A control method and control system for a multi-agent water plant process system

Abstract

The present invention relates to a control method and control system for a multi-agent water plant process system, the method comprising: constructing a control objective function for maximizing the global benefit of the water plant process system; performing process simulation on a single agent of the water plant process system according to constraints to obtain an optimal control solution for the single agent; grouping and integrating the optimal control solutions, and performing multi-objective iterative optimization on at least one multi-agent collaborative control solution set formed according to the control objective function to obtain a multi-agent collaborative control strategy that meets the control objective function; dynamically adjusting the multi-agent collaborative control strategy according to real-time change information of the water plant process system, and deploying the adjusted optimal multi-agent collaborative control strategy to the water plant process system. The present invention manages the competition and cooperation relationship between single agents through an innovative coordination mechanism to ensure that each single agent will not damage the overall harmony and water production efficiency of the water plant while pursuing its own optimization goals.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to a control method and control system for a multi-agent water plant process system. Background Art

[0002] With the continuous advancement of science and technology, the production control system of water plants has gradually integrated technologies such as big data, machine learning and image recognition on the basis of traditional automatic control, and developed a more intelligent control system to enable the water production process of water plants to achieve energy saving and consumption reduction effects.

[0003] Currently, based on water plant water production processes, existing control systems that can utilize intelligent control technologies generally include: intelligent alum dosing systems, intelligent chlorination systems, intelligent backwash systems, intelligent pump distribution systems, intelligent sludge removal systems, and plant-wide water balance systems. These individual agents typically utilize edge deployment, self-learning, and independent operation, achieving specific control objectives by controlling their own individual agents. For example, the intelligent alum dosing system can reduce coagulant dosage while ensuring that the effluent turbidity of the sedimentation tank meets internal control standards, typically saving 5% to 20% in chemical consumption. The intelligent backwash system can maintain the healthy operation of the filter (or membrane filter), reduce production accidents, save 5% to 10% in energy consumption, and reduce recycled water consumption. However, there are competitive interactions between these individual agents. For example, while intelligent alum dosing reduces chemical consumption, it may also increase filter load in the filtration system, thereby reducing the flushing effectiveness of the intelligent backwash system. Furthermore, maintaining a high liquid level in the clear water tank increases the residence time of chlorine in the water, causing the residual chlorine in the water to decay over time. Consequently, the intelligent chlorination system needs to consume more chlorine to maintain the residual chlorine concentration in the water.

[0004] Therefore, those skilled in the art are in urgent need of a technical solution that can effectively resolve the competition and game relationship between single-agent controls when collaboratively controlling a multi-agent water plant process system. Summary of the Invention

[0005] (1) Technical issues to be resolved

[0006] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides a control method and control system for a multi-agent water plant process system, which solves the technical problem that the existing water plant control system only considers the energy-saving and consumption-reducing effect of a single agent, thereby increasing the operating costs of other single agents.

[0007] (2) Technical solution

[0008] In order to achieve the above objectives, the main technical solutions adopted by the present invention include:

[0009] In a first aspect, an embodiment of the present invention provides a control method for a multi-agent water plant process system, comprising:

[0010] Taking the minimization of production cost and optimization of water quality of the water plant process system as the dual control objectives, a control objective function for maximizing the global benefits of the water plant process system is constructed;

[0011] Conduct independent process simulation for each single agent in the water plant process system according to the preset constraints to obtain the optimal control solution for each single agent;

[0012] The optimal control solutions are grouped and integrated to form at least one multi-agent collaborative control solution set. Based on the control objective function, the multi-agent collaborative control solution set is subjected to multi-objective iterative optimization including exchange optimization, production optimization, and hybrid optimization to obtain a multi-agent collaborative control strategy that meets the control objective function.

[0013] Based on the real-time change information of the water plant process system, the multi-agent collaborative control strategy is dynamically adjusted, and the optimal multi-agent collaborative control strategy obtained by adjustment is deployed to the water plant process system.

[0014] Optionally, with the dual control objectives of minimizing the production cost and optimizing the water quality of the water plant process system, the control objective function for maximizing the global benefit of the water plant process system is constructed, including:

[0015] Obtain production cost information and water quality information of the water plant process system. Production cost information includes water production process cost and water supply process cost;

[0016] Based on the water supply quality information and the preset water supply quality target, the deviation between the water supply quality and the water supply quality target is obtained;

[0017] Based on the preset collaborative reward function and interference penalty function between single agents, combined with production cost information and the deviation between the actual water quality and the water quality target, a control objective function is constructed to maximize the global benefits of the water plant process system with the dual control objectives of minimizing the production cost and optimizing the water quality of the water plant process system.

[0018] Among them, the control objective function is:

[0019] ;

[0020] Where, k 1 is the weight of water quality, k 2 is the weight of water production cost, k 3 is the weight of water supply cost, k 4 is the weight of the collaborative reward function, k 5 is the weight of the interference penalty function, is the deviation between the actual water quality and the water quality target, V punit The cost of water production process, FT in is the water inlet flow rate, V sunit The cost of water supply process, PT is the actual water supply pressure, FT out is the water supply flow, R 1 is the collaborative reward function between single agents, R 2 is the interference penalty function between single agents.

[0021] Optionally, an independent process simulation is performed on each single agent of the water plant process system according to preset constraints to obtain the optimal control solution for each single agent, including:

[0022] Based on the process type of each single agent in the water plant process system, the dual control objectives are decomposed to obtain the dual control sub-objectives of minimizing the production cost and optimizing the output results of each single agent;

[0023] Traverse all dual control sub-goals and obtain the constraints for each single agent to perform process simulation based on the influencing factors of the single agent operation;

[0024] Conduct independent process simulation for each single agent in the water plant process system based on the constraints, analyze the simulation output results and process costs, and obtain the optimal control solution for each single agent;

[0025] The constraint expression is:

[0026] ;

[0027] Where, For a single agent i The minimum dosage of the agent, For a single agent i The dosage of the agent, For a single agent i The maximum dosage of the agent, For a single agent i The minimum liquid level, Representing a single agent i The liquid level, Representing a single agent i The maximum liquid level, For a single agent i Medium equipment j The number of runs, For a single agent iMedium equipment j The maximum number of runs, For a single agent i Medium equipment a and equipment b Only one can be run at a time.

[0028] Optionally, the optimal control solutions are grouped and integrated to form at least one multi-agent collaborative control solution set, and a multi-objective iterative optimization including exchange optimization, production optimization, and hybrid optimization is performed on the multi-agent collaborative control solution set according to the control objective function to obtain a multi-agent collaborative control strategy that satisfies the control objective function, including:

[0029] According to the acquired single-agent process type, the optimal control solutions are grouped and integrated to form at least one multi-agent collaborative control solution set;

[0030] Based on the preset reward and punishment function between single agents, the multi-agent collaborative control solution set is optimized through multi-objective iterative optimization including exchange optimization, production optimization and hybrid optimization to obtain the multi-agent collaborative control strategy solution set;

[0031] Based on the control objective function of maximizing the global benefit of the water plant process system, the multi-agent collaborative control strategy solution set is converged to obtain a multi-agent collaborative control strategy that meets the control objective function;

[0032] Among them, the reward and punishment functions include: collaboration reward function and interference penalty function;

[0033] The collaborative reward function is:

[0034] ;

[0035] Where, λ is the weight of the efficient collaboration reward, δ the weight of the reward for inefficient collaboration;

[0036] The interference penalty function is:

[0037] R 2=- μ ;

[0038] Where, μ is the intensity of the interference penalty.

[0039] Optionally, based on a preset reward and penalty function between single agents, a multi-objective iterative optimization including exchange optimization, production optimization and hybrid optimization is performed on the multi-agent collaborative control solution set to obtain the multi-agent collaborative control strategy solution set including:

[0040] Obtain the reward and punishment weights between single agents based on the preset reward and punishment function between single agents;

[0041] Perform multi-objective coordinated optimization on the multi-agent collaborative control solution set, including exchange optimization, production optimization, and hybrid optimization, to obtain the initial multi-agent collaborative control strategy solution set;

[0042] Through the genetic multi-objective optimization algorithm and combined with the reward and punishment weights, the initial multi-agent collaborative control strategy solution set is iteratively updated to obtain the multi-agent collaborative control strategy solution set.

[0043] Optionally, based on the control objective function of maximizing the global benefit of the water plant process system, the multi-agent collaborative control strategy solution set is converged to obtain a multi-agent collaborative control strategy that satisfies the control objective function, including:

[0044] Traverse each multi-agent collaborative control strategy in the multi-agent collaborative control strategy solution set, and select and obtain a multi-agent collaborative control strategy that meets the set minimum water quality target;

[0045] According to the control objective function of maximizing the global benefit of the water plant process system, the global benefit data of the water plant process system of each multi-agent collaborative control strategy after screening is obtained;

[0046] The multi-agent collaborative control strategy with the largest global benefit data of the water plant process system is determined as the multi-agent collaborative control strategy that meets the control objective function.

[0047] Optionally, dynamically adjusting the multi-agent collaborative control strategy based on the acquired real-time change information of the water plant process system, and deploying the adjusted optimal multi-agent collaborative control strategy to the water plant process system includes:

[0048] Obtain real-time information on changes in the water plant's process systems, including changes in the plant's environment, water supply targets, and production costs;

[0049] Based on real-time change information, the multi-agent collaborative control strategy is dynamically adjusted to obtain the optimal multi-agent collaborative control strategy for the water plant process system;

[0050] The optimal multi-agent collaborative control strategy is deployed to the water plant process system to make the operating conditions of the water plant process system optimal.

[0051] In a second aspect, an embodiment of the present invention provides a control system for a multi-agent water plant process system, comprising:

[0052] The control objective function construction module is used to construct a control objective function that maximizes the global benefits of the water plant process system, with the dual control objectives of minimizing the production cost and optimizing the water quality of the water plant process system;

[0053] The single-agent control optimization module is used to perform independent process simulation on each single agent of the water plant process system according to preset constraints to obtain the optimal control solution for each single agent;

[0054] The multi-agent control optimization module is used to group and integrate the optimal control solutions to form at least one multi-agent collaborative control solution set, and perform multi-objective iterative optimization of the multi-agent collaborative control solution set based on the control objective function, including exchange optimization, production optimization, and hybrid optimization, to obtain a multi-agent collaborative control strategy that meets the control objective function;

[0055] The control strategy dynamic adjustment module is used to dynamically adjust the multi-agent collaborative control strategy based on the real-time change information of the water plant process system, and deploy the adjusted optimal multi-agent collaborative control strategy to the water plant process system.

[0056] In a third aspect, an embodiment of the present invention provides a multi-agent water plant process system, including:

[0057] A water plant process brain module, configured to execute the above-mentioned steps of a control method for a multi-agent water plant process system;

[0058] The water plant single intelligent body module is connected to the water plant process brain module and is used to execute the water production process of the water plant when receiving control instructions from the water plant process brain module.

[0059] Optionally, the single intelligent module of the water plant includes: an intelligent alum addition module, an intelligent chlorination module, an intelligent backwash module, an intelligent sludge discharge module, an intelligent pump distribution module, an intelligent water balance module, an intelligent monitoring module and an equipment management module.

[0060] (3) Beneficial effects

[0061] The present invention provides a beneficial effect: a control method for a multi-agent water plant process system, which adopts the optimization control objective of maximizing the overall benefits of the water plant process system, comprehensively considers the control constraints of each individual agent and the advantages of multi-agent linkage, and provides a new dynamic optimal collaborative control scheme for the water plant process system through multi-objective coordinated optimization. This method manages the competition and cooperation between individual agents through an efficient coordination mechanism, ensuring that each individual agent pursues its own optimization goal without compromising the overall harmony and water production efficiency of the water plant process system.

[0062] In addition, the present invention can also adaptively adjust the collaborative control strategy of the water plant process system based on the operating data of the water plant process system and external environmental change information, and achieve more precise and dynamic control by continuously optimizing the control strategy process. It not only significantly improves the stability and intelligence level of the water plant process system operation, but also effectively reduces the energy consumption of the water plant process system operation and improves resource utilization efficiency, so as to ensure that the indicators of water quality and water quantity can be continuously optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A flow chart of a control method for a multi-agent water plant process system provided by one embodiment of the present invention;

[0064] Figure 2 A schematic diagram of a process flow of a multi-agent water plant process system provided by one embodiment of the present invention;

[0065] Figure 3 A schematic diagram of a process flow of a single-agent process simulation provided by one embodiment of the present invention;

[0066] Figure 4 An optimal solution set diagram of an intelligent alum adding system provided by one embodiment of the present invention;

[0067] Figure 5 A schematic diagram of a process flow of genetic multi-objective optimization provided by one embodiment of the present invention;

[0068] Figure 6 An optimal solution set diagram of the optimal solution frontier PF between the intelligent alum addition system and the intelligent backwashing system provided in one embodiment of the present invention;

[0069] Figure 7 A schematic diagram of a process for converging a multi-agent collaborative control strategy solution set according to an embodiment of the present invention;

[0070] Figure 8 A schematic diagram of a process for implementing dynamic adjustment of a multi-agent collaborative control strategy according to an embodiment of the present invention;

[0071] Figure 9 A schematic diagram of the composition of a multi-agent water plant process system provided by one embodiment of the present invention;

[0072] Figure 10 A schematic diagram of a process flow for collaborative control of a multi-agent water plant process system provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0073] In order to better explain the present invention and facilitate understanding, the present invention is described in detail below through specific implementation methods in conjunction with the accompanying drawings.

[0074] Prior to this, in order to facilitate understanding of the technical solution provided by the present invention, some basic information related to the technical solution of this application is first introduced below, and the solution of this application can be subsequently adjusted and controlled based on this basic information.

[0075] Multi-agent: A multi-agent system generally refers to a collection of multiple independent agents, each of which operates independently and communicates and coordinates with each other.

[0076] Alum addition process: During the water treatment process of a water plant, alum addition process is to add coagulant into the raw water (the coagulant commonly used in water plants is polyaluminium chloride, Poly Aluminium Chloride, referred to as PAC). Under the action of the coagulant, the colloids and fine suspended matter in the water are coagulated into flocs and then continuously precipitated, thereby reducing the sensory indicators of water quality such as turbidity and color of the raw water.

[0077] Chlorination process: During the water production process of a water plant, chlorination involves adding disinfectants to the water (sodium hypochlorite is the commonly used disinfectant in water plants). Chlorination kills bacteria and deadly microorganisms in the water and retains a certain residual chlorine content in the water leaving the plant to prevent bacterial growth in the process from leaving the water plant to the end user.

[0078] Filtration process: During the water production process of the water plant, the filtration process is to remove suspended matter, microorganisms, etc. in the water through sand filters (using quartz stone as filter material), carbon filters (using activated carbon as filter material), membrane filters (using fiber membranes as filter materials) and other process bodies to achieve the purpose of reducing water turbidity.

[0079] Backwashing process: When the filter is filtering, impurities such as suspended matter in the water are retained in the filter material. After a period of time, the filtering effect of the filter will decrease. Backwashing is performed through air flushing, air-water mixed flushing, water flushing, etc. to remove impurities in the filter layer and restore the filtering effect of the filter.

[0080] Intelligent pump matching technology: During the water production process of the water plant, the intelligent pump matching technology combines the pump units in the water supply pump room through different demand strategies (such as water supply pressure demand, water supply flow demand, pump combination efficiency, pump health, etc.) to achieve the best operating effect.

[0081] refer to Figure 1As shown, an embodiment of the present invention proposes a control method for a multi-agent water plant process system. The executor of the method of this embodiment can be a water plant process brain module of the multi-agent water plant process system. The water plant process brain module connects each single agent in the multi-agent water plant process system. The control method includes: taking the minimization of production cost and optimization of water quality of the water plant process system as the dual control objectives, constructing a control objective function for maximizing the global benefit of the water plant process system; performing independent process simulation on each single agent of the water plant process system according to preset constraints to obtain the optimal control scheme for each single agent; grouping and integrating the optimal control schemes to form at least one multi-agent collaborative control scheme set, and performing multi-objective iterative optimization of the multi-agent collaborative control scheme set including exchange optimization, production optimization and hybrid optimization according to the control objective function to obtain a multi-agent collaborative control strategy that meets the control objective function; dynamically adjusting the multi-agent collaborative control strategy according to the acquired real-time change information of the water plant process system, and deploying the adjusted optimal multi-agent collaborative control strategy to the water plant process system.

[0082] This embodiment, by maximizing the global benefits of the water plant's process system as its optimization control objective, comprehensively considers the control constraints of each individual agent and the advantages of multi-agent collaboration. Through multi-objective coordinated optimization, it provides a new, dynamic, and optimal collaborative control solution for the water plant's process system. This solution manages the competition and cooperation between individual agents through an efficient coordination mechanism, ensuring that each agent pursues its own optimization goals without compromising the overall harmony and water production efficiency of the water plant's process system.

[0083] In addition, this embodiment can also adaptively adjust the collaborative control strategy of the water plant process system based on the operating data of the water plant process system and external environmental change information, and achieve more precise and dynamic control by continuously optimizing the control strategy process. It not only significantly improves the stability and intelligence level of the water plant process system operation, but also effectively reduces the energy consumption of the water plant process system operation and improves resource utilization efficiency, so as to ensure that the indicators of water quality and water quantity can be continuously optimized.

[0084] To better understand the above technical solutions, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. Rather, these embodiments are provided to enable a clearer and more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0085] Specifically, refer to Figure 1 As shown, this embodiment proposes a control method for a multi-agent water plant process system, including:

[0086] S100. Taking the minimization of production cost and optimization of water quality of the water plant process system as the dual control objectives, a control objective function for maximizing the global benefit of the water plant process system is constructed.

[0087] The control objectives of maximizing the global benefits of the water plant process system are to minimize the production costs of the water plant process system and optimize the water quality, where a minimum water quality target is set. Figure 2 As shown in the figure, the production link of the water plant process system adopts a fixed operation mode or does not introduce an intelligent control module, and its production cost remains basically unchanged. Therefore, the global benefit maximization is provided by the sub-agents that constitute the multi-agent.

[0088] In this embodiment, step S100 may include steps S110-S130:

[0089] S110. Obtain production cost information and water quality information of the water plant process system, where the production cost information includes water production process cost and water supply process cost.

[0090] The production cost includes the cost of the water production process section and the water supply process section. The water supply process section is the water supply pump room, and the remaining process sections are all water production process sections. The cost of each process section is mainly composed of equipment power costs, chemical consumption costs, and equipment usage costs.

[0091] Water production process cost V punit It is the total cost of equipment power cost, chemical consumption cost and equipment use cost (equipment use cost includes equipment maintenance cost and residual value consumption cost) in each water production process section. Its expression is:

[0092] (1)

[0093] In formula (1), n is the number of water production process stages, vp i Water production process i Equipment power costs, vm i Water production process i The cost of drug consumption, vd i Water production process i equipment usage fees.

[0094] Water delivery process cost V punit Power cost of equipment in the water delivery process V s and equipment usage fees Vq The total cost is expressed as:

[0095] V punit = V s + V q (2)

[0096] S120. Obtain the degree of deviation between the water supply quality and the water supply quality target based on the water supply quality information and the preset water supply quality target.

[0097] Water supply quality information includes water supply pressure, water supply turbidity, water supply residual chlorine and water supply pH, actual water supply quality W out The actual water supply pressure PT , Actual water supply turbidity TU , Actual water supply residual chlorine CL and the actual pH of the water supply PH composition.

[0098] W out ={ PT , TU , CL , PH} (3)

[0099] Water supply quality targets By water supply pressure target PT tg , water supply turbidity target TU tg , water supply residual chlorine target CL tg and water supply pH targets PH tg composition.

[0100] (4)

[0101] Actual water supply quality W out and water supply quality targets The deviation degree can describe the quality of water supply. The formula for obtaining is:

[0102] (5)

[0103] S130. Based on the preset collaborative reward function and interference penalty function between single intelligent agents, combined with the production cost information and the deviation between the actual water supply quality and the water supply quality target, a control objective function for maximizing the global benefit of the water plant process system is constructed with the dual control objectives of minimizing the production cost and optimizing the water quality of the water plant process system.

[0104] Among them, the control objective function is:

[0105] (6)

[0106] In formula (6), k 1 is the weight of water quality, k 2 is the weight of water production cost, k 3 is the weight of water supply cost, k 4 is the weight of the collaborative reward function, k 5 is the weight of the interference penalty function, FT in is the water inlet flow rate, FT out is the water supply flow, R 1 is the collaborative reward function between single agents, R 2 is the interference penalty function between single agents.

[0107] S200: Perform independent process simulation on each single agent of the water plant process system according to preset constraints to obtain the optimal control solution for each single agent.

[0108] Each single agent in the multi-agent water plant process system is able to achieve the optimal state of its own control effect, especially its own self-intelligent regulation under the influence of changes in the relevant influencing factors of the single agent itself. With the help of the ability of the single agent itself, the actual process performance is simulated through the simulation input of the process to achieve the optimal solution of the single agent control. It mainly includes simulating the input of different influencing factors, giving the specific output state performance expectations and process cost statistics through the agent itself, and then analyzing the output state results and statistical results to obtain the optimal control plan of the agent. The specific process is as follows Figure 3 .

[0109] In this embodiment, step S200 may include steps S210-S230:

[0110] S210. Based on the acquired process type of each single intelligent agent in the water plant process system, the dual control objectives are decomposed to obtain the dual control sub-objectives of minimizing the production cost of each single intelligent agent and optimizing the output result.

[0111] Since the process types of each single intelligent body in the water plant process system are different, the dual control objectives of minimizing the production cost and optimizing the water quality of the water plant process system can be decomposed into different sub-objectives. For example, in the intelligent alum addition system, its dual control sub-objectives are minimizing the amount of chemical addition and optimizing the effluent turbidity; in the intelligent chlorination system, its dual control sub-objectives are minimizing the amount of chemical addition and optimizing the effluent residual chlorine content; in the intelligent backwashing system, its dual control sub-objectives are optimizing the effluent turbidity and minimizing the flushing cost; in the intelligent sludge discharge system, its dual control sub-objectives are optimizing the effluent turbidity and optimizing the sludge discharge cost; in the intelligent pump distribution system, its dual control sub-objectives are optimizing the equipment operating cost and optimizing the pump distribution efficiency.

[0112] S220 traverses all dual control sub-goals, and obtains the constraint conditions for each single agent to perform process simulation based on the influencing factors of the single agent operation.

[0113] Due to the different process types, the influencing factors affecting the operation of single intelligent agents are also different. For example, the main influencing factors of the intelligent alum addition system include: water flow, turbidity, temperature, pH and drug consumption. The main influencing factors of the intelligent chlorination system include: water flow, turbidity, temperature, residual chlorine target and drug consumption. The main influencing factors of the intelligent backwash system include: water flow, turbidity, backwash cycle, number of backwashes, backwash intensity and backwash duration. The main influencing factors of the intelligent sludge discharge system include: water flow, turbidity, sludge discharge valve, sludge discharge vehicle, sludge discharge duration and sludge discharge cycle. The main influencing factors of the intelligent pump distribution system include: network time-sharing water volume, pressure, clear water tank level, pump group matching and operation frequency. By analyzing the dual control sub-goals of each single intelligent agent and the influencing factors of the single intelligent agent operation, the constraints for each single intelligent agent to perform independent process simulation can be obtained. The expression of the constraint conditions is:

[0114] (7)

[0115] In formula (7), For a single agent i The minimum dosage of the agent, For a single agent i The dosage of the agent, For a single agent i The maximum dosage of the agent, For a single agent i The minimum liquid level, Representing a single agent i The liquid level, Representing a single agent i The maximum liquid level, For a single agent i Medium equipmentj The number of runs, For a single agent i Medium equipment j The maximum number of runs, For a single agent i Medium equipment a and equipment b Only one can be run at a time.

[0116] S230. Perform independent process simulation on each single agent of the water plant process system according to the constraint conditions, and analyze the simulation output results and process costs to obtain the optimal control solution for each single agent.

[0117] In a specific embodiment, according to the constraints of the intelligent alum adding system, several control inputs of the alum adding process simulation are set, and the process simulation results and process costs of each set of control inputs are counted; then, by analyzing the process simulation results and process costs, the following are obtained: Figure 4 The optimal solution set diagram of the intelligent alum adding system is shown in the figure. The solutions (○) on the curve are all the optimal solutions of the intelligent alum adding system following the change of influent turbidity. The solutions (*) above the curve are the non-optimal solutions with excessive alum addition. The solutions (×) below the curve are the non-optimal solutions with insufficient alum addition. Finally, according to Figure 4 The optimal solution set curve is used to reversely infer the control input of the alum addition process simulation to obtain the optimal control solution of the intelligent alum addition system.

[0118] S300. Group and integrate the optimal control schemes to form at least one multi-agent collaborative control scheme set, and perform multi-objective iterative optimization of the multi-agent collaborative control scheme set including exchange optimization, production optimization and hybrid optimization according to the control objective function to obtain a multi-agent collaborative control strategy that meets the control objective function.

[0119] In this embodiment, step S300 may include steps S310-S330:

[0120] S310. According to the acquired process type of the single agent, the optimal control solutions are grouped and integrated to form at least one multi-agent collaborative control solution set.

[0121] By single-agent process type, the optimal control solutions of single agents are grouped and integrated to form at least one multi-agent collaborative control solution set. For example, the optimal control solutions of the intelligent alum addition system and the intelligent backwash system are integrated to form the first multi-agent collaborative control solution set for the flocculation sedimentation process; the optimal control solutions of the intelligent alum addition system, the intelligent backwash system, and the intelligent sludge discharge system are integrated to form the second multi-agent collaborative control solution set for the flocculation sedimentation process; and the optimal control solutions of the intelligent chlorination system and the intelligent pump distribution system are integrated to form the multi-agent collaborative control solution set for the chlorination process.

[0122] S320: Based on a preset reward and penalty function between individual agents, perform multi-objective iterative optimization on the set of multi-agent collaborative control solutions, including exchange optimization, production optimization, and hybrid optimization, to obtain a set of multi-agent collaborative control strategy solutions. The reward and penalty function includes a collaborative reward function and an interference penalty function.

[0123] When multiple agents work together to optimize the operation of the entire water plant, additional collaborative rewards can be given. The collaborative reward function is:

[0124] (8)

[0125] In formula (8), λ is the weight of the efficient collaboration reward, δ is the weight of the reward for inefficient collaboration.

[0126] Efficient collaboration primarily involves the multi-agent water plant process system making rational decisions based on global objectives, avoiding redundant work and wasted energy. The reward weight for efficient collaboration ranges from 1 to 10. For small-scale optimization or coordination of non-core tasks, the reward weight is low (e.g., 1-3). For multi-agent collaboration that optimizes energy efficiency and reduces costs while meeting effluent quality standards, the reward weight can be set to a high-mid range (e.g., 7-9).

[0127] The inefficient collaboration reward weight is used to deduct rewards when agents fail to cooperate effectively, resulting in energy waste and inefficient operations. This weight is used to encourage system adjustments. For some inefficient collaboration scenarios, the inefficient collaboration reward weight ranges from -1 to -5. If the system operates inefficiently and the water plant equipment operates at high load for extended periods, the inefficient collaboration reward weight can range from -2 to -4. If the collaborative operation of multiple agents results in energy waste, the inefficient collaboration reward weight can range from -3 to -5.

[0128] The interference penalty function is:

[0129] R 2=- μ (9)

[0130] In formula (9), μ is the intensity of the interference penalty.

[0131] Furthermore, step S320 may include steps S321-S323:

[0132] S321. Obtain reward and punishment weights between single agents based on a preset reward and punishment function between single agents.

[0133] S322. Perform multi-objective coordinated optimization on the multi-agent collaborative control solution set, including exchange optimization, production optimization, and hybrid optimization, to obtain the initial multi-agent collaborative control strategy solution set.

[0134] When the exchange is optimal, the multi-agent collaborative control strategy under any combination of the optimal control schemes of the single agent cannot exceed the maximum control benefit effect.

[0135] When production is optimal, the control plan of each single agent is on the production possibility frontier of the configuration.

[0136] When hybrid optimal, each collaborative control strategy in the solution set of intelligent collaborative control strategies can reflect the control objectives of the water plant process system.

[0137] S323. Through the genetic multi-objective optimization algorithm, combined with the reward and punishment weights, the initial multi-agent collaborative control strategy solution set is iteratively updated to obtain the multi-agent collaborative control strategy solution set.

[0138] In a specific embodiment, referring to Figure 5 As shown, the genetic multi-objective optimization algorithm is used to iteratively update the first multi-agent collaborative control solution set of the flocculation sedimentation process integrated with the optimal control solution of the intelligent alum addition system and the intelligent backwashing system. First, the multi-agent collaborative control strategy solution set that maximizes the global benefits of the multi-agents when the water quality meets the standards is calculated through the reward and punishment function to resolve the competitive relationship between the single agents and prevent the single agents from developing in a selfish direction. Then, the genetic multi-objective optimization algorithm is used to iteratively update the initial multi-agent collaborative control strategy solution set including crossover, mutation and merging to generate the offspring solution set of the first multi-agent collaborative control solution set. Finally, when the number of iterations G meets the set termination evolution iteration number Gen, it is determined that the offspring solution set obtained in the last iteration is the collaborative control strategy solution set of the intelligent alum addition system and the intelligent backwashing system. At the same time, the following can also be obtained: Figure 6 The optimal solution set of the optimal solution frontier PF between the intelligent alum addition system and the intelligent backwashing system is shown.

[0139] S330. Based on the control objective function of maximizing the global benefit of the water plant process system, the multi-agent collaborative control strategy solution set is converged to obtain a multi-agent collaborative control strategy that meets the control objective function.

[0140] The solution set of the multi-agent collaborative control strategy needs to be further combined with the control objective function of maximizing the global benefit of the water plant process system to converge to the global optimal solution. The specific convergence process is as follows: Figure 7 As shown. Further, step S330 may include steps S331-S333:

[0141] S331. Traverse each multi-agent collaborative control strategy in the multi-agent collaborative control strategy solution set, and screen and obtain a multi-agent collaborative control strategy that meets the set minimum water quality target.

[0142] S332. Based on the control objective function of maximizing the global benefit of the water plant process system, obtain the global benefit data of the water plant process system for each multi-agent collaborative control strategy after screening.

[0143] S333. Determine the multi-agent collaborative control strategy that maximizes the global benefit data of the water plant process system as the multi-agent collaborative control strategy that meets the control objective function.

[0144] S400. Dynamically adjust the multi-agent collaborative control strategy based on the acquired real-time change information of the water plant process system, and deploy the optimal multi-agent collaborative control strategy obtained from the adjustment to the water plant process system.

[0145] When the system influencing factors such as user demand, water environment, water target, cost, weight, etc. change, it is necessary to re-iterate and re-optimize the solution to achieve the dynamic response of the water plant process control system to real-time changes, so as to meet the working conditions of the water plant process system in the optimal state. The dynamic adjustment of the multi-agent collaborative control strategy is implemented as follows: Figure 8 As shown, Figure 8 Where th1, th2…thn are the threshold targets for re-triggers of multi-agent collaborative control strategy adjustment in the corresponding dimensions.

[0146] In this embodiment, step S400 may include steps S410-S430:

[0147] S410: Acquire real-time change information of the water plant process system, where the real-time change information includes water plant environment change information, water supply target change information, and production cost change information.

[0148] S420. Dynamically adjust the multi-agent collaborative control strategy based on real-time change information to obtain the optimal multi-agent collaborative control strategy for the water plant process system.

[0149] S430. Deploy the optimal multi-agent collaborative control strategy to the water plant process system to make the operating conditions of the water plant process system optimal.

[0150] In addition, this embodiment also provides a control system for a multi-agent water plant process system, including:

[0151] The control objective function construction module is used to construct a control objective function that maximizes the global benefits of the water plant process system with the dual control objectives of minimizing the production cost and optimizing the water quality of the water plant process system.

[0152] The single-agent control optimization module is used to perform independent process simulation on each single-agent in the water plant process system according to preset constraints to obtain the optimal control solution for each single-agent.

[0153] The multi-agent control optimization module is used to group and integrate the optimal control schemes to form at least one multi-agent collaborative control scheme set, and perform multi-objective iterative optimization of the multi-agent collaborative control scheme set including exchange optimization, production optimization and hybrid optimization according to the control objective function to obtain a multi-agent collaborative control strategy that meets the control objective function.

[0154] The control strategy dynamic adjustment module is used to dynamically adjust the multi-agent collaborative control strategy based on the real-time change information of the water plant process system, and deploy the adjusted optimal multi-agent collaborative control strategy to the water plant process system.

[0155] Furthermore, this embodiment also provides a multi-agent water plant process system, including:

[0156] The water plant process brain module is used to execute the control method steps of the multi-agent water plant process system described above.

[0157] The water plant single intelligent body module is connected to the water plant process brain module and is used to execute the water production process of the water plant when receiving control instructions from the water plant process brain module.

[0158] In this embodiment, reference Figure 9 As shown, the single intelligent module of the water plant includes: intelligent alum addition module, intelligent chlorination module, intelligent backwash module, intelligent sludge discharge module, intelligent pump distribution module, intelligent water balance module, intelligent monitoring module and equipment management module.

[0159] The intelligent alum addition module is deployed in the coagulant addition process section of the water plant. The module can achieve fully automatic and precise control of the coagulant dosage based on the specific requirements of the inlet flow, turbidity, temperature, pH and sedimentation tank effluent turbidity, thereby ensuring the continuous stability of the sedimentation tank effluent water quality and effectively reducing the consumption of chemicals.

[0160] The intelligent chlorination module is deployed in the chlorination process section of the water plant. This module can fully automatically and accurately control the disinfectant dosage based on the requirements of water flow, temperature, pH, and water quality, ensuring stable water quality, reducing drug consumption, and reducing disinfection by-products.

[0161] The intelligent backwash module is deployed in the backwash process section of the water plant. Based on parameters such as inlet flow, turbidity, filter water supply turbidity, liquid level, water supply valve opening, and head loss, it automatically and accurately controls the backwash cycle and intensity, maintaining the normal operation of the filter, improving backwash efficiency, and reducing energy consumption.

[0162] The intelligent sludge discharge module is deployed in the flocculation and sedimentation tank sludge discharge process section of the water plant. This module can adjust the sludge discharge mode, sludge discharge cycle and sludge discharge duration of the flocculation and sedimentation zones according to parameters such as water inlet flow and turbidity, maintaining the healthy and normal operation of the flocculation and sedimentation system, improving sludge discharge efficiency, and saving energy and reducing consumption.

[0163] The intelligent pump distribution module is deployed in the water supply pump room or lift pump room process section of the water plant. This module regulates the pump-machine combination while meeting the water supply pressure, water supply volume, and health status of the pump group to achieve the optimal operating effect of the pump-machine combination and thus reduce energy consumption.

[0164] The intelligent water balance module monitors the volume of incoming water, outgoing water, backwash water, and return water in real time, and issues timely warnings to precisely control the supply-demand differential rate. It also regulates the liquid level in the clear water reservoir to maintain a reasonably high level, effectively reducing the power consumption of the water supply units during the water supply process.

[0165] The intelligent monitoring module comprehensively collects the operating status and fault signals of all process equipment within the water plant, including pumps, blowers, valves, mixers, and more. It also aggregates data from various test instruments, including flow meters, level gauges, pressure gauges, turbidity analyzers, residual chlorine monitors, and pH meters, as well as information from all intelligent instruments, including power monitoring instruments and temperature inspection systems. This enables centralized and efficient monitoring of production data across the entire water plant.

[0166] The equipment management module manages the entire life cycle of the plant's process equipment, including basic equipment ledger information, equipment operation, equipment alarms, equipment repair and maintenance plans, equipment repair and maintenance lists, and equipment residual values, providing a decision-making basis for the water plant's water production costs and equipment expenses.

[0167] In summary, the embodiment of the present invention provides a control method and control system for a multi-agent water plant process system. Figure 10As shown, first, based on the water environment and water treatment objectives of the water plant, independent process simulation is carried out to obtain the optimal control solution set of a single agent; secondly, combined with the competition and game relationship between each single agent, a multi-objective iterative optimization is performed to obtain a multi-agent collaborative control strategy solution set; then, combined with the single cost status and the comprehensive effect of the solution, a multi-agent collaborative control strategy that meets the control objective function is obtained; finally, based on the real-time change information of the water plant process system such as water environment, objectives, and costs, the multi-agent collaborative control strategy is dynamically adjusted through iteration to achieve the dynamic response of the control system to real-time changes. The present invention provides a new set of dynamic optimal collaborative control solutions for the water plant process system, which manages the competition and cooperation relationship between single agents through an efficient coordination mechanism to ensure that each single agent will not damage the overall harmony and water production efficiency of the water plant process system while pursuing its own optimization goals. At the same time, the present invention can also adaptively adjust the collaborative control strategy of the water plant process system based on the operating data of the water plant process system and external environmental change information, and achieve more precise and dynamic control by continuously optimizing the control strategy process. It not only significantly improves the stability and intelligence level of the water plant process system operation, but also effectively reduces the energy consumption of the water plant process system operation and improves resource utilization efficiency, so as to ensure that the indicators of water quality and water quantity can be continuously optimized.

[0168] Since the systems / devices described in the above embodiments of the present invention are systems / devices used to implement the methods of the above embodiments of the present invention, those skilled in the art will be able to understand the specific structures and variations of these systems / devices based on the methods described in the above embodiments of the present invention, and thus will not be described in detail here. All systems / devices used in the methods of the above embodiments of the present invention are within the scope of protection of the present invention.

[0169] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0170] The present invention is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions.

[0171] It should be noted that, in the description of the present invention, the word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The present invention can be implemented by means of hardware comprising several distinct components and by means of a suitably programmed computer. The use of the words first, second, third, etc., is merely for convenience and does not imply any order. These words should be understood as part of the component name.

[0172] In addition, it should be noted that, in the description of this specification, the description of the terms "one embodiment", "some embodiments", "embodiment", "example", "specific example" or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0173] Although preferred embodiments of the present invention have been described, those skilled in the art will be able to make additional changes and modifications to these embodiments after obtaining the basic inventive concepts.

[0174] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the invention.

Claims

1. A control method for a multi-agent water plant process system, characterized in that: include: Taking the minimization of production cost and optimization of water quality of the water plant process system as the dual control objectives, a control objective function for maximizing the global benefits of the water plant process system is constructed; Based on the process type of each single agent in the water plant process system, the dual control objectives are decomposed into dual control sub-objectives for each single agent. The process simulation of the single agent is carried out in combination with the preset constraints to obtain the optimal control solution for each single agent. Based on the acquired single-agent process type, the optimal control solutions are grouped and integrated to form at least one multi-agent collaborative control solution set. Based on the preset reward and punishment function between single agents, the multi-agent collaborative control solution set is subjected to multi-objective iterative optimization including exchange optimization, production optimization, and hybrid optimization to obtain a multi-agent collaborative control strategy solution set. Based on the control objective function of maximizing the global benefit of the water plant process system, the multi-agent collaborative control strategy solution set is converged to obtain a multi-agent collaborative control strategy that meets the control objective function. Dynamically adjust the multi-agent collaborative control strategy based on the real-time change information of the water plant process system, and deploy the optimal multi-agent collaborative control strategy obtained from the adjustment to the water plant process system; Among them, when the exchange is optimal, the multi-agent collaborative control strategy under any combination of the optimal control solutions of the single agent cannot exceed the maximum control benefit effect; when the production is optimal, the control solution of each single agent is on the production possibility frontier of the configuration; when the hybrid is optimal, each collaborative control strategy in the solution set of the agent collaborative control strategy can reflect the control objective of the water plant process system; The reward and punishment functions include: collaborative reward function and interference penalty function; the collaborative reward function is: ； Where, λ is the weight of the efficient collaboration reward, δ the weight of the reward for inefficient collaboration; The interference penalty function is: R 2=－ μ ； Where, μ is the intensity of the interference penalty.

2. The method according to claim 1, wherein Taking the minimization of production costs and optimization of water quality of the water plant process system as the dual control objectives, the control objective function for maximizing the global benefits of the water plant process system is constructed, including: Obtain production cost information and water quality information of the water plant process system. Production cost information includes water production process cost and water supply process cost; Based on the water supply quality information and the preset water supply quality target, the deviation between the water supply quality and the water supply quality target is obtained; Based on the preset collaborative reward function and interference penalty function between single agents, combined with production cost information and the deviation between the actual water quality and the water quality target, a control objective function is constructed to maximize the global benefits of the water plant process system with the dual control objectives of minimizing the production cost and optimizing the water quality of the water plant process system. Among them, the control objective function is: ； Where, k 1 is the weight of water quality, k 2 is the weight of water production cost, k 3 is the weight of water supply cost, k 4 is the weight of the collaborative reward function, k 5 is the weight of the interference penalty function, is the deviation between the actual water quality and the water quality target, V punit The cost of water production process, FT in is the water inlet flow rate, V sunit The cost of water supply process, PT is the actual water supply pressure, FT out is the water supply flow, R 1 is the collaborative reward function between single agents, R 2 is the interference penalty function between single agents.

3. The method according to claim 1, wherein Based on the process type of each single agent in the water plant process system, the dual control objectives are decomposed into dual control sub-objectives for each single agent. The process simulation of the single agent is carried out in combination with the preset constraints to obtain the optimal control solution for each single agent, including: Based on the process type of each single agent in the water plant process system, the dual control objectives are decomposed to obtain the dual control sub-objectives of minimizing the production cost and optimizing the output results of each single agent; Traverse all dual control sub-goals and obtain the constraints for each single agent to perform process simulation based on the influencing factors of the single agent operation; Conduct independent process simulation for each single agent in the water plant process system based on the constraints, analyze the simulation output results and process costs, and obtain the optimal control solution for each single agent; The constraint expression is: ； Where, For a single agent i The minimum dosage of the agent, For a single agent i The dosage of the agent, For a single agent i The maximum dosage of the agent, For a single agent i The minimum liquid level, Representing a single agent i The liquid level, Representing a single agent i The maximum liquid level, For a single agent i Medium equipment j The number of runs, For a single agent i Medium equipment j The maximum number of runs, For a single agent i Medium equipment a and equipment b Only one can be run at a time.

4. The method according to claim 1, wherein Based on the preset reward and punishment function between single agents, the multi-agent collaborative control solution set is subjected to multi-objective iterative optimization including exchange optimization, production optimization and hybrid optimization. The multi-agent collaborative control strategy solution set is obtained, including: Obtain the reward and punishment weights between single agents based on the preset reward and punishment function between single agents; Perform multi-objective coordinated optimization on the multi-agent collaborative control solution set, including exchange optimization, production optimization, and hybrid optimization, to obtain the initial multi-agent collaborative control strategy solution set; Through the genetic multi-objective optimization algorithm and combined with the reward and punishment weights, the initial multi-agent collaborative control strategy solution set is iteratively updated to obtain the multi-agent collaborative control strategy solution set.

5. The method according to claim 1, wherein According to the control objective function of maximizing the global benefit of the water plant process system, the multi-agent collaborative control strategy solution set is converged to obtain the multi-agent collaborative control strategy that meets the control objective function, including: Traverse each multi-agent collaborative control strategy in the multi-agent collaborative control strategy solution set, and select and obtain a multi-agent collaborative control strategy that meets the set minimum water quality target; According to the control objective function of maximizing the global benefit of the water plant process system, the global benefit data of the water plant process system of each multi-agent collaborative control strategy after screening is obtained; The multi-agent collaborative control strategy with the largest global benefit data of the water plant process system is determined as the multi-agent collaborative control strategy that meets the control objective function.

6. The method according to claim 1, wherein Based on the real-time change information of the water plant process system, the multi-agent collaborative control strategy is dynamically adjusted, and the optimal multi-agent collaborative control strategy obtained by adjustment is deployed to the water plant process system, including: Obtain real-time information on changes in the water plant's process systems, including changes in the plant's environment, water supply targets, and production costs; Based on real-time change information, the multi-agent collaborative control strategy is dynamically adjusted to obtain the optimal multi-agent collaborative control strategy for the water plant process system; The optimal multi-agent collaborative control strategy is deployed to the water plant process system to make the operating conditions of the water plant process system optimal.

7. A control system for a multi-agent water plant process system, characterized in that: include: The control objective function construction module is used to construct a control objective function that maximizes the global benefits of the water plant process system, with the dual control objectives of minimizing the production cost and optimizing the water quality of the water plant process system; The single-agent control optimization module is used to decompose the dual control objectives into dual control sub-objectives for each single agent based on the process type of each single agent in the water plant process system, and perform single-agent process simulation in combination with preset constraints to obtain the optimal control solution for each single agent; The multi-agent control optimization module groups and integrates the optimal control solutions according to the process types of the single agents obtained to form at least one multi-agent collaborative control solution set; Based on the preset reward and punishment function between single agents, the multi-agent collaborative control solution set is subjected to multi-objective iterative optimization including exchange optimization, production optimization and hybrid optimization to obtain the multi-agent collaborative control strategy solution set; based on the control objective function of maximizing the global benefit of the water plant process system, the multi-agent collaborative control strategy solution set is converged to obtain a multi-agent collaborative control strategy that meets the control objective function; among them, when the exchange is optimal, the multi-agent collaborative control strategy under any combination of the optimal control schemes of the single agent cannot exceed the maximum control benefit effect; when the production is optimal, the control scheme of each single agent is on the configured production possibility frontier; when the hybrid is optimal, each collaborative control strategy in the agent collaborative control strategy solution set can reflect the control objective of the water plant process system; the reward and punishment functions include: collaborative reward function and interference penalty function; the collaborative reward function is: ； Where, λ is the weight of the efficient collaboration reward, δ the weight of the reward for inefficient collaboration; The interference penalty function is: R 2=－ μ ； Where, μ The intensity of the interference penalty; The control strategy dynamic adjustment module is used to dynamically adjust the multi-agent collaborative control strategy based on the real-time change information of the water plant process system, and deploy the adjusted optimal multi-agent collaborative control strategy to the water plant process system.

8. A multi-agent water plant process system, characterized in that: include: A water plant process brain module, configured to execute the steps of a control method for a multi-agent water plant process system as described in any one of claims 1 to 6; The water plant single intelligent body module is connected to the water plant process brain module and is used to execute the water production process of the water plant when receiving control instructions from the water plant process brain module.

9. The multi-agent water plant process system according to claim 8, characterized in that: The single intelligent module of the water plant includes: intelligent alum addition module, intelligent chlorination module, intelligent backwash module, intelligent sludge discharge module, intelligent pump distribution module, intelligent water balance module, intelligent monitoring module and equipment management module.

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