Optimization treatment method and equipment for urban water control system and medium

Abstract

The invention provides an optimization treatment method and device for an urban water control system and a medium. Comprising the steps of obtaining treatment engineering measures of a plurality of urban drainage systems; determining a project amount upper limit value and an investment cost upper limit value corresponding to each treatment project measure to generate a plurality of project amount combinations; performing simulation evaluation processing on the project amount combinations to obtain investment amount data, sewage entering plant biochemical oxygen demand concentration data and full life cycle carbon emission intensity values corresponding to the project amount combinations; and obtaining a preset multi-target optimization model, and performing Pareto frontier treatment on the investment amount data, the sewage entering plant biochemical oxygen demand concentration data and the full life cycle carbon emission intensity value corresponding to each project amount combination to output a multi-target tradeoff scheme set of the urban water control system. The comprehensiveness of urban water control is improved, and the urban water control requirement is met.

Description

Technical Field

[0001] The present application relates to the technical field of drainage systems, and in particular to an optimization treatment method, equipment and medium for an urban water treatment system. Background Art

[0002] The urban drainage system is an important part of the urban infrastructure, which realizes the functions of flood control and drainage, sewage treatment, maintenance of public health, management of water resources and protection of the environment. The urban drainage system governance project requires investment optimization and effectiveness evaluation to improve the governance effect of the urban drainage system.

[0003] In the prior art, optimization methods with a single objective or a small number of objectives are usually adopted, as well as linear programming, multi-objective decision analysis or simple heuristic algorithms to control investment costs or optimize a single indicator; however, the optimization processing methods of urban water management systems in the prior art are limited to the optimization of a single indicator and cannot find the global optimal solution.

[0004] Therefore, in the prior art, there is a problem that the optimization treatment method of the urban water management system is not comprehensive enough and accurate enough. Summary of the invention

[0005] The embodiments of the present application provide an optimization processing method, equipment and medium for an urban water management system, so as to achieve the effect of improving the comprehensiveness and accuracy of the optimization processing method for an urban water management system.

[0006] In a first aspect, an embodiment of the present application provides an optimization processing method for an urban water management system, comprising:

[0007] Obtain engineering measures for the management of drainage systems in multiple cities;

[0008] Determine the upper limit of engineering quantity and investment cost corresponding to each control engineering measure;

[0009] Generate multiple combinations of engineering quantities according to the engineering quantity upper limit and investment cost upper limit corresponding to each control engineering measure;

[0010] Conduct simulation and evaluation of the engineering quantity combinations to obtain the corresponding investment amount data, biochemical oxygen demand concentration data of sewage entering the plant and carbon emission intensity value of the whole life cycle for each engineering quantity combination;

[0011] Obtaining a preset multi-objective optimization model, wherein the objective functions of the multi-objective optimization model include an economic cost minimization objective function for optimizing investment amount data, a sewage inlet biochemical oxygen demand concentration maximization objective function for optimizing sewage inlet biochemical oxygen demand concentration data, and a life cycle carbon emission intensity minimization objective function for optimizing the life cycle carbon emission intensity value;

[0012] Using the preset multi-objective optimization model, the Pareto frontier processing is performed on the investment amount data, the biochemical oxygen demand concentration data of the sewage entering the plant, and the carbon emission intensity value of the whole life cycle corresponding to each engineering quantity combination to output a set of multi-objective trade-off solutions for the urban water treatment system. In one possible implementation, multiple engineering quantity combinations are generated according to the engineering quantity upper limit value and the investment cost upper limit value corresponding to each treatment engineering measure, including:

[0013] A random generation method is adopted to generate multiple engineering quantity combinations according to the engineering quantity upper limit values ​​and investment cost upper limit values ​​corresponding to each control engineering measure, and the engineering quantity values ​​of the generated engineering quantity combinations are gradually increased through an applicable random generation algorithm until the engineering quantity values ​​reach the preset engineering quantity upper limit value.

[0014] In a possible implementation, simulation and evaluation processing is performed on the engineering quantity combinations to obtain investment amount data, biochemical oxygen demand concentration data of sewage entering the plant, and carbon emission intensity values ​​over the entire life cycle corresponding to each engineering quantity combination, including:

[0015] Get the preset construction sequence logic;

[0016] According to the preset construction sequence logic, determine the influencing data of key parameters corresponding to each combination of engineering quantities;

[0017] Obtain a preset system dynamics simulation model;

[0018] According to the impact data of key parameters, the preset system dynamics simulation model is used to simulate and evaluate the environmental benefits, so as to obtain the evaluation results of the environmental benefits corresponding to each combination of engineering quantities;

[0019] Based on the environmental benefit assessment results, the investment amount data, biochemical oxygen demand concentration data of sewage entering the plant and carbon emission intensity value over the entire life cycle corresponding to each engineering quantity combination are determined.

[0020] In a possible implementation, the preset multi-objective optimization model is a non-dominated sorting genetic algorithm model after optimization and improvement;

[0021] Among them, the optimization and improvement processing for the non-dominated sorting genetic algorithm model includes population non-dominated sorting processing and genetic operator genetic processing.

[0022] In one possible implementation, the population non-dominated sorting process includes:

[0023] In the process of optimizing with the preset multi-objective optimization model, the fast non-dominated sorting algorithm is used to stratify the individuals in the population.

[0024] In a possible implementation, the genetic operator genetic processing includes:

[0025] In the process of optimization using a preset multi-objective optimization model, individuals with fitness higher than a preset fitness threshold are selected from the population, recombined using simulated binary crossover, and adjusted through multi-point mutation operations to maintain population diversity.

[0026] In a possible implementation, the economic cost minimization objective function for optimizing the investment amount data is:

[0027]

[0028] Among them, TDC is the economic cost, is the initial construction cost, is the operation and maintenance cost in year t, is the discount rate and T is the operating period of the project.

[0029] In a possible implementation, the life cycle carbon emission intensity minimization objective function for optimizing the life cycle carbon emission intensity value is:

[0030]

[0031] Among them, CEI is the carbon emission intensity of the entire life cycle. is the carbon emissions during the construction phase of the drainage system project. is the carbon emissions during the demolition phase, is the carbon emissions during the operation and maintenance phase, is the carbon emission in the sludge disposal stage, is the carbon emission during the extended stage of water discharge to receiving water bodies, T is the operation period of the project, It is the average annual water inflow during the operating life.

[0032] In a possible implementation, after adopting a preset multi-objective optimization model to perform Pareto frontier processing on the investment amount data, the biochemical oxygen demand concentration data of the sewage entering the plant, and the carbon emission intensity value of the whole life cycle corresponding to each engineering quantity combination to output a set of multi-objective trade-off solutions, it also includes:

[0033] On the preset display interface, the multi-objective trade-off scheme in the multi-objective trade-off scheme set is displayed; among them, the multi-objective trade-off scheme includes the investment amount data after Pareto frontier treatment, the biochemical oxygen demand concentration data of the sewage entering the plant after Pareto frontier treatment, and the full life cycle carbon emission intensity value after Pareto frontier treatment.

[0034] In a second aspect, an embodiment of the present application provides an optimization processing device for an urban water management system, including: a memory, a processor;

[0035] Memory stores computer-executable instructions;

[0036] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0037] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementations of the first aspect.

[0038] In a fourth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementations of the first aspect.

[0039] The embodiments of the present application provide an optimization treatment method, equipment and medium for an urban water treatment system. By obtaining the treatment engineering measures of multiple urban drainage systems and determining the upper limit of the engineering quantity and the upper limit of the investment cost of each measure, a variety of engineering quantity combinations are generated; these combinations are simulated and evaluated to obtain data on the investment amount, the biochemical oxygen demand concentration of sewage entering the plant, and the carbon emission intensity of the whole life cycle; these data are optimized using a preset multi-objective optimization model, wherein the objective function of the multi-objective optimization model includes an economic cost minimization objective function for optimizing the investment amount data, a sewage entering the plant biochemical oxygen demand concentration data maximization objective function for optimizing the sewage entering the plant biochemical oxygen demand concentration data, and a life cycle carbon emission intensity minimization objective function for optimizing the life cycle carbon emission intensity value. The optimization treatment method for the urban water treatment system proposed in this application improves the level of environmental protection and effectively improves the efficiency of resource utilization by optimizing sewage treatment and carbon emissions; the multi-objective optimization model is used to ensure the comprehensiveness and accuracy of the optimization treatment method for the urban water treatment system. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0041] Figure 1 A schematic diagram of the optimization system architecture of an urban water treatment system provided in this application;

[0042] Figure 2 A schematic diagram of the process of optimizing the urban water treatment system provided in this application Figure 1 ;

[0043] Figure 3 A schematic diagram of the process of optimizing the urban water treatment system provided in this application Figure 2 ;

[0044] Figure 4 Schematic diagram of the multi-objective trade-off solution provided for this application Figure 1 ;

[0045] Figure 5 Schematic diagram of the optimal multi-objective trade-off solution provided for this application;

[0046] Figure 6 A schematic diagram of the process of optimizing the urban water treatment system provided in this application Figure 3 ;

[0047] Figure 7 Schematic diagram of another optimization treatment method for urban water treatment system provided in this application Figure 4 ;

[0048] Figure 8 A schematic diagram of the structure of the optimization treatment device for the urban water treatment system provided in this application;

[0049] Fig. 9 A schematic diagram of the structure of the optimization treatment equipment for the urban water treatment system provided in this application.

[0050] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0051] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0052] It should be noted that the data involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0053] First, the terms involved in this application are explained:

[0054] Urban drainage system management project refers to the construction and renovation of drainage systems used to solve urban water pollution problems and improve the quality of urban water environment, including the design and construction of facilities such as sewage treatment, rainwater discharge and sewage pipe networks.

[0055] Carbon emissions refer to greenhouse gas emissions calculated by global warming potential, where greenhouse gases include carbon dioxide (CO 2 ), methane (CH 4 ) and nitrous oxide (N 2 O).

[0056] Since urban drainage systems need to constantly change to meet urban drainage needs, it is necessary to optimize investment and evaluate the effectiveness of urban drainage system management projects to improve the management effect of urban drainage systems.

[0057] However, existing technologies usually use optimization methods with a single objective or a small number of objectives, combined with linear programming, multi-objective decision analysis or simple heuristic algorithms to control investment costs or optimize a single indicator; this results in the lack of comprehensiveness of the optimization treatment methods for urban water management systems, and the overall management results of urban drainage system management projects are relatively low.

[0058] Based on this, in order to solve the above problems, the core idea of ​​this application is: by determining the engineering quantity combination of each treatment project measure, further determining the investment amount data, sewage inlet biochemical oxygen demand concentration data and life cycle carbon emission intensity value corresponding to each engineering quantity combination, and then through a multi-objective optimization method, continuously optimize the urban drainage system treatment project to select a multi-objective trade-off plan for the urban water treatment system and improve the comprehensiveness of the optimization treatment method of the urban water treatment system.

[0059] Optionally, Figure 1 This is a schematic diagram of the optimization system architecture of an urban water treatment system provided in this application. Figure 1 As shown, the optimization processing system architecture of the urban water management system includes at least one of a data acquisition device 101, a processing device 102 and a display device 103.

[0060] It is understandable that the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the above architecture. In other feasible implementations of the present application, the above architecture may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently, which can be determined according to the actual application scenario and is not limited here. Figure 1 The components shown may be implemented in hardware, software, or a combination of software and hardware.

[0061] In a specific implementation process, the data acquisition device 101 may include an input / output interface and may also include a communication interface. The data acquisition device 101 may be connected to the processing device via the input / output interface or the communication interface.

[0062] The processing device 102 can determine the engineering quantity upper limit and investment cost upper limit corresponding to each treatment engineering measure; generate multiple engineering quantity combinations according to the engineering quantity upper limit and investment cost upper limit corresponding to each treatment engineering measure; simulate and evaluate the engineering quantity combinations to obtain the investment amount data, sewage inlet biochemical oxygen demand concentration data and life cycle carbon emission intensity value corresponding to each engineering quantity combination; obtain a preset multi-objective optimization model, wherein the objective function of the multi-objective optimization model includes an economic cost minimization objective function for optimizing the investment amount data, a sewage inlet biochemical oxygen demand concentration data maximization objective function for optimizing the sewage inlet biochemical oxygen demand concentration data and a life cycle carbon emission intensity minimization objective function for optimizing the life cycle carbon emission intensity value; use the preset multi-objective optimization model to perform Pareto frontier processing on the investment amount data, sewage inlet biochemical oxygen demand concentration data and life cycle carbon emission intensity value corresponding to each engineering quantity combination, so as to output a multi-objective trade-off solution set for the urban water treatment system. The display device 103 can also be a touch display screen or a screen of a terminal device, which is used to receive user instructions while displaying the above content to achieve interaction with the user.

[0063] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0064] Figure 2 A schematic diagram of the process of optimizing the urban water treatment system provided in this application Figure 1 ,like Figure 2 As shown, the method includes:

[0065] S201. Obtain engineering measures for the management of drainage systems in multiple cities.

[0066] In this embodiment, the engineering measures for the management of the urban drainage system include repairing leakage points of diversion pipes, repairing leakage points of combined pipes, repairing mixed and wrong connection points, building a new pipeline network, separating rainwater and sewage, building a new regulating reservoir, adding a regulating reservoir, building a new underground sewage treatment plant, building a new ground sewage treatment plant, sponge engineering and ecological engineering, and sludge treatment, among which sludge treatment includes concentration and dehydration, anaerobic digestion, aerobic fermentation, drying and incineration, and incineration and pyrolysis of sludge; sponge engineering refers to increasing the city's rainwater absorption and retention capacity through natural and artificial means; ecological engineering uses ecological principles and technologies to carry out engineering measures for environmental protection and restoration.

[0067] S202. Determine the upper limit of the engineering quantity and the upper limit of the investment cost corresponding to each control engineering measure.

[0068] In this embodiment, for example, in each control engineering measure, the upper limit value of the engineering quantity for repairing diversion pipe leakage points can be set to 100, the upper limit value of the engineering quantity for repairing combined pipe leakage points can be set to 100, the upper limit value of the engineering quantity for repairing mixed connection points can be set to 100, the upper limit value of the engineering quantity for building a new pipeline network can be set to 100 kilometers, the upper limit value of the engineering quantity for rainwater and sewage diversion can be set to 100 hectares, the upper limit value of the engineering quantity for building a new regulating reservoir can be set to 1 million cubic meters, the upper limit value of the engineering quantity for building an additional regulating reservoir can be set to 1 million cubic meters, the upper limit value of the engineering quantity for building a new underground sewage treatment plant can be set to 1 million cubic meters, the upper limit value of the engineering quantity for building a new ground sewage treatment plant can be set to 1 million cubic meters, the upper limit value of the engineering quantity for sponge engineering can be set to 100 hectares, the upper limit value of the engineering quantity for ecological engineering can be set to 100 hectares, and the upper limit value of the engineering quantity for sludge treatment can be set to 100 tons; and the upper limit value of the investment cost corresponding to each control engineering measure is obtained.

[0069] S203. Generate multiple engineering quantity combinations according to the engineering quantity upper limit values ​​and investment cost upper limit values ​​corresponding to each control engineering measure.

[0070] Optionally, multiple combinations of engineering quantities are generated according to the engineering quantity upper limit values ​​and investment cost upper limit values ​​corresponding to each control engineering measure, including:

[0071] A random generation method is adopted to generate multiple engineering quantity combinations according to the engineering quantity upper limit values ​​and investment cost upper limit values ​​corresponding to each control engineering measure, and the engineering quantity values ​​of the generated engineering quantity combinations are gradually increased through an applicable random generation algorithm until the engineering quantity values ​​reach the preset engineering quantity upper limit value.

[0072] In this embodiment, the random generation method uses uniform random sampling, and generates random integers within a specified range through the np.random.randint (a function for generating random integers) function. Specifically, for each governance engineering measure, an appropriate random generation algorithm is used to randomly select m engineering quantity values ​​within the range of 0 to the preset engineering quantity upper limit max_value, and during the generation process, ensure that each engineering quantity value is uniformly distributed within its specified range, and the probability of each engineering quantity value appearing is equal, so as to avoid the engineering quantity value repetition rate being higher than the preset threshold; wherein, the number of engineering quantity samples for each governance engineering measure can be dynamically set according to the case capacity, and the case capacity refers to the limit on the number of available resources for the governance engineering measure.

[0073] For example, among the various control engineering measures, the upper limit of the engineering workload for repairing the leakage point of the diversion pipe is x 1 The upper limit of the engineering workload for repairing the combined pipe leakage point is x 2 The upper limit of the engineering workload for repairing mixed-up contact points is x 3The upper limit of the engineering volume of the new pipeline network is x 4 km, the upper limit of the engineering volume for rainwater and sewage diversion is x 5 Hectares, the upper limit of the construction volume of the new storage pond is x 6 Ten thousand cubic meters, the upper limit of the engineering volume of the additional storage tank is x 7 Ten thousand cubic meters, the upper limit of the construction volume of the new underground sewage plant is x 8 Ten thousand cubic meters, the upper limit of the construction volume of the new ground sewage treatment plant is x 9 Ten thousand cubic meters, the upper limit of the sponge project is x 10 Hectares, the upper limit of the ecological engineering project is x 11 Hectares, the upper limit of the sludge treatment project is x 12 Tons, the engineering quantity value of the engineering quantity combination can be generated from 0 until the engineering quantity value reaches the preset engineering quantity upper limit, where x 1 , x 2 , x 3 , x 4 , x 5 , x 6 , x 7 , x 8 , x 9 , x 10 , x 11 , x 12 are all preset integers greater than or equal to 0; the number of samples m for each project is set according to the actual needs and case capabilities obtained 1 , m 2 , m 3 , m 4 , m 5 , m 6 , m 7 , m 8 , m 9 , m 10 , m 11 , m 12 The engineering quantity combination is composed of a random combination of multiple governance engineering measures, and the engineering quantity value of each governance measure is independently sampled and generated.

[0074] The total number of engineering quantity combinations n is:

[0075]

[0076] In the formula, i is an integer greater than or equal to 1 and less than or equal to 12, and m is i is the number of samples taken for each control engineering measure.

[0077] Among them, the initial construction cost of each combination of engineering quantities is the product of the upper limit of the investment cost corresponding to each governance engineering measure and the engineering quantity value corresponding to each governance engineering measure.

[0078] S204. Simulate and evaluate the engineering quantity combinations to obtain the investment amount data, biochemical oxygen demand concentration data of sewage entering the plant, and carbon emission intensity value over the entire life cycle corresponding to each engineering quantity combination.

[0079] In this embodiment, simulation and evaluation are performed on the engineering quantity combinations to obtain the investment amount data, biochemical oxygen demand concentration data of sewage entering the plant and carbon emission intensity value over the entire life cycle corresponding to each engineering quantity combination, so as to provide parameters for subsequent multi-objective optimization.

[0080] S205. Obtain a preset multi-objective optimization model, wherein the objective functions of the multi-objective optimization model include an economic cost minimization objective function for optimizing investment amount data, a sewage inlet biochemical oxygen demand concentration data maximization objective function for optimizing sewage inlet biochemical oxygen demand concentration data, and a full life cycle carbon emission intensity minimization objective function for optimizing the full life cycle carbon emission intensity value.

[0081] Optionally, the economic cost minimization objective function for optimizing the investment amount data is:

[0082]

[0083] Among them, TDC is the economic cost, is the initial construction cost, is the operation and maintenance cost in year t, is the discount rate and T is the operating period of the project.

[0084] In this embodiment, the economic cost of the urban drainage system is continuously optimized by continuously adjusting the engineering quantities of various control engineering measures, so as to minimize the economic cost of the urban drainage system and improve the economic benefits of the urban drainage system.

[0085] Optionally, the life cycle carbon emission intensity minimization objective function for optimizing the life cycle carbon emission intensity value is:

[0086]

[0087] Among them, CEI is the carbon emission intensity of the entire life cycle. is the carbon emissions during the construction phase of the drainage system project. is the carbon emissions during the demolition phase, is the carbon emissions during the operation and maintenance phase, is the carbon emission in the sludge disposal stage, is the carbon emission during the extended stage of water discharge to receiving water bodies, T is the operation period of the project, It is the average annual water inflow during the operating life.

[0088] In this embodiment, the life cycle carbon emission intensity refers to the carbon emissions generated by the urban drainage system in treating a unit volume of sewage during its entire life cycle, wherein the carbon emissions generated during the entire life cycle include the sum of the carbon emissions during the entire life cycle and the extended boundary carbon emissions.

[0089] By adjusting the engineering volume of each treatment project measure, the carbon emissions over the entire life cycle can be minimized, thereby effectively reducing carbon emissions and improving the environmental benefits of urban drainage system projects.

[0090] Optionally, based on the impact of various treatment engineering measures on sewage quality, the biochemical oxygen demand concentration data of sewage entering the plant is calculated, and the biochemical oxygen demand concentration data of sewage entering the plant is maximized as the biochemical oxygen demand concentration data maximization objective function for optimizing the biochemical oxygen demand concentration data of sewage entering the plant.

[0091] S206. Using a preset multi-objective optimization model, Pareto frontier processing is performed on the investment amount data, biochemical oxygen demand concentration data of sewage entering the plant, and carbon emission intensity value of the whole life cycle corresponding to each engineering quantity combination to output a set of multi-objective trade-off solutions for the urban water treatment system. Figure 3 As shown in the figure, after setting the parameters of the preset multi-objective optimization model, each combination of engineering quantities is simulated and evaluated, and the investment amount data, biochemical oxygen demand concentration data of sewage entering the plant and carbon emission intensity value of the whole life cycle corresponding to each combination of engineering quantities are obtained, which are input into the preset multi-objective optimization model for population initialization; wherein, population initialization refers to initializing the parameters of the preset multi-objective optimization model to randomly generate an initial population P that meets the constraints. 0 In the embodiment of the present application, the constraints include investment restrictions, emission standards and benefit requirements.

[0092] For example, P 0 ={X 1 ,X 2 ,…,X N}, where N represents the initial population P 0 The value of N is the number of engineering quantity combinations; X i is the initial population P 0 The individual represents the i-th combination of engineering quantities and the corresponding investment amount data, biochemical oxygen demand concentration data of sewage entering the plant and carbon emission intensity value of the whole life cycle, where i is an integer greater than or equal to 1 and less than or equal to N.

[0093] Optionally, the preset multi-objective optimization model is a non-dominated sorting genetic algorithm model after optimization and improvement;

[0094] Among them, the optimization and improvement processing for the non-dominated sorting genetic algorithm model includes population non-dominated sorting processing and genetic operator genetic processing.

[0095] In this embodiment, multi-objective optimization refers to obtaining a solution that optimizes all objectives simultaneously while satisfying multiple objectives or constraints; the non-dominated sorting genetic algorithm is a multi-objective optimization algorithm that selects the optimal solution through non-dominated sorting and crowding distance calculation to handle optimization problems with multiple competing objectives. For example, the multi-objective in this embodiment refers to the investment amount, the biochemical oxygen demand concentration of sewage entering the plant, and the carbon emission intensity of the entire life cycle; multi-objective optimization refers to minimizing the investment amount data, maximizing the biochemical oxygen demand concentration of sewage entering the plant, and minimizing the carbon emission intensity value of the entire life cycle.

[0096] Furthermore, the preset multi-objective optimization model provided in the present application maintains the diversity of the population while improving the global search efficiency by adding an adaptive crossover mutation operator to the non-dominated sorting genetic algorithm model and setting the crossover probability to 0.7 to 0.9 and the mutation probability to 0.01 to 0.1.

[0097] Optionally, the population non-dominated sorting process includes:

[0098] In the process of optimizing with the preset multi-objective optimization model, the fast non-dominated sorting algorithm is used to stratify the individuals in the population.

[0099] In this embodiment, the individuals in the population are divided into k Pareto front layers F by using a fast non-dominated sorting algorithm, where F includes F 1 To F i ; and determine the dominance relationship between individuals to obtain different depth dominance layers; where i represents the level of the Pareto frontier layer, and i is an integer less than or equal to k, F 1 As the optimal dominant layer, when screening, the individuals at the bottom level with smaller levels in the Pareto front layer are given priority.

[0100] For example, determining the dominance relationship between individuals and obtaining different deep dominance layers includes: if individual X 1 The target values ​​of {investment amount, biochemical oxygen demand concentration of sewage entering the plant, and carbon emission intensity over the entire life cycle} are {20, 30, 0.5} respectively; individual X 2 The target values ​​of {investment amount, biochemical oxygen demand concentration of sewage entering the plant, and carbon emission intensity throughout the life cycle} are {20, 30, 0.6} respectively. 1 In the investment amount, the target of biochemical oxygen demand concentration of sewage entering the plant is not listed in individual X 2 , and is better than individual X in terms of carbon emission intensity over the entire life cycle 2 Therefore, it is considered that individual X1 Domination of individuals;

[0101] By determining the dominance relationship between individuals, all non-dominated solutions in the initial population are obtained. Non-dominated solutions refer to all individuals that are not dominated by other individuals. The individuals in the population are normalized to the interval [0, 1] through the linear normalization method, avoiding the objective function dominating the optimization process due to a large numerical range, resulting in a single optimization indicator.

[0102] And individuals are associated with reference points to balance and optimize different objective functions, wherein the reference points include points consisting of the best objective value or the worst objective value of each objective function in the current population.

[0103] Among them, genetic operator genetic processing includes:

[0104] In the process of optimization using a preset multi-objective optimization model, individuals with fitness higher than a preset fitness threshold are selected from the population, recombined using simulated binary crossover, and adjusted through multi-point mutation operations to maintain population diversity.

[0105] In this embodiment, selecting an individual whose fitness is higher than a preset fitness threshold from the population may refer to sorting the target values ​​based on the objective function, and selecting an individual corresponding to the target value that meets the condition according to the preset fitness threshold;

[0106] For example, simulated binary crossover is used to perform recombination to generate offspring individuals β, The crossover expression is:

[0107]

[0108] In the formula, is the normalization parameter, is the distribution index.

[0109] Binary crossover is a crossover operation used in real-coded genetic algorithms, which generates offspring through probability distribution so that the similarity between offspring and parents can be controlled. In this embodiment, simulated binary crossover is used for recombination, which effectively improves the diversity of the population.

[0110] Adjusting the individual through the multi-point mutation operation includes randomly selecting an individual from the population based on a preset mutation rate, determining a mutation point to mutate, and in this embodiment, the mutation point may refer to a target of the individual;

[0111] For example, for the offspring individuals Perform polynomial mutation operations to change the asynchronous length Controlled by the following formula to maintain population diversity:

[0112]

[0113] in, is the normalization parameter, is the variation distribution index.

[0114] Recombination is performed by simulating binary crossover, and individuals are adjusted through multi-point mutation operations to generate a parent population to maintain the diversity of the parent population. After obtaining the parent population, each engineering quantity combination is simulated and evaluated again, and the investment amount data, sewage inlet biochemical oxygen demand concentration data and life cycle carbon emission intensity value corresponding to each engineering quantity combination are input into the preset multi-objective optimization model to obtain the offspring population. At this time, the size of the offspring population is N, and the value of N is the number of engineering quantity combinations; and the parent population and the offspring population are merged to obtain the merged population, and the size of the merged population is 2N;

[0115] The individuals in the merged population are stratified using a fast non-dominated sorting algorithm, and the distances between the individuals in the merged population and the reference point are calculated. Individuals whose correlation with the reference point is higher than a preset correlation threshold are selected, and the merged population is updated to obtain an updated population.

[0116] Based on the distance between individuals in the updated population and the reference point and the correlation between individuals and the reference point, the optimal individuals are retained to obtain a new population to ensure the diversity and convergence of the new population.

[0117] In another possible implementation, if the optimal individual is not retained to obtain a new population based on the distance between the individuals in the updated population and the reference point and the correlation between the individuals and the reference point, the number of iterations is increased by 1, and the genetic operator genetic processing is returned to enter the next iterative cycle.

[0118] If the number of iterations is greater than the preset value or the Pareto solution set converges, the algorithm is terminated and the Pareto frontier solution set is output, where the Pareto frontier solution set is a set of multi-objective trade-off solutions.

[0119] Optionally, after adopting a preset multi-objective optimization model to perform Pareto frontier processing on the investment amount data, sewage inlet biochemical oxygen demand concentration data and life cycle carbon emission intensity value corresponding to each engineering quantity combination to output a set of multi-objective trade-off solutions for the urban water treatment system, it also includes:

[0120] On the preset display interface, the multi-objective trade-off scheme in the multi-objective trade-off scheme set is displayed; among them, the multi-objective trade-off scheme includes the investment amount data after Pareto frontier treatment, the biochemical oxygen demand concentration data of the sewage entering the plant after Pareto frontier treatment, and the full life cycle carbon emission intensity value after Pareto frontier treatment.

[0121] In this embodiment, for example, Figure 4 The multi-objective trade-off scheme shown Figure 1 When the total investment is 0 yuan, the annual average BOD concentration is 40.39 mg / L; the optimal multi-objective trade-off solution is as follows: Figure 5 shown.

[0122] The method for optimizing the urban water treatment system provided in the embodiment of the present application determines different combinations of engineering quantities through engineering treatment measures, comprehensively considers the investment amount corresponding to each combination of engineering quantities, the biochemical oxygen demand concentration of sewage entering the plant, and the carbon emission intensity over the entire life cycle, and uses a preset multi-objective optimization model for optimization processing to obtain a multi-objective trade-off solution to achieve optimization processing of the urban water treatment system; improves the economic, environmental and social benefits of the optimization processing of the urban water treatment system, and comprehensively optimizes the urban water treatment system; wherein the preset multi-objective optimization model is a non-dominant sorting genetic algorithm model after optimization and improvement processing, which solves the deficiencies of traditional optimization algorithms in multi-objective trade-off capabilities and adaptability to complex systems.

[0123] Figure 6 A schematic diagram of the process of optimizing the urban water treatment system provided in this application Figure 3 ,like Figure 6 As shown, in this embodiment Figure 2 Based on the embodiment, the simulation evaluation process of the engineering quantity combination in the above step S204 is carried out to obtain the investment amount data, the biochemical oxygen demand concentration data of the sewage entering the plant and the carbon emission intensity value of the whole life cycle corresponding to each engineering quantity combination. The method includes:

[0124] S601, obtaining a preset construction sequence logic.

[0125] In this embodiment, the preset construction sequence logic refers to the implementation sequence of various control engineering measures.

[0126] S602. Determine the influencing data of key parameters corresponding to each combination of engineering quantities according to the preset construction sequence logic.

[0127] In this embodiment, the influencing data of the key parameters corresponding to each engineering quantity combination include daily rainfall data, per capita water consumption (unit: liter), domestic sewage COD (chemical oxygen demand) concentration (unit: mg / L), domestic sewage NH 3-N (ammonia nitrogen) concentration (unit: mg / L), domestic sewage TP (total phosphorus) concentration (unit: mg / L), proportion of impervious area, impervious surface runoff coefficient, permeable surface runoff coefficient, storage capacity (unit: 10,000 cubic meters), sewage treatment volume (unit: 10,000 cubic meters), sewage treatment plant effluent standard, combined area density, diversion area population (unit: 10,000 people), diversion area area (unit: square kilometers), diversion area leakage points (unit: places), diversion area infiltration rate, mixing and wrong connection points (unit: places), area affected by mixing and wrong connection (unit: square kilometers), combined area population (unit: 10,000 people), combined area area (unit: square kilometers), combined area leakage points (unit: places), combined area infiltration rate, combined area water collection coefficient.

[0128] S603: Obtain a preset system dynamics simulation model.

[0129] In this embodiment, the preset system dynamics simulation model is the UWO (Urban Water Operator) model, wherein the UWO model is a model obtained by training the SWWM (Storm Water Management Model) model.

[0130] S604. Based on the impact data of key parameters, a preset system dynamics simulation model is used to perform simulation evaluation of environmental benefits to obtain evaluation results of environmental benefits corresponding to each combination of engineering quantities.

[0131] In this embodiment, the evaluation results of the environmental benefits corresponding to each combination of engineering quantities include overflow pollution data, urban non-point source pollution data, carbon emission reduction data, and daily average inlet biochemical oxygen demand concentration and annual average inlet biochemical oxygen demand concentration.

[0132] S605. Based on the environmental benefit assessment results, determine the investment amount data, biochemical oxygen demand concentration data of sewage entering the plant and carbon emission intensity value over the entire life cycle corresponding to each engineering quantity combination.

[0133] In this embodiment, the whole life cycle includes the drainage system engineering construction stage, demolition stage, operation and maintenance stage, sludge disposal stage, and extension stage of water discharge to receiving water bodies; according to the environmental benefit assessment results, the investment amount data, biochemical oxygen demand concentration data of sewage entering the plant and the carbon emission intensity value of the whole life cycle corresponding to each engineering quantity combination are determined to complete multi-objective optimization.

[0134] The optimization processing method for the urban water management system provided in the embodiment of the present application evaluates and processes the influence data of key parameters corresponding to each engineering quantity combination obtained through a preset system dynamics simulation model, obtains an environmental benefit evaluation result, and improves the environmental benefit of the optimization of the urban water management system; based on the environmental benefit evaluation result, the investment amount data corresponding to each engineering quantity combination, the biochemical oxygen demand concentration data of the sewage entering the plant and the carbon emission intensity value over the whole life cycle are determined, thereby proposing multi-objective optimization indicators, thereby improving the comprehensiveness of the urban water management system.

[0135] Figure 7 Schematic diagram of another optimization treatment method for urban water treatment system provided in this application Figure 4 ,like Figure 7 As shown, the method includes:

[0136] The first step is to set up treatment engineering measures, including rainwater and sewage diversion, new pipe network, repair of diversion pipe leakage points, repair of combined pipe leakage points, repair of mixed and wrong connection points, new or additional storage tanks, new ground sewage treatment plants, new underground sewage treatment plants, sludge concentration and dehydration, sludge anaerobic digestion, sludge aerobic fermentation, sludge drying and incineration, sludge incineration pyrolysis, sponge engineering and ecological engineering;

[0137] The second step is to determine the values ​​of key parameters based on the set governance engineering measures; the key parameters include regional rainfall in the statistical yearbook, land use and drainage design parameters, mixed connection points and leakage points of the pipe network drainage system, energy consumption, drug consumption, sludge treatment and disposal volume, sewage plant effluent concentration and water volume, resource and energy recycling volume, and vegetation area;

[0138] The third step is to calibrate the model based on key parameters, determine overflow pollution, urban non-point source pollution, biochemical oxygen demand concentration of sewage entering the plant, calculate economic costs, and calculate the carbon emission intensity of the entire life cycle through carbon accounting. The entire life cycle includes the construction and demolition stage, sewage treatment stage, sludge treatment stage, sludge disposal stage and receiving water body extension stage;

[0139] The fourth step is to use the preset multi-objective optimization model to perform multi-objective optimization processing based on the biochemical oxygen demand concentration of sewage entering the plant, economic costs, and carbon emission intensity over the entire life cycle to determine the multi-objective trade-off solution for the urban water management system; among which, the optimization goals are to maximize the biochemical oxygen demand concentration of sewage entering the plant, minimize the economic costs, and minimize the carbon emission intensity over the entire life cycle.

[0140] Figure 8 The schematic diagram of the structure of the optimization treatment device for the urban water treatment system provided in this application is as follows: Figure 8 As shown, the optimization treatment device for the urban water treatment system provided in this embodiment includes:

[0141] The first acquisition module 801 is used to acquire management engineering measures for multiple urban drainage systems.

[0142] The determination module 802 is used to determine the upper limit value of the engineering quantity and the upper limit value of the investment cost corresponding to each control engineering measure.

[0143] The generation module 803 is used to generate multiple engineering quantity combinations according to the engineering quantity upper limit value and the investment cost upper limit value corresponding to each control engineering measure.

[0144] Evaluation module 804 is used to simulate and evaluate the engineering quantity combinations to obtain the investment amount data, biochemical oxygen demand concentration data of sewage entering the plant and the carbon emission intensity value of the whole life cycle corresponding to each engineering quantity combination;

[0145] The second acquisition module 805 is used to acquire a preset multi-objective optimization model, wherein the objective functions of the multi-objective optimization model include an economic cost minimization objective function for optimizing the investment amount data, a sewage inlet biochemical oxygen demand concentration data maximization objective function for optimizing the sewage inlet biochemical oxygen demand concentration data, and a life cycle carbon emission intensity minimization objective function for optimizing the life cycle carbon emission intensity value;

[0146] The optimization module 806 is used to use a preset multi-objective optimization model to perform Pareto frontier processing on the investment amount data, biochemical oxygen demand concentration data of sewage entering the plant and carbon emission intensity value over the entire life cycle corresponding to each engineering quantity combination, so as to output a set of multi-objective trade-off solutions for the urban water management system.

[0147] In a possible implementation, the generating module 803 is further configured to:

[0148] A random generation method is adopted to generate multiple engineering quantity combinations according to the engineering quantity upper limit values ​​and investment cost upper limit values ​​corresponding to each control engineering measure, and the engineering quantity values ​​of the generated engineering quantity combinations are gradually increased through an applicable random generation algorithm until the engineering quantity values ​​reach the preset engineering quantity upper limit value.

[0149] In a possible implementation, the evaluation module 804 is further configured to:

[0150] Get the preset construction sequence logic;

[0151] According to the preset construction sequence logic, determine the influencing data of key parameters corresponding to each combination of engineering quantities;

[0152] Obtain a preset system dynamics simulation model;

[0153] According to the impact data of key parameters, the preset system dynamics simulation model is used to simulate and evaluate the environmental benefits, so as to obtain the evaluation results of the environmental benefits corresponding to each combination of engineering quantities;

[0154] Based on the environmental benefit assessment results, the investment amount data, biochemical oxygen demand concentration data of sewage entering the plant and carbon emission intensity value over the entire life cycle corresponding to each engineering quantity combination are determined.

[0155] Optionally, the preset multi-objective optimization model is a non-dominated sorting genetic algorithm model after optimization and improvement;

[0156] Among them, the optimization and improvement processing for the non-dominated sorting genetic algorithm model includes population non-dominated sorting processing and genetic operator genetic processing.

[0157] Optionally, the population non-dominated sorting process includes:

[0158] In the process of optimizing with the preset multi-objective optimization model, the fast non-dominated sorting algorithm is used to stratify the individuals in the population.

[0159] Optionally, the genetic operator genetic processing includes:

[0160] In the process of optimization using a preset multi-objective optimization model, individuals with fitness higher than a preset fitness threshold are selected from the population, recombined using simulated binary crossover, and adjusted through multi-point mutation operations to maintain population diversity.

[0161] Optionally, the economic cost minimization objective function for optimizing the investment amount data is:

[0162]

[0163] Among them, TDC is the economic cost, is the initial construction cost, is the operation and maintenance cost in year t, is the discount rate and T is the operating period of the project.

[0164] Optionally, the life cycle carbon emission intensity minimization objective function for optimizing the life cycle carbon emission intensity value is:

[0165]

[0166] Among them, CEI is the carbon emission intensity of the entire life cycle. is the carbon emissions during the construction phase of the drainage system project. is the carbon emissions during the demolition phase, is the carbon emissions during the operation and maintenance phase, is the carbon emission in the sludge disposal stage, is the carbon emission during the extended stage of water discharge to receiving water bodies, T is the operation period of the project, It is the average annual water inflow during the operating life.

[0167] In a possible implementation, after adopting a preset multi-objective optimization model to perform Pareto frontier processing on the investment amount data, the biochemical oxygen demand concentration data of the sewage entering the plant, and the carbon emission intensity value of the whole life cycle corresponding to each engineering quantity combination to determine the multi-objective trade-off scheme of the urban water treatment system, it also includes:

[0168] A display module is used to display a multi-objective trade-off scheme in a multi-objective trade-off scheme set on a preset display interface; wherein the multi-objective trade-off scheme includes investment amount data after Pareto frontier treatment, biochemical oxygen demand concentration data of sewage entering the plant after Pareto frontier treatment, and full life cycle carbon emission intensity value after Pareto frontier treatment.

[0169] The optimization processing device for the urban water treatment system provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and this embodiment will not be described in detail here.

[0170] Fig. 9 This is a schematic diagram of the structure of the optimization treatment equipment for the urban water treatment system provided in this application. Fig. 9 As shown, the optimization processing device of the urban water management system provided in this embodiment includes: at least one processor 901 and a memory 902. Optionally, the optimization processing device of the urban water management system also includes a communication component 903. The processor 901, the memory 902 and the communication component 903 are connected via a bus 904.

[0171] In a specific implementation process, at least one processor 901 executes the computer execution instructions stored in the memory 902, so that at least one processor 901 executes the above method.

[0172] The specific implementation process of the processor 901 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0173] In the above embodiments, it should be understood that the processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the invention can be directly implemented as a hardware processor, or can be implemented by a combination of hardware and software modules in the processor.

[0174] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (NVM), such as at least one disk storage.

[0175] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of this application is not limited to only one bus or one type of bus.

[0176] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0177] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0178] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0179] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0180] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0181] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0182] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0183] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0184] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. An optimization treatment method for an urban water management system, characterized in that: include: Obtain engineering measures for the management of drainage systems in multiple cities; Determine the upper limit of engineering quantity and investment cost corresponding to each control engineering measure; Generate multiple engineering quantity combinations according to the engineering quantity upper limit values ​​and investment cost upper limit values ​​corresponding to the various control engineering measures; Conduct simulation and evaluation processing on the engineering quantity combinations to obtain investment amount data, biochemical oxygen demand concentration data of sewage entering the plant and carbon emission intensity value of the whole life cycle corresponding to each engineering quantity combination; Obtaining a preset multi-objective optimization model, wherein the objective functions of the multi-objective optimization model include an economic cost minimization objective function for optimizing the investment amount data, a sewage inlet biochemical oxygen demand concentration maximization objective function for optimizing the sewage inlet biochemical oxygen demand concentration data, and a life cycle carbon emission intensity minimization objective function for optimizing the life cycle carbon emission intensity value; The preset multi-objective optimization model is used to perform Pareto frontier processing on the investment amount data, biochemical oxygen demand concentration data of sewage entering the plant, and carbon emission intensity value over the entire life cycle corresponding to each engineering quantity combination, so as to output a set of multi-objective trade-off solutions for the urban water management system.

2. The method according to claim 1, characterized in that: The generating of multiple engineering quantity combinations according to the engineering quantity upper limit value and the investment cost upper limit value corresponding to each of the control engineering measures includes: A random generation method is adopted to generate multiple engineering quantity combinations according to the engineering quantity upper limit values ​​and investment cost upper limit values ​​corresponding to the various control engineering measures, and the engineering quantity values ​​of the generated engineering quantity combinations are gradually increased through an applicable random generation algorithm until the engineering quantity values ​​reach the preset engineering quantity upper limit value.

3. The method according to claim 1, characterized in that The simulation evaluation process is performed on the engineering quantity combinations to obtain the investment amount data, the biochemical oxygen demand concentration data of the sewage entering the plant and the carbon emission intensity value of the whole life cycle corresponding to each engineering quantity combination, including: Get the preset construction sequence logic; According to the preset construction sequence logic, determining the influencing data of the key parameters corresponding to each combination of engineering quantities; Obtain a preset system dynamics simulation model; According to the impact data of the key parameters, the preset system dynamics simulation model is used to perform simulation evaluation of environmental benefits to obtain evaluation results of environmental benefits corresponding to each combination of engineering quantities; Based on the environmental benefit assessment results, the investment amount data, biochemical oxygen demand concentration data of sewage entering the plant and the carbon emission intensity value for the entire life cycle corresponding to each engineering quantity combination are determined.

4. The method according to claim 1, characterized in that: The preset multi-objective optimization model is a non-dominated sorting genetic algorithm model after optimization and improvement processing; wherein the optimization and improvement processing for the non-dominated sorting genetic algorithm model includes population non-dominated sorting processing and genetic operator genetic processing.

5. The method according to claim 4, characterized in that The population non-dominated sorting process includes: In the process of optimizing by using the preset multi-objective optimization model, a fast non-dominated sorting algorithm is used to stratify individuals in the population.

6. The method according to claim 4, characterized in that The genetic operator genetic processing includes: In the process of performing optimization processing using the preset multi-objective optimization model, selecting individuals whose fitness is higher than a preset fitness threshold from the population; Recombination was performed using simulated binary crossover, and individuals were adjusted through multi-point mutation operations to maintain population diversity.

7. The method according to claim 4, characterized in that The economic cost minimization objective function for optimizing the investment amount data is: in, For economic cost, is the initial construction cost, for Annual operation and maintenance costs, is the discount rate, The operating cycle of the project.

8. The method according to claim 4, characterized in that The life cycle carbon emission intensity minimization objective function for optimizing the life cycle carbon emission intensity value is: in, is the carbon emission intensity of the entire life cycle, is the carbon emissions during the construction phase of the drainage system project. is the carbon emissions during the demolition phase, is the carbon emissions during the operation and maintenance phase, is the carbon emission in the sludge disposal stage, is the carbon emissions during the extended phase of water discharge to receiving water bodies, The operation cycle of the project, It is the average annual water inflow during the operating life.

9. The method according to any one of claims 1 to 3, characterized in that: After the preset multi-objective optimization model is used to perform Pareto frontier processing on the investment amount data, the biochemical oxygen demand concentration data of the sewage entering the plant, and the carbon emission intensity value of the whole life cycle corresponding to each engineering quantity combination to output a set of multi-objective trade-off solutions for the urban water treatment system, the method further includes: On a preset display interface, a multi-objective trade-off scheme in the multi-objective trade-off scheme set is displayed; wherein, the multi-objective trade-off scheme includes investment amount data after Pareto frontier treatment, biochemical oxygen demand concentration data of sewage entering the plant after Pareto frontier treatment, and full life cycle carbon emission intensity value after Pareto frontier treatment.

10. An optimization treatment device for an urban water treatment system, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the optimization processing method for the urban water treatment system as described in any one of claims 1 to 9.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the optimization processing method for an urban water management system according to any one of claims 1 to 9.

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