An electrical control method and system applied to a variable frequency water pump

Abstract

The application discloses an electrical control method and system applied to a variable frequency water pump, relates to the technical field of electrical control, and comprises the following steps: collecting real-time state data and real-time load data of the water pump; predicting the load demand of the water pump in the next period according to the real-time load data and the real-time state data, and obtaining predicted load data of the water pump; acquiring meteorological data in the next period, and generating constraint conditions of a frequency converter according to the meteorological data; constructing a target function of the frequency converter according to the predicted load data; optimizing parameters of the target function under the constraint conditions, obtaining optimal parameters of the frequency converter, and performing electrical control on the water pump according to the optimal parameters. The application can avoid the operation of the water pump in a high energy consumption interval or the occurrence of pressure fluctuation.

Description

Technical Field

[0001] This invention relates to the field of electrical control technology, and in particular to an electrical control method and system for variable frequency water pumps. Background Technology

[0002] Variable frequency water pumps, as core equipment in fluid transport systems, are widely used in urban water supply, industrial circulating cooling, agricultural irrigation, and secondary water supply for high-rise buildings. In urban water supply systems, pumps must cope with the periodic fluctuations of high loads during morning and evening peak water usage and low loads at night; their operating status directly affects the stability of water supply pressure and the user experience. In industrial production, pumps need to match the flow and pressure requirements of different processes on the production line, with loads dynamically changing with the production rhythm; energy consumption can account for 20% to 30% of total industrial energy consumption. In agricultural irrigation, pump operation is affected by factors such as crop growth cycles and soil moisture, and must adapt to changes in meteorological conditions such as temperature and precipitation in open-air environments. In these scenarios, the operating efficiency, energy consumption level, and safety stability of the pumps not only affect the system operating costs but also the overall system reliability.

[0003] Existing electrical control methods for variable frequency water pumps mostly employ closed-loop regulation strategies based on real-time load feedback. This involves collecting data such as current flow rate and pressure through sensors and adjusting the inverter's output frequency in real time to match the immediate load. However, this control method has significant limitations: its regulation relies on real-time data feedback and lacks forward-looking prediction of load changes in the next cycle. When the load changes abruptly, the control response exhibits lag, which can easily lead to the pump operating in a high-energy-consumption range or experiencing pressure fluctuations. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies that cause water pumps to operate in high energy consumption ranges or experience pressure fluctuations, and to propose an electrical control method and system for variable frequency water pumps.

[0005] To address the problems existing in the prior art, the present invention adopts the following technical solution:

[0006] An electrical control method for a variable frequency water pump includes:

[0007] S1. Collect real-time status data and real-time load data of the water pump;

[0008] S2. Based on real-time load data and real-time status data, predict the load demand of the water pump in the next cycle to obtain the predicted load data of the water pump.

[0009] S3. Obtain the meteorological data for the next cycle and generate the constraints for the frequency converter based on the meteorological data;

[0010] S4. Construct the objective function of the frequency converter based on the predicted load data;

[0011] S5. Under constraints, optimize the parameters of the objective function to obtain the optimal parameters of the frequency converter, and perform electrical control of the water pump based on the optimal parameters.

[0012] Preferably, the real-time status data and real-time load data of the water pump are collected, including:

[0013] Sensors are deployed on the water pump body to collect raw data of the water pump;

[0014] The raw data is denoised to obtain real-time status data and real-time load data. The real-time status data includes the pump's operating current, voltage, speed, and equipment temperature, while the real-time load data includes pipeline flow and system pressure.

[0015] Preferably, the load demand of the water pump in the next cycle is predicted based on real-time load data and real-time status data to obtain the predicted load data of the water pump, including:

[0016] Obtain historical load data and historical status data of the water pump;

[0017] Historical load datasets and historical state datasets are used as training samples. The training samples are then input into the time series prediction model for training to obtain the load prediction model.

[0018] Real-time load data and real-time status data are input into the load forecasting model, and the load forecasting model outputs the load demand forecast for the next period as the forecast load data.

[0019] Preferably, the meteorological data for the next cycle is acquired, and constraints for the frequency converter are generated based on the meteorological data, including:

[0020] Collect ambient temperature and rainfall data for the next cycle, and use these data as meteorological data.

[0021] The upper limit of the inverter's operating temperature is determined based on the ambient temperature, and the upper limit of the water pump's pipeline flow rate is determined based on the rainfall.

[0022] The upper limit of operating temperature and the upper limit of pipeline flow are used as constraints.

[0023] Preferably, the objective function of the frequency converter is constructed based on the predicted load data, including:

[0024] The core control parameters of the frequency converter are determined to be the output frequency and the target speed;

[0025] Based on the flow and pressure demands in the predicted load data, a correlation model is established between the pump's energy consumption per unit time and the core control parameters.

[0026] With the goal of minimizing total energy consumption during the predicted load cycle, the minimum total energy consumption is expressed as an objective function of the core control parameters and the predicted load data.

[0027] Preferably, the parameters of the objective function are optimized to obtain the optimal parameters of the frequency converter, including:

[0028] The parameters of the objective function are optimized using the update formula of the gradient descent algorithm;

[0029] When the change in the value of the objective function is less than a preset threshold, the approximate minimum value of the objective function is obtained, and the optimal parameters of the frequency converter are determined based on the approximate minimum value.

[0030] Preferably, the water pump is electrically controlled according to optimal parameters, including:

[0031] The pump drive command is generated based on the output frequency and target speed of the optimal parameters.

[0032] The water pump is electrically controlled according to the drive command.

[0033] To address the above problems, the present invention also provides an electrical control system for a variable frequency water pump, the system comprising:

[0034] The data acquisition module is used to collect real-time status data and real-time load data of the water pump.

[0035] The load data generation module is used to predict the load demand of the water pump in the next cycle based on real-time load data and real-time status data, and obtain the predicted load data of the water pump.

[0036] The constraint module is used to acquire meteorological data for the next cycle and generate constraint conditions for the frequency converter based on the meteorological data.

[0037] The function construction module is used to construct the objective function of the frequency converter based on the predicted load data.

[0038] The control module is used to optimize the parameters of the objective function under constraints to obtain the optimal parameters of the frequency converter, and then perform electrical control of the water pump based on the optimal parameters.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] 1. This invention addresses the lack of foresight in existing technologies by introducing a load demand forecasting mechanism. Specifically, it collects historical load and status data of water pumps, using these data as training samples to train a time series prediction model, resulting in a load forecasting model capable of learning the load variation patterns with status. Real-time load and status data are then input into this model, outputting the predicted load demand value for the next period. This process enables the system to anticipate load change trends in the next period, breaking away from the passive adjustment mode that relies solely on real-time data feedback. It provides data support for adjusting water pump operating parameters in advance, fundamentally reducing response lag caused by insufficient prediction of load changes.

[0041] 2. In this invention, a precise response to load surges is achieved by constructing an objective function and optimizing parameters based on constraints. Based on predicted load data, an objective function is constructed with the goal of minimizing total energy consumption. Simultaneously, upper limits for operating temperature and pipeline flow rate, generated from meteorological data, are introduced as constraints. The parameters of the objective function are optimized using a gradient descent algorithm to obtain the optimal parameters for the frequency converter. Then, drive commands are generated based on these optimal parameters to control the water pump operation. This approach allows the water pump to adjust to a highly efficient operating state matching the predicted load before load surges occur. This avoids high energy consumption due to response lag and ensures system pressure stability during load fluctuations through constraints, effectively solving the problems of excessive energy consumption and pressure fluctuations caused by load surges in existing technologies. Attached Figure Description

[0042] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0043] Figure 1 This is a flowchart illustrating an electrical control method for a variable frequency water pump according to an embodiment of the present invention.

[0044] Figure 2 This is a functional block diagram of an electrical control system for a variable frequency water pump, provided as an embodiment of the present invention. Detailed Implementation

[0045] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0046] Example: This example provides an electrical control method for a variable frequency water pump. See [link to example]. Figure 1 Specifically, including:

[0047] S1. Collect real-time status data and real-time load data of the water pump;

[0048] In embodiments of the present invention, the collection of real-time status data and real-time load data of the water pump includes:

[0049] Sensors are deployed on the water pump body to collect raw data of the water pump;

[0050] The raw data is denoised to obtain real-time status data and real-time load data. The real-time status data includes the pump's operating current, voltage, speed, and equipment temperature, while the real-time load data includes pipeline flow and system pressure.

[0051] Specifically, to acquire real-time status and load data of the water pump, various types of sensors are first deployed at appropriate locations on the pump body. These sensors are used to acquire raw data generated during pump operation. After deployment, the sensors continuously collect raw data containing information such as electrical signals, mechanical motion, and environmental characteristics during pump operation. Subsequently, an appropriate noise reduction algorithm is used to process the collected raw data, removing noise factors such as electromagnetic interference and mechanical vibration noise. After noise reduction processing, real-time status data and real-time load data are separated. The real-time status data includes operating current and voltage, which reflect the electrical operating status of the pump, rotational speed, which reflects the mechanical operation, and equipment temperature, which characterizes the thermal state of the equipment. The real-time load data includes pipeline flow rate, which reflects the pipeline's transport capacity, and system pressure, which reflects the system's working pressure. This provides accurate and reliable basic data support for subsequent data-based analysis and control.

[0052] Specifically, real-time status data refers to a set of dynamic parameters reflecting the electrical, mechanical, and thermal characteristics of the pump during operation. This includes operating current, voltage, speed, and equipment temperature. Operating current represents the quantified current of the motor's electrical load; voltage is the voltage supply parameter for the motor and electrical circuits; speed is the mechanical motion frequency parameter of the pump impeller; and equipment temperature reflects the thermal state of key components such as the pump motor and casing. Real-time load data refers to a set of dynamic parameters reflecting the pump's output and system demands. This includes pipeline flow rate and system pressure. Pipeline flow rate is the volumetric or mass flow rate of the pumped medium within the pipeline; and system pressure is the pressure level at the pump's output and in the system circuit. During data acquisition, raw data containing electrical signals and physical quantity fluctuations is first obtained using current sensors, temperature sensors, and flow sensors deployed on the pump itself. This raw data undergoes filtering and noise reduction processing to extract accurate real-time status and load data that reflect the pump's operating status and system load demands. This provides fundamental data support for subsequent intelligent pump control and energy efficiency optimization.

[0053] S2. Based on real-time load data and real-time status data, predict the load demand of the water pump in the next cycle to obtain the predicted load data of the water pump.

[0054] Specifically, by predicting the pump's load demand for the next cycle based on real-time load and status data, the system obtains predicted pump load data, allowing for advance insight into future load trends during pump operation. Based on this predicted data, the system can optimize and adjust pump operating parameters in advance, such as adjusting pump speed via frequency converters, ensuring the pump meets actual load demands while maintaining high-efficiency operation and reducing energy consumption. Furthermore, it allows for advance planning of equipment maintenance, scheduling repairs based on predicted high-load periods to prevent equipment failures due to excessive load, improving equipment reliability and lifespan, ensuring stable operation of the entire water supply system, and reducing system pressure fluctuations and water supply instability caused by sudden load changes.

[0055] In an embodiment of the present invention, the load demand of the water pump in the next cycle is predicted based on real-time load data and real-time status data to obtain predicted load data for the water pump, including:

[0056] Obtain historical load data and historical status data of the water pump;

[0057] Historical load datasets and historical state datasets are used as training samples. The training samples are then input into the time series prediction model for training to obtain the load prediction model.

[0058] Real-time load data and real-time status data are input into the load forecasting model, and the load forecasting model outputs the load demand forecast for the next period as the forecast load data.

[0059] Specifically, to predict the pump load demand for the next cycle based on real-time load and status data, and to obtain predicted load data, historical data preparation is first carried out. This involves acquiring historical load and status data for the pump. The historical load dataset includes records of load-related data such as pipeline flow and system pressure from different cycles during the pump's past operation. The historical status data includes status parameters such as pump operating current, voltage, speed, and equipment temperature within the corresponding cycle. Next, the prediction model is trained using the acquired historical load and status datasets as training samples. These training samples are input into the time series prediction model, and through iterative learning of the internal algorithm, the model learns the patterns of load demand changes with status in the historical data, thus completing the training of the load prediction model. Finally, load demand prediction is executed. The currently collected real-time load and status data are input into the trained load prediction model. The model analyzes and processes the input data based on the learned patterns and outputs the predicted load demand value for the pump in the next cycle. This predicted value serves as the predicted load data, providing data support for subsequent optimized pump control.

[0060] S3. Obtain the meteorological data for the next cycle and generate the constraints for the frequency converter based on the meteorological data;

[0061] Specifically, by acquiring meteorological data for the next cycle and generating inverter constraints, the pump-inverter system can proactively adapt to environmental changes. Ambient temperature and rainfall, as key meteorological factors, directly affect the system's operating boundaries: determining the upper limit of inverter operating temperature based on ambient temperature can proactively avoid the risk of inverter overheating due to rising ambient temperatures, ensuring equipment hardware safety and continuous operation; determining the upper limit of pipeline flow based on rainfall can prevent overcurrent and overpressure problems caused by rainwater inflow, protecting the structural integrity of the pipeline system; integrating meteorological constraints into system control allows the optimization of inverter control parameters to better match the actual environmental load, avoiding blindly pursuing energy saving under extreme weather conditions leading to equipment overload, or conservative operation resulting in energy waste, achieving optimal energy efficiency within the system's safe operating boundaries, and improving the entire water supply system's adaptability to meteorological environments and its long-term stable operation.

[0062] In an embodiment of the present invention, meteorological data for the next cycle is acquired, and constraints for the frequency converter are generated based on the meteorological data, including:

[0063] Collect ambient temperature and rainfall data for the next cycle, and use these data as meteorological data.

[0064] The upper limit of the inverter's operating temperature is determined based on the ambient temperature, and the upper limit of the water pump's pipeline flow rate is determined based on the rainfall.

[0065] The upper limit of operating temperature and the upper limit of pipeline flow are used as constraints, and the constraints are as follows:

[0066]

[0067] In the formula, T max It is the upper limit of the operating temperature, Q max It is the upper limit of the pipeline flow rate, T(f,Q) pred Q(f,Q) represents the actual operating temperature of the water pump. pred ) represents the actual flow rate of the water pump in the pipeline, f is the core control parameter of the frequency converter, and Q is the actual flow rate of the water pump in the pipeline. pred It refers to the load in the predicted load data.

[0068] Specifically, to obtain meteorological data for the next cycle and generate constraints for the frequency converter accordingly, meteorological data collection is first conducted. For the environment where the water pump operates, ambient temperature and rainfall information for the next cycle are collected, and these two types of data reflecting environmental meteorological characteristics are identified as meteorological data. Then, based on the collected ambient temperature, analysis is performed, considering the frequency converter's hardware characteristics, heat dissipation capacity, and safe operation requirements. Combining the impact of ambient temperature on the frequency converter's heat dissipation environment, the temperature threshold at which the frequency converter can operate safely and stably under these ambient temperature conditions is determined, i.e., the upper limit of the frequency converter's operating temperature. Simultaneously, based on the collected rainfall, its impact on the pipeline system where the water pump is located is analyzed. Considering the impact of the system, the drainage capacity of the pipeline, the flood control requirements of the system, and other factors, the maximum flow rate that the water pump pipeline can safely bear under the corresponding rainfall conditions is determined, i.e., the upper limit of the water pump pipeline flow rate. Finally, the determined upper limit of operating temperature and upper limit of pipeline flow rate are correlated with the actual operating temperature and flow rate parameters of the water pump, and constraints are constructed in the form of mathematical expressions. Specifically, the actual operating temperature does not exceed the upper limit of operating temperature, and the actual pipeline flow rate does not exceed the upper limit of pipeline flow rate. These conditions are used to constrain the frequency converter control process, providing a safety boundary basis for subsequent optimization of frequency converter operating parameters based on predicted load data, and ensuring the reliable operation of the system under the influence of meteorological environment.

[0069] S4. Construct the objective function of the frequency converter based on the predicted load data;

[0070] Specifically, constructing the target function for the frequency converter based on predicted load data enables precise matching of the converter's control direction with the actual load demand of the water pump. By incorporating predicted load data into the target function, the function clearly reflects the energy consumption variation patterns under different load conditions, providing targeted guidance for frequency converter parameter optimization. This ensures that the water pump operates within its lowest energy consumption and highest efficiency range while meeting predicted load requirements, avoiding energy waste caused by load fluctuations and improving the overall system energy efficiency. Furthermore, this target setting based on predicted data makes frequency converter control more forward-looking, reducing the lag in real-time adjustments and ensuring the stability and economy of water pump operation.

[0071] In an embodiment of the present invention, the objective function of the frequency converter is constructed based on the predicted load data, including:

[0072] The core control parameters of the frequency converter are determined to be the output frequency and the target speed;

[0073] Based on the flow and pressure demands in the predicted load data, a correlation model is established between the pump's energy consumption per unit time and the core control parameters.

[0074] With the goal of minimizing total energy consumption during the predicted load cycle, the minimum total energy consumption is expressed as an objective function of the core control parameters and the predicted load data, where the objective function is as follows:

[0075]

[0076] In the formula, min F(f,Q) pred P(f,Q) is the objective function. pred ) is the energy consumption function of the water pump per unit time, f is the core control parameter of the frequency converter, and Q is the energy consumption function of the water pump per unit time. pred t represents the load in the predicted load data, and t represents the load cycle duration of the predicted load data.

[0077] Specifically, to achieve the objective function of the frequency converter based on the predicted load data, the core parameters for the frequency converter to regulate the water pump are first identified. Analysis determines that the core regulation parameters are the output frequency and the target speed, which directly affect the adjustment of the water pump's operating status. Next, the predicted load data is studied in depth to extract the flow and pressure demand information contained within it. Based on the water pump's working principle and energy consumption characteristics, a correlation model between the water pump's energy consumption per unit time and the aforementioned core regulation parameters is established through energy consumption tests and theoretical derivations under different operating conditions. This model can accurately depict the response law of the water pump's energy consumption per unit time when the core regulation parameters change. Then, with the goal of achieving the minimum total energy consumption of the water pump within the predicted load cycle, the correlation model is further expanded. The minimum total energy consumption is constructed in the form of a mathematical expression, incorporating the core regulation parameters and the predicted load data to form the objective function. The objective function is expressed by integrating the water pump's energy consumption per unit time over the duration of the predicted load cycle.

[0078] S5. Under constraints, optimize the parameters of the objective function to obtain the optimal parameters of the frequency converter, and perform electrical control of the water pump based on the optimal parameters.

[0079] In embodiments of the present invention, the parameters of the objective function are optimized to obtain the optimal parameters of the frequency converter, including:

[0080] The parameters of the objective function are optimized using the update formula of the gradient descent algorithm;

[0081] When the change in the value of the objective function is less than a preset threshold, the approximate minimum value of the objective function is obtained, and the optimal parameters of the frequency converter are determined based on the approximate minimum value.

[0082] Specifically, to optimize the parameters of the objective function and obtain the optimal parameters of the frequency converter, the gradient descent algorithm is first used to optimize the parameters of the objective function. The gradient descent algorithm adjusts the parameter values ​​step by step based on the gradient information of the objective function, iteratively updating in the direction that makes the objective function value better. During the iterative process of parameter optimization, the changes in the objective function value are continuously monitored. When the change in the objective function value is less than a pre-set threshold, it is considered that the objective function has approached its approximate minimum value. Based on the parameter state corresponding to this approximate minimum value, the optimal parameters of the frequency converter are determined. This provides the parameter configuration with optimal energy consumption performance for the precise control of the frequency converter, ensuring that the water pump operates in a highly efficient and energy-saving manner within the corresponding load cycle.

[0083] In an embodiment of the present invention, electrical control of the water pump based on optimal parameters includes:

[0084] The pump drive command is generated based on the output frequency and target speed of the optimal parameters.

[0085] The water pump is electrically controlled according to the drive command.

[0086] Specifically, to achieve electrical control of the water pump based on optimal parameters, firstly, based on the output frequency and target speed of the inverter's optimal parameters obtained in the previous optimization, the output frequency and target speed are converted into drive commands that can be recognized and executed by the water pump driver according to the command generation rules of the water pump electrical control system. The specific values ​​and timing logic of the frequency adjustment and speed control in the commands are clearly defined. Then, through the hardware execution path of the water pump electrical control, the generated drive commands are transmitted to the water pump's drive unit. The drive unit precisely adjusts its own output according to the command content, controlling the water pump's motor power supply frequency, speed, and other operating parameters, thereby enabling the water pump to operate according to the operating state corresponding to the optimal parameters. This ensures that the water pump meets the load requirements while achieving efficient and stable operation, thus achieving the goal of system energy saving and performance optimization.

[0087] like Figure 2 The diagram shown is a functional block diagram of an electrical control system for a variable frequency water pump provided in an embodiment of the present invention.

[0088] In this embodiment, the functions of each module / unit are as follows:

[0089] The data acquisition module is used to collect real-time status data and real-time load data of the water pump.

[0090] The load data generation module is used to predict the load demand of the water pump in the next cycle based on real-time load data and real-time status data, and obtain the predicted load data of the water pump.

[0091] The constraint module is used to acquire meteorological data for the next cycle and generate constraint conditions for the frequency converter based on the meteorological data.

[0092] The function construction module is used to construct the objective function of the frequency converter based on the predicted load data.

[0093] The control module is used to optimize the parameters of the objective function under constraints to obtain the optimal parameters of the frequency converter, and then perform electrical control of the water pump based on the optimal parameters.

[0094] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An electrical control method for a variable frequency water pump, characterized in that, Includes the following steps: S1. Collect real-time status data and real-time load data of the water pump; S2. Based on real-time load data and real-time status data, predict the load demand of the water pump in the next cycle to obtain the predicted load data of the water pump. S3. Obtain meteorological data for the next cycle, and generate constraints for the frequency converter based on the meteorological data, including: Collect ambient temperature and rainfall data for the next cycle, and use these data as meteorological data. The upper limit of the inverter's operating temperature is determined based on the ambient temperature, and the upper limit of the water pump's pipeline flow rate is determined based on the rainfall. The upper limit of operating temperature and the upper limit of pipeline flow rate are used as constraints. S4. Construct the objective function of the frequency converter based on the predicted load data, including: The core control parameters of the frequency converter are determined to be the output frequency and the target speed; Based on the flow and pressure demands in the predicted load data, a correlation model is established between the pump's energy consumption per unit time and the core control parameters. With the goal of minimizing total energy consumption during the predicted load cycle, the minimum total energy consumption is expressed as an objective function of the core control parameters and the predicted load data. S5. Under constraints, optimize the parameters of the objective function to obtain the optimal parameters of the frequency converter, and perform electrical control of the water pump based on the optimal parameters; The parameters of the objective function are optimized to obtain the optimal parameters of the frequency converter, including: The parameters of the objective function are optimized using the update formula of the gradient descent algorithm; When the change in the value of the objective function is less than a preset threshold, the approximate minimum value of the objective function is obtained, and the optimal parameters of the frequency converter are determined based on the approximate minimum value. Electrical control of the water pump based on optimal parameters includes: The pump drive command is generated based on the output frequency and target speed of the optimal parameters. The water pump is electrically controlled according to the drive command.

2. The electrical control method for a variable frequency water pump according to claim 1, characterized in that, Collect real-time status data and real-time load data of the water pump, including: Sensors are deployed on the water pump body to collect raw data of the water pump; The raw data is denoised to obtain real-time status data and real-time load data. The real-time status data includes the pump's operating current, voltage, speed, and equipment temperature, while the real-time load data includes pipeline flow and system pressure.

3. The electrical control method for a variable frequency water pump according to claim 1, characterized in that, Based on real-time load data and real-time status data, the load demand of the water pump in the next cycle is predicted, resulting in predicted load data for the water pump, including: Obtain historical load data and historical status data of the water pump; Historical load datasets and historical state datasets are used as training samples. The training samples are then input into the time series prediction model for training to obtain the load prediction model. Real-time load data and real-time status data are input into the load forecasting model, and the load forecasting model outputs the load demand forecast for the next period as the forecast load data.

4. An electrical control system for a variable frequency water pump, applied to the electrical control method for a variable frequency water pump as described in any one of claims 1-3, characterized in that, The system includes: The data acquisition module is used to collect real-time status data and real-time load data of the water pump. The load data generation module is used to predict the load demand of the water pump in the next cycle based on real-time load data and real-time status data, and obtain the predicted load data of the water pump. The constraint module is used to acquire meteorological data for the next cycle and generate constraint conditions for the frequency converter based on the meteorological data. The function construction module is used to construct the objective function of the frequency converter based on the predicted load data. The control module is used to optimize the parameters of the objective function under constraints to obtain the optimal parameters of the frequency converter, and then perform electrical control of the water pump based on the optimal parameters.

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