Intelligent scheduling system for waterworks

Abstract

The invention discloses a waterworks intelligent scheduling system, and particularly relates to the field of waterworks scheduling, and the waterworks intelligent scheduling system comprises a data collection module, a scheduling model calculation module, a water inflow adjustment module and a control instruction issuing module, and the data collection module is used for obtaining scheduling operation data from each device and sensor of a waterworks in real time; a standardized data format is adopted to carry out structured processing on the scheduling operation data, and the data is transmitted to other modules of the system in a network communication mode; the dispatching operation data comprises water inlet amount, water outlet amount, sludge discharge amount, backwashing water amount, sampling water amount, pressure water amount and reservoir liquid level. Various data of a water plant are integrated and analyzed, including water inlet and outlet quantity, sludge discharge quantity, filter tank backwashing queue and water quantity, pressure water quantity, sampling water quantity, reservoir liquid level and the like, and the water quantity change condition and the water quantity adjustment requirement in the plant are predicted through model calculation, so that a scheduling scheme is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of water plant dispatching, and more specifically, to an intelligent dispatching system for a water plant. Background Art

[0002] Traditional water plant dispatching mainly relies on human experience to perceive water use patterns, and finally forms an empirical dispatching plan by observing the water volume of each process section and the changes in the liquid level of the water purification facility. The company's dispatching center issues water output and water pressure requirements to the water plant according to the user's water demand. The water plant management personnel put forward requirements for the control range of the clear water reservoir level according to the operation requirements. The water plant dispatching personnel adjust the water intake and the operating parameters of each process link according to the water output, water pressure and reservoir level requirements. Focusing on the overall goal of building the Jujiaqiao Smart Water Plant, it is planned to introduce a smart scheduling model, establish a full-process scheduling system from entering the plant to leaving the plant, realize automatic and dynamic control of water volume, and replace manual experience with machine calculations. Summary of the invention

[0003] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an intelligent scheduling system for a water plant, which integrates and analyzes various data of the water plant, including water inlet and outlet, sludge water volume, filter tank backwash queue and water volume, pressure water volume, sampling water volume, reservoir liquid level, etc., and predicts the changes in water volume in the plant through model calculation, so as to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a water plant intelligent dispatching system, comprising a data acquisition module, a dispatching model calculation module, a water intake adjustment module, and a control instruction issuing module; The data acquisition module is used to obtain the dispatching operation data from various equipment and sensors in the water plant in real time, and use standardized data format to structure the dispatching operation data, and transmit it to other modules of the system through network communication; the dispatching operation data includes water inlet, water outlet, sludge water volume, backwash water volume, sampling water volume, pressure water volume, and reservoir liquid level; The dispatch model calculation module calculates water volume and liquid level based on the collected dispatch operation data through the preset dispatch evaluation model; the dispatch model calculation module generates corresponding water inflow adjustment instructions based on the water volume calculation and liquid level prediction to maintain the reservoir liquid level within the preset target range; The water inflow adjustment module is used to calculate the results of the dispatching evaluation model and determine whether the water inflow needs to be adjusted based on the calculated results, thereby controlling the reservoir liquid level; The control instruction issuing module realizes data flow by communicating with the PLC unit of the water plant, and obtains the scheduling and operation data of the water plant in real time based on the interaction between the PLC unit and the host computer; the control instruction issuing module obtains the scheduling calculation results according to the adjustment instructions of the scheduling model calculation module, generates corresponding control instructions based on the scheduling calculation results, and transmits them to the PLC to execute automatic adjustment of water inlet and liquid level.

[0005] In a preferred embodiment, the scheduling evaluation model of the scheduling model calculation module includes a water quantity model; The water volume model includes:

[0006] in is the water inflow to the water plant; The amount of water supplied to the water plant; To calculate time; is the reservoir area; is the change of reservoir liquid level; is the amount of mud water discharged; It is the time for mud discharge; is the number of filter tanks; is the backwash water volume; is the amount of water sampled; The pressure water volume for factory use.

[0007] In a preferred embodiment, the scheduling logic of the scheduling model calculation module includes: Obtain scheduling operation data based on the data acquisition module; Determine whether sedimentation tank sludge discharge, filter tank backwashing, sampling water volume and pressure water volume adjustment are required in the next cycle; Based on water plant inflow and outflow Under the condition of no change, the reservoir level change trend for:

[0008] Calculate the time when the reservoir liquid level reaches the control limit ,pass Refers to the difference between the water intake of a water plant and the water supply of a water plant;

[0009]

[0010] Determine whether the water inflow of the water plant needs to be adjusted: based on the time parameters preset by the system ,when , then the water plant water inlet control instruction is issued; when , then the water inflow of the water plant will not be adjusted; The model obtains the current liquid level , Next determination time target liquid level , calculate the amount of water that needs to be adjusted to reach the target level within the set time :

[0011] when , do not adjust the water plant water intake; when ,according to Adjust; when , then according to ;in is the minimum adjustable water volume; The maximum adjustable water volume; The water volume calculation is now completed.

[0012] In a preferred embodiment, it also includes a scheduling mode selection module; The scheduling mode selection module includes energy-saving mode and stable mode; Energy-saving mode is used to keep the reservoir level continuously high, with the goal of reducing the energy consumption of water output; The stable mode is used to keep the water inflow stable so that the water treatment process can run stably.

[0013] In a preferred embodiment, it also includes a user interface module, a data analysis and optimization module, and a network communication module; The user interface module is used to provide operators with real-time viewing of reservoir level changes, scheduling model calculation results, and mud discharge and backwashing information; users set the target parameters of energy-saving mode or stable mode on the interface according to the user interface module, and the system executes the scheduling task; The data analysis and optimization module analyzes historical data to find out the water volume relationship and liquid level change rules of each process link in the water plant, and optimizes the scheduling model accordingly; The network communication module is used for data communication between various devices in the system; the network communication module is connected to the PLC unit by using RSLogix5000 to realize real-time data collection and instruction issuance. Through network communication, the system adjusts the water intake in real time.

[0014] In a preferred embodiment, the data analysis and optimization module predicts the change of reservoir liquid level based on the dispatching and operating data of the water plant as input variables through multiple regression analysis, dynamic time window optimization and real-time feedback mechanism, and optimizes the adjustment of water intake according to the prediction results; Through the data analysis and optimization module, the reservoir level changes are analyzed and the Indicates at time Predicted change in reservoir level;

[0015] in is the regression coefficient; is the number of input variables; For the input variables; It is a dynamic adjustment item; Optimize the regression coefficients by minimizing the mean square error (MSE) , whose expression is:

[0016] in is the actual reservoir level change; is the number of historical data points; are the parameter sets of regression coefficients and adjustment terms respectively.

[0017] In a preferred embodiment, for the calculation of the regression coefficient in the data analysis and optimization module, the optimization of the regression coefficient is calculated by the normal equation:

[0018]

[0019] in is the regression coefficient vector, which indicates the influence of each input variable on the change of reservoir level; is the input matrix, Contains data for all time steps, each column represents an input variable, and the shape is ,in is the number of data points; is the transpose of the input matrix; is the output vector, which contains the actual liquid level change data at each time step and has the shape of , that is, the liquid level change corresponding to each time step.

[0020] In a preferred embodiment, based on the predicted results of the liquid level change in the data analysis and optimization module, Indicates at time The water intake volume that needs to be adjusted at all times is used to calculate the water intake volume;

[0021] in represents the output of regression analysis; is the number of input variables used in the regression analysis; is the index of the input variable; Constraints are imposed on water intake adjustment based on data analysis and optimization modules;

[0022] in Indicates taking and The valley between Indicates taking and The peak between Real-time adjustment of regression coefficients based on feedback mechanism;

[0023] in is the learning rate.

[0024] Technical effects and advantages of the present invention: The present invention adopts network communication to integrate and analyze various data of the water plant, including water inlet and outlet, sludge discharge water volume, filter tank backwash queue and water volume, pressure water volume, sampling water volume, reservoir liquid level, etc. Through model calculation, the water volume change in the plant and the water volume adjustment demand are predicted, thereby optimizing the scheduling plan.

[0025] By using algorithms and control decisions, human experience is summarized into a mathematical model. Through data collection, model calculation, and automated control, the water intake can be adjusted in real time and accurately according to the requirements of the company's dispatching center and the operation needs of the water plant, the reservoir liquid level can be controlled, the water volume can be precisely controlled, the dispatching efficiency can be improved, and the goal of "unmanned dispatching, low-carbon operation" can be achieved.

[0026] Set up a variety of dispatching modes for easy selection and switching: Set up energy-saving mode and stable mode, and choose to switch to the corresponding dispatching mode according to the operation of the water plant to meet the operation needs of different time periods; the energy-saving mode aims to keep the reservoir liquid level at a high level, which can reduce the energy consumption of water output; the stable mode aims to stabilize the water inlet of the whole plant, ensuring the stability of water production process links such as dosing, clarifier inlet and sludge discharge, filter inlet and backwashing. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is the reservoir liquid level control target diagram of the present invention.

[0028] Figure 2 It is the reservoir liquid level trend diagram of the present invention.

[0029] Figure 3 It is a trend diagram of the predicted liquid level and the actual liquid level of the present invention.

[0030] Figure 4 Develop page 1 for the host computer of the present invention.

[0031] Figure 5 Develop page 2 for the host computer of the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] Refer to the instruction manual Figure 1-Figure 5 , a water plant intelligent dispatching system according to an embodiment of the present invention includes a data acquisition module, a dispatching model calculation module, a water intake adjustment module, and a control instruction issuing module; The data acquisition module is used to obtain the dispatching operation data from various equipment and sensors in the water plant in real time, and use standardized data format to structure the dispatching operation data, and transmit it to other modules of the system through network communication; the dispatching operation data includes water inlet, water outlet, sludge water volume, backwash water volume, sampling water volume, pressure water volume, and reservoir liquid level; The dispatch model calculation module calculates water volume and liquid level based on the collected dispatch operation data through the preset dispatch evaluation model; the dispatch model calculation module generates corresponding water inflow adjustment instructions based on the water volume calculation and liquid level prediction to maintain the reservoir liquid level within the preset target range; The water inflow adjustment module is used to calculate the results of the dispatching evaluation model and determine whether the water inflow needs to be adjusted based on the calculated results, thereby controlling the reservoir liquid level; The control instruction issuing module realizes data flow by communicating with the PLC unit of the water plant, and obtains the scheduling and operation data of the water plant in real time based on the interaction between the PLC unit and the host computer; the control instruction issuing module obtains the scheduling calculation results according to the adjustment instructions of the scheduling model calculation module, generates corresponding control instructions based on the scheduling calculation results, and transmits them to the PLC to execute automatic adjustment of water inlet and liquid level.

[0034] The dispatching evaluation model of the dispatching model calculation module includes water volume model, reservoir liquid level change model, and water plant water inflow model; The water volume model includes:

[0035] The reservoir level change model includes:

[0036] The water plant inflow model includes:

[0037] in is the water inflow of the water plant, in m³ / h; is the water supply of the water plant, in m³ / h; is the calculation time, in h; is the reservoir area in m²; is the change in reservoir level, in m; is the amount of mud and water discharged, in m³; is the mud discharge time, in h; is the number of filter tanks, which is a pure number without unit; is the backwash water volume, in m³; is the sampled water volume, in m³; It is the factory pressure water volume, in m³.

[0038] The scheduling logic of the scheduling model calculation module includes: Obtain scheduling operation data based on the data acquisition module; is the calculated value, which comes from the derivation of the scheduling model calculation module; It is a set value, which is preset by the system or the user; The collected data is obtained by the data collection module; Determine whether sedimentation tank sludge discharge (according to the set plan), filter tank backwashing (automatically read queue data), sampling water volume (constant value) and pressure water volume (constant value) need to be adjusted in the next cycle; Based on water plant inflow and outflow Under the condition of no change, the reservoir level change trend for:

[0039] Calculate the time when the reservoir liquid level reaches the control limit ,pass Refers to the difference between the water intake of a water plant and the water supply of a water plant;

[0040]

[0041] Determine whether the water inflow of the water plant needs to be adjusted: based on the time parameters preset by the system ,when , then the water plant water inlet control instruction is issued; when , then the water inflow of the water plant will not be adjusted; The model obtains the current liquid level , Next determination time target liquid level , calculate the amount of water that needs to be adjusted to reach the target level within the set time :

[0042] when , do not adjust the water plant water intake; when ,according to Adjust; when , then according to ;in is the minimum adjustable water volume; The maximum adjustable water volume; The water volume calculation is now completed.

[0043] In the embodiments of the present invention, some key parameters used are obtained by: The water inflow of the water plant is collected in real time by the electromagnetic flowmeter installed on the water inlet pipe of the water plant, with a collection frequency of 1 time / minute. The data is standardized by the data acquisition module and then transmitted; the water supply of the water plant is collected by the flowmeter of the outlet pipe section of the water plant's clear water tank; the reservoir level is collected in real time by the reservoir level sensor with an accuracy of ±1mm; auxiliary parameters such as backwash water volume, sludge discharge water volume, sampling water volume, pressure water volume, etc. are generated by the control program prediction or on-site preset values, and compared and corrected with the actual sampling data;

[0044] Also includes a scheduling mode selection module; The scheduling mode selection module includes energy-saving mode and stable mode; Energy-saving mode is used to keep the reservoir level continuously high, with the goal of reducing the energy consumption of water output; The stable mode is used to keep the water inflow stable so that the water treatment process can run stably.

[0045] It also includes a user interface module, a data analysis and optimization module, and a network communication module; The user interface module is used to provide operators with real-time viewing of reservoir level changes, scheduling model calculation results, and mud discharge and backwashing information; users set the target parameters of energy-saving mode or stable mode on the interface according to the user interface module, and the system executes the scheduling task; The data analysis and optimization module analyzes historical data to find out the water volume relationship and liquid level change rules of each process link in the water plant, and optimizes the scheduling model accordingly to improve the scheduling accuracy of the system; The network communication module is used for data communication between various devices in the system; the network communication module is connected to the PLC unit using RSLogix5000 to achieve real-time data collection and command issuance. Through network communication, the system adjusts the water intake in real time and ensures the stability of the water plant operation.

[0046] The data analysis and optimization module uses multiple regression analysis, dynamic time window optimization and real-time feedback mechanism to predict changes in reservoir liquid levels based on the water plant's dispatching and operation data as input variables, and optimizes the adjustment of water intake based on the prediction results; Through the data analysis and optimization module, the reservoir level changes are analyzed and the Indicates at time Predicted change in reservoir level;

[0047] in is the regression coefficient, which indicates the influence of each input variable on the change of reservoir level. Will change over time Dynamic adjustment to reflect the relationship between real-time system status and historical data; is the number of input variables; For the input variables, indicating the Input factors of scheduling operation data at a certain time; Dynamic adjustment items include system errors or unpredictable external factors; Optimize the regression coefficients by minimizing the mean square error (MSE) , whose expression is:

[0048] in is the actual reservoir level change, that is, The actual observed changes in reservoir levels at all times; is the number of historical data points, i.e., the number of time steps used to train the regression model; They are the parameter sets of regression coefficients and adjustment terms, respectively. When optimizing the objective function, their values ​​are adjusted to minimize the error.

[0049] For the calculation of regression coefficients in the data analysis and optimization module, the optimization of regression coefficients is calculated by the normal equation:

[0050]

[0051] in is the regression coefficient vector, which indicates the influence of each input variable on the change of reservoir level; is the input matrix, Contains data for all time steps, each column represents an input variable, and the shape is ,in is the number of data points; is the transpose of the input matrix, used to calculate the regression coefficients; is the output vector, which contains the actual liquid level change data at each time step and has the shape of , that is, the liquid level change corresponding to each time step.

[0052] Based on the predicted results of liquid level changes in the data analysis and optimization module, Indicates at time The water intake volume that needs to be adjusted at all times is used to calculate the water intake volume;

[0053] in It represents the output of regression analysis, which represents the weighted sum of the impact of each process water volume on the reservoir level in the dispatching operation data after adjustment by the regression coefficient; is the number of input variables used in the regression analysis; is the index of the input variable; Constraints are imposed on water intake adjustment based on data analysis and optimization modules;

[0054] in Indicates taking and The valley between Indicates taking and The peak between Real-time adjustment of regression coefficients based on feedback mechanism;

[0055] in is the learning rate, which is used to control the speed of adjusting the regression coefficient, and its value is between 0 and 1; What needs to be explained about the above scheme is that the function of the data acquisition module is to collect relevant operation data from various equipment and sensors of the water plant in real time through the network communication system, such as water intake, water supply, sludge discharge, backwash water, etc.; these data will be transmitted to other modules of the system after standardized processing, providing accurate basic data for subsequent calculations; the timeliness and accuracy of data acquisition are the prerequisites for the effective operation of the entire dispatching system, especially in the complex water plant environment, the real-time update of data enables the system to respond to changes quickly and make adjustments; The scheduling model calculation module calculates water volume and liquid level based on the collected data. It uses complex algorithms and models to predict reservoir liquid level changes and generate corresponding water intake adjustment instructions. Liquid level prediction not only ensures that the reservoir liquid level is always maintained within a safe range, but also dynamically adjusts water volume according to different process requirements to improve the operating efficiency of the water plant. For example, in certain periods, the water plant may need more water intake to cope with demand peaks, while in energy-saving mode, the water intake control is more precise to reduce energy consumption. The water inflow adjustment module decides whether to adjust the water inflow according to the results of the scheduling model calculation module. It not only automatically calculates the water inflow, but also determines whether it needs to be adjusted according to the actual situation (such as mud discharge, backwashing, etc.). In the case of large changes in reservoir liquid level, the system can automatically adjust the water inflow to avoid manual intervention, reduce the occurrence of human errors, avoid water resource waste and ensure the stable operation of the water plant. The control instruction issuing module realizes the automatic adjustment of water inflow and liquid level through the communication interface with the PLC unit; as the core of the water plant automation control system, the PLC unit can receive instructions from the scheduling model calculation module and adjust the opening of the water flow valve in real time to control the water inflow and liquid level; The energy-saving mode and stable mode settings in the scheduling mode selection module can be automatically switched according to different operating requirements; for example, during peak demand periods, the system will switch to stable mode to ensure the stable operation of various water treatment processes in the water plant; and during non-peak periods, the energy-saving mode reduces the energy consumption of water output by maintaining a high reservoir level. This mode switching not only improves the flexibility of water plant operation, but also optimizes the use of resources; The data analysis and optimization module can not only predict the changes in reservoir liquid level through multivariate regression analysis, dynamic time window optimization and real-time feedback mechanism, but also optimize the dispatching model based on historical data, making the adjustment of water intake more accurate. When long-term dispatching optimization is required, historical data can be used to find the operation rules of the water plant and adjust the dispatching strategy in real time. The network communication module connects RSLogix5000 with the PLC unit to ensure data interaction among various devices in the system. This module provides efficient communication support for the system, making it possible to transmit real-time data and issue instructions, further enhancing the automation level of the system. In addition, regarding network communication and scheduling logic programming, it should be noted that according to the determined scheduling logic, a scheduling model is established. First, the raw water PLC (L32E series) is connected via Ethernet using RSLogix5000, and MSG instructions are added to establish communication between PLCs, and the relevant parameters of the filter tank PLC (L61 series) and the alum adding PLC (L551C series) are read, so as to realize the real-time acquisition of the parameters required for model calculation; After establishing the communication, programming is performed in the raw water PLC. There are 6 programs in total, which are divided into 1 main program for calling subroutines and 5 subroutines for realizing functions: 1) Main program: calls sub-function program; 2) Mode selection: divided into automatic mode and manual mode. Automatic mode means automatic calculation using the scheduling model. You can choose stable mode or energy-saving mode. The energy-saving mode aims to keep the reservoir level high to reduce the energy consumption of water output. The stable mode aims to stabilize the water inflow of the whole plant. Through the regulation and storage of the clear water tank, it ensures the stability of water production process links such as dosing, clarifier water inflow and sludge discharge, filter water inflow and backwashing. Manual mode means switching to traditional manual scheduling. 3) Parameter setting: Manually set the target parameters and limit parameters of energy-saving mode / stable mode; set the sludge discharge plan half an hour in advance according to the operating conditions; read the filter tank operation time and consider the backwash water consumption in advance for model calculation; 4) Calculation of backwash water volume: When the filter tank has been running for 48 hours, start to lift the backwash water to the water tower. By reading the filter tank running time, consider the amount of water required for backwashing half an hour in advance; 5) Calculation of mud discharge water volume: Half an hour before mud discharge, the staff on duty inputs mud discharge signal into the upper computer, and the model starts to calculate the mud discharge water volume half an hour later; 6) Valve opening calculation and control: Through the backwash water volume calculation and sludge discharge water volume calculation, as well as the inlet and outlet water flow, reservoir liquid level and other parameters, calculate the time t1 for the reservoir level to reach the target level and the water inlet volume that needs to be adjusted, control the valve opening, adjust it multiple times, and gradually approach the water inlet volume, so as to achieve automatic scheduling of the water inlet volume target according to the water supply, reservoir liquid level target and intermediate process water loss, reduce the workload of operating personnel, and effectively improve scheduling accuracy and water supply efficiency.

[0056] Regarding page development, after completing the scheduling logic programming work, add a smart scheduling interface to the host computer, including "energy-saving mode" and "stable mode" parameter input, model-related calculation values, mud discharge and backwashing conditions, reservoir level trend chart, etc. The operator can set relevant parameters on the host computer, the model automatically calculates, and sends control instructions through the PLC to adjust the water intake; for Figure 1-Figure 3 It should be noted that the implementation method of the present invention mainly collects production operation data in real time through network communication, establishes a scheduling model in the water plant industrial control network by programming, and collects and calculates in real time and automatically, replacing manual operation; Figure 1-Figure 3 The horizontal axis is time, Figure 1-Figure 3 The vertical axis in is the liquid level.

[0057] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A water plant intelligent dispatching system, including a data acquisition module, a dispatching model calculation module, a water intake adjustment module, and a control instruction issuing module, characterized in that: The data acquisition module is used to obtain the dispatching operation data from various equipment and sensors in the water plant in real time, and use standardized data format to structure the dispatching operation data, and transmit it to other modules of the system through network communication; the dispatching operation data includes water inlet, water outlet, sludge water volume, backwash water volume, sampling water volume, pressure water volume, and reservoir liquid level; The dispatch model calculation module calculates water volume and liquid level based on the collected dispatch operation data through the preset dispatch evaluation model; the dispatch model calculation module generates corresponding water inflow adjustment instructions based on the water volume calculation and liquid level prediction to maintain the reservoir liquid level within the preset target range; The water inflow adjustment module is used to calculate the results of the dispatching evaluation model and determine whether the water inflow needs to be adjusted based on the calculated results, thereby controlling the reservoir liquid level; The control instruction issuing module realizes data flow by communicating with the PLC unit of the water plant, and obtains the scheduling and operation data of the water plant in real time based on the interaction between the PLC unit and the host computer; the control instruction issuing module obtains the scheduling calculation results according to the adjustment instructions of the scheduling model calculation module, generates corresponding control instructions based on the scheduling calculation results, and transmits them to the PLC to execute automatic adjustment of water inlet and liquid level.

2. According to claim 1, the intelligent dispatching system for a water plant is characterized by: The scheduling evaluation model of the scheduling model calculation module includes a water volume model; The water volume model includes: ， in is the water inflow to the water plant; The amount of water supplied to the water plant; To calculate time; is the reservoir area; is the change of reservoir liquid level; is the amount of mud water discharged; It is the time for mud discharge; is the number of filter tanks; is the backwash water volume; is the amount of water sampled; The pressure water volume for factory use.

3. The intelligent dispatching system for a water plant according to claim 2 is characterized in that: The scheduling logic of the scheduling model calculation module includes: Obtain scheduling operation data based on the data acquisition module; Determine whether sedimentation tank sludge discharge, filter tank backwashing, sampling water volume and pressure water volume adjustment are required in the next cycle; Based on water plant inflow and outflow Under the condition of no change, the reservoir level change trend for: ， Calculate the time when the reservoir liquid level reaches the control limit ,pass Refers to the difference between the water intake of a water plant and the water supply of a water plant; ， ， Determine whether the water inflow of the water plant needs to be adjusted: based on the time parameters preset by the system ,when , then the water plant water inlet control instruction is issued; when , then the water inflow of the water plant will not be adjusted; The model obtains the current liquid level , Next determination time target liquid level , calculate the amount of water that needs to be adjusted to reach the target level within the set time : ， when , do not adjust the water plant water intake; when ,according to Adjust; when , then according to ;in is the minimum adjustable water volume; The maximum adjustable water volume; The water volume calculation is now completed.

4. The intelligent dispatching system for a water plant according to claim 3 is characterized in that: Also includes a scheduling mode selection module; The scheduling mode selection module includes energy-saving mode and stable mode; Energy-saving mode is used to keep the reservoir level continuously high, with the goal of reducing the energy consumption of water output; The stable mode is used to keep the water inflow stable so that the water treatment process can run stably.

5. The intelligent dispatching system for a water plant according to claim 4 is characterized in that: It also includes a user interface module, a data analysis and optimization module, and a network communication module; The user interface module is used to provide operators with real-time viewing of reservoir level changes, scheduling model calculation results, and mud discharge and backwashing information; users set the target parameters of energy-saving mode or stable mode on the interface according to the user interface module, and the system executes the scheduling task; The data analysis and optimization module analyzes historical data to find out the water volume relationship and liquid level change rules of each process link in the water plant, and optimizes the scheduling model accordingly; The network communication module is used for data communication between various devices in the system; the network communication module is connected to the PLC unit by using RSLogix5000 to realize real-time data collection and instruction issuance. Through network communication, the system adjusts the water intake in real time.

6. The intelligent dispatching system for a water plant according to claim 5 is characterized in that: The data analysis and optimization module uses multiple regression analysis, dynamic time window optimization and real-time feedback mechanism to predict the change of reservoir liquid level based on the dispatching and operation data of the water plant as input variables, and optimizes the adjustment of water intake according to the prediction results; Through the data analysis and optimization module, the reservoir level changes are analyzed and the Indicates at time Predicted change in reservoir level; ， in is the regression coefficient; is the number of input variables; For the input variables; It is a dynamic adjustment item; Optimize the regression coefficients by minimizing the mean square error MSE , whose expression is: ， in is the actual reservoir level change; is the number of historical data points; are the parameter sets of regression coefficients and adjustment terms respectively.

7. The intelligent dispatching system for a waterworks according to claim 6 is characterized by: For the calculation of regression coefficients in the data analysis and optimization module, the optimization of regression coefficients is calculated by the normal equation: ， ， in is the regression coefficient vector, which indicates the influence of each input variable on the change of reservoir level; is the input matrix, Contains data for all time steps, each column represents an input variable, and the shape is ,in is the number of data points; is the transpose of the input matrix; is the output vector, which contains the actual liquid level change data at each time step and has the shape of , that is, the liquid level change corresponding to each time step.

8. The intelligent dispatching system for a waterworks according to claim 7 is characterized in that: Based on the predicted results of liquid level changes in the data analysis and optimization module, Indicates at time The water intake volume that needs to be adjusted at all times is used to calculate the water intake volume; ， in represents the output of regression analysis; is the number of input variables used in the regression analysis; is the index of the input variable; Constraints are imposed on water intake adjustment based on data analysis and optimization modules; ， in Indicates taking and The valley between Indicates taking and The peak between Real-time adjustment of regression coefficients based on feedback mechanism; ， in is the learning rate.

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