Intelligent balance control method, system and equipment for water volume of water plant and storage medium

Abstract

The invention relates to the technical field of water production of waterworks, in particular to an intelligent balance control method, system and equipment for the water volume of a water plant and a storage medium, and the method comprises the steps: S1, obtaining historical data of the water plant, carrying out the preprocessing, obtaining the preprocessed historical data, and carrying out the early warning of abnormal data; s2, inputting the preprocessed historical data into a pre-trained water volume prediction network to obtain the optimal water supply volume of the water plant at the current stage; s3, in response to the water volume control mode selected by the user, adjusting corresponding weights in the water volume prediction network in different modes to obtain the optimal water supply volume of the water plant in each mode at the current stage; and S4, sending the optimal water supply quantity of the water plant in each mode at the current stage to a corresponding water pump or valve through the intelligent control terminal so as to adjust the corresponding water supply quantity of the water plant.

Description

Technical Field

[0001] The present invention relates to the technical field of water production in waterworks, and particularly to an intelligent balance control method, system, device and storage medium for the water volume of a waterworks. Background Art

[0002] During the entire water supply process, the waterworks undertakes extremely crucial and complex tasks. On the one hand, it is necessary to strictly control the water production cost while ensuring that the quality of the discharged water meets the relevant national standards; on the other hand, it must also ensure the stability of water supply to meet the continuous water use needs of urban production and life. Currently, the waterworks has actively carried out a large number of technical researches in terms of meeting the discharge water quality standards and reducing the water production cost, and has achieved a series of application results. By adopting advanced water treatment processes and equipment and continuously optimizing the water production process, rich experience has been accumulated in improving water quality and reducing costs.

[0003] However, in terms of ensuring the stability of water supply, the current dispatching method of the waterworks still has obvious deficiencies. Currently, most of the dispatching work during the operation of the waterworks relies on traditional experience. On-site staff mainly manually adjust the water intake of the waterworks based on the time distribution law of the peak and valley stages of the water supply in the past waterworks and the current water supply pressure value. This judgment method based on experience and the current situation of the waterworks lacks accurate quantitative analysis and scientific prediction model support, resulting in difficult to achieve precise and stable regulation of the water volume of the waterworks. During the peak and low water use periods, there are often situations where the clear water tank of the waterworks overflows and wastes water resources, or is emptied due to insufficient water supply, thus affecting the normal water supply. In addition, the process control of the waterworks has extremely high requirements for the stability of the water intake. The frequent fluctuations of the water intake will have an adverse impact on each production link of the waterworks, greatly increasing the difficulty of optimizing the operation control of the waterworks process. Summary of the Invention

[0004] In order to overcome the deficiencies of the prior art, the present application provides an intelligent balance control method, system, device and storage medium for the water volume of a waterworks, aiming to break through the limitations of traditional experience-based dispatching, achieve precise regulation of the water intake volume of the waterworks, and provide strong support for the stable operation and efficient management of the waterworks.

[0005] The technical means adopted by the present invention to solve its technical problems is: an intelligent balance control method for the water volume of a waterworks, the improvement lies in that it includes the following steps: Step S1, obtain the historical data of the waterworks and perform preprocessing to obtain the preprocessed historical data, and at the same time give early warnings for abnormal data; Step S2, input the preprocessed historical data into a pre-trained water volume prediction network to obtain the optimal water supply volume at the current stage of the waterworks; Step S3: In response to the water volume control mode selected by the user, adjust the corresponding weights in the water volume prediction network under different modes to obtain the optimal water supply volume of the water plant at the current stage under each mode; Step S4: Send the optimal water supply volume of the water plant at the current stage under each mode to the corresponding water pump or valve through the intelligent control terminal to adjust the water supply volume of the water plant accordingly.

[0006] After step S2 in the above technical solution, the method further includes: Step S5: Compare and analyze the actual water supply volume with the predicted water supply volume, calculate the prediction error. When the prediction error exceeds the set threshold, feedback the actual water supply volume data, error data, etc. to the water volume prediction network to retrain the network model parameters.

[0007] Step S1 in the above technical solution includes: Step S101: Convert the acquired real-time data into high-quality data content through data cleaning, data integration, data reduction, and data transformation; Step S102: For the preprocessed data, when important data is abnormal, conduct data early warning through, including but not limited to, redundant data replacement, similar data replacement, historical data simulation, and abnormal timing, and set a three-level alarm mode: attention required mode, processing required mode, and system cut-off mode; Step S103: Based on the alarm mode, switch the system operation state, where the attention required mode and the processing required mode do not need to cut off the water supply system, but need to send alarm information to guide relevant personnel to handle it; the system cut-off mode needs to autonomously cut off the water supply system and notify relevant staff to handle the on-site situation.

[0008] The water volume prediction network in step S2 of the above technical solution includes a water plant water supply volume prediction model, a water plant water production process simulation model, and a multi-objective greedy optimization model. Among them, The water plant water supply volume prediction model predicts the hourly water supply volume of the water plant in the next 24h based on the historical water supply volume of the water plant: (1); Among them, a t,i is the predicted water supply volume, t is the date, i represents the time, and the value range is 0 - 23, m t,j is the input data for performing water supply volume prediction, j represents the influence factor number, including but not limited to historical water supply volume, historical meteorological data, historical date type, and F() is the constructed time series prediction model; The water production process simulation model of the water plant, based on the water volume, liquid level and equipment signal data in the operation process of the water plant, real-time simulates the water supply volume and the change of the clear water tank liquid level based on the change of the raw water volume in the operation process of the water plant: (2); where b t,k is the water supply volume, c t,k is the clear water tank liquid level, n t,k is the equipment signal data and the online instrument data of other water plants, and G() is the constructed sequential water production process simulation model, which is used to predict the change of the water supply volume and the clear water tank liquid level at the next moment based on the data of the previous moment; The multi-objective greedy optimization model, by connecting the water supply volume prediction model of the water plant and the water production process simulation model of the water plant, and based on the clear water tank liquid level target, water supply energy consumption and power consumption index required for operation, generates the optimal value of the raw water volume of the water plant: (3); (4); where f t,k is the optimal value of the raw water volume output by the model, t represents the weight set for the corresponding control target, x i represents the corresponding control target, including but not limited to the clear water tank liquid level rising target, the power consumption reduction target of water supply, and the water supply cost reduction target; y i represents the corresponding operation target, including but not limited to the upper and lower limits of the clear water tank liquid level control and the upper and lower limits of the equipment control in the process operation.

[0009] In the above technical solution, the multi-objectives include but are not limited to: stable intake water volume, clear water tank liquid level within a reasonable range, reduction of water supply unit consumption, and reasonable use of peak-valley electricity prices for water production, and the weights set for its single parameters are adjusted and optimized based on specific water plants.

[0010] In the above technical solution, the water volume control mode in step S3 is generated by the following logic: Based on the control target of the stable intake water volume mode, fully meet the requirement that the fluctuation range of the intake water volume does not exceed the maximum water volume fluctuation range required by the water plant operation process, and ensure that the clear water tank liquid level does not run dry or overflow during the operation of the water plant; Based on the control target of the lowest energy consumption mode, under the condition of meeting the stable intake water volume mode, reduce the water production unit consumption during the operation of the water plant, that is, the power consumption required per ton of water, and its targets include but are not limited to raising the clear water tank liquid level of the water plant to reduce the energy consumption of the water supply pump; Based on the control objective of the lowest electricity cost mode, under the condition of meeting the stable water inflow mode, using the peak-valley electricity price strategy, it is realized that as little water is produced as possible during the peak electricity price period and as much water is produced as possible during the valley electricity price period, so as to achieve the lowest electricity cost required in the operation process of the water plant.

[0011] In the above technical solution, the intelligent control terminal includes, but is not limited to, the combination of a PLC control cabinet and corresponding lines, and at the same time has the functions of remote monitoring and manual intervention. Users can remotely monitor the water supply volume of the water plant through a mobile terminal or a monitoring center. When an emergency occurs, the operation parameters of the water pump or valve can be manually adjusted to achieve emergency intervention in the water supply volume of the water plant.

[0012] The technical means adopted by the present invention to solve its technical problems is: an intelligent water volume balance control system for a water plant, including: A data processing and early warning module, which is used to obtain the historical data of the water plant and perform preprocessing to obtain the preprocessed historical data, and at the same time give early warnings for abnormal data; An intelligent water volume prediction module, which is used to input the preprocessed historical data into a pre-trained water volume prediction network to obtain the optimal water supply volume of the water plant at the current stage; A control mode selection module, which is used to respond to the water volume control mode selected by the user, adjust the corresponding weights in the water volume prediction network under different modes, and obtain the optimal water supply volume of the water plant at the current stage under each mode; An intelligent water volume control module, which sends the optimal water supply volume of the water plant at the current stage under each mode to the corresponding water pump or valve through the intelligent control terminal to adjust the corresponding water supply volume of the water plant.

[0013] The technical means adopted by the present invention to solve its technical problems is: a device, including: at least one processor and at least one memory, wherein, Program instructions or codes are stored on the memory; The program instructions or codes are loaded and executed by the processor, so that the electronic device realizes the intelligent water volume balance control method for the water plant as described above.

[0014] The technical means adopted by the present invention to solve its technical problems is: a storage medium, on which program instructions or codes are stored, and the program instructions or codes are loaded and executed by a processor to realize the intelligent water volume balance control method for the water plant as described above.

[0015] The beneficial effects of the present invention are: A method for intelligent balance control of water volume in a water plant is proposed for the balance of water production and supply in the water plant, realizing the prediction and control of the water intake volume of the water plant; in addition, aiming at the application requirements of different water plants, multiple intelligent control modes for water balance are proposed for water volume control, which better realizes the intelligent control of the water volume in the water plant and achieves the corresponding energy-saving and consumption-reducing goals. Description of the Drawings

[0016] Figure 1 It is a flowchart of a method for intelligent balance control of water volume in a water plant shown in an embodiment of the present invention; Figure 2 It is a flowchart of step S1 shown in an embodiment of the present invention; Figure 3 It is a flowchart of another method for intelligent balance control of water volume in a water plant shown in an embodiment of the present invention; Figure 4 It is a structural block diagram of a system for intelligent balance control of water volume in a water plant shown in an embodiment of the present invention; Figure 5 It is a schematic structural diagram of an electronic device shown in an embodiment of the present invention. Detailed Embodiments

[0017] The present invention will be further described below in conjunction with the drawings and embodiments.

[0018] The concept, specific structure and technical effects generated by the present invention will be clearly and completely described below in conjunction with the embodiments and drawings to fully understand the purpose, features and effects of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, other embodiments obtained by those skilled in the art without creative efforts fall within the scope of protection of the present invention. In addition, all the connection / connection relationships involved in the patent do not refer to the direct connection of components alone, but refer to the more optimal connection structure that can be formed by adding or reducing connection accessories according to the specific implementation situation. The various technical features in the present invention can be combined with each other without conflict.

[0019] As Figure 1 shown, the present application provides a method for intelligent balance control of water volume in a water plant, including the following steps: Step S1: Obtain the historical data of the water plant and perform preprocessing to obtain the preprocessed historical data, and at the same time give early warnings for abnormal data.

[0020] Specifically, the historical data of the water plant includes the historical water supply volume of the water plant, meteorological data, date type, water volume, liquid level and equipment operation status data during the operation of the water plant, and the target value of the operation of the water plant.

[0021] In a possible implementation manner, asFigure 2 As shown in Figure 2 , step 1 includes: Step S101: Convert the acquired real-time data into high-quality data content through data cleaning, data integration, data reduction, and data transformation.

[0022] Among them, data cleaning specifically includes: identifying and removing duplicate data, marking and processing missing data. For data fields with a missing rate lower than 10%, the mean filling method is used for filling; for data fields with a missing rate higher than 10%, machine learning algorithms are used for predictive filling. The data integration is to integrate the water plant operation data, meteorological data, and user water consumption data from different data sources to eliminate data conflicts. The data reduction reduces the data dimension and data volume through attribute selection and value reduction. The data transformation includes standardizing the data to make it conform to a normal distribution with a mean of 0 and a standard deviation of 1.

[0023] Step S102: For the preprocessed data, when important data is abnormal, conduct data warning through, but not limited to, redundant data replacement, similar data replacement, historical data simulation, and abnormal timing, and set a three-level alarm mode: attention required mode, processing required mode, and system cut-off mode.

[0024] Among them, the attention required mode means that the data is abnormal, but there are redundant instruments to supplement. It is necessary to manually judge whether it is necessary to inspect the instrument on site, but it does not affect the system operation. The processing required mode means that the data has a relatively serious abnormality, but fitting can be provided for compensation. It is necessary to manually inspect the instrument and network on site and other issues, and it will not affect the system operation in a short time. The system cut-off mode means that the data abnormality cannot be compensated, and it is necessary for manual intervention in the system for control, and check the on-site instrument and equipment conditions for repair. Step S103: Based on the alarm mode, switch the system operation state. Among them, the attention required mode and the processing required mode do not need to cut off the water supply system, but need to send alarm information to guide relevant personnel to handle it. The system cut-off mode needs to independently cut off the water supply system and notify relevant staff to handle the on-site situation.

[0025] Through the above embodiments, the detailed data cleaning, integration, reduction, and transformation operations in step S101 can effectively improve data quality. Identifying and removing duplicate data avoids data redundancy interference. Appropriate filling methods are used for data fields with different missing rates, ensuring data integrity. Data integration eliminates conflicts between different data sources, making the data more consistent. Data reduction reduces the data dimension and volume, improving data processing efficiency. The standardized data transformation helps the subsequent model better learn data features, laying a solid foundation for accurate water volume prediction.

[0026] Meanwhile, the set three - level alarm mode and the corresponding processing methods achieve precise hierarchical control of abnormal data. The "attention - needed mode" only prompts manual judgment when there are minor anomalies in the data and there are redundant instruments for supplementation, reducing unnecessary interference and ensuring the continuity of the normal operation of the system; the "processing - needed mode" guides manual inspection in a timely manner to avoid abnormal expansion and affect system operation when dealing with relatively serious anomalies under the condition of fit - compensable; the "system - cut - off mode" quickly cuts off the system when the data anomaly cannot be compensated, preventing serious production accidents caused by incorrect data and ensuring the safety and reliability of the water plant operation.

[0027] Step S2: Input the pre - processed historical data into a pre - trained water volume prediction network to obtain the optimal water supply volume at the current stage of the water plant.

[0028] In a possible implementation, the water volume prediction network includes a water plant water supply volume prediction model, a water treatment process simulation model of the water plant, and a multi - objective greedy optimization model. Among them, The water plant water supply volume prediction model predicts the hourly water supply volume of the water plant in the next 24h based on the historical water supply volume of the water plant: (1); where a t,i is the predicted water supply volume, t is the date, i represents the time, with a value range of 0 - 23, m t,j is the input data for performing water supply volume prediction, j represents the influence factor number, including but not limited to historical water supply volume, historical meteorological data, historical date type, and F() is the constructed time - series prediction model.

[0029] Optionally, the time - series prediction model F() is constructed using a long short - term memory network (LSTM). By extracting and analyzing the input data such as historical water supply volume, historical meteorological data, and historical date type, the model parameters are trained to improve the accuracy of predicting the hourly water supply volume of the water plant in the next 24h.

[0030] The water treatment process simulation model of the water plant, based on the water volume, liquid level, and equipment signal data during the operation of the water plant, real - time simulates the water supply volume based on the change of raw water volume and the change of clear water tank liquid level during the operation of the water plant: (2); where b t,k is the water supply volume, c t,k is the clear water tank liquid level, n t,k is the equipment signal data and other on - line instrument data of the water plant, and G() is the constructed time - series water treatment process simulation model, which is used to predict the water supply volume and the change of clear water tank liquid level at the next moment based on the data of the previous moment.

[0031] Optionally, the sequential water production process simulation model G() is constructed based on a hybrid architecture of convolutional neural network (CNN) and recurrent neural network (RNN). CNN is used to extract the spatial features of equipment signal data, and RNN is used to analyze the time series characteristics of water volume and liquid level, so as to achieve accurate real-time simulation of the changes in water supply volume and clear water tank liquid level during water plant operation.

[0032] The multi-objective based greedy optimization model generates the optimal value of the raw water volume of the water plant by connecting the water supply volume prediction model of the water plant and the water production process simulation model of the water plant, and based on the clear water tank level target, water supply energy consumption and water supply power consumption indicators required for operation: (3); (4); Among them, f t,k is the optimal value of raw water volume output by the model, t represents the weight set for the corresponding control target, and x i Indicates the corresponding control target, including but not limited to the target of raising the liquid level of the clean water tank, the target of reducing water supply power consumption, and the target of reducing water supply costs; i Indicates the corresponding operating objectives, including but not limited to the upper and lower limits of the clear water tank level control and the upper and lower limits of the equipment control for process operation.

[0033] Optionally, the weight t set for the corresponding control target is determined by the analytic hierarchy process (AHP), which first constructs a hierarchical model of the control target, then determines the relative importance of each control target through expert scoring, and then calculates the weight t corresponding to each control target to achieve the reasonable generation of the optimal value of the raw water volume of the water plant.

[0034] Through the above embodiments, the water volume prediction network realizes the full-chain intelligence of the water supply system from demand prediction, process simulation to optimization control. On the basis of ensuring water supply safety, it effectively reduces operating costs and improves the refined management level of the water plant, with significant economic and social benefits.

[0035] Step S3, in response to the water volume control mode selected by the user, the corresponding weights in the water volume prediction network under different modes are adjusted to obtain the optimal water supply volume under each mode at the current stage of the water plant.

[0036] In a possible implementation, the water volume control mode is generated using the following logic: Based on the control target of the stable water inflow mode, we strive to ensure that the fluctuation range of water inflow does not exceed the maximum water volume fluctuation range required by the water plant operation process, and ensure that the liquid level in the clear water tank does not evacuate or overflow during the operation of the water plant; Based on the control objective of the lowest energy consumption mode, under the condition of meeting the stable water inflow mode, reduce the unit water production power consumption during the operation of the water plant, that is, the power consumption required per ton of water. Its objectives include but are not limited to raising the liquid level of the clear water tank in the water plant to reduce the energy consumption of the water supply pump; Based on the control objective of the lowest electricity cost mode, under the condition of meeting the stable water inflow mode, use the peak-valley electricity price strategy to minimize water production during the peak electricity price period and maximize water production during the valley electricity price period, so as to minimize the electricity cost required during the operation of the water plant.

[0037] Optionally, the multi-objectives include but are not limited to: stable water inflow, the liquid level of the clear water tank within a reasonable range, reduction of the unit water supply power consumption, and reasonable use of the peak-valley electricity price for water production. The weights set for its individual parameters are adjusted and optimized based on the specific water plant.

[0038] Through the above embodiments, through multi-dimensional objective optimization, for the water supply mode selected according to the real-time needs of users, by adjusting the weight t of the control objective setting in the corresponding mode in the above formula (3), it is possible to significantly reduce the operation cost and energy consumption while ensuring water supply safety, and at the same time improve the intelligent level and management efficiency of the system, with good economic and environmental benefits.

[0039] Step S4: Send the optimal water supply volume of each mode at the current stage of the water plant to the corresponding water pump or valve through the intelligent control terminal to adjust the water supply volume of the water plant accordingly.

[0040] In a possible implementation manner, the intelligent control terminal includes but is not limited to the combination of a PLC control cabinet and the corresponding lines, and at the same time has the functions of remote monitoring and manual intervention. Users can remotely monitor the water supply volume of the water plant through a mobile terminal or a monitoring center. When an emergency occurs, the operation parameters of the water pump or valve can be manually adjusted to achieve emergency intervention in the water supply volume of the water plant.

[0041] Through the above embodiments, the intelligent control terminal significantly improves the control accuracy, emergency response ability and management efficiency of the water supply system through automation, remote control and intelligent means, and at the same time reduces the operation and maintenance cost and safety risk, providing strong support for the modern operation of the water plant.

[0042] In another possible implementation manner, as Figure 3 shown, after step S2, the method further includes: Step S5: Compare and analyze the actual water supply volume with the predicted water supply volume, calculate the prediction error. When the prediction error exceeds the set threshold, feedback the actual water supply volume data, error data, etc. to the water volume prediction network to retrain the network model parameters.

[0043] By comparing the actual and predicted water volumes in real time, when the error exceeds a threshold (e.g., ±5%), model retraining is triggered, which can improve the long-term prediction accuracy by 15% - 20%. At the same time, the feedback mechanism enables the water volume prediction network to have a complete intelligent closed loop of "perception - decision - execution - learning", continuously improving performance without manual intervention, and significantly enhancing the intelligent level and long-term operation value of the water supply system.

[0044] The following is an embodiment of the system of the present application, which can be used to execute the intelligent water volume balance control method for water plants involved in the present application. For details not disclosed in the embodiment of the system of the present application, please refer to the method embodiment of the intelligent water volume balance control method for water plants involved in the present application.

[0045] Please refer to Figure 4 , an embodiment of the present application provides an intelligent water volume balance control system 20 for a water plant. The system 20 includes: a data processing and warning module 201, an intelligent water volume prediction module 202, a control mode selection module 203, and an intelligent water volume control module 204.

[0046] Among them, the data processing and warning module 201 is used to obtain the historical data of the water plant and perform preprocessing to obtain the preprocessed historical data, and at the same time give warnings for abnormal data; The intelligent water volume prediction module 202 is used to input the preprocessed historical data into a pre-trained water volume prediction network to obtain the optimal water supply volume at the current stage of the water plant; The control mode selection module 203 is used to respond to the water volume control mode selected by the user, adjust the corresponding weights in the water volume prediction network under different modes, and obtain the optimal water supply volume under each mode at the current stage of the water plant; The intelligent water volume control module 204 sends the optimal water supply volume under each mode at the current stage of the water plant to the corresponding water pump or valve through an intelligent control terminal to adjust the water supply volume of the corresponding water plant.

[0047] It should be noted that when the intelligent water volume balance control system for the water plant provided in the above embodiment performs intelligent water volume balance control for the water plant, only the above-mentioned division of each functional module is used for illustration. In actual application, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the intelligent water volume balance control system for the water plant will be divided into different functional modules to complete all or part of the functions described above. The above-mentioned modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0048] In addition, the intelligent water volume balance control system and the method embodiment of the intelligent water volume balance control method for a water plant provided in the above embodiments belong to the same concept. The specific ways in which each module performs operations have been described in detail in the method embodiment, and will not be elaborated here.

[0049] Please refer to Figure 5 , an electronic device 4000 is provided in an embodiment of the present application.

[0050] In Figure 5 , among them, the data interaction between the processor 4001 and the memory 4003 can be realized through at least one communication bus 4002. The communication bus 4002 may include a path for transmitting data between the processor 4001 and the memory 4003. The communication bus 4002 can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 4002 can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, Figure 5 only a thick line is used to represent it in

[0051] Optionally, the electronic device 4000 may further include a transceiver 4004, and the transceiver 4004 can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data, etc. It should be noted that in practical applications, the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation to the embodiments of the present application.

[0052] The processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logical blocks, modules, and circuits described in connection with the disclosure of this application. The processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc. The memory 4003 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory), or other type of dynamic storage device that can store information and instructions. It may also be an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or any other medium that can be used to carry or store desired program instructions or code in the form of instruction or data structures and can be accessed by the electronic device 4000, but is not limited thereto.

[0053] Program instructions or code are stored on the memory 4003, and the processor 4001 can read the program instructions or code stored in the memory 4003 through the communication bus 4002.

[0054] When the program instructions or code are executed by the processor 4001, the intelligent water volume balance control method for the water plant in the above embodiments is implemented.

[0055] In addition, an embodiment of the present application provides a storage medium on which program instructions or code are stored, and the program instructions or code are loaded and executed by a processor to implement the intelligent water volume balance control method for the water plant as described above.

[0056] In an embodiment of the present application, a computer program product is provided. The computer program product includes program instructions or code, which are stored in a storage medium. A processor of an electronic device reads the program instructions or code from the storage medium, loads and executes the program instructions or code, so that the electronic device implements the intelligent water volume balance control method for a water plant as described above.

[0057] The above is a specific description of the preferred embodiment of the present invention, but the present invention is not limited to the above embodiment. Those skilled in the art can make various equivalent deformations or substitutions without departing from the spirit of the present invention, and these equivalent deformations or substitutions are all included in the scope defined by the claims of the present application.

Claims

1. An intelligent water volume balance control method for a waterworks, characterized in that, The following steps are involved: Step S1, obtaining historical data of the water plant and preprocessing it to obtain preprocessed historical data, and issuing an early warning for abnormal data; Step S2, inputting the pre-processed historical data into a pre-trained water volume prediction network to obtain the optimal water supply volume of the water plant at the current stage; Step S3, in response to the water volume control mode selected by the user, adjusting the corresponding weights in the water volume prediction network under different modes to obtain the optimal water supply volume under each mode at the current stage of the water plant; Step S4: Send the optimal water supply volume in each mode of the water plant at the current stage to the corresponding water pump or valve through the intelligent control terminal to adjust the water supply volume of the corresponding water plant.

2. The intelligent balance control method for water volume in a waterworks according to claim 1, characterized in that, After step S2, the method further includes: Step S5, compare and analyze the actual water supply volume with the predicted water supply volume, calculate the prediction error, and when the prediction error exceeds the set threshold, feed back the actual water supply volume data and error data to the water volume prediction network to retrain the network model parameters.

3. The intelligent water volume balance control method for a waterworks according to claim 1, characterized in that The step S1 comprises: Step S101, converting the acquired real-time data into high-quality data content through data cleaning, data integration, data reduction and data transformation; Step S102: for the pre-processed data, when important data is abnormal, data warning is performed through methods including but not limited to redundant data replacement, similar data replacement, historical data simulation, and abnormal timing, and three levels of alarm modes are set: attention mode, processing mode, and system cut-off mode; Step S103: Based on the alarm mode, the system operation state is switched. The attention mode and the processing mode do not need to cut off the water supply system, but an alarm message needs to be issued to guide relevant personnel to handle it; the system cut-off mode requires autonomously cutting off the water supply system and notifying relevant personnel to deal with the on-site situation.

4. The intelligent balance control method for water volume in a water plant according to claim 1, characterized in that The water volume prediction network in step S2 includes a water plant water supply prediction model, a water plant water production process simulation model, and a multi-objective greedy optimization model, wherein: The water supply prediction model of the water plant predicts the hourly water supply of the water plant in the next 24 hours based on the historical water supply of the water plant: （1）； where a t,i is the predicted water supply volume, t is the date, i represents the time, with a value range of 0 - 23, m t,j is the input data for performing water supply volume prediction, j represents the influencing factor number, including but not limited to historical water supply volume, historical meteorological data, historical date type, and F() is the constructed time series prediction model; The water production process simulation model of the water plant is based on the water volume, liquid level and equipment signal data during the operation of the water plant, and simulates the water supply volume and the change of the clear water tank liquid level based on the change of the raw water volume during the operation of the water plant in real time: （2）； Among them, b t,k is the water supply volume, c t,k is the clear water tank liquid level, n t,k is the equipment signal data and the online instrument data of other water plants. G() is the constructed time-series water production process simulation model, which is used to predict the changes in the water supply volume and the clear water tank liquid level at the next moment based on the data at the previous moment; The multi-objective based greedy optimization model generates the optimal value of the raw water volume of the water plant by connecting the water supply volume prediction model of the water plant and the water production process simulation model of the water plant, and based on the clear water tank level target, water supply energy consumption and water supply power consumption indicators required for operation: （3）； （4）； Among them, f t,k is the optimal value of the raw water flow output by the model, t represents the weight set for the corresponding control target, and x i represents the corresponding control target, including but not limited to the target of raising the clear water tank level, the target of reducing the power consumption for water supply, and the target of reducing the water supply cost; y i represents the corresponding operation target, including but not limited to the upper and lower limits of the clear water tank level control and the upper and lower limits of the equipment control for the process operation.

5. The intelligent balance control method for water volume in a waterworks according to claim 4, wherein The multiple objectives include but are not limited to: stable water inflow, clear water tank level within a reasonable range, reduced water supply unit consumption, and reasonable use of peak and valley electricity prices for water production. The weights set for individual parameters are adjusted and optimized based on the specific water plant.

6. The intelligent balance control method for water volume in a water plant according to claim 1, characterized in that, The water volume control mode in step S3 is generated using the following logic: Based on the control target of the stable water inflow mode, we strive to ensure that the fluctuation range of water inflow does not exceed the maximum water volume fluctuation range required by the water plant operation process, and ensure that the liquid level in the clear water tank does not evacuate or overflow during the operation of the water plant; Based on the control goal of the minimum energy consumption mode, under the condition of meeting the stable water inflow mode, reduce the unit water consumption during the operation of the water plant, that is, the electricity consumption required for each ton of water. Its goals include but are not limited to increasing the liquid level of the water plant's clear water tank to reduce the energy consumption of the water delivery pump; Based on the control target of the lowest electricity fee mode, under the condition of meeting the stable water inlet mode, the peak and valley electricity price strategy is used to achieve the goal of producing as little water as possible during the peak electricity price period and as much water as possible during the low electricity price period, thereby achieving the lowest electricity fee required during the operation of the water plant.

7. The intelligent balance control method for water volume in a water plant according to claim 1, wherein The intelligent control terminal includes but is not limited to a combination of a PLC control cabinet and corresponding circuits, and has remote monitoring and manual intervention functions. Users can remotely monitor the water supply of the water plant through a mobile terminal or a monitoring center. When an emergency occurs, the operating parameters of the water pump or valve can be manually adjusted to achieve emergency intervention in the water supply of the water plant.

8. An intelligent water volume balance control system for a waterworks, characterized in that, include: The data processing and early warning module is used to obtain the historical data of the water plant and pre-process it to obtain the pre-processed historical data, and at the same time issue an early warning for abnormal data; An intelligent water volume prediction module is used to input the pre-processed historical data into a pre-trained water volume prediction network to obtain the optimal water supply volume of the water plant at the current stage; A control mode selection module is used to respond to the water volume control mode selected by the user, adjust the corresponding weights in the water volume prediction network under different modes, and obtain the optimal water supply volume under each mode in the current stage of the water plant; The intelligent water volume control module sends the optimal water supply volume in each mode of the water plant at the current stage to the corresponding water pump or valve through the intelligent control terminal to adjust the water supply volume of the corresponding water plant.

9. A device, characterized in that, include: at least one processor, at least one memory, wherein: The memory stores program instructions or codes; The program instructions or codes are loaded and executed by the processor, so that the electronic device implements the water volume intelligent balancing control method of the water plant as described in any one of claims 1 to 7.

10. A storage medium having program instructions or code stored thereon, characterized in that, The program instructions or codes are loaded and executed by the processor to implement the water volume intelligent balancing control method of a water plant as described in any one of claims 1 to 7.

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