A control method and system for optimizing balanced water inflow between urban sewage treatment plants

Abstract

The present invention discloses a control method and system for optimizing the balanced water inflow between urban sewage treatment plants, including: constructing an operation parameter database and a step cost database for a sewage treatment system and a sewage allocation system; constructing a sewage volume optimization configuration model, obtaining restriction conditions based on the operation parameter database and the step cost database, and obtaining the sewage volume optimization configuration in combination with the sewage volume optimization configuration model; obtaining monitoring data of the sewage treatment system and the sewage allocation system, constructing a prediction model based on the water volume of the sewage treatment plant and the front feedback of the sewage allocation system, and obtaining the total water inflow of the sewage treatment system based on the prediction model and the monitoring data; issuing a deployment instruction to the sewage allocation system or a spanning instruction to the sewage treatment system based on the total water inflow. The present invention can automatically adapt to different data features without manual parameter adjustment; has high computational efficiency, can process large-scale data, and generate accurate prediction results in a short time, helping users understand and explain data changes.

Description

Technical Field

[0001] The present invention belongs to the field of smart water technology, and in particular relates to a control method and system for optimizing balanced water inflow between urban sewage treatment plants. Background Art

[0002] The trade-off between operating costs and sewage volume has always been the primary issue in sewage plant operations. However, sewage systems generally have problems such as low accuracy in the dispatch and dispatch of sewage collection systems, different operating loads of various sewage plants, different sewage treatment prices depending on the treatment process, and the inability to accurately implement a water volume distribution model with prices ranging from low to high. There is still room for improvement in saving sewage treatment costs. Especially in recent years, many cities in my country have faced many requirements such as urbanization, upgrading and transformation of sewage systems, and improvement of intelligent operation and management of sewage systems, resulting in complex and changeable operating conditions of sewage systems. At this stage, breaking the traditional segmentation of sewage treatment system elements and implementing coordinated operation of plant and website systems is the key to improving the efficiency of urban sewage collection and treatment, ensuring that urban watershed water quality meets standards and optimizes cost-effectiveness.

[0003] Due to the complex topological relationship and uncertain flow input of the sewage system, it is difficult to make an accurate assessment of the operation status of the sewage system only through local and simple empirical judgments, and it is impossible to provide a scientific basis for the systematic decision-making of "balancing the sewage flow into the plant and adjusting the operating load of each plant". Therefore, it is necessary to build an effective control method and system for optimizing the precise allocation of sewage between urban sewage plants based on the comprehensive capabilities of sewage treatment facilities, to achieve a comprehensive assessment of the sewage system and provide quantitative allocation results, to help managers scientifically allocate the sewage treatment volume and sewage treatment costs of the sewage system, to achieve the control of precise sewage allocation between urban sewage plants, to ensure safe and efficient operation while reducing financial expenditures. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a control method and system for optimizing the balanced water inflow between urban sewage treatment plants, so as to solve the problems existing in the above prior art.

[0005] To achieve the above object, the present invention provides a control method for optimizing the balanced water inflow between urban sewage treatment plants, comprising:

[0006] Construct the operating parameter database and tiered cost database of the sewage treatment system;

[0007] Construct the operation parameter database and tiered cost database of the sewage distribution system;

[0008] Constructing a sewage volume optimization configuration model, obtaining restriction conditions based on an operation parameter database and a step cost database, and obtaining a sewage volume optimization configuration based on the sewage volume optimization configuration model and the restriction conditions;

[0009] Acquire monitoring data of a sewage treatment system and a sewage distribution system, construct a prediction model based on the water volume of a sewage treatment plant and the front feedback of a sewage distribution system, and obtain the total water volume of several sewage treatment systems based on the prediction model and the monitoring data;

[0010] Obtain the dispatching rules, take the hourly discharge of the sewage treatment plant as the determining variable, and take the water volume limit as the boundary condition. Based on the dispatching rules and the total water inflow, issue dispatching instructions to the sewage dispatching system or issue crossing instructions to several sewage treatment systems to achieve balanced water inflow control among sewage treatment plants.

[0011] Optionally, the process of constructing an operating parameter database of a sewage treatment system includes:

[0012] Collect the guaranteed water volume, design water volume, maximum load water volume, and actual daily treatment water volume of several sewage treatment systems, divide the levels according to the periodicity and seasonality of the sewage volume, obtain all possible arrangements of uncertain factors, and generate several scenarios through arrangement and combination. Divide the actual daily treatment water volume data according to the scenarios, and obtain the total daily treatment water volume based on the actual daily treatment water volume of several sewage treatment systems; build an operation parameter database based on the data of several sewage treatment plants and the total daily treatment water volume.

[0013] Optionally, obtain a historical data set of sewage volume to be treated, remove the sewage data corresponding to the rainy days in the data set, change them to missing values, obtain a seasonal time series of dry season flow, use regression analysis to calculate the sewage volume fluctuation under rainfall conditions, and obtain the dry season flow based on the sewage volume fluctuation. The dry season flow is the water inflow of the sewage treatment plant during non-rainy periods. The dry season flow combined with the real-time rainfall is the total water inflow of the sewage treatment plant; wherein the seasonal time series includes missing values ​​and outliers.

[0014] Optionally, the process of constructing a ladder cost database for a wastewater treatment system includes:

[0015] The treatment costs of several sewage treatment systems are obtained, and a functional relationship between the inlet water volume and the treatment cost is constructed based on the guaranteed water volume, the designed water volume, and the maximum load water volume. The total cost of the sewage treatment system is obtained according to the functional relationship, and a stepped cost database of the sewage treatment system is constructed based on the treatment cost, the functional relationship, and the total cost.

[0016] Optionally, the process of constructing an operating parameter database of a sewage distribution system includes:

[0017] Collect the allocation relationship path, design load, maximum load and daily allocation water volume of the sewage allocation system, calculate the total allocation water volume of several allocation systems based on the daily allocation water volume; build an operation parameter database of the sewage allocation system based on the collected data and the total allocation water volume.

[0018] Optionally, the process of constructing a ladder cost database for the wastewater distribution system includes:

[0019] Collect the allocation costs of several sewage allocation systems, build a functional relationship between the allocation water volume and cost according to the designed water volume and maximum load of the sewage allocation system, obtain the total treatment cost of several sewage allocation systems based on the functional relationship, and build a ladder cost database of the sewage allocation system according to the allocation cost, functional relationship and total treatment cost.

[0020] Optionally, the process of obtaining the optimal configuration of sewage volume based on the sewage volume optimal configuration model and the constraint conditions includes:

[0021] Obtain the treatment water volume allocation and initialize the vector between the sewage treatment plant cost and the treatment water volume, obtain the restriction conditions based on the operation parameter database and the ladder cost database, and traverse and screen all the treatment water volume allocation states according to the restriction conditions; calculate the allocation cost and total cost of the screened treatment water volume allocation, and obtain the optimal sewage volume configuration according to the allocation cost and the total cost.

[0022] Optionally, extract the dispatching rules under different scenarios and apply the decision tree to refine them to obtain the dispatching rules;

[0023] The process of obtaining a decision tree includes: identifying the sewage treatment volume and counting and dividing the nodes; creating branches according to the sewage treatment volume and season; dividing the data set and performing recursive processing until all instances under each branch obtain classification results and obtain a decision tree.

[0024] The present invention also provides a control system for optimizing the balanced water inflow between urban sewage treatment plants, comprising:

[0025] Database construction module, optimization configuration module, prediction module, and scheduling module;

[0026] The data construction module is used to construct an operation parameter database and a ladder cost database for a sewage treatment system, and an operation parameter database and a ladder cost database for a sewage allocation system;

[0027] The optimization configuration module is used to construct a sewage volume optimization configuration model, obtain restriction conditions based on the operation parameter database and the ladder cost database, and obtain sewage volume optimization configuration based on the sewage volume optimization configuration model and the restriction conditions;

[0028] The prediction module is used to obtain monitoring data of the sewage treatment system and the sewage allocation system, build a prediction model based on the water volume of the sewage treatment plant and the front feedback of the sewage allocation system, and obtain the total water volume of several sewage treatment systems based on the prediction model and the monitoring data;

[0029] The scheduling module is used to obtain scheduling rules, take the hourly discharge of the sewage treatment plant as the determining variable, and take the water volume limit as the boundary condition. Based on the scheduling rules and the total water inflow, it issues a dispatching instruction to the sewage distribution system or issues a crossing instruction to several sewage treatment systems to achieve balanced water inflow control among sewage treatment plants.

[0030] Compared with the prior art, the present invention has the following advantages and technical effects:

[0031] The present invention proposes a universal optimization and dispatching rule for precise allocation of sewage between urban sewage treatment plants, which includes four key components: (1) a prophet algorithm to extract dry season flow and its uncertainty range; (2) sewage inflow scenario generation; (3) an optimization model for the optimal allocation scheme for each scenario; and (4) a decision tree to help derive the dispatching rule. The present invention can automatically adapt to different data characteristics without manual adjustment of parameters. It has high computational efficiency and can process large-scale data, generate accurate prediction results in a short time, and help users understand and interpret data changes. It provides some statistical indicators, such as the degree of influence of time series, to facilitate users' further analysis and decision-making. Accurate monitoring data and sufficient computing power will allow further research on RTC strategies and provide parameter basis for water intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0033] Figure 1 A schematic diagram of a control method for optimizing balanced water inflow between urban sewage treatment plants according to an embodiment of the present invention;

[0034] Figure 2 This is a schematic diagram of the logical relationship layout of the sewage treatment plant, station, and storage tank according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0036] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0037] This paper proposes a universal control rule for optimizing the precise allocation of sewage between urban sewage treatment plants, which is based on four key components: (1) a prophet algorithm to extract dry season flow and its uncertainty range; (2) sewage inflow scenario generation; (3) an optimization model for the optimal allocation scheme for each scenario; and (4) a decision tree to help derive the scheduling rules.

[0038] First of all, it should be noted that the sewage system in this embodiment refers to the sewage treatment plant and the sewage pipe network, sewage pumping station and storage tank within its service area. The sewage treatment system includes the pipe network and sewage treatment plant; the sewage distribution system includes the pipe network, sewage pumping station and storage tank; the monitoring indicators are the main flow indicators in front of the sewage treatment plant, sewage pumping station and storage tank.

[0039] Embodiment 1

[0040] like Figure 1-2 As shown, this embodiment provides a control method and system for optimizing the balanced water inflow between urban sewage treatment plants, including the following steps S1 to S6:

[0041] S1. Establish a database of the treatment capacity and operating parameter values ​​of the urban sewage treatment system. The treatment capacity and operating parameter values ​​of the sewage treatment system are specifically as follows;

[0042] (1) Collect the guaranteed water volume A of n sewage treatment plants i 、Design water volume B i and maximum load water volume C i ;

[0043] The uncertainty range of sewage plant inflow is between the guaranteed water volume and the maximum load volume of the sewage plant. The uncertainty of sewage includes but is not limited to trend changes, seasonal changes and holiday effects. Under the premise of not significantly changing the sewage volume law, the sewage data corresponding to the rainy days are eliminated and replaced with missing values ​​to obtain a seasonal time series of dry season flow containing missing values ​​and outliers. Use regression analysis or catchment area models to calculate the sewage volume fluctuations caused by rainfall to obtain the corrected dry season flow, that is, the inflow volume of the sewage plant during the non-rainy period;

[0044] x(t)=g(t)+s(t)+h(t)+ε t

[0045] where g(t) is the trend function that models non-periodic variations in the time series values, s(t) represents cyclical variations (e.g., weekly and yearly seasonality), and h(t) represents the effect of potentially irregularly scheduled holidays that occur on one or more days. t represents any special changes that the model does not accommodate; we then make a parametric assumption that ε t is normally distributed;

[0046] (2) Collect the actual daily water treatment volume of n sewage treatment plants, which are x1, x2, ..., x n ;

[0047] The actual water inflow is determined according to the actual situation of the basin, and the scenario is set. Different levels are divided according to the periodicity and seasonality, all possible arrangements of uncertain factors are made, and multiple scenarios are generated through arrangement and combination. Some scenario analyses are as follows:

[0048] Scenario 1: Spring and autumn, the inflow is mostly sewage balance flow, Q is the water plant inflow flow:

[0049]

[0050] In the process of hydraulic calculation of the pipe network, it is assumed that the sewage volume is a stable uniform flow (design flow);

[0051] Scenario 2: Summer without rainfall;

[0052] Scenario 3: Summer rainfall scenario, the flow into the plant is mostly sewage-constrained flow:

[0053] In order to ensure efficient operation of the sewage treatment plant, the maximum flow rate (C i ) Proper load control;

[0054] Scenario 4: Winter scenario, the incoming water is mostly sewage and the load is appropriately controlled;

[0055] In order to ensure the efficient operation of the sewage treatment plant, a guaranteed flow rate (A i ) Proper load control;

[0056] 3) Calculate the total daily water volume X treated by n sewage treatment plants:

[0057]

[0058] Determine the actual water inflow according to the actual situation of the basin, and calculate the total daily water treatment volume of the sewage plant under different scenarios according to the determined scenarios;

[0059] The actual operation experience of several sewage treatment plants is obtained, and the treatment capacity and operation parameter value database are obtained according to the above method, as shown in the following table:

[0060] Table 1

[0061]

[0062] According to the daily water inflow rules of the sewage treatment plant, the scene allocation is shown in the following table:

[0063] Table 2

[0064]

[0065] S2. Establish a database of the ladder cost components of the urban sewage treatment system. The ladder cost components are as follows:

[0066] (1) Collect the treatment costs y of n sewage treatment plants i , covering the power cost D i 、Pharmacy cost E i and labor cost F i wait;

[0067] y i =D i +E i +F i

[0068] (2) Based on the guaranteed sewage volume A i 、Design water volume B i and maximum load C i Calculate different water inflow and cost y i The functional relationship between

[0069]

[0070] (3) Calculate the total daily treatment cost Y of n sewage treatment plants:

[0071]

[0072] According to the above method, the step-by-step cost is obtained as shown in the following table:

[0073] Table 3

[0074]

[0075] S3. Establish a database of urban sewage distribution system capacity and operating parameter values;

[0076] (1) Collect the distribution relationship paths in the sewage distribution system such as pumping stations and storage tanks;

[0077] (2) Design load H of sewage collection and distribution system such as pumping station and storage tank i And maximum load I i ;

[0078] (3) Collect the daily water volume d of m distribution systems i , and calculate the total water volume D of all deployment systems:

[0079]

[0080] Table 4

[0081]

[0082]

[0083] S4. Establish a database of the tiered cost components of the urban sewage distribution system;

[0084] (1) The cost of collecting m sewage distribution systems is z i , such as power cost J i and labor cost K i wait;

[0085] z i =J i +K i

[0086] (2) Calculate different allocation water volumes and costs Z based on the designed water volume and maximum load of the sewage allocation system i The functional relationship between

[0087]

[0088] (3) Calculate the total daily treatment cost Z of m sewage treatment plants:

[0089]

[0090] Since the cost of water transfer is much less than the cost of sewage treatment, the cost component of water transfer may not be considered temporarily in special circumstances;

[0091] S5. Establish an optimization model based on the total control cost reduction target, and complete the optimal allocation of sewage treatment plant sewage volume according to the system minimum load, design load, maximum load, sewage treatment cost and sewage allocation cost as limiting conditions;

[0092] (1) Initialize the sewage treatment plant cost y i and the vector between the amount of treated water x;

[0093] Determine the fluctuation range of sewage treatment at the sewage treatment plant under different scenarios based on the actual water inflow in the basin;

[0094] x=[A i :0.1:B i ,B i :0.1,C i ]

[0095] y i =zeros(size(x));

[0096] Table 5

[0097] Scenario Initialize the vector between the treated water volumes Water volume fluctuation range Scene 1 x_values ​​= [] <![CDATA[for x=[A i :0.1:B i ]]]> Scene 2 x_values ​​= [] <![CDATA[for x=[A i :0.1,C i ]]]>

[0098] (2) Traverse all possible treatment water allocations;

[0099] Table 6

[0100]

[0101]

[0102] (3) Ensure that other conditions for water allocation are met;

[0103] For example, the maximum water transfer volume between sewage treatment plant 1 and sewage treatment plant 2 is t i ≤ design load H of pump station or storage tank i ; (4) Ensure that the predicted total treated water volume is equal to Q;

[0104] Table 7

[0105] Scenario Predict the conditions under which the total treated water volume is equal to Q Scene 1 <![CDATA[if x1+x1+……+xn=Q,Q=25:0.1:40]]> Scene 2 <![CDATA[if x1+x1+……+xn=Q,Q=40:0.1:52]]>

[0106] (5) Calculate the treatment cost of the sewage treatment plant;

[0107] Table 8

[0108]

[0109] (6) Calculate deployment costs;

[0110] z i =f(d)

[0111] if z i >H i ,z i =f(d);

[0112]

[0113] (7) Calculate total cost;

[0114]

[0115] (8) Add the total cost to the relationship vector between the total amount of sewage treatment and the total cost;

[0116] cost = [cost; total_cost]

[0117] x i _values=[x i _values; x i ]

[0118] end

[0119] (9) Create a traversal result table;

[0120] M=table(x 1_ values,x 2_values,x 3_ values,x 4_ values,cost,'VariableNames',{'x1','x2','x3',

[0121] 'x4','Cost'});

[0122] (10) Find the optimal solution;

[0123] [min_cost,min_index]=min(M.cost);

[0124] min_row = M(min_indes,:);

[0125] (11) Display the optimal solution result;

[0126] disp(min_row)

[0127] The above model was solved by using Matlab software linear programming function, and the optimal configuration of water inlet to the sewage treatment plant under normal circumstances was obtained. After applying the corresponding method for feedback control, the system achieved the maximum cost-effectiveness compared with the original sewage treatment plant operation mode;

[0128] S6. Establish a prediction model based on the water volume of the sewage plant and the pre-feedback of the sewage allocation system to achieve data collection, advance configuration and rapid scheduling;

[0129] (1) Mining the dispatching rules of all scenarios, applying the decision tree algorithm to refine them, and obtaining the dispatching rules. For example, the maximum water allocation volume t between sewage treatment plant 1 and sewage treatment plant 2 i ≤ design load H of pump station or storage tank i ;

[0130] For the allocation and dispatch of river basin sewage, including offline dispatch rules based on historical statistical data and uncertain factors;

[0131] For the allocation and dispatch of river basin sewage, including real-time control (RTC) with powerful online monitoring systems, high-precision models and strong computing power, effective methods for handling real-time dispatch, suitable for internal operations of sewage plants or pumping stations;

[0132] The amount of sewage and season are used as conditions, and the allocation scheme is used as a decision. The decision tree can use information theory to summarize the abundance sample attributes in a set of irregular cases and deduce classification rules;

[0133] Since sewage volume is a continuous attribute, the possible value set of a quantity is infinite. Identify the sewage volume and divide the nodes by count. Then, create branches based on sewage volume and season. Divide the data set and recursively process until all instances under each branch have obtained classification results. Thus, a decision tree is formed, and each path branch on the tree is a rule.

[0134] (2) Based on the monitoring data before the sewage treatment plant, the total water inflow Q of n sewage treatment plants is determined by using mathematical models, physical models and experience. The models include but are not limited to the Prophet Algorithm, Long Short-Term Memory Neural Network (LSTM), etc.

[0135] (3) Determine the value of Q and find the optimal result;

[0136]

[0137] disp(min_row)

[0138]

[0139] disp(min_row)

[0140] if Q>C i

[0141] disp('cross instruction')

[0142] end

[0143] (4) Based on the predicted Q value and the optimal result, a dispatch instruction is issued to the dispatch system or a crossing instruction is issued to the sewage treatment plant. When obtaining the dispatch instruction, the hourly discharge of the sewage treatment plant is used as the determining variable and the water volume limit is used as the boundary condition to ensure that the dispatch path between different sewage treatment plants is feasible.

[0144] Predict water flow changes in front of the sewage plant, in front of the pump station and in front of the storage tank based on the prediction model and actual collected monitoring data;

[0145] Taking the hourly cumulative value as an example, the water volume forecast data granularity needs to be calculated. The basic logic is: hourly water inflow = difference in water storage before and after + total water discharge;

[0146] Table 9

[0147]

[0148] The method provided in this embodiment can quickly and efficiently evaluate the degree of change in regional water inflow and improve the efficiency of sewage diversion; it can optimize the allocation of sewage water diversion, improve the overall treatment efficiency of the sewage system, reduce energy consumption, and provide a scientific basis for the integrated operation of sewage treatment plants.

[0149] In another aspect, the present invention discloses a computer-readable storage medium, wherein the storage medium stores a computer program. When a processor executes the computer program, the processor executes the steps in any of the above methods.

[0150] In addition, the present invention also discloses a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the processor executes any step in the above method.

[0151] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A control method for optimizing the balanced water inflow between urban sewage treatment plants, characterized in that: The following steps are involved: Construct the operating parameter database and tiered cost database of the sewage treatment system; The process of building an operating parameter database for a sewage treatment system includes: Collect the guaranteed water volume, design water volume, maximum load water volume, and actual daily treated water volume of several sewage treatment plants, divide the levels according to the periodicity and seasonality of the sewage volume, obtain all possible arrangements of uncertain factors, and generate several scenarios through arrangement and combination. According to the scenarios, divide the actual daily treated water volume data, and obtain the total daily treated water volume based on the actual daily treated water volume of several sewage treatment plants; build an operation parameter database based on the data of several sewage treatment plants and the total daily treated water volume; The process of constructing a ladder cost database for a wastewater treatment system includes: Obtaining the treatment costs of several sewage treatment plants, constructing a functional relationship between the inflow water volume and the treatment cost based on the guaranteed water volume, the designed water volume, and the maximum load water volume, obtaining the total cost of the sewage treatment plant according to the functional relationship, and constructing a ladder cost database of the sewage treatment system based on the treatment cost, the functional relationship, and the total cost; Construct the operation parameter database and tiered cost database of the sewage distribution system; The process of constructing a ladder cost database for wastewater distribution systems includes: Collect the allocation costs of several sewage allocation systems, build a functional relationship between the allocation water volume and the allocation cost according to the design water volume and maximum load of the sewage allocation system, obtain the total allocation costs of several sewage allocation systems based on the functional relationship, and build a ladder cost database of the sewage allocation system according to the allocation cost, the functional relationship, and the total allocation costs of several sewage allocation systems; Constructing a sewage volume optimization configuration model, obtaining restriction conditions based on an operation parameter database and a step cost database, and obtaining a sewage volume optimization configuration based on the sewage volume optimization configuration model and the restriction conditions; The process of obtaining the optimal configuration of sewage volume based on the sewage volume optimal configuration model and the constraint conditions includes: Obtain the treatment water volume distribution and initialize the vector between the treatment cost and the treatment water volume of each sewage treatment plant, obtain the restriction conditions based on the operation parameter database and the ladder cost database, and traverse and screen all the treatment water volume distribution states according to the restriction conditions; calculate the total allocation cost and the total cost of several sewage allocation systems corresponding to the screened treatment water volume distribution states, the total cost is obtained by summing the total allocation cost of several sewage allocation systems under different treatment water volume distribution states and the total treatment cost of several sewage treatment plants, and obtain the optimal sewage volume configuration according to the total allocation cost and the total cost of several sewage allocation systems; Acquire monitoring data of sewage treatment plants and sewage distribution systems, construct a prediction model based on the water volume of sewage treatment plants and the front feedback of sewage distribution systems, and obtain the total water volume of several sewage treatment plants based on the prediction model and the monitoring data; Obtain the scheduling rules, take the hourly discharge of the sewage treatment plant as the determining variable, and the water volume limit as the boundary condition, and issue a dispatching instruction to the sewage distribution system or a crossing instruction to several sewage treatment plants based on the scheduling rules and the total water inflow, so as to achieve balanced water inflow control among sewage treatment plants.

2. The control method for optimizing the balanced water inflow between urban sewage treatment plants according to claim 1 is characterized in that: Obtain the historical data set of sewage volume to be treated, remove the sewage data corresponding to the rainy days in the data set, change it to missing values, obtain the seasonal time series of dry season flow, use regression analysis to calculate the sewage volume fluctuation under rainfall conditions, and obtain the dry season flow based on the sewage volume fluctuation. The dry season flow is the water inflow of the sewage treatment plant during the non-rainy period. The dry season flow combined with the real-time rainfall is the total water inflow of the sewage treatment plant; wherein, the seasonal time series includes missing values ​​and outliers.

3. The control method for optimizing the balanced water inflow between urban sewage treatment plants according to claim 1 is characterized in that: The process of building an operating parameter database for a sewage distribution system includes: Collect the allocation relationship path, design load, maximum load and daily allocation water volume of the sewage allocation system, calculate the total allocation water volume of several sewage allocation systems based on the daily allocation water volume; build an operating parameter database of the sewage allocation system based on the collected data and the total allocation water volume.

4. The control method for optimizing the balanced water inflow between urban sewage treatment plants according to claim 1 is characterized in that: Extract dispatching rules under different scenarios and apply decision trees to refine them to obtain dispatching rules; The process of obtaining a decision tree includes: identifying the sewage treatment volume and counting and dividing the nodes; creating branches according to the sewage treatment volume and season; dividing the data set and performing recursive processing until all instances under each branch obtain classification results and obtain a decision tree.

5. A control system for optimizing the balanced water inflow between urban sewage treatment plants based on the method according to any one of claims 1 to 4, characterized in that: include: Database construction module, optimization configuration module, prediction module, and scheduling module; The data construction module is used to construct an operation parameter database and a ladder cost database for a sewage treatment system, and an operation parameter database and a ladder cost database for a sewage allocation system; The optimization configuration module is used to construct a sewage volume optimization configuration model, obtain restriction conditions based on the operation parameter database and the ladder cost database, and obtain sewage volume optimization configuration based on the sewage volume optimization configuration model and the restriction conditions; The prediction module is used to obtain monitoring data of sewage treatment plants and sewage allocation systems, build a prediction model based on the water volume of sewage treatment plants and the front feedback of sewage allocation systems, and obtain the total water volume of several sewage treatment plants based on the prediction model and the monitoring data; The scheduling module is used to obtain scheduling rules, with the hourly discharge of the sewage treatment plant as the determining variable and the water volume limit as the boundary condition. Based on the scheduling rules and the total water inflow, it sends allocation instructions to the sewage allocation system or sends crossing instructions to several sewage treatment plants to achieve balanced water inflow control among sewage treatment plants.

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