Warmer intelligent regulation and control system and method based on variable frequency ion group concentration

Abstract

The invention discloses a warmer intelligent regulation and control system and method based on variable frequency ion group concentration, and relates to the technical field of variable frequency intelligent regulation and control. A stable working interval generation module; and a multi-objective optimization and execution module. Environmental parameters and voltage and current sampling signals are periodically collected, all collected data are subjected to preprocessing and demand evaluation, the discharge stability is judged according to the current environmental parameters and the discharge state, a safe working interval is fitted in real time, and the comprehensive comfort of heating and purification is dynamically optimized in the safe working interval. Each target parameter is intelligently distributed to carry out multi-target optimization, parameter adjustment is cooperatively controlled through a layered structure, and an optimal parameter scheduling strategy is formulated and output, so that the problems that in the prior art, due to sudden change of high-voltage output and ion release amount during switching of different working gears, linearly controllable ion output cannot be realized, and the efficiency is high are effectively solved. And the air purification effect is not enough.

Description

Technical Field

[0001] This invention relates to the field of frequency conversion intelligent control technology, and in particular to an intelligent control system and method for heaters based on frequency conversion ion cluster concentration. Background Technology

[0002] With the increasing demand for healthy indoor environments and smart homes, heaters are gradually integrating multiple functions such as air purification and intelligent control. They use temperature sensors to collect real-time indoor temperature data, air quality sensors to detect particulate matter and harmful gases, and air ion detectors to monitor the real-time concentration of positive and negative ions. By periodically reading these real-time data, the system comprehensively assesses indoor heating and air purification needs, generating corresponding control commands. These commands include adjusting heating power and setting the ion release frequency. Specifically, the controller adjusts the variable frequency drive of the ion generator to control the release of positive and negative ions from the high-voltage discharge electrodes to purify the air. It also automatically adjusts the heating power based on the deviation between the set temperature and the actual room temperature, achieving precise temperature control and energy savings. Furthermore, based on air quality conditions and negative ion concentration requirements, it can intelligently start / stop or adjust the frequency of ion release, achieving adaptive control of the air purification effect.

[0003] For example, Chinese Invention Patent CN220121163U discloses a multifunctional heater integrated circuit and its multifunctional heater. The multifunctional heater integrated circuit includes: an AC-to-DC circuit, an LED dimming control circuit, a DC step-down constant voltage circuit, an LD0 5V step-down constant voltage circuit, a control relay circuit, and a wireless receiver. The AC-to-DC circuit provides power; the LED dimming control circuit controls the LED lighting; the AC-to-DC circuit powers the microcontroller and the load; the control relay circuit selectively connects the heater, the blower and / or ventilation motor, and the negative ion generator. Using this circuit, commands are received and controlled via a wireless receiver, avoiding the risk of electric shock from manual operation. The control commands are sent to the microcontroller for decomposition, and the microcontroller module selectively controls the LED lights, heater, blower and / or ventilation motor, and negative ion generator according to different commands, achieving multiple functions.

[0004] Ion generators utilize the corona discharge effect of high-voltage electrodes to ionize water molecules in the air, simultaneously producing both positive and negative ions. The discharge modes mainly include corona discharge and micro-arc discharge. Within a certain voltage range of the high-voltage power supply output, corona discharge can maintain a stable, linear, and controllable ion output. However, once this voltage range is exceeded, it easily enters the hysteresis zone of streamer or micro-arc discharge. In this hysteresis zone, the discharge mode intermittently jumps between corona discharge and streamer or micro-arc discharge. Furthermore, during the instantaneous transition of the discharge mode to streamer or micro-arc, due to the abrupt change in local high-energy electron density and energy distribution, the ozone generation rate will experience a short-term sharp increase. Both situations—where the output voltage approaches the minimum voltage of the stable corona discharge window or exceeds the stable corona discharge window—make it difficult to maintain a stable corona discharge. This causes the output voltage to more easily fall into the hysteresis zone, resulting in unstable ion output and the risk of excessive ozone. Therefore, existing technologies suffer from technical problems such as abrupt changes in high-voltage output and ion release when switching between different operating levels, making it impossible to achieve linear and controllable ion output and leading to insufficient air purification effects. Summary of the Invention

[0005] To address the technical problem that existing technologies lack a closed-loop and wide linear adjustment mechanism, leading to abrupt changes in speed adjustment and an inability to achieve linearly controllable ion output, resulting in insufficient air purification effect, this invention provides a smart control system and method for heaters based on variable frequency ion cluster concentration. The technical solution is as follows:

[0006] On one hand, a smart heater control system based on variable frequency ion cluster concentration is provided. This system includes: a data acquisition and demand assessment module, a stable operating range generation module, and a multi-objective optimization and execution module. The data acquisition and demand assessment module is used to periodically collect environmental parameters, high-voltage sampling signals, and discharge current signals, preprocess the collected data, and perform demand assessment on the preprocessed data to obtain heating demand results and air purification demand results. The stable operating range generation module is used to determine the discharge stability based on the demand assessment results, current environmental parameters, and discharge state, and fit a safe operating range in real time. Within the safe operating range, it dynamically optimizes the overall comfort of heating and purification. The multi-objective optimization and execution module is used to intelligently allocate various target parameters for multi-objective optimization. At the same time, it coordinates the control parameter adjustment through a hierarchical structure, formulates the optimal parameter scheduling strategy, and outputs it to the underlying driver interface.

[0007] On the other hand, a method for intelligent control of heaters based on variable frequency ion cluster concentration is provided. This method includes: periodically collecting environmental parameters, high-voltage sampling signals, and discharge current signals; preprocessing the collected data; evaluating the demand of the preprocessed data to obtain heating demand results and air purification demand results; determining discharge stability and fitting a safe operating range in real time based on the demand evaluation results, according to the current environmental parameters and discharge state; dynamically optimizing the overall comfort of heating and purification within the safe operating range; intelligently allocating various target parameters for multi-objective optimization; simultaneously coordinating parameter adjustment through a hierarchical structure; formulating the optimal parameter scheduling strategy and outputting it to the underlying driver interface.

[0008] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0009] 1. Based on a hierarchical closed-loop structure and an adaptively generated stable operating range, the intelligent control system achieves coordinated optimization of multiple objectives such as heater heating power, air ion concentration, ozone generation, and airflow. Unlike existing technologies that rely solely on fixed-level voltage boosting or single-loop open-loop adjustment, which can easily lead to ion output jumps and excessive ozone, this system can dynamically identify discharge stability regions, avoiding the risks of discharge hysteresis and nonlinear instability caused by high-voltage level jumps. It can achieve linear controllability of ion output, real-time suppression of ozone risks, and dynamic allocation of heat energy and airflow resources as needed, significantly improving air purification effect, heating experience, and overall system safety and reliability.

[0010] 2. Using a sliding time window as the unit, and integrating multiple factors such as relative humidity, air volume, temperature, peak-to-average power ratio of high-voltage signals, and number of false fires, the discharge stability criterion is used to screen and dynamically update the set of stable operating points in real time during operation. Based on historical data, it automatically fits the safe operating range of discharge. Compared with the existing technology that only relies on experience to set or static thresholds for the safe range, it can actively track the optimal discharge window under different environmental parameters, realize a self-learning adjustment mechanism for discharge stability with strong environmental adaptability and long-term reliability, and effectively avoid problems such as unstable discharge, frequent false fires, and abnormal ozone fluctuations.

[0011] 3. A hierarchical control structure is adopted, decoupling the high-voltage target setting from the current closed-loop tracking. The first control loop, a low-frequency loop, is responsible for slow, global high-voltage, airflow, and heating strategy adjustments. The second control loop, a high-frequency loop, is responsible for high-speed adjustment of the discharge current and anomaly detection. This architecture differs from traditional single-loop control or methods that rely solely on high voltage to directly adjust ion output. It can achieve smooth transition and safe limiting of target parameters in each control cycle, preventing gear jumps, system overshoot, and output instability caused by external disturbances or small signal changes. This significantly improves the continuity and controllability of ion concentration output, optimizing the air purification experience while providing stable heating.

[0012] 4. The system integrates heating demand, environmental purification, and safety protection into a multi-objective adaptive optimization mechanism. By combining multiple dimensions such as whether the ion exposure level meets the standard, the operating conditions of the discharge range, and the upper limit of energy consumption, the system achieves dynamic balance and coordinated control of multiple objectives such as ion output, temperature comfort, energy consumption optimization, and abnormal operating condition protection through graded pressure increase with different time lengths, air volume compensation, operating time adjustment, and target switching strategies with hysteresis range. Compared with the single-objective or passive protection of existing technologies, this greatly improves the system's adaptive capability and achieves more effective intelligent regulation. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0014] Figure 1 A schematic diagram of the structure of a heater intelligent control system based on variable frequency ion cluster concentration provided in an embodiment of the present invention;

[0015] Figure 2 A flowchart for demand assessment and determination provided in embodiments of the present invention;

[0016] Figure 3 A flowchart for determining a safe working area is provided in an embodiment of the present invention;

[0017] Figure 4 The circuit diagram of the ion cluster schematic provided in the embodiment of the present invention;

[0018] Figure 5 A schematic diagram of the added components to the ion cluster schematic diagram provided in an embodiment of the present invention;

[0019] Figure 6 A flowchart of a smart heater control method based on variable frequency ion cluster concentration provided in an embodiment of the present invention. Detailed Implementation

[0020] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0021] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0022] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0023] like Figure 1 The diagram shown is a structural schematic of a heater intelligent control system based on variable frequency ion cluster concentration provided in an embodiment of the present invention. The system includes: a data acquisition and demand assessment module, a stable working range generation module, and a multi-objective optimization and execution module.

[0024] Among them, the data acquisition and demand assessment module is used to periodically collect environmental parameters, high-voltage sampling signals and discharge current signals, preprocess the collected data, and conduct demand assessment on the preprocessed data to obtain heating demand results and air purification demand results.

[0025] The stable working range generation module is used to determine the discharge stability based on the demand assessment results, current environmental parameters, and discharge state, and to fit a safe working range in real time. Within the safe working range, it dynamically optimizes the overall comfort of heating and purification.

[0026] The multi-objective optimization and execution module is used to intelligently allocate parameters for each objective to perform multi-objective optimization. At the same time, it coordinates parameter adjustment through a hierarchical structure, formulates the optimal parameter scheduling strategy, and outputs it to the underlying driver interface.

[0027] Furthermore, environmental parameters, high-voltage sampling signals, and discharge current signals are periodically collected, and the collected data are preprocessed. The specific steps are as follows: within the preset collection period, various indoor environmental parameters, high-voltage raw sampling signals, and discharge current sampling signals are collected simultaneously, and all types of data are stored according to a unified timestamp. The various environmental parameters include, but are not limited to, actual temperature, relative humidity, air volume, and PM concentration.

[0028] The original high-voltage sampling signal and the discharge current sampling signal are filtered to remove short-term abnormal spikes and noise; the temperature, relative humidity, air volume and PM concentration are processed by moving average to improve data stability and robustness.

[0029] In this embodiment, during the data preprocessing stage, differentiated processing methods are adopted for signals with different characteristics. For the original high-voltage sampling signal, which is highly susceptible to electromagnetic interference caused by power grid fluctuations and switching actions, a median-based preprocessing is first applied to effectively remove instantaneous abnormal spikes with durations in the millisecond range. Subsequently, a low-pass digital filter is used to effectively suppress high-frequency noise and retain the true high-voltage waveform characteristics, providing a clean signal source for subsequent accurate analysis of ion generation intensity.

[0030] For environmental parameters such as temperature, relative humidity, air volume, and PM concentration, which do not fluctuate significantly in the short term, averaging is performed using a sliding time window of the sampling period. Air volume refers to the actual output air volume of the heater fan unit under the current operating conditions, which is obtained by collecting air volume sensors in the air duct. This method, by calculating the arithmetic mean of the measurement values ​​over multiple consecutive periods, can smooth out random deviations caused by airflow disturbances or small fluctuations in the sensor, significantly improving the stability of the data and the robustness of long-term trends, and providing a more reliable data foundation for the system to assess the environmental status and formulate control strategies.

[0031] like Figure 2 The flowchart shown is a demand assessment and determination flowchart provided by an embodiment of the present invention. The specific design logic is as follows: obtain the set temperature and the actual indoor temperature to calculate the temperature difference, and extract historical temperature rise data to calculate the temperature rise slope; if the temperature difference and historical temperature rise slope conditions are met, it is determined that the heating capacity is insufficient and the target heating power is increased; otherwise, it is determined that the heating response is good and the heating power is reduced or maintained.

[0032] Furthermore, a demand assessment is conducted on the preprocessed data. Specifically, the current heating demand is dynamically assessed based on the difference between the set temperature and the actual indoor temperature, combined with the historical temperature rise slope. The set temperature represents the target temperature value that the user hopes to maintain or achieve.

[0033] When the temperature difference is greater than or equal to the set temperature difference threshold, or when the historical temperature rise rate is lower than the set rate threshold under a given heating power, the controller determines that the current heating capacity is insufficient and increases the target heating power according to a preset step size. When the temperature difference is less than the set temperature difference threshold and the historical temperature rise rate is higher than the set rate threshold, the controller determines that the current heating response is too fast and decreases the target heating power according to a preset step size. The set temperature difference threshold is represented by the sum and average of historical temperature difference thresholds in the historical demand assessment process in the database. The set rate threshold is represented by the sum and average of historical rate thresholds in the historical demand assessment process in the database. If both the temperature difference and the historical temperature rise rate are within the set normal range, the current heating power remains unchanged. The set normal range represents the range corresponding to the maximum and minimum values ​​of the historical normal range in the historical demand assessment process in the database, including the maximum and minimum values ​​of the normal range.

[0034] Read the pre-processed PM concentration, obtain the ion exposure index and the deviation from the target value. The PM concentration is used to characterize the pollution level of suspended particulate matter in the air. The ion exposure index is used to quantify the control index of the intensity of ion purification effect applied to the air medium and breathing zone within a set time. Comprehensively evaluate the current required purification intensity. The set time represents a time point set through the historical ion exposure index acquisition process.

[0035] In this embodiment, the historical temperature rise slope refers to the average rate of temperature increase obtained by the controller applying a least-squares linear fitting algorithm to periodically collected indoor actual temperature data within a set time window (e.g., 5 minutes) in historical data. The unit is degrees Celsius per minute. The time window can be flexibly set according to the actual room thermal inertia. The optimal time window is matched according to the thermal inertia level mapping table. For example, the first time window (e.g., 8-10 minutes) is selected for the first level of thermal inertia, the second time window (e.g., 5-7 minutes) is selected for the second level of thermal inertia, and the third time window (e.g., 5-7 minutes) is selected for the third level of thermal inertia. A time window (e.g., 2-4 minutes) is used to improve the accuracy and real-time performance of the temperature rise slope assessment. The current heating power value is recorded, and the historical temperature rise slope measured at this constant power is compared with the expected temperature rise slope. If the historical temperature rise slope is lower than the expected set temperature rise difference, it is determined that the current heat capacity or heat loss is large, and the heating power needs to be appropriately increased (e.g., each increase should not exceed 10% of the maximum heating power, and the minimum step size should be set to 5%). The rate of increase of heating power should be limited to avoid temperature overshoot. The set temperature rise difference is represented by the sum and average of the historical temperature rise differences in the historical temperature rise slope comparison process in the database.

[0036] PM concentration can be preferentially referred to as PM2.5 concentration, which is collected periodically by air quality sensors. The controller classifies PM2.5 concentration into five levels—excellent, good, lightly polluted, moderately polluted, and heavily polluted—according to national or international air quality classification standards (such as GB3095-2012). Each level corresponds to a purification demand coefficient, for example, 0.2, 0.4, 0.6, 0.8, and 1.0. The purification demand intensity is a weighted sum of the PM level coefficient and the ion exposure deviation, with PM concentration as the primary weight and ion exposure as a supplementary correction term. The deviation in ion exposure is assessed using a tiered decision-making approach. When PM concentration is at or above the level of light pollution and ion exposure does not meet the standard, the purification demand intensity is set to high, instructing an increase in the high-pressure target value and airflow. When PM concentration is at or above the level of light pollution but ion exposure meets the standard, the purification demand is set to medium, prioritizing an increase in the high-pressure target value. When PM concentration is below the level of light pollution but ion exposure does not meet the standard, the purification demand is set to low, appropriately reducing the high-pressure target value to save energy and reduce ozone. All assessment steps are equipped with upper and lower hysteresis zones to avoid frequent control switching due to sensor vibration.

[0037] Furthermore, the ion exposure index and its deviation from the target value are obtained. The specific steps are as follows: based on the pre-processed high-pressure sampling signal and air volume, combined with the set calibration coefficient, the current ion exposure index is calculated and determined.

[0038] If the current ion exposure level is lower than the set target value, it is determined to be insufficient exposure, and the corresponding deviation is recorded. The deviation can be defined as the difference between the target value and the actual exposure level, and is used as the basis for adjustment of the subsequent multi-objective optimization scheduling module. If the current ion exposure level is not lower than the set target value, it is determined to be adequate exposure. The set target value is represented by the sum and average of the historical target values ​​in the historical ion exposure determination process in the database.

[0039] In this embodiment, the ion exposure index EIE(t) is calculated using the following standardized formula. The ion exposure index is an estimated value of the equivalent ion exposure, which is obtained by mapping the high-voltage discharge intensity to the actual ion exposure that the user can be exposed to. A set of calibration coefficients is obtained in advance through experimental calibration. The calibration coefficients are used to describe the correspondence between high-voltage discharge intensity, air volume and ion concentration:

[0040] EIE(t) = α × C(t) × Q(t);

[0041] Wherein, α is the calibration coefficient, which compensates for differences in ion output efficiency caused by different models, discharge electrode structures, sampling point locations, etc., through factory experiments or on-site calibration; C(t) uses a preset calibration relationship to convert the high-voltage sampling signal into an equivalent estimate of ion release. The equivalent ion concentration mapped from the high-voltage sampling signal is adjusted step by step in the factory test (e.g., 5.0kV, 5.5kV, 6.0kV...7.5kV). At each high-voltage value, the actual discharge output ion release is collected. With the high-voltage sampling signal as the independent variable and the ion release as the dependent variable, the empirical mapping formula is obtained using the least squares method. The obtained parameters and mapping formula are fixed in the control system software, and the equivalent estimate of ion release can be obtained in real time through mapping during subsequent operation; Q(t) represents the air volume at the current moment (unit: m). 3 / h or L / min); t refers to each time point. Within the set integration time window, the ion concentration and air volume are averaged and filtered respectively, and calculated according to the above formula to obtain the estimated value of the equivalent ion exposure in the user's breathing zone.

[0042] Subsequently, the above-mentioned ion exposure index is compared with the preset target ion exposure value: if the ion exposure index is lower than the target value, it is determined that the ion exposure is insufficient and its deviation is calculated; if the ion exposure index reaches or exceeds the target value, it is determined that the ion exposure meets the standard. In order to avoid frequent state switching caused by sensor noise, the exposure status mark is only updated when the standard conditions are met for several consecutive cycles (such as 2 to 5 cycles).

[0043] like Figure 3The flowchart shown is a process for determining the safe working range provided in an embodiment of the present invention. The specific design logic is as follows: input environmental parameters and signals, then calculate the peak-to-average power ratio, count the number of false fires and detect EN_trip, then determine whether the stability conditions are met. If they are met, record the point as a stable point and add it to the historical set, then fit the stable working range in real time, and finally output the working range and the stable point; if they are not met, then perform an abnormal prompt or limit down operation.

[0044] Furthermore, based on the demand assessment results and the current environmental parameters and discharge status, the discharge stability is determined and a safe operating range is fitted in real time. Specifically, this includes: collecting environmental parameters, high-voltage filter values, discharge peak-to-average power ratio (PAPR), and the number of false fires in real time, using a sliding time window as the unit. Environmental parameters include, but are not limited to, relative humidity, air volume, and temperature. The discharge stability conditions include at least: the PAPR is less than the set PAPR threshold, the number of false fires within the sliding time window is less than the set number of false fires, and no forced interruption event occurs in the discharge circuit. The set PAPR threshold is represented by the sum and average of historical PAPR thresholds in the historical discharge stability determination process in the database, and the set number of false fires is represented by the sum and average of historical false fires in the historical discharge stability determination process in the database.

[0045] If all the above conditions are met, the current environmental parameters and discharge state are recorded as a stable point, and this point is added to the historical stable point set. Using all historical stable points, the discharge safe operating range is obtained in real time with the environmental parameters and high-voltage operating parameters corresponding to the historical stable points as independent variables. In each subsequent control cycle, it is determined whether the current real-time operating condition is within the safe operating range. If it is within the safe operating range, the high-voltage target is allowed to be increased; otherwise, the amplitude is limited or the voltage is reduced, prioritizing the protection of discharge stability and system safety.

[0046] In this embodiment, the discharge peak-to-average power ratio (PAPR) refers to the ratio of the maximum value to the average value of the discharge current signal within the sliding time window. It is calculated in real time using time-domain analysis. The PAPR is obtained by collecting data in batches under typical stable coronal discharge conditions and statistically analyzing the PAPR distribution range within the sliding window. Typically, the PAPR is 1.4, and it can be dynamically adjusted based on long-term operating data. False fire indicates an abnormal start-up that does not meet the normal triggering conditions. The number of false fires is set by adding a margin to the maximum value of abnormal pulses within a unit window under normal operating conditions, for example, 2 times per window, and can be adaptively adjusted. Forced interruption events are determined in real time by the hardware protection signal (EN_trip) of the high-voltage power supply self-test circuit.

[0047] If and only if all stability conditions are met within the current sliding time window (i.e., the discharge peak-to-average ratio is lower than the set discharge peak-to-average ratio threshold, the number of false fires is lower than the set number of false fires, and no forced interruption occurs), the environmental parameters, high-voltage filter value, average discharge current, peak-to-average ratio, number of false fires, and other data in this window are packaged into stable point entries and stored in the historical stable point set. This set uses a circular queue structure to store the most recent N (e.g., 100) data entries to avoid the influence of outdated data on the judgment. Multivariate regression analysis or partition mean and interpolation algorithms are applied to the historical stable point set to fit the discharge safe working range with relative humidity, air volume, temperature, and high-voltage filter value as independent variables in real time. Each environmental parameter is divided into multiple segments, and then the maximum and minimum allowable ranges of high voltage or current in each segment are statistically analyzed as the stable working range of that segment.

[0048] Furthermore, the overall comfort of heating and air purification is dynamically optimized within the safe working range. The specific process is as follows: when the current working point is within the safe working range, the temperature comfort range is determined based on the difference between the actual indoor temperature and the set temperature.

[0049] If the exposure meets the standard and the actual indoor temperature is within the comfortable temperature range, the current control parameters remain unchanged. If the exposure meets the standard but the temperature is not within the comfortable range, it is determined that the temperature is insufficient, and the heating unit parameters are adjusted first to raise the room temperature to the comfortable range. If the exposure is insufficient but the temperature is within the comfortable range, it is determined that the purification is insufficient, and the ion output parameters are increased first to make up for the ion exposure, while the heating unit output is suppressed to prevent the temperature from becoming too high. If the exposure is insufficient and the actual indoor temperature is not within the comfortable temperature range, a choice is made between temperature priority and ion priority based on the user's target needs to dynamically optimize the user's overall comfort.

[0050] In this embodiment, the actual indoor temperature is periodically acquired and compared with the user-set temperature. The comfort range is defined by the fluctuation of the set temperature by ΔT. For example, ΔT is preferably ±1.5℃. If the user-set temperature is 24℃, the comfort range is 22.5℃~25.5℃. If the actual temperature is within this range, it is considered comfortable; otherwise, it is considered uncomfortable. User priority settings and dynamic switching are implemented. For example, the user selects "temperature priority" or "purification priority" mode through the system settings interface. Priorities can be comprehensively sorted based on season, scenario, and user selection. If the system detects that neither temperature nor ion levels meet the standards, it first increases the priority target parameter (heating power or high-pressure target) according to the current priority. If the priority target parameter is continuously adjusted to reach a preset upper limit or if no positive change exceeding a preset range is detected in the target indicator (such as room temperature or ion exposure) within a specified period, it is determined that coordinated adjustment is needed. The airflow is automatically increased to enhance the spatial distribution and exchange efficiency of heat or ions, thereby accelerating the achievement of the comfort range or purification target.

[0051] When the actual temperature is within the comfortable range and the ion exposure meets the standard, the current control parameters (heating power, high pressure target value, air volume) are kept unchanged, with only minor adjustments to suppress long-term drift. When only one target is not met, the corresponding parameter is prioritized for improvement, and the maximum adjustment step and rate of change are set (e.g., heating power is not increased by more than 10% at a time, and high pressure target is not increased by more than 0.5kV at a time) to prevent overshoot and jitter. When both targets are not met, the primary target parameter is improved first, while the secondary target parameter is improved as an auxiliary measure. However, the total energy consumption of all parameters is dynamically allocated under the premise that it does not exceed the system's preset upper limit to avoid system overload.

[0052] Furthermore, intelligent allocation of various target parameters for multi-objective optimization is performed. The specific steps are as follows: when insufficient exposure is detected, the current operating condition is determined based on the safe operating range and the discharge peak-to-average ratio.

[0053] If the current operating condition is within the safe operating range of discharge, then under the premise of meeting the upper limit constraint of total power, the high voltage target value is increased by the preset first step. If the ion exposure does not reach the preset increase standard after the high voltage target value is adjusted multiple times, the air volume is increased in coordination to make up for the ion exposure first.

[0054] If the current operating condition is in the boundary area of ​​the safe operating range, that is, when the discharge peak-to-average ratio reaches the preset threshold range, the increase of the high voltage target value is limited. Only the second step size adjustment is allowed, and the air volume is increased and the operating time is extended to compensate for the ion exposure gap. The second step size is smaller than the first step size adjustment.

[0055] If the current operating condition is in the unstable discharge range, the high voltage will be increased and the basic purification effect and safety will be maintained by simply increasing the air volume in a conservative operating strategy.

[0056] When exposure is detected to meet the standard, the high-pressure target value is reduced to reduce the risk of ozone formation and overall energy consumption. At the same time, the high-pressure target value is avoided from frequently rising or falling by setting an upper and lower tolerance range for ion exposure. The set ion exposure is represented by the sum and average of historical ion exposure values ​​from the historical multi-objective optimization process in the database.

[0057] In this embodiment, when the current ion exposure level is detected to be lower than the target value set by the user, the current operating condition is first determined based on the aforementioned safe operating range of discharge. The discharge peak-to-average power ratio is used as the main stability indicator. When the peak-to-average power ratio is lower than the first discharge peak-to-average power ratio threshold (e.g., 1.4) and the current high voltage value is within the stable operating range obtained by historical fitting, the high voltage target value is increased by a preset first step size (e.g., 0.3 to 0.5 kV or proportionally) under the premise of meeting the upper limit constraint of the total system power. If necessary, the air volume is increased by a fixed step size or proportionally (e.g., 5%–10%) to prioritize the replenishment of ion exposure. The upper limit of total power includes the total power of the heating unit, fan and high voltage module to avoid high load causing system overheating or power supply limitation.

[0058] When the peak-to-average power ratio (PAPR) is between the first and second discharge PAPR thresholds (e.g., 1.4 to 1.6), or when the current high-voltage point is within a preset range (e.g., within 5%) of the stable range boundary, the increase in the high-voltage target value is limited. Fine-tuning is only allowed by a preset smaller second step size. Typically, the second step size is 1 / 3 to 1 / 5 of the first step size. In this case, ion compensation is achieved by increasing the air volume and extending the operating time. The air volume can be increased by 5%–15% increments, and the operating time can be extended by increasing the duty cycle of the cycle or shortening the sleep time to avoid a large increase in high voltage leading to instability.

[0059] When the peak-to-average ratio exceeds the second discharge peak-to-average ratio threshold or the operating condition has exceeded the stable range, the rise of the high-pressure target value is frozen, the current high pressure is maintained or the high pressure value is slightly reduced according to the preset value, and the basic purification effect is maintained by increasing the air volume (such as by 10%–20%). The high pressure is no longer allowed to be increased in order to avoid further entering the stream or micro-arc discharge state and reduce the risk of discharge instability and instantaneous ozone spikes.

[0060] Furthermore, through hierarchical structure collaborative control parameter adjustment, specifically including: using the target high voltage value, the upper limit of the allowable target discharge current, and the target air volume obtained from multi-objective optimization as reference inputs for hierarchical closed-loop control.

[0061] The first control stage takes the high-voltage target value as the main control object, performs PI regulation at a preset low update frequency, and applies slope and amplitude limits to the high-voltage target output according to the current environmental parameters and the safe operating range, generating the actual high-voltage reference value after smoothing constraints and the corresponding target discharge current upper limit.

[0062] The second control loop takes the actual discharge current as the main control object. It samples the discharge current in real time at a preset high sampling frequency and performs PI closed-loop control to ensure that the actual discharge current tracks the reference value given by the outer loop without exceeding the upper limit of the target discharge current. Based on this, it adjusts the driving amount of the programmable high-voltage power supply, heating unit and fan.

[0063] The first control link focuses on major goals and trends, achieving slow and smooth constraints. The second control link is responsible for specific execution and precise and rapid tracking. When the second control link detects an abnormal discharge peak-to-average power ratio, an abnormal increase in the number of false fires, or actual operating conditions exceeding the safe operating range, it feeds back the abnormality flag to the first control link, triggering a voltage reduction, soft start, or degraded operation strategy, thus forming a hierarchical closed-loop control relationship of "multi-objective optimized scheduling - slow constraints of the first control link - rapid execution of the second control link - status feedback".

[0064] In this embodiment, voltage reduction refers to immediately lowering the high-voltage target value when the system detects an anomaly (such as exceeding the discharge peak-to-average power ratio, an abnormal increase in the number of false fires, or operating conditions exceeding the safe range) to return the discharge state to a safe and stable operating range. This method can quickly eliminate the risks caused by excessively high voltage. Soft start means that when an anomaly occurs, the controller slowly restores the high-voltage target value and / or heating power according to a ramp curve, rather than directly restoring it to the original value, to prevent new anomalies caused by sudden changes. This is often used in the recovery phase after a short-term power outage or abnormal restart. Degraded operation strategy refers to automatically reducing the system's operating level when the system detects a long-term or continuous anomaly that cannot be resolved by conventional measures. For example, it may limit the maximum high-voltage output, reduce the heating power, or shut down some functions to prioritize system safety and hardware lifespan. Degraded operation is usually accompanied by alarm prompts to alert for manual intervention.

[0065] Based on the output of the multi-objective optimization scheduling module, the target high voltage value, the upper limit of the allowable target discharge current, and the target air volume are periodically (e.g., every 1 second) acquired synchronously and used as reference inputs for subsequent hierarchical closed-loop control. The above parameters are transmitted between modules of the control system through the software bus and updated in real time.

[0066] The first control loop takes the high-voltage target value as the main control object. At a preset low update frequency (such as 1 - 10 Hz), the proportional-integral (PI) algorithm is used to adjust the actual high-voltage output. To prevent instantaneous large changes in the high-voltage target, a first-order finite slope limit (such as no more than 0.5 kV per adjustment) and an amplitude limit (in the high-voltage range of 5 - 7.5 kV) are imposed on the high-voltage target output to ensure a smooth transition of the high-voltage output. The smoothing constraint uses the weighted moving average method to ensure the continuity of the high-voltage reference value. The first control loop makes dynamic linkage adjustments to the high-voltage reference value and the corresponding upper limit of the target discharge current according to the current environmental parameters and the safe operating range. In a specific embodiment, when the high-voltage target value increases, the upper limit of the target discharge current increases equally, and the total increase does not exceed the set current limit of the maximum allowable current. All adjustment processes are automatically completed by the control program without manual intervention, ensuring the linkage response and safe operation of the system under dynamic working conditions. The set current limit is represented by the result of summing and averaging the historical current limits in the historical first control loop process in the database.

[0067] The second control loop takes the actual discharge current as the main control object. It samples the discharge current in real time at a high sampling frequency (such as 10 - 50 kHz) and uses the proportional-integral (PI) closed-loop algorithm to adjust it, so that the actual discharge current accurately tracks the high-voltage reference value output by the first control loop on the premise of not exceeding the upper limit of the target current. The closed-loop output directly adjusts the programmable high-voltage power supply module to achieve fine control of the discharge intensity.

[0068] To avoid instruction conflicts between the two control loops, an interlock control strategy is adopted. When the second control loop detects an abnormality and gives a feedback, the first control loop is forced to enter a protection or limit state and no longer accepts new boosting / current increasing instructions until the abnormality is eliminated before it can resume the normal control process. The controller sets hierarchical priorities to ensure that the fast loop (the second control loop) has the final decision-making power in terms of ensuring discharge safety.

[0069] As Figure 4 shown, it is the circuit diagram of the ion group schematic diagram provided by the embodiment of the present invention. The core purpose is to convert low-voltage DC through control and boosting transformation to output high-voltage AC or high-voltage pulses, and then form corona discharge in the air through electrodes to generate positive and negative ions (ion group) to achieve functions such as air purification. The alternately conducting Q1 and Q2 achieve the push-pull excitation mode of the transformer. The high-frequency pulse is boosted to the high-voltage side and two single-pole high-voltage pulse signals are output through D1 and D2, and are respectively applied to the electrodes FLZ1 and FLZ5 under the alternating drive of the controller, so as to form an alternating reverse high-voltage electric field between the electrodes and realize the release of positive and negative ions (ion group).

[0070] As Figure 5As shown, this is the added component part of the ion cluster schematic diagram provided in the embodiment of the present invention. The specific connection method is as follows: the input terminal of the high-voltage sampling is connected to... Figure 4 The output ports (FLZ1 / FLZ5) in the system convert the kilovolt-level high voltage into a low-voltage sampling signal proportional to the high voltage amplitude through parallel high-resistance voltage divider resistors and rectifier filter networks. The output terminals are connected to the analog sampling channel of the controller to obtain the high voltage filtering value and the high voltage sampling signal required for ion exposure calculation.

[0071] Current sampling is connected in series with the output of the pre-amplifier DC / DC power supply and Figure 4 Between the power supply terminals FLZ2, the primary drive current is detected by a current sampling resistor, and after differential amplification and filtering, the discharge current sampling signal is output to the controller and the discharge stability detection module.

[0072] The input terminals of the discharge stability detection are connected to the output terminals of the current sampling module and the high voltage sampling module, respectively. The discharge peak-to-average power ratio and false fire flag are obtained through peak detection, average detection and comparison calculation. The discharge stability results are fed back to the controller to construct the safe operating range of discharge and trigger the degradation control logic.

[0073] Furthermore, an optimal parameter scheduling strategy is formulated and output to the underlying drive interface. The specific steps are as follows: based on the target high pressure value, target air volume, and target heating power, control commands are sent to the underlying drive interface within a preset control cycle to adjust the drive duty cycle of the heating unit, the fan speed, and the target value of the high pressure before the ion source.

[0074] During the execution of control commands, the actual working status and feedback parameters of the heating unit, fan and high-voltage power supply are monitored in real time. When an execution abnormality is detected, a degraded operation or retry mechanism is automatically triggered, the relevant target parameters are adjusted within a limited range and the control commands are reissued to ensure the safety and control stability of the system under abnormal operating conditions.

[0075] In this embodiment, based on the target high voltage, target air volume, and target heating power determined by the multi-objective optimization scheduling module, the controller sends control commands to each actuator through the underlying drive interface within each preset control cycle (e.g., 1 second or 10 seconds). The underlying drive interface includes, but is not limited to: the PWM control signal of the heating unit (0–100% duty cycle, step size such as 1–5%), the fan speed control signal (PWM or voltage signal, speed 0–3000 rpm, step size such as 100 rpm), and the programmable DC / DC control command of the ion source pre-stage high voltage (e.g., 5–7.5kV). (Step 0.1–0.5kV); The target high voltage value is converted into an analog or digital control signal according to the high voltage power supply setting curve ratio and then sent to the programmable high voltage power supply. The setting curve ratio is represented by the sum and average of the historical curve ratios in the historical high voltage power supply conversion process in the database; The target air volume is converted into the PWM control signal of the fan drive circuit by looking up the table or converting it according to the ratio and then sent; The target heating power is converted into the PWM duty cycle of the heating unit according to the rated maximum power ratio and then sent. All converted command values ​​are verified by the equipment parameter table and the safety range to prevent parameter out-of-bounds and hardware risks.

[0076] The system synchronously collects and monitors parameters such as the actual power / temperature, current, and voltage of the heating unit, the actual speed / current / temperature rise of the fan, and the actual output / feedback current of the high-voltage power supply. Within each sampling period, all feedback parameters are compared with the target command, and multiple levels of anomaly criteria are set up.

[0077] The anomaly detection and degradation or retry strategy means that when the above-mentioned execution anomaly is detected, the controller will immediately trigger a degradation operation or retry mechanism. The degradation strategy includes: reducing the high pressure target value and / or heating power and air volume; temporarily shutting down the abnormal execution unit, retaining core functions or degrading to safe mode, recording the anomaly information, and entering it into the system log.

[0078] like Figure 6 The diagram shows a flowchart of a smart heater control method based on variable frequency ion cluster concentration provided by an embodiment of the present invention. The method includes the following steps:

[0079] S1 periodically collects environmental parameters, high-voltage sampling signals, and discharge current signals. It preprocesses the collected data and performs demand assessment on the preprocessed data to obtain heating demand results and air purification demand results.

[0080] S2, based on the demand assessment results, determines the discharge stability according to the current environmental parameters and discharge status, and fits the safe working range in real time. Within the safe working range, it dynamically optimizes the overall comfort of heating and purification.

[0081] S3 intelligently allocates parameters for each objective to perform multi-objective optimization. At the same time, it coordinates parameter adjustment through a hierarchical structure, formulates the optimal parameter scheduling strategy, and outputs it to the underlying driver interface.

[0082] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0083] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0084] In various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0085] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0086] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0087] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A smart control system for a heater based on variable frequency ion cluster concentration, characterized in that, include: The module includes data acquisition and demand assessment, a stable working range generation module, and a multi-objective optimization and execution module. The data acquisition and demand assessment module is used to periodically collect environmental parameters, high-voltage sampling signals and discharge current signals, preprocess the collected data, and assess the demand of the preprocessed data to obtain heating demand results and air purification demand results. The stable working range generation module is used to determine the discharge stability based on the demand assessment results, current environmental parameters, and discharge state, and to fit a safe working range in real time, dynamically optimizing the overall comfort of heating and purification within the safe working range. The multi-objective optimization and execution module is used to intelligently allocate parameters for each objective to perform multi-objective optimization. At the same time, it coordinates the adjustment of control parameters through a hierarchical structure, formulates the optimal parameter scheduling strategy, and outputs it to the underlying driver interface.

2. The intelligent control system for a heater based on variable frequency ion cluster concentration as described in claim 1, characterized in that, The periodic acquisition of environmental parameters, high-voltage sampling signals, and discharge current signals, followed by preprocessing of the acquired data, involves the following steps: Within the preset collection period, various indoor environmental parameters, high-voltage raw sampling signals, and discharge current sampling signals are collected simultaneously, and all types of data are stored according to a unified timestamp. The various environmental parameters include actual temperature, relative humidity, air volume, and PM concentration. The original high-voltage sampling signal and the discharge current sampling signal are filtered to remove short-term abnormal spikes and noise. Temperature, relative humidity, air volume, and PM concentration are processed using moving averages to improve data stability and robustness.

3. The intelligent control system for a heater based on variable frequency ion cluster concentration as described in claim 2, characterized in that, The process of conducting demand assessments on the preprocessed data to obtain heating demand results and air purification demand results is as follows: Based on the difference between the set temperature and the actual indoor temperature, and combined with the historical temperature rise slope, the current heating demand is dynamically assessed. When the temperature difference is greater than or equal to the set temperature difference threshold, or when the historical temperature rise rate is lower than the set rate threshold under the given heating power, the controller determines that the current heating capacity is insufficient and increases the target heating power according to the preset step size; when the temperature difference is less than the set temperature difference threshold and the historical temperature rise rate is higher than the set rate threshold, the controller determines that the current heating response is too fast and decreases the target heating power according to the preset step size; if both the temperature difference and the historical temperature rise rate are within the set normal range, the current heating power remains unchanged. The pre-processed PM concentration is read, and the ion exposure index and its deviation from the target value are obtained. The PM concentration is used to characterize the pollution level of suspended particulate matter in the air at present, and the ion exposure index is used to quantify the control index of the intensity of ion purification effect applied to the breathing area within a set time, and to comprehensively evaluate the current required purification intensity.

4. The intelligent control system for a heater based on variable frequency ion cluster concentration as described in claim 3, characterized in that, The specific steps for obtaining the ion exposure index and its deviation from the target value are as follows: Based on the pre-processed high-voltage sampling signal and air volume, combined with the set calibration coefficient, the current ion exposure index is calculated and determined. The high-voltage sampling signal reflects the discharge intensity of the ion source pre-stage, and the air volume reflects the air transport and diffusion capacity in the room. If the current ion exposure is lower than the set target value, it is determined to be insufficient exposure, and the corresponding deviation is recorded; If the current ion exposure level is not lower than the set target value, it is determined that the exposure meets the standard.

5. The intelligent control system for a heater based on variable frequency ion cluster concentration as described in claim 1, characterized in that, The process of determining discharge stability and fitting a safe operating range in real time based on the demand assessment results, according to current environmental parameters and discharge status, specifically includes: The high-voltage filter value, discharge peak-to-average power ratio, and number of false fires are collected in real time using a sliding time window as the unit. The discharge stability conditions include: the discharge peak-to-average power ratio is less than the set discharge peak-to-average power ratio threshold, the number of false fires within the sliding time window is less than the set number of false fires, and no forced interruption event occurs in the discharge circuit; If all the above conditions are met, the current environmental parameters and discharge state are recorded as a stable point, and this point is added to the historical stable point set. By utilizing all historical stable points, the safe operating range of discharge is obtained in real time through fitting, with environmental parameters and high-voltage operating parameters as independent variables. In each subsequent control cycle, it is determined whether the current real-time operating condition is within the safe operating range; if it is within the safe operating range, the high voltage target is allowed to be increased; otherwise, the amplitude is limited or the voltage is reduced, prioritizing the protection of discharge stability and system safety.

6. The intelligent control system for a heater based on variable frequency ion cluster concentration as described in claim 5, characterized in that, The specific process of dynamically optimizing the overall comfort of heating and air purification within the safe working range is as follows: When the current working point is within the safe working range, the temperature comfort range is determined based on the difference between the actual indoor temperature and the set temperature. If the exposure is within the acceptable range and the actual indoor temperature is within the comfortable temperature range, maintain the current control parameters unchanged. If the exposure meets the standard but the temperature is not in the comfortable range, it is determined that the temperature is insufficient, and the heating unit parameters are adjusted first to raise the room temperature to the comfortable range; If the exposure is insufficient but the temperature is within the comfortable range, it is determined that the purification is insufficient. The ion output parameters are increased first to make up for the ion exposure, while the output of the heating unit is suppressed to prevent the temperature from becoming too high. If the exposure is insufficient and the actual indoor temperature is not within the comfortable temperature range, then a choice will be made between temperature priority and ion priority based on the user's target needs, so as to dynamically optimize the user's overall comfort.

7. The intelligent control system for a heater based on variable frequency ion cluster concentration as described in claim 1, characterized in that, The intelligent allocation of target parameters for multi-objective optimization involves the following steps: When insufficient exposure is detected, the current operating condition is determined based on the safe operating range and the discharge peak-to-average ratio: If the current operating condition is within the safe operating range of discharge, then, under the premise of meeting the upper limit constraint of total power, increase the high voltage target value according to the preset first step length, and increase the air volume if necessary, so as to prioritize the replenishment of ion exposure. If the current operating condition is in the boundary area of ​​the safe operating range, that is, when the discharge peak-to-average ratio is close to the preset threshold, the increase of the high voltage target value is limited, and only the second step size is allowed to be finely adjusted in conjunction with increasing the air volume and extending the operating time to compensate for the ion exposure gap. The second step size is smaller than the adjustment range of the first step size. If the current operating condition is in the unstable discharge range, the high voltage will be frozen, and the basic purification effect and safety will be maintained by simply increasing the air volume in a conservative operation strategy. When exposure is detected to meet the standard, the high-pressure target value is reduced to decrease the risk of ozone formation and overall energy consumption. At the same time, the high-pressure target value is avoided from frequently rising and falling by setting upper and lower hysteresis ranges for ion exposure.

8. The intelligent control system for a heater based on variable frequency ion cluster concentration as described in claim 7, characterized in that, The hierarchical structure-based collaborative control parameter adjustment specifically includes: The target high voltage value, the upper limit of the allowable target discharge current, and the target air volume obtained from multi-objective optimization are used as reference inputs for hierarchical closed-loop control. The first control stage takes the high voltage target value as the main control object, performs PI regulation at a preset low update frequency, and applies slope and amplitude limits to the high voltage target output according to the current environmental parameters and the safe working range, generating the actual high voltage reference value after smoothing constraints and the corresponding target discharge current upper limit; The second control loop takes the actual discharge current as the main control object. It samples the discharge current in real time at a preset high sampling frequency and performs PI closed-loop control so that the actual discharge current tracks the reference value given by the outer loop without exceeding the upper limit of the target discharge current, and adjusts the drive quantity of the programmable high voltage power supply, heating unit and fan accordingly. When the second control loop detects an abnormal discharge peak-to-average power ratio, an abnormal increase in the number of false fires, or actual operating conditions exceeding the safe operating range, it feeds back the abnormality flag to the first control loop, triggering a voltage reduction, soft start, or degraded operation strategy.

9. The intelligent control system for a heater based on variable frequency ion cluster concentration as described in claim 8, characterized in that, The specific steps for formulating the optimal parameter scheduling strategy and outputting it to the underlying driver interface are as follows: Based on the target high pressure value, target air volume and target heating power, control commands are sent to the underlying drive interface within a preset control cycle to adjust the drive duty cycle of the heating unit, the fan speed and the target value of the ion source pre-stage high pressure. During the execution of control commands, the actual working status and feedback parameters of the heating unit, fan and high-voltage power supply are monitored in real time. When an execution abnormality is detected, a degraded operation or retry mechanism is automatically triggered, the relevant target parameters are adjusted within a limited range and the control commands are reissued.

10. A method for intelligent control of a heater based on variable frequency ion cluster concentration, characterized in that, Includes the following steps: S1 periodically collects environmental parameters, high-voltage sampling signals, and discharge current signals. It preprocesses the collected data and performs demand assessment on the preprocessed data to obtain heating demand results and air purification demand results. S2, based on the demand assessment results, determines the discharge stability according to the current environmental parameters and discharge status, and fits the safe working range in real time. Within the safe working range, it dynamically optimizes the overall comfort of heating and purification. S3 intelligently allocates parameters for each objective to perform multi-objective optimization. At the same time, it coordinates parameter adjustment through a hierarchical structure, formulates the optimal parameter scheduling strategy, and outputs it to the underlying driver interface.

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