Control method and device, softening water purification equipment, electronic equipment, medium and product

Abstract

The invention relates to the technical field of household water purification, and provides a control method and device, softening water purification equipment, electronic equipment, a medium and a product, the softening water purification equipment comprises a capacitive deionization system, and the capacitive deionization system is used for removing ions and salt in water. The control method of the softening water purification equipment comprises the following steps: acquiring historical operation data, a current state and user preference data of the softening water purification equipment; inputting the historical operation data, the current state and the user preference data into a prediction model to obtain a prediction result output by the prediction model; the prediction result comprises water demand and water quality change; determining working parameters of the capacitive deionization system according to the water demand, the water quality change and the current state; and controlling the capacitive deionization system according to the working parameters so as to output purified water meeting the water demand. Through the mode, the intelligent self-adaptive control characteristic of the softening water purification equipment is enhanced, and accurate water quality control, predictive maintenance and efficient, energy-saving and personalized water quality treatment are realized.

Description

Technical Field

[0001] The present application relates to the field of household water purification technology, and in particular to control methods, devices, softening water purification equipment, electronic equipment, media and products. Background Art

[0002] Traditional household softening and water purification systems mainly use ion exchange resins or reverse osmosis technology. Due to the use of fixed parameter control, it is difficult to adapt to changes in water quality and different user needs, resulting in low treatment efficiency and energy waste.

[0003] At the same time, traditional household softening and water purification systems lack intelligence and adaptability, and are unable to achieve precise water quality control and predictive maintenance. Summary of the invention

[0004] The present application aims to solve at least one of the technical problems existing in the related art. To this end, the present application proposes a control method for softening water purification equipment, which strengthens the intelligent adaptive control characteristics of the softening water purification equipment, realizes accurate water quality control and predictive maintenance, and efficient, energy-saving, and personalized water quality treatment.

[0005] The present application also proposes a control device for softening and purifying water equipment, softening and purifying water equipment, electronic equipment, storage medium and computer program product.

[0006] According to the control method of the softening water purification equipment of the first aspect embodiment of the present application, the softening water purification equipment includes a capacitor deionization system, which is used to remove ions and salts in water. The control method of the softening water purification equipment includes: obtaining historical operation data, current status and user preference data of the softening water purification equipment; inputting the historical operation data, current status and user preference data into the prediction model to obtain the prediction results output by the prediction model; wherein the prediction results include water demand and water quality changes; the prediction model is a neural network model trained based on a sample set of operation data of the softening water purification equipment; determining the working parameters of the capacitor deionization system according to the water demand, water quality changes and current status; and controlling the capacitor deionization system according to the working parameters to output purified water that meets the water demand.

[0007] According to the control method of the softening water purification equipment in the embodiment of the present application, by inputting historical operation data, current status and user preference data into the prediction model, the prediction results including water demand and water quality changes can be obtained, so that the capacitor deionization system can be controlled according to the prediction results to output purified water that meets the demand, thereby improving the water purification efficiency, enhancing the stability of water quality, and having strong adaptability; and it can also meet user preferences, realize personalized water quality customization, and improve user satisfaction.

[0008] According to one embodiment of the present application, controlling a capacitor deionization system according to working parameters includes: controlling the capacitor deionization system to perform water purification according to the working parameters; determining that the purified water output by the capacitor deionization system does not meet the water demand, adjusting the working parameters of the capacitor deionization system to obtain new working parameters; controlling the capacitor deionization system according to the new working parameters to output purified water that meets the water demand.

[0009] According to one embodiment of the present application, after controlling the capacitor deionization system according to the working parameters to output purified water that meets the water demand, it also includes: recording the working parameters of the softening water purification equipment during this operation as new historical operating data; and updating the prediction model according to the new historical operating data and / or user feedback information.

[0010] According to one embodiment of the present application, after controlling the capacitive deionization system according to the working parameters to output purified water that meets the water demand, it also includes: inspecting the performance of the electrodes in the capacitive deionization system to obtain inspection results; if it is determined in the inspection results that the electrode performance cannot meet the preset requirements, regenerating the electrodes.

[0011] According to an embodiment of the present application, the control method of the softening water purification equipment also includes: obtaining local electricity price information; adjusting the start-up time of the capacitor deionization system according to the electricity price information to achieve energy consumption optimization.

[0012] According to one embodiment of the present application, the control method of the softening water purification equipment also includes: sending the historical operation data and current status of the softening water purification equipment to a remote server; the remote server is used to remotely control and / or diagnose faults of the softening water purification equipment based on the historical operation data and the current status; and receiving and executing control instructions issued by the remote server.

[0013] According to the control device of the softening water purification equipment in the second aspect of the embodiment of the present application, the softening water purification equipment includes a capacitor deionization system, which is used to remove ions and salts in water. The control device of the softening water purification equipment includes: a data acquisition module, used to acquire historical operation data, current status data and user preference data of the softening water purification equipment; a prediction module, used to input the historical operation data, current status and user preference data into the prediction model to obtain the prediction result output by the prediction model; wherein the prediction result includes water demand and water quality change; the prediction model is a neural network model trained based on the operation data sample set of the softening water purification equipment; a parameter determination module, used to determine the working parameters of the capacitor deionization system according to the water demand, water quality change and current status; a control module, used to control the capacitor deionization system according to the working parameters, so as to output purified water that meets the water demand.

[0014] According to the third aspect of the present application, the softening and purifying water equipment includes a capacitive deionization system and a controller. The capacitive deionization system is used to remove ions and salts from water; the controller is used to execute the control method of the above-mentioned softening and purifying water equipment.

[0015] According to the electronic device of the fourth aspect embodiment of the present application, it includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a control method for softening and purifying water equipment as described above.

[0016] According to the non-transitory computer-readable storage medium of the fifth aspect embodiment of the present application, a computer program is stored thereon, and when the computer program is executed by a processor, a control method for a softening and purifying water device as described above is implemented.

[0017] According to the computer program product of the sixth aspect of the present application, the computer program includes a computer program, which, when executed by a processor, implements a control method for any of the above-mentioned softening and water purification equipment.

[0018] The above one or more technical solutions in the embodiments of the present application have at least one of the following technical effects: By inputting historical operation data, current status and user preference data into the prediction model, the prediction results including water demand and water quality changes can be obtained, so that the capacitive deionization system can be controlled according to the prediction results to output purified water that meets the demand, thereby improving the water purification efficiency, improving the stability of water quality, and having strong adaptability; and it can also meet user preferences, realize personalized water quality customization, and improve user satisfaction; Furthermore, the combination of adaptive PID controller, fuzzy control system, LSTM neural network and predictive maintenance algorithm strengthens the intelligent adaptive control characteristics of softening water purification equipment, realizes precise water quality control and predictive maintenance as well as efficient, energy-saving and personalized water quality treatment; Furthermore, the effluent quality can be continuously monitored, the control parameters can be adjusted dynamically, and the machine learning model can be updated regularly to optimize the control strategy.

[0019] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 This is one of the flow charts of the control method of the softening water purification equipment provided in the embodiment of the present application.

[0022] Figure 2 It is a schematic diagram of the system architecture of the control method of the softening water purification equipment provided in the embodiment of the present application.

[0023] Figure 3 This is the second flow chart of the control method of the softening water purification equipment provided in the embodiment of the present application.

[0024] Figure 4 It is a schematic diagram of the functional modules of the softening water purification equipment provided in the embodiment of the present application.

[0025] Figure 5 It is a structural schematic diagram of a control device for a softening water purification device provided in an embodiment of the present application.

[0026] Figure 6 It is a schematic diagram of the physical structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The following is a further detailed description of the implementation of the present application in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application but cannot be used to limit the scope of the present application.

[0028] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiments of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0029] It should be noted that all actions of acquiring signals, information or data in this application are carried out in compliance with the relevant data protection laws and policies of the country where they are located, and with the authorization given by the owner of the corresponding device.

[0030] The present application embodiment provides a control method for a softening water purification device, see Figure 1 , Figure 1 This is one of the flow charts of the control method of the softening water purification equipment provided in the embodiment of the present application.

[0031] In this embodiment, the control method of the softening water purification equipment may include steps 110 to 140, and each step is specifically as follows: Step 110: Obtain historical operation data, current status and user preference data of the softening water purification equipment.

[0032] The main function of the softening water purification equipment is to remove calcium and magnesium ions in water, thereby reducing the hardness of water, preventing the formation of scale, and improving the use effect of water and the life of the equipment. In this embodiment, the softening water purification equipment includes a capacitive deionization (CDI) system, which is also called a CDI electrode system for removing ions and salts from water.

[0033] The CDI electrode system includes an anode and a cathode, which are usually electrodes made of conductive materials.

[0034] In the CDI electrode system, the functions of the electrode include: Ion adsorption: When current passes through the electrode, an electric field is formed on the electrode surface, and ions in the water (such as sodium ions and chloride ions) are adsorbed to the electrode surface. Ion release: When the electric field of the electrode system is reversed or the current stops, the ions on the electrode are released back into the water, restoring the electrode to its original state, that is, electrode regeneration.

[0035] The performance of the CDI electrode system is affected by factors such as electrode material, surface area, pore structure, distance between electrodes and applied voltage. Optionally, the electrodes usually use carbon-based materials (such as activated carbon) or other materials with high conductivity and large specific surface area to improve the efficiency of deionization and the life of the electrodes.

[0036] Alternatively, the CDI electrode system may also use a selective CDI electrode system. The selective CDI electrode system can preferentially remove calcium and magnesium ions. In the selective CDI electrode system, the electrode uses a surface-modified porous carbon material, such as a graphene / activated carbon composite material; a chelating agent such as EDTA (ethylenediaminetetraacetic acid) can be used for surface functionalization to enhance the Ca 2+ and Mg 2+ selectivity; the electrode is made of honeycomb structure using 3D printing technology to increase the specific surface area.

[0037] In this step, historical operation data, current status and user preference data of the softening water purification equipment are obtained; wherein the current status may include monitoring data obtained by real-time monitoring of water quality, such as TDS (Total Dissolved Solids), pH and hardness data.

[0038] TDS is the total amount of all solid matter dissolved in water, including salts, minerals, organic matter, and other dissolved substances. It is usually expressed in milligrams per liter (mg / L) or parts per million (ppm). pH is the acidity or alkalinity of water, a measure of the concentration of hydrogen ions in an aqueous solution. Water hardness refers to the total amount of calcium and magnesium ions dissolved in water. These ions react with other components in the water, affecting the properties and effectiveness of the water.

[0039] The user preference data can be obtained by analyzing the user's operating instructions for the softening and water purification equipment.

[0040] Step 120: Input historical operation data, current status and user preference data into the prediction model to obtain the prediction results output by the prediction model; the prediction results include water demand and water quality changes.

[0041] The historical operation data, current status and user preference data collected in the above steps are input into the prediction model, and the prediction model can predict water demand and water quality change trends based on the input data.

[0042] Among them, the prediction model is a neural network model trained based on the operating data sample set of the softening and water purification equipment.

[0043] The prediction model used in this embodiment is a neural network model that has been trained in advance. Optionally, the neural network model may be an LSTM (Long Short-Term Memory) model.

[0044] The LSTM model is a special recurrent neural network (RNN). Based on the RNN, three gating units are introduced: input gate, forget gate and output gate. These gating units are used to control the flow and update of information.

[0045] Optionally, when training the LSTM model, methods such as back-propagation weighting can be used to optimize gradient calculations to improve training efficiency.

[0046] Step 130: Determine the operating parameters of the capacitive deionization system according to water demand, water quality changes and current status.

[0047] After obtaining the prediction results of the prediction model, the working parameters of the capacitive deionization system can be further determined based on the prediction results and the current state of the softening water purification equipment.

[0048] Optionally, a fuzzy control system may be used to determine initial control parameters as operating parameters.

[0049] A fuzzy control system is a control system based on fuzzy logic theory. It processes input signals and generates control outputs through fuzzy rules and fuzzy reasoning mechanisms. The operation of a fuzzy control system can mainly include four steps: fuzzification, determination of the fuzzy rule base, fuzzy reasoning, and defuzzification.

[0050] Fuzzification refers to the process of converting input signals from exact values ​​to fuzzy sets. The fuzzification process is achieved by mapping the input quantity to membership values ​​in fuzzy sets, which are usually represented by linguistic variables such as "high", "medium", and "low".

[0051] A fuzzy rule base contains a set of fuzzy rules that define the relationship between input and output. The rules are usually in the form of IF-THEN statements.

[0052] Fuzzy reasoning refers to the use of a fuzzy rule base to reason with the fuzzified input signal to generate a fuzzy control output. The reasoning process calculates the results of the fuzzy rules and combines them with the input membership values ​​to derive the control strategy.

[0053] Defuzzification refers to converting fuzzy control output into precise control value. The defuzzification process usually uses weighted average method, centroid method and other methods to calculate the specific value of control output, which will be applied to the actual control system.

[0054] Exemplarily, the fuzzy control rule can be expressed as follows: IF (TDS is High) AND (hardness is High) THEN (voltage is High); IF (TDS is Low) AND (hardness is Low) THEN (voltage is Low).

[0055] Step 140: Control the capacitor deionization system according to the operating parameters to output purified water that meets water demand.

[0056] After determining the working parameters through the above steps, the capacitive deionization system can be controlled according to the working parameters to output purified water that meets the water demand.

[0057] It should be noted that due to the certain errors in the reasoning of the fuzzy control system, when the capacitor deionization system is controlled according to the working parameters for the first time, it may not be possible to immediately output clean water that meets the water demand. Therefore, in this case, an adaptive PID controller can be introduced to fine-tune the working parameters to output clean water that meets the water demand. The adaptive PID controller can adjust the CDI electrode voltage and current: accurately control the operation of the CDI system.

[0058] Optionally, the step of controlling the capacitive deionization system according to the working parameters to output purified water that meets the water demand may specifically include: The capacitor deionization system is controlled according to the working parameters to perform water purification; if it is determined that the purified water output by the capacitor deionization system does not meet the water demand, the working parameters of the capacitor deionization system are adjusted to obtain new working parameters; the capacitor deionization system is controlled according to the new working parameters to output purified water that meets the water demand.

[0059] In this embodiment, in order to output purified water that meets water demand, the capacitor deionization system can be controlled to perform water purification according to the working parameters; thereafter, it is necessary to detect whether the purified water output by the capacitor deionization system meets the water demand. If it does not meet the water demand, the working parameters of the capacitor deionization system can be readjusted to obtain new working parameters, and purified water that meets the water demand can be output based on the new working parameters.

[0060] Optionally, this embodiment provides an intelligent voltage control algorithm in combination with a PID controller to automatically optimize operating parameters according to water quality. The adaptive PID controller can be executed according to the following formula: ; Among them, u(t) is the control signal, e(t) is the error signal, Kp, Ki, and Kd are the proportional, integral, and differential gains, respectively.

[0061] In summary, the control method of the softening water purification equipment in the embodiment of the present application can obtain prediction results including water demand and water quality changes by inputting historical operation data, current status and user preference data into the prediction model, so as to control the capacitor deionization system according to the prediction results to output purified water that meets the demand, thereby improving the water purification efficiency, improving the water quality stability, and having strong adaptability; and it can also fit user preferences, realize personalized water quality customization, and improve user satisfaction; further, it combines an adaptive PID controller, a fuzzy control system, an LSTM neural network and a predictive maintenance algorithm to enhance the intelligent adaptive control characteristics of the softening water purification equipment, can continuously monitor the outlet water quality, dynamically adjust the control parameters, and realize precise water quality control and predictive maintenance as well as efficient, energy-saving and personalized water quality treatment.

[0062] Based on any of the above embodiments, the steps after controlling the capacitor deionization system according to the working parameters to output purified water that meets the water demand specifically include: The working parameters of the softening water purification equipment during this operation are recorded as new historical operation data; and the prediction model is updated according to the new historical operation data and / or user feedback information.

[0063] In this embodiment, after the softening water purification equipment completes an operation, the various working parameters of this operation can be recorded as new historical operation data, and user feedback information can be received. Finally, based on the new historical operation data and / or user feedback information, the machine learning model can be regularly updated to optimize the control strategy.

[0064] This embodiment integrates online water quality monitoring and hardness adjustment functions, and a water use pattern recognition and prediction system based on machine learning. Through CDI technology and intelligent control, the system can achieve efficient softening and purification of water, while significantly reducing energy consumption and wastewater volume, and providing purified water with adjustable hardness to meet the different water needs of households.

[0065] Based on any of the above embodiments, the steps after controlling the capacitor deionization system according to the working parameters to output purified water that meets the water demand specifically include: The performance of the electrodes in the capacitive deionization system is checked to obtain a check result; if it is determined in the check result that the performance of the electrodes cannot meet the preset requirements, the electrodes are regenerated.

[0066] In this embodiment, after the water discharge is completed, the performance of the electrodes in the capacitive deionization system can also be checked to obtain the inspection results. If the inspection results show that the electrode performance can meet the preset requirements, no processing is required; if the inspection results show that the electrode performance cannot meet the preset requirements, the electrodes are regenerated.

[0067] CDI technology uses the double electric layer formed on the electrode surface to adsorb ions in water. As time goes by, the electrode will gradually reach the adsorption saturation state. At this time, regeneration is required to release the adsorbed ions and restore the adsorption capacity of the electrode. If the saturated electrode is not regenerated in time, the deionization efficiency of the CDI system will be seriously affected.

[0068] Therefore, in this embodiment, it is necessary to check the performance of the electrode, and the electrode that cannot meet the preset requirements is regenerated to quickly remove the accumulated ions on the electrode, so that the electrode has efficient adsorption performance again, thereby improving the processing efficiency of the entire system. In this embodiment, the electrode is cleaned and the function is restored inside the system, realizing the in-situ regeneration technology of the electrode and reducing wastewater discharge.

[0069] Based on any of the above embodiments, the control method of the softening water purification equipment further includes the following steps: Obtain local electricity price information; adjust the startup time of the capacitor deionization system according to the electricity price information to achieve energy consumption optimization.

[0070] Since dynamic electricity prices are implemented in some areas, electricity prices are different in different time periods. Therefore, in order to optimize energy consumption, the embodiment can also obtain local electricity price information and adjust the start time of the capacitor deionization system according to the electricity price information to optimize energy consumption.

[0071] Specifically, the capacitor deionization system may be started when the electricity price is low, and may be shut down when the electricity price is high, thereby alleviating the pressure on the power grid, reducing electricity bills, and saving costs.

[0072] It should be noted that other modules in the softening water purification equipment can also adjust the start time according to the electricity price information.

[0073] Based on any of the above embodiments, the control method of the softening water purification equipment further includes the following steps: The historical operation data and current status of the softening water purification equipment are sent to the remote server; the remote server is used to remotely control and / or diagnose faults of the softening water purification equipment based on the historical operation data and the current status; and receive and execute control instructions issued by the remote server.

[0074] In this embodiment, the softening water purification device also has the functions of remote monitoring and diagnosis, and supports system control and fault diagnosis through mobile devices. Specifically, the historical operation data and current status of the softening water purification device can be sent to a remote server, and the remote server can be connected to the mobile device. The remote server remotely controls and / or diagnoses faults of the softening water purification device based on the historical operation data and current status; the softening water purification device receives and executes the control instructions issued by the remote server.

[0075] Optionally, the contents of fault diagnosis include but are not limited to: 1. Check whether the water inlet pressure is normal; 2. Check and adjust whether the valve is working properly; 3. Test the outlet water hardness to confirm whether the expected softening effect is achieved; 4. Check the brine system to confirm whether the salt supply is sufficient and the brine ratio is correct; 5. Confirm whether the equipment is regenerated as planned and check the regeneration cycle settings; 6. Check the indicator lights and display information on the control panel; 7. Test the function of the sensor.

[0076] Based on the above embodiments, the softening water purification equipment of the present application can perform the following operations: 1. System initialization, reading historical data and user settings.

[0077] 2. Real-time monitoring of incoming water quality and flow.

[0078] 3. The machine learning module predicts short-term water demand and water quality changes.

[0079] 4. The fuzzy control system determines the initial control parameters based on the prediction results.

[0080] 5. PID controller accurately adjusts CDI electrode voltage and current.

[0081] 6. Continuously monitor the effluent quality and dynamically adjust the control parameters.

[0082] 7. Regularly update machine learning models and optimize control strategies.

[0083] 8. Analyze system performance and predict maintenance needs.

[0084] See also Figure 2 , Figure 2 It is a schematic diagram of the system architecture of the control method of the softening water purification equipment provided in the embodiment of the present application.

[0085] In the control method of this embodiment, the main executor is the central processing unit, and the functional modules involved may include a water quality sensor group, a CDI electrode system, a flow sensor, a power management unit, a valve control system, a data storage unit, a machine learning module, a PID controller and a user interface.

[0086] Among them, the main components of the architecture diagram include data acquisition layer, intelligent control layer, user interface, data storage unit and LSTM neural network. The data acquisition layer includes a water quality sensor group for measuring TDS, pH and hardness data, a flow sensor and a CDI electrode system for electrode status monitoring. The intelligent control layer uses an adaptive PID controller, a fuzzy control system, an LSTM neural network and a predictive maintenance algorithm. The execution layer can include a CDI electrode system, a power management unit and a valve control system.

[0087] The user interface may include a touch screen or an application APP; the LSTM neural network may include an input layer (water quality, flow, time data), LSTM layer 1 and layer 2, a fully connected layer and an output layer (water use prediction, water quality trend).

[0088] The central processing unit can also be a microcontroller unit (MCU). The central processing unit obtains TDS, pH and hardness data through the water quality sensor group, obtains flow data through the flow sensor, reads data through the data storage unit, obtains optimization parameters through the machine learning module, obtains control signals through the PID controller, and obtains user input through the user interface. At the same time, the central processing unit sends electrode control signals to the CDI electrode system, sends power control signals to the power management unit, sends valve control signals to the valve control system, stores data in the data storage unit, sends historical data to the machine learning module, sends set values ​​to the PID controller, and sends system status feedback to the user interface.

[0089] The core of the embodiment of the present application is an intelligent adaptive control system, which mainly includes: 1. Multi-parameter real-time monitoring module: integrated with TDS, pH, hardness, flow and other sensors.

[0090] 2. High-precision PID controller: used for precise regulation of CDI electrode voltage and current.

[0091] 3. Machine learning optimization module: optimizes control parameters based on historical data and predicts water usage patterns.

[0092] 4. Adaptive fuzzy control system: automatically adjusts the treatment strategy according to changes in water quality.

[0093] 5. Predictive maintenance algorithms: monitor system performance and predict maintenance needs.

[0094] 6. User behavior analysis module: learn user habits and adjust processing parameters in a personalized manner.

[0095] Based on this, the control method of the softening water purification equipment in the embodiment of the present application improves the processing efficiency by 30% and reduces energy consumption by 25%; the water quality stability is improved by 50% and the adaptability is strong; the maintenance cost is reduced by 40% and the system life is extended by 20%; personalized water quality customization is achieved and user satisfaction is improved.

[0096] See also Figure 3 , Figure 3 This is the second flow chart of the control method of the softening water purification equipment provided in the embodiment of the present application.

[0097] The user turns on the softening and purifying equipment, the softening and purifying equipment system starts, the system performs self-test and initialization processing, checks the status of all hardware components, ensures that all components are working properly, and initializes various parameters. Then read historical data and user settings, load past operating data and user preferences. The system reads the user's personalized settings (if any), or uses the default settings. This can include target water quality, treatment mode, etc.

[0098] After that, the system loads the LSTM model and prepares the neural network model for prediction.

[0099] Continuously collect current water quality parameters and water consumption, and monitor water quality and flow in real time. The system reads various indicators of incoming water, such as hardness, TDS, pH value, etc. through built-in sensors.

[0100] Based on historical data and current status, LSTM predicts short-term water demand and water quality changes. The fuzzy control system determines the initial control parameters and sets the initial parameters based on the prediction results and current status. The PID controller adjusts the CDI electrode voltage and current to achieve precise control of the operation of the CDI system. The CDI process is then started to start the water purification process.

[0101] During this process, the system analyzes the difference between the current water quality and the target water quality. Based on the water quality analysis results, the system sets the specific parameters of the CDI treatment, such as voltage, treatment time, etc. The CDI electrode system is started to start the softening water treatment process.

[0102] The system calculates the optimal electrode voltage based on the Gouy-Chapman-Stern theory. The electrode surface charge density σ is: .

[0103] Where ε is the dielectric constant, ε0 is the vacuum dielectric constant, κ is the Debye length, k is the Boltzmann constant, T is the absolute temperature, z is the ion valence, e is the electron charge, and ψ0 is the surface potential.

[0104] The CDI process is based on the principle of electrosorption. When voltage is applied, charged ions are adsorbed to the oppositely charged electrode. The ion removal efficiency η is: η=(C0-C) / C0*100%.

[0105] Where C0 is the initial ion concentration and C is the ion concentration after treatment.

[0106] After the softening water purification equipment outputs water, the output water quality is monitored in real time, the treated water quality is continuously tested, and it is determined whether the output water quality reaches the target water quality of the preset water quality standard. If it reaches it, the treated water is output; if it does not reach it, it returns to the PID controller adjustment step and readjusts the CDI electrode voltage and current to output the treated, qualified purified water.

[0107] Update the system status and data storage, and record the various parameters of this operation. The system also performs performance analysis, evaluates the system's operating efficiency and effectiveness, and determines whether the CDI electrode needs maintenance (such as regeneration). If necessary, it performs the maintenance procedure; if not, it uses the new operating data to update the prediction model, receives user feedback and makes necessary adjustments, and completes user feedback and setting adjustments.

[0108] If the water does not need to be treated temporarily, the system enters the standby state and waits for the next use. During the standby process, it can be determined whether the operation is terminated. If it is terminated, the system is completely shut down; if it is not terminated, it returns to the real-time monitoring step and continues to monitor the water quality and flow in real time.

[0109] On the other hand, the present application also provides a softening water purification device. Figure 4 , Figure 4 It is a schematic diagram of the functional modules of the softening water purification equipment provided in the embodiment of the present application.

[0110] The softening and purifying water equipment may include a capacitive deionization system and a controller. The capacitive deionization system is used to remove ions and salts from water; and the controller is used to execute the control method of the softening and purifying water equipment.

[0111] Optionally, the softening water purification equipment may also include a water inlet, a pretreatment module, a water quality monitoring module, a user interaction interface, an intelligent control unit, a selective CDI electrode system, an electrode in-situ regeneration system, a hardness adjustment module, a wastewater treatment module, a post-treatment module and a water outlet, etc.

[0112] Among them, the pre-treatment module is equivalent to a filter; the post-treatment module can perform mineralization / disinfection and other treatments; the user interaction interface can refer to an application APP / display screen. The selective CDI electrode system, the electrode in-situ regeneration system, and the hardness adjustment module can be integrated in the capacitive deionization system; the intelligent control unit is integrated in the controller.

[0113] In the selective CDI electrode system, surface-modified porous carbon materials such as graphene / activated carbon composites are used; chelating agents such as EDTA are used for surface functionalization to enhance Ca 2+ and Mg 2+ selectivity; use 3D printing technology to make honeycomb structure and increase specific surface area.

[0114] The intelligent control unit includes a real-time water quality monitoring module (hardness, TDS, pH, etc.), a water use pattern recognition algorithm based on machine learning, and an adaptive voltage regulation system.

[0115] The electrode in-situ regeneration system adopts a reverse polarity flushing mechanism, an ultrasonic cleaning module and an electrochemical redox cleaning technology.

[0116] The hardness adjustment module includes a post-processing unit that can adjust the ratio of calcium and magnesium ions and a precise TDS control system.

[0117] In the user interaction interface, it can be controlled through a smartphone APP, and real-time water quality display and adjustment functions are also available.

[0118] The working process of softening water purification equipment is as follows: 1) The system monitors the incoming water quality in real time, especially hardness and TDS.

[0119] 2) The intelligent control unit predicts the need for purified water based on water quality data and water usage patterns.

[0120] 3) The system automatically adjusts the operating voltage and processing time of the CDI electrode.

[0121] 4) Selectively remove calcium and magnesium ions during water purification.

[0122] 5) The electrodes are regularly regenerated in situ to reduce wastewater discharge.

[0123] 6) Users can select the required water hardness through the APP, and the system will adjust the post-processing parameters accordingly.

[0124] 7) Machine learning algorithms continuously optimize system parameters and improve long-term operating efficiency.

[0125] For example 1, the softening and purifying equipment can be a standard intelligent household softening and purifying system with a processing capacity of 500L / day. Its intelligent features may include: 1. Automatic identification of three common water use modes (drinking, washing, and bathing); 2. Personalized customization of water quality, with five different hardness levels set; 3. Energy consumption optimization, automatic adjustment of operating time according to electricity prices.

[0126] Example 2: The softening and water purification equipment can be a large-capacity smart home integrated softening and water purification system with a processing capacity of 1000L / day. Its intelligent features may include: 1. Integration with smart home system and support for voice control; 2. Multi-point water supply, each water outlet can independently set water quality parameters; 3. Adaptive learning function to master household water use patterns within a week; 4. Remote monitoring and fault diagnosis functions.

[0127] Example 3: The softening and purifying equipment can be a standard household softening and purifying system with a processing capacity of 400L / day and an inlet water hardness of 250-350 mg / L CaCO3; the outlet water hardness is adjustable, the default is 50-100 mg / L CaCO3; the energy consumption is <0.5kWh / m³; the wastewater rate is <5%; its features may include: 1. Compact design, with dimensions of 45cm x 25cm x 60cm, suitable for installation under kitchen cabinets; 2. Smartphone APP control, supporting remote monitoring and adjustment; 3. Automatic mixing valve, which can adjust the outlet water hardness according to user needs; 4. Silent design, operating noise <40dB.

[0128] Example 4: The softening and purifying equipment can be a large-capacity household softening and purifying system with a processing capacity of 400L / day and an inlet water hardness of 300-400 mg / L CaCO3; the outlet water hardness is adjustable, the default is 60-120 mg / L CaCO3; the energy consumption is <0.55kWh / m³; the wastewater rate is <6%; its features may include: 1. Dual outlet design, which can provide drinking water and domestic water at the same time; 2. Integrated water quality analyzer, real-time monitoring of multiple water quality parameters; 3. Automatic cleaning and disinfection functions to reduce maintenance requirements; 4. Intelligent water use analysis to provide water-saving suggestions.

[0129] Example 5: The softening and purifying equipment can be a smart home integrated softening and purifying system with a processing capacity of 600L / day and an inlet water hardness of 200-400 mg / L CaCO3; the outlet water hardness is adjustable, with a default of 20-150 mg / L CaCO3; energy consumption <0.52kWh / m³; wastewater rate <5.5%; its features may include: 1. Integration with smart home systems, support for voice control and scene modes; 2. Adaptive learning function, automatically adjusting water quality according to user habits; 3. Zoned water supply, different hardness can be set for different water use points; 4. 7-inch touch screen, real-time display of water quality parameters and system status.

[0130] On the other hand, the present application also provides a control device for a softening water purification equipment. The control device for the softening water purification equipment provided by the present application is described below. The control device for the softening water purification equipment described below and the control method for the softening water purification equipment described above can be referenced to each other.

[0131] See also Figure 5 , Figure 5 It is a structural schematic diagram of a control device of a softening and purifying water device provided in an embodiment of the present application. In this embodiment, the softening and purifying water device includes a capacitive deionization system, which is used to remove ions and salts from water.

[0132] The control device of the softening water purification equipment may include a data acquisition module 510 , a prediction module 520 , a parameter determination module 530 and a control module 540 .

[0133] The data acquisition module 510 is used to acquire the historical operation data, current status data and user preference data of the softening and purifying water equipment.

[0134] The prediction module 520 is used to input historical operation data, current status and user preference data into the prediction model to obtain the prediction results output by the prediction model; the prediction results include water demand and water quality changes; the prediction model is a neural network model trained based on the operation data sample set of the softening and water purification equipment.

[0135] The parameter determination module 530 is used to determine the operating parameters of the capacitive deionization system according to water demand, water quality changes and current status.

[0136] The control module 540 is used to control the capacitor deionization system according to the working parameters so as to output purified water that meets the water demand.

[0137] Based on any of the above embodiments, the control module 540 can be specifically used to: control the capacitor deionization system to perform water purification according to the working parameters; if it is determined that the purified water output by the capacitor deionization system does not meet the water demand, adjust the working parameters of the capacitor deionization system to obtain new working parameters; control the capacitor deionization system according to the new working parameters to output purified water that meets the water demand.

[0138] Based on any of the above embodiments, the control device of the softening water purification equipment also includes an update module, which can be specifically used to: record the working parameters of the softening water purification equipment during this operation as new historical operation data; and update the prediction model according to the new historical operation data and / or user feedback information.

[0139] Based on any of the above embodiments, the control device of the softening water purification equipment also includes an electrode performance module, which can be specifically used to: check the electrode performance in the capacitive deionization system to obtain inspection results; if it is judged in the inspection results that the electrode performance cannot meet the preset requirements, the electrode is regenerated.

[0140] Based on any of the above embodiments, the control device of the softening water purification equipment also includes an energy consumption optimization module, which can be specifically used to: obtain local electricity price information; adjust the start-up time of the capacitor deionization system according to the electricity price information to achieve energy consumption optimization.

[0141] Based on any of the above embodiments, the control device of the softening water purification equipment also includes a remote control module, which can be specifically used for: sending the historical operation data and current status of the softening water purification equipment to a remote server; the remote server is used to remotely control and / or diagnose faults of the softening water purification equipment according to the historical operation data and the current status; and receiving and executing control instructions issued by the remote server.

[0142] In summary, the embodiment of the present application provides a control device for a softening water purification device, the softening water purification device includes a capacitor deionization system, the capacitor deionization system is used to remove ions and salts in water, and the control device of the softening water purification device includes: a data acquisition module, used to obtain historical operation data, current state data and user preference data of the softening water purification device; a prediction module, used to input historical operation data, current state and user preference data into a prediction model to obtain a prediction result output by the prediction model; wherein the prediction result includes water demand and water quality change; the prediction model is a neural network model trained based on a sample set of operation data of the softening water purification device; a parameter determination module, used to determine the working parameters of the capacitor deionization system according to water demand, water quality change and current state; a control module, used to control the capacitor deionization system according to the working parameters, so as to output purified water that meets the water demand. In the above manner, the embodiment of the present application strengthens the intelligent adaptive control characteristics of the softening water purification device, realizes precise water quality control and predictive maintenance, as well as efficient, energy-saving and personalized water quality treatment.

[0143] In another aspect, the present application also provides an electronic device. Figure 6 , Figure 6 6 is a schematic diagram of the physical structure of an electronic device provided in an embodiment of the present application, and the electronic device may include: a processor (processor) 610, a communication interface (Communications Interface) 620, a memory (memory) 630 and a communication bus 640, wherein the processor 610, the communication interface 620, and the memory 630 communicate with each other through the communication bus 640. The processor 610 can call the logic instructions in the memory 630 to execute the following method: Obtain historical operation data, current status and user preference data of the softening water purification equipment; input the historical operation data, current status and user preference data into the prediction model to obtain the prediction results output by the prediction model; wherein the prediction results include water demand and water quality changes; the prediction model is a neural network model trained based on the operation data sample set of the softening water purification equipment; determine the working parameters of the capacitor deionization system according to the water demand, water quality changes and current status; control the capacitor deionization system according to the working parameters to output purified water that meets the water demand.

[0144] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the relevant technology. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0145] In another aspect, an embodiment of the present application discloses a computer program product, wherein the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program includes program instructions. When the computer program instructions are executed by a computer, the computer can perform the methods provided by the above-mentioned method embodiments, for example, including: Obtain historical operation data, current status and user preference data of the softening water purification equipment; input the historical operation data, current status and user preference data into the prediction model to obtain the prediction results output by the prediction model; wherein the prediction results include water demand and water quality changes; the prediction model is a neural network model trained based on the operation data sample set of the softening water purification equipment; determine the working parameters of the capacitor deionization system according to the water demand, water quality changes and current status; control the capacitor deionization system according to the working parameters to output purified water that meets the water demand.

[0146] In another aspect, an embodiment of the present application further provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method provided in each of the above embodiments is implemented, for example, including: Obtain historical operation data, current status and user preference data of the softening water purification equipment; input the historical operation data, current status and user preference data into the prediction model to obtain the prediction results output by the prediction model; wherein the prediction results include water demand and water quality changes; the prediction model is a neural network model trained based on the operation data sample set of the softening water purification equipment; determine the working parameters of the capacitor deionization system according to the water demand, water quality changes and current status; control the capacitor deionization system according to the working parameters to output purified water that meets the water demand.

[0147] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0148] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the relevant technology can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiment.

[0149] Finally, it should be noted that the above implementation modes are only used to illustrate the present application, rather than to limit the present application. Although the present application is described in detail with reference to the embodiments, a person skilled in the art should understand that various combinations, modifications or equivalent substitutions of the technical solutions of the present application do not depart from the spirit and scope of the technical solutions of the present application, and should be included in the scope of the claims of the present application.

Claims

1. A control method for softening water purification equipment, characterized in that: The softening water purification equipment includes a capacitive deionization system, which is used to remove ions and salts in water. The control method of the softening water purification equipment includes: Obtaining historical operation data, current status and user preference data of the softening and purifying water device; Inputting the historical operation data, the current state and the user preference data into a prediction model to obtain a prediction result output by the prediction model; wherein the prediction result includes water demand and water quality changes; the prediction model is a neural network model trained based on a sample set of operation data of the softening and water purification equipment; Determining operating parameters of the capacitive deionization system according to the water demand, the water quality change and the current state; The capacitive deionization system is controlled according to the operating parameters to output purified water that meets the water demand.

2. The control method of the softening water purification equipment according to claim 1, characterized in that: The controlling the capacitive deionization system according to the operating parameters comprises: Controlling the capacitive deionization system to perform water purification according to the working parameters; Determining that the purified water output by the capacitive deionization system does not meet the water demand, adjusting the operating parameters of the capacitive deionization system to obtain new operating parameters; The capacitive deionization system is controlled according to the new operating parameters to output purified water that meets the water demand.

3. The control method of the softening water purification equipment according to claim 1, characterized in that: After the capacitive deionization system is controlled according to the working parameters to output purified water that meets the water demand, the method further includes: Recording the working parameters of the softening and purifying water equipment during this operation as new historical operation data; The prediction model is updated according to the new historical operation data and / or user feedback information.

4. The control method of the softening water purification equipment according to claim 1, characterized in that: After the capacitive deionization system is controlled according to the working parameters to output purified water that meets the water demand, the method further includes: Checking the performance of electrodes in the capacitive deionization system to obtain a check result; If it is determined in the inspection result that the performance of the electrode cannot meet the preset requirements, the electrode is regenerated.

5. The control method of the softening water purification equipment according to any one of claims 1 to 4, characterized in that: The control method of the softening water purification equipment also includes: Get local electricity price information; The start-up time of the capacitive deionization system is adjusted according to the electricity price information to achieve energy consumption optimization.

6. The control method of the softening water purification equipment according to any one of claims 1 to 4, characterized in that: The control method of the softening water purification equipment also includes: Sending the historical operation data and current status of the softening and purifying water device to a remote server; the remote server is used to remotely control and / or diagnose faults of the softening and purifying water device according to the historical operation data and the current status; Receive and execute the control instructions sent by the remote server.

7. A control device for softening water purification equipment, characterized in that: The softening and purifying water equipment comprises a capacitive deionization system, which is used to remove ions and salts in water. The control device of the softening and purifying water equipment comprises: A data acquisition module, used to acquire historical operation data, current status data and user preference data of the water softening and purification equipment; A prediction module, used for inputting the historical operation data, the current state and the user preference data into a prediction model to obtain a prediction result output by the prediction model; wherein the prediction result includes water demand and water quality change; the prediction model is a neural network model trained based on an operation data sample set of the softening and water purification equipment; A parameter determination module, used to determine the operating parameters of the capacitive deionization system according to the water demand, the water quality change and the current state; A control module is used to control the capacitive deionization system according to the working parameters so as to output purified water that meets the water demand.

8. A softening water purification device, characterized in that: It comprises a capacitive deionization system and a controller, wherein the capacitive deionization system is used to remove ions and salts in water; and the controller is used to execute the control method of the softening and purifying water equipment as claimed in any one of claims 1 to 6.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the control method of the softening and purifying water equipment according to any one of claims 1 to 6 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the control method of the softening and purifying water device according to any one of claims 1 to 6 is implemented.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the control method of the softening and purifying water device according to any one of claims 1 to 6 is implemented.

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