Optimization Processing Method, Equipment and Medium of Urban Water Treatment System

Abstract

The present application provides an optimization processing method, device and medium for an urban water treatment system. The method includes: obtaining the governance engineering measures of multiple urban drainage systems; determining the upper limit values of the engineering quantities and the upper limit values of the investment costs corresponding to each governance engineering measure to generate multiple engineering quantity combinations; performing simulation evaluation processing on the engineering quantity combinations to obtain the investment amount data, the influent biochemical oxygen demand concentration data and the whole-life cycle carbon emission intensity values corresponding to each engineering quantity combination; obtaining a preset multi-objective optimization model, and performing Pareto frontier processing on the investment amount data, the influent biochemical oxygen demand concentration data and the whole-life cycle carbon emission intensity values corresponding to each engineering quantity combination to output a multi-objective trade-off scheme set of the urban water treatment system. This can improve the comprehensiveness of urban water treatment and meet the urban water treatment requirements.

Description

Technical Field

[0001] This application relates to the technical field of drainage systems, and particularly to an optimization processing method, device, and medium for an urban water treatment system. Background Art

[0002] The urban drainage system is an important part of urban infrastructure, realizing functions such as flood control and drainage, sewage treatment, maintaining public health, managing water resources, and protecting the environment; the governance project of the urban drainage system requires investment optimization and effectiveness evaluation to improve the governance effect of the urban drainage system.

[0003] In the prior art, single-objective or a small number of objective optimization methods are usually adopted, and linear programming, multi-objective decision analysis, or simple heuristic algorithms are used to control investment costs or optimize a single index; however, the optimization processing method of the urban water treatment system in the prior art is only limited to the optimization of a single index and cannot find the global optimal solution.

[0004] Therefore, in the prior art, there is a problem of insufficient comprehensiveness and accuracy in the optimization processing method of the urban water treatment system. Summary of the Invention

[0005] The embodiments of this application provide an optimization processing method, device, and medium for an urban water treatment system to achieve the effect of improving the comprehensiveness and accuracy of the optimization processing method of the urban water treatment system.

[0006] In a first aspect, the embodiments of this application provide an optimization processing method for an urban water treatment system, including:

[0007] Obtain the governance engineering measures of multiple urban drainage systems;

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

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

[0010] Conduct a simulation evaluation process on the engineering quantity combinations to obtain the investment amount data, the influent sewage biochemical oxygen demand concentration data, and the whole-life cycle carbon emission intensity value corresponding to each engineering quantity combination;

[0011] Obtain a preset multi-objective optimization model, where the objective function of the multi-objective optimization model includes an economic cost minimization objective function for optimizing the investment amount data, a sewage influent biochemical oxygen demand concentration maximization objective function for optimizing the influent sewage biochemical oxygen demand concentration data, and a whole-life cycle carbon emission intensity minimization objective function for optimizing the whole-life cycle carbon emission intensity value;

[0012] Using a preset multi-objective optimization model, perform Pareto front processing on the investment amount data, influent biochemical oxygen demand concentration data, and full life cycle carbon emission intensity values corresponding to each engineering quantity combination, so as to output a set of multi-objective trade-off solutions for the urban water treatment system. In a possible implementation manner, according to the upper limit values of the engineering quantities and the upper limit values of the investment costs corresponding to each treatment engineering measure, generate multiple engineering quantity combinations, including:

[0013] Adopt a random generation method. According to the upper limit values of the engineering quantities and the upper limit values of the investment costs corresponding to each treatment engineering measure, generate multiple engineering quantity combinations, and gradually increase the engineering quantity values of the generated engineering quantity combinations through an applicable random generation algorithm until the engineering quantity values reach the preset upper limit values of the engineering quantities.

[0014] In a possible implementation manner, perform simulation evaluation processing on the engineering quantity combinations to obtain the investment amount data, influent biochemical oxygen demand concentration data, and full life cycle carbon emission intensity values corresponding to each engineering quantity combination, including:

[0015] Obtain the preset construction sequence logic;

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

[0017] Obtain the preset system dynamics simulation model;

[0018] According to the influence data of the key parameters, use the preset system dynamics simulation model to perform simulation evaluation processing of the environmental benefits, so as to obtain the evaluation results of the environmental benefits corresponding to each engineering quantity combination;

[0019] According to the evaluation results of the environmental benefits, determine the investment amount data, influent biochemical oxygen demand concentration data, and full life cycle carbon emission intensity values corresponding to each engineering quantity combination.

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

[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 a possible implementation manner, the population non-dominated sorting processing includes:

[0023] During the process of using the preset multi-objective optimization model for optimization processing, use the fast non-dominated sorting algorithm to stratify the individuals in the population.

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

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

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

[0027]

[0028] where 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 operation period of the project.

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

[0030]

[0031] where CEI is the life-cycle carbon emission intensity, is the carbon emission amount in the construction stage of the drainage system project, is the carbon emission amount in the demolition stage, is the carbon emission amount in the operation and maintenance stage, is the carbon emission amount in the sludge disposal stage, is the carbon emission amount in the extended stage where water is discharged to the receiving water body, and T is the operation period of the project, is the average annual influent volume within the operation years.

[0032] In a possible implementation, after performing Pareto front processing on the investment amount data, the influent biochemical oxygen demand concentration data, and the life-cycle carbon emission intensity value corresponding to each engineering quantity combination using a preset multi-objective optimization model to output a multi-objective trade-off solution set, it further includes:

[0033] On a preset display interface, displaying the multi-objective trade-off solutions in the multi-objective trade-off solution set; among them, the multi-objective trade-off solutions include the investment amount data after Pareto front processing, the influent biochemical oxygen demand concentration data after Pareto front processing, and the life-cycle carbon emission intensity value after Pareto front processing.

[0034] In a second aspect, an optimization processing device for an urban water treatment system provided by an embodiment of the present application includes: a memory, a processor;

[0035] The memory stores computer execution instructions;

[0036] The processor executes computer-executable instructions stored in the memory, enabling the processor to execute as described in the first aspect and / or various possible embodiments of the first aspect.

[0037] In a third aspect, an embodiment of the present application provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the first aspect and / or various possible embodiments of the first aspect as described above when executed by a processor.

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

[0039] An optimization processing method, device, and medium for an urban water treatment system provided by an embodiment of the present application. By obtaining the governance engineering measures of multiple urban drainage systems, determining the upper limit values of the engineering quantities and investment costs of each measure, and generating multiple engineering quantity combinations; simulating and evaluating these combinations to obtain data on the investment amount, the concentration of biochemical oxygen demand in the sewage entering the plant, and the carbon emission intensity in the whole life cycle; using a preset multi-objective optimization model to optimize this data, where the objective function of the multi-objective optimization model includes an economic cost minimization objective function for optimizing the investment amount data, a biochemical oxygen demand concentration data maximization objective function for optimizing the concentration of biochemical oxygen demand in the sewage entering the plant data, and a whole life cycle carbon emission intensity minimization objective function for optimizing the whole life cycle carbon emission intensity value. The optimization processing method for the urban water treatment system proposed in the present application improves the environmental protection level and effectively improves the resource utilization efficiency by optimizing sewage treatment and carbon emissions; using a multi-objective optimization model ensures the comprehensiveness and accuracy of the optimization processing method for the urban water treatment system. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present application and used together with the specification to explain the principles of the present application.

[0041] Figure 1 It is a schematic diagram of the architecture of an optimization processing system for an urban water treatment system provided by the present application;

[0042] Figure 2 It is a flowchart of an optimization processing method for an urban water treatment system provided by the present application Figure 1 ;

[0043] Figure 3 It is a flowchart of an optimization processing method for an urban water treatment system provided by the present application Figure 2 ;

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

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

[0046] Figure 6 Flow schematic diagram of an optimization processing method for an urban water treatment system provided by this application Figure 3 ;

[0047] Figure 7 Flow schematic diagram of another optimization processing method for an urban water treatment system provided by this application Figure 4 ;

[0048] Figure 8 Structural schematic diagram of an optimization processing device for an urban water treatment system provided by this application;

[0049] Figure 9 Structural schematic diagram of an optimization processing equipment for an urban water treatment system provided by this application.

[0050] Through the above-mentioned drawings, specific embodiments of this application have been shown, and there will be more detailed descriptions hereinafter. These drawings and text descriptions are not intended to limit the scope of the concept of this application in any way, but to illustrate the concept of this application to those skilled in the art by referring to specific embodiments. Detailed Description of the Embodiments

[0051] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. On the contrary, they are merely examples of devices and methods consistent with some aspects of this 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 users or fully authorized by all parties. Moreover, the collection, use, and processing of relevant data need to comply with relevant laws, regulations, and standards, and corresponding operation entrances are provided for users to choose to authorize or reject.

[0053] First, the nouns involved in this application will be explained:

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

[0055] Carbon emissions refer to the greenhouse gas emissions calculated through the global warming potential value, where the greenhouse gases include carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O).

[0056] Since the urban drainage system needs to change continuously to meet the urban drainage requirements, accordingly, it is necessary to optimize the investment and evaluate the effectiveness of the urban drainage system governance project to improve the governance effect of the urban drainage system.

[0057] However, the existing technologies usually adopt single-objective or few-objective optimization methods, combined with linear programming, multi-objective decision analysis, or simple heuristic algorithms, to control the investment cost or optimize a single indicator; resulting in insufficient comprehensiveness of the optimization method for the urban water treatment system and relatively low overall governance effectiveness of the urban drainage system governance project.

[0058] Based on this, to solve the above problems, the core concept of this application is as follows: by determining the engineering quantity combinations of each governance engineering measure, further determining the investment amount data, the influent biochemical oxygen demand concentration data of sewage, and the full-life-cycle carbon emission intensity values corresponding to each engineering quantity combination, and then through the multi-objective optimization method, continuously optimizing the urban drainage system governance project to select a multi-objective trade-off scheme for the urban water treatment system and improve the comprehensiveness of the optimization method for the urban water treatment system.

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

[0060] It can be understood that the structure illustrated in the embodiments of this application does not constitute a specific limitation on the above architecture. In other feasible embodiments of this application, the above architecture may include more or fewer components than those shown in the figure, or combine certain components, or split certain components, or have different component arrangements, which can be specifically determined according to the actual application scenario and will not be limited here. Figure 1 The components shown can be implemented in hardware, software, or a combination of software and hardware.

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

[0062] The processing device 102 can determine the upper limit values of the engineering quantities and the upper limit values of the investment costs corresponding to each treatment engineering measure; generate multiple engineering quantity combinations according to the upper limit values of the engineering quantities and the upper limit values of the investment costs corresponding to each treatment engineering measure; perform simulation evaluation processing on the engineering quantity combinations to obtain the investment amount data, the influent biochemical oxygen demand concentration data of the sewage, and the full life cycle carbon emission intensity values 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, an influent biochemical oxygen demand concentration data maximization objective function for optimizing the influent biochemical oxygen demand concentration data of the sewage, and a full life cycle carbon emission intensity minimization objective function for optimizing the full life cycle carbon emission intensity value; use the preset multi-objective optimization model to perform Pareto frontier processing on the investment amount data, the influent biochemical oxygen demand concentration data of the sewage, and the full life cycle carbon emission intensity values corresponding to each engineering quantity combination 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 the 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 technical problems will be described in detail below with specific embodiments. These specific embodiments below 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 with reference to the drawings.

[0064] Figure 2 Schematic flow of an optimization processing method for an urban water treatment system provided by the present application Figure 1 as Figure 2 shown, the method includes:

[0065] S201. Obtain treatment engineering measures for multiple urban drainage systems.

[0066] In this embodiment, the treatment engineering measures for the urban drainage system include repairing the leakage points of the diverted pipes, repairing the leakage points of the combined pipes, repairing the misconnection points, building new pipe networks, rain and sewage diversion, building new storage ponds, expanding storage ponds, building new underground sewage treatment plants, building new above-ground sewage treatment plants, sponge projects and ecological projects, and sludge treatment. Among them, sludge treatment includes the treatment of sludge by concentration and dehydration, anaerobic digestion, aerobic fermentation, drying and incineration, and incineration pyrolysis; sponge projects refer to increasing the rainwater absorption and retention capacity of cities through natural and artificial means; ecological projects are engineering measures for environmental protection and restoration using ecological principles and technologies.

[0067] S202. Determine the upper limit values of the engineering quantities and the upper limit values of the investment costs corresponding to each treatment engineering measure.

[0068] In this embodiment, for example, among the various treatment engineering measures, the upper limit of the engineering quantity for repairing the leakage points of the diversion pipes can be set to 100, the upper limit of the engineering quantity for repairing the leakage points of the combined sewage pipes can be set to 100, the upper limit of the engineering quantity for repairing the mixed and misconnected points can be set to 100, the upper limit of the engineering quantity for constructing new pipe networks can be set to 100 kilometers, the upper limit of the engineering quantity for rainwater and sewage diversion can be set to 100 hectares, the upper limit of the engineering quantity for constructing new storage ponds can be set to 1 million cubic meters, the upper limit of the engineering quantity for adding storage ponds can be set to 1 million cubic meters, the upper limit of the engineering quantity for constructing new underground sewage treatment plants can be set to 1 million cubic meters, the upper limit of the engineering quantity for constructing new above-ground sewage treatment plants can be set to 1 million cubic meters, the upper limit of the engineering quantity for sponge projects can be set to 100 hectares, the upper limit of the engineering quantity for ecological projects can be set to 100 hectares, and the upper limit of the engineering quantity for sludge treatment can be set to 100 tons; and the upper limit of the investment cost corresponding to each treatment engineering measure is obtained.

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

[0070] Optionally, generating multiple engineering quantity combinations according to the upper limit of the engineering quantity and the upper limit of the investment cost corresponding to each treatment engineering measure includes:

[0071] Adopt a random generation method. According to the upper limit of the engineering quantity and the upper limit of the investment cost corresponding to each treatment engineering measure, generate multiple engineering quantity combinations, and gradually increase the engineering quantity value of the generated engineering quantity combinations through an applicable random generation algorithm until the engineering quantity value reaches the preset upper limit of the engineering quantity.

[0072] In this embodiment, the random generation method adopts 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 treatment engineering measure, an appropriate random generation algorithm is adopted to randomly extract m engineering quantity values within the range of 0 to the preset upper limit of the engineering quantity max_value, and during the generation process, it is ensured that each engineering quantity value is evenly distributed within its specified range and the probability of each engineering quantity value appearing is equal, so as to avoid the repetition rate of the engineering quantity values being higher than the preset threshold; among them, the sampling quantity of the engineering quantity for each treatment engineering measure can be dynamically set according to the case-solving ability, and the case-solving ability refers to the limit of the available resources for the treatment engineering measure.

[0073] For example, among various treatment engineering measures, the upper limit value of the engineering quantity for repairing the leakage points of the diversion pipes is x1, the upper limit value of the engineering quantity for repairing the leakage points of the combined sewer pipes is x2, the upper limit value of the engineering quantity for repairing the misconnected points is x3, the upper limit value of the engineering quantity for constructing new pipe networks is x4 kilometers, the upper limit value of the engineering quantity for rainwater and sewage diversion is x5 hectares, the upper limit value of the engineering quantity for constructing new storage ponds is x6 cubic meters, the upper limit value of the engineering quantity for expanding storage ponds is x7 cubic meters, the upper limit value of the engineering quantity for constructing new underground sewage treatment plants is x8 cubic meters, the upper limit value of the engineering quantity for constructing new above-ground sewage treatment plants is x9 cubic meters, the upper limit value of the engineering quantity for sponge projects is x 10 hectares, and the upper limit value of the engineering quantity for ecological projects is x 11 hectares. The upper limit value of the engineering quantity for sludge treatment is x 12 tons. The engineering quantity value of the engineering quantity combination can be generated starting from 0 until the engineering quantity value reaches the preset upper limit value of the engineering quantity. Among them, x1, x2, x3, x4, x5, x6, x7, x8, x9, x 10 , x 11 , x 12 are all preset integers greater than or equal to 0. According to the obtained actual requirements and the calculation example capabilities, the sampling sample numbers m1, m2, m3, m4, m5, m6, m7, m8, m9, m 10 , m 11 , m 12 of each project are set. The engineering quantity combination is composed of random combinations of multiple treatment engineering measures, and the engineering quantity values of each treatment measure are independently sampled and generated.

[0074] Then the total number n of the engineering quantity combinations 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 i is the sampling sample number of each treatment engineering measure.

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

[0078] S204. Perform simulation evaluation processing on the engineering quantity combination to obtain the investment amount data, the concentration data of biochemical oxygen demand in the sewage entering the plant, and the full life cycle carbon emission intensity value corresponding to each engineering quantity combination.

[0079] In this embodiment, by performing simulation evaluation processing on the engineering quantity combination, the investment amount data, the concentration data of biochemical oxygen demand in the sewage entering the plant, and the full life cycle carbon emission intensity value corresponding to each engineering quantity combination are obtained, providing parameters for subsequent multi-objective optimization.

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

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

[0082]

[0083] where TDC is the economic cost, is the initial construction cost, is the operation and maintenance cost in the t-th year, is the discount rate, and T is the operation cycle of the project.

[0084] In this embodiment, by continuously adjusting the quantities of various treatment engineering measures, the economic cost of the urban drainage system is continuously optimized, so that the economic cost of the urban drainage system is minimized, and the economic benefit of the urban drainage system is improved.

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

[0086]

[0087] where CEI is the life cycle carbon emission intensity, is the carbon emission amount in the drainage system engineering construction stage, is the carbon emission amount in the demolition stage, is the carbon emission amount in the operation and maintenance stage, is the carbon emission amount in the sludge disposal stage, is the carbon emission amount in the extended stage of discharging water to the receiving water body, T is the operation cycle of the project, is the average annual water inflow within the operation years.

[0088] In this embodiment, the life cycle carbon emission intensity refers to the carbon emission amount generated by the urban drainage system per unit volume of sewage treated during the whole life cycle. Among them, the carbon emission amount generated during the whole life cycle includes the sum of the carbon emission amount during the whole life cycle and the extended boundary carbon emission amount.

[0089] By adjusting the quantities of various treatment engineering measures to minimize the life cycle carbon emission amount, the carbon emission amount is effectively reduced, and the environmental protection benefit of the urban drainage system project is improved.

[0090] Optionally, based on the impact of each governance engineering measure on the sewage quality, the influent biochemical oxygen demand concentration data of the sewage is calculated, and the influent biochemical oxygen demand concentration data of the sewage is maximized as the objective function for maximizing the influent biochemical oxygen demand concentration data of the optimized sewage.

[0091] S206. Using a preset multi-objective optimization model, perform Pareto front processing on the investment amount data, influent biochemical oxygen demand concentration data, and full-life cycle carbon emission intensity values corresponding to each engineering quantity combination to output a set of multi-objective trade-off solutions for the urban water treatment system. In this embodiment, as Figure 3 shown, after setting the parameters of the preset multi-objective optimization model; perform simulation evaluation processing on each engineering quantity combination, and input the investment amount data, influent biochemical oxygen demand concentration data, and full-life cycle carbon emission intensity values corresponding to each engineering quantity combination into the preset multi-objective optimization model for population initialization; where population initialization refers to initializing the parameters of the preset multi-objective optimization model to randomly generate an initial population P0 that satisfies the constraint conditions. In the embodiments of the present application, the constraint conditions include investment restrictions, discharge standards, and benefit requirements.

[0092] For example, P0 = {X1, X2, …, X N}, where N represents the size of the initial population P0, and the value of N is the number of engineering quantity combinations; X i is an individual of the initial population P0, representing the i-th engineering quantity combination and the corresponding investment amount data, influent biochemical oxygen demand concentration data, and full-life cycle carbon emission intensity value, and 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 processing;

[0094] where 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 under the condition of satisfying multiple objectives or constraint conditions; 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 the optimization problem of multiple competing objectives. For example, the multi-objectives in this embodiment refer to investment amount, influent biochemical oxygen demand concentration, and full-life cycle carbon emission intensity; multi-objective optimization refers to minimizing the investment amount data, maximizing the influent biochemical oxygen demand concentration data, and minimizing the full-life cycle carbon emission intensity value.

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

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

[0098] During the optimization process using 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 through the fast non-dominated sorting algorithm, and F includes F1 to F i ; and the dominance relationship between individuals is determined to obtain different depth dominance layers; where i represents the level of the Pareto front layer, and i is an integer less than or equal to k, and F1 is the optimal dominance layer. When screening, the individuals in the lower-level Pareto front layer with a smaller level are preferentially retained.

[0100] For example, determining the dominance relationship between individuals to obtain different depth dominance layers includes: if the objective values of individual X1 {investment amount, influent biochemical oxygen demand concentration, life cycle carbon emission intensity} are {20, 30, 0.5}; the objective values of individual X2 {investment amount, influent biochemical oxygen demand concentration, life cycle carbon emission intensity} are {20, 30, 0.6}, then individual X1 is not inferior to individual X2 in terms of the objectives of investment amount and influent biochemical oxygen demand concentration, and is superior to individual X2 in terms of the objective of life cycle carbon emission intensity. Therefore, it is considered that individual X1 dominates individual;

[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, and the population individuals are normalized to the [0, 1] interval through the linear normalization method, avoiding the optimization process being dominated by the objective function due to too large a numerical range, resulting in a single optimization index.

[0102] And the individuals are associated with the reference points to balance the optimization of different objective functions, where the reference points include the points composed of the optimal or worst objective values of each objective function in the current population.

[0103] Among them, the genetic operator genetic process includes:

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

[0105] In this embodiment, selecting individuals with fitness higher than a preset fitness threshold from the population may refer to sorting the objective values based on the objective function, and selecting the individuals corresponding to the objective values that meet the conditions according to the preset fitness threshold;

[0106] For example, simulated binary crossover is used for recombination to generate offspring individual β, and the offspring individual The crossover operation 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. Offspring are generated through a probability distribution, making the similarity between offspring and parents controllable; in this embodiment, simulated binary crossover is used for recombination, effectively enhancing the diversity of the population;

[0110] Adjusting individuals through multi-point mutation operations includes determining mutation points for mutation on randomly selected individuals from the population based on a preset mutation rate. In this embodiment, the mutation point can refer to the objective of the individual;

[0111] For example, performing polynomial mutation operation on the offspring individual The mutation step size is controlled by the following formula to maintain the diversity of the population:

[0112]

[0113] Among them, is the normalization parameter, is the mutation distribution index.

[0114] Recombination is performed through simulated binary crossover, and individuals are adjusted through multi-point mutation operations to generate the parent population to maintain the diversity of the parent population. After obtaining the parent population, each engineering quantity combination is again subjected to simulated evaluation processing, and the corresponding investment amount data, influent biochemical oxygen demand concentration data, and full-life cycle carbon emission intensity values of each engineering quantity combination are input into a 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] Use the fast non-dominated sorting algorithm to stratify the individuals in the merged population, calculate the distance between the individuals in the merged population and the reference point, select the individuals with a higher relevance to the reference point than the preset relevance threshold, and update the merged population to obtain the updated population;

[0116] Based on the distance between the individuals in the updated population and the reference point and the relevance between the individuals and the reference point, retain the optimal individuals to obtain a new population, so as to ensure the diversity and convergence of the new population.

[0117] In another possible implementation, if the optimal individuals are not retained based on the distance between the individuals in the updated population and the reference point and the relevance between the individuals and the reference point to obtain a new population, then increase the iteration count by 1 and return to the genetic operator for genetic processing to enter the next iteration loop.

[0118] If the iteration count is greater than the preset value or the Pareto solution set converges, terminate the algorithm and output the Pareto front solution set, where the Pareto front solution set is a set of multi-objective trade-off solutions.

[0119] Optionally, after performing Pareto front processing on the investment amount data, influent sewage biochemical oxygen demand concentration data, and whole-life cycle carbon emission intensity values corresponding to each engineering quantity combination using a preset multi-objective optimization model to output a set of multi-objective trade-off solutions for the urban water treatment system, it further includes:

[0120] On a preset display interface, display the multi-objective trade-off solutions in the set of multi-objective trade-off solutions; among them, the multi-objective trade-off solutions include the investment amount data after Pareto front processing, the influent sewage biochemical oxygen demand concentration data after Pareto front processing, and the whole-life cycle carbon emission intensity values after Pareto front processing.

[0121] In this embodiment, for example, as Figure 4 shown in the schematic diagram of the multi-objective trade-off solution Figure 1 , when the total investment amount is 0 yuan, the average annual BOD (influent sewage biochemical oxygen demand) concentration is 40.39 mg / L; the optimal multi-objective trade-off solution is as Figure 5 shown.

[0122] The optimization processing method for the urban water treatment system provided by the embodiments of the present application determines different combinations of engineering quantities through engineering treatment measures, comprehensively considers the investment amount, the concentration of biochemical oxygen demand in the influent sewage, and the target of the carbon emission intensity in the whole life cycle corresponding to each combination of engineering quantities, and uses a preset multi-objective optimization model for optimization processing to obtain a multi-objective trade-off scheme, so as to realize the optimization processing of the urban water treatment system; it 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; among them, the preset multi-objective optimization model is a non-dominated sorting genetic algorithm model after optimization and improvement, which solves the deficiencies of traditional optimization algorithms in terms of multi-objective trade-off ability and adaptability to complex systems.

[0123] Figure 6 Schematic flow of an optimization processing method for an urban water treatment system provided by the present application Figure 3 , such as Figure 6 shown. On the basis of the Figure 2 embodiment, the simulation and evaluation processing of the combination of engineering quantities in step S204 above is elaborated to obtain the investment amount data, the concentration data of biochemical oxygen demand in the influent sewage, and the carbon emission intensity values in the whole life cycle corresponding to each combination of engineering quantities. The method includes:

[0124] S601. Obtain the preset construction sequence logic.

[0125] In this embodiment, the preset construction sequence logic refers to the implementation sequence of each engineering treatment measure.

[0126] S602. According to the preset construction sequence logic, determine the influence data of the key parameters corresponding to each combination of engineering quantities.

[0127] In this embodiment, the influence data of the key parameters corresponding to each combination of engineering quantities includes daily rainfall data, per capita water consumption (unit: liter), COD (chemical oxygen demand) concentration of domestic sewage (unit: mg / l), NH3-N (ammonia nitrogen) concentration of domestic sewage (unit: mg / l), TP (total phosphorus) concentration of domestic sewage (unit: mg / l), proportion of impervious area, runoff coefficient of impervious surface, runoff coefficient of pervious surface, storage capacity (unit: 10,000 cubic meters), sewage treatment volume (unit: 10,000 cubic meters), effluent standard of sewage treatment plant, density of combined sewer area, population of separate sewer area (unit: 10,000 people), area of separate sewer area (unit: square kilometers), number of leakage points in separate sewer area (unit: place), infiltration rate of separate sewer area, number of mixed and misconnected points (unit: place), area affected by mixed and misconnected (unit: square kilometers), population of combined sewer area (unit: 10,000 people), area of combined sewer area (unit: square kilometers), number of leakage points in combined sewer area (unit: place), infiltration rate of combined sewer area, water collection coefficient of combined sewer area.

[0128] S603. Obtain the preset system dynamics simulation model.

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

[0130] S604. According to the influence data of the key parameters, perform simulation evaluation processing on the environmental benefits by using the preset system dynamics simulation model to obtain the evaluation results of the environmental benefits corresponding to each combination of project quantities.

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

[0132] S605. According to the evaluation results of the environmental benefits, determine the investment amount data, influent biochemical oxygen demand concentration data of the sewage treatment plant, and the full life cycle carbon emission intensity value corresponding to each combination of project quantities.

[0133] In this embodiment, the full life cycle includes the drainage system engineering construction stage, demolition stage, operation and maintenance stage, sludge disposal stage, and the stage of water discharged to the receiving water body extended stage; according to the evaluation results of the environmental benefits, determine the investment amount data, influent biochemical oxygen demand concentration data of the sewage treatment plant, and the full life cycle carbon emission intensity value corresponding to each combination of project quantities to complete multi-objective optimization.

[0134] The optimization processing method of the urban water treatment system provided by the embodiment of the present application evaluates the influence data of the key parameters corresponding to each combination of project quantities obtained by using the preset system dynamics simulation model to obtain the evaluation results of the environmental benefits, improving the environmental benefits of the optimization of the urban water treatment system; according to the evaluation results of the environmental benefits, determine the investment amount data, influent biochemical oxygen demand concentration data of the sewage treatment plant, and the full life cycle carbon emission intensity value corresponding to each combination of project quantities, thereby proposing multi-objective optimization indicators and improving the comprehensiveness of the urban water treatment system.

[0135] Figure 7 It is a flow schematic diagram of another optimization processing method of the urban water treatment system provided by the present application Figure 4 , such as Figure 7 shown, this method includes:

[0136] The first step is to set up governance engineering measures, where the governance engineering measures include rainwater and sewage diversion, construction of new pipe networks, repair of leakage points in diverted pipes, repair of leakage points in combined pipes, repair of misconnected and wrongly connected points, construction or addition of storage ponds, construction of above-ground sewage treatment plants, construction of underground sewage treatment plants, sludge thickening and dewatering, anaerobic digestion of sludge, aerobic fermentation of sludge, sludge drying and incineration, sludge incineration pyrolysis, sponge projects and ecological projects;

[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, drainage design parameters for land use, misconnected and leakage points in the pipe network drainage system, energy consumption, chemical consumption, sludge treatment and disposal volume, effluent concentration and volume of the sewage treatment plant, resource and energy recycling volume, and vegetation area;

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

[0139] The fourth step is to use a preset multi-objective optimization model to perform multi-objective optimization processing based on the biochemical oxygen demand concentration of the sewage entering the plant, economic cost, and carbon emission intensity of the whole life cycle to determine the multi-objective trade-off scheme for the urban water treatment system; among them, the optimization objectives are to maximize the biochemical oxygen demand concentration of the sewage entering the plant, minimize the economic cost, and minimize the carbon emission intensity of the whole life cycle.

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

[0141] The first acquisition module 801 is used to acquire the governance engineering measures of 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 governance engineering measure.

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

[0144] The evaluation module 804 is used to perform simulation evaluation processing on the engineering quantity combinations to obtain the investment amount data, biochemical oxygen demand concentration data of the sewage entering the plant, and carbon emission intensity values of the whole life cycle corresponding to each engineering quantity combination;

[0145] The second acquisition module 805 is configured to acquire a preset multi-objective optimization model, where the objective function of the multi-objective optimization model includes an economic cost minimization objective function for optimizing investment amount data, a sewage influent biochemical oxygen demand concentration data maximization objective function for optimizing sewage influent 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 configured to perform Pareto front processing on the investment amount data, sewage influent biochemical oxygen demand concentration data, and life cycle carbon emission intensity value corresponding to each engineering quantity combination by using the preset multi-objective optimization model, so as to output a multi-objective trade-off solution set for the urban water treatment system.

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

[0148] Adopt a random generation method to generate multiple engineering quantity combinations according to the upper limit values of the engineering quantities and the upper limit values of the investment costs corresponding to each treatment engineering measure, and gradually increase the engineering quantity values of the generated engineering quantity combinations through an applicable random generation algorithm until the engineering quantity values reach the preset upper limit values of the engineering quantities.

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

[0150] Acquire a preset construction sequence logic;

[0151] Determine the influence data of the key parameters corresponding to each engineering quantity combination according to the preset construction sequence logic;

[0152] Acquire a preset system dynamics simulation model;

[0153] Perform simulation evaluation processing of the environmental benefits by using the preset system dynamics simulation model according to the influence data of the key parameters, so as to obtain the evaluation results of the environmental benefits corresponding to each engineering quantity combination;

[0154] Determine the investment amount data, sewage influent biochemical oxygen demand concentration data, and life cycle carbon emission intensity value corresponding to each engineering quantity combination according to the evaluation results of the environmental benefits.

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

[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 processing includes:

[0158] In the process of optimizing using a preset multi-objective optimization model, the individuals in the population are stratified using the fast non-dominated sorting algorithm.

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

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

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

[0162]

[0163] where 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 operation cycle of the project.

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

[0165]

[0166] where CEI is the life-cycle carbon emission intensity, is the carbon emission during the construction stage of the drainage system project, is the carbon emission during the demolition stage, is the carbon emission during the operation and maintenance stage, is the carbon emission during the sludge disposal stage, is the carbon emission during the extended stage of discharging water to the receiving water body, T is the operation cycle of the project, is the average annual water inflow within the operation years.

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

[0168] A display module for displaying the multi-objective trade-off scheme in the multi-objective trade-off scheme set on a preset display interface; wherein, the multi-objective trade-off scheme includes the investment amount data after Pareto front processing, the influent biochemical oxygen demand concentration data after Pareto front processing, and the life-cycle carbon emission intensity value after Pareto front processing.

[0169] The optimization processing device of the urban water treatment system provided in this embodiment can execute the method provided in the above method embodiment, and its implementation principle and technical effects are similar. Details are not described herein in this embodiment.

[0170] Figure 9 It is a schematic structural diagram of the optimization processing device of the urban water treatment system provided in this application. As Figure 9 shown, the optimization processing device of the urban water treatment system provided in this embodiment includes: at least one processor 901 and a memory 902. Optionally, the optimization processing device of the urban water treatment system further includes a communication component 903. Among them, the processor 901, the memory 902, and the communication component 903 are connected through a bus 904.

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

[0172] For the specific implementation process of the processor 901, reference can be made to the above method embodiment, and its implementation principle and technical effects are similar. Details are not described herein again in this embodiment.

[0173] In the above embodiment, it should be understood that the processor may be a central processing unit (English: Central Processing Unit, abbreviated: CPU), or other general-purpose processors, digital signal processors (English: Digital Signal Processor, abbreviated: DSP), application specific integrated circuits (English: Application Specific Integrated Circuit, abbreviated: ASIC), etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the invention can be directly implemented by the execution of the hardware processor, or implemented by the combination of the 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 (Non-volatile Memory, NVM), such as at least one disk memory.

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

[0176] The present application also provides a computer-readable storage medium storing computer-executable instructions, and when the processor executes the computer-executable instructions, the above method is implemented.

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

[0178] An exemplary readable storage medium is coupled to the processor, enabling the processor to read information from 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 (ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in a device.

[0179] The division of units is only a logical function division. In actual implementation, there may be other division methods. For example, 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 displayed or discussed coupling or direct coupling or communication connection between each other can be an indirect coupling or communication connection through some interfaces, devices, or units, and can be in electrical, mechanical, or other forms.

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

[0181] In addition, in each embodiment of the present invention, each functional unit may be integrated into a processing unit, may exist separately physically as each unit, 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, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in each embodiment of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, etc., which can store program codes.

[0183] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When this program is executed, it executes the steps including the above method embodiments; and the aforementioned storage medium includes: ROMs, RAMs, magnetic disks, or optical discs, etc., which can store program codes.

[0184] Finally, it should be noted that: after considering the specification and practicing the invention disclosed herein, those skilled in the art will readily think of other implementation schemes of the present invention. 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 general knowledge or conventional technical means in the technical field not disclosed in the present invention. It is not limited to the exact structure described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. An optimization processing method for an urban water treatment system, characterized in that Including: Obtaining the treatment engineering measures for multiple urban drainage systems; Determining the upper limit values of the engineering quantities and the upper limit values of the investment costs corresponding to each treatment engineering measure; Generating multiple engineering quantity combinations according to the upper limit values of the engineering quantities and the upper limit values of the investment costs corresponding to each treatment engineering measure; Performing a simulation evaluation process on the engineering quantity combinations to obtain the investment amount data, the influent biochemical oxygen demand concentration data, and the life cycle carbon emission intensity values corresponding to each engineering quantity combination; the life cycle carbon emission intensity value refers to the carbon emissions generated during the life cycle of treating a unit volume of sewage in the urban drainage system; Obtaining 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 maximum influent biochemical oxygen demand concentration objective function for optimizing the influent biochemical oxygen demand concentration data, and a minimum life cycle carbon emission intensity objective function for optimizing the life cycle carbon emission intensity values; Using the preset multi-objective optimization model to perform Pareto front processing on the investment amount data, the influent biochemical oxygen demand concentration data, and the life cycle carbon emission intensity values corresponding to each engineering quantity combination, so as to output a set of multi-objective trade-off solutions for the urban water treatment system; The minimum life cycle carbon emission intensity objective function for optimizing the life cycle carbon emission intensity values is: Among them, is the carbon emission intensity of the whole life cycle, is the carbon emission of the drainage system engineering construction stage, is the carbon emission of the demolition stage, is the carbon emission of the operation and maintenance stage, is the carbon emission of the sludge disposal stage, is the carbon emission of the extended stage where water is discharged into the receiving water body, is the operation cycle of the project, is the average annual water inflow within the operation years; The preset multi-objective optimization model is a non-dominated sorting genetic algorithm model after optimization and improvement processing; 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; an adaptive crossover and mutation operator is added to the non-dominated sorting genetic algorithm model; The population non-dominated sorting processing includes: during the optimization process using the preset multi-objective optimization model, using the fast non-dominated sorting algorithm to stratify the individuals in the population; The genetic operator genetic processing includes: during the optimization process using the preset multi-objective optimization model, selecting individuals with fitness higher than a preset fitness threshold from the population; using simulated binary crossover for recombination, and adjusting the individuals through multi-point mutation operations to maintain population diversity.

2. The method according to claim 1, wherein The generating multiple engineering quantity combinations according to the upper limit values of the engineering quantities and the upper limit values of the investment costs corresponding to each treatment engineering measure includes: Adopting a random generation method, generating multiple engineering quantity combinations according to the upper limit values of the engineering quantities and the upper limit values of the investment costs corresponding to each treatment engineering measure, and gradually increasing the engineering quantity values of the generated engineering quantity combinations through an applicable random generation algorithm until the engineering quantity values reach the preset upper limit values of the engineering quantities.

3. The method according to claim 1, wherein The performing a simulation evaluation process on the engineering quantity combinations to obtain the investment amount data, the influent biochemical oxygen demand concentration data, and the life cycle carbon emission intensity values corresponding to each engineering quantity combination includes: Obtaining a preset construction sequence logic; Determining the influence data of the key parameters corresponding to each engineering quantity combination according to the preset construction sequence logic; Obtaining a preset system dynamics simulation model; Performing simulation evaluation processing on environmental benefits by using the preset system dynamics simulation model according to the impact data of the key parameters, so as to obtain the evaluation results of the environmental benefits corresponding to each combination of project quantities; Determining the investment amount data, the influent biochemical oxygen demand concentration data, and the full life cycle carbon emission intensity values corresponding to each combination of project quantities according to the evaluation results of the environmental benefits; 4. The method according to claim 1, wherein 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.

5. The method according to any one of claims 1 to 3, characterized in that, After performing Pareto frontier processing on the investment amount data, the influent biochemical oxygen demand concentration data, and the full life cycle carbon emission intensity values corresponding to each combination of project quantities by using the preset multi-objective optimization model to output a set of multi-objective trade-off schemes for the urban water treatment system, it further includes: Displaying the multi-objective trade-off schemes in the set of multi-objective trade-off schemes on a preset display interface; wherein, the multi-objective trade-off schemes include the investment amount data after Pareto frontier processing, the influent biochemical oxygen demand concentration data after Pareto frontier processing, and the full life cycle carbon emission intensity values after Pareto frontier processing.

6. An optimization processing device for an urban water treatment system, characterized in that, Including: At least one processor; And A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable 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 of the urban water treatment system as described in any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that, Computer-executable instructions are stored in the computer-readable storage medium, and when the computer-executable instructions are executed by a processor, they are used to implement the optimization processing method of the urban water treatment system as described in any one of claims 1 to 5.

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