A method and system for low-carbon regulation of urban lake clusters

Abstract

This invention provides a low-carbon regulation method and system for urban lake clusters, comprising: acquiring geospatial, topographic, meteorological, hydrological observation, and watershed management data of the target lake; calculating inflow and pollutant concentration data based on this data; then, combining the topographic data of the target lake, simulating and calculating the flow field distribution and water quality index data of the target lake; constructing an optimization model with the objectives of minimizing gate pump energy consumption and maximizing lake water quality improvement; solving the optimization model based on inflow, pollutant concentration, flow field distribution, water quality index, gate pump power coefficient, gate pump flow rate and head, and seasonal duration to obtain gate pump regulation parameter set data, thereby achieving low-carbon regulation of the urban lake cluster. The low-carbon regulation method and system for urban lake clusters provided by this invention can optimize water volume by allocating seasonal changes, regulate inflow, improve water quality, enhance water flow, promote the restoration of the lake's ecological environment, reduce energy consumption, and achieve green and low-carbon governance.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of urban lake group water resource management and ecological environment improvement, and particularly relates to a low-carbon regulation method and system for urban lake group. BACKGROUND

[0002] Urban lakes play a significant role in the ecological system, as 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 over-concentration of urban population and the complex surrounding environment exacerbate the pollution of water quality and ecological degradation of lakes. Many lakes have poor connectivity, insufficient water exchange, and slow water flow, especially in summer, which easily forms dead water zones, leading to the proliferation of algae and causing water blooms, thereby severely damaging water health and ecological balance. Current methods for improving urban lakes mainly include physical, chemical, and ecological measures. Physical measures such as pollution barriers and sedimentation tanks can remove some pollutants, but they cannot fundamentally solve the problems of water flow and water quality deterioration. Chemical measures such as adding flocculants and oxidants can quickly remove some pollutants, but they can easily cause secondary pollution and are complex to operate and costly. Ecological measures such as plant and microbial restoration rely on natural processes, have a slow effect, and are sensitive to environmental conditions, and have limited effect in lakes with poor water quality and flow. Furthermore, in terms of water resource regulation, many urban lakes lack effective regulation mechanisms and cannot flexibly adjust the water inflow according to seasonal changes. Traditional single dam regulation methods lack scientific basis and cannot accurately control water flow or effectively improve water quality. Moreover, this regulation method does not fully consider energy consumption, and frequent start-stop of the dam pump not only wastes energy but also can disrupt the ecological balance, which goes against the principles of green low-carbon and sustainable development.

[0003] Under the background of the prior art, urban lake management measures relying on physical, chemical, and ecological measures have limited effectiveness. At the same time, traditional water resource regulation mechanisms lack scientificity and mostly rely on single dam regulation, without fully considering energy consumption and the impact on the lake's ecological environment. Under the combined influence of these factors, urban lakes may experience water quality deterioration, massive algae proliferation leading to water blooms, ecological system imbalance, and energy waste, which severely affects the ecological function and sustainable development of urban lakes. SUMMARY

[0004] The present application aims to provide a low-carbon regulation method and system for urban lake group to solve the above technical problems, avoid the inability to effectively coordinate various factors in the face of complex lake hydrological characteristics and poor water flow, optimize water allocation through seasonal changes, flexibly adjust the inflow, improve water quality, enhance water flow, and promote the restoration of the lake's ecological environment, further reduce energy consumption, and achieve the goal of green low-carbon management.

[0005] In order to solve the above technical problems, the present application provides a kind of urban lake group low carbon regulation method, comprising the following steps:

[0006] Obtain the geographic space data of target lake, topographic data, meteorological data, hydrological observation data and basin management data;

[0007] Based on the geographic space data, meteorological data, hydrological observation data and basin management data, the inflow data and pollutant concentration data of target lake are calculated;

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

[0009] An optimization model is constructed to minimize the energy consumption of gate pump and maximize the improvement of lake water quality;

[0010] Based on the inflow data, pollutant concentration data, flow field distribution data, water quality index data of target lake, gate pump power coefficient, gate pump flow data, gate pump lift data and seasonal length, the optimization model is solved to obtain gate pump regulation parameter set data, and the low carbon regulation of urban lake group is realized.

[0011] In the above scheme, based on the geographic space data, meteorological data, hydrological observation data and basin management data of target lake, the hydrological processes such as precipitation, runoff and evaporation can be simulated, and then the inflow data and pollutant concentration data are calculated to provide input data for subsequent calculation. Then based on the topographic data, meteorological data and inflow data and pollutant concentration data of target lake, the flow field distribution data and water quality index data of target lake are obtained by numerical simulation of lake water flow, pollutant transport and deposition process. Then an optimization model is constructed to minimize the energy consumption of gate pump and maximize the improvement of lake water quality, which is the basis for solving the gate pump regulation parameter set data. Finally, the optimization model is solved by combining the inflow data, pollutant concentration data, flow field distribution data, water quality index data of target lake, gate pump power coefficient, gate pump flow data, gate pump lift data and seasonal length, to simulate the improvement effect of gate pump control on lake water quality and ecological environment, generate gate pump regulation parameter set data, realize low carbon regulation of urban lake group, and achieve the optimization of water allocation through seasonal change, flexible adjustment of inflow, improvement of water quality, enhancement of water flow and promotion of lake ecological environment repair, further reduction of energy consumption, and realization of green and low carbon management goal.

[0012] Further, it also includes:

[0013] Based on the arrangement of multiple inflow ports of target lake, the flow of each inflow port is adjusted and quantified;

[0014] Based on the flow of each lake inlet, the growth rate of algae is calculated to represent the effect of water bloom prevention and control, and the specific calculation process is as follows:

[0015]

[0016] In the formula, represents the growth rate of algae; represents the maximum growth rate of algae; represents the pumping flow; represents the critical flow for inhibiting the growth of algae; represents a function related to temperature and nutrients; wherein T represents water temperature, nutrient N represents nitrogen, and nutrient P represents phosphorus.

[0017] In the above scheme, by calculating the growth rate of algae, the effect of the flow of each lake inlet on water bloom prevention and control can be quantified. With this quantitative index, the specific improvement effect of the lake ecological environment after low-carbon regulation of the urban lake group is simulated, so that the effectiveness of the provided low-carbon regulation strategy can be more directly and scientifically evaluated, thereby providing strong support for the sustainable management of urban lake groups.

[0018] Further, the optimization model constructed to minimize the energy consumption of the gate pump and maximize the improvement of the lake water quality comprises:

[0019] An HSI index model for the spawning period of fish 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 .

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

[0021]

[0022] Wherein:

[0023]

[0024] In the formula, is the total energy consumption objective function; is the water quality comprehensive index objective function; , represents the total flow of each season into the lake; Ts represents the total number of seasons; , represents the proportion of the flow of each lake inlet, satisfying ; represents the power coefficient of the th gate pump; represents the flow of the th gate pump in season ; represents the lift of the th gate pump in season ; denotes the length of the season t; , , , , denotes the weight coefficient, ; denotes the dissolved oxygen; denotes the transparency; denotes the total nitrogen; denotes the total phosphorus;

[0025] The constraint conditions of the optimization model:

[0026] a. Water balance constraint:

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

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

[0029] c. Water quality compliance constraint:

[0030] d. Ecological flow constraint:

[0031] In the formula, denotes the minimum ecological base flow; denotes the average annual runoff of the watershed obtained by the inflow data of the target lake;

[0032] e. Dynamic rainstorm response constraint:

[0033] denotes the summer inflow data of the target lake.

[0034] In the above scheme, a multi-objective optimization model considering the total energy consumption and water quality comprehensive index is proposed. The model adjusts the total inflow of different seasons and the proportion of each inflow, combined with water balance constraint, equipment physical limit of gate pump, water quality compliance constraint, ecological flow constraint and dynamic rainstorm response constraint, to carry out multi-objective optimization. By introducing the multi-objective optimization model and each constraint condition, the balance between each target is ensured, and the situation that excessive deviation to a certain target in the optimization process leads to the ineffective realization of other targets is avoided. Finally, the optimization model is obtained, which provides support for subsequent model solving and obtaining gate pump control parameter set data.

[0035] Further, the HSI index model of the fish spawning period is constructed, and the HSI index model is solved based on the flow field distribution data, the water quality index data and the water temperature to obtain , comprising:

[0036] constructing an HSI index model of a fish spawning period;

[0037] solving the HSI index based on the flow field distribution data, the water quality index data and the water temperature, and the specific calculation process is:

[0038]

[0039] In the formula, represents the suitability index of the fish spawning period, represents the dissolved oxygen concentration in the water; ; represents the flow rate of the fish spawning area in the lake; ; represents the water temperature; represents the optimal temperature for fish spawning; 、 、 The weight represents the importance of dissolved oxygen, flow rate and water temperature to the suitability of fish spawning.

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

[0041] Further, the optimization model is solved based on the inflow data, the pollutant concentration data, the flow field distribution data of the target lake, the water quality index data, the gate pump power coefficient, the gate pump flow data, the gate pump head data and the seasonal length to obtain the gate pump control parameter set data, and the low-carbon regulation of the urban lake group is realized, comprising:

[0042] The optimization model is solved based on the non-dominated sorting genetic algorithm to obtain the gate pump control parameter set data, and the low-carbon regulation of the urban lake group is realized, specifically:

[0043] Encode the total inflow of each season and the proportion of each inflow into the chromosome;

[0044] Generate an initial solution that satisfies the constraint conditions of the optimization model based on the chromosome as the initial population;

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

[0046] According to the total energy consumption target and water quality comprehensive index of each initial solution, the initial population is sorted by Pareto non-dominated sorting, and the sorted population is obtained;

[0047] Based on the sorted population and genetic operation, a child population is generated;

[0048] The above operations are repeatedly executed, when the Pareto front variation rate meets the preset threshold or reaches the maximum iteration number, the iteration is stopped, and the optimal solution on the approximate Pareto front is returned;

[0049] Based on the chromosome of the final child population corresponding to the optimal solution, the gate pump control parameter set data is obtained, and the low-carbon regulation of the urban lake group is realized.

[0050] In the above scheme, the non-dominated sorting genetic algorithm is used as the core of multi-objective optimization to solve the optimization model. The total inflow of each season and the proportion of the inflow of each inlet are encoded as chromosomes, which facilitates the subsequent operation and optimization of these variables by the non-dominated sorting genetic algorithm. The total inflow of each season and the inflow of each inlet are mapped to the gene space of the genetic algorithm, allowing the algorithm to search for the optimal solution in this abstract space, improving 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. Then, the performance of each initial solution in terms of energy consumption and water quality improvement is quantified to obtain the total energy consumption target and water quality comprehensive index of each initial solution, providing objective evaluation indicators for subsequent Pareto non-dominated sorting. Further, the initial population is divided into different Pareto levels according to the non-dominated relationship of the total energy consumption target and water quality comprehensive index of each initial solution. Through this sorting method, solutions with good performance in both total energy consumption target and water quality comprehensive index can be quickly selected, providing a high-quality parent population for subsequent genetic operations and speeding up the convergence of the algorithm to the optimal solution. Then, the above operations are repeatedly executed to continuously optimize the population and gradually approach the optimal solution. By monitoring the change rate of the Pareto frontier, 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 frontier is returned. The maximum number of iterations is used as a forced stopping condition to prevent the algorithm from running indefinitely. Finally, the final offspring population chromosome obtained by using the non-dominated sorting genetic algorithm is decoded into actual gate pump control parameter sets, which can be directly applied to actual regulation engineering of urban lake groups, achieving precise control of gate pumps, and further achieving low-carbon regulation of urban lake groups. Through seasonal optimization of water allocation, flexible adjustment of inlet flow, improvement of water quality, enhancement of water flow, and promotion of lake ecological environment restoration, energy consumption is further reduced, and the goal of green and low-carbon governance is achieved.

[0051] Further, the initial solution satisfying the constraint conditions of the optimization model is generated based on the chromosome, as the initial population, comprising:

[0052] The original initial solution is generated based on the chromosome, and the generated original initial solution is checked. The original initial solution satisfying the constraint conditions of the optimization model is set as a directly qualified solution, and the original initial solution not satisfying the constraint conditions of the optimization model is set as a to-be-repaired solution.

[0053] The to-be-repaired solution is repaired to make the to-be-repaired solution satisfy the constraint conditions of the optimization model, and a repaired qualified solution is obtained.

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

[0055] In the above scheme, generating uniformly distributed original initial solutions helps to reduce the possibility of the algorithm falling into a local optimal solution. By checking the original initial solutions and classifying, 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 and avoid wasting computational resources on invalid solutions. It also lays the foundation for selecting a high-quality initial population. Then, the to-be-repaired solutions become repaired qualified solutions after repair, which can meet the constraints of the optimization model, increasing the number of effective solutions in the initial population and improving the overall quality of the initial population. This ensures that the subsequent genetic algorithm searches within a feasible solution space, making the algorithm more reliable and stable and improving the probability of finding a global optimal solution. Integrating the directly qualified solutions and the repaired qualified solutions forms the initial population, ensuring that the initial population contains both solutions that meet the conditions without processing and feasible solutions that have been repaired, enriching the diversity of the initial population.

[0056] Further, the initial population is sorted according to the total energy consumption target and the water quality comprehensive index of each initial solution to obtain a sorted population, including:

[0057] Based on the total energy consumption target and the water quality comprehensive index of each initial solution, each initial solution in the initial population is sorted and divided into a Pareto level.

[0058] According to the divided Pareto level, each initial solution is sorted to obtain a first sorted population.

[0059] The crowding degree of each initial solution in the first sorted population is calculated.

[0060] In the same Pareto level, the initial solutions are sorted according to the crowding degree to obtain a second sorted population.

[0061] The sorted population is obtained according to the first sorted population and the second sorted population.

[0062] In the above scheme, the initial solutions are divided into Pareto levels, so that those initial solutions which are relatively superior in both total energy consumption target and water quality comprehensive index are in higher levels. According to the Pareto level, the initial solutions are sorted to construct the first sorted population, and the solutions in the front will be more competitive in the multi-objective optimization process and more likely to be selected as parents to participate in the evolution step of the genetic algorithm. 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 falling into local optimum by concentrating too much in some local region. According to the crowding degree, the initial solutions in the same Pareto level are sorted again to obtain the second sorted population. This operation ensures that the quality of the initial solutions is not reduced, and preferentially selects those initial solutions which have relatively few initial solutions around them and are more valuable for exploration. It not only maintains the advancement of the population in the direction of multi-objective optimization, but also fully excavates the diversity of the solutions in the same level, which is conducive to discovering new better solution combinations and promoting continuous optimization of the algorithm. By comprehensively considering the Pareto level advantage emphasized by the first sorted population and the crowding degree advantage excavated by the second sorted population, the sorted population is finally obtained, which contains not only the solutions that perform well in the early stage of multi-objective optimization, but also the solutions that have potential in maintaining diversity, providing a high-quality and diversified basis for the subsequent genetic operations based on the sorted population.

[0063] Further, the sorted population and the genetic operation generate a child population, including:

[0064] The sorted population is selected, crossed and mutated to generate a child population.

[0065] In the above scheme, the sorted population is selected based on the Pareto level and the crowding degree to preferentially select better individuals. The selected individuals are crossed and mutated to generate a child population.

[0066] Further, 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 data, gate pump head data and seasonal length, the optimization model is solved to obtain the gate pump control parameter set data, and the low-carbon control of the urban lake group is realized, which further includes:

[0067] Ensure that the initial solutions in the child population meet all the hard constraints and soft constraints when coding;

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

[0069] The penalty function is As a soft constraint, it is added to the total energy consumption objective function and water quality comprehensive index objective function.

[0070] In the above scheme, by strict hard constraint and soft constraint, the diffusion of invalid solution in the offspring population is avoided. For the initial solution slightly violating the soft constraint (such as the water quality comprehensive index close to the critical value of exceeding the standard), by moderately increasing its objective function value, the competitiveness of the initial solution in the process of Pareto non-dominated sorting and genetic algorithm optimization is reduced, and the algorithm is guided to gradually optimize in the direction of meeting low-carbon energy consumption and ensuring good water quality.

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

[0072] The data acquisition module is used to acquire geographical space data, terrain data, meteorological data, hydrological observation data and basin management data of the target lake.

[0073] The data calculation module is used to calculate inflow data and pollutant concentration data of the target lake based on the geographical space data, meteorological data, hydrological observation data and basin management data.

[0074] The flow field and water quality simulation module is used to simulate and calculate flow field distribution data and water quality index data of the target lake based on the terrain 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 minimum energy consumption of the gate pump and the maximum improvement of the water quality of the lake as the target.

[0076] The parameter solving module is used to solve the optimization model based on the inflow data, pollutant concentration data, flow field distribution data, water quality index data of the target lake, gate pump power coefficient, gate pump flow data, gate pump head data and seasonal length, acquire gate pump regulation parameter set data, and realize low-carbon regulation of the urban lake groups.

[0077] The urban lake group low-carbon regulation system provided by the scheme has simple structure, and in actual application, only geographical space data, terrain data, meteorological data, hydrological observation data and basin management data of the target lake need to be obtained through the data acquisition module. Then, based on the geographical space data, meteorological data, hydrological observation data and basin management data of the target lake, the data calculation module is used to simulate hydrological processes such as precipitation, runoff and evaporation, and then the inflow data and pollutant concentration data are calculated to provide input data for subsequent calculation. Then, 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 is used to numerically simulate the lake water flow, pollutant transport and deposition process, and the flow field distribution data and water quality index data of the target lake are obtained. Then, through the model construction module, an optimization model with the minimum energy consumption of the gate pump and the maximum improvement of the lake water quality as the target is constructed, which is used as the basis for solving the gate pump regulation parameter set data. Finally, combined with the inflow data, pollutant concentration data, flow field distribution data, water quality index data of the target lake, gate pump power coefficient, gate pump flow data, gate pump head data and seasonal length, the optimization model is solved through the parameter solving module, the improvement effect of the gate pump control on the lake water quality and ecological environment is simulated, the gate pump regulation parameter set data is generated, the urban lake group low-carbon regulation is realized, and the seasonal change optimization of water allocation, flexible adjustment of inflow, improvement of water quality, enhancement of water flow and promotion of lake ecological environment repair can be achieved, further reducing energy consumption and realizing the green and low-carbon management target. BRIEF DESCRIPTION OF DRAWINGS

[0078] Figure 1 A flowchart of a low-carbon regulation method for urban lake groups provided by an embodiment of the present application is shown in the figure.

[0079] Figure 2 An architecture diagram of a low-carbon regulation system for urban lake groups provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0080] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0081] The specific steps of the low-carbon regulation method for urban lake groups provided by the embodiment are shown in Figure 1 , including:

[0082] Obtain geographical space data, terrain data, meteorological data, hydrological observation data and basin management data of the target lake.

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

[0084] simulate and calculate 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;

[0085] construct an optimization model with the objectives of minimizing the energy consumption of the gate pump and maximizing the improvement of the water quality of the lake;

[0086] solve the optimization model based on the inflow data, pollutant concentration data, flow field distribution data, water quality index data of the target lake, gate pump power coefficient, gate pump flow data, gate pump head data and seasonal time length, obtain gate pump regulation parameter set data, and realize low-carbon regulation and control of the urban lake group.

[0087] In this embodiment, to improve the water quality of Xinghu Lake, the lake is taken as a target lake for low-carbon regulation. An optimization scheme for dam regulation is proposed to improve the water quality and water flow by adjusting the flow proportion of multiple inflow outlets of Xinghu Lake. Based on the geographical spatial data, meteorological data, hydrological observation data and basin 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, groundwater recharge and pollutant load of the basin outlet, and then obtain the inflow data and pollutant concentration data of the lake, which provide input data for subsequent simulation and provide basic boundary conditions for the inflow of the lake. The geographical spatial data includes DEM, land use type map and soil type map, and the meteorological data includes precipitation, air temperature and relative humidity. Then, based on the topographic data, meteorological data, inflow data and pollutant concentration data of the target lake, the EFDC model is used to simulate the numerical simulation of the lake water flow, pollutant transport and deposition process by the three-dimensional finite difference method, and the flow field distribution data and water quality index data of the target lake are obtained. The model is arranged with multiple inflow outlets based on the actual situation of Xinghu Lake, and the improvement effect of the dam pump control on the water quality and ecological environment of the lake is simulated by adjusting the flow of each inflow outlet. The lake topographic data includes high-resolution topographic map. Then an optimization model is constructed to minimize the energy consumption of the dam pump and maximize the improvement of the water quality of the lake, which is used as the basis for solving the dam pump regulation parameter set data. Finally, the optimization model is solved by combining the inflow data, pollutant concentration data, flow field distribution data, water quality index data of the target lake, dam pump power coefficient, dam pump flow data, dam pump head data and seasonal length, to simulate the improvement effect of the dam pump control on the water quality and ecological environment of the lake, generate the dam pump regulation parameter set data, realize the low-carbon regulation of the urban lake group, and achieve the optimization of water allocation through seasonal changes, flexible adjustment of inflow flow, improvement of water quality, enhancement of water flow and promotion of lake ecological environment restoration, further reduction of energy consumption, and realization of green and low-carbon management goal. The above process is realized by designing an interface program to seamlessly connect the inflow 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 inflow data and pollutant concentration data of the basin; then the results are taken as the input of the EFDC model to simulate the changes of water dynamics and water quality in the lake. By combining the two models, the interaction of water flow, pollutant propagation, ecological environment and other factors in the lake can be comprehensively reflected, and accurate data support can be provided for optimizing lake management and resource allocation. During the dynamic simulation of the model, the flow proportion of each inflow outlet is adjusted to analyze the influence of different flow allocation schemes on the lake water exchange frequency, water quality improvement and ecological system health. In summer and autumn, the flow proportion can be appropriately increased to improve the lake water exchange efficiency due to the abundant water resources in the basin; in winter and spring, the flow is optimized according to the resource limitation to maintain the stability of the water quality.The model output includes flow field distribution data (such as flow rate, residence time) and water quality index data (such as transparency, nutrient salt concentration) of the target lake, providing quantitative basis for flow regulation. Finally, by designing different inflow configuration schemes and regulation strategies, the influence of gate pump regulation on lake water quality improvement and energy consumption reduction is analyzed, thereby providing scientific basis for lake optimization management. In this embodiment, by combining the SWAT hydrological model and the EFDC hydrodynamic model, the water resources and lake hydrodynamic processes are comprehensively simulated, and a comprehensive model integrating hydrology, water quality and ecological optimization is constructed, thereby providing accurate data support and decision basis for scientific governance of the lake. In this embodiment, by combining the amount of water resources in the basin and the seasonal changes of the lake, the water quantity in each season is accurately allocated to optimize the effect of water quality improvement and lake ecological environment restoration. In addition, by combining the regulation schemes of the inflow of multiple lake inlets, the inflow of the lake inlets can be flexibly adjusted, further improving the flowability of the lake water body and the water quality transparency.

[0088] Further, it also includes:

[0089] Based on the arrangement of multiple lake inlets of the target lake, the flow of each lake inlet is adjusted and quantified;

[0090] Based on the flow of each lake inlet, the growth rate of algae is calculated to represent the effect of water bloom prevention and control, and the specific calculation process is as follows:

[0091]

[0092] In the formula, represents the growth rate of algae; represents the maximum growth rate of algae; represents the pumping flow; represents the critical flow for inhibiting the growth of algae; represents a function related to temperature and nutrients; wherein 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 effect of the flow of each lake inlet on water bloom prevention and control can be quantified. With this quantitative index, the specific improvement effect of the lake ecological environment after low-carbon regulation of the urban lake group is simulated, thereby more intuitively and scientifically evaluating the effectiveness of the provided low-carbon regulation strategy, and providing strong support for the sustainable governance of the urban lake group.

[0094] Further, the construction of the optimization model with the minimum gate pump energy consumption and the maximum lake water quality improvement as the target includes:

[0095] An HSI index model for the spawning period of fish is constructed, and the HSI index model is solved based on the flow field distribution data, the water quality index data and the water temperature, and is calculated.

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

[0097]

[0098] Wherein:

[0099]

[0100] In the formula: is the total energy consumption objective function; is the water quality comprehensive index objective function; , indicates the total inflow of each season into the lake; Ts indicates the total number of seasons; , indicates the proportion of the inflow of each lake mouth, satisfying ; indicates the power coefficient of the i th gate pump; indicates the flow of the i th gate pump in season ; indicates the head of the i th gate pump in season ; indicates the time length of season t; , , , , , , , indicates the weight coefficient, ; indicates the dissolved oxygen; indicates the transparency; indicates the total nitrogen; indicates the total phosphorus;

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

[0102] a, water balance constraint:

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

[0104] b, equipment physical limit of the i th gate pump:

[0105] c, water quality compliance constraint:

[0106] d, ecological flow constraint:

[0107] In the formula, indicates the minimum ecological base flow; represents the average annual runoff of the watershed obtained by the inflow data of the target lake;

[0108] e. Dynamic rainstorm response constraint:

[0109] represents the summer inflow data of the target lake.

[0110] In this embodiment, a multi-objective optimization model is proposed, which comprehensively considers the total energy consumption and the water quality comprehensive index. The model adjusts the total inflow of different seasons and the proportion of the inflow of each lake mouth, combines the water balance constraint, the physical limitation of the gate pump device, the water quality standard constraint, the ecological flow constraint and the dynamic rainstorm response constraint, and performs multi-objective optimization. Among them, is the water quality comprehensive index (dimensionless), which is calculated by weighting the key indicators, represents the length of season t, such as summer = 92 × 86400 s = 92 × 86400 s, , , , , According to the ecological priority setting, the water balance constraint needs to ensure that the total inflow of each season is consistent with the inflow data of the target lake simulated by SWAT. The ecological flow constraint represents the minimum ecological base flow in the dry season, and the dynamic rainstorm response constraint represents the upper limit of the flow in the rainy season to prevent flood overflow. By introducing the multi-objective optimization model and each constraint condition, the balance between each target is ensured, and the situation that other targets cannot be effectively realized due to excessive deviation of a target in the optimization process is avoided. Finally, the optimization model is obtained, which provides support for subsequent solution of the model and obtaining the gate pump control parameter set data. This model not only optimizes the utilization efficiency of water resources, but also balances the economic benefits and sustainable development of ecological environment in water resources management. This not only helps to reduce the management cost, but also helps to achieve the green and low-carbon management goal. In this embodiment, the multi-objective optimization model is introduced, and the synergistic effect of multiple decision factors such as water quality improvement, energy consumption minimization, water resources balance and optimization of gate pump regulation strategy is considered. Ensure that while realizing water quality and ecological improvement, the energy consumption is minimized to the maximum extent, ensure the balance between each target, and avoid the situation that other targets cannot be effectively realized due to excessive deviation of a target in the optimization process.

[0111] Further, the HSI index model of the fish spawning period is constructed, and the HSI index model is solved based on the flow field distribution data, the water quality index data and the water temperature to obtain , including:

[0112] The HSI index model of the fish spawning period is constructed;

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

[0114]

[0115] In the formula, An index indicating the suitability of fish during their spawning season. This indicates the concentration of dissolved oxygen in the water; ; This indicates the flow rate of water in the fish spawning area of ​​a lake; ; Indicates water temperature; This indicates the optimal temperature for fish to spawn; , , The weights are used to represent the importance of the three factors—dissolved oxygen, flow rate, and water temperature—to the suitability of fish spawning.

[0116] In this embodiment, an HSI index model for fish spawning season was constructed to quantify the comprehensive impact of environmental factors on fish suitability during spawning. The model incorporates dissolved oxygen concentration, water flow rate, and water temperature, and uses weighting to reflect the relative importance of each factor. The calculated... The index can intuitively reflect the suitability of a lake's ecological environment for fish spawning. A higher index indicates that the area is favorable for fish spawning under current environmental conditions; a lower index suggests the need for adjustments or improvements to relevant environmental factors. Therefore, this HSI index model can assist in the development of optimization models. Considering the suitability for fish spawning, adjusting the total inflow and the proportion of inflow at each inlet can avoid negative impacts on fish spawning due to improper flow control.

[0117] Furthermore, based on 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 seasonal duration, the optimization model is solved to obtain a set of gate pump control parameters, thereby achieving low-carbon regulation of the urban lake cluster, including:

[0118] The optimization model is solved using a non-dominated sorting genetic algorithm to obtain the set of gate pump control parameters, thereby achieving low-carbon regulation of the urban lake cluster. Specifically:

[0119] The total inflow into the lake in each season and the proportion of inflow into each lake outlet are encoded as chromosomes;

[0120] An initial solution that satisfies the constraints of the optimization model is generated based on chromosomes, and this solution is used as the initial population.

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

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

[0123] generate a child population based on the sorted population and genetic operations;

[0124] perform the above operations in a loop, and stop iteration when a Pareto frontier change rate meets a preset threshold or a maximum iteration number is reached, and return an optimal solution on an approximate Pareto frontier;

[0125] decode a chromosome of a final child population corresponding to the optimal solution to obtain gate pump regulation parameter set data, and realize low-carbon regulation of the urban lake group.

[0126] In this embodiment, a non-dominated sorting genetic algorithm is used as the core of multi-objective optimization, and a closed-loop solution process is constructed by combining the SWAT and EFDC models to solve the optimization model. The total inflow x and the inflow ratio y of each inlet are encoded as chromosomes to facilitate the subsequent operation and optimization of these variables by the non-dominated sorting genetic algorithm. x is real-coded, with each gene representing the total flow of a season, such as spring, summer, autumn, and winter. y is normalized vector-coded, with each gene representing the flow ratio of a certain inlet, and the sum of all ratios is 1. The seasonal total inflow values and the inflow values of each inlet are mapped to the gene space of the genetic algorithm, allowing the algorithm to search for the optimal solution in this abstract space, improving efficiency. Then, ensure that the solutions in the initial population meet the constraint conditions of the optimization model at the beginning, to 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, and obtain the total energy consumption target and water quality comprehensive index of each initial solution, providing objective evaluation indicators for subsequent Pareto non-dominated sorting. Further, according to the non-dominated relationship of the total energy consumption target and water quality comprehensive index of each initial solution, the initial population is divided into different Pareto levels. Through this sorting method, solutions with good performance in both total energy consumption target and water quality comprehensive index can be quickly selected, providing a high-quality parent population for subsequent genetic operations and speeding up the convergence of the algorithm to the optimal solution. Then, the above operations are executed in a loop to continuously optimize the population, allowing the algorithm to gradually approach the optimal solution. By monitoring the change rate of the Pareto frontier, 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 frontier is returned. The maximum number of iterations is used as a forced stop condition to prevent the algorithm from running indefinitely. Finally, the final offspring population chromosome obtained by using the non-dominated sorting genetic algorithm is decoded into actual gate pump control parameter sets, which can be directly applied to actual regulation engineering of urban lake groups, achieving precise control of gate pumps, and further achieving the goal of green and low-carbon management. This embodiment designs an optimization model with the goal of minimizing gate pump energy consumption and maximizing lake water quality improvement, considering seasonal water scheduling, water resource balance, energy consumption constraints, and water quality requirements, and dynamically adjusting the inflow of each inlet. Finally, the genetic algorithm is used to solve the problem, outputting the optimal configuration scheme of the total inflow and the inflow ratio of each inlet, providing scientific guidance for sustainable management of lake water environment.

[0127] Further, the initial solution satisfying the constraint conditions of the optimization model is generated based on the chromosome, as the initial population, comprising:

[0128] The original initial solution with uniform distribution is generated based on chromosomes, and the original initial solution meeting the constraint condition of the optimization model is set as a directly qualified solution, and the original initial solution not meeting the constraint condition of the optimization model is set as a to-be-repaired solution;

[0129] The to-be-repaired solution is repaired to make the to-be-repaired solution meet the constraint condition of the optimization model, and a repaired qualified solution is obtained;

[0130] The directly qualified solution and the repaired qualified solution jointly constitute an initial solution as an initial population.

[0131] In the embodiment, generating the original initial solution with uniform distribution by Latin hypercube sampling (LHS) helps to reduce the possibility of the algorithm falling into a local optimal solution. By checking the original initial solution and classifying, solutions meeting and not meeting the constraint condition of the optimization model can be quickly identified. This ensures that subsequent calculations focus on valuable solutions and avoids wasting computing resources on invalid solutions. It also lays the foundation for screening a high-quality initial population. Then, the to-be-repaired solution becomes a repaired qualified solution after repair, such as adjusting the flow proportion normalization, so as to meet the constraint condition of the optimization model, increase the number of effective solutions in the initial population, and thus improve the overall quality of the initial population. This ensures that the subsequent genetic algorithm searches in a feasible solution space, making the algorithm more reliable and stable, and improving the probability of finding a global optimal solution. Integrating the directly qualified solution and the repaired qualified solution forms the initial population, ensuring that the initial population contains both solutions that meet the conditions without processing and feasible solutions that are repaired, and enriching the diversity of the initial population.

[0132] Further, the initial population is sorted according to the total energy consumption target and the water quality comprehensive index of each initial solution to obtain a sorted population, including:

[0133] Based on the total energy consumption target and the water quality comprehensive index of each initial solution, each initial solution in the initial population is sorted and divided into a Pareto level;

[0134] According to the divided Pareto level, each initial solution is sorted to obtain a first sorted population;

[0135] The crowding degree of each initial solution in the first sorted population is calculated;

[0136] In the same Pareto level, the initial solutions are sorted according to the crowding degree to obtain a second sorted population;

[0137] The sorted population is obtained according to the first sorted population and the second sorted population.

[0138] In this embodiment, the initial solutions are divided into Pareto levels, and level 1 is the optimal frontier, so that those initial solutions that are relatively superior in both total energy consumption target and water quality comprehensive index are at a higher level. The initial solutions are sorted according to the Pareto levels to construct a first sorted population, and the solutions in the front will be more competitive in the multi-objective optimization process and are more likely to be selected as parents to participate in the evolution step of the genetic algorithm. 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 falling into local optimum by concentrating too much in some local regions. Within the same Pareto level, the initial solutions are sorted again according to the crowding degree to obtain a second sorted population. This operation prioritizes those initial solutions that have fewer initial solutions around them and are more valuable for exploration, without reducing the quality of the initial solutions. This not only maintains the advancement of the population in the direction of multi-objective optimization, but also fully excavates the diversity of solutions within the same level, which is conducive to discovering new better solution combinations and promoting continuous optimization of the algorithm. By combining the Pareto level advantage emphasized by the first sorted population and the crowding degree advantage excavated by the second sorted population, the sorted population is finally obtained, which contains solutions that perform well in the early stage of multi-objective optimization and solutions that have potential in maintaining diversity, providing a high-quality and diversified basis for subsequent genetic operations based on the sorted population.

[0139] Further, the sorted population and genetic operations are used to generate a child population, including:

[0140] The sorted population is selected, crossed and mutated to generate a child population.

[0141] In this embodiment, the sorted population is selected based on the Pareto level and the crowding degree, and the selected individuals are crossed and mutated to generate a child population. Tournament selection is used to select individuals with high Pareto level and large crowding degree. 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 inflow 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 seasonal length, the optimization model is solved to obtain gate pump control parameter set data, and low-carbon control of urban lake groups is realized, which further includes:

[0143] When encoding, ensure that the initial solutions in the child population meet all the hard constraints and soft constraints;

[0144] The hard constraints include water balance constraints, device physical limitations of gate pumps, water quality compliance constraints, ecological flow constraints, and dynamic storm response constraints, for directly limiting the initial solution space when encoding;

[0145] The penalty function As a soft constraint, it is added to the total energy consumption objective function and the water quality comprehensive index objective function.

[0146] In the above scheme, through strict hard constraints and soft constraints, the spread of invalid solutions in the offspring population is avoided. For initial solutions that slightly violate soft constraints (such as water quality comprehensive indicators close to the critical value of exceeding the standard), the target function value is moderately increased, so that its competitiveness is reduced in the process of Pareto non-dominated sorting and genetic algorithm optimization, and the algorithm is gradually optimized in the direction of meeting low-carbon energy consumption and ensuring good water quality.

[0147] Further, it also includes:

[0148] An algorithm acceleration strategy is adopted for the non-dominated sorting genetic algorithm.

[0149] The algorithm acceleration strategy uses a surrogate model to replace 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 for candidate optimal solution verification; parallel computing uses MPI parallel technology to run multiple SWAT / EFDC instances simultaneously, which can shorten the single iteration time.

[0151] Further, the gate pump control parameter set data includes:

[0152] The gate pump control parameter set data includes core control parameters, auxiliary decision parameters, and verification and monitoring parameters.

[0153] The core control parameters include total inflow, inflow ratio at the inflow, and gate pump operation parameters in each season.

[0154] The auxiliary decision parameters include a Pareto frontier solution set and real-time control thresholds.

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

[0156] In this embodiment, the total inflow of each season is used to guide the annual macro-distribution of water resources and balance the demand in wet and dry seasons. The inflow proportion of the lake mouth is used to fine-tune the flow of each inlet and optimize the flow path of the water body and the diffusion of pollutants. The gate opening: 60%, pump station frequency: 45Hz, daily operation period: 8:00-18:00 of the gate pump operation parameters are used to directly guide the on-site equipment operation, and ensure that the theoretical scheme is converted into actual control action. The Pareto frontier solution set can provide 10 groups of energy consumption-water quality trade-off schemes (such as A scheme: energy consumption reduction 15%, water quality standard rate 90%; B scheme: energy consumption reduction 5%, water quality standard rate 95%), which are used to support managers to select the most suitable scheme according to actual needs (economic vs. ecological priority). Real-time control thresholds such as water quality exceeding threshold (TN≥2mg / L triggers re-optimization) and energy consumption warning threshold (single-day energy consumption exceeds 1000kWh), are used to build adaptive control logic to ensure stable operation of the system. Model verification indicators such as Nash-Sutcliffe coefficient (SWAT runoff simulation >0.6) and EFDC water quality error (RMSE <TN 0.3mg / L) are used to evaluate the reliability of the model and ensure the credibility of the optimization results. Long-term ecological tracking parameters such as sediment TP release rate and phytoplankton biomass interannual variation are used to monitor the long-term impact of the regulation scheme on the lake ecology and support dynamic modification of the scheme.

[0157] See Figure 2 The embodiment provides a low-carbon regulation system for urban lake groups, which comprises a data acquisition module, a data calculation module, a flow field and water quality simulation module, a model construction module and a parameter solving module, wherein:

[0158] The data acquisition module is used to acquire geographic spatial data, terrain data, meteorological data, hydrological observation data and basin management data of the target lake;

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

[0160] The flow field and water quality simulation module is used to simulate and calculate flow field distribution data and water quality index data of the target lake based on terrain 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 minimum gate pump energy consumption and the maximum lake water quality improvement as the target;

[0162] The parameter solving module is configured to solve the optimization model based on the inflow data, the pollutant concentration data, the flow field distribution data of the target lake, the water quality index data, the gate pump power coefficient, the gate pump flow data, the gate pump lift data and the seasonal length, to obtain the gate pump regulation parameter set data, and to realize low-carbon regulation of the urban lake group.

[0163] The urban lake group low-carbon regulation system provided by the embodiment is simple in structure, and only needs to obtain geographical space data, terrain data, meteorological data, hydrological observation data and basin management data of the target lake through the data acquisition module in actual application. The embodiment proposes an urban lake group low-carbon regulation system in view of the characteristics of the Zhaqing Star Lake urban lake group, multiple lake inlets and multiple gate pump regulation, and the demand for ecological environment restoration. The urban lake group low-carbon regulation system can simulate hydrological processes such as precipitation, runoff and evaporation based on the geographical space data, meteorological data, hydrological observation data and basin management data of the target lake through the data calculation module, and then calculate the inflow data and pollutant concentration data to provide input data for subsequent calculation. Then, the flow field and water quality simulation module is used to simulate the numerical simulation of the lake water flow, pollutant transport and deposition process based on the terrain data, meteorological data, inflow data and pollutant concentration data of the target lake, so as to obtain the flow field distribution data and water quality index data of the target lake. Then, a optimization model is constructed by the model construction module, which takes the minimization of gate pump energy consumption and the maximization of lake water quality improvement as the target, and is used as the basis for solving the gate pump regulation parameter set data. Finally, the optimization model is solved by the parameter solving module in combination with 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 data, gate pump head data and seasonal length, so as to simulate the improvement effect of the gate pump control on the lake water quality and ecological environment, generate the gate pump regulation parameter set data, realize the low-carbon regulation of the urban lake group, and achieve the optimization of water allocation through seasonal changes, the flexible regulation of the inflow of the lake inlet, the improvement of water quality, the enhancement of water flow and the restoration of the ecological environment of the lake. The whole process of water conservancy engineering optimization scheduling is researched, and a low-carbon-green technical system is formed. The energy consumption is further reduced, and the green low-carbon management goal is realized. The embodiment proposes a solution of systematic control of gate pump energy consumption optimization based on the concept of green low-carbon, carbon reduction and sustainable development of ecological environment, and realizes the dual goals of energy consumption reduction of water conservancy engineering and improvement of lake ecological environment by combining the multi-objective optimization method of seasonal water resource scheduling and water balance. The method effectively improves the water quality and ecological environment of the lake by optimizing water allocation and flow regulation. The embodiment closely combines the characteristics of the connectivity of the urban lake group, and proposes an optimization framework based on the joint application of hydrological and hydrodynamic models, which is used for accurately calculating lake water resources, regulating inflow and improving lake ecological environment. By optimizing the inflow of the lake inlet and the proportion of water in different seasons, the embodiment can effectively reduce the energy consumption of the inflow gate pump, and significantly improve the water quality and ecological environment of the lake. The technology has the characteristics of high efficiency, low carbon and water saving priority, and can provide a green, low-carbon and intelligent technical solution for urban lake environment management, which has a wide market application prospect, and is especially suitable for coping with challenges such as climate change and water resource shortage.The embodiment reduces energy consumption and wastewater discharge to the greatest extent through seasonal water resource scheduling and energy consumption optimization, and significantly improves the utilization efficiency of lake water resources. It meets the requirements of the national water-saving and pollution reduction policy, can significantly reduce water pollution and improve water resource utilization efficiency, and has significant practical benefits. It solves many challenges in lake basin water resource management and has strong operability and practical significance. The embodiment emphasizes comprehensive system management of the lake basin, optimizes multiple dimensions of hydrology, hydrodynamics, ecology and energy consumption, uses innovative technology combining hydrology and hydrodynamic models, and provides a systematic lake water resource management and environmental protection scheme. Combined with seasonal changes, the water resources in different seasons are comprehensively scheduled to solve many problems such as water resource waste, ecological restoration and energy consumption. By optimizing multiple targets such as lake inflow, inflow proportion, energy efficiency and water quality, the comprehensive goal of efficient allocation of water resources, improvement of ecological environment and minimization of energy consumption is achieved. The water-saving, carbon-reducing and efficiency-increasing management framework constructed by the project not only has high application value, but also provides a promotable technical scheme for similar lake management projects.

[0164] The above is the preferred embodiment of the present application. It should be noted that for those skilled in the art, without departing from the principles of the present application, several improvements and refinements can be made, which are also considered within the scope of protection of the present application.

Claims

1. A low-carbon regulation method for urban lake groups, characterized in that, The method comprises the following steps: obtaining geographical spatial data, terrain data, meteorological data, hydrological observation data and basin management data of a target lake; calculating inflow data and pollutant concentration data of the target lake based on the geographical spatial data, meteorological data, hydrological observation data and basin management data; simulating and calculating flow field distribution data and water quality index data of the target lake based on the terrain data, meteorological data, inflow data and pollutant concentration data of the target lake; constructing an optimization model with the minimum energy consumption of the gate pump and the maximum improvement of the water quality of the lake as the objectives; specifically: constructing an HSI index model for the spawning period of fish; solving the HSI index based on the flow field distribution data, water quality index data and water temperature, and the specific calculation process is as follows: In the formula, an index indicating the suitability of the spawning period of fish, an index indicating the concentration of dissolved oxygen in water; ; an index indicating the flow rate of water in the fish spawning area in a lake; ; an index indicating the water temperature; an index indicating the optimum temperature for fish spawning; , , The weight indicates the importance of the three factors, dissolved oxygen, flow rate, and water temperature, to the suitability of fish spawning. The optimization model is in the form of: wherein: In the formula: is the total energy consumption objective function; is the water quality comprehensive index objective function; , indicates the total inflow of each season into the lake; Ts indicates the total number of seasons; , indicates the proportion of the inflow of each lake mouth, satisfying ; indicates the power coefficient of the th gate pump; indicates the flow of the th gate pump in season ; indicates the head of the th gate pump in season ; indicates the duration of season t; , , , , indicates the weight coefficient, ; indicates dissolved oxygen; indicates transparency; indicates total nitrogen; indicates total phosphorus; The constraint conditions of the optimization model are: a. Water balance constraint: In the formula: is the inflow data of the target lake; b. Device physical limitations of the ith gate pump: c. Water quality compliance constraints: d. Ecological flow constraints: In the formula, represents the minimum ecological base flow; represents the average annual runoff of the watershed obtained through the inflow data of the target lake; e. Dynamic storm response constraints: represents summer inflow data through the target lake; solving the optimization model based on the inflow data, pollutant concentration data, flow field distribution data, water quality index data of the target lake, gate pump power coefficient, gate pump flow data, gate pump head data and seasonal length, obtaining gate pump control parameter set data, and realizing low-carbon regulation and control of the urban lake group.

2. The low-carbon regulation method for urban lake groups according to claim 1, characterized in that, It also includes: arranging multiple inflow openings in the target lake, adjusting and quantifying the flow of each inflow opening; calculating the growth rate of algae to represent the water bloom prevention and control effect based on the flow of each inflow opening, and the specific calculation process is as follows: wherein represents the growth rate of the algae; represents the maximum growth rate of the algae; represents the pumping flow rate; represents the critical flow rate for inhibiting the growth of the algae; represents a function of temperature, nutrients; wherein T represents water temperature, nutrient N represents nitrogen, and nutrient P represents phosphorus.

3. The low-carbon regulation method for urban lake groups according to claim 1, characterized in that, The solving of the optimization model based on the inflow data, pollutant concentration data, flow field distribution data, water quality index data of the target lake, gate pump power coefficient, gate pump flow data, gate pump head data and seasonal length, obtaining gate pump control parameter set data, and realizing low-carbon regulation and control of the urban lake group, includes: solving the optimization model based on the non-dominated sorting genetic algorithm, obtaining gate pump control parameter set data, and realizing low-carbon regulation and control of the urban lake group, specifically: encoding the total inflow of each season and the proportion of the flow of each inflow opening into a chromosome; generating an initial solution that meets the constraint conditions of the optimization model based on the chromosome as an initial population; calculating the total energy consumption target and water quality comprehensive index of each initial solution; 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, and obtaining a sorted population; generating a child population based on the sorted population and genetic operations; recursively executing the above operations, stopping iteration when the Pareto frontier change rate meets a preset threshold or the maximum number of iterations is reached, and returning the optimal solution on the approximate Pareto frontier; decoding the final child population chromosome corresponding to the optimal solution to obtain gate pump control parameter set data, and realizing low-carbon regulation and control of the urban lake group.

4. The low-carbon regulation method for urban lake groups according to claim 3, characterized in that, The generation of an initial solution that meets the constraint conditions of the optimization model based on the chromosome as an initial population includes: generating uniformly distributed original initial solutions based on the chromosome, and checking the generated original initial solutions, setting the original initial solutions that meet the constraint conditions of the optimization model as directly qualified solutions, and setting the original initial solutions that do not meet the constraint conditions of the optimization model as solutions to be repaired; Repair the to-be-repaired solution to make the to-be-repaired solution meet the constraint conditions of the optimization model, and obtain a repaired qualified solution; The direct qualified solution and the repaired qualified solution jointly constitute an initial solution as an initial population.

5. The low-carbon regulation method for urban lake groups according to claim 3, characterized in that, The initial population is sorted according to the total energy consumption target and the water quality comprehensive index of each initial solution to obtain a sorted population, including: Each initial solution in the initial population is sorted according to the total energy consumption target and the water quality comprehensive index, and the Pareto level of each initial solution is divided; Each initial solution is sorted according to the divided Pareto level to obtain a first sorted population; The crowding degree of each initial solution in the first sorted population is calculated; The initial solutions in the same Pareto level are sorted according to the crowding degree to obtain a second sorted population; The sorted population is obtained according to the first sorted population and the second sorted population.

6. The low-carbon regulation method for urban lake groups according to claim 3, characterized in that, The sorted population is sorted according to the first sorted population and the second sorted population. The optimization model is solved 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 data, gate pump head data and seasonal length to obtain gate pump control parameter set data, and realize low-carbon control of the urban lake group, and further including:

7. The low-carbon regulation method for urban lake groups according to claim 3, characterized in that, Ensure that the initial solution in the offspring population meets all the hard constraints and soft constraints during coding; The hard constraints include water balance constraints, device physical limitations of the gate pump, water quality standard constraints, ecological flow constraints and dynamic rainstorm response constraints, which are used to directly limit the initial solution space during coding; The data acquisition module, the data calculation module, the flow field and water quality simulation module, the model construction module and the parameter solving module are included, wherein: The penalty function is added to the total energy consumption objective function and the water quality comprehensive index objective function as a soft constraint.

8. A low-carbon regulation system for urban lake groups, characterized in that, The data acquisition module is used to obtain geographic spatial data, terrain data, meteorological data, hydrological observation data and basin 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 geographic spatial data, meteorological data, hydrological observation data and basin 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 terrain 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 minimum gate pump energy consumption and the maximum lake water quality improvement as the target; specifically: An HSI index model for the fish spawning period is constructed; The HSI index is solved based on the flow field distribution data, water quality index data and water temperature, and the specific calculation process is: The optimization model has the form: In the formula, an index indicating the suitability of the spawning period of fish, an index indicating the concentration of dissolved oxygen in water; ; an index indicating the flow rate of water in the fish spawning area in a lake; ; an index indicating water temperature; an index indicating the optimum temperature for fish spawning; , , The weight indicates the importance of the three factors, dissolved oxygen, flow rate, and water temperature, to the suitability of fish spawning. Wherein: The constraint conditions of the optimization model are: In the formula: is the total energy consumption objective function; is the water quality comprehensive index objective function; , indicates the total inflow of each season; Ts indicates the total number of seasons; , indicates the proportion of the inflow of each lake mouth, satisfying ; indicates the power coefficient of the th gate pump; indicates the flow of the th gate pump in season ; indicates the head of the th gate pump in season ; indicates the duration of season t; , , , , indicates the weight coefficient, ; indicates dissolved oxygen; indicates transparency; indicates total nitrogen; indicates total phosphorus; The parameter solving 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 data, gate pump head data and seasonal length to obtain gate pump control parameter set data, and realize low-carbon control of the urban lake group. a. Water balance constraint: In the formula: is the inflow data of the target lake; b. Device physical limits of the ith gate pump: c. Water quality compliance constraints: d. Ecological flow constraints: In the formula, represents the minimum ecological base flow; represents the average annual runoff of the watershed obtained through the inflow data of the target lake; e. Dynamic storm response constraints: represents summer inflow data through the target lake; ​

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