Intelligent water plant control system

Abstract

The invention relates to the technical field of water supply, and provides an intelligent water plant control system, and the system is characterized in that a physical sensing layer is used for collecting multi-dimensional operation data including water quality parameters, equipment states, flow, pressure and energy consumption in real time through an Internet of Things sensor and an intelligent instrument disposed in each process unit of a water plant; the edge calculation layer is used for carrying out cleaning, filtering and normalization preprocessing on the collected original data; the digital twin platform layer is used for constructing a virtual water plant model which is completely consistent with a physical water plant in geometrical morphology, physical law and operation logic; the multi-agent decision-making layer comprises a plurality of agents with specific functions, and each agent is responsible for control decision-making of a specific process unit or subsystem of the water plant; the application service layer is used for issuing a control instruction to an execution mechanism of a physical water plant, and the system can achieve the full-process autonomous perception, intelligent prediction, collaborative decision and precise control of the intelligent water plant, and achieves the economical efficiency, safety and high efficiency of the operation of the water plant.

Description

Technical Field

[0001] This invention relates to the field of water supply technology, and in particular to a smart water plant control system. Background Technology

[0002] Water treatment plants are a core component of urban infrastructure, and their safe, efficient, and energy-saving operation is directly related to public health and social development. Traditional water treatment plant control systems are mainly based on PLCs (Programmable Logic Controllers) and SCADA (Supervisory Control and Data Acquisition) systems.

[0003] As disclosed in application number CN202121794899.0, a novel smart water plant SCADA intelligent monitoring and control platform device circulates air inside the shell, thereby cooling and dissipating heat inside the shell, improving the heat dissipation effect of electrical components inside the shell, reducing equipment malfunctions, and filtering outdoor air by setting multiple sets of filter cartridges to reduce outdoor dust entering the shell and improve the protection effect of the equipment; it includes a shell, a grille, a support device, a central processing unit, a gas collection hood, a distribution box, multiple sets of exhaust pipes, multiple sets of protective nets, multiple sets of first electric fans, a dustproof net, multiple sets of filter cartridges, multiple sets of air inlet pipes, multiple sets of drying layers, and multiple sets of second electric fans. The side wall of the shell is connected to an inspection port, the outer edge of the grille is connected to the inner side wall of the shell, the support device is installed on the top of the grille, and the central processing unit is installed on the support device.

[0004] However, while existing systems have made significant progress in automation, some problems still exist. For example, process parameter adjustment, equipment fault diagnosis, and emergency event handling are highly dependent on the experience of operators. Due to the lack of refined control, the power consumption (mainly pump stations) and chemical consumption of water plants often have considerable room for optimization, resulting in high operating costs. Furthermore, the control systems of each process unit (such as water intake, chemical dosing, sedimentation, filtration, and disinfection) are relatively independent, making data exchange difficult and lacking a global optimization perspective. Summary of the Invention

[0005] To address the aforementioned problems, this invention provides a smart water plant control system.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A smart water plant control system includes: a physical sensing layer, an edge computing layer, a digital twin platform layer, a multi-agent decision-making layer, and an application service layer. The physical sensing layer collects multi-dimensional operational data in real time, including water quality parameters, equipment status, flow rate, pressure, and energy consumption, through IoT sensors and smart meters deployed in various process units of the water plant. The edge computing layer, connected to the physical sensing layer, performs preprocessing such as cleaning, filtering, and normalization on the collected raw data and executes local control logic with high real-time requirements. The digital twin platform layer, also connected to the edge computing layer, constructs a virtual water plant model that is completely consistent with the physical water plant's geometry, physical laws, and operational logic, and receives... Real-time data drives the model to evolve dynamically, enabling real-time mapping, historical retrospection, and future prediction of the physical water plant's status. The multi-agent decision-making layer, connected to the digital twin platform layer, contains multiple specialized agents. Each agent is responsible for the control decisions of a specific process unit or subsystem of the water plant. The agents interact and negotiate with each other through preset communication protocols and collaborative mechanisms to jointly generate a set of control instructions oriented towards the global optimal goal. The application service layer, connected to the multi-agent decision-making layer, is used to issue control instructions to the execution mechanisms of the physical water plant and present the water plant's operating status, prediction results, and optimization suggestions to the user in a visual form, providing a human-machine interface.

[0008] The physical sensing layer deploys high-precision IoT sensors at key nodes of the water plant (such as raw water inlets, outlets of each process section, and key points of the pipeline network) and core equipment (pumps, valves, and agitators). These sensors include turbidity meters, pH meters, residual chlorine analyzers, online COD (chemical oxygen demand) monitors, flow meters, pressure gauges, electricity meters, and vibration sensors. These sensors collect data at high frequencies (such as second-level or minute-level) and aggregate the data to the edge computing layer through wireless communication technologies such as industrial Ethernet, 5G, or LoRa.

[0009] The edge computing layer is deployed in the field close to the data source, and its function is:

[0010] 1. Perform noise reduction, outlier removal, data calibration, and format standardization on the raw sensor data to ensure the quality of the uploaded data;

[0011] 2. Execute local control tasks with extremely high response speed requirements, such as equipment safety interlock protection and emergency shutdown, to ensure that the underlying production safety is not affected when a failure occurs in the cloud or upper-level system;

[0012] 3. Cache the processed data and upload it to the digital twin platform layer as needed or on a regular schedule to reduce the network and computing pressure on the central server.

[0013] The digital twin platform layer constructs a virtual water plant that is mapped 1:1 to the physical water plant, interacts with it, and is synchronized in real time. Using BIM (Building Information Modeling) technology, a three-dimensional geometric model of the water plant is built. Mechanism models such as hydraulic models (e.g., pipe network hydraulic models), water quality models (e.g., residual chlorine decay models, disinfection byproduct generation models), and equipment energy consumption models are integrated with data-driven models (e.g., neural network prediction models) to form a "living" model that reflects the actual physical laws and behaviors of the water plant.

[0014] The digital twin platform layer receives real-time data from the edge computing layer and continuously corrects the model's state and parameters through data assimilation techniques (such as Kalman filtering and particle filtering) to ensure that the virtual model is synchronized with the physical water plant. Based on the synchronized model, "what-if" analysis can be performed. For example, it can predict whether the current dosage can guarantee the effluent quality if the influent turbidity suddenly increases within the next hour; it can also predict the energy consumption and cost over the next 24 hours under different pump combinations, thus providing a safe and efficient "testing ground" for multi-agent decision-making.

[0015] The multi-agent decision layer decomposes the complex global control problem into multiple sub-problems, each handled by a specialized agent.

[0016] Based on the water plant's processes and control objectives, multiple intelligent agents are set up, such as:

[0017] Intelligent dosing system: The goal is to minimize chemical costs while ensuring that the effluent water quality meets standards. Its inputs are the influent water quality and quantity predicted by digital twins, and its output is the optimal dosing dosage setting.

[0018] Pump station intelligence: The goal is to minimize pump station power consumption while meeting water supply pressure and flow requirements. Its inputs are water demand forecast and pipeline pressure model, and its outputs are the optimal pump start-up / shutdown combination and operating frequency.

[0019] The filtration agent aims to maximize the filtration cycle of the filter bed, ensure the quality of the filtered water, and minimize backwash water and energy consumption. Its inputs are filter head loss and turbidity, and its outputs are the optimal backwash trigger timing and parameters.

[0020] Water quality assurance intelligent agent: As a global supervisor, its goal is to ensure that the final effluent water quality meets 100% standards. It continuously monitors the predicted water quality values ​​of each process stage, and when potential risks are detected, it has the authority to issue "mandatory adjustment" instructions to the chemical dosing intelligent agent, filtration intelligent agent, etc.

[0021] The agents do not work independently, but communicate through a cooperative bus. Their decision-making process is as follows:

[0022] Independent decision-making: Each agent generates a preliminary locally optimal decision scheme based on its own observations and objectives, using its internally trained reinforcement learning model (such as DQN, PPO algorithm).

[0023] Information exchange: Each intelligent agent broadcasts its decision-making plan, expected benefits (such as how much energy is saved or how much cost is reduced), and usage of public resources (such as energy consumption and medicine) to other relevant intelligent agents.

[0024] Conflict Negotiation: When a conflict of objectives arises (e.g., a pump station agent wants to reduce frequency to save energy, but this leads to insufficient pipeline pressure and affects water supply security), a collaborative negotiation module is activated. This module can employ an auction mechanism based on game theory or a contract network protocol. For example, a water quality assurance agent can "bid" (promise to save more costs elsewhere) to "purchase" the pump station agent to increase pressure, ultimately achieving a Pareto optimal solution that benefits all parties.

[0025] The application service layer is responsible for applying the decisions made in the virtual world to the physical world and presenting the information to the user.

[0026] The control commands generated by the multi-agent decision-making layer are distributed to the actuators of the physical water plant, such as PLCs, frequency converters, and electric valves, via industrial protocols such as OPC UA and Modbus. Managers can intuitively see real-time changes in water flow, equipment status, and water quality parameters, and can also "see through" the internal workings of the equipment to view predictive maintenance information. Based on the prediction results of digital twins, the system can issue early warnings of water quality exceeding standards and equipment failures several hours or even days in advance, providing preliminary diagnostic causes and handling suggestions. The application service layer can also provide advanced functions such as operation optimization suggestions, energy consumption analysis reports, and cost-benefit analysis to assist managers in strategic decision-making.

[0027] Preferably, the digital twin platform layer includes: a geometric model module, a physical model module, a behavioral model module, and a data fusion and driving module. The geometric model module is used to construct a three-dimensional visualization model of the water plant's buildings, equipment, and pipelines; the physical model module is used to embed mechanism models such as hydraulics, chemical reactions, and microbial degradation to simulate the real changes in water during the treatment process; the behavioral model module is used to integrate equipment operation rules, control logic, and process constraints to simulate the behavioral response of the water plant under different operating conditions; and the data fusion and driving module is used to receive real-time data from the edge computing layer and correct model parameters through data assimilation technology to ensure the synchronization and consistency between the virtual model and the physical water plant.

[0028] Preferably, the agents in the multi-agent decision-making layer include at least: a chemical dosing agent, a pumping station agent, a filtration agent, and a water quality assurance agent. The chemical dosing agent is responsible for optimizing the dosage of coagulants, disinfectants, and other chemicals. The pumping station agent is responsible for the start-up, shutdown, and variable frequency speed control decisions of the intake and delivery pumps to optimize the power consumption of water distribution. The filtration agent is responsible for optimizing the backwashing cycle, intensity, and duration of the filter bed. The water quality assurance agent is responsible for overall water quality safety monitoring and early warning, and when a risk of water quality exceeding standards is predicted, it sends adjustment requests to other relevant agents.

[0029] Preferably, the collaborative mechanism of the multi-agent decision-making layer is based on a framework combining reinforcement learning and game theory. Each agent trains the optimal strategy through extensive trial and error learning on a digital twin platform. In real-time operation, when the objectives of the agents conflict, such as the pump station agent pursuing energy saving while the chemical dosing agent pursues water quality, Pareto optimal solutions are achieved through game theory methods such as negotiation or auction.

[0030] A control method for a smart water plant control system includes the following steps:

[0031] S1: The physical sensing layer collects real-time data from the entire water plant process, while the edge computing layer cleans and preprocesses the data.

[0032] S2: Input the preprocessed data into the digital twin platform layer to drive the virtual model to run synchronously with the physical water plant, and predict key indicators such as water quality, water quantity, and energy consumption in the near future based on the current state.

[0033] S3: The multi-agent decision layer obtains the current state and prediction results of the digital twin platform. Each agent calculates and negotiates through a collaborative mechanism based on its own goals and global constraints to generate a set of optimal control instructions.

[0034] S4: The application service layer converts the optimal control commands into standard signals and sends them to the actuators of the physical water plant to complete the actual control of the water plant;

[0035] S5: Feed back the actual results data after execution to the digital twin platform and the multi-agent decision-making layer to evaluate the decision-making effect and use it as new training samples to optimize and continuously learn the agent's policy model online.

[0036] Preferably, in step S3, the collaborative decision-making process specifically includes:

[0037] S31: Each agent independently generates a preliminary decision scheme based on local observation information and using the trained reinforcement learning model;

[0038] S32: Through the communication network between intelligent agents, they exchange their respective decision-making schemes, objective function values, and resource usage;

[0039] S33: If a target conflict is detected, the collaborative negotiation module is activated. Using an algorithm based on contract network or game theory, conflicting resources are redistributed or the decision-making scheme is adjusted until a consensus scheme that satisfies global constraints and has the highest overall benefits is reached.

[0040] S34: Outputs the final consensus control instruction set.

[0041] The advantages of this invention are as follows: Through a multi-agent collaborative decision-making mechanism, it breaks down traditional "information silos," achieving coordinated optimization of various process units in the water plant, resolving the inherent contradictions between energy saving, consumption reduction, and quality improvement, and maximizing overall comprehensive benefits; by setting up a digital twin platform layer, the system is endowed with powerful predictive capabilities, enabling it to anticipate potential risks and problems and adjust control strategies in advance, transforming passive handling into proactive prevention, greatly improving the robustness and safety of water plant operation; the system can perform autonomous decision-making and optimization control 24 / 7, freeing operators from tedious daily monitoring and parameter adjustments, allowing them to focus on higher-level system management and anomaly handling, significantly reducing reliance on expert experience; through refined dosing control and pump station optimization, it can save 10%-20% on chemical costs and 8%-15% on electricity consumption. Predictive maintenance can reduce unplanned equipment downtime, extend equipment life, and lower maintenance costs. The system can track the entire chain of water quality changes in real time and respond quickly and accurately to any factors that affect water quality, ensuring that the effluent quality is consistently better than national standards and providing a solid guarantee for public drinking water safety. The system adopts a modular design, and when adding a new process unit or control target, only the corresponding intelligent agent needs to be added. At the same time, through continuous learning, multiple intelligent agents can continuously adapt to changes such as aging water plant equipment and process modifications, achieving continuous evolution of control strategies. Attached Figure Description

[0042] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0043] Figure 1 This is a schematic diagram of the structure of the present invention;

[0044] Figure 2 This is a flowchart of the control method of the present invention;

[0045] Figure 3 This is a flowchart of the collaborative decision-making process of the various intelligent agents in this invention. Detailed Implementation

[0046] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0047] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0048] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0049] Example 1, combined with Figure 1 , Figure 2 and Figure 3 Explanation:

[0050] Taking a water treatment plant with a daily processing capacity of 500,000 tons as an example, the system described in this invention is used for intelligent transformation.

[0051] The first step is to construct a physical sensing layer and an edge computing layer for system deployment: add or upgrade online water quality analyzers (turbidity, pH, residual chlorine, COD, etc.) at key locations such as raw water intake, sedimentation tank outlet, filtered water, and treated water; install smart meters and vibration sensors on all water intake and delivery pumps; add smart pressure and flow meters to the main pipeline network; and deploy edge computing gateways in sedimentation tank and filter areas to be responsible for local data preprocessing and emergency control logic.

[0052] The second step is to build a digital twin platform layer, using laser scanning and BIM technology to construct a high-precision 3D model of the entire water plant, including the plant buildings, equipment, pipelines, instruments, etc.

[0053] Establish a full-process hydraulic model from water intake to water distribution to simulate the pressure and flow changes of water in pipelines and structures. Establish a water quality model of coagulation-sedimentation-filtration-disinfection to simulate the changes of indicators such as turbidity, organic matter, and residual chlorine along the process. Based on historical operating data, train a neural network model to predict the short-term trend of raw water quality (such as turbidity) and establish an energy consumption-flow-efficiency characteristic model of water pumps.

[0054] The real-time collected data is then connected to the digital twin platform via an API interface. The extended Kalman filter algorithm is used to correct the parameters of the hydraulic model and water quality model in real time, ensuring that the operating status of the virtual water plant is less than 5% different from that of the actual water plant.

[0055] The third step is to train and deploy multi-agent systems.

[0056] Design intelligent agents for chemical dosing, pumping stations, filters, and water quality assurance. Use a digital twin platform as the training environment and employ a proximal strategy optimization algorithm to perform reinforcement learning training on each agent.

[0057] The dosing agent's reward function is designed as R = w1 * (effluent turbidity compliance rate) - w2 * (chemical cost), where w1 and w2 are weights. The agent continuously tries different dosing amounts in a virtual environment, learning the optimal strategy for handling different raw water qualities.

[0058] Pump station agent: The reward function is designed as R = w3 * (pipeline pressure qualification rate) - w4 * (power consumption), where w3 and w4 are weights. The agent learns how to combine pumps and adjust frequencies to meet water supply demand with the lowest energy consumption.

[0059] The trained agent model is deployed to the decision server. In actual operation, for example, when the digital twin platform predicts that the raw water turbidity will soar by 50 NTU in 2 hours, the dosing agent immediately calculates that the dosing amount needs to be increased by 20%. The pump station agent predicts that the water demand will remain unchanged and maintains the current operation plan. The water quality protection agent evaluates the dosing agent's plan, deems it feasible, and does not intervene. The coordination bus reaches a consensus and issues the instruction to "increase the dosing amount by 20%".

[0060] The fourth step is application and interaction.

[0061] A WebGL-based 3D visualization monitoring platform was developed, allowing managers to view real-time flow, pressure, and water quality data at any point on their office computers by dragging and zooming the 3D model. When the system predicts that "Filter A will reach the backwash head loss threshold in 3 hours," a warning will pop up on the screen, suggesting that "backwashing be scheduled during off-peak hours at night to save costs." The system automatically generates daily operation reports, clearly displaying key performance indicators such as daily chemical savings, electricity savings, and water quality compliance rate.

[0062] Through the application of this embodiment, the water plant can reduce its overall operating costs by about 15% within one year after the renovation, significantly improve the stability of the effluent water quality, and reduce the failure rate of key equipment by 30%, thus realizing the transformation and upgrading from a traditional automated water plant to a modern smart water plant.

[0063] The working principle of this invention is as follows: During operation, the physical perception layer collects real-time data from the entire water plant process, while the edge computing layer cleans and preprocesses the data. The preprocessed data is then input into the digital twin platform layer, driving the virtual model to run synchronously with the physical water plant and predicting key indicators such as water quality, water quantity, and energy consumption in the near future based on the current state. The multi-agent decision-making layer obtains the current state and prediction results of the digital twin platform. Each agent, based on its own goals and global constraints, performs calculations and negotiations through a collaborative mechanism to generate a set of optimal control commands. The application service layer converts the optimal control commands into standard signals and sends them to the execution mechanisms of the physical water plant to complete the actual control of the water plant. The actual effect data after execution is fed back to the digital twin platform and the multi-agent decision-making layer for evaluating the decision-making effect and as new training samples for online optimization and continuous learning of the agent's strategy model. This invention enables intelligent water plant to achieve full-process autonomous perception, intelligent prediction, collaborative decision-making, and precise control, achieving economic efficiency, safety, and high efficiency in water plant operation.

[0064] For those skilled in the art, the present invention is not limited to the details of the exemplary embodiments described above, and can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention; therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Therefore, it is intended that all variations falling within the meaning and scope of equivalents of the claims be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0065] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any minor modifications, equivalent substitutions, and improvements made to the above embodiments based on the technical essence of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart water plant control system, characterized in that, include: The system consists of a physical sensing layer, an edge computing layer, a digital twin platform layer, a multi-agent decision-making layer, and an application service layer. The physical sensing layer is used to collect multi-dimensional operational data in real time, including water quality parameters, equipment status, flow rate, pressure, and energy consumption, through IoT sensors and smart meters deployed in various process units of the water plant. The edge computing layer connects to the physical sensing layer and is used to clean, filter, and normalize the collected raw data for preprocessing, and to execute local control logic with high real-time requirements. The digital twin platform layer connects to the edge computing layer and is used to construct a virtual water plant model that is completely consistent with the physical water plant's geometry, physical laws, and operating logic. It receives real-time data to drive the model to evolve dynamically, realizing real-time mapping, historical backtracking, and future prediction of the physical water plant's state. The multi-agent decision-making layer connects to the digital twin platform layer and contains multiple agents with specialized functions. Each agent is responsible for the control decisions of a specific process unit or subsystem of the water plant. The agents interact and negotiate with each other through preset communication protocols and collaborative mechanisms to jointly generate a set of control instructions oriented towards the global optimal goal. The application service layer connects to the multi-agent decision-making layer and is used to issue control instructions to the execution mechanism of the physical water plant and present the water plant's operating status, prediction results, and optimization suggestions to the user in a visual form, providing a human-computer interaction interface.

2. The intelligent water plant control system according to claim 1, characterized in that, The digital twin platform layer includes a geometric model module, a physical model module, a behavioral model module, and a data fusion and driving module. The geometric model module is used to construct a 3D visualization model of the water plant's buildings, equipment, and pipelines. The physical model module is used to embed mechanism models such as hydraulics, chemical reactions, and microbial degradation to simulate the real changes in water during the treatment process. The behavioral model module is used to integrate equipment operation rules, control logic, and process constraints to simulate the water plant's behavioral responses under different operating conditions. The data fusion and driving module is used to receive real-time data from the edge computing layer and correct model parameters through data assimilation technology to ensure the synchronization and consistency between the virtual model and the physical water plant.

3. The intelligent water plant control system according to claim 1, characterized in that, The agents in the multi-agent decision-making layer include at least: a chemical dosing agent, a pumping station agent, a filtration agent, and a water quality assurance agent. The chemical dosing agent is responsible for optimizing the dosage of coagulants, disinfectants, and other chemicals. The pumping station agent is responsible for the start-up, shutdown, and variable frequency speed control decisions of the intake and delivery pumps to optimize the power consumption of water distribution. The filtration agent is responsible for optimizing the backwashing cycle, intensity, and duration of the filter bed. The water quality assurance agent is responsible for overall water quality safety monitoring and early warning, and when a risk of water quality exceeding standards is predicted, it sends adjustment requests to other relevant agents.

4. A smart water plant control system according to claim 1 or 3, characterized in that, The collaborative mechanism of the multi-agent decision-making layer is based on a framework combining reinforcement learning and game theory. Each agent learns through extensive trial and error on a digital twin platform to develop the optimal strategy. In real-time operation, when the objectives of the agents conflict, such as the pump station agent pursuing energy conservation while the chemical dosing agent pursues water quality, Pareto optimal solutions are achieved through game theory methods such as negotiation or auction.

5. A control method for a smart water plant control system according to any one of claims 1-4, characterized in that, Includes the following steps: S1: The physical sensing layer collects real-time data from the entire water plant process, while the edge computing layer cleans and preprocesses the data. S2: Input the preprocessed data into the digital twin platform layer to drive the virtual model to run synchronously with the physical water plant, and predict key indicators such as water quality, water quantity, and energy consumption in the near future based on the current state. S3: The multi-agent decision layer obtains the current state and prediction results of the digital twin platform. Each agent calculates and negotiates through a collaborative mechanism based on its own goals and global constraints to generate a set of optimal control instructions. S4: The application service layer converts the optimal control commands into standard signals and sends them to the actuators of the physical water plant to complete the actual control of the water plant; S5: Feed back the actual results data after execution to the digital twin platform and the multi-agent decision-making layer to evaluate the decision-making effect and use it as new training samples to optimize and continuously learn the agent's policy model online.

6. The control method for a smart water plant control system according to claim 5, characterized in that, In step S3, the collaborative decision-making process specifically includes: S31: Each agent independently generates a preliminary decision scheme based on local observation information and using the trained reinforcement learning model; S32: Through the communication network between intelligent agents, they exchange their respective decision-making schemes, objective function values, and resource usage; S33: If a target conflict is detected, the collaborative negotiation module is activated. Using an algorithm based on contract network or game theory, conflicting resources are redistributed or the decision-making scheme is adjusted until a consensus scheme that satisfies global constraints and has the highest overall benefits is reached. S34: Outputs the final consensus control instruction set.

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