Intelligent Optimization Scheduling Method for the Whole Process of Waterworks Based on Adaptive Feedback Control

Abstract

The present invention discloses an intelligent optimization scheduling method for the whole process of a water plant based on adaptive feedback control. S1. It is divided into a low-demand stage, a medium-demand stage, and a high-demand stage; S2. In the low-demand stage, the medium-demand stage, and the high-demand stage, the real-time monitoring data set of the water plant is obtained correspondingly; S3. A multi-stage evolutionary game model is constructed based on the real-time monitoring data set of the water plant; S4. Generate the local optimal scheduling plan for the current stage; S5. Use the adaptive feedback control mechanism according to the local optimal scheduling plan output by the evolutionary game model; S6. When the global optimal scheduling of the system in each stage; S7. Optimize the scheduling plan in advance; S8. Display the optimization scheduling effect through an integrated intelligent scheduling platform, monitor the operation status of the entire water plant, and generate a scheduling report for the reference of management personnel. The present invention effectively improves the predictability and foresight of the scheduling system through the prediction model, ensuring that the water plant is always in an efficient and safe operating state.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent water plants, and particularly to an intelligent optimization scheduling method for the whole process of a water plant based on adaptive feedback control. Background Art

[0002] In the prior art, the optimization of the water plant scheduling system mainly relies on traditional rules and preset parameters for control. Usually, fixed operation strategies are adopted to adjust the water pump flow rate, chemical dosage, and filtration speed. The control process of the traditional system often based on historical data and empirical parameters lacks sensitivity to real-time changes. With the changes of external and internal conditions such as water quality, water consumption demand, and energy consumption, the traditional scheduling system is difficult to respond in a timely manner, often resulting in waste of resources, increased energy consumption, and even affecting the overall operation efficiency and water quality compliance rate of the water plant.

[0003] Another typical problem of the prior art is that the scheduling system lacks a dynamic feedback mechanism. In actual operation, the technological processes of the water plant are complex and changeable, and the states of each link may fluctuate due to various factors such as the environment, equipment aging, or water quality changes. Since the traditional system cannot collect and feedback the state changes of each technological link in real time, it is unable to adjust the strategy in a timely manner to cope with these fluctuations. The scheduling scheme often lags behind the actual operation state, resulting in low operation efficiency and even causing safety problems.

[0004] In summary, the prior art has the following disadvantages in water plant scheduling: First, the traditional scheduling system relies on fixed parameters and preset rules, and cannot adaptively adjust according to real-time data, making it difficult to cope with the dynamic changes of the external environment and internal conditions of the system; Second, it lacks the ability of multi-objective optimization and cannot balance multiple optimization objectives such as water quality, energy consumption, and cost; Finally, the feedback mechanism of the existing system is not perfect enough to form a closed-loop control, resulting in the inability to respond to the changes in operation in a timely manner, affecting the overall operation efficiency and safety of the water plant. Summary of the Invention

[0005] An object of the present invention is to propose an intelligent optimization scheduling method for the whole process of a water plant based on adaptive feedback control. The present invention effectively improves the predictability and foresight of the scheduling system through a prediction model, ensuring that the water plant is always in an efficient and safe operation state.

[0006] An intelligent optimization scheduling method for the whole process of a water plant based on adaptive feedback control according to an embodiment of the present invention includes the following steps:

[0007] S1. Dynamically divide the operation process of the water plant into a low-demand stage, a medium-demand stage, and a high-demand stage based on real-time parameters such as water quality parameters, energy consumption data, water consumption demand, and equipment operation status;

[0008] S2. In the low-demand stage, medium-demand stage, and high-demand stage, obtain the real-time monitoring dataset of the water plant, including water quality parameters, energy consumption data, equipment status, and external environment parameters, and establish a real-time data acquisition system related to each process link;

[0009] S3. Based on the real-time monitoring dataset of the water plant, construct a multi-stage evolutionary game model. Take each process link in the water plant as a game participant, define the strategy set and payoff function of each game participant. The payoff function includes water quality compliance rate, energy consumption, and cost optimization objectives;

[0010] S4. In each stage, use the evolutionary game model to simulate the strategy evolution among process links, calculate and optimize the strategy combination of each game participant through the replicator dynamics equation, and generate the local optimal scheduling plan for the current stage;

[0011] S5. Utilize the adaptive feedback control mechanism. According to the local optimal scheduling plan output by the evolutionary game model, adjust the pump flow rate, chemical dosage, and filtration speed of each process link in the water plant in real time, and continuously collect new real-time monitoring data during the adjustment process and feedback it to the control system to form a closed-loop feedback regulation;

[0012] S6. When the operating conditions or external environment of the water plant change, according to the local optimal scheduling plan of the evolutionary game model and the adaptive feedback control mechanism, dynamically adjust the operating stage and switch the strategy combination of each process link to achieve the global optimal scheduling of the system in each stage;

[0013] S7. Combine historical monitoring data and real-time monitoring data to predict the possible future system change trends of the water plant and optimize the scheduling plan in advance;

[0014] S8. Display the optimized scheduling effect through an integrated intelligent scheduling platform, monitor the operating status of the entire water plant, and generate a scheduling report for the reference of management personnel.

[0015] Optionally, the S1 includes:

[0016] S11. Obtain the water quality parameter P, energy consumption data E, water demand D, and equipment operating status parameter S of the water plant operation, and use the data as input to form the real-time dataset X(t) of the water plant;

[0017] S12. Based on the real-time dataset X(t), set the demand stage division criteria of the water plant as the low-demand stage L, medium-demand stage M, and high-demand stage H. The demand stage division criteria are defined by the threshold intervals of each parameter:

[0018] The interval of the water quality parameter P is [P low , P high , the interval of the energy consumption data E is [E low , E high, the interval of water demand D is [D low , D high , and the interval of equipment status parameter S is [S low , S high ;

[0019] S13. Determine the demand stage to which each parameter belongs at a certain moment t through the real-time data set X(t), and use the classification function F(X(t)) to classify the operation status of the water plant:

[0020] L, if P(t) ∈ [P low , P mid and E(t) ∈ [E low , E mid and D(t) ∈ [D low , D mid and S(t) ∈ [S low , S mid F(X(t)) = {M, if P(t) ∈ [P mid , P high and E(t) ∈ [E mid , E high and D(t) ∈ [D mid , D high and S(t) ∈ [S mid , S high ;

[0021] H, if P(t) > P high or E(t) > E high or D(t) > D high or S(t) > S high

[0022] S14. According to the real-time data set X(t) and the classification function F(X(t)), dynamically divide the operation process of the water plant into a low-demand stage, a medium-demand stage, and a high-demand stage.

[0023] Optionally, the S2 includes:

[0024] S21. Obtain the real-time monitoring data sets of each process link of the water plant in the low-demand stage, medium-demand stage, and high-demand stage of the water plant, including water quality parameter P(t), energy consumption data E(t), equipment status S(t), and external environment parameter W(t):

[0025] P(t) represents the relevant parameters of the water quality of the water plant at a certain moment t, including turbidity, pH value, and chemical oxygen demand (COD);

[0026] E(t) represents the energy consumption data of the water treatment plant at time t, including the total energy consumption and the sub-item energy consumption of each process link;

[0027] S(t) represents the operating state parameters of the main equipment in the water treatment plant, including the pump pressure, the filter state, and the working state of the dosing system;

[0028] W(t) represents the external environmental parameters, including the temperature, humidity, and water quality parameters of the external water source;

[0029] S22. Construct a real-time data acquisition system DCS(t) based on the water quality parameters, energy consumption data, equipment status, and external environmental parameters monitored in real time for each process link. The real-time data acquisition system independently monitors and collects data for each process link in different demand stages, sets the acquisition frequency f of the real-time data acquisition system, and sets different real-time data acquisition frequencies f in the low-demand stage, medium-demand stage, and high-demand stage L , f M , f H :

[0030] f L is the data acquisition frequency in the low-demand stage;

[0031] f M is the data acquisition frequency in the medium-demand stage;

[0032] f H is the data acquisition frequency in the high-demand stage;

[0033] S24. Dynamically adjust the parameter settings of the real-time data acquisition system DCS(t) according to the monitoring requirements of each stage, and construct a real-time monitoring data set of the water treatment plant:

[0034] X(t) = {P(t), E(t), S(t), W(t)}.

[0035] Optionally, the S3 includes:

[0036] S31. Construct a multi-stage evolutionary game model based on the real-time monitoring data set of the water treatment plant, and use the pumps, dosing systems, and filtration systems in the water treatment plant as game participants A i , where i = 1, 2, 3;

[0037] S32. Define the strategy set S of each game participant A i of, and the strategy set includes different operation modes and operating parameters: i For pumps, the strategy set includes different flow rate settings;

[0038] For the dosing system, the strategy set includes different dosing amount settings;

[0039] For the filtration system, the strategy set includes different filtration rate settings;

[0040] For the filtration system, the policy set includes different filtration speed settings;

[0041] S33. Define the payoff function U for each game participant i (S i , S -i ), the payoff function aims at the water quality compliance rate, energy consumption, and cost optimization:

[0042] U i (S i , S -i ) = α i P(t) - β i E(t) - γ i C(t);

[0043] Among them, P(t) represents the water quality compliance rate, E(t) represents the energy consumption of the water pump, chemical dosing system, or filtration system, C(t) represents the cost of the corresponding process link, and α i , β i , γ i are the weight coefficients for each participant, corresponding to the goals of water quality compliance rate, energy consumption, and cost optimization respectively.

[0044] Optionally, the construction process of the local optimal scheduling plan in the current stage includes that in each operation stage, the game participants update their strategies according to the complex replicator dynamics equation:

[0045]

[0046] Among them, x i is the proportion of the strategy S i , representing the participation degree of the water pump, chemical dosing system, and filtration system in the game. U i (S i , S -i , t) is the payoff function of the game participant A i , which changes with time t. is the change rate of the water quality compliance rate with time, representing the change trend of the current water plant's water quality. is the change rate of the energy consumption with time, representing the change trend of the energy consumption of each process link. is the change rate of the equipment status with time, representing the change trend of the operating status of the water pump, chemical dosing system, and filtration system. λ i , μ i , ν i are the weight coefficients respectively:

[0047] λ i represents the sensitivity of process link A i to the change in the water quality compliance rate, reflecting the impact of the water pump, chemical dosing system, and filtration system on the water quality;

[0048] μ i Represents process step A i The sensitivity to changes in energy consumption determines the regulation for minimizing energy consumption;

[0049] ν i Represents process step A i The sensitivity to changes in equipment status determines the degree of influence on equipment maintenance and adjustment;

[0050] Is the average payoff for all game participants:

[0051]

[0052] Where n is the number of process steps participating in the game.

[0053] Optionally, the S5 includes:

[0054] S51. Using an adaptive feedback control mechanism, according to the locally optimal scheduling scheme output by the evolutionary game model, adjust the operating parameters of each process step of the water plant in real time, including the pump flow rate Q(t), the chemical dosage M(t), and the filtration rate V(t);

[0055] S52. During the adjustment process, collect the real-time monitoring data set X'(t) of the water treatment plant in real time, and transmit the real-time monitoring data set X'(t) of the water plant to the control system through the feedback mechanism to update the participant payoff function U′ i (S i ,S -i ,t) in the multi-stage evolutionary game model, so that the multi-stage evolutionary game model is dynamically adjusted according to the latest operating state and external environment parameters;

[0056] S53. According to the feedback data and the strategy combination output by the evolutionary game model, use the adaptive feedback control equation to optimize the adjustment of process parameters:

[0057]

[0058] Where λ1, λ2, λ3 are adjustment coefficients, controlling the response speed of the pump, chemical dosing system, and filtration system to the adjustment

[0059] ″′, U Q (t), U M (t), U V (t) are the payoff functions of the pump, chemical dosing system, and filtration system respectively, Is the average payoff function of all game participants, Is the change rate of water quality parameters, Is the second derivative of the external environment parameters, is the change rate of energy consumption, is the square of the change rate of the device status parameter, and α1, α2, α3 are the influence weights of the water quality parameter change on each link, and β1, β2, β3 are the influence weights of the energy consumption change on each link.

[0060] S54. Through the closed-loop feedback regulation mechanism, the system dynamically optimizes the operation parameters of the process links according to the real-time monitoring data of the water plant. Each process link continuously updates its revenue function and control parameters during the adjustment process to achieve the optimal dynamic regulation effect, forming an adaptive closed-loop regulation system.

[0061] Optionally, the S6 includes:

[0062] S61. When the operating conditions of the water plant or the external environment parameter W(t) change, the change rate of the external environment is monitored in real time Combined with the real-time monitoring data set of the water plant, calculate the local optimal scheduling scheme through the multi-stage evolutionary game model;

[0063] S62. Dynamically adjust the operating stage of the water plant according to the calculated local optimal scheduling scheme, switch between the low-demand stage, medium-demand stage, and high-demand stage, and update the operation parameters of the process links through the participant strategy combination in the multi-stage evolutionary game model;

[0064] S63. Use the adaptive feedback control mechanism to dynamically adjust the strategy combination of the process links according to the changes in the external environment and operating conditions. When the strategy combination of the process links changes, the control system automatically dynamically optimizes the operation parameters of each link of the water plant according to the adaptive feedback control mechanism to achieve the global optimal scheduling in each stage;

[0065] S64. As the operating stage switches, the control system adjusts the revenue function and strategy combination of each process link according to the real-time data, so that the water plant always maintains the global optimal scheduling when the external environment and operating conditions change.

[0066] Optionally, the S7 includes:

[0067] S71. Combine the historical monitoring data X hist (t) and the real-time monitoring data X(t) of the water plant to construct a prediction model for analyzing the system change trend;

[0068] S72. In the prediction model, calculate the change trend equations of the water quality parameter P, energy consumption data E, water demand D, and device operating status parameter S through the time series analysis method:

[0069]

[0070] Among them, X pred(t + Δt) is the predicted value at the future moment t + Δt, X(t) represents any water quality parameter P(t), energy consumption data E(t), water demand D(t), or equipment operation status parameter S(t), and α X and β X are the weight coefficients of the corresponding parameters respectively;

[0071] S74. Optimize the water plant scheduling plan according to the water quality fluctuations, energy consumption, water demand, and equipment load change trends output by the prediction model, and adjust the operation parameters of each process link of the water plant before the system change occurs.

[0072] The beneficial effects of the present invention are:

[0073] (1) Through the adaptive feedback control mechanism combined with real-time monitoring data and the multi-stage evolutionary game model, the present invention dynamically adjusts the operation parameters of each process link during the operation of the water plant. The system performs closed-loop regulation according to the real-time collected data, can quickly respond and automatically adjust the scheduling plan when the external environment or internal operation conditions change, ensuring that the water plant always operates in the optimal state. Compared with the traditional fixed-parameter scheduling, the present invention shows extremely high flexibility and adaptability in dealing with water quality fluctuations, energy consumption changes, and equipment load changes, greatly improving the response speed and efficiency of the system.

[0074] (2) By constructing a multi-stage evolutionary game model, each process link in the water plant is regarded as a game participant, and the revenue function of each participant is defined, comprehensively considering multiple objectives such as water quality compliance rate, energy consumption optimization, and cost control. Through the strategy evolution of the replicator dynamics equation, local optimality can be achieved in each stage, and the strategy combination in different stages can be dynamically adjusted through the adaptive feedback control mechanism, so as to achieve global optimal scheduling.

[0075] (3) By using time series analysis and prediction models in combination with historical monitoring data and real-time monitoring data, the present invention can accurately predict the future system change trends of the water plant. Through the prediction results, the scheduling plan can be optimized in advance before the system change occurs, avoiding the impact of emergencies on the operation of the water plant. Compared with the lag adjustment in the prior art, the present invention effectively improves the predictability and forward-looking of the scheduling system through the prediction model, ensuring that the water plant always operates in an efficient and safe state. Description of the Drawings

[0076] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0077] Figure 1 is a flowchart of an intelligent optimization scheduling method for the whole process of a water plant based on adaptive feedback control proposed by the present invention;

[0078] Figure 2 This is a flowchart of the game strategy combination of the process links based on the multi-stage evolutionary game model in an intelligent optimization scheduling method for the whole process of a water treatment plant based on adaptive feedback control proposed by the present invention. Detailed implementation manners

[0079] Now, the present invention will be further described in detail with reference to the accompanying drawings. These drawings are all simplified schematic diagrams, only illustrating the basic structure of the present invention in a schematic manner, so they only show the components related to the present invention.

[0080] Refer to Figure 1-2 , an intelligent optimization scheduling method for the whole process of a water treatment plant based on adaptive feedback control, comprising the following steps:

[0081] S1. Dynamically divide the operation process of the water treatment plant into a low-demand stage, a medium-demand stage, and a high-demand stage based on real-time parameters such as water quality parameters, energy consumption data, water usage demand, and equipment operation status.

[0082] S2. In the low-demand stage, the medium-demand stage, and the high-demand stage, respectively obtain the real-time monitoring data set of the water treatment plant, including water quality parameters, energy consumption data, equipment status, and external environment parameters, and establish a real-time data acquisition system related to each process link.

[0083] S3. Construct a multi-stage evolutionary game model based on the real-time monitoring data set of the water treatment plant, regard each process link in the water treatment plant as a game participant, define the strategy set and the revenue function of each game participant, and the revenue function includes water quality compliance rate, energy consumption, and cost optimization objectives.

[0084] S4. In each stage, use the evolutionary game model to simulate the strategy evolution between each process link, calculate and optimize the strategy combination of each game participant through the replicator dynamics equation, and generate the local optimal scheduling plan for the current stage.

[0085] S5. Utilize the adaptive feedback control mechanism to adjust the water pump flow rate, chemical dosing amount, and filtration speed of each process link in the water treatment plant in real time according to the local optimal scheduling plan output by the evolutionary game model, and continuously collect new real-time monitoring data during the adjustment process and feedback it to the control system to form a closed-loop feedback regulation.

[0086] S6. When the operating conditions of the water treatment plant or the external environment change, dynamically adjust the operating stage according to the local optimal scheduling plan of the evolutionary game model and the adaptive feedback control mechanism, and switch the strategy combination of each process link to enable the global optimal scheduling of the system in each stage.

[0087] S7. Combine historical monitoring data and real-time monitoring data to predict the possible future system change trends of the water treatment plant and optimize the scheduling plan in advance.

[0088] S8. Display the optimized scheduling effect through the integrated intelligent scheduling platform, monitor the operation status of the entire water plant, and generate a scheduling report for the reference of management personnel.

[0089] In this embodiment, S1 includes:

[0090] S11. Obtain the water quality parameter P, energy consumption data E, water demand D, and equipment operation status parameter S of the water plant operation, and use the data as input to form the real-time data set X(t) of the water plant;

[0091] S12. Based on the real-time data set X(t), set the demand stage division criteria of the water plant as the low-demand stage L, medium-demand stage M, and high-demand stage H. The demand stage division criteria are defined by the threshold intervals of each parameter:

[0092] The interval of the water quality parameter P is [P low , P high , the interval of the energy consumption data E is [E low , E high , the interval of the water demand D is [D low , D high , and the interval of the equipment status parameter S is [S low , S high ;

[0093] S13. Determine the demand stage to which each parameter belongs at a certain moment t by calculating the real-time data set X(t), and use the classification function F(X(t)) to classify the operation status of the water plant:

[0094] L, if P(t) ∈ [P low , P mid and E(t) ∈ [E low , E mid and D(t) ∈ [D low , D mid and S(t) ∈ [S low , S mid F(X(t)) = {M, if P(t) ∈ [P mid , P high and E(t) ∈ [E mid , E high and D(t) ∈ [D mid , D high and S(t) ∈ [S mid , S high ; H, if P(t) > P high or E(t) > E high or D(t) > D highor S(t) > S high

[0095] S14. Dynamically divide the operation process of the water plant into a low - demand stage, a medium - demand stage, and a high - demand stage according to the real - time data set X(t) and the classification function F(X(t)).

[0096] In this embodiment, S2 includes:

[0097] S21. Obtain the real - time monitoring data sets of each process link in the water plant in the low - demand stage, medium - demand stage, and high - demand stage of the water plant, including water quality parameters P(t), energy consumption data E(t), equipment status S(t), and external environment parameters W(t):

[0098] P(t) represents the relevant parameters of the water quality of the water plant at a certain moment t, including turbidity, pH value, and chemical oxygen demand (COD);

[0099] E(t) represents the energy consumption data of the water plant at moment t, including the total energy consumption and the sub - item energy consumption of each process link;

[0100] S(t) represents the operation status parameters of the main equipment in the water plant, including pump pressure, filter status, and the working status of the dosing system;

[0101] W(t) represents the external environment parameters, including air temperature, humidity, and the water quality parameters of the external water source;

[0102] S22. Construct a real - time data acquisition system DCS(t) based on the real - time monitored water quality parameters, energy consumption data, equipment status, and external environment parameters of each process link. The real - time data acquisition system independently monitors and collects data for each process link in different demand stages, sets the acquisition frequency f of the real - time data acquisition system, and sets different real - time data acquisition frequencies f L , f M , f H :

[0103] f L is the data acquisition frequency in the low - demand stage;

[0104] f M is the data acquisition frequency in the medium - demand stage;

[0105] f H is the data acquisition frequency in the high - demand stage;

[0106] S24. Dynamically adjust the parameter settings of the real - time data acquisition system DCS(t) according to the monitoring requirements of each stage to construct a real - time monitoring data set of the water plant:

[0107] X(t) = {P(t), E(t), S(t), W(t)}.

[0108] In this embodiment, S3 includes:

[0109] S31. Construct a multi - stage evolutionary game model based on the real - time monitoring data set of the water plant, taking the water pumps, chemical dosing systems, and filtration systems in the water plant as game participant A i , where i = 1, 2, 3;

[0110] S32. Define the strategy set S i of each game participant A i , and the strategy set includes different operation modes and operating parameters:

[0111] For the water pumps, the strategy set includes different flow rate settings;

[0112] For the chemical dosing system, the strategy set includes different chemical dosing amount settings;

[0113] For the filtration system, the strategy set includes different filtration speed settings;

[0114] S33. Define the pay - off function U i (S i , S -i ) for each game participant, and the pay - off function aims at the water quality compliance rate, energy consumption, and cost optimization:

[0115] U i (S i , S -i ) = α i P(t) - β i E(t) - γ i C(t);

[0116] Wherein, P(t) represents the water quality compliance rate, E(t) represents the energy consumption of the water pumps, chemical dosing systems, or filtration systems, C(t) represents the cost of the corresponding process link, and α i , β i , γ i are the weight coefficients for each participant, corresponding to the goals of water quality compliance rate, energy consumption, and cost optimization respectively.

[0117] In this embodiment, the construction process of the local optimal scheduling scheme in the current stage includes that in each operation stage, the game participants update their strategies according to the complex replicator dynamics equation:

[0118]

[0119] Wherein, x i is the strategy S iThe proportion represents the participation of the water pump, chemical dosing system, and filtration system in the game, U i (S i ,S -i ,t) is the payoff function of the game participant A i , which changes with time t is the change rate of the water quality compliance rate over time, representing the change trend of the current water plant's water quality is the change rate of energy consumption over time, representing the change trend of energy consumption in each process link is the change rate of the equipment status over time, representing the change trend of the operating status of the water pump, chemical dosing system, and filtration system, λ i , μ i , ν i are the weight coefficients respectively:

[0120] λ i represents the sensitivity of process link A i to the change in the water quality compliance rate, reflecting the impact of the water pump, chemical dosing system, and filtration system on the water quality

[0121] μ i represents the sensitivity of process link A i to the change in energy consumption, determining the regulation for minimizing energy consumption

[0122] ν i represents the sensitivity of process link A i to the change in the equipment status, determining the degree of influence on equipment maintenance and adjustment

[0123] is the average payoff of all game participants:

[0124]

[0125] where n is the number of process links participating in the game

[0126] In this embodiment, S5 includes:

[0127] S51. Using an adaptive feedback control mechanism, according to the local optimal scheduling scheme output by the evolutionary game model, adjust the operation parameters of each process link of the water plant in real time, including the water pump flow rate Q(t), chemical dosing amount M(t), and filtration speed V(t);

[0128] S52. During the adjustment process, collect the real-time monitoring data set X'(t) of the water plant in real time, and transmit the real-time monitoring data set X'(t) of the water plant to the control system through the feedback mechanism to update the participant payoff function U′ in the multi-stage evolutionary game model i (S i ,S -i, t) such that the multi-stage evolutionary game model is dynamically adjusted according to the latest operating status and external environment parameters;

[0129] S53. According to the feedback data and the strategy combination output by the evolutionary game model, use the adaptive feedback control equation to optimize and adjust the process parameters:

[0130]

[0131]

[0132] where λ1, λ2, λ3 are adjustment coefficients to control the response speed of the water pump, chemical dosing system, and filtration system to the adjustment,

[0133] ″′, U Q (t), U M (t), U V (t) are the revenue functions of the water pump, chemical dosing system, and filtration system respectively, is the average revenue function of all game participants, is the change rate of water quality parameters, is the second derivative of the external environment parameters, is the change rate of energy consumption, is the square of the change rate of equipment status parameters, and α1, α2, α3 are the influence weights of water quality parameter changes on each link, and β1, β2, β3 are the influence weights of energy consumption changes on each link.

[0134] S54. Through the closed-loop feedback regulation mechanism, the system dynamically optimizes the operating parameters of the process links according to the real-time monitoring data of the water plant. Each process link continuously updates its revenue function and control parameters during the adjustment process to achieve the optimal dynamic regulation effect, forming an adaptive closed-loop regulation system.

[0135] In this embodiment, S6 includes:

[0136] S61. When the operating conditions of the water plant or the external environment parameters W(t) change, the change rate of the external environment is monitored in real time Combined with the real-time monitoring data set of the water plant, calculate the local optimal scheduling scheme through the multi-stage evolutionary game model;

[0137] S62. Dynamically adjust the operating stage of the water plant according to the calculated local optimal scheduling scheme, switch between the low-demand stage, medium-demand stage, and high-demand stage, and update the operating parameters of the process links through the participant strategy combination in the multi-stage evolutionary game model;

[0138] S63. Utilize an adaptive feedback control mechanism to dynamically adjust the strategy combination of the technological processes according to changes in the external environment and operating conditions. When the strategy combination of the technological processes changes, the control system automatically performs dynamic optimization on the operating parameters of each link in the water plant according to the adaptive feedback control mechanism to achieve the global optimal scheduling at each stage.

[0139] S64. As the operating stage switches, the control system adjusts the revenue function and strategy combination of each technological process according to real-time data, enabling the water plant to always maintain the global optimal scheduling when the external environment and operating conditions change.

[0140] In this embodiment, S7 includes:

[0141] S71. Combine the historical monitoring data X hist (t) of the water plant and the real-time monitoring data X(t) to construct a prediction model for analyzing the system change trend;

[0142] S72. In the prediction model, calculate the change trend equations of water quality parameters P, energy consumption data E, water demand D, and equipment operating status parameter S through time series analysis methods:

[0143]

[0144] where X pred (t + Δt) is the predicted value at the future moment t + Δt, X(t) represents any water quality parameter P(t), energy consumption data E(t), water demand D(t), or equipment operating status parameter S(t), and α X and β X are the weight coefficients of the corresponding parameters respectively;

[0145] S74. Optimize the water plant scheduling plan according to the change trends of water quality fluctuations, energy consumption, water demand, and equipment load output by the prediction model, and adjust the operating parameters of each technological process in the water plant before the system change occurs.

[0146] Example 1:

[0147] To verify the feasibility of the present invention in the intelligent optimization scheduling of the entire water plant process, the implementer conducted experiments using the actual water plant operation dataset of a certain city and the simulated changing environment dataset. The dataset of the water plant covered the operation data from January 2021 to June 2023, including water quality parameters, energy consumption data, equipment operating status, and external environmental conditions. Based on these data, the implementer also generated a simulated changing environment dataset under peak periods and equipment failures to evaluate the scheduling optimization ability of the present invention in complex scenarios.

[0148] The implementer divides the dataset into a training set and a test set in a ratio of 8:2. The training set contains the historical operation data of the past 2.5 years, which is used to train the strategy combination and adaptive feedback mechanism in the multi-stage evolutionary game model, while the test set is used to verify the real-time response and scheduling optimization performance of the system in a complex environment.

[0149] In the data collection and preprocessing stage, the implementer uses multiple process parameters such as pump flow rate, chemical dosage, and filtration rate as input features, and the water quality compliance rate, energy consumption, and equipment load as the main output targets. At the same time, the implementer models the impact of the external environment, which includes factors such as temperature, humidity, and water source water quality changes. The data is obtained in real time through a sensor network. The implementer uses the Adaptive Moment Estimation optimizer to train the multi-stage evolutionary game model, sets the learning rate to 0.001, and the training is carried out for 500 epochs with the batch size set to 32.

[0150] To adapt to the optimization process of the present invention, the implementer takes each process link of the water plant as a game participant and constructs a strategy combination model based on multi-stage games. Through the feedback mechanism, the system dynamically adjusts the strategy combination according to water quality fluctuations, energy consumption data, and equipment status changes, and generates the optimal operation parameters for each process link.

[0151] In practical applications, the implementer focuses on comparing the performance of the intelligent optimization scheduling method of the present invention with the traditional fixed-parameter scheduling method, especially the response ability under peak periods and complex environmental conditions. The specific experimental results and data are shown in Table 1 below:

[0152] Table 1 Comparison of experimental data between the intelligent optimization scheduling method of the present invention and the traditional scheduling method

[0153] Comparison Items Traditional Method Method of the Present Invention Pump Energy Consumption (kWh) 16000 (per day) 13500 (per day) Dosing Quantity (g / L) 0.85 (uneven) 0.75 (accurate control) Water Quality Compliance Rate 88% (decrease during peak period) 97% (stable throughout) Filter System Load 90% (often overloaded) 75% (stable operation) Dispatch Response Time (minutes) 45 - 60 (manual adjustment) 10 - 15 (system automatic adjustment) System Operation Cost (yuan) 52000 (per day) 43000 (per day)

[0154] During the training process, the multi-stage evolutionary game model of the present invention optimizes the strategy based on the actual dataset and the simulated dataset. The traditional method relies on fixed parameters set manually and is difficult to cope with the dynamic changes in the operation of the water plant. However, the present invention can automatically adjust the pump flow rate, chemical dosage, and filtration rate when detecting water quality fluctuations or equipment status changes through evolutionary games and adaptive feedback control mechanisms, greatly improving the real-time performance and accuracy of scheduling.

[0155] To further prove the effectiveness of the present invention, the implementer introduced two types of extreme cases into the test set: the first type is the peak water usage period, and the second type is the simulation of equipment failures. By comparing the scheduling effects in these two types of situations, the implementer found that in the peak water usage period and during equipment failures, the traditional method increased the pump energy consumption by 25%, and it was difficult to accurately control the chemical dosage, resulting in water quality fluctuations and frequent overloading of the filtration system. However, the optimized method of the present invention can automatically adjust the strategy under these extreme conditions, reducing the pump energy consumption by 15%, controlling the chemical dosage accurately within 0.01 g / L, maintaining the water quality compliance rate above 97%, and reducing the load of the filtration system by 15%.

[0156] In addition, in the comparative tests under different seasons and different environmental conditions, the system of the present invention showed high robustness. For example, under the low-temperature conditions in winter, due to the insufficient response of the traditional scheduling system to environmental changes, the water quality compliance rate dropped to 85%. However, through the adaptive adjustment of the pump flow rate and chemical dosage of the present invention, the water quality compliance rate was maintained above 96%. Under the high-temperature and high-humidity conditions in summer, the system of the present invention can still operate efficiently. Compared with the traditional system, the energy consumption was reduced by about 18%. Table 2 below further shows the performance comparison between the system of the present invention and the traditional system under different environmental conditions:

[0157] Table 2 Performance Comparison between the System of the Present Invention and the Traditional System

[0158]

[0159]

[0160] In addition, the present invention also combines historical data and real-time data to predict the water quality fluctuations and equipment load changes in the next few hours, and optimizes the scheduling plan in advance. For example, when it is predicted that the external temperature will rise and the water demand will increase in the next two hours, the system automatically adjusts the flow rate and chemical dosage of the pump to ensure that the water quality is stable and the equipment load will not exceed the tolerable range before the peak period arrives.

[0161] In summary, through the data sets of real water plants and the data sets of the simulation environment in this embodiment, a detailed experimental comparison was made between the method of the present invention and the traditional method, verifying the feasibility and superiority of the present invention in complex environments. Through the full-process intelligent optimization scheduling method of the water plant of the present invention, the water plant can greatly improve the scheduling efficiency, reduce energy consumption, ensure the water quality compliance rate, and perform excellently in dealing with complex situations such as peak periods and equipment failures.

[0162] Through an adaptive feedback control mechanism, this invention combines real-time monitoring data with a multi-stage evolutionary game model to dynamically adjust the operating parameters of each process link during the operation of the water plant. The system performs closed-loop regulation based on the real-time collected data, can quickly respond and automatically adjust the scheduling plan when the external environment or internal operating conditions change, ensuring that the water plant always operates in an optimal state. Compared with traditional fixed-parameter scheduling, this invention shows extremely high flexibility and adaptability in dealing with water quality fluctuations, energy consumption changes, and equipment load changes, significantly improving the response speed and efficiency of the system.

[0163] This invention constructs a multi-stage evolutionary game model, regarding each process link in the water plant as a game participant, and defines the profit function of each participant, comprehensively considering multiple objectives such as water quality compliance rate, energy consumption optimization, and cost control. Through the strategy evolution of the replicator dynamics equation, local optimality can be achieved in each stage, and the strategy combination in different stages can be dynamically adjusted through the adaptive feedback control mechanism, thus achieving global optimal scheduling.

[0164] This invention combines historical monitoring data with real-time monitoring data and uses time series analysis and prediction models to accurately predict the future system change trend of the water plant. Through the prediction results, the scheduling plan can be optimized in advance before the system change occurs, avoiding the impact of emergencies on the operation of the water plant. Compared with the lag adjustment in the prior art, this invention effectively improves the predictability and foresight of the scheduling system through the prediction model, ensuring that the water plant always operates in an efficient and safe state.

[0165] The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent substitutions or changes, shall be covered by the protection scope of the present invention.

Claims

1. A full-process intelligent optimization scheduling method for a water plant based on adaptive feedback control, characterized in that: The steps include: S1. Dynamically divide the operation process of the water plant into low demand stage, medium demand stage and high demand stage based on water quality parameters, energy consumption data, water demand and real-time parameters of equipment operation status; S2. Obtain real-time monitoring data sets of the water plant in the low demand stage, medium demand stage and high demand stage, including water quality parameters, energy consumption data, equipment status and external environment parameters, and establish a real-time data collection system related to each process link; S3. A multi-stage evolutionary game model is constructed based on the real-time monitoring data set of the water plant. Each process link in the water plant is taken as a game participant, and the strategy set and profit function of each game participant are defined. The profit function includes the water quality compliance rate, energy consumption and cost optimization goals; S4. In each stage, the evolutionary game model is used to simulate the strategy evolution between each process link, and the strategy combination of each game participant is calculated and optimized by replicating the dynamic equation to generate the local optimal scheduling plan for the current stage; S5. Using the adaptive feedback control mechanism, according to the local optimal scheduling plan output by the evolutionary game model, the pump flow, dosage and filtration speed of each process link of the water plant are adjusted in real time, and new real-time monitoring data are continuously collected during the adjustment process and fed back to the control system to form a closed-loop feedback regulation; S6. When the operating conditions or external environment of the water plant change, the operation stage is dynamically adjusted according to the local optimal scheduling scheme of the evolutionary game model and the adaptive feedback control mechanism, and the strategy combination of each process link is switched to achieve the global optimal scheduling of the system at each stage; S7. Combine historical monitoring data with real-time monitoring data to predict the possible system change trend of the water plant in the future and optimize the dispatching plan in advance; S8. The optimized scheduling effect is displayed through the integrated intelligent scheduling platform, the operating status of the entire water plant is monitored and a scheduling report is generated for the reference of managers.

2. According to claim 1, a method for intelligent optimization and scheduling of the entire process of a water plant based on adaptive feedback control is characterized in that: The S1 includes: S11. Obtain water quality parameters P, energy consumption data E, water demand D and equipment operation status parameters S of the water plant operation, and use the data as input to form a real-time data set X(t) of the water plant; S12. Based on the real-time data set X(t), the demand stage division standard of the water plant is set as low demand stage L, medium demand stage M and high demand stage H. The demand stage division standard is defined by the threshold range of each parameter: The interval of water quality parameter P is [P low ,P high ], the energy consumption data E is in the range of [E low ,E high ], the interval of water demand D is [D low ,D high ], the interval of the equipment state parameter S is [S low ,S high ]; S13. Determine the demand stage to which each parameter belongs at a certain time t by calculating the real-time data set X(t), and use the classification function F(X(t)) to classify the operation status of the water plant: P(t) represents the water quality parameters of the water plant at a certain time t; E(t) represents the energy consumption data of the water plant at a certain time t; D(t) represents the water demand of the water plant at a certain time t; S(t) represents the operating status parameters of the main equipment in the water plant at a certain time t; S14. According to the real-time data set X(t) and the classification function F(X(t)), the operation process of the water plant is dynamically divided into a low demand stage, a medium demand stage and a high demand stage.

3. According to claim 1, a method for intelligent optimization and scheduling of the entire process of a water plant based on adaptive feedback control is characterized in that: The S2 includes: S21. Obtain real-time monitoring data sets of each process link of the water plant during the low demand stage, medium demand stage and high demand stage of the water plant, including water quality parameters P(t), energy consumption data E(t), equipment status S(t) and external environment parameters W(t): P(t) represents the relevant parameters of the water quality of the water plant at a certain time t, including turbidity, pH value and chemical oxygen demand (COD); E(t) represents the energy consumption data of the water plant at time t, including total energy consumption and sub-item energy consumption of each process link; S(t) represents the operating status parameters of the main equipment in the water plant, including pump pressure, filter status, and working status of the dosing system; W(t) represents the external environmental parameters, including temperature, humidity and water quality parameters of the external water source; S22. Based on the real-time monitoring of water quality parameters, energy consumption data, equipment status and external environmental parameters of each process link, a real-time data acquisition system DCS(t) is constructed. The real-time data acquisition system independently monitors and collects data for each process link in different demand stages, and sets the acquisition frequency f of the real-time data acquisition system. Different real-time data acquisition frequencies f are set in the low demand stage, medium demand stage and high demand stage respectively. L ,f M ,f H : f L The frequency of data collection for the low demand phase; f M The frequency of data collection for the medium demand stage; f H The frequency of data collection for the high-demand phase; S24. Dynamically adjust the parameter settings of the real-time data acquisition system DCS(t) according to the monitoring requirements of each stage to build a real-time monitoring data set for the water plant: X(t)={P(t),E(t),S(t),W(t)}.

4. According to claim 1, a method for intelligent optimization and scheduling of the entire process of a water plant based on adaptive feedback control is characterized in that: The S3 includes: S31. A multi-stage evolutionary game model is constructed based on the real-time monitoring data set of the water plant, and the water pump, dosing system and filtration system in the water plant are used as game participants A. i , where i = 1, 2, 3; S32. Define each game participant A i The strategy set S i , the policy set contains different operation modes and operating parameters: For pumps, the strategy set includes different flow settings; For dosing systems, the strategy set includes different dosing amount settings; For the filtering system, the policy set includes different filtering speed settings; S33. Define the payoff function U for each game participant i (S i ,S -i ), the revenue function aims at optimizing water quality compliance rate, energy consumption and cost: U i (S i ,S -i )=a i P(t)-β i E(t)-γ i C(t); Among them, P(t) represents the water quality compliance rate, E(t) represents the energy consumption of the water pump, dosing system or filtration system, C(t) represents the cost of the corresponding process link, α i , β i , γ i The weight coefficients for each participant correspond to the goals of water quality compliance rate, energy consumption and cost optimization.

5. According to claim 1, a method for intelligent optimization and scheduling of the entire process of a water plant based on adaptive feedback control is characterized in that: The process of constructing the local optimal scheduling solution in the current stage includes that in each operation stage, the game participants update their strategies according to the complex replication dynamic equation: Among them, x i For strategy S i The proportion of water pump, dosing system and filtration system in the game, U i (S i ,S -i ,t) is the game participant A i The profit function changes with time t, is the rate of change of water quality compliance rate over time, indicating the changing trend of water quality in the current water plant. is the rate of change of energy consumption over time, indicating the changing trend of energy consumption in each process link. is the rate of change of equipment status over time, indicating the change trend of the operating status of the water pump, dosing system and filtration system, λ i , μ i , ν i They are weight coefficients respectively: λ i Indicates process step A i Sensitivity to changes in water quality compliance rates, reflecting the impact of water pumps, dosing systems and filtration systems on water quality; μ i Indicates process step A i Sensitivity to changes in energy consumption determines regulation to minimize energy consumption; ν i Indicates process step A i The sensitivity to changes in equipment status determines the degree of impact on equipment maintenance and adjustments; is the average payoff of all game participants: Among them, n is the number of process links participating in the game.

6. According to claim 1, a method for intelligent optimization and scheduling of the entire process of a water plant based on adaptive feedback control is characterized in that: The S5 includes: S51. Using the adaptive feedback control mechanism, according to the local optimal scheduling scheme output by the evolutionary game model, the operating parameters of each process link of the water plant are adjusted in real time, including the pump flow rate Q(t), the dosage M(t) and the filtration speed V(t); S52. During the adjustment process, the real-time monitoring data set X'(t) of the water transfer plant is collected in real time, and the real-time monitoring data set X'(t) of the water transfer plant is transmitted to the control system through the feedback mechanism to update the participant benefit function U' in the multi-stage evolutionary game model i (S i ,S -i ,t), so that the multi-stage evolutionary game model can be dynamically adjusted according to the latest operating status and external environment parameters; S53. Based on the feedback data and the strategy combination output by the evolutionary game model, the process parameters are optimized and adjusted using the adaptive feedback control equation: Among them, λ1, λ2, λ3 are adjustment coefficients, which control the speed of the water pump, dosing system and filtration system to respond to the adjustment, U′ Q (t),U′ M (t),U′ V (t) are the profit functions of the water pump, dosing system and filtration system respectively, is the average payoff function of all game participants, is the rate of change of water quality parameters, is the second-order derivative of the external environment parameter, is the rate of change of energy consumption, is the square of the rate of change of equipment status parameters, α1, α2, α3 are the weights of the impact of water quality parameter changes on each link, β1, β2, β3 are the weights of the impact of energy consumption changes on each link; S54. Through the closed-loop feedback regulation mechanism, the system dynamically optimizes the operating parameters of the process links according to the real-time monitoring data of the water plant. During the adjustment process, each process link continuously updates its profit function and control parameters to achieve the optimal dynamic regulation effect, forming an adaptive closed-loop regulation system.

7. The method for intelligent optimization and scheduling of the whole process of a water plant based on adaptive feedback control according to claim 1 is characterized in that: The S6 includes: S61. When the water plant operating conditions or external environmental parameters W(t) change, real-time monitoring of the change rate of the external environment Combined with the real-time monitoring data set of the water plant, the local optimal scheduling plan is calculated through a multi-stage evolutionary game model; S62. Dynamically adjust the operation phase of the water plant according to the calculated local optimal scheduling plan, switch between the low demand phase, the medium demand phase and the high demand phase, and update the operating parameters of the process link through the combination of participant strategies in the multi-stage evolutionary game model; S63. Use the adaptive feedback control mechanism to dynamically adjust the strategy combination of the process link according to the changes in the external environment and operating conditions. When the strategy combination of the process link changes, the control system automatically optimizes the operating parameters of each link of the water plant according to the adaptive feedback control mechanism to achieve the global optimal scheduling at each stage; S64. As the operation phase switches, the control system adjusts the profit function and strategy combination of each process link according to real-time data so that the water plant always maintains the global optimal scheduling when the external environment and operating conditions change.

8. The method for intelligent optimization and scheduling of the whole process of a water plant based on adaptive feedback control according to claim 1 is characterized in that: The S7 includes: S71. Combined with water plant historical monitoring data X hist (t) and real-time monitoring data X(t) to build a prediction model for analyzing system change trends; S72. In the prediction model, the change trend equation of water quality parameter P, energy consumption data E, water demand D and equipment operation status parameter S is calculated by time series analysis method: Among them, X pred (t+Δt) is the predicted value at the future time t+Δt, X(t) represents any water quality parameter P(t), energy consumption data E(t), water demand D(t) or equipment operation status parameter S(t), α X and β X are the weight coefficients of the corresponding parameters respectively; S74. Optimize the water plant dispatch plan based on the water quality fluctuations, energy consumption, water demand and equipment load change trends output by the prediction model, and adjust the operating parameters of each process link of the water plant before system changes occur.

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