Multi-objective optimized sewage treatment resource allocation method and system

Abstract

The invention discloses a multi-objective optimized sewage treatment resource allocation method and system, and relates to the related technical field of sewage treatment.The method comprises the steps that a sewage source set of a target sewage treatment plant is determined; analyzing sewage treatment node requirements to generate a sequence of each treatment node; performing combination verification on any two processing node sequences to generate a plurality of sewage processing modules; calling a preset multi-target optimization constraint to carry out configuration optimization of sewage treatment resources, and generating a plurality of resource configuration results and a corresponding polygon optimization equilibrium relationship; and returning to the management terminal of the target sewage treatment plant. The technical problems of low treatment efficiency and relatively high energy consumption caused by non-uniform configuration of sewage treatment resources and lack of comprehensive allocation of multi-objective optimization in the prior art are solved, the optimal configuration of the sewage treatment resources is realized, and the technical effects of improving the sewage treatment efficiency and reducing the energy consumption are achieved.

Description

Technical Field

[0001] This application relates to the technical field of sewage treatment, and specifically relates to a sewage treatment resource allocation method and system for multi-objective optimization. Background Art

[0002] Sewage treatment is a process of purifying sewage and discharging it up to standard, and may also involve the rational allocation of various resources such as energy, materials, and equipment. However, traditional sewage treatment methods usually adopt a centralized treatment mode, that is, various types of sewage are treated uniformly, relying on a single technical means and a fixed treatment process, without fully considering the diversity of sewage sources. The treatment capacity and resource allocation cannot be designed individually according to the specific characteristics of different sewage sources. Due to different sewage sources, there are significant differences in parameters such as water quality and flow rate, which may lead to resource waste or poor treatment effects, and lack of flexible adaptability. When facing the changing sewage treatment requirements, it is difficult to make quick adjustments and optimizations. In addition, because the resource allocation of traditional sewage treatment is often not optimized, the use efficiency of equipment and energy is relatively low, which not only increases the operating cost of the sewage treatment plant, but also poses a hidden danger of secondary pollution to the environment.

[0003] Therefore, in the current related technologies, there are technical problems such as uneven sewage treatment resource allocation and lack of comprehensive allocation for multi-objective optimization, resulting in low treatment efficiency and high energy consumption. Summary of the Invention

[0004] This application provides a sewage treatment resource allocation method for multi-objective optimization, and the method includes: determining a set of sewage sources of a target sewage treatment plant; analyzing the sewage treatment node requirements for each sewage source in the set of sewage sources to generate each treatment node sequence; performing a merging verification on any two treatment node sequences in each of the treatment node sequences to generate multiple sewage treatment modules; for the multiple sewage treatment modules, calling a preset multi-objective optimization constraint to perform configuration optimization of sewage treatment resources to generate multiple resource allocation results and corresponding polygon optimization equilibrium relationships; and returning the multiple resource allocation results and the polygon optimization equilibrium relationships to the management terminal of the target sewage treatment plant.

[0005]

[0006] ​In a possible implementation manner, the multi-objective optimized sewage treatment resource allocation method further performs the following processing: extracting a first treatment node sequence corresponding to a first sewage source and a second treatment node sequence corresponding to a second sewage source from each of the treatment node sequences; performing a merging cost analysis on the first treatment node sequence and the second treatment node sequence to generate a first merged node network and a first merging cost index; if the first merging cost index is less than a preset cost index, generating a first sewage treatment module with the first merged node network and adding it to the multiple sewage treatment modules.

[0007] In a possible implementation manner, the multi-objective optimized sewage treatment resource allocation method further performs the following processing: identifying identical nodes in the first treatment node sequence and the second treatment node sequence to generate an identical node set, a first differentiated node set, and a second differentiated node set; using the identical node set, the first differentiated node set, and the second differentiated node set to perform sequence merging to construct the first merged node network; performing a branch node introduction complexity identification on the first merged node network to generate the first merging cost index, where the branch node introduction complexity identification includes a branch node quantity ratio and an introduction position quantity analysis.

[0008] In a possible implementation manner, the multi-objective optimized sewage treatment resource allocation method further performs the following processing: establishing a mapping relationship between the multiple sewage treatment modules and the sewage sources; based on the mapping relationship, extracting the first sewage source corresponding to the first sewage treatment module; performing a dynamic analysis of the historical sewage generation amount and the sewage pollutant components of the first sewage source to construct a first sewage generation amount concentration value and a first pollutant content concentration value; using the satisfaction of the first sewage generation amount concentration value and the first pollutant content concentration value as constraints, and combining the preset multi-objective optimization constraints to perform an optimization of the sewage treatment resources of the first sewage treatment module, generating a first resource allocation result and a first polygon optimization equilibrium relationship, and adding them to the multiple resource allocation results and the polygon optimization equilibrium relationships.

[0009] In a possible implementation manner, the multi-objective optimized sewage treatment resource allocation method further performs the following processing: extracting each treatment node in the first sewage treatment module and determining the required sewage treatment equipment types for each treatment node; performing sewage treatment fitting based on the first sewage generation amount concentration value, the first pollutant content concentration value, and the required sewage treatment equipment types to generate a sewage treatment equipment configuration constraint space for each treatment node; calling the preset multi-objective optimization constraints to screen the sewage treatment equipment configuration result closest to the preset multi-objective optimization constraints in the sewage treatment equipment configuration constraint space, generating the first resource allocation result and the first polygon optimization equilibrium relationship.

[0010] In a possible implementation, the multi-objective optimization sewage treatment resource allocation method further performs the following processing: The preset multi-objective optimization constraint is a radar chart for multiple optimization indicators, where the multiple optimization indicators at least include treatment efficiency, treatment cost, and treatment power consumption. By obtaining the evaluation weights and index thresholds corresponding to the multiple optimization indicators, the relationship construction of the radar chart is performed to generate the preset multi-objective optimization constraint.

[0011] In a possible implementation, the multi-objective optimization sewage treatment resource allocation method further performs the following processing: Taking each sewage source as a constraint, sewage treatment sample data is collected to generate each sewage treatment sample data; based on each sewage treatment sample data, sewage treatment nodes are identified and arranged in the treatment order to generate each treatment node sequence.

[0012] This application also provides a multi-objective optimization sewage treatment resource allocation system, including: a sewage source set determination unit for determining the sewage source set of the target sewage treatment plant; a treatment node sequence generation unit for analyzing the sewage treatment node requirements for each sewage source in the sewage source set to generate each treatment node sequence; a sewage treatment module generation unit for merging and verifying any two treatment node sequences in each treatment node sequence to generate multiple sewage treatment modules; a sewage treatment resource allocation optimization unit for calling the preset multi-objective optimization constraint to perform the configuration optimization of sewage treatment resources for the multiple sewage treatment modules, generating multiple resource allocation results and corresponding polygon optimization equilibrium relationships; and a resource allocation result return unit for returning the multiple resource allocation results and the polygon optimization equilibrium relationships to the management terminal of the target sewage treatment plant.

[0013] It is intended to determine the sewage source set of the target sewage treatment plant through the multi-objective optimization sewage treatment resource allocation method and system proposed in this application; analyze the sewage treatment node requirements to generate each treatment node sequence; merge and verify any two treatment node sequences to generate multiple sewage treatment modules; call the preset multi-objective optimization constraint to perform the configuration optimization of sewage treatment resources, generating multiple resource allocation results and corresponding polygon optimization equilibrium relationships; and return them to the management terminal of the target sewage treatment plant. This solves the technical problems in the prior art, such as uneven allocation of sewage treatment resources and lack of comprehensive allocation with multi-objective optimization, resulting in low treatment efficiency and high energy consumption, realizes the optimal allocation of sewage treatment resources, and achieves the technical effects of improving sewage treatment efficiency and reducing energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments of the present disclosure will be briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the systems according to the embodiments of the present application. It should be understood that the operations above or below do not necessarily need to be performed precisely in sequence. On the contrary, according to the needs, various steps can be performed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several steps can be removed from these processes.

[0015] Figure 1 Schematic flowchart of the multi-objective optimization sewage treatment resource allocation method provided by the embodiments of the present application;

[0016] Figure 2 Schematic structural diagram of the multi-objective optimization sewage treatment resource allocation system provided by the embodiments of the present application.

[0017] Explanation of reference numerals: sewage source set determination unit 10, treatment node sequence generation unit 20, sewage treatment module generation unit 30, sewage treatment resource allocation optimization unit 40, resource allocation result return unit 50. Detailed implementation manners

[0018] The above description is only an overview of the technical solutions of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the description. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically gives the detailed implementation manners of the present application.

[0019] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

[0020] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments. However, it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict. The terms "first" and "second" are only used to distinguish similar objects and do not represent a specific order for the objects. The terms "comprising" and "having", and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server that comprises a series of steps or units need not be limited to those steps or units clearly listed, but may include other steps or modules not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application.

[0021] The embodiments of this application provide a multi-objective optimization method for sewage treatment resource allocation, as Figure 1 shown, the method includes:

[0022] Step S100, determining the set of sewage sources of the target sewage treatment plant.

[0023] Preferably, different sewage sources have different water qualities, flow rates, pollutant characteristics, etc. The treatment methods for different sewage sources may also be different. Therefore, obtaining multiple specific sewage sources of the input sewage to the target sewage treatment plant and forming a set of sewage sources may include, but is not limited to, domestic sewage, that is, sewage from residents' daily life, such as sewage generated from kitchens, toilets, baths, laundries, etc. in residential communities, shopping malls, schools, hospitals, etc. The pollutant components of domestic sewage mainly include organic matter, nitrogen, phosphorus, etc.; industrial sewage, that is, sewage generated in various industrial production processes. According to different industrial types, the water quality of industrial sewage varies greatly and may contain heavy metals, toxic and harmful chemical substances, oil pollutants, etc., such as chemical plants, paper mills, textile mills, food processing plants, etc.; agricultural sewage, that is, sewage in agricultural production processes, including runoff generated after farmland irrigation, pollution brought by chemical fertilizers and pesticides used in agricultural production, such as farms, farmland irrigation water, agricultural waste discharge, etc.; construction sewage, such as slurry, construction wastewater, washing water, etc. generated at construction sites, usually containing a high content of solid particles, chemical pollutants, etc.

[0024] Step S200, analyzing the sewage treatment node requirements for each sewage source in the set of sewage sources and generating each treatment node sequence.

[0025] Preferably, a demand analysis is performed according to the characteristics of each sewage source in the sewage source set, that is, to analyze which sewage treatment nodes (i.e., sewage treatment steps or treatment units) are required for each sewage source, and generate the execution order of the treatment nodes (i.e., the treatment node sequence) according to the demand analysis results. Specifically, the sewage treatment node refers to each step or unit operation in the sewage treatment process. Each node represents an independent treatment process, usually with different functions and treatment objectives. Common sewage treatment nodes include pretreatment nodes (such as screens and sedimentation tanks for removing larger impurities), biological treatment nodes (such as activated sludge tanks and biological filters for removing organic matter in water), chemical treatment nodes (such as chemical sedimentation tanks for removing heavy metals or pollutants that are difficult to biodegrade), filtration nodes (such as sand filtration tanks and membrane filtration for further removing suspended solids and fine particles in water), disinfection nodes (such as ultraviolet disinfection tanks and chlorination disinfection tanks for removing pathogenic microorganisms in water), and sludge treatment nodes (such as sludge dewatering and sludge anaerobic digestion for treating the sludge generated in the sewage treatment process).

[0026] Preferably, the water quality characteristics of each sewage source (such as pollutant type, concentration, flow rate, etc.) are different, and its treatment requirements (i.e., the required sewage treatment nodes) are also different. Specifically, it includes water quality analysis, such as pollutant types and water quality concentrations; sewage source characteristic analysis, such as domestic sewage, industrial sewage, agricultural sewage, etc.; flow rate and time window analysis, according to the flow rate and treatment time requirements of the sewage source, analyze whether it is necessary to perform treatment in different time periods; by analyzing the water quality requirements and treatment node characteristics of each sewage source, the treatment steps that each sewage source needs to go through can be determined, and the execution order of the treatment nodes, that is, the treatment node sequence, can be generated. Specifically, based on the water quality characteristics, pollutant types, concentrations, etc. of the sewage, appropriate treatment nodes are selected, and the execution order of the nodes is arranged according to the flow path and treatment requirements of the sewage. For example, the pretreatment node is usually before the biological treatment, and the chemical treatment usually needs to be carried out after the biological treatment, so as to achieve targeted treatment of sewage from different sources, thereby ensuring the treatment effect and the rational use of resources.

[0027] Further, step S200 further includes step S210, collecting sewage treatment sample data with each sewage source as a constraint to generate each sewage treatment sample data; step S220, identifying sewage treatment nodes based on each sewage treatment sample data and arranging them in the treatment order to generate each treatment node sequence.

[0028] Preferably, multiple sewage sources are used as constraint conditions to collect sewage treatment sample data, including sewage flow data, pollutant concentration data, treatment effect data, etc., to form sewage treatment sample data sets corresponding to each sewage source, which describe information such as water quality changes and flow fluctuations during the treatment process of each sewage source; then, based on the sewage treatment sample data and the sewage treatment process flow, considering the treatment requirements of each sewage source (e.g., removing organic matter, removing nitrogen and phosphorus, etc.) and the functions and characteristics of treatment nodes (e.g., biological reaction tanks are used to degrade organic matter, sedimentation tanks are used to remove suspended solids), the sewage treatment nodes that each sewage source needs to pass through are identified. Finally, according to the water quality and treatment requirements of each sewage source, in the logical order of the process flow, the order of the treatment nodes for each sewage source is determined, that is, a treatment node sequence is generated, which reflects each step and its execution order in the sewage treatment process, thereby ensuring the continuity, efficiency, and stability of the sewage treatment process.

[0029] Step S300, merge and verify any two treatment node sequences in the respective treatment node sequences to generate multiple sewage treatment modules.

[0030] Preferably, according to different sewage sources and the corresponding treatment node sequences, each pair of treatment node sequences is merged and verified to form multiple effective sewage treatment modules, thereby optimizing the sewage treatment process and structure. Specifically, each sewage source constitutes different treatment node sequences according to its water quality characteristics and treatment requirements. Each treatment node sequence may include multiple individual treatment units (such as pretreatment, chemical treatment, biological treatment, disinfection, etc.). For example, the treatment node sequence of domestic sewage may include, screen → sedimentation tank → biological reaction tank → disinfection tank, and the treatment node sequence of industrial sewage may include, chemical sedimentation tank → physical filtration → biological treatment → disinfection; then, according to the treatment requirements of different sewage sources, the sewage treatment process is optimized, and multiple independent treatment steps are combined to form sewage treatment modules, which respectively complete different tasks of sewage treatment. Specifically, any two treatment node sequences are selected for verification, that is, to verify whether these two sequences are complementary or compatible in the treatment process. For example, some physical or chemical treatment steps may not match the time and flow requirements of biological treatment. After merging, the treatment order is adjusted to ensure the coordination between each node, and then multiple sewage treatment modules are generated, which can be configured for different sewage sources or different treatment requirements. Through the merge verification of the treatment node sequences, it is helpful to flexibly adjust the combination of treatment modules according to different sewage sources and requirements, meet the treatment requirements of different water qualities, and ensure the efficient and stable operation of sewage treatment.

[0031] Further, step S300 further includes step S310 of extracting a first treatment node sequence corresponding to a first sewage source and a second treatment node sequence corresponding to a second sewage source from each of the treatment node sequences; step S320 of performing a merging cost analysis on the first treatment node sequence and the second treatment node sequence to generate a first merged node network and a first merging cost index; and step S330 of, if the first merging cost index is less than a preset cost index, generating a first sewage treatment module based on the first merged node network and adding it to the plurality of sewage treatment modules.

[0032] Preferably, based on the treatment node sequences of different sewage sources, through merging analysis, it is evaluated whether the merged treatment network is cost-effective, and finally it is decided whether to add the merged treatment module to the overall sewage treatment module. Specifically, the first treatment node sequence corresponding to the first sewage source and the second treatment node sequence corresponding to the second sewage source are respectively identified and extracted from each of the treatment node sequences, where the first sewage source and the second sewage source are any two arbitrarily obtained sewage sources, and the first treatment node sequence and the second treatment node sequence are any two node sequences in each of the treatment node sequences; then, the different treatment node sequences (the first treatment node sequence and the second treatment node sequence) from different sewage sources are merged, and a merging cost analysis is performed, that is, the costs in terms of equipment sharing cost, treatment efficiency, energy consumption, etc. that may be brought about by the merger of the two treatment node sequences are evaluated, and then a first merged node network and a first merging cost index are generated. The first merged node network refers to the treatment network formed after merging the first treatment node sequence and the second treatment node sequence, reflecting the overall treatment process and structure after resource and equipment sharing. The first merging cost index refers to the cost evaluation index of the node network formed after merging, usually including a comprehensive evaluation of cost, energy consumption, time efficiency, etc., reflecting the economy and feasibility of the merged treatment network.

[0033] Preferably, if the merging cost (the first merging cost index) is lower than the preset cost standard (the preset cost index), it indicates that the merged treatment module meets the expected requirements in terms of economy, efficiency, etc. and can be implemented. The preset cost index is usually a threshold set based on the budget of the sewage treatment plant, cost control objectives or requirements, etc. If the merging cost meets the preset index requirements, the corresponding first sewage treatment module is merged and generated, including the merged treatment steps, which may involve the coordination and integration of the treatment steps of multiple sewage sources to more effectively utilize resources, reduce costs or improve treatment efficiency, and finally added to the plurality of sewage treatment modules to achieve more efficient, energy-saving and economical sewage treatment.

[0034] Further, step S320 further includes step S321 of identifying the same nodes in the first processing node sequence and the second processing node sequence to generate a set of same nodes, as well as a first set of distinguishing nodes and a second set of distinguishing nodes; step S322 of building a sequence merge with the set of same nodes, the first set of distinguishing nodes and the second set of distinguishing nodes to build the first merged node network; step S323 of identifying the complexity of introducing branch nodes in the first merged node network to generate the first merging cost metric, where the identification of the complexity of introducing branch nodes includes the proportion of the number of branch nodes and the analysis of the number of introduction positions.

[0035] Preferably, identify the nodes with similar functions or the same processing tasks in the first processing node sequence and the second processing node sequence to form a set of same nodes. For example, both sequences may contain processing nodes such as "sedimentation tank" or "disinfection tank". Then, identify the processing nodes with different functions or tasks in the first processing node sequence and the second processing node sequence to form a first set of distinguishing nodes and a second set of distinguishing nodes, which respectively correspond to the unique processing nodes in each sewage source treatment sequence; by integrating the set of same nodes with the set of distinguishing nodes, that is, integrating the processing steps, equipment and functions of the two processing sequences, build the merged processing node network, generate the first merged node network, and achieve resource sharing and process optimization; in the process of merging the processing node sequences, a "branch node" refers to an additional or newly added node in the merged node network, and thus it is necessary to identify the complexity of introducing branch nodes, including the proportion of the number of branch nodes and the analysis of the number of introduction positions. Among them, the proportion of the number of branch nodes refers to the proportion of branch nodes in the total nodes in the merged network. A larger number of branch nodes means a higher network complexity, which may affect the efficiency, maintenance cost and operation difficulty of the processing process; the analysis of the number of introduction positions refers to analyzing the specific positions where the branch nodes appear and how they affect the overall network structure and operation. The positions where the branch nodes are introduced may affect the resource flow, processing capacity or operation flexibility. For example, some branch nodes may appear on important processing paths, increasing the complexity of the entire processing process; finally, based on the complexity evaluation of the number and position of the branch nodes, calculate the first merging cost metric to measure whether the merged processing network is effective and whether it is worth implementing, and ultimately achieve a more efficient, energy-saving and economical sewage treatment module.

[0036] Step S400: For the multiple sewage treatment modules, call the preset multi-objective optimization constraints to optimize the allocation of sewage treatment resources, and generate multiple resource allocation results and the corresponding polygon optimization equilibrium relationships.

[0037] Preferably, based on multiple treatment modules, through a multi-objective optimization method, resources are reasonably allocated to achieve the best treatment effect and generate optimization results. Specifically, the optimization objectives may include minimizing costs, for example, reducing energy consumption, reducing equipment maintenance costs, maximizing efficiency, improving treatment efficiency, shortening treatment time, or improving sewage treatment capacity, maximizing resource utilization, for example, optimizing the use of energy and chemicals, reducing the discharge of wastewater and sludge, minimizing environmental impact, and reducing carbon emissions, energy consumption and other environmental loads in the sewage treatment process; the corresponding optimization constraints may be the limitation of equipment processing capacity, the coordination requirements between treatment modules, the minimum / maximum operating load of each sewage treatment unit, the requirements for water quality discharge and the maximum energy consumption of the equipment, etc.; the configuration optimization of sewage treatment resources refers to the reasonable allocation of sewage treatment resources (such as equipment, energy, hydraulic load, chemicals, etc.) among multiple sewage treatment modules, so that the entire treatment process achieves the best economic and environmental Efficiency is usually achieved by formulating the best resource allocation plan based on the needs, operating conditions, resource availability, etc. of different modules, and then generating multiple feasible resource allocation plans, that is, multiple resource allocation results, in which each plan will have different resource allocation and optimization target realization levels; in the multi-objective optimization process, a polygonal optimization equilibrium relationship (that is, the trade-off relationship between multiple objectives) is generated, which is usually represented by the Pareto frontier, that is, when multiple objectives are met, it is impossible to optimize one objective without affecting the optimal solution set of other objectives, representing the optimal set of different resource allocation plans, each of which shows a balance between different objectives; in more complex multi-objective optimization, the results of different plans are represented by geometric figures (such as polygons), each vertex represents an optimization goal, and under different objective constraints, the weight of each objective is different, which will form optimization equilibrium relationships of different shapes, thereby ensuring the reasonable allocation of resources and maximization of treatment effects in the sewage treatment process.

[0038] Furthermore, step S400 also includes step S410, establishing a mapping relationship between the multiple sewage treatment modules and the sewage sources; step S420, extracting the first sewage source corresponding to the first sewage treatment module based on the mapping relationship; step S430, performing a dynamic analysis of the historical sewage generation and sewage pollutant components of the first sewage source, and constructing a first sewage generation concentration value and a first pollutant content concentration value; step S440, optimizing the configuration of sewage treatment resources of the first sewage treatment module in combination with the preset multi-objective optimization constraints to satisfy the first sewage generation concentration value and the first pollutant content concentration value as constraints, generating a first resource configuration result and a first polygonal optimization balance relationship, and adding the multiple resource configuration results and the polygonal optimization balance relationship.

[0039] Preferably, a correspondence is established between the sewage treatment module and the sewage source, that is, different sewage sources (such as domestic sewage, industrial sewage, agricultural sewage, etc.) need to be treated through specific treatment modules to ensure that each sewage source can be treated through an appropriate treatment process. Then, according to the established mapping relationship, the first sewage source (such as domestic sewage) associated with the first sewage treatment module (such as a biological treatment module) is extracted, and then the input sources (information such as water quality, water volume, and pollutants) of the first sewage treatment module are obtained; then, a dynamic analysis of the historical sewage generation volume and the components of sewage pollutants is carried out on the first sewage source. Among them, analyzing the historical sewage generation volume means analyzing the historical generation volume of the sewage source (such as the sewage volume per month or per quarter) to understand the fluctuation law of sewage from different sources, usually from the historical monitoring data or flow measurement of the sewage treatment plant, and constructing a first concentrated value of sewage generation volume, that is, the statistical result of the historical sewage generation volume, reflecting the typical water production volume of the sewage source in a certain time period (such as one year or several months), for example, the mode of the sewage generation volume; analyzing the components of sewage pollutants means analyzing the components of the main pollutants in the sewage (such as ammonia nitrogen, total phosphorus, chemical oxygen demand COD, etc.) and tracking their component changes to predict the treatment load and required treatment intensity of sewage in different time periods, and constructing a first concentrated value of pollutant content, that is, the statistical result of the concentration of the main pollutants (such as COD, ammonia nitrogen, phosphorus, etc.) in the sewage, reflecting the pollution degree of the sewage source.

[0040] Preferably, taking the first concentrated value of sewage generation volume and the first concentrated value of pollutant content as constraint conditions, and considering other preset multi-objective optimization constraints (such as minimizing cost, maximizing treatment capacity, reducing energy consumption, etc.) at the same time, mathematical models and algorithms (such as linear programming, genetic algorithms, etc.) are used to optimize the allocation of sewage treatment resources, so that the sewage treatment can not only achieve the required treatment effect, but also reach the optimum in terms of cost, time, energy, etc., and then generate a first resource allocation result and a first polygon optimization equilibrium relationship. Among them, the first resource allocation result refers to the generated sewage treatment resource allocation plan, including the resource allocation for the first sewage source, such as equipment configuration, energy distribution, and treatment capacity optimization. The first polygon optimization equilibrium relationship refers to the trade-off relationship between different objectives in the multi-objective optimization process, showing the optimization balance relationship between each objective (such as cost, efficiency, energy consumption, etc.) under different resource allocation plans. Each point represents a resource allocation plan, and different regions in the figure show the relationships of different optimization objectives; multiple different sewage sources and treatment modules can generate multiple different resource allocation results and optimization equilibrium relationships to ensure that the resource allocation between different modules is coordinated and efficient, thereby improving the resource utilization efficiency of the sewage treatment plant and reducing costs.

[0041] Further, step S440 further includes step S441, which extracts each processing node in the first sewage treatment module and determines the required sewage treatment equipment types for each processing node; step S442, which performs sewage treatment fitting based on the first sewage generation volume concentration value, the first pollutant content concentration value, and the required sewage treatment equipment types to generate a sewage treatment equipment configuration constraint space for each processing node; step S443, which calls a preset multi-objective optimization constraint to screen the sewage treatment equipment configuration result closest to the preset multi-objective optimization constraint in the sewage treatment equipment configuration constraint space, and generates the first resource configuration result and the first polygon optimization equilibrium relationship.

[0042] Preferably, multiple processing nodes in the sewage treatment module are identified and extracted (such as a screen, sedimentation tank, bioreactor, disinfection tank, etc.). Each processing node undertakes different processing tasks and requires specific types of equipment to perform the tasks. Then, the required sewage treatment equipment types are determined. For example, the screen node requires a screen device, the bioreactor requires an activated sludge tank or other bioreactors, and the disinfection tank requires an ultraviolet disinfection device or a chlorination disinfection device, etc., to ensure that the tasks of each node can be effectively completed. Then, sewage treatment fitting is performed based on the first sewage generation volume concentration value, the first pollutant content concentration value, and the required sewage treatment equipment types, that is, the appropriate equipment configuration is matched according to the sewage generation volume and pollutant content to ensure that the selected treatment equipment can handle the actual sewage volume and cope with the pollutant concentration, preventing the equipment from being excessive or insufficient, thereby ensuring the treatment effect. Furthermore, a sewage treatment equipment configuration constraint space (the range or space of allowable equipment configuration) for each processing node is generated, that is, which equipment types, quantities, scales, and configuration methods can be selected. For example, in the processing node of the bioreactor, there may be multiple equipment choices, but each choice has constraints on processing capacity, operating cost, and energy consumption. Using the preset multi-objective optimization constraint (cost, processing efficiency, energy consumption, and environmental protection, etc.), the closest equipment configuration result is screened, that is, the most suitable equipment configuration plan is screened to balance all objectives. Finally, the first resource configuration result and the first polygon optimization equilibrium relationship are obtained, so as to ensure that the configuration of sewage treatment resources not only meets the sewage treatment requirements but also achieves a good balance among multiple objectives such as cost, efficiency, and energy consumption.

[0043] Further, step S443 further includes that the preset multi-objective optimization constraint is a radar chart for multiple optimization indicators, where the multiple optimization indicators at least include processing efficiency, processing cost, and processing power consumption. The relationship construction of the radar chart is generated by obtaining the evaluation weights and index thresholds corresponding to the multiple optimization indicators to generate the preset multi-objective optimization constraint.

[0044] Preferably, a radar chart (also known as a spider chart) is a two-dimensional graph used to display multiple variables and is suitable for multi-dimensional analysis. In the multi-objective optimization of sewage treatment, a radar chart can help show the relationships between different optimization metrics (such as treatment efficiency, cost, power consumption, etc.). Each objective (such as treatment efficiency, treatment cost, treatment power consumption) is represented on an axis of the radar chart, and each sewage treatment plan will have corresponding scores on these axes. The shape of the radar chart can display the advantages and disadvantages of each plan in different objectives. Among them, the multiple optimization metrics at least include treatment efficiency, treatment cost, and treatment power consumption. Treatment efficiency represents the treatment capacity in the sewage treatment process, that is, the amount of sewage that can be treated per unit time or the amount of pollutants that can be removed. The higher the treatment efficiency, the faster the system can treat sewage. Treatment cost includes equipment cost, operation cost, maintenance cost, etc. Treatment efficiency and cost are inversely related. Improving treatment efficiency often increases the input cost and needs to be balanced. Treatment power consumption refers to the energy consumed in the sewage treatment process, especially in terms of power and other forms of energy consumption.

[0045] Preferably, obtain the evaluation weights and index thresholds corresponding to multiple optimization metrics to construct a radar chart. Specifically, different optimization objectives (such as efficiency, cost, power consumption) may have different priorities for decision-makers. For example, in some cases, treatment efficiency may be more important than cost, while in other cases, energy conservation (reducing power consumption) may be considered the top priority. The index threshold refers to the minimum or maximum acceptable value for each optimization objective. For example, the treatment efficiency cannot be lower than a certain standard, the cost cannot exceed the budget, and the power consumption must be kept within an acceptable range. Then, according to the weights and thresholds of each optimization metric, through multi-objective optimization algorithms (such as linear programming, genetic algorithms, etc.), map the relationships of these objectives onto the radar chart. The position of each sewage treatment plan in the graph will be calculated and calibrated according to its performance on each objective, thereby generating preset multi-objective optimization constraints, which helps to find the best balance between multiple objectives, optimize the resource allocation of the sewage treatment system, and at the same time ensure that the actual requirements are met in terms of efficiency, cost, and power consumption.

[0046] Step S500, return the multiple resource allocation results and the polygon optimization equilibrium relationship to the management terminal of the target sewage treatment plant.

[0047] Preferably, multiple possible resource allocation schemes obtained through multi-objective optimization, as well as the trade-offs between different optimization objectives (i.e., "polygonal optimization equilibrium relationship"), are transmitted back to the management terminal of the sewage treatment plant. Here, the management terminal refers to the management control system of the sewage treatment plant, which is used to monitor and manage the entire sewage treatment process in real time. It may include computer equipment in the central control room, terminal equipment of operators, etc. Specifically, multiple resource allocation results and the polygonal optimization equilibrium relationship (such as the Pareto frontier graph) are visualized and presented to the operators. The management personnel can find the best balance scheme between different objectives. According to the returned optimization results, the management personnel of the sewage treatment plant can further adjust the resource allocation, scheduling plan or operation strategy. For example, adjust the load distribution of the sewage treatment module, optimize the dosage of chemical agents, equipment operation load, etc., and implement the optimization scheme; not only improve the operation efficiency of the sewage treatment plant, but also be able to make flexible adjustments according to actual needs and real-time data, so as to achieve intelligent and automated resource optimization management.

[0048] In the foregoing, reference is made to Figure 1 A multi-objective optimization method for sewage treatment resource allocation according to an embodiment of the present invention is described in detail. Next, reference will be made to Figure 2 Describe a multi-objective optimization system for sewage treatment resource allocation according to an embodiment of the present invention.

[0049] The multi-objective optimization system for sewage treatment resource allocation according to an embodiment of the present invention is used to solve the technical problems of uneven sewage treatment resource allocation and lack of comprehensive allocation of multi-objective optimization, resulting in low treatment efficiency and high energy consumption. As Figure 2 shown, the multi-objective optimization system for sewage treatment resource allocation includes: a sewage source set determination unit 10, a treatment node sequence generation unit 20, a sewage treatment module generation unit 30, a sewage treatment resource allocation optimization unit 40, and a resource allocation result return unit 50.

[0050] The sewage source set determination unit 10 is used to determine the sewage source set of the target sewage treatment plant; the treatment node sequence generation unit 20 is used to analyze the sewage treatment node requirements for each sewage source in the sewage source set and generate each treatment node sequence; the sewage treatment module generation unit 30 is used to merge and verify any two treatment node sequences in the respective treatment node sequences to generate multiple sewage treatment modules; the sewage treatment resource allocation optimization unit 40 is used to call a preset multi-objective optimization constraint to optimize the allocation of sewage treatment resources for the multiple sewage treatment modules, generate multiple resource allocation results and the corresponding polygonal optimization equilibrium relationship; the resource allocation result return unit 50 is used to return the multiple resource allocation results and the polygonal optimization equilibrium relationship to the management terminal of the target sewage treatment plant.

[0051] Next, the specific configuration of the sewage treatment module generation unit 30 will be described in detail. The sewage treatment module generation unit 30 further includes: extracting the first treatment node sequence corresponding to the first sewage source and the second treatment node sequence corresponding to the second sewage source from each of the treatment node sequences; performing a merging cost analysis on the first treatment node sequence and the second treatment node sequence to generate a first merged node network and a first merging cost index; if the first merging cost index is less than a preset cost index, generating a first sewage treatment module with the first merged node network and adding it to the multiple sewage treatment modules.

[0052] Next, the specific configuration of the sewage treatment module generation unit 30 will be further described in detail. The sewage treatment module generation unit 30 further includes: identifying identical nodes in the first treatment node sequence and the second treatment node sequence to generate an identical node set, a first differentiated node set, and a second differentiated node set; using the identical node set, the first differentiated node set, and the second differentiated node set to perform sequence merging to construct the first merged node network; performing a complexity identification of branch node introduction on the first merged node network to generate the first merging cost index, where the complexity identification of branch node introduction includes the proportion of the number of branch nodes and the analysis of the number of introduction positions.

[0053] Next, the specific configuration of the sewage treatment resource configuration optimization unit 40 will be described in detail. The sewage treatment resource configuration optimization unit 40 further includes: establishing a mapping relationship between the multiple sewage treatment modules and the sewage sources; based on the mapping relationship, extracting the first sewage source corresponding to the first sewage treatment module; performing a dynamic analysis of the historical sewage generation amount and the sewage pollutant components of the first sewage source to construct a first sewage generation amount concentration value and a first pollutant content concentration value; using the satisfaction of the first sewage generation amount concentration value and the first pollutant content concentration value as constraints, and combining the preset multi-objective optimization constraints to perform configuration optimization of the sewage treatment resources of the first sewage treatment module, generating a first resource configuration result and a first polygon optimization equilibrium relationship, and adding them to the multiple resource configuration results and the polygon optimization equilibrium relationship.

[0054] Next, the specific configuration of the sewage treatment resource allocation optimization unit 40 will be described in detail. The sewage treatment resource allocation optimization unit 40 further includes: extracting each processing node in the first sewage treatment module, and determining the required sewage treatment equipment types for each processing node; performing sewage treatment fitting based on the first sewage generation amount concentration value, the first pollutant content concentration value, and the required sewage treatment equipment types, to generate a sewage treatment equipment configuration constraint space for each processing node; invoking a preset multi-objective optimization constraint to screen the sewage treatment equipment configuration result closest to the preset multi-objective optimization constraint within the sewage treatment equipment configuration constraint space, to generate the first resource allocation result and the first polygon optimization equilibrium relationship.

[0055] Next, the specific configuration of the sewage treatment resource allocation optimization unit 40 will be described in detail. The sewage treatment resource allocation optimization unit 40 further includes: the preset multi-objective optimization constraint is a radar chart for multiple optimization indicators, where the multiple optimization indicators at least include processing efficiency, processing cost, and processing power consumption. By obtaining the evaluation weights and index thresholds corresponding to the multiple optimization indicators, the relationship construction of the radar chart is performed to generate the preset multi-objective optimization constraint.

[0056] Next, the specific configuration of the processing node sequence generation unit 20 will be described in detail. The processing node sequence generation unit 20 further includes: performing cross-task node identification based on the visualization parameter import path to obtain conflict processing nodes; configuring the priority value and tolerance time limit of the cross-task according to the synchronous visualization rule; aiming at maximizing the dynamic update timeliness evaluation result, performing cross-task path timing optimization and resetting on the conflict processing nodes according to the priority value and tolerance time limit of the cross-task.

[0057] The multi-objective optimization sewage treatment resource allocation system provided by the embodiments of the present invention can execute the multi-objective optimization sewage treatment resource allocation method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0058] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or the server. The included units and modules are only divided according to the functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the present invention.

[0059] The above specific embodiments do not constitute a limitation to the protection scope of this application. Those skilled in the art should understand that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of this application shall be included within the protection scope of this application.

Claims

1. A multi-objective optimization method for wastewater treatment resource allocation, characterized in that: include: Determine the set of sewage sources for the target sewage treatment plant; Analyzing the sewage treatment node requirements of each sewage source in the sewage source set to generate each treatment node sequence; Merging and verifying any two processing node sequences in the processing node sequences to generate multiple sewage treatment modules; For the multiple sewage treatment modules, calling preset multi-objective optimization constraints to optimize the configuration of sewage treatment resources, generating multiple resource configuration results and corresponding polygonal optimization equilibrium relationships; The multiple resource allocation results and the polygonal optimization balance relationship are returned to the management terminal of the target sewage treatment plant.

2. The multi-objective optimization method for sewage treatment resource allocation according to claim 1, characterized in that: Any two processing node sequences in the processing node sequences are merged and verified to generate multiple sewage treatment modules, including: Extracting a first processing node sequence corresponding to a first sewage source and a second processing node sequence corresponding to a second sewage source from each of the processing node sequences; Performing a merging cost analysis on the first processing node sequence and the second processing node sequence to generate a first merging node network and a first merging cost index; If the first merging cost index is less than a preset cost index, a first sewage treatment module is generated by using the first merging node network and added into the plurality of sewage treatment modules.

3. The multi-objective optimization method for sewage treatment resource allocation according to claim 2, characterized in that: Performing a merging cost analysis on the first processing node sequence and the second processing node sequence to generate a first merging node network and a first merging cost index includes: Identify identical nodes on the first processing node sequence and the second processing node sequence to generate an identical node set, a first distinguishing node set, and a second distinguishing node set; The same node set, the first distinguishing node set and the second distinguishing node set are used to build a sequence merge to build the first merged node network; The complexity of introducing branch nodes is identified for the first merge node network to generate the first merge cost index, wherein the complexity of introducing branch nodes includes analyzing the proportion of the number of branch nodes and the number of introduced positions.

4. The multi-objective optimization method for sewage treatment resource allocation according to claim 1, characterized in that: For the multiple sewage treatment modules, the preset multi-objective optimization constraints are called to optimize the configuration of sewage treatment resources, and multiple resource configuration results and corresponding polygonal optimization equilibrium relationships are generated, including: Establishing a mapping relationship between the plurality of sewage treatment modules and sewage sources; Based on the mapping relationship, extracting a first sewage source corresponding to the first sewage treatment module; Performing a dynamic analysis of the historical sewage generation and sewage pollutant composition of the first sewage source, and constructing a first sewage generation concentration value and a first pollutant content concentration value; Based on the constraints of satisfying the first sewage generation concentration value and the first pollutant content concentration value, the configuration of the sewage treatment resources of the first sewage treatment module is optimized in combination with the preset multi-objective optimization constraints, and a first resource configuration result and a first polygon optimization balance relationship are generated, and the multiple resource configuration results and the polygon optimization balance relationship are added.

5. The multi-objective optimization method for sewage treatment resource allocation according to claim 4, characterized in that: The configuration optimization of the sewage treatment resources of the first sewage treatment module is performed in combination with the preset multi-objective optimization constraint to meet the first sewage generation concentration value and the first pollutant content concentration value as constraints, and a first resource configuration result and a first polygon optimization equilibrium relationship are generated, including: Extracting each processing node in the first sewage treatment module and determining the type of sewage treatment equipment required for each processing node; Perform sewage treatment fitting based on the first sewage generation concentrated value, the first pollutant content concentrated value and the required sewage treatment equipment type to generate a sewage treatment equipment configuration constraint space for each treatment node; The preset multi-objective optimization constraint is called to screen the sewage treatment equipment configuration result closest to the preset multi-objective optimization constraint in the sewage treatment equipment configuration constraint space, and the first resource configuration result and the first polygon optimization balance relationship are generated.

6. The multi-objective optimization method for sewage treatment resource allocation according to claim 5, characterized in that: The preset multi-objective optimization constraint is a radar chart for multiple optimization indicators, wherein the multiple optimization indicators include at least processing efficiency, processing cost, and processing power consumption. The relationship of the radar chart is constructed by obtaining the evaluation weights and indicator thresholds corresponding to the multiple optimization indicators to generate the preset multi-objective optimization constraint.

7. The multi-objective optimization method for sewage treatment resource allocation according to claim 1, characterized in that: Analyze the sewage treatment node requirements for each sewage source in the sewage source set to generate each treatment node sequence, including: Collecting sewage treatment sample data based on the various sewage sources as constraints to generate various sewage treatment sample data; Sewage treatment nodes are identified based on the various sewage treatment sample data and arranged in a treatment order to generate the various treatment node sequences.

8. The multi-objective optimization sewage treatment resource allocation system is characterized by: The system is used to implement the multi-objective optimization sewage treatment resource allocation method according to any one of claims 1 to 7, and the system comprises: A sewage source set determination unit, used to determine the sewage source set of a target sewage treatment plant; A processing node sequence generating unit, used for analyzing the sewage treatment node requirements of each sewage source in the sewage source set, and generating each processing node sequence; A sewage treatment module generation unit, used for merging and verifying any two processing node sequences in the processing node sequences to generate multiple sewage treatment modules; A sewage treatment resource configuration optimization unit, for invoking preset multi-objective optimization constraints to optimize the configuration of sewage treatment resources for the plurality of sewage treatment modules, and generating a plurality of resource configuration results and corresponding polygonal optimization equilibrium relationships; The resource configuration result returning unit is used to return the multiple resource configuration results and the polygon optimization balance relationship to the management terminal of the target sewage treatment plant.

Images