Electrical control method and system applied to variable frequency water pump

Abstract

The invention discloses an electrical control method and system applied to a variable frequency water pump, and relates to the technical field of electrical appliance control, and the method comprises the 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 to obtain predicted load data of the water pump; acquiring meteorological data of the next period, and generating constraint conditions of the frequency converter according to the meteorological data; constructing a target function of the frequency converter according to the predicted load data; and under the constraint condition, optimizing the parameters of the target function to obtain the optimal parameters of the frequency converter, and performing electric appliance control on the water pump according to the optimal parameters. The water pump can be prevented from running in a high-energy-consumption interval or pressure fluctuation can be avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electrical control, and in particular to an electrical control method and system applied to a variable frequency water pump. BACKGROUND

[0002] As the core equipment of fluid delivery system, variable frequency water pumps are widely used in urban water supply, industrial circulating cooling, agricultural irrigation, high-rise building secondary water supply and other scenarios. In the urban water supply system, the water pump needs to cope with the periodic fluctuations of high load in the morning and evening peak and low load at night, and its operating state directly affects the stability of the water supply pressure and the experience of residents using water; in industrial production, the water pump needs to match the flow and pressure requirements of different processes of the production line, and the load changes dynamically with the production rhythm, and the energy consumption accounts for 20% to 30% of the total industrial energy consumption; in agricultural irrigation, the water pump operation is affected by factors such as crop growth cycle and soil moisture, and needs to adapt to changes in temperature, precipitation and other meteorological conditions in the open environment. In these scenarios, the operating efficiency, energy consumption level and safety and stability of the water pump not only relate to the system operation cost, but also affect the reliability of the overall system.

[0003] The existing electrical control method of variable frequency water pump mostly adopts a closed-loop adjustment strategy based on real-time load feedback, that is, the current flow, pressure and other data are collected by sensors, and the output frequency of the frequency converter is adjusted in real time to match the instantaneous load. However, this control method has significant limitations: its adjustment depends on real-time data feedback, lacks forward-looking prediction of the next cycle load change, and when the load changes suddenly, the control response has a lag, which easily leads to the water pump running in a high energy consumption interval or pressure fluctuation. SUMMARY

[0004] The purpose of the present application is to solve the problem in the prior art that the water pump runs in a high energy consumption interval or pressure fluctuation, and to provide an electrical control method and system applied to a variable frequency water pump.

[0005] In order to solve the problems in the prior art, the present application adopts the following technical scheme:

[0006] An electrical control method applied to a variable frequency water pump, comprising:

[0007] S1, collecting real-time state data and real-time load data of the water pump;

[0008] S2, predicting the load demand of the water pump in the next cycle according to the real-time load data and the real-time state data, and obtaining predicted load data of the water pump;

[0009] S3, obtaining meteorological data of the next cycle, and generating constraint conditions of the frequency converter according to the meteorological data;

[0010] S4, constructing a target function of the frequency converter according to the predicted load data;

[0011] S5, optimizing parameters of the target function under the constraint condition to obtain optimal parameters of the frequency converter, and performing electrical control on the water pump according to the optimal parameters.

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

[0013] A sensor is deployed on the water pump body, and original data of the water pump are collected through the sensor;

[0014] The original data are denoised to obtain the real-time state data and the real-time load data, the real-time state data including operating current, voltage, rotating speed and equipment temperature of the water pump, and the real-time load data including pipeline flow and system pressure.

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

[0016] The historical load data set and the historical state data of the water pump are obtained;

[0017] The historical load data set and the historical state data set are taken as training samples, and the training samples are input into a time series prediction model for training to obtain a load prediction model;

[0018] The real-time load data and the real-time state data are input into the load prediction model, and the load prediction model outputs a predicted value of load demand in the next cycle as the predicted load data.

[0019] Preferably, meteorological data in the next cycle are obtained, and constraint conditions of the frequency converter are generated according to the meteorological data, including:

[0020] The environmental temperature and the rainfall in the next cycle are collected, and the environmental temperature and the rainfall are taken as the meteorological data;

[0021] The running temperature upper limit of the frequency converter is determined based on the environmental temperature, and the pipeline flow upper limit of the water pump is determined based on the rainfall;

[0022] The running temperature upper limit and the pipeline flow upper limit are taken as the constraint conditions.

[0023] Preferably, a target function of the frequency converter is constructed according to the predicted load data, including:

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

[0025] Based on flow demand and pressure demand in the predicted load data, an associated model of unit time energy consumption of the water pump and the core control parameters is established;

[0026] The lowest total energy consumption in the predicted load cycle is taken as a target, and the lowest total energy consumption is represented as a target function of core control parameters and predicted load data.

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

[0028] The parameters of the target function are optimized by an update formula of a gradient descent algorithm;

[0029] When the value of the target function changes less than a preset threshold, an approximate minimum value of the target function is obtained, and the optimal parameters of the frequency converter are determined according to the approximate minimum value.

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

[0031] The drive instruction of the water pump is generated based on the output frequency and the target rotating speed of the optimal parameters;

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

[0033] In order to solve the above problems, the application further provides an electrical control system applied to a variable frequency water pump, the system comprising:

[0034] A collection module is configured to collect real-time state data and real-time load data of the water pump;

[0035] A load data generation module is configured to predict the load demand of the water pump in the next cycle according to the real-time load data and the real-time state data, and obtain predicted load data of the water pump;

[0036] A constraint module is configured to obtain meteorological data of the next cycle, and generate constraint conditions of the frequency converter according to the meteorological data;

[0037] A function construction module is configured to construct a target function of the frequency converter according to the predicted load data;

[0038] A control module is configured to optimize the parameters of the target function under the constraint conditions, obtain optimal parameters of the frequency converter, and electrically control the water pump according to the optimal parameters.

[0039] Compared with the prior art, the application has the following beneficial effects:

[0040] 1. In the present application, the problem of lack of foresight in the prior art is solved by introducing a load demand prediction mechanism. Specifically, it collects historical load data sets and historical state data of the water pump, inputs these data as training samples into the time series prediction model for training, obtains a load prediction model that can learn the load change rule with the state, inputs real-time load data and real-time state data into the model, and outputs the predicted load data as the predicted load demand value of the next period. This process enables the system to anticipate the load change trend in the next period in advance, breaks the passive adjustment mode that relies only on real-time data feedback, provides data support for adjusting the water pump operating parameters in advance, and reduces the response lag caused by insufficient prediction of load changes.

[0041] 2. In the present application, by constructing an objective function and combining constraint conditions for parameter optimization, accurate response to load mutation is realized. Based on the predicted load data, an objective function with the lowest total energy consumption as the target is constructed, and the upper limit of operating temperature and the upper limit of pipeline flow generated based on meteorological data are introduced as constraint conditions. The optimal parameters of the frequency converter are obtained by optimizing the parameters of the objective function through the gradient descent algorithm. Then, the driving instructions are generated according to the optimal parameters to control the water pump operation. This way enables the water pump to adjust to the efficient operation state matching the predicted load in advance before the load mutation, avoiding high energy consumption caused by response lag, and ensuring the pressure stability of the system during load fluctuation through constraint conditions, effectively solving the problems of high energy consumption and pressure fluctuation caused by load mutation in the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0042] The accompanying drawings, which are included to provide a further understanding of the present application, constitute a part of this application and illustrate embodiments of the present application and its description, which do not constitute an improper limitation of the present application. In the drawings:

[0043] Figure 1 A flowchart of an electrical control method applied to a variable frequency water pump according to an embodiment of the present application is provided.

[0044] Figure 2 A functional module diagram of an electrical control system applied to a variable frequency water pump according to an embodiment of the present application is provided. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments.

[0046] Embodiment: The present embodiment provides an electrical control method applied to a variable frequency water pump, referring to Figure 1 , specifically, including:

[0047] S1, collect real-time state data and real-time load data of the water pump;

[0048] In the embodiments of the present application, the real-time state data and the real-time load data of the water pump are collected, including:

[0049] Deploying sensors on the water pump body, and collecting original data of the water pump through the sensors;

[0050] Performing noise reduction processing on the original data to obtain real-time state data and real-time load data, the real-time state data including operating current, voltage, rotating speed and equipment temperature of the water pump, and the real-time load data including pipeline flow and system pressure.

[0051] Specifically, to realize the collection of the real-time state data and the real-time load data of the water pump, first, a plurality of types of sensors are deployed at reasonable positions of the water pump body, which are used to obtain original data generated in the operation process of the water pump; after the deployment, the sensors continuously collect original data containing electrical signals, mechanical movements and environmental characteristics and other information in the operation process of the water pump; then, an appropriate noise reduction algorithm is used to process the collected original data, to remove noise factors such as electromagnetic interference and mechanical vibration clutter, and after the noise reduction processing, the real-time state data and the real-time load data are separated therefrom, wherein the real-time state data covers operating current, voltage, rotating speed and equipment temperature, which can reflect the electrical operation condition of the water pump, the mechanical operation condition and the thermal state of the equipment, and the real-time load data contains pipeline flow and system pressure, which can reflect the pipeline conveying capacity and the system working pressure, to provide accurate and reliable basic data support for subsequent data-based analysis and control.

[0052] Specifically, the real-time state data refers to a dynamic parameter set reflecting the electrical, mechanical and thermal characteristics of the water pump body during operation, including operating current, voltage, rotating speed and equipment temperature, wherein the operating current presents the amount of current of the motor electrical load, the voltage is the voltage supply parameter of the motor and the electrical circuit, the rotating speed is the mechanical movement frequency parameter of the water pump impeller rotation, and the equipment temperature reflects the thermal state of the water pump motor, shell and other key components. The real-time load data refers to a dynamic parameter set reflecting the output of the water pump and the demand of the system, including pipeline flow and system pressure, wherein the pipeline flow is the volume or mass flow rate of the water pump conveying medium in the pipeline, and the system pressure is the pressure level of the output end of the water pump and the system circuit. During the collection operation, the current sensor, temperature sensor, flow sensor and other sensors deployed on the water pump body are used to obtain original data containing electrical signals and physical quantity fluctuations, and then through filtering, noise reduction and other signal processing, the real-time state data and the real-time load data accurately reflecting the operating state of the water pump and the load demand of the system are separated and extracted from the original data, to provide basic data support for subsequent intelligent control and energy efficiency optimization of the water pump.

[0053] S2, 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 to obtain predicted load data of the water pump;

[0054] Specifically, by 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, the predicted load data of the water pump is obtained, which can anticipate the load change trend of the water pump in the future. Based on these prediction data, the system can optimize and adjust the operation parameters of the water pump in advance, such as adjusting the speed of the water pump in advance through the frequency converter, so that the water pump can always remain in the high-efficiency operation interval while meeting the actual load demand, thereby reducing energy consumption. In addition, it can also plan equipment maintenance in advance, reasonably arrange maintenance according to the predicted high-load period, avoid equipment failure caused by high load, improve the reliability and service life of the equipment, and ensure the stable operation of the entire water supply system, reduce the system pressure fluctuation and water supply instability caused by load mutation.

[0055] In the embodiments of the present application, the predicted load data of the water pump is obtained by 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, which comprises:

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

[0057] The historical load data set and the historical state data set are used as training samples, and the training samples are input into a time series prediction model for training to obtain a load prediction model;

[0058] The real-time load data and the real-time state data are input into the load prediction model, and the load prediction model outputs the predicted value of the load demand in the next period as the predicted load data.

[0059] Specifically, to realize the prediction of the next cycle load demand of the water pump according to real-time load data and real-time state data and obtain predicted load data, first, historical data preparation work is carried out to obtain a historical load data set and historical state data of the water pump, wherein the historical load data set covers load-related data records such as pipeline flow and system pressure of the water pump in different cycles in the past running process, and the historical state data includes state parameters such as operating current, voltage, speed and equipment temperature of the water pump in the corresponding cycle; then, a prediction model is trained, the historical load data set and the historical state data set obtained above are taken as training samples, the training samples are input into a time series prediction model, the model learns the law of load demand changing with state in the historical data through iterative learning of the algorithm inside the model, so as to complete the training of the load prediction model; finally, load demand prediction is performed, the real-time load data and real-time state data collected at present are input into the trained load prediction model, the model analyzes and processes the input data according to the learned law, and outputs the predicted value of the load demand of the water pump in the next cycle, which is taken as the predicted load data to provide data support for subsequent optimization control of the water pump.

[0060] S3, acquiring meteorological data of the next cycle, and generating constraint conditions of the frequency converter according to the meteorological data;

[0061] Specifically, by acquiring meteorological data of the next cycle and generating constraint conditions of the frequency converter, the water pump-frequency converter system can actively adapt to environmental changes. Environmental temperature and rainfall, as key meteorological factors, directly affect the system operation boundary: the upper limit of the operating temperature of the frequency converter is determined based on the environmental temperature, which can avoid the risk of over-temperature failure of the frequency converter due to environmental temperature rise in advance, and ensure the safety and continuous operation of the equipment hardware; the upper limit of the pipeline flow is determined based on the rainfall, which can prevent problems such as overcurrent and overpressure caused by rainwater flowing into the pipeline, and protect the structural integrity of the pipeline system; by integrating meteorological-related constraints into system control, the optimization of the frequency converter control parameters can be more in line with the actual environmental load, avoiding blind pursuit of energy saving under extreme weather, or energy waste caused by conservative operation, so as to realize energy efficiency optimization within the safe operation boundary of the system and improve the self-adaptation ability and long-term stable operation level of the entire water supply system to the meteorological environment.

[0062] In the embodiments of the present application, the meteorological data of the next cycle is acquired, and the constraint conditions of the frequency converter are generated according to the meteorological data, which includes:

[0063] Acquiring the environmental temperature and rainfall of the next cycle, and taking the environmental temperature and rainfall as meteorological data;

[0064] Determining the upper limit of the operating temperature of the frequency converter based on the environmental temperature, and determining the upper limit of the pipeline flow of the water pump based on the rainfall;

[0065] The upper limit of the operating temperature and the upper limit of the pipeline flow are taken as constraint conditions, wherein the constraint conditions are as follows:

[0066]

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

[0068] Specifically, to achieve the acquisition of the meteorological data of the next period and generate the constraint conditions of the frequency converter based on the same, first, the meteorological data collection work is carried out, and the ambient temperature and rainfall information in the next period are collected according to the environment in which the water pump operates, and the two types of data reflecting the environmental meteorological characteristics are determined as the meteorological data; then, based on the collected ambient temperature, the temperature threshold value at which the frequency converter can be safely and stably operated under the ambient temperature condition is determined, that is, the upper limit of the operating temperature of the frequency converter, by considering the hardware characteristics, heat dissipation capacity and safety operation requirements of the frequency converter and the influence of the ambient temperature on the heat dissipation environment of the frequency converter; at the same time, according to the collected rainfall, the influence of the rainfall on the pipeline system in which the water pump is located is analyzed, and the maximum flow that can be safely carried by the pipeline of the water pump under the corresponding rainfall condition is determined, that is, the upper limit of the pipeline flow of the water pump, by considering the drainage capacity of the pipeline and the flood control demand of the system; finally, the determined upper limit of the operating temperature and the upper limit of the pipeline flow are associated with the temperature and flow parameters of the actual operation of the water pump, and the constraint conditions are constructed in the form of mathematical expressions, specifically, the actual operating temperature does not exceed the upper limit of the operating temperature, and the actual pipeline flow does not exceed the upper limit of the pipeline flow, to form the conditions for constraining the control process of the frequency converter, to provide a safety boundary basis for subsequent optimization of the operating parameters of the frequency converter based on the predicted load data, and to ensure the reliable operation of the system under the influence of the meteorological environment.

[0069] S4, constructing a target function of the frequency converter according to the predicted load data;

[0070] Specifically, the target function of the frequency converter is constructed according to the predicted load data, which can accurately match the control direction of the frequency converter with the actual load demand of the water pump. By incorporating the predicted load data into the target function, the function can clearly reflect the energy consumption change law under different load conditions, provide targeted guidance for frequency converter parameter optimization, ensure that the water pump always operates in the most energy-efficient interval while meeting the predicted load demand, avoid energy waste caused by load fluctuations, and improve the overall energy efficiency of the system. At the same time, this target setting based on predicted data makes the frequency converter control more forward-looking, reduces the hysteresis of real-time adjustment, and ensures the stability and economy of the water pump operation.

[0071] In the embodiment of the application, the target function of the frequency converter is constructed according to the predicted load data, including:

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

[0073] Based on the flow demand and the pressure demand in the predicted load data, a correlation model of the unit time energy consumption of the water pump and the core control parameters is established;

[0074] The lowest total energy consumption in the predicted load cycle is taken as a target, and the lowest total energy consumption is expressed as a target function of the core control parameters and the predicted load data, wherein the target function is as follows:

[0075]

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

[0077] Specifically, to construct the target function of the frequency converter according to the predicted load data, first, the core parameters of the frequency converter for regulating the water pump are determined, and it is determined through analysis that the core control parameters are the output frequency and the target rotating speed, which will directly act on the running state adjustment of the water pump; then, the flow demand and the pressure demand information contained in the predicted load data are extracted, and based on the working principle and energy consumption characteristics of the water pump, the correlation model between the unit time energy consumption of the water pump and the above core control parameters is established through energy consumption testing, theoretical derivation and other ways of the water pump under different operating conditions, which can accurately depict the response law of the unit time energy consumption of the water pump when the core control parameters change; then, the lowest total energy consumption of the water pump in the predicted load cycle is taken as a target, and the correlation model is further expanded, the lowest total energy consumption is constructed in the form of a mathematical expression, the core control parameters and the predicted load data are included, and a target function is formed, and the target function is expressed by integrating the unit time energy consumption function of the water pump within the predicted load cycle length.

[0078] S5, under the constraint condition, the parameters of the target function are optimized to obtain the optimal parameters of the frequency converter, and the water pump is electrically controlled according to the optimal parameters.

[0079] In the embodiment of the application, the parameters of the target function are optimized to obtain the optimal parameters of the frequency converter, including:

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

[0081] When the value of the objective function changes less than a preset threshold, an approximate minimum value of the objective function is obtained, and the optimal parameters of the frequency converter are determined according to the approximate minimum value.

[0082] Specifically, to optimize the parameters of the objective function and obtain the optimal parameters of the frequency converter, first, the update formula of the gradient descent algorithm is used to optimize the parameters of the objective function. The gradient descent algorithm adjusts the parameter value step by step according to the gradient information of the objective function, and iteratively updates in the direction of making the value of the objective function better. During the iteration process of parameter optimization, the change of the value of the objective function is continuously monitored. When the value of the objective function changes less than the preset threshold, it is considered that the approximate minimum value of the objective function has been approached. Based on the parameter state corresponding to the approximate minimum value, the optimal parameters of the frequency converter are determined. The optimal energy consumption performance is provided for the precise control of the frequency converter, and the water pump is ensured to run in an efficient and energy-saving manner in the corresponding load cycle.

[0083] In the embodiment of the present application, the water pump is electrically controlled according to the optimal parameters, including:

[0084] The drive instruction of the water pump is generated based on the output frequency and the target speed of the optimal parameters;

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

[0086] Specifically, to electrically control the water pump according to the optimal parameters, first, based on the output frequency and the target speed in the optimal parameters of the frequency converter obtained in the previous optimization, the output frequency and the target speed are converted into a drive instruction that can be recognized and executed by the water pump driver according to the instruction generation rule of the water pump electrical control system, and the specific values and timing logic of the frequency adjustment and speed control in the instruction are determined. Then, the generated drive instruction is transmitted to the driving device of the water pump through the hardware execution channel of the water pump electrical control, and the driving device accurately adjusts its output according to the instruction content to control the operating parameters such as the motor power frequency and speed of the water pump, so that the water pump works in the operating state corresponding to the optimal parameters, and the water pump realizes efficient and stable operation while meeting the load demand, achieving the goal of system energy saving and performance optimization.

[0087] As shown in Figure 2 , it is a functional module diagram of an electrical control system applied to a variable frequency water pump according to an embodiment of the present application.

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

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

[0090] a load data generation module configured to predict a load demand of the water pump in a next period according to real-time load data and real-time state data, and obtain predicted load data of the water pump;

[0091] a constraint module configured to obtain meteorological data in the next period, and generate a constraint condition of the frequency converter according to the meteorological data;

[0092] a function construction module configured to construct a target function of the frequency converter according to the predicted load data;

[0093] a control module configured to optimize parameters of the target function under the constraint condition, obtain optimal parameters of the frequency converter, and perform electrical control on the water pump according to the optimal parameters.

[0094] The above merely describes a preferred embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can make equivalent replacements or changes to the technical solution and the inventive concept of the present application within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. An electrical control method applied to a variable frequency water pump, characterized in that, The method comprises the following steps: S1, collecting real-time state data and real-time load data of the water pump; S2, 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; S3, obtaining meteorological data of the next period, and generating constraint conditions of the frequency converter according to the meteorological data; S4, constructing a target function of the frequency converter according to the predicted load data; S5, 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.

2. The electrical control method for a variable frequency water pump according to claim 1, wherein The collection of real-time state data and real-time load data of the water pump comprises: deploying sensors on the water pump body to collect raw data of the water pump through the sensors; performing noise reduction processing on the raw data to obtain real-time state data and real-time load data, wherein the real-time state data comprises operating current, voltage, rotating speed and equipment temperature of the water pump, and the real-time load data comprises pipeline flow and system pressure.

3. The electrical control method for a variable frequency water pump according to claim 1, wherein The prediction of the load demand of the water pump in the next period according to the real-time load data and the real-time state data comprises: obtaining a historical load data set and a historical state data set of the water pump; using the historical load data set and the historical state data set as training samples, inputting the training samples into a time series prediction model for training to obtain a load prediction model; inputting the real-time load data and the real-time state data into the load prediction model, and outputting a predicted value of the load demand in the next period as the predicted load data.

4. The electrical control method for a variable frequency water pump according to claim 1, wherein The obtaining of meteorological data of the next period and the generation of constraint conditions of the frequency converter according to the meteorological data comprise: collecting environmental temperature and rainfall of the next period, and taking the environmental temperature and the rainfall as meteorological data; determining an upper limit of the operating temperature of the frequency converter based on the environmental temperature, and determining an upper limit of the pipeline flow of the water pump based on the rainfall; taking the upper limit of the operating temperature and the upper limit of the pipeline flow as the constraint conditions.

5. The electrical control method for a variable frequency water pump according to claim 4, wherein The construction of the target function of the frequency converter according to the predicted load data comprises: determining that the core control parameters of the frequency converter are output frequency and target rotating speed; establishing a correlation model of the unit time energy consumption of the water pump and the core control parameters based on the flow demand and the pressure demand in the predicted load data; taking the minimum total energy consumption in the predicted load period as a target, and expressing the minimum total energy consumption as a target function of the core control parameters and the predicted load data.

6. The electrical control method for a variable frequency water pump according to claim 1, wherein The optimization of the parameters of the target function to obtain the optimal parameters of the frequency converter comprises: optimizing the parameters of the target function through an update formula of a gradient descent algorithm; when the value of the target function changes by less than a preset threshold, an approximate minimum value of the target function is obtained, and the optimal parameters of the frequency converter are determined according to the approximate minimum value.

7. The electrical control method for a variable frequency water pump according to claim 1, wherein The electrical control on the water pump according to the optimal parameters comprises: generating driving instructions of the water pump based on the output frequency and the target rotating speed of the optimal parameters; and performing electrical control on the water pump according to the driving instructions.

8. An electrical control system applied to a variable frequency water pump, characterized in that, The system comprises: a collection module configured to collect real-time state data and real-time load data of the water pump; a load data generation module configured to predict 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 obtain predicted load data of the water pump; The constraint module is configured to acquire meteorological data of a next period, and generate a constraint condition of the frequency converter according to the meteorological data; The function construction module is configured to construct a target function of the frequency converter according to the predicted load data; The control module is configured to optimize parameters of the target function under the constraint condition, obtain optimal parameters of the frequency converter, and perform electrical control on the water pump according to the optimal parameters.

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