of the final offspring population corresponding to the optimal solution to obtain the data of the gate-pump regulation parameter set, and achieve low-carbon regulation of urban lake groups.

[0126] In this embodiment, the non-dominated sorting genetic algorithm is used as the core of multi-objective optimization, and the SWAT and EFDC models are combined to construct a closed-loop solution process to solve the optimization model. The total inflow x of each season and the proportion y of the flow at each inlet are encoded into chromosomes, which is convenient for the non-dominated sorting genetic algorithm to perform subsequent operations and optimizations on these variables. The real number encoding is used for x, and each gene represents the total flow of a season, such as the four dimensions of spring, summer, autumn, and winter. The normalized vector encoding is used for y, and each gene represents the proportion of the flow at a certain inlet, and the sum of all proportions is 1. The numerical values of the total inflow of each season and the flow values of each inlet are mapped to the gene space of the genetic algorithm, so that the algorithm can search for the optimal solution in this abstract space, improving the efficiency. Then, ensure that the solutions in the initial population meet the constraints of the optimization model from the beginning, avoid the generation of invalid solutions in subsequent calculations, and reduce unnecessary waste of computing resources. Then, quantify the performance of each initial solution in terms of energy consumption and water quality improvement, obtain the total energy consumption target and the comprehensive water quality index of each initial solution, and provide an objective evaluation index for the subsequent Pareto non-dominated sorting. Further, stratify according to the non-dominated relationship of the total energy consumption target and the comprehensive water quality index of each initial solution, and divide the initial population into different Pareto levels. Through this sorting method, the solutions with better total energy consumption target and comprehensive water quality index can be quickly screened out, providing a high-quality parental population for subsequent genetic operations and accelerating the convergence of the algorithm to the optimal solution. Then, loop through the above operations, continuously optimize the population, and make the algorithm gradually approach the optimal solution. By monitoring the change rate of the Pareto front, when the change rate is less than the preset threshold, it means that the algorithm has approached convergence, and the optimal solution on the approximate Pareto front is returned. The maximum number of iterations is used as a forced stop condition to prevent the algorithm from running infinitely. Finally, decode the chromosomes of the final offspring population optimized by the non-dominated sorting genetic algorithm into the actual gate-pump regulation parameter set. These parameters can be directly applied to the actual regulation project of urban lake groups to achieve precise control of the gate pumps, and then achieve low-carbon regulation of urban lake groups. It can optimize the water volume allocation through seasonal changes, flexibly adjust the flow at the inlets, improve the water quality, enhance the water body fluidity and promote the restoration of the lake ecological environment, further reduce the energy consumption, and achieve the green and low-carbon governance goal. In this embodiment, an optimization model with the goal of minimizing the gate-pump energy consumption and maximizing the improvement of lake water quality is designed, and the conditions such as seasonal water volume scheduling, water resource balance, energy consumption constraints, and water quality requirements are comprehensively considered to dynamically adjust the flow at each inlet. Finally, through the solution of the genetic algorithm, the optimal allocation scheme of the total inflow of each season and the proportion of the flow at each inlet is output, providing scientific guidance for the sustainable management of the lake water environment.

[0127] Further, generating an initial solution that satisfies the constraint conditions of the optimization model based on the chromosome as the initial population includes:

[0128] Generating a raw initial solution with a uniform distribution based on the chromosome, and checking the generated raw initial solution. Set the raw initial solution that satisfies the constraint conditions of the optimization model as a directly qualified solution, and the raw initial solution that does not satisfy the constraint conditions of the optimization model as a solution to be repaired;

[0129] Repairing the solution to be repaired so that the solution to be repaired satisfies the constraint conditions of the optimization model, and obtaining a repaired qualified solution;

[0130] Combining the directly qualified solution and the repaired qualified solution to form the initial solution as the initial population.

[0131] In this embodiment, generating a raw initial solution with a uniform distribution by Latin Hypercube Sampling (LHS) helps reduce the possibility of the algorithm falling into a local optimal solution. By checking and classifying the raw initial solution, solutions that meet and do not meet the constraint conditions of the optimization model can be quickly identified. This ensures that subsequent calculations focus on valuable solutions, avoiding wasting computing resources on invalid solutions, and also laying a foundation for screening out a high-quality initial population. Then, let the solution to be repaired become a repaired qualified solution after repair. The repair method is to adjust the flow ratio normalization, so as to meet the constraint conditions of the optimization model, increase the number of valid solutions in the initial population, thereby improving the overall quality of the initial population, ensuring that the subsequent genetic algorithm searches within a feasible solution space, making the algorithm more reliable and stable, and increasing the probability of the algorithm finding the global optimal solution. Integrating the directly qualified solution and the repaired qualified solution to form the initial population ensures that the initial population contains both solutions that meet the conditions without treatment and repaired feasible solutions, enriching the diversity of the initial population.

[0132] Further, performing Pareto non-dominated sorting on the initial population according to the total energy consumption target and water quality comprehensive index of each initial solution to obtain the sorted population includes:

[0133] Based on the total energy consumption target and water quality comprehensive index of each initial solution, performing hierarchical sorting on each initial solution in the initial population and dividing the Pareto rank of each initial solution;

[0134] Sorting each initial solution according to the divided Pareto rank to obtain the first sorted population;

[0135] Calculating the crowding degree of each initial solution in the first sorted population;

[0136] Sorting the initial solutions according to the crowding degree in the same Pareto rank to obtain the second sorted population;

[0137] Obtain the sorted population based on the first sorted population and the second sorted population.

[0138] In this embodiment, Pareto ranks are assigned to each initial solution, where level 1 is the optimal front, such that the initial solutions that are relatively dominant in terms of both the total energy consumption target and the comprehensive water quality index are at a higher rank. The initial solutions are sorted according to the Pareto ranks to construct the first sorted population. The solutions at the front are more competitive in the multi-objective optimization process and are more likely to be selected as parents to participate in the evolutionary steps of the genetic algorithm in subsequent selection operations. Calculate the crowding degree of each initial solution in the first sorted population, which reflects the density of each initial solution in the solution space and can measure the diversity of the region where each initial solution is located, avoiding the algorithm from being overly concentrated in some local regions and falling into local optima. Within the same Pareto rank, the initial solutions are sorted again according to the crowding degree to obtain the second sorted population. This operation preferentially selects those initial solutions with relatively fewer surrounding initial solutions and greater exploration value on the premise of ensuring that the quality of the initial solutions is not reduced. It not only maintains the advancement of the population in the direction of multi-objective optimization but also fully explores the diversity of solutions within the same rank, facilitating the discovery of new and better solution combinations and promoting the continuous optimization of the algorithm. By integrating the Pareto rank advantage emphasized by the first sorted population and the crowding degree advantage within the same rank mined by the second sorted population, the sorted population is finally obtained, which contains both the solutions that performed outstandingly in the early stage of multi-objective optimization and the solutions with potential in maintaining diversity, providing a high-quality and diversified basis for subsequent genetic operations based on the sorted population.

[0139] Further, generating an offspring population based on the sorted population and genetic operations includes:

[0140] Perform selection, crossover, and mutation operations on the sorted population to generate an offspring population.

[0141] In this embodiment, selection is performed based on the sorted population, and relatively better individuals are preferentially selected according to indicators such as Pareto rank and crowding degree. The selected individuals are subjected to crossover and mutation operations to generate an offspring population. The tournament selection method is used for selection, and individuals with a high Pareto rank and a large crowding degree are preferentially selected. Simulated binary crossover (SBX) is used for crossover, with a crossover probability of 0.8. Polynomial mutation is used for mutation, with a mutation probability of 0.1.

[0142] Further, based on the incoming lake flow data, pollutant concentration data, flow field distribution data of the target lake, water quality index data, gate-pump power coefficient, gate-pump flow data, gate-pump head data, and season duration, solving the optimization model to obtain the gate-pump regulation parameter set data and realizing the low-carbon regulation of urban lake groups further includes:

[0143] Ensure that the initial solutions in the offspring population satisfy all hard constraints and soft constraints during coding;

[0144] The hard constraints include water balance constraints, equipment physical limitations of sluice pumps, water quality compliance constraints, ecological flow constraints, and dynamic rainstorm response constraints, which are used to directly limit the initial solution space during coding;

[0145] The penalty function P = max(0, excess quantity) 2 is used as a soft constraint and added to the total energy consumption objective function and the comprehensive water quality index objective function.

[0146] In the above solution, through strict hard constraints and soft constraints, the diffusion of invalid solutions in the offspring population is avoided. For the initial solutions that slightly violate the soft constraints (such as the comprehensive water quality index approaching the critical value of exceeding the standard), by moderately increasing their objective function values, their competitiveness in the Pareto non-dominated sorting and genetic algorithm optimization process decreases, guiding the algorithm to gradually optimize in the direction of meeting both low-carbon energy consumption and ensuring excellent water quality.

[0147] Furthermore, it also includes:

[0148] Adopting an algorithm acceleration strategy for the non-dominated sorting genetic algorithm.

[0149] The algorithm acceleration strategy uses a surrogate model replacement or parallel computing.

[0150] In this embodiment, the surrogate model replacement means that in the optimization iteration, the Kriging surrogate model is used to replace the EFDC full model, which can quickly predict the water quality response, and only the complete model is called to verify the candidate optimal solutions; parallel computing uses the MPI parallel technology to run multiple SWAT / EFDC instances simultaneously, which can shorten the single iteration time.

[0151] Furthermore, the data of the gate pump regulation parameter set includes:

[0152] The data of the gate pump regulation parameter set includes core regulation parameters, auxiliary decision-making parameters, and verification and monitoring parameters.

[0153] The core regulation parameters include the total inflow into the lake in each season, the proportion of the inflow at the lake inlet, and the operation parameters of the sluice pumps.

[0154] The auxiliary decision-making parameters include the Pareto front solution set and the real-time regulation threshold.

[0155] The verification and monitoring parameters include model verification indicators and long-term ecological tracking parameters.

[0156] In this embodiment, the total inflow into the lake in each season is used to guide the macro-allocation of water resources throughout the year and balance the demands during the wet and dry seasons. The proportion of the flow at the lake inlet is used to precisely control the flow at each inlet and optimize the water body flow path and pollutant diffusion. The operating parameters of the sluice pumps, such as the gate opening: 60%, the pump station frequency: 45 Hz, and the daily operating period: 8:00 - 18:00, are used to directly guide the on-site equipment operation to ensure the conversion of the theoretical plan into actual control actions. The Pareto front solution set can provide 10 sets of energy consumption - water quality trade-off plans (such as Plan A: energy consumption reduced by 15%, water quality compliance rate of 90%; Plan B: energy consumption reduced by 5%, water quality compliance rate of 95%) to support the manager in selecting the most suitable plan according to the actual needs (economy vs. ecological priority). The real-time regulation thresholds, such as the water quality exceeding threshold (re-trigger re-optimization when TN ≥ 2 mg / L) and the energy consumption warning threshold (alarm when the single-day energy consumption exceeds 1000 kWh), are used to construct an adaptive control logic to ensure the stable operation of the system. The model verification indicators, such as the Nash - Sutcliffe coefficient (SWAT runoff simulation > 0.6) and the EFDC water quality error (RMSE < TN 0.3 mg / L), are used to evaluate the reliability of the model and ensure the credibility of the optimization results. The long-term ecological tracking parameters, such as the sediment TP release rate and the interannual change of phytoplankton biomass, are used to monitor the long-term impact of the regulation plan on the lake ecosystem and support the dynamic modification of the plan.

[0157] Please refer to Figure 2 , this embodiment provides a low-carbon regulation system for urban lake groups, including a data acquisition module, a data calculation module, a flow field and water quality simulation module, a model construction module, and a parameter solution module, where:

[0158] The data acquisition module is used to obtain the geospatial data, topographic data, meteorological data, hydrological observation data, and watershed management data of the target lake;

[0159] The data calculation module is used to calculate the inflow data and pollutant concentration data of the target lake based on the geospatial data, meteorological data, hydrological observation data, and watershed management data;

[0160] The flow field and water quality simulation module is used to simulate and calculate the flow field distribution data and water quality index data of the target lake based on the topographic data, meteorological data, inflow data, and pollutant concentration data of the target lake;

[0161] The model construction module is used to construct an optimization model with the goal of minimizing the sluice pump energy consumption and maximizing the improvement of the lake water quality;

[0162] The parameter solving module is used to solve the optimization model based on the inflow data into the lake, pollutant concentration data, flow field distribution data of the target lake, water quality index data, gate-pump power coefficient, gate-pump flow rate data, gate-pump head data, and seasonal duration, so as to obtain the gate-pump regulation parameter set data and achieve low-carbon regulation of the urban lake group.

[0163] The low-carbon regulation system for urban lake groups provided in this embodiment has a simple structure. In practical applications, only the geospatial data, topographic data, meteorological data, hydrological observation data, and watershed management data of the target lake need to be obtained through the data acquisition module. In view of the characteristics of the Zhaoqing Star Lake urban lake group, the situation of multiple lake inlets, the regulation of multiple sluice pumps, and the need for ecological environment restoration, this embodiment proposes a low-carbon regulation system for urban lake groups. Based on the geospatial data, meteorological data, hydrological observation data, and watershed management data of the target lake, the data calculation module can simulate hydrological processes such as precipitation, runoff, and evaporation, and then calculate the inflow data and pollutant concentration data to provide input data for the follow-up. Then, based on the topographic data, meteorological data, inflow data, and pollutant concentration data of the target lake, the flow field and water quality simulation module numerically simulates the water body flow, pollutant transport, and sedimentation processes of the lake to obtain the flow field distribution data and water quality index data of the target lake. Then, through the model construction module, an optimization model with the goal of minimizing the energy consumption of sluice pumps and maximizing the improvement of lake water quality is constructed as the basis for solving the sluice pump regulation parameter set data. Finally, combined with the inflow data, pollutant concentration data, flow field distribution data of the target lake, water quality index data, sluice pump power coefficient, sluice pump flow data, sluice pump head data, and season duration, the parameter solving module solves the optimization model, simulates the improvement effect of sluice pump control on lake water quality and ecological environment, generates the sluice pump regulation parameter set data, and realizes the low-carbon regulation of urban lake groups. It can achieve the optimization of water allocation through seasonal changes, flexibly adjust the inflow at the lake inlet, improve water quality, enhance water body fluidity, and promote the restoration of the lake ecological environment. It conducts a full-process study on the optimization of water conservancy projects using hydrological and hydrodynamic simulation - optimization algorithms, forming a low-carbon - green technology system. Further reduce energy consumption and achieve the goal of green and low-carbon governance. Based on the concepts of green and low-carbon, carbon reduction, and sustainable development of the ecological environment, this embodiment proposes a systematic solution for optimizing the control of sluice pump energy consumption, combines the multi-objective optimization method of seasonal water resource scheduling and water volume balance, and realizes the dual goals of reducing the energy consumption of water conservancy projects and improving the lake ecological environment. This method effectively improves the water quality and ecological environment of the lake by optimizing water allocation and flow regulation. This embodiment closely combines the characteristics of the connectivity of urban lake groups and proposes an optimization framework based on the joint application of hydrological and hydrodynamic models to accurately calculate lake water resources, regulate the inflow into the lake, and improve the lake ecological environment. By optimizing the inflow and the proportion of water volume in different seasons, this embodiment can effectively reduce the energy consumption of the inlet sluice pumps while significantly improving the water quality and ecological environment of the lake. This technology has the characteristics of high efficiency, low carbon, and water conservation priority, can provide a green, low-carbon, and intelligent technology solution for urban lake environmental management, and has broad market application prospects, especially suitable for coping with challenges such as climate change and water resource shortage.Through seasonal water resource scheduling and energy consumption optimization, this embodiment minimizes energy consumption and wastewater discharge to the greatest extent, significantly improving the utilization efficiency of lake water resources. It meets the requirements of the national water conservation and pollution reduction policies, can significantly reduce water body pollution, improve water resource utilization efficiency, and has significant practical benefits. It solves many challenges in the water resource management of the lake basin, has strong operability and practical significance. This embodiment emphasizes the comprehensive system governance of the lake basin. Through multi-dimensional optimization of hydrology, hydrodynamics, ecology, and energy consumption, and by adopting innovative technologies that combine hydrological and hydrodynamic models, it provides a systematic set of lake water resource management and environmental protection solutions. Combining with seasonal changes, it comprehensively schedules water resources in different seasons, solving problems in multiple aspects such as water resource waste, ecological restoration, and energy consumption. By optimizing multiple targets such as the lake water inflow, the proportion of the water inflow at the water inlet, energy efficiency, and water quality, it realizes the comprehensive goals of efficient water resource allocation, ecological environment improvement, and minimum energy consumption. The governance framework of water conservation, carbon reduction, and efficiency increase constructed by the project not only has high application value but also provides a popularizable technical solution for similar lake governance projects.

[0164] The above is the preferred implementation manner of the present invention. It should be noted that for those of ordinary skill in the art of this technology, without departing from the principle of the present invention, several improvements and refinements can still be made, and these improvements and refinements are also regarded as the protection scope of the present invention.

Claims

1. A low-carbon regulation method for urban lake groups, characterized in that, Including the following steps: Obtain the geospatial data, topographic data, meteorological data, hydrological observation data, and watershed management data of the target lake; Based on the geospatial data, meteorological data, hydrological observation data, and watershed management data, calculate the inflow data and pollutant concentration data of the target lake; Based on the topographic data, meteorological data, inflow data, and pollutant concentration data of the target lake, simulate and calculate the flow field distribution data and water quality index data of the target lake; Construct an optimization model with the goal of minimizing the energy consumption of sluice pumps and maximizing the improvement of lake water quality; Based on the inflow data, pollutant concentration data, flow field distribution data, water quality index data, sluice pump power coefficient, sluice pump flow data, sluice pump head data, and season duration of the target lake, solve the optimization model to obtain the sluice pump regulation parameter set data, and achieve low-carbon regulation of urban lake groups.

2. The low-carbon regulation method for an urban lake group according to claim 1, wherein It also includes: Arrange multiple lake inlets based on the target lake, and adjust and quantify the flow of each lake inlet; Based on the flow of each lake inlet, calculate the growth rate of algae to characterize the prevention and control effect of water blooms. The specific calculation process is: where μ algae represents the growth rate of algae; μ max represents the maximum growth rate of algae; Q pump represents the pumping flow rate; Q crit represents the critical flow rate for inhibiting the growth of algae; f(T, N, P) represents a function of temperature and nutrients; where T represents the water temperature, the nutrient N represents nitrogen, and the nutrient P represents phosphorus.

3. A low-carbon regulation method for urban lake groups according to claim 1, characterized in that The construction of the optimization model with the goal of minimizing the energy consumption of sluice pumps and maximizing the improvement of lake water quality includes: Construct the HSI index model for the fish spawning period, and solve the HSI index model based on the flow field distribution data, water quality index data and water temperature to calculate the HSI spawn ; The general form of the optimization model is: Minimize Z=z1(x,y)-z2(x,y) Where: where: z1 is the total energy consumption objective function; z2 is the comprehensive water quality index objective function; x = [x1, x2, x3, x4] represents the total inflow into the lake in each season; Ts represents the total number of seasons; y = [y1, y2, y3, y4] represents the proportion of the flow at each lake inlet, satisfying P i represents the power coefficient of the i-th sluice pump; Q i,t represents the flow rate of the i-th sluice pump in season t; H i,t represents the head of the i-th sluice pump in season t; Δt represents the duration of season t; α, β, γ, δ, η represent the weight coefficients, α + β + γ + δ + η = 1; DO represents dissolved oxygen; SD represents transparency; TN represents total nitrogen; TP represents total phosphorus; The constraint conditions of the optimization model: a. Water balance constraint: where: Q t is the inflow data of the target lake; b. Equipment physical limitations of the i-th sluice pump: c. Water quality compliance constraint: d. Ecological flow constraint: x winter ≥Q eco =0.3·Q averageannual Wherein, Q eco represents the minimum ecological base flow; Q averageannual represents the average annual runoff of the basin obtained from the inflow data of the target lake; e. Dynamic rainstorm response constraint: x summer ≤1.2·Q summer Q summer represents the summer inflow data through the target lake.

4. The low-carbon regulation method for an urban lake group according to claim 3, characterized in that, The HSI index model for the fish spawning period is constructed, and the HSI index model is solved based on the flow field distribution data, water quality index data, and water temperature to calculate the HSI spawn , including: Construct an HSI index model for the fish spawning period; Based on the flow field distribution data, water quality index data, and water temperature, solve the HSI index. The specific calculation process is: Where, HSI spawn represents the suitability index during the fish spawning period, and DO represents the dissolved oxygen concentration in water; DO opt represents the optimal dissolved oxygen concentration for fish spawning; Q represents the water flow rate in the fish spawning area of the lake; DQ opt represents the optimal flow rate for fish spawning; T represents the water temperature; T opt represents the optimal temperature for fish spawning; the weights ω1, ω2, and ω3 are used to represent the relative importance of the three factors of dissolved oxygen, flow rate, and water temperature on the fish spawning suitability.

5. A low-carbon regulation method for urban lake groups according to claim 3, characterized in that The above-mentioned process of solving the optimization model based on the inflow data, pollutant concentration data, flow field distribution data, water quality index data, sluice pump power coefficient, sluice pump flow data, sluice pump head data, and season duration of the target lake to obtain the sluice pump regulation parameter set data and achieve low-carbon regulation of urban lake groups includes: Solve the optimization model based on the non-dominated sorting genetic algorithm to obtain the sluice pump regulation parameter set data and achieve low-carbon regulation of urban lake groups. Specifically: Encode the total inflow of each season and the proportion of the flow of each lake inlet into chromosomes; Generate an initial solution that satisfies the constraint conditions of the optimization model based on the chromosomes as the initial population; Calculate the total energy consumption target and water quality comprehensive index of each initial solution; Perform Pareto non-dominated sorting on the initial population according to the total energy consumption target and water quality comprehensive index of each initial solution to obtain the sorted population; Generate an offspring population based on the sorted population and genetic operations; Loop and execute the above operations. When the change rate of the Pareto front meets the preset threshold or reaches the maximum number of iterations, stop the iteration and return the optimal solution on the approximate Pareto front; Decode based on the chromosomes of the final offspring population corresponding to the optimal solution to obtain the sluice pump regulation parameter set data and achieve low-carbon regulation of urban lake groups.

6. The low-carbon regulation method for an urban lake group according to claim 5, wherein The above-mentioned generation of an initial solution that satisfies the constraint conditions of the optimization model based on the chromosomes as the initial population includes: Generate the original initial solutions with uniform distribution based on chromosomes, and check the generated original initial solutions. Set the original initial solutions that meet the constraints of the optimization model as directly qualified solutions, and the original initial solutions that do not meet the constraints of the optimization model as solutions to be repaired; Repair the solutions to be repaired to make them meet the constraints of the optimization model, and obtain the repaired qualified solutions; Collectively constitute the initial solutions from the directly qualified solutions and the repaired qualified solutions as the initial population.

7. A low-carbon regulation method for urban lake groups according to claim 5, characterized in that Pareto non-dominated sorting is performed on the initial population according to the total energy consumption target and water quality comprehensive index of each initial solution to obtain the sorted population, including: Based on the total energy consumption target and water quality comprehensive index of each initial solution, perform hierarchical sorting on each initial solution in the initial population and divide the Pareto rank of each initial solution; Sort each initial solution according to the divided Pareto rank to obtain the first sorted population; Calculate the crowding degree of each initial solution in the first sorted population; Sort the initial solutions according to the crowding degree within the same Pareto rank to obtain the second sorted population; Obtain the sorted population based on the first sorted population and the second sorted population.

8. A low-carbon regulation method for an urban lake group according to claim 5, characterized in that Generate the offspring population based on the sorted population and genetic operations, including; Perform selection, crossover, and mutation operations on the sorted population to generate the offspring population.

9. A low-carbon regulation method for urban lake groups according to claim 5, characterized in that Based on the inflow data, pollutant concentration data, flow field distribution data of the target lake, water quality index data, gate-pump power coefficient, gate-pump flow rate data, gate-pump head data, and season duration, solve the optimization model to obtain the gate-pump regulation parameter set data, and achieve low-carbon regulation of urban lake groups. It also includes: Ensure that the initial solutions in the offspring population meet all hard constraints and soft constraints during coding; The hard constraints include water balance constraints, equipment physical limitations of gate-pumps, water quality compliance constraints, ecological flow constraints, and dynamic rainstorm response constraints, which are used to directly limit the initial solution space during coding; The penalty function P = max(0, over - scalar) 2 is added as a soft constraint to the total energy consumption objective function and the comprehensive water quality index objective function.

10. A low-carbon regulation system for urban lake groups, characterized in that, Include a data acquisition module, a data calculation module, a flow field and water quality simulation module, a model construction module, and a parameter solution module, where: The data acquisition module is used to obtain the geographical spatial data, topographic data, meteorological data, hydrological observation data, and watershed management data of the target lake; The data calculation module is used to calculate the inflow data and pollutant concentration data of the target lake based on the geographical spatial data, meteorological data, hydrological observation data, and watershed management data; The flow field and water quality simulation module is used to simulate and calculate the flow field distribution data and water quality index data of the target lake based on the topographic data, meteorological data, inflow data, and pollutant concentration data of the target lake; The model construction module is used to construct an optimization model with the goal of minimizing the energy consumption of gate-pumps and maximizing the improvement of lake water quality; The parameter solution module is used to solve the optimization model based on the inflow data, pollutant concentration data, flow field distribution data of the target lake, water quality index data, gate-pump power coefficient, gate-pump flow rate data, gate-pump head data, and season duration, and obtain the gate-pump regulation parameter set data to achieve low-carbon regulation of urban lake groups.

Images