Self-calibration control method and system for ion yield drift of negative ion purifier

Abstract

The invention relates to the technical field of air purification, and discloses a negative ion purifier ion yield drift self-calibration control method and system, and the method comprises the steps: obtaining the accumulated operation time of an air pump to determine a mechanical fatigue reference drift amount; constructing a theoretical evaporation model to solve a theoretical moisture evaporation rate and a theoretical conductivity reference slope, comparing the theoretical moisture evaporation rate and the theoretical conductivity reference slope with an actual conductivity change slope to obtain a deviation index, executing water quality state binary judgment according to the deviation index, and generating a water quality damping drift amount; superposing the mechanical fatigue reference drift distance and the water quality damping drift distance to obtain a total performance drift distance, generating a target driving instruction, and combining the target driving instruction with the operation envelope to execute clamping output; according to the method, conductivity virtual high interference caused by natural evaporation is effectively eliminated through the thermodynamic model, layered accurate compensation of hardware aging and water quality pollution is achieved, and the risk of excessive compensation and air pump dry burning caused by misjudgment is avoided while the constant ion yield is maintained.

Description

Technical Field

[0001] This invention relates to the field of air purification technology, and more specifically, to a method and system for self-calibrating control of ion production drift in a negative ion purifier. Background Technology

[0002] Negative ion air purifiers utilize the Leonard effect—the principle behind water droplets breaking apart under the shearing force of high-speed airflow to generate negative ions—and are widely used in improving indoor air quality, settling particulate matter, and sterilization. To maintain consistent purification efficiency, precise control of the air pump pressure and water quality is crucial.

[0003] In the prior art, for example, Chinese patent application CN109974157A discloses a remotely controllable silent negative ion purifier. This purifier integrates an air quality detector, a PLC controller, and a wireless transmitter, enabling remote monitoring and silent operation. Its main focus is on optimizing the device structure and improving the user experience. Another example is Chinese patent application CN119983454A, which discloses a control method and device for a micro-sized negative oxygen ion generator. This technology utilizes machine learning models to analyze air quality information and enhances electrodes with quantum dot materials to optimize ionization efficiency. It also constructs a distributed purification network to achieve task allocation, emphasizing the improvement of electrode materials and networked intelligent collaboration.

[0004] While existing technologies have made progress in terms of structural convenience and improved ionization efficiency, they still have certain limitations in maintaining constant ion production control throughout the entire life cycle. During the long-term operation of negative ion purifiers, the water conductivity signal, a core feedback variable, exhibits a "polysemous" physical meaning, making it difficult for the system to distinguish between natural water evaporation and concentration caused by environmental thermodynamic factors and water quality deterioration caused by the intrusion of external pollutants. Specifically, in high-temperature and low-humidity environments, natural water evaporation leads to a physical increase in the mineral concentration in the water tank, causing a rise in conductivity. However, the surface tension characteristics of the water do not change substantially, and the excitation efficiency of the Leonard effect remains at a normal level. Conversely, if the increase in conductivity is caused by the introduction of external impurities, it directly alters the surface tension of the water, physically inhibiting droplet breakage and resulting in a real decrease in ion production. Existing control logic lacks the ability to decouple these two distinct physical processes, often employing a single negative feedback mechanism: once an increase in conductivity is detected, it is judged as a deterioration in water quality, forcibly increasing the air pump power. This misjudgment can cause the system to perform an incorrect "overcompensation" operation under the condition of natural evaporation and concentration, resulting in a surge of ineffective energy consumption. In addition, natural evaporation is usually accompanied by a drop in water level. At this time, the high-power drive air pump is very likely to induce dry burning under low water level, which accelerates the thermal aging and fatigue damage of the rubber diaphragm, ultimately leading to a significant reduction in equipment life and failure to maintain the expected ion generation. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of existing technologies, this invention provides a self-calibration control method and system for ion output drift in negative ion purifiers. By constructing a thermodynamic evaporation model based on environmental parameters, a natural benchmark for conductivity changes is established. The evaporation and concentration components in the actual detection signal are precisely decoupled from the actual water pollution components. Combined with the mechanical fatigue benchmark drift determined by the cumulative operating time of the air pump, the total performance drift is generated through linear superposition. This invention effectively eliminates falsely high conductivity readings caused by natural evaporation. While achieving precise compensation for both hardware aging and water quality changes, it also eliminates the risk of air pump dry-burning caused by over-compensation through an envelope clamping mechanism, ensuring constant ion output and operational safety throughout the entire lifespan of the equipment.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A self-calibration control method for ion output drift in a negative ion air purifier includes:

[0008] Obtain the cumulative operating time of the air pump, and determine the mechanical fatigue baseline drift based on the cumulative operating time of the air pump;

[0009] Calculate the saturated vapor pressure difference at the water-air interface, obtain the theoretical water evaporation rate based on the saturated vapor pressure difference at the water-air interface, deduce the theoretical conductivity baseline slope based on the theoretical water evaporation rate, obtain the actual conductivity change slope, compare the actual conductivity change slope with the theoretical conductivity baseline slope to obtain the conductivity deviation index, perform a binary judgment of water quality status based on the conductivity deviation index, and generate water quality damping drift based on the judgment result.

[0010] The total performance drift is obtained by linearly superimposing the mechanical fatigue reference drift and the water quality damping drift. The target drive command is generated based on the total performance drift. It is determined whether to perform running envelope clamping on the target drive command, and the target drive command or the clamped target drive command is output.

[0011] The method for determining the mechanical fatigue reference drift includes:

[0012] Using the cumulative operating time of the air pump as an index, the pre-stored diaphragm stiffness life cycle decay curve is queried, and the current pressure decay percentage is determined by linear interpolation.

[0013] The drive power compensation value is calculated based on the current pressure attenuation percentage and is defined as the mechanical fatigue reference drift.

[0014] The calculation method for the saturated vapor pressure difference at the water-vapor interface includes:

[0015] Collect the current ambient temperature, ambient humidity and internal heat load of the equipment, calculate the actual temperature of the water body based on the internal heat load of the equipment, and calculate the saturated vapor pressure at the water-air interface based on the actual temperature of the water body.

[0016] Calculate the actual partial pressure of water vapor in the air based on the ambient temperature and humidity. Subtract the actual partial pressure of water vapor in the air from the saturated vapor pressure at the water vapor interface to obtain the saturated vapor pressure difference at the water vapor interface.

[0017] The method for performing binary determination of water quality status includes:

[0018] The conductivity deviation index is compared with the preset evaporation tolerance threshold. If the conductivity deviation index is less than or equal to the evaporation tolerance threshold, it is determined to be in a natural concentration state. If the conductivity deviation index is greater than the evaporation tolerance threshold, it is determined to be in a contamination intrusion state.

[0019] The method for generating water quality damping drift based on the determination result is as follows:

[0020] If the determination result is a natural concentration state, the water quality damping drift is set to zero; if the determination result is a pollution intrusion state, the conductivity deviation index is input into the pre-stored impurity damping mapping function to calculate the corresponding water quality damping drift.

[0021] The method for generating target driving instructions based on total performance drift includes:

[0022] The voltage-power response characteristic curve of the air pump is invoked, and the drive voltage increment is calculated in reverse addressing with the total performance drift as the target value. The drive voltage increment is then superimposed on the base drive voltage to generate the target drive command.

[0023] The method for determining whether to perform a runtime envelope clamp on the target driver instruction includes:

[0024] The remaining water volume is estimated based on the theoretical water evaporation rate and the cumulative operating time of the air pump since the last water filling. The estimated remaining water volume is then compared with the preset water volume safety threshold. If the estimated remaining water volume is greater than or equal to the water volume safety threshold, the running envelope clamp is not executed, and the target drive command is directly output. If the estimated remaining water volume is less than the water volume safety threshold, the running envelope clamp is executed on the target drive command.

[0025] The cumulative running time of the air pump since the last water addition is the current reading of the water addition timer. The water addition timer resets to zero and restarts timing when a water addition event is detected.

[0026] The method for detecting the water addition event is as follows: the conductivity value of the water inside the negative ion purifier tank is continuously monitored by a conductivity sensor. When the conductivity value drops more than a preset conductivity drop threshold within a preset detection time window, it is determined that a water addition event has been detected.

[0027] The method for performing runtime envelope clamping on the target driving instruction includes:

[0028] Determine the upper limit of available drive power based on the estimated remaining water volume, and convert the drive voltage value corresponding to the target drive command into the power value corresponding to the target drive command.

[0029] If the power value corresponding to the target drive command is less than or equal to the upper limit of available drive power, the target drive command is kept unchanged and output directly; if the power value corresponding to the target drive command is greater than the upper limit of available drive power, the drive voltage value corresponding to the target drive command is forcibly truncated to the drive voltage value corresponding to the upper limit of available drive power, and the truncated drive voltage value is output as the clamped target drive command.

[0030] A self-calibration control system for ion output drift in a negative ion purifier, used to implement the aforementioned self-calibration control method for ion output drift in a negative ion purifier, the system comprising:

[0031] Mechanical fatigue drift module: used to acquire the cumulative operating time of the air pump and determine the mechanical fatigue baseline drift amount based on the cumulative operating time of the air pump;

[0032] Water quality damping drift module: used to calculate the saturated vapor pressure difference at the water-air interface, obtain the theoretical water evaporation rate based on the saturated vapor pressure difference at the water-air interface, calculate the theoretical conductivity baseline slope based on the theoretical water evaporation rate, obtain the actual conductivity change slope, compare the actual conductivity change slope with the theoretical conductivity baseline slope to obtain the conductivity deviation index, perform a binary judgment of water quality status based on the conductivity deviation index, and generate the water quality damping drift amount based on the judgment result;

[0033] Drive command generation module: It is used to linearly superimpose the mechanical fatigue reference drift and the water quality damping drift to obtain the total performance drift, generate the target drive command based on the total performance drift, determine whether to perform running envelope clamping on the target drive command, and output the target drive command or the clamped target drive command.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0035] This invention effectively solves the control distortion problem caused by the inability to distinguish between natural water evaporation and concentration and the intrusion of exogenous pollutants in traditional control methods by constructing a theoretical evaporation model and dynamically comparing the theoretical conductivity benchmark slope with the actual conductivity change slope. This method utilizes thermodynamic principles to establish a natural benchmark for conductivity changes, decoupling the "pseudo-pollution" evaporation component from the actual water quality damping component in the actual detection signal. This allows for accurate identification of the true pollution intrusion state in the binary judgment of water quality status, preventing the system from erroneously performing power increase operations due to the physical increase in conductivity values ​​under natural concentration conditions. By combining the independent calculation of mechanical fatigue benchmark drift with the linear superposition of total performance drift, a full-dimensional compensation architecture covering both hardware mechanical decay and water quality chemical decay is constructed. Furthermore, an operational envelope clamping mechanism is introduced at the output end, ensuring a constant ion output throughout the entire life cycle of the negative ion purifier while fundamentally eliminating the increased ineffective energy consumption and the risk of dry burning at low water levels caused by misjudging high concentration conditions. This achieves coordinated control of precise self-calibration and intrinsic safety. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart of a method for self-calibrating control of ion output drift in a negative ion purifier, provided by an embodiment of the present invention.

[0038] Figure 2 This is a schematic diagram of the Leonard effect generating negative ions provided in an embodiment of the present invention;

[0039] Figure 3 This is a schematic diagram of the aging process of the rubber diaphragm provided in an embodiment of the present invention;

[0040] Figure 4 A flowchart for the binary determination of water quality status provided in an embodiment of the present invention;

[0041] Figure 5 This is a schematic diagram of the detection of a sudden drop in conductivity during a water addition event provided in an embodiment of the present invention;

[0042] Figure 6 This is a functional block diagram of a negative ion purifier ion output drift self-calibration control system provided in an embodiment of the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Example 1:

[0045] Please see Figure 1 As shown, this embodiment provides a self-calibration control method for ion output drift in a negative ion purifier, including:

[0046] Step S10: Obtain the cumulative running time of the air pump, use the cumulative running time of the air pump to query the pre-stored diaphragm stiffness life cycle decay curve, determine the current pressure decay percentage, and calculate the mechanical fatigue reference drift based on the current pressure decay percentage.

[0047] Further, step S10 includes:

[0048] Step S11: Read and accumulate the working time of the air pump in real time to obtain the cumulative running time of the air pump. Use the cumulative running time of the air pump as an index to query the pre-stored diaphragm stiffness life cycle decay curve and determine the current pressure decay percentage through linear interpolation.

[0049] Step S12: Calculate the drive power compensation value required to restore the factory pressure output based on the current pressure attenuation percentage, and define the drive power compensation value as the mechanical fatigue reference drift amount.

[0050] Specifically, negative ion air purifiers generate negative ions using the Leonard effect principle. Please refer to [link / reference needed]. Figure 2 , Figure 2 This schematically illustrates the Leonard effect process, in which a complete water droplet is transformed into negative ions. (For example...) Figure 2 As shown, a complete water droplet is impacted and broken into tiny water mist particles by a high-pressure airflow. During this process, charge separation occurs, generating negatively charged ions. The Leonard effect refers to the charge separation phenomenon that occurs when a water droplet breaks apart under aerodynamic force. An air pump (in this embodiment, a dual-head air pump structure) serves as the core power source, outputting... Figure 2 The high-pressure airflow shown drives the water droplets to impact and break apart. The output pressure of the air pump directly determines the degree of droplet breakage and the efficiency of negative ion generation. The rubber diaphragm inside the air pump continuously bears mechanical stress during its reciprocating motion. Please refer to [link to relevant documentation]. Figure 3 , Figure 3 The aging process of a rubber diaphragm is illustrated schematically. For example... Figure 3As shown, with the increase of cumulative operating time, the new diaphragm, which originally had strong resilience and good airtightness, gradually evolves into an aged diaphragm with microcrack propagation and hardening of elastic modulus. With the accumulation of operating time, the rubber diaphragm undergoes the aforementioned... Figure 3 The hardening of the elastic modulus and the propagation of microcracks shown lead to a decrease in the resilience and airtightness of the diaphragm. Figure 3 The reciprocating motion shown in the diagram causes a gradual decrease in output pressure due to physical aging, resulting in a gradual decrease in the output pressure of the air pump under the same driving voltage, which in turn leads to a gradual decrease in negative ion production. The cumulative running time of the air pump is obtained through a timer integrated inside the controller. The controller is installed on the PCB main control board of the negative ion purifier. The PCB main control board is the core circuit board of the device, integrating the controller, drive circuit, sensor interface, and power management module. The timer continuously accumulates the count when the air pump is powered on and pauses counting when the air pump is powered off or in standby mode. The accumulated time data is stored in non-volatile memory, which is a storage medium that retains data even after power failure. This ensures that the historical accumulated running time can be read after the device is powered on again without recounting. This design allows the cumulative running time of the air pump to truly reflect the actual working process of the air pump diaphragm rather than just recording the duration of a single power-on. The diaphragm stiffness lifecycle decay curve is a characteristic curve obtained through accelerated aging tests during the product development phase and pre-stored in the controller's storage unit. This curve plots the cumulative operating time of the air pump on the horizontal axis and the percentage decrease in pump output pressure relative to the factory-calibrated pressure on the vertical axis. The accelerated aging test is conducted by continuously running multiple sets of the same model of air pumps under temperatures and frequencies higher than normal operating conditions. The actual output pressure of each set of air pumps is measured at fixed time intervals and compared with the factory-calibrated pressure to calculate the percentage decrease. The data from multiple sets of tests are statistically averaged and fitted to generate the decay curve. The fatigue characteristics of rubber diaphragms follow the stress relaxation law of polymer materials. The decay rate is relatively fast in the early stages of operation, and gradually slows down as the operating time increases. Therefore, the diaphragm stiffness lifecycle decay curve typically exhibits a non-linear logarithmic decay pattern rather than a simple linear decreasing pattern.

[0051] The process of using the cumulative operating time of the air pump as an index to look up the diaphragm stiffness lifecycle decay curve is implemented using a combination of table lookup and linear interpolation. The diaphragm stiffness lifecycle decay curve is stored in the controller as discrete data points, each containing a cumulative operating time node and its corresponding pressure decay percentage value. When the actual acquired cumulative operating time of the air pump falls exactly on a pre-stored node, the pressure decay percentage value corresponding to that node is directly read as the current pressure decay percentage. When the actual acquired cumulative operating time of the air pump falls between two adjacent pre-stored nodes, the system locates the adjacent node interval where the time value is located, extracts the cumulative operating time values ​​and corresponding pressure decay percentage values ​​of the two nodes before and after, and calculates the current pressure decay percentage using linear interpolation. Compared to running complex fitting functions in real time, the method of combining table lookup and linear interpolation significantly reduces the computational load on the controller, allowing the determination of the pressure decay percentage to be completed within milliseconds, meeting the response requirements of real-time control.

[0052] The pressure attenuation percentage characterizes the decrease in the current air pump output pressure relative to the factory calibration pressure. This percentage directly reflects the degree of elasticity loss of the rubber diaphragm due to long-term reciprocating motion. The principle of calculating the drive power compensation value required to restore the factory pressure output based on the pressure attenuation percentage is based on the air pump's pressure-power response characteristics: there is a positive correlation between the air pump's output pressure and drive power. When diaphragm aging causes a drop in output pressure, it is necessary to increase the drive power to compensate for this pressure loss. The calculation process for the drive power compensation value is as follows: Multiply the pressure attenuation percentage by the pre-stored factory calibration pressure value to obtain the absolute amount of pressure loss. Subtract the absolute amount of pressure loss from the factory calibration pressure value to obtain the current attenuated pressure value. The absolute amount of pressure loss serves as an intermediate calculation quantity, converting the pressure attenuation percentage from a relative quantity to an absolute pressure difference with physical dimensions. This determines the position of the current attenuated pressure value on the pressure-power response characteristic curve. On the air pump's pressure-power response characteristic curve, first, the drive power corresponding to the current pressure operating point is located based on the current attenuated pressure value. Then, the drive power corresponding to the factory pressure operating point is located based on the factory calibration pressure value. Subtract the drive power corresponding to the current pressure operating point from the drive power corresponding to the factory pressure operating point to obtain the power increment required to compensate for the pressure loss. This power increment is the drive power compensation value. The air pump's pressure-power response characteristic curve is also obtained through experimental calibration during the product development phase and pre-stored in the controller. This curve describes the correspondence between different drive power levels and the actual output pressure of the air pump. Because the response sensitivity of the air pump varies at different operating points, the pressure-power response characteristic curve usually exhibits a non-linear shape. Therefore, the calculation of the drive power compensation value needs to be performed by segmented querying or interpolation on the curve in combination with the current basic drive power level, so as to ensure that the compensation amount can accurately correspond to the actual pressure recovery requirements rather than using a simple amplification with a fixed ratio.

[0053] The significance of defining the drive power compensation value as the mechanical fatigue reference drift is that: the mechanical fatigue reference drift characterizes the energy increment required to overcome the mechanical aging of the air pump itself, assuming the water quality remains consistently pure and ideal. This drift is a value independent of water quality conditions, reflecting the unavoidable physical degradation at the hardware level. The mechanical fatigue reference drift points to the fatigue aging of the rubber diaphragm as its physical cause and serves as a fixed basis component for subsequent total compensation calculations, characterizing the extent to which ion production deviates from the factory setting. The mechanical fatigue reference drift is calculated independently of water quality changes, allowing subsequent steps to add the additional degradation caused by water quality changes as independent components, forming a layered compensation architecture of "mechanical aging compensation plus water quality degradation compensation."

[0054] Step S10 matches the accumulated operating time of the air pump with the pre-stored diaphragm stiffness life cycle decay curve, transforming the abstract accumulated time into a concrete percentage of pressure decay, and then calculating the quantified mechanical fatigue reference drift, thus realizing real-time tracking and quantified compensation of the air pump hardware aging degree. The pre-stored curve is generated based on the statistical average results of multiple samples, which can filter out individual differences and fluctuations of single devices and provide a more universal description of the decay law. The drive power compensation value is defined as an independent mechanical fatigue reference drift and calculated separately from the subsequent water quality damping drift, so that the compensation architecture presents a clear physical hierarchy. Each drift component has a clear physical correspondence. This hierarchical architecture not only improves the compensation accuracy, but also facilitates system fault diagnosis. When the growth rate of the mechanical fatigue reference drift is abnormally fast, it can indicate that the air pump may have abnormal wear and needs maintenance. When the water quality damping drift is consistently high, it can indicate to the user that the water source in the water tank needs to be replaced. The mechanical fatigue baseline drift output in step S10 is used as an unconditional fixed compensation component to participate in the subsequent total drift synthesis. This ensures that the system can continuously compensate for the hardware aging of the air pump regardless of changes in water quality. This allows the baseline of ion production of the negative ion purifier to be maintained throughout its entire life cycle, avoiding the problem of "good effect when new, but diminishing effect after long-term use" that occurs in traditional products due to neglecting hardware aging.

[0055] Step S20: Calculate the saturated vapor pressure difference at the water-air interface, obtain the theoretical water evaporation rate based on the saturated vapor pressure difference at the water-air interface, deduce the theoretical conductivity baseline slope based on the theoretical water evaporation rate, obtain the actual conductivity change slope, compare the actual conductivity change slope with the theoretical conductivity baseline slope to obtain the conductivity deviation index, perform a binary judgment of water quality status based on the conductivity deviation index, and generate water quality damping drift based on the judgment result.

[0056] During long-term operation of a negative ion purifier, the conductivity of the water in the tank continuously changes. This increase in conductivity has two distinct physical causes: one is the concentration of solutes due to natural evaporation. In high-temperature, low-humidity environments, water molecules escape from the liquid surface into the air, reducing the volume of water remaining in the tank. While the total amount of mineral ions dissolved in the water remains constant, the solute concentration passively increases due to volume contraction, leading to a rise in conductivity. However, the surface tension of the water does not substantially change, and the efficiency of the Leonard effect in generating negative ions is not significantly affected. The other cause is the intrusion of external pollutants. Users pour impurities from tap water into the tank, or dust particles from the air fall into the tank, increasing the total amount of dissolved ions or suspended particles. The conductivity increases abruptly due to the increased solute volume, and the surface tension of the water changes due to the surface activity of the impurities, thus reducing the efficiency of the Leonard effect in generating negative ions. Traditional control methods rely solely on conductivity values ​​for judgment, failing to distinguish between the two causes mentioned above. When water evaporation leads to increased conductivity, it is mistakenly interpreted as water quality deterioration, prompting an increase in pump power for compensation. This overcompensation not only wastes energy but may also lead to pump burnout at low water levels. Conversely, when contaminant intrusion causes increased conductivity, insufficient compensation cannot effectively counteract the decrease in ion production due to increased surface tension. Step S20 constructs a theoretical evaporation model to predict the natural increase in conductivity under the assumption of no pollution. By comparing the actual measured rate of conductivity change with the theoretical prediction, the evaporation concentration component and the contaminant intrusion component in the conductivity signal are decoupled and separated, achieving an accurate determination of the true state of water quality.

[0057] Further, step S20 includes:

[0058] Step S21: Collect the current ambient temperature, ambient humidity and internal heat load of the equipment, calculate the saturated vapor pressure difference at the water-vapor interface, construct a theoretical evaporation model, and obtain the theoretical water evaporation rate based on the saturated vapor pressure difference at the water-vapor interface.

[0059] Step S22: Based on the theoretical water evaporation rate, deduce the upward trend of conductivity caused by natural evaporation and generate the theoretical conductivity benchmark slope.

[0060] Ambient temperature and humidity are collected through temperature and humidity sensors installed inside the equipment. These sensors employ capacitive or resistive sensing principles and can output the current ambient temperature and relative humidity values ​​in real time. The internal heat load refers to the heat generated by the air pump and PCB main control board during operation, which has a heating effect on the water tank. The rubber diaphragm of the air pump generates heat due to internal friction during reciprocating motion, and the power devices on the PCB main control board generate heat due to resistive losses when drive current passes through them. This heat is transferred to the water tank wall through air convection and thermal conduction within the equipment, causing the actual temperature of the water inside the tank to be higher than the ambient temperature. The quantification of the internal heat load of the equipment is achieved through a pre-stored heat load characteristic table. This table takes the current drive power of the air pump and the cumulative operating time as input parameters and outputs the corresponding internal heat load value. The heat load characteristic table is generated during the product development phase through thermal testing and calibration. The calibration method involves continuously running the equipment at different drive power levels, measuring the surface temperature of the air pump body, the surface temperature of the PCB main control board, and the water tank wall temperature using temperature sensors, and calculating the actual heat transfer power from the air pump and PCB main control board to the water tank at each drive power level based on the measured temperature differences and known structural component thermal resistance parameters. Simultaneously, the impact of cumulative operating time on the heat transfer power is recorded. As the cumulative operating time increases, the air pump rubber diaphragm... Aging leads to an increase in the internal friction coefficient, and the junction temperature of power devices on the PCB main control board gradually rises due to long-term operation. This causes the heat generation power under the same drive power to show a slow increasing trend with the cumulative operating time. The heat generation power data measured under different combinations of drive power and cumulative operating time are organized into a two-dimensional lookup table with drive power and cumulative operating time as dual indices. During operation, the controller performs bilinear interpolation calculations on this two-dimensional lookup table based on the current drive power value and cumulative operating time value, thereby outputting the internal heat load value of the equipment under the corresponding operating condition. The controller calculates the temperature rise of the water body relative to the ambient temperature based on the internal heat load value and the thermal resistance parameter of the equipment. The actual temperature of the water body is obtained by adding the ambient temperature and the temperature rise. The purpose of collecting the three parameters of ambient temperature, ambient humidity, and internal heat load of the equipment is to obtain all the thermodynamic boundary conditions affecting water evaporation. Ambient temperature determines the thermal motion energy of water molecules, ambient humidity determines the existing water vapor content in the air, and internal heat load of the equipment corrects the deviation between the actual water temperature and the ambient temperature. The three together constitute the complete input for evaporation rate calculation.

[0061] The saturated vapor pressure difference at the water-air interface is the thermodynamic potential difference that drives water evaporation. The calculation process is as follows: Based on the actual temperature of the water body, the saturated vapor pressure at the water-air interface is calculated using the Antoine equation, an empirical formula describing the relationship between the saturated vapor pressure of a pure substance and temperature. The actual partial pressure of water vapor in the air is calculated based on the ambient temperature and humidity; this actual partial pressure equals the saturated vapor pressure corresponding to the ambient temperature multiplied by the relative humidity. The saturated vapor pressure difference is obtained by subtracting the actual partial pressure of water vapor in the air from the saturated vapor pressure at the water-air interface. The physical meaning of the saturated vapor pressure difference is that the water vapor concentration at the water-air interface is higher than the water vapor concentration in the surrounding air. This concentration difference drives water molecules to diffuse from the liquid surface into the air; the larger the saturated vapor pressure difference, the stronger the driving force for evaporation. The theoretical evaporation model is constructed based on Fick's diffusion law and the mass transfer boundary layer theory. Fick's diffusion law describes that the diffusion rate of a substance from a high-concentration region to a low-concentration region is proportional to the concentration gradient. The mass transfer boundary layer theory describes the existence of a stationary air layer above the liquid surface, which water vapor molecules must cross to enter the main airflow. The theoretical evaporation model simplifies the water tank surface as a planar evaporation source, uses the saturated vapor pressure difference as the driving force for evaporation, and employs the mass transfer coefficient as a quantitative representation of boundary layer resistance. The mass transfer coefficient is related to the airflow state inside the equipment and is determined through calibration tests during product development and pre-stored in the controller. The calibration method for the mass transfer coefficient is as follows: Under controlled ambient temperature and humidity conditions, the equipment is continuously operated using a water tank with a known initial water volume. The actual remaining water volume is measured at fixed time intervals. The actual evaporated water volume is calculated based on the difference between the initial water volume and the actual remaining water volume. The actual evaporated water volume is compared with the predicted value of the theoretical evaporation model based on the assumed mass transfer coefficient. The mass transfer coefficient value that best fits the model prediction value with the actual measurement value is calculated using the least squares method or other parameter identification methods. This calibration process is repeated under multiple sets of different ambient temperature and humidity conditions to establish a correspondence table or fitting function between the mass transfer coefficient and environmental parameters, which is pre-stored in the controller. During operation, the controller queries the mass transfer coefficient correspondence table or substitutes the values ​​into the fitting function based on the currently collected ambient temperature and humidity to obtain the current mass transfer coefficient value. The theoretical water evaporation rate is calculated using the mass transfer rate equation, which expresses the relationship between the mass of water evaporated per unit area per unit time, the saturated vapor pressure difference, and the mass transfer coefficient. Substituting the effective evaporation area of ​​the water tank surface into the mass transfer rate equation, we obtain the mass of water escaping from the entire liquid surface per unit time, which is the theoretical water evaporation rate. The theoretical water evaporation rate characterizes the rate at which water is lost from the tank due to natural evaporation under current environmental conditions.

[0062] The theoretical conductivity baseline slope is generated based on the law of conservation of solute, which states that in a closed system without the addition of external substances, the total amount of solute remains constant. When solvent evaporation causes a decrease in solution volume, the solute concentration must increase proportionally. The decrease in water volume per unit time is calculated based on the theoretical water evaporation rate and the density of water. This decrease equals the theoretical water evaporation rate divided by the density of water. The current water volume in the tank is assumed to be the initial volume at the time of the last addition minus the cumulative evaporated water volume since the last addition. The cumulative evaporated water volume is obtained by integrating the decrease in water volume per unit time. There is a positive linear correlation between water conductivity and solute concentration; conductivity increases synchronously with increasing solute concentration. The proportionality coefficient between the rate of change in concentration and the rate of change in conductivity is determined through calibration experiments during product development and is pre-stored as a fixed constant in the controller. The controller directly uses this proportionality coefficient when calculating the theoretical conductivity baseline slope. The derivation process of the theoretical conductivity baseline slope is as follows: According to the law of conservation of solute, the solute concentration equals the total solute volume divided by the water volume. The total solute volume equals the product of the initial conductivity value measured under stable conditions after the last water addition and the initial volume corresponding to the initial water volume at that time, divided by the proportionality coefficient between the rate of concentration change and the rate of conductivity change. The initial conductivity value is obtained by collecting the conductivity sensor output value within a preset stable time window after the water addition event is detected, and calculating the arithmetic mean of multiple sampling values ​​within the stable time window. The preset stable time window refers to the time period required for the newly added water to fully mix with the original water in the tank after the water addition event is detected. In the initial stage after the water addition event, the newly added low conductivity water has not yet fully mixed with the original high conductivity water, and the conductivity sensor reading is in a state of violent fluctuation. The conductivity value collected under this fluctuating state cannot be obtained. To accurately reflect the true conductivity level of the mixed water, sampling is only required after the water conductivity has stabilized following the completion of a preset stabilization time window. The duration of the preset stabilization time window is determined experimentally during product development. Under different initial water volumes and added water volumes, the time required for the conductivity reading to stabilize and converge after water addition is recorded. The longest convergence time among multiple sets of test results, plus a safety margin, is taken as the duration of the preset stabilization time window. For example, the duration of the preset stabilization time window can be set between 30 and 120 seconds. When the water volume decreases due to evaporation, the rate of change of solute concentration is equal to the total solute multiplied by the rate of change of water volume and then divided by the square of the water volume. The rate of change of water volume is replaced by the theoretical water evaporation rate divided by the density of water. The rate of change of solute concentration is converted into the rate of change of conductivity through a proportionality coefficient to obtain the theoretical conductivity baseline slope.The physical meaning of the theoretical conductivity baseline slope is: under ideal conditions assuming no external pollutants enter the water tank, the rate at which conductivity increases over time solely due to natural evaporation of water. This slope value is entirely determined by the current ambient temperature, humidity, and water volume, and is unrelated to whether the water quality is polluted. The theoretical conductivity baseline slope serves as a benchmark for conductivity changes, providing a standard for determining whether actual conductivity changes deviate from the natural evaporation pattern.

[0063] Step S23, see Figure 4 The actual conductivity is collected in real time, and the actual conductivity is differentiated over time to obtain the slope of the actual conductivity change. The difference between the actual conductivity change slope and the theoretical conductivity reference slope is used to obtain the conductivity deviation index.

[0064] Step S24: Compare the conductivity deviation index with the preset evaporation tolerance threshold to perform a binary judgment of water quality status. If the conductivity deviation index is less than or equal to the evaporation tolerance threshold, it is judged as a natural concentration state and the water quality damping drift is set to zero. If the conductivity deviation index is greater than the evaporation tolerance threshold, it is judged as a pollution intrusion state. The conductivity deviation index is then input into the pre-stored impurity damping mapping function to calculate the corresponding water quality damping drift.

[0065] Actual conductivity is acquired using a conductivity sensor installed inside the water tank. This sensor employs a two-electrode or four-electrode measurement principle, calculating the water's conductivity by applying an AC voltage to the electrodes and measuring the current flowing through the water. The slope of the actual conductivity change is calculated using time differentiation, which calculates the rate of change of a physical quantity over time. In discrete sampling systems, this is typically achieved using a differential approximation method. The differential approximation method works as follows: the controller stores the conductivity measurements at two adjacent sampling times. The conductivity value at the later time is subtracted from the conductivity value at the earlier time to obtain the conductivity change. This change is then divided by the time interval between the two sampling times to obtain the actual conductivity slope. To eliminate the interference of single-measurement noise on the slope calculation, the controller can use a sliding window averaging method, averaging the conductivity changes over multiple consecutive sampling periods before calculating the slope. The length of the sliding window is determined based on the measurement noise level of the conductivity sensor and the system response speed requirements. The physical meaning of the slope of the actual conductivity change is: the actual rate of increase of the conductivity of the water in the tank at the current moment. This slope value includes the superimposed effect of two causes: natural evaporation and concentration and possible pollution intrusion.

[0066] The conductivity deviation index is calculated by comparing the actual conductivity change slope with the theoretical conductivity baseline slope. The comparison method is as follows: subtract the theoretical conductivity baseline slope from the actual conductivity change slope to obtain the conductivity deviation index. The conductivity deviation index characterizes the degree of deviation between the actual conductivity change rate and the theoretical evaporation prediction value. A larger deviation indicates a greater component of non-evaporation factors in the conductivity increase, and a higher possibility of pollution intrusion. A deviation close to zero indicates that the conductivity increase fully conforms to the natural evaporation law, and the water quality has not been affected by external pollution. The conductivity deviation index removes the evaporation concentration effect from the conductivity signal, extracting the effective signal reflecting the true water quality changes. This allows subsequent water quality status judgments to be based on the pure signal after artifact removal, avoiding misjudgments caused by evaporation concentration.

[0067] The binary judgment of water quality status requires a preset evaporation tolerance threshold as the judgment boundary. The setting of the evaporation tolerance threshold is based on factors including the measurement accuracy of the conductivity sensor, the error range of environmental parameter acquisition, and the prediction deviation of the theoretical evaporation model. Conductivity sensors inherently exhibit measurement noise during the measurement process. This noise causes fluctuations in the calculated slope of the actual conductivity change, meaning that even if the water quality remains unchanged, the conductivity deviation index may exhibit non-zero random fluctuations. Environmental temperature and humidity sensors also have measurement errors, which are transmitted to the prediction results of the theoretical evaporation model, causing a deviation between the theoretical conductivity baseline slope and the actual natural evaporation slope. The value of the evaporation tolerance threshold should cover the combined range of the aforementioned measurement noise and model errors, ensuring that the conductivity deviation index does not exceed the threshold and trigger a false judgment when the water quality is indeed unpolluted. The method for determining the evaporation tolerance threshold is as follows: during the product development stage, pure water is used to conduct long-term evaporation tests under different ambient temperature and humidity conditions. The fluctuation range of the conductivity deviation index is continuously recorded. The upper limit of the fluctuation range is multiplied by the safety factor as the evaporation tolerance threshold. The safety factor is usually greater than 1 to reserve a margin to cope with the complexity of the on-site environment. For example, the safety factor can be set between 1.2 and 1.5.

[0068] The binary judgment of water quality status has two possible outcomes: When the conductivity deviation is less than or equal to the evaporation tolerance threshold, the current water quality is determined to be in a naturally concentrated state. This indicates that the increase in conductivity is entirely due to solute concentration caused by water evaporation, the physicochemical properties of the water body have not changed substantially, the efficiency of the Leonard effect is unaffected, and no additional compensation is needed. The controller forcibly sets the water quality damping drift to zero. When the conductivity deviation is greater than the evaporation tolerance threshold, the current water quality is determined to be in a contamination-invaded state. This indicates that the increase in conductivity has an abnormal component exceeding the natural evaporation pattern, suggesting the possible introduction of external impurities into the water body. These impurities alter the surface tension characteristics of the water body, and the efficiency of the Leonard effect is indeed reduced, requiring corresponding power compensation. The water quality damping drift is set to zero in the naturally concentrated state to avoid unnecessary power increase caused by the system's artificially high conductivity signal. When the water level in the tank decreases to a low level due to evaporation, if the system still performs a large power compensation based on the artificially high conductivity, the air pump will operate at high power in the low water level state, increasing the risk of dry burning and accelerating the wear of the rubber diaphragm.

[0069] The calculation of water quality damping drift under polluted intrusion conditions is achieved through an impurity damping mapping function. This function is a pre-stored nonlinear mapping relationship in the controller, describing the correspondence between the conductivity deviation index and the degree of ion production attenuation caused by water quality. The impurity damping mapping function is constructed based on the physical mechanism of the Leonard effect: when water droplets break apart under aerodynamic forces, charge separation occurs. The greater the surface tension of the water body, the more difficult it is for the water droplets to break apart, and the fewer negative ions are generated. After exogenous impurities enter the water body, impurity ions and particulate matter alter the hydrogen bond network structure between water molecules, leading to changes in the surface tension of the water body. When the impurity concentration increases to a certain level, the increase in surface tension will significantly inhibit the droplet breaking effect. The conductivity deviation index reflects the degree of impurity entry into the water body; a larger deviation index indicates a higher impurity concentration, a greater deviation of the surface tension from the pure water state, and a more severe attenuation of ion production. The impurity damping mapping function typically exhibits a non-linear characteristic. When the conductivity deviation is small, the water quality damping drift increases slowly with the deviation; when the conductivity deviation is large, the water quality damping drift increases rapidly with the deviation. This non-linear characteristic reflects the response of surface tension to impurity concentration. The calibration of the impurity damping mapping function is performed during the product development phase: a known concentration of impurities is gradually added to pure water to simulate the contamination intrusion process. At each impurity concentration level, the percentage decrease in conductivity deviation and actual ion production is measured. Based on the percentage decrease in ion production, the power increment required to restore the factory ion production, i.e., the water quality damping drift, is calculated. The corresponding data points of conductivity deviation and water quality damping drift are curve-fitted to generate the impurity damping mapping function. Under contamination intrusion conditions, the controller substitutes the current conductivity deviation into the impurity damping mapping function and calculates the corresponding water quality damping drift using table lookup or interpolation methods.

[0070] Step S20 constructs a theoretical evaporation model to predict the natural evaporation pattern, quantifies the deviation between actual changes and theoretical predictions using the conductivity deviation index, distinguishes between the natural concentration state and the pollution intrusion state using a binary water quality state determination, and converts the deviation into a power compensation quantity using an impurity damping mapping function. This achieves decoupling and separation of the evaporation concentration component and the pollution intrusion component in the conductivity signal, enabling the system to accurately identify the true state of water quality and generate targeted water quality damping drift. The introduction of the theoretical evaporation model incorporates environmental temperature and humidity information into the input parameters for water quality determination. Traditional methods rely solely on the conductivity value itself and cannot obtain environmental information. However, this scheme calculates the theoretical water evaporation rate through environmental parameters, and then derives the theoretical conductivity change pattern from the theoretical water evaporation rate, establishing a causal chain between environmental conditions and expected conductivity changes. This allows the controller to predict the natural change trend of conductivity based on current environmental conditions, thereby comparing the actual measured value with the predicted trend to identify anomalies. The application of the law of conservation of solute links the macroscopic evaporation rate with the microscopic change in solute concentration. The evaporation rate affects the concentration through volume change, and the concentration change affects the conductivity through electrochemical properties. This physical derivation chain ensures that the calculation of the theoretical conductivity baseline slope has a clear thermodynamic basis rather than empirical fitting. The binary determination design of water quality state makes the system's response to water quality changes exhibit a step characteristic rather than a continuous gradual characteristic. In the natural concentration state, the water quality damping drift is strictly zero, ensuring that the system does not generate any power adjustment for water quality. In the contaminated state, the water quality damping drift increases accordingly according to the degree of deviation, ensuring that the system fully resists the increase in surface tension. This step characteristic avoids uncertain compensation behavior in the fuzzy transition zone between the natural concentration state and the contaminated state. The impurity damping mapping function pre-calibrates and stores the complex nonlinear relationship between the conductivity deviation index and the power compensation amount. During operation, only table lookup or interpolation calculations are needed to obtain the compensation amount, avoiding complex physicochemical calculations in each control cycle, thus reducing the real-time computational load while ensuring compensation accuracy. The water quality damping drift output in step S20 and the mechanical fatigue reference drift output in step S10 are physically independent of each other. The former is caused by changes in the chemical properties of the water, while the latter is caused by the mechanical performance degradation of the air pump. After being calculated separately, they are superimposed in step S30 to form a hierarchical architecture that quantifies and compensates for hardware degradation and water quality degradation in parallel. This hierarchical architecture allows the trend of change of each drift component to be tracked independently, providing a quantitative basis for equipment fault diagnosis and maintenance prompts.

[0071] Step S30: Linearly superimpose the mechanical fatigue reference drift and the water quality damping drift to obtain the total performance drift. Based on the total performance drift, reverse address the voltage-power response characteristic curve of the air pump to generate the target drive command. Determine whether it is necessary to perform running envelope clamping on the target drive command, and output the target drive command or the clamped target drive command.

[0072] Further, step S30 includes:

[0073] Step S31: Perform a linear superposition operation on the mechanical fatigue reference drift and the water quality damping drift to obtain the total performance drift.

[0074] The mechanical fatigue baseline drift represents the fixed attenuation component caused by the hardening of the elastic modulus and the decrease in airtightness of the air pump's rubber diaphragm due to long-term reciprocating motion. The water quality damping drift represents the dynamic attenuation component caused by changes in water surface tension due to the intrusion of exogenous pollutants. These two drift components are physically independent and have distinct mechanisms of action, but both lead to a decrease in the efficiency of negative ion generation through the Leonard effect, requiring compensation by increasing the air pump's drive power. The total performance drift is calculated using a linear superposition operation, directly adding the mechanical fatigue baseline drift and the water quality damping drift. The physical basis of this linear superposition operation is that the effects of mechanical attenuation and water quality attenuation on ion production are independent and additive. Mechanical attenuation leads to a decrease in air pump output pressure, thus weakening the force of droplet breakage; water quality attenuation leads to an increase in water surface tension, thus increasing the resistance to droplet breakage. Both effects act simultaneously on the droplet breakage process, and their respective attenuation contributions can be directly added together. Linear superposition allows the contributions of the two drift components to be tracked independently. When the total performance drift increases abnormally, the source of the degradation can be quickly located by analyzing the changing trends of each component, whether it is hardware aging or water quality deterioration, providing a quantitative basis for equipment maintenance and fault diagnosis.

[0075] The unit of the total performance drift is consistent with that of the mechanical fatigue reference drift and the water quality damping drift, both being in the dimension of power. This represents the additional drive power increment required to restore ion production to the factory setting. When the system is in a natural concentration state, the water quality damping drift output in step S20 is zero, and the total performance drift is only equal to the mechanical fatigue reference drift. The system only compensates for power loss due to pump hardware aging and will not overcompensate due to artificially high conductivity signals. This characteristic allows the system to remain restrained when conductivity increases due to water evaporation, avoiding unnecessary power increases and the resulting energy waste and dry-burning risks. When the system is in a contamination intrusion state, step S20 outputs a non-zero water quality damping drift, and the total performance drift is equal to the sum of the mechanical fatigue reference drift and the water quality damping drift. The system simultaneously performs dual power compensation for pump hardware aging and water quality degradation, ensuring that ion production can be fully restored to the factory setting. For example, when the air pump has accumulated to the middle of its design life, the mechanical fatigue reference drift may correspond to about 10% of the rated power. If the water quality is pure at this time, the total performance drift is only 10%. If the water quality is polluted at this time, the water quality damping drift corresponds to 5% of the rated power, and the total performance drift rises to 15%. The power compensation range is dynamically adjusted according to the actual degree of attenuation.

[0076] Step S32: Call the voltage-power response characteristic curve of the air pump, use the total performance drift as the target value to reverse address the required drive voltage increment, and add the drive voltage increment to the base drive voltage to generate the target drive command.

[0077] The target drive command is a drive voltage value, representing the drive voltage level required by the air pump within the current control cycle. The control cycle refers to the time interval for the controller to execute one complete control operation. The voltage-power response characteristic curve describes the actual drive power consumed by the air pump at different drive voltage levels. This curve is different from the pressure-power response characteristic curve used in step S10. The pressure-power response characteristic curve describes the relationship between drive power and output pressure, used to convert pressure attenuation into power compensation. The voltage-power response characteristic curve is used to convert power compensation into drive voltage increment. The voltage-power response characteristic curve has drive voltage as the horizontal axis and drive power as the vertical axis. The curve shape reflects the efficiency characteristics of the air pump in converting electrical energy into mechanical energy. The voltage-power response characteristic curve is obtained through experimental calibration during the product development stage and pre-stored in the controller. The calibration method is as follows: when the air pump is in a brand new state, the drive voltage is increased in fixed steps, and the drive current is measured at each voltage level. The drive power value is calculated based on the product of the drive voltage and the drive current. The voltage value and the corresponding power value are paired to form data points and curve fitting is performed to generate the voltage-power response characteristic curve. The voltage-power response of an air pump typically exhibits a non-linear characteristic. In the low-voltage region, the power growth rate with increasing voltage is low; in the medium-voltage region, the growth rate is high; and near the rated voltage, the growth rate decreases again and tends to saturate. This non-linear characteristic stems from the elastic properties of the air pump diaphragm and the pressure dependence of the air path resistance. The base drive voltage refers to the default drive voltage value of the air pump calibrated at the factory. The unit of the base drive voltage is voltage. This drive voltage value corresponds to the drive power level required for the air pump to produce the designed ion output when it is in brand-new condition and using pure water. The value of the base drive voltage is determined through calibration tests during the product development phase and is pre-stored in the controller.

[0078] Reverse addressing refers to the process of calculating the required input increment from a known target output increment, which is the opposite of the forward process of finding the output from a known input. The implementation process of reverse addressing is as follows: The total performance drift is used as the target power increment value to be compensated. The power operating point corresponding to the current base drive voltage is located on the voltage-power response characteristic curve. From this operating point, the system moves forward along the curve until the power increase equals the total performance drift. The voltage value corresponding to the moved position is read, and this voltage value is subtracted from the current base drive voltage to obtain the drive voltage increment. Since the voltage-power response characteristic curve is non-linear, the same power increment corresponds to different voltage increments at different operating points. Therefore, reverse addressing needs to be calculated in conjunction with the current operating point position to ensure that the compensation amount accurately matches the actual requirements. The voltage-power response characteristic curve stored in the controller is represented in the form of discrete data points. When the target power increment falls between two adjacent data points, a linear interpolation method is used to calculate the corresponding voltage increment. The interpolation method is consistent with the method used in step S10 to query the diaphragm stiffness lifetime decay curve.

[0079] Step S33: Calculate the remaining water volume based on the theoretical water evaporation rate and the cumulative operating time of the air pump since the last water addition. Compare the remaining water volume estimate with the preset water volume safety threshold. If the remaining water volume estimate is greater than or equal to the water volume safety threshold, the system is determined to be in the full-power feasible region, and no running envelope clamping is required; the target drive command is directly output. If the remaining water volume estimate is less than the water volume safety threshold, the running envelope clamping is performed on the target drive command.

[0080] The cumulative operating time of the air pump since the last water filling is obtained through a combination of water filling event detection and an independent timer. Please refer to [link / reference]. Figure 5 , Figure 5 This diagram illustrates the principle of detecting a sudden drop in conductivity during water addition. For example... Figure 5 As shown, before a water addition event occurs, the conductivity of the water body shows a slow upward trend with evaporation and concentration. A significant sudden drop in conductivity within a short period is considered a water addition event. The reason for this sudden drop in conductivity is that the water added to the tank is usually purified or distilled water, whose conductivity is significantly lower than the original water in the tank, which has increased due to evaporation, concentration, or contamination. The newly added low-conductivity water mixes with the existing high-conductivity water, causing the overall conductivity to drop rapidly, forming... Figure 5 The detectable significant drop features are shown in the diagram. The criteria for determining a water addition event are set as follows: combined with... Figure 5As shown, if the conductivity value decreases beyond a preset conductivity drop threshold within a preset detection time window, the detection time window is a sliding observation period used by the controller to monitor whether a sudden drop in conductivity occurs. At each sampling moment, the controller compares the current conductivity value with the conductivity value recorded at the start of the detection time window, calculating the decrease in conductivity within that window period. The detection time window slides forward continuously with each sampling moment to achieve continuous monitoring of water addition events. The duration of the detection time window must balance detection sensitivity and anti-interference capability; a duration that is too short will cause sensor malfunction. Instantaneous fluctuations caused by measurement noise can be misidentified as sudden drops in conductivity. Excessive duration of these fluctuations will delay the response to water addition events. The detection time window is determined during product development based on the conductivity sensor's sampling period and the typical duration of a user's water addition operation. For example, the detection time window can be set between 10 and 60 seconds. The conductivity drop threshold must cover the range of conductivity fluctuations during normal evaporation to avoid misjudgments. For example, the conductivity drop threshold can be set to two to three times the maximum conductivity fluctuation within the detection time window during normal evaporation. Figure 5 As shown, after experiencing a sudden drop in conductivity and reaching a stable state after water addition, it means that when a water addition event is detected, the controller resets an independent water addition timer to zero and restarts the timing. This water addition timer is independent of the timer that records the cumulative running time of the air pump in step S10. The water addition timer only accumulates counts when the air pump is powered on and in operation, and pauses counting when the air pump is powered off or in standby mode. The cumulative running time of the air pump since the last water addition is the current reading of the water addition timer, representing the length of time the air pump has been operating since the most recent water addition event.

[0081] The calculation of the remaining water volume estimate is based on the principle of water balance: the current remaining water volume in the tank equals the initial water volume at the time of the last filling minus the cumulative evaporation since the last filling. The initial water volume is obtained as follows: after a water filling event is detected, if the device is equipped with a level sensor, the output value of the level sensor is directly read and converted into water volume; if the device is not equipped with a level sensor, it is assumed that the user fills the tank to the design capacity each time, and the design capacity is used as the initial water volume. The calculation of the cumulative evaporation is achieved by integrating the theoretical water evaporation rate over the cumulative operating time of the air pump since the last filling. Since the theoretical water evaporation rate dynamically adjusts with changes in ambient temperature and humidity, the integration process is implemented using a discrete accumulation method: in each control cycle, the current theoretical water evaporation rate is multiplied by the control cycle duration to obtain the evaporation increment within that cycle; the evaporation increments from all cycles are summed to obtain the cumulative evaporation since the last filling. The remaining water volume estimate equals the initial water volume minus the cumulative evaporation. This estimate reflects the current amount of water available for the air pump in the tank and serves as the basis for subsequent safety verification.

[0082] The water safety threshold refers to the lower limit of the water level in the tank. When the remaining water level is below this threshold, the air pump will face the risk of dry burning if it continues to operate at high power. Dry burning refers to the air pump continuing to operate when the water level in the tank is insufficient, leading to overheating of the pump body, accelerated aging of the rubber diaphragm, or even burnout. The water safety threshold is set based on the air pump's heat dissipation characteristics, the heat resistance temperature of the rubber diaphragm, and the minimum usable water level of the tank structure. The water safety threshold is usually set between 15% and 25% of the tank's design capacity to ensure that the air pump can still operate safely for a short period when the water level drops to the safety threshold, allowing users to add water in time. The estimated remaining water volume is compared with the water safety threshold to produce two judgment results: when the estimated remaining water volume is greater than or equal to the water safety threshold, the system is determined to be in the full power feasible region, indicating that the water volume in the tank is sufficient and the air pump can operate safely at any power level. The controller directly outputs the target drive command to the air pump drive circuit. When the estimated remaining water volume is less than the water safety threshold, the system is determined to be in a low water level warning state. The available power range of the air pump is limited, and the target drive command needs to be clamped by the running envelope before output.

[0083] The operating envelope refers to the power boundary curve of the air pump under different water level conditions, indicating its safe operation. The operating envelope is plotted with the remaining water volume on the horizontal axis and the upper limit of available drive power on the vertical axis. The curve shape reflects the constraint relationship between water volume and available power. The construction of the operating envelope is based on the air pump's thermal balance analysis: the heat generated during air pump operation needs to be dissipated through the heat absorption and evaporation of the water in the tank. The less water there is, the weaker the heat dissipation capacity, and the lower the upper limit of the available drive power. When the water volume is sufficient, the heat dissipation capacity is adequate, and the upper limit of available power equals the air pump's rated power. When the water volume drops to near the safe water volume threshold, the heat dissipation capacity is limited, and the upper limit of available power needs to be reduced accordingly to prevent overheating. The operating envelope curve is obtained and pre-stored in the controller during the product development phase through thermal simulation or experimental calibration. The calibration method is as follows: the air pump is continuously driven at different power levels under different water level conditions. The pump body temperature is monitored, and the power value corresponding to the temperature just reaching the safe upper limit is recorded. The water level value and the corresponding upper limit power value are paired to form data points, and curve fitting is performed to generate the operating envelope curve.

[0084] Operating envelope clamping refers to the operation of constraining the target drive command within the power boundary defined by the operating envelope curve. The specific execution process of clamping is as follows: Based on the current estimated remaining water volume, the corresponding available drive power upper limit is retrieved from the operating envelope curve. The drive voltage value corresponding to the target drive command is converted into the corresponding power value using the voltage-power response characteristic curve. This power value is compared with the available drive power upper limit. If the power value corresponding to the target drive command is less than or equal to the available drive power upper limit, the target drive command is output directly without adjustment. If the power value corresponding to the target drive command is greater than the available drive power upper limit, the target drive command is forcibly truncated to the drive voltage value corresponding to the available drive power upper limit, and the truncated drive voltage value is output as the clamped target drive command. The physical significance of the clamping operation is that, under low water level conditions, some ion production recovery is sacrificed in exchange for the safe operation of the air pump, avoiding overheating and damage to the air pump due to the pursuit of complete ion production recovery. While the clamped target drive command may not fully restore the ion output to the factory setting, it can achieve the maximum compensation effect within a safe range under the current water volume conditions. Simultaneously, it prompts the user to add water via the display screen or indicator lights, guiding them to replenish water in time to restore the system's full power compensation capability. The target drive command, or the clamped target drive command, is output to the air pump drive circuit via the PCB main control board. The drive circuit adjusts the duty cycle of the drive voltage or pulse width modulation signal output to the air pump according to the output target drive command, thereby changing the air pump's speed and output pressure. The increase in drive voltage increases the reciprocating amplitude or frequency of the air pump diaphragm, increasing the gas-liquid flow rate per unit time. This enhances the power of the high-pressure airflow driving the water droplets to impact and break up, improving the efficiency of the Leonard effect in generating negative ions, ultimately bringing the ion output back to a level close to or equal to the factory setting, completing the closed-loop self-calibration process.

[0085] Step S30 combines the mechanical fatigue baseline drift and the water quality damping drift into a total performance drift through linear superposition. The total performance drift is then converted into a drive voltage increment via the reverse addressing voltage-power response characteristic curve. The remaining water volume is estimated using water addition event detection and evaporation integration. Finally, the drive power is constrained at low water levels using an envelope clamp, thus achieving a complete conversion link from multidimensional drift to safe drive commands. The reverse addressing process, combined with the current operating point, ensures that the compensation on the nonlinear characteristic curve accurately corresponds to the actual requirements, avoiding undercompensation or overcompensation caused by fixed-ratio amplification. Water addition event detection utilizes the conductivity drop characteristic to automatically identify user water addition behavior, eliminating the need for manual triggering or additional water addition sensors, reducing complexity and hardware costs. The envelope clamp, while ensuring the safe operation of the air pump, achieves maximum compensation under the current water volume conditions, balancing safety and performance recovery goals, avoiding the extreme practices of abrupt shutdown at low water levels or continued operation ignoring safety risks common in traditional methods. The target drive command or clamped target drive command output in step S30 ultimately drives the air pump to adjust the operating parameters, so that the concentration of negative ions generated by the Leonard effect returns to the factory setting value, completing the closed-loop process of ion production drift self-calibration control, and achieving the goal of constant performance of the negative ion purifier throughout its entire life cycle.

[0086] Example 2:

[0087] This embodiment, based on Embodiment 1, provides a self-calibration control system for ion production drift in a negative ion purifier, such as... Figure 6 As shown, it includes:

[0088] Mechanical fatigue drift module: used to acquire the cumulative operating time of the air pump and determine the mechanical fatigue baseline drift amount based on the cumulative operating time of the air pump;

[0089] Water quality damping drift module: used to calculate the saturated vapor pressure difference at the water-air interface, obtain the theoretical water evaporation rate based on the saturated vapor pressure difference at the water-air interface, calculate the theoretical conductivity baseline slope based on the theoretical water evaporation rate, obtain the actual conductivity change slope, compare the actual conductivity change slope with the theoretical conductivity baseline slope to obtain the conductivity deviation index, perform a binary judgment of water quality status based on the conductivity deviation index, and generate the water quality damping drift amount based on the judgment result;

[0090] Drive command generation module: It is used to linearly superimpose the mechanical fatigue reference drift and the water quality damping drift to obtain the total performance drift, generate the target drive command based on the total performance drift, determine whether the target drive command needs to be clamped by the running envelope, and output the target drive command or the clamped target drive command.

[0091] Furthermore, in the mechanical fatigue drift module, the method for determining the mechanical fatigue reference drift amount includes:

[0092] Using the cumulative operating time of the air pump as an index, the pre-stored diaphragm stiffness life cycle decay curve is queried, and the current pressure decay percentage is determined by linear interpolation.

[0093] The drive power compensation value is calculated based on the current pressure attenuation percentage and is defined as the mechanical fatigue reference drift.

[0094] Furthermore, in the water quality damping drift module, the calculation method for the saturated vapor pressure difference at the water-air interface includes:

[0095] Collect the current ambient temperature, ambient humidity and internal heat load of the equipment, calculate the actual temperature of the water body based on the internal heat load of the equipment, and calculate the saturated vapor pressure at the water-air interface based on the actual temperature of the water body.

[0096] Calculate the actual partial pressure of water vapor in the air based on the ambient temperature and humidity. Subtract the actual partial pressure of water vapor in the air from the saturated vapor pressure at the water vapor interface to obtain the saturated vapor pressure difference at the water vapor interface.

[0097] The method for performing binary determination of water quality status includes:

[0098] The conductivity deviation index is compared with the preset evaporation tolerance threshold. If the conductivity deviation index is less than or equal to the evaporation tolerance threshold, it is determined to be in a natural concentration state. If the conductivity deviation index is greater than the evaporation tolerance threshold, it is determined to be in a contamination intrusion state.

[0099] Furthermore, in the driver instruction generation module, the method for generating target driver instructions based on total performance drift includes:

[0100] The voltage-power response characteristic curve of the air pump is invoked, and the required drive voltage increment is calculated in reverse addressing with the total performance drift as the target value. The drive voltage increment is then superimposed on the base drive voltage to generate the target drive command.

[0101] The method for determining whether a runtime envelope clamp needs to be applied to the target driver instruction includes:

[0102] The remaining water volume is estimated based on the theoretical water evaporation rate and the cumulative operating time of the air pump since the last water filling. The estimated remaining water volume is then compared with the preset water volume safety threshold. If the estimated remaining water volume is greater than or equal to the water volume safety threshold, there is no need to execute the running envelope clamp, and the target drive command is directly output. If the estimated remaining water volume is less than the water volume safety threshold, the running envelope clamp is executed on the target drive command.

[0103] The methods and systems of this application may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the method is for illustrative purposes only, and the steps of the method of this application are not limited to the order specifically described above, unless otherwise specifically stated.

[0104] In addition, the parts of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of the corresponding technical solutions in the prior art have not been described in detail, so as to avoid excessive elaboration.

[0105] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A self-calibration control method for ion output drift in a negative ion purifier, characterized in that, The method includes: Obtain the cumulative operating time of the air pump, and determine the mechanical fatigue baseline drift based on the cumulative operating time of the air pump; Calculate the saturated vapor pressure difference at the water-air interface, obtain the theoretical water evaporation rate based on the saturated vapor pressure difference at the water-air interface, deduce the theoretical conductivity baseline slope based on the theoretical water evaporation rate, obtain the actual conductivity change slope, compare the actual conductivity change slope with the theoretical conductivity baseline slope to obtain the conductivity deviation index, perform a binary judgment of water quality status based on the conductivity deviation index, and generate water quality damping drift based on the judgment result. The total performance drift is obtained by linearly superimposing the mechanical fatigue reference drift and the water quality damping drift. The target drive command is generated based on the total performance drift. It is determined whether to perform running envelope clamping on the target drive command, and the target drive command or the clamped target drive command is output.

2. The self-calibration control method for ion output drift of a negative ion purifier according to claim 1, characterized in that, The method for determining the mechanical fatigue reference drift includes: Using the cumulative operating time of the air pump as an index, the pre-stored diaphragm stiffness life cycle decay curve is queried, and the current pressure decay percentage is determined by linear interpolation. The drive power compensation value is calculated based on the current pressure attenuation percentage and is defined as the mechanical fatigue reference drift.

3. The self-calibration control method for ion output drift in a negative ion purifier according to claim 2, characterized in that, The calculation method for the saturated vapor pressure difference at the water-vapor interface includes: Collect the current ambient temperature, ambient humidity and internal heat load of the equipment, calculate the actual temperature of the water body based on the internal heat load of the equipment, and calculate the saturated vapor pressure at the water-air interface based on the actual temperature of the water body. Calculate the actual partial pressure of water vapor in the air based on the ambient temperature and humidity. Subtract the actual partial pressure of water vapor in the air from the saturated vapor pressure at the water vapor interface to obtain the saturated vapor pressure difference at the water vapor interface.

4. The self-calibration control method for ion output drift in a negative ion purifier according to claim 3, characterized in that, The method for performing binary determination of water quality status includes: The conductivity deviation index is compared with the preset evaporation tolerance threshold. If the conductivity deviation index is less than or equal to the evaporation tolerance threshold, it is determined to be in a natural concentration state. If the conductivity deviation index is greater than the evaporation tolerance threshold, it is determined to be in a contamination intrusion state.

5. The self-calibration control method for ion output drift of a negative ion purifier according to claim 4, characterized in that, The method for generating water quality damping drift based on the determination result is as follows: If the determination result is a natural concentration state, the water quality damping drift is set to zero; if the determination result is a pollution intrusion state, the conductivity deviation index is input into the pre-stored impurity damping mapping function to calculate the corresponding water quality damping drift.

6. The self-calibration control method for ion output drift in a negative ion purifier according to claim 5, characterized in that, The method for generating target driving instructions based on total performance drift includes: The voltage-power response characteristic curve of the air pump is invoked, and the drive voltage increment is calculated in reverse addressing with the total performance drift as the target value. The drive voltage increment is then superimposed on the base drive voltage to generate the target drive command.

7. The self-calibration control method for ion output drift of a negative ion purifier according to claim 6, characterized in that, The method for determining whether to perform a runtime envelope clamp on the target driver instruction includes: The remaining water volume is estimated based on the theoretical water evaporation rate and the cumulative operating time of the air pump since the last water filling. The estimated remaining water volume is then compared with the preset water volume safety threshold. If the estimated remaining water volume is greater than or equal to the water volume safety threshold, the running envelope clamp is not executed, and the target drive command is directly output. If the estimated remaining water volume is less than the water volume safety threshold, the running envelope clamp is executed on the target drive command.

8. The self-calibration control method for ion output drift of a negative ion purifier according to claim 7, characterized in that, The cumulative running time of the air pump since the last water addition is the current reading of the water addition timer. The water addition timer resets to zero and restarts timing when a water addition event is detected. The method for detecting the water addition event is as follows: the conductivity value of the water inside the negative ion purifier tank is continuously monitored by a conductivity sensor. When the conductivity value drops more than a preset conductivity drop threshold within a preset detection time window, it is determined that a water addition event has been detected.

9. A self-calibration control method for ion output drift in a negative ion purifier according to claim 8, characterized in that, The method for performing runtime envelope clamping on the target driving instruction includes: Determine the upper limit of available drive power based on the estimated remaining water volume, and convert the drive voltage value corresponding to the target drive command into the power value corresponding to the target drive command. If the power value corresponding to the target drive command is less than or equal to the upper limit of available drive power, the target drive command is kept unchanged and output directly; if the power value corresponding to the target drive command is greater than the upper limit of available drive power, the drive voltage value corresponding to the target drive command is forcibly truncated to the drive voltage value corresponding to the upper limit of available drive power, and the truncated drive voltage value is output as the clamped target drive command.

10. A self-calibration control system for ion output drift in a negative ion purifier, used to implement the self-calibration control method for ion output drift in a negative ion purifier according to any one of claims 1-9, characterized in that, The system includes: Mechanical fatigue drift module: used to acquire the cumulative operating time of the air pump and determine the mechanical fatigue baseline drift amount based on the cumulative operating time of the air pump; Water quality damping drift module: used to calculate the saturated vapor pressure difference at the water-air interface, obtain the theoretical water evaporation rate based on the saturated vapor pressure difference at the water-air interface, calculate the theoretical conductivity baseline slope based on the theoretical water evaporation rate, obtain the actual conductivity change slope, compare the actual conductivity change slope with the theoretical conductivity baseline slope to obtain the conductivity deviation index, perform a binary judgment of water quality status based on the conductivity deviation index, and generate the water quality damping drift amount based on the judgment result; Drive command generation module: It is used to linearly superimpose the mechanical fatigue reference drift and the water quality damping drift to obtain the total performance drift, generate the target drive command based on the total performance drift, determine whether to perform running envelope clamping on the target drive command, and output the target drive command or the clamped target drive command.

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