Air purifier dust cleaning control method and device, air purifier and storage medium

Abstract

The application relates to the technical field of energy-saving electrical appliances, and discloses an air purifier dust cleaning control method and device, an air purifier and a storage medium, the method comprises the following steps: obtaining operation data of the air purifier, wherein the operation data comprises an inlet particle concentration, an outlet particle concentration, a real-time air volume corresponding to a fan gear, an effective operation duration and an inlet humidity; calculating a real-time purification efficiency and an accumulated dust flux based on the operation data; converting the accumulated dust flux into an estimated filter screen dust dry weight by using an adaptive nonlinear dust prediction model; and jointly judging the estimated filter screen dust dry weight and the decay rate of the actual purification efficiency relative to the benchmark efficiency by using double thresholds, so that the insufficient robustness caused by single parameter misjudgment is effectively avoided; and finally, a graded dust cleaning strategy is matched according to the determined dust accumulation level, which prevents the wear of the filter screen and the dust cleaning structure caused by invalid dust cleaning, ensures complete cleaning when the dust is seriously accumulated, and significantly prolongs the service life of the filter screen and reduces energy consumption.

Description

Technical Field

[0001] This invention relates to the field of energy-saving electrical appliance technology, and in particular to a method, device, air purifier and storage medium for controlling dust removal in an air purifier. Background Technology

[0002] During long-term operation, air purifiers continuously trap particulate matter in the air, causing dust to accumulate on the filter surface and inside the fibers. This directly leads to three major problems: First, increased wind resistance and soaring fan energy consumption, with energy consumption increasing by more than 30% for the same airflow. Second, decreased purification efficiency and reduced particulate matter interception capacity, failing to achieve the advertised purification effect. Third, dust accumulation breeds bacteria and mold, producing odors and secondary pollution, endangering user health.

[0003] Existing technologies for monitoring and maintaining dust accumulation on air filters have significant technical shortcomings and cannot meet the needs of consumer-grade air purifiers. The core pain points are as follows: 1. Fixed-cycle maintenance plan: The industry-standard fixed reminders of cleaning every 3 months and replacing the filter every 6-12 months completely ignore the impact of differences in indoor air quality, equipment usage frequency, and operating conditions. Since indoor PM2.5 concentrations can vary by more than 100 times in different regions and scenarios, a fixed cycle can easily lead to over-maintenance. For example, frequent dust cleaning can accelerate filter fiber wear and shorten its lifespan; or it can lead to serious delays in maintenance, such as failing to remind you when the filter is covered in excessive dust, resulting in equipment failure and a surge in energy consumption.

[0004] 2. Differential Pressure Sensing Detection Solution: This solution, which determines the degree of dust accumulation by detecting the pressure difference before and after the filter, is the mainstream solution for commercial equipment. However, it has fatal flaws: First, it has a severe lag, as it can only be detected when the dust accumulates into a dense dust cake, causing a significant increase in air resistance. By this time, the equipment has already been operating in a degraded state of high energy consumption and low efficiency for a long time. Second, it has extremely poor anti-interference ability, and the detection results are greatly affected by the fan speed, temperature and humidity, fan aging, and power grid voltage fluctuations, resulting in a high misjudgment rate over long-term use. Third, it cannot adapt to light to moderate dust accumulation scenarios with automatic dust removal structures, as slight dust accumulation will not cause changes in air resistance, making it impossible to achieve early prediction and preventive dust removal.

[0005] 3. Optical / Image Direct Detection Solution: This solution directly observes the dust accumulation on the filter using a camera or laser scanning module. However, it suffers from high costs and poor environmental adaptability. Dust, water mist, and temperature and humidity changes within the purifier cavity can contaminate the lens and interfere with the detection results. The accuracy also decreases rapidly with long-term use, making it completely unsuitable for mass production of consumer-grade home products.

[0006] 4. Defects of automatic dust removal control scheme: If a dust removal logic with fixed duration and frequency is adopted, and it is not linked to the actual dust accumulation level, it will not only fail to solve the problem of excessive dust accumulation, but also fail to avoid structural wear and noise interference caused by ineffective dust removal.

[0007] Therefore, existing technologies still need to be improved and developed. Summary of the Invention

[0008] In order to overcome the shortcomings of the prior art, the present invention aims to provide an air purifier dust removal control method, device, air purifier and storage medium, which aims to solve the technical problems of low prediction accuracy, slow response and unreasonable dust removal strategy of the prior art for air purifier filter dust accumulation.

[0009] The first aspect of this invention provides a method for controlling dust removal in an air purifier, comprising the steps of: acquiring operational data of the air purifier during the current purification cycle, the operational data including inlet particulate matter concentration, outlet particulate matter concentration, real-time airflow corresponding to the fan speed setting, effective operating time, and inlet humidity; calculating the real-time purification efficiency and the cumulative dust flux at the end of the current operating cycle based on the operational data; and inputting the cumulative dust flux into a preset adaptive nonlinear dust prediction model to obtain the estimated cumulative dry weight of dust on the filter, wherein the expression of the preset adaptive nonlinear dust prediction model is: ,in, To estimate the dry weight of dust on the filter screen, For the calibrated intercept term; The linear deposition coefficient; It is a nonlinear saturation coefficient. The cumulative dust flux at the end of the current operating cycle is used as the basis. Based on the estimated cumulative dry weight of dust on the filter and the rate of decrease in real-time purification efficiency relative to the baseline purification efficiency, the current filter dust accumulation level is determined, where the baseline purification efficiency is the initial purification efficiency of the air purifier filter in a clean state. Based on the filter dust accumulation level, a corresponding filter cleaning strategy is matched and executed. The filter cleaning strategy includes a no-cleaning operation strategy, a light cleaning operation strategy, and a deep cleaning operation strategy.

[0010] Optionally, in a first implementation of the first aspect of the present invention, the formula for calculating the real-time purification efficiency based on the operating data is: ,in, For real-time purification efficiency, The concentration of particulate matter at the air inlet. This refers to the particulate matter concentration at the air outlet.

[0011] Optionally, in a second implementation of the first aspect of the present invention, the formula for calculating the cumulative dust flux at the end of the current operating cycle based on the operating data is: ,in, This represents the cumulative dust flux prior to this operation. The concentration of particulate matter at the air inlet; The baseline purification efficiency for the new filter; This is the calibrated humidity correction factor; This represents the real-time airflow corresponding to the fan speed setting. Effective runtime is measured in minutes.

[0012] Optionally, in a third implementation of the first aspect of the present invention, determining the current filter dust accumulation level based on the estimated cumulative dry weight of dust on the filter and the rate of decrease in real-time purification efficiency relative to the baseline purification efficiency includes the step of: comparing the estimated cumulative dry weight of dust on the filter with a plurality of preset dust accumulation level thresholds to obtain a first comparison result, wherein the plurality of dust accumulation level thresholds include a light dust accumulation threshold whose values ​​increase sequentially. Moderate dust accumulation threshold Heavy dust threshold The attenuation rate of the real-time purification efficiency relative to the benchmark purification efficiency is compared with a preset attenuation threshold to obtain a second comparison result; the current filter dust accumulation level is determined by combining the first comparison result and the second comparison result, and the filter dust accumulation level includes: micro dust accumulation level, light dust accumulation level, medium dust accumulation level and heavy dust accumulation level.

[0013] Optionally, in a fourth implementation of the first aspect of the present invention, determining the current filter dust accumulation level by combining the first comparison result and the second comparison result includes the step of: if the first comparison result indicates that the estimated cumulative dry weight of dust on the filter is less than the light dust accumulation threshold. If the second comparison result shows that the attenuation rate is less than the attenuation threshold, then the current filter dust accumulation level is determined to be the micro dust accumulation level; if the first comparison result shows that the estimated cumulative dry weight of dust on the filter is less than the light dust accumulation threshold... If the second comparison result is that the attenuation rate is greater than or equal to the attenuation threshold, then the current filter dust accumulation level is determined to be moderate dust accumulation level; if the first comparison result is that the estimated cumulative dry weight of dust on the filter is greater than the light dust accumulation threshold. And less than or equal to the moderate dust accumulation threshold. If the second comparison result is that the attenuation rate is less than the attenuation threshold, then the current filter dust accumulation level is determined to be a light dust accumulation level; if the first comparison result is that the estimated cumulative dry weight of dust on the filter is greater than the light dust accumulation threshold. And less than or equal to the moderate dust accumulation threshold. If the second comparison result is that the attenuation rate is greater than or equal to the attenuation threshold, then the current filter dust accumulation level is determined to be moderate dust accumulation level; if the first comparison result is that the estimated cumulative dry weight of dust on the filter is greater than the moderate dust accumulation threshold... And less than or equal to the heavy dust accumulation threshold. If the first comparison result is that the estimated cumulative dry weight of dust on the filter is greater than the heavy dust accumulation threshold, then the current filter dust accumulation level is directly determined to be moderate dust accumulation level; If the current dust accumulation level is determined to be heavy dust accumulation level, then the current filter dust accumulation level will be directly determined.

[0014] Optionally, in a fifth implementation of the first aspect of the present invention, the step of matching and executing a corresponding filter cleaning strategy according to the filter dust accumulation level includes the following steps: when the filter dust accumulation level is a micro dust accumulation level or a light dust accumulation level, a strategy of not triggering a cleaning operation is executed; when the filter dust accumulation level is a moderate dust accumulation level, a light cleaning operation strategy is executed; and when the filter dust accumulation level is a heavy dust accumulation level, a deep cleaning operation strategy is executed.

[0015] Optionally, in the sixth implementation of the first aspect of the present invention, the non-triggered dust cleaning operation strategy is to not perform a dust cleaning operation; the light dust cleaning operation strategy is to control the filter screen to rotate 1 revolution, the brush sweeping structure to lightly touch the filter screen with 30-80% of the preset pressure value, and the dust suction structure to maintain low power operation with 30-80% of the rated power; the deep dust cleaning operation strategy is to control the filter screen to rotate at least 2 revolutions, the brush sweeping structure to adhere to the filter screen with a preset pressure value, and the dust suction structure to maintain full power operation with 100% of the rated power.

[0016] Optionally, in the seventh implementation of the first aspect of the present invention, the step of matching and executing a corresponding filter cleaning strategy according to the filter dust accumulation level further includes the steps of: obtaining the updated purification efficiency of the air purifier under fixed operating conditions, and calculating the purification efficiency recovery degree based on the updated purification efficiency; based on the purification efficiency recovery degree, matching and executing one or more of the following operations: resetting the cumulative dust flux, reducing the dust accumulation level threshold, and pushing manual maintenance reminders, while storing the parameter change data before and after cleaning into a historical database.

[0017] Optionally, in the eighth implementation of the first aspect of the present invention, the step of matching and executing one or more of the following operations based on the purification efficiency recovery degree: resetting the cumulative dust flux, reducing the dust accumulation level threshold, and pushing a manual maintenance reminder, includes the following steps: if the purification efficiency recovery degree is greater than or equal to the first recovery threshold, then the cumulative dust flux is reset, and the parameter change data before and after cleaning is stored in the historical database; if the purification efficiency recovery degree is less than the first recovery threshold but greater than or equal to the second recovery threshold, then the cumulative dust flux is not reset, only the cumulative dust flux increment within the current cleaning cycle is cleared, and the dust accumulation level threshold for triggering the next cleaning is lowered based on the purification efficiency recovery degree, and the parameter change data before and after cleaning is stored in the historical database; if the purification efficiency recovery degree is less than the second recovery threshold, then it is determined that the filter is irreversibly clogged by deep dust accumulation or the filter life is exhausted, and a prompt message indicating that the filter needs manual maintenance or replacement is generated.

[0018] Optionally, in a ninth implementation of the first aspect of the present invention, the air purifier dust removal control method further includes the step of: periodically or event-triggeredly refitting and updating the coefficients of the adaptive nonlinear dust prediction model based on the historical database.

[0019] Optionally, in the tenth implementation of the first aspect of the present invention, based on the historical database, the coefficients of the adaptive nonlinear dust prediction model are periodically or event-triggered, and then refitted and updated, including the steps of: after each number of effective purification operations or after each dust removal operation, checking whether there are m sets of data pairs in the historical database consisting of cumulative dust flux and corresponding actual dust dry weight; if so, using least squares regression to refit the adaptive nonlinear dust prediction model. Refit, Update and .

[0020] Optionally, in the eleventh implementation of the first aspect of the present invention, the acquisition of the actual dust dry weight includes the following steps: conducting a calibration experiment on the same type of filter screen in advance, measuring the real-time purification efficiency under different dust dry weights, establishing a mapping relationship between dust dry weight and real-time purification efficiency, and forming a calibration fitting function; using the calibration fitting function, calculating the current real-time purification efficiency. This is mapped to the corresponding actual dry weight of dust.

[0021] A second aspect of the present invention provides a control device for an air purifier, comprising: an acquisition module for acquiring operating data of the air purifier during the current purification cycle, the operating data including inlet particulate matter concentration, outlet particulate matter concentration, real-time airflow corresponding to the fan speed setting, effective operating time, and inlet humidity; a calculation module for calculating real-time purification efficiency and cumulative dust flux at the end of the current operating cycle based on the operating data; and a prediction module for inputting the cumulative dust flux into a preset adaptive nonlinear dust prediction model to obtain a predicted cumulative dust dry weight of the filter, wherein the expression of the preset adaptive nonlinear dust prediction model is: ,in, To estimate the dry weight of dust on the filter screen, For the calibrated intercept term; The linear deposition coefficient; It is a nonlinear saturation coefficient. The cumulative dust flux at the end of the current operating cycle; the level judgment module is used to determine the current filter dust accumulation level based on the estimated cumulative dry weight of dust on the filter and the decay rate of the real-time purification efficiency relative to the benchmark purification efficiency, wherein the benchmark purification efficiency is the initial purification efficiency of the air purifier filter in a clean state; the execution module is used to match and execute the corresponding filter cleaning strategy according to the filter dust accumulation level, wherein the filter cleaning strategy includes a no-cleaning operation strategy, a light cleaning operation strategy, and a deep cleaning operation strategy.

[0022] A third aspect of the present invention provides an air purifier, comprising: a memory and at least one processor, wherein the memory stores computer-readable instructions, and the memory and the at least one processor are interconnected via a circuit; the at least one processor invokes the computer-readable instructions in the memory to cause the electronic device to perform various steps of the air purifier dust removal control method described above.

[0023] A fourth aspect of the present invention provides a computer-readable storage medium storing computer-readable instructions that, when executed on a computer, cause the computer to perform the various steps of the air purifier dust removal control method described above.

[0024] Beneficial Effects: This invention acquires the operating data of an air purifier and simultaneously calculates real-time purification efficiency and cumulative dust flux. Then, it utilizes an adaptive nonlinear dust prediction model to convert the cumulative dust flux into an estimated filter dust dry weight. This allows for precise matching of the entire dust accumulation cycle pattern using quadratic nonlinear characteristics, achieving proactive prediction. Furthermore, it employs a dual-threshold joint judgment based on the estimated filter dust dry weight and the rate of decrease in actual purification efficiency relative to the baseline efficiency, effectively avoiding insufficient robustness caused by misjudgment of a single parameter. Finally, it matches a graded cleaning strategy based on the determined dust accumulation level, addressing micro / light dust accumulation. The system performs light cleaning when there is no dust, and deep cleaning when there is heavy dust. This not only prevents wear and tear on the filter and cleaning structure caused by ineffective cleaning, but also ensures thorough cleaning when there is heavy dust accumulation. This significantly extends the life of the filter and reduces energy consumption. At the same time, the method of this invention is based entirely on the original sensors of the air purifier, with zero incremental hardware cost. Furthermore, it can be combined with cleaning effect verification and adaptive model updates to allow the prediction model to be continuously corrected as the filter ages and the sensor drifts. Ultimately, this achieves low-cost, high-precision, lag-free, robust, and fully life-cycle-free intelligent control of filter cleaning. Attached Figure Description

[0025] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0026] Figure 1 This is a flowchart of an air purifier dust removal control method provided in an embodiment of the present invention.

[0027] Figure 2 Another flowchart of an air purifier dust removal control method provided in an embodiment of the present invention.

[0028] Figure 3 This is a schematic diagram of the structure of an air purifier dust removal control device provided by the present invention.

[0029] Figure 4 This is a schematic diagram of the air purifier structure provided by the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0031] The air purifier dust removal control method provided in this invention can be applied to various types of air purifiers, including but not limited to household air purifiers, commercial air purifiers, fresh air systems, and air conditioning equipment with air purification functions.

[0032] Please see Figure 1 , Figure 1 A flowchart of an air purifier dust removal control method provided by the present invention is shown in the figure, which includes steps S100-S400, wherein, S100. Obtain the operating data of the air purifier during the current purification cycle. The operating data includes the inlet particulate matter concentration, the outlet particulate matter concentration, the real-time air volume corresponding to the fan speed, the effective operating time, and the inlet humidity. Specifically, when a user turns on the air purifier, the device collects the following operational data in real time at a preset sampling period (e.g., 1 minute): inlet particulate matter concentration, outlet particulate matter concentration, real-time airflow corresponding to the fan speed setting, effective operating time, and inlet humidity. The inlet particulate matter concentration is measured by the inlet PM2.5 sensor, reflecting the indoor pollution level; the outlet particulate matter concentration is measured by the outlet PM2.5 sensor, reflecting the cleanliness of the air after filtration by the air purifier's filter; the real-time airflow corresponding to the fan speed setting is read by the fan speed setting acquisition module, and the correspondence between airflow and speed setting is calibrated at the factory and stored in non-volatile memory; the effective operating time is recorded by the timing module, for example, only when the fan speed is higher than a preset minimum speed threshold (e.g., 20% of the rated speed) is it counted as effective operation, avoiding interference from low-speed invalid operation; the inlet humidity is collected by the humidity sensor and used to subsequently correct for dust deposition characteristics.

[0033] This step makes full use of the air purifier's original standard sensors without increasing any hardware costs. The above multi-dimensional data provides the basis for subsequent accurate calculation of dust interception. Among them, the inlet particulate matter concentration determines the pollution load, the air volume and duration determine the total amount of air handled, humidity affects dust adhesion, and the outlet particulate matter concentration is directly related to the purification effect.

[0034] S200. Based on the operating data, calculate the real-time purification efficiency and the cumulative dust flux at the end of the current operating cycle; In this step, real-time purification efficiency is an important indicator for evaluating filter performance degradation and can be directly used to assist in judgment; cumulative dust flux represents the total mass of dust theoretically intercepted by the filter (after humidity correction). Its calculation process does not depend on the current actual state of the filter, but only on the integration of inlet particulate matter concentration, real-time air volume and effective operating time. Therefore, it has foresight and no lag, and overcomes the lag defect of the traditional pressure difference method that depends on changes in wind resistance. It can predict the filter load before dust accumulation significantly affects wind resistance, and achieve early warning.

[0035] Specifically, step S200 includes two parallel computing tasks. The first is to calculate the real-time purification efficiency based on the running data. For each sampling time i, the formula for the real-time purification efficiency is: , where is the real-time purification efficiency, is the particulate matter concentration at the air inlet, is the particulate matter concentration at the air outlet. This formula directly reflects the interception ability of the filter for particulate matter in the current state. When the filter is clean, is close to the reference purification efficiency calibrated at the factory (such as 99.5%); as the dust accumulation increases, the pores of the filter are blocked, and some particulate matter penetrates the filter, gradually decreases.

[0036] Secondly, after the end of one operation cycle of the air purifier (that is, when the user turns on the air purifier until it shuts down or the device goes into standby), calculate the total effective dust interception amount of this operation and update the cumulative dust passage. The calculation formula is: , where is the cumulative dust flux before this operation; is the reference purification efficiency of a brand-new filter; is the calibrated humidity correction coefficient; is the real-time air volume corresponding to the fan gear; is the effective operation duration in minutes. In this embodiment, the humidity correction coefficient is calibrated through experiments. For example, when RH ≤ 30%, = 0.85; when 30% < RH ≤ 70%, = 1.0; when RH > 70%, = 1.15; the humidity correction coefficient improves the calculation accuracy in different environments.

[0037] S300. Input the cumulative dust flux into a preset adaptive non-linear dust prediction model to obtain the estimated dry weight of the accumulated dust on the filter; Specifically, the dust accumulation process on the filter shows non-linear characteristics: in the initial stage, dust fills the pores of the filter fibers, and the deposition rate is relatively fast; in the middle stage, a dust cake is formed, and the deposition rate tends to be linear; in the later stage, the deep layer of the fibers is blocked, and the deposition rate gradually saturates. To accurately describe this characteristic, this embodiment uses an adaptive non-linear dust prediction model with a quadratic term to calculate the estimated dry weight of the accumulated dust on the filter.

[0038] In this embodiment, the expression of the preset adaptive non-linear dust prediction model is: , where is the estimated dry weight of the dust on the filter, is the calibrated intercept term, which is used to correct the inherent deviation of the system (such as the zero drift of the sensor), and the initial value calibrated at the factory is 0 or a small constant; The linear deposition coefficient corresponds to the basic deposition efficiency of dust, with dimensions in g / mg. The initial value is obtained through experimental calibration, for example, by fitting a standard dust sample onto a brand-new filter screen. The nonlinear saturation coefficient is negative and is used to capture the trend of the deposition rate decreasing with the increase of dust accumulation. The initial value is also calibrated experimentally. This represents the cumulative dust flux at the end of the current operating cycle.

[0039] The adaptive nonlinear dust prediction model provided in this embodiment can accurately match the full-cycle characteristics of dust accumulation on the filter screen, and its prediction accuracy is far superior to that of traditional linear models or fixed-cycle methods. At the same time, the model has a simple structure and low computational cost, making it suitable for real-time operation on low-cost microcontrollers, providing an accurate basis for graded dust removal decisions.

[0040] S400. Based on the estimated cumulative dry weight of dust on the filter and the rate of decrease in real-time purification efficiency relative to the baseline purification efficiency, determine the current dust accumulation level of the filter, wherein the baseline purification efficiency is the initial purification efficiency of the air purifier filter in a clean state. This step employs a dual-threshold joint judgment mechanism, combining the estimated cumulative dry weight of dust on the filter and the attenuation rate, to determine the current dust accumulation level on the filter. This dual-threshold joint judgment mechanism greatly enhances the robustness of the system. When the model-predicted cumulative dry weight of dust on the filter, S, is too low (e.g., due to sensor drift or special dust types) but the actual efficiency has already significantly decreased, the auxiliary attenuation rate condition can promptly trigger the dust cleaning operation, preventing the equipment from operating inefficiently for extended periods. This embodiment's dual-threshold joint judgment mechanism effectively avoids misjudgments caused by factors such as sensor drift, changes in dust characteristics, and power grid fluctuations. It maintains stable monitoring and control performance in various scenarios, including clean home environments, heavy smog environments, and special environments such as high humidity, multiple pets, and heavy oil fumes.

[0041] S500. Based on the dust accumulation level of the filter screen, match and execute the corresponding filter screen cleaning strategy. The filter screen cleaning strategy includes a non-triggered cleaning operation strategy, a light cleaning operation strategy, and a deep cleaning operation strategy.

[0042] This step can match different filter cleaning strategies according to different levels of filter dust accumulation. This graded strategy breaks the traditional crude logic of cleaning when the dust reaches the threshold. It avoids structural wear and noise caused by ineffective cleaning, and can thoroughly clean when the dust accumulation is serious, thus extending the life of the filter and the cleaning structure.

[0043] This invention acquires the operating data of an air purifier and simultaneously calculates real-time purification efficiency and cumulative dust flux. Then, it utilizes an adaptive nonlinear dust prediction model to convert the cumulative dust flux into an estimated filter dust dry weight. This allows for precise matching of the entire dust accumulation cycle pattern using quadratic nonlinear characteristics, achieving proactive prediction. Furthermore, it employs a dual-threshold joint judgment of the estimated filter dust dry weight and the rate of decrease in actual purification efficiency relative to the baseline efficiency, effectively avoiding insufficient robustness caused by misjudgment based on a single parameter. Finally, it matches a graded cleaning strategy based on the determined dust accumulation level, ensuring no action is taken during periods of slight / light dust accumulation. Light cleaning for moderate dust accumulation and deep cleaning for heavy dust accumulation prevents wear on the filter and cleaning structure from ineffective cleaning, while ensuring thorough cleaning in cases of severe dust accumulation. This significantly extends the filter's lifespan and reduces energy consumption, achieving energy-saving effects. Furthermore, the method fully reuses the purifier's existing sensors, resulting in zero incremental hardware costs. It can also be combined with cleaning effect verification and adaptive model updates, allowing the prediction model to continuously correct itself as the filter ages and the sensor drifts. Ultimately, this achieves low-cost, high-precision, lag-free, robust, and fully lifecycle-free intelligent control of filter dust accumulation.

[0044] In some implementations, step S400 includes the following sub-steps: S401. The estimated cumulative dry weight of dust on the filter screen is compared with multiple preset dust accumulation level thresholds to obtain a first comparison result. The multiple dust accumulation level thresholds include a light dust accumulation threshold with progressively increasing values. Moderate dust accumulation threshold Heavy dust threshold ; S402. The attenuation rate of the real-time purification efficiency relative to the benchmark purification efficiency is compared with a preset attenuation threshold to obtain a second comparison result. S403. Based on the first comparison result and the second comparison result, determine the current dust accumulation level of the filter screen. The dust accumulation level of the filter screen includes: micro dust accumulation level, light dust accumulation level, medium dust accumulation level and heavy dust accumulation level.

[0045] Specifically, this embodiment pre-sets three dust accumulation level thresholds with progressively increasing values, including a light dust accumulation threshold. This indicates that there is a small amount of dust on the filter surface, which has not yet significantly affected the purification efficiency; moderate dust accumulation threshold. This indicates that dust accumulation is quite noticeable, and purification efficiency has begun to decline slightly; heavy dust accumulation threshold. This indicates severe dust accumulation, a significant decrease in purification efficiency, and a marked increase in energy consumption. For example, the threshold for light dust accumulation... 1.5g, moderate dust accumulation threshold 3.5g, heavy dust accumulation threshold It contains 6.0g.

[0046] The estimated cumulative dry weight of dust on the filter screen, S, calculated in step 300, is compared with the three thresholds mentioned above to determine the range in which S falls: is it less than...? Between and Between, between and Between, or greater than or equal to The comparison result is the first comparison result. This first comparison result directly reflects the quantitative assessment of dust accumulation based on the theoretical model. Because the model uses nonlinear prediction and considers humidity correction, this result has high accuracy in most normal scenarios and can be used as the primary judgment basis. This embodiment divides the dust accumulation level threshold into three levels, providing a refined decision-making basis for subsequent graded dust removal.

[0047] In step S402, the rate of decrease in real-time purification efficiency relative to the baseline purification efficiency is first calculated: ,in, This refers to the initial purification efficiency of the air purifier filter in a clean state, i.e., the baseline purification efficiency. To ensure real-time purification efficiency, for example, a preset attenuation threshold of 15% is used, and then the calculated attenuation rate is... The result is compared with the threshold to obtain a second comparison result, which is the judgment. Is it less than 15%, or greater than or equal to 15%? Real-time purification efficiency is a direct reflection of the actual filtration performance of the filter and is not affected by prediction model errors. When the filter efficiency drops rapidly due to special reasons (such as oily particles, moisture, or localized damage), the attenuation rate will increase prematurely even if the estimated cumulative dust dry weight S of the filter has not yet reached the moderate or heavy threshold. Therefore, the second comparison result can be used for actual performance verification and can compensate for the shortcomings of model prediction.

[0048] In step S403, the first comparison result (the threshold range where S lies) and the second comparison result (whether the attenuation rate is ≥ the attenuation threshold) are logically combined to comprehensively determine four dust accumulation levels: micro dust accumulation level, light dust accumulation level, moderate dust accumulation level, and heavy dust accumulation level. This step specifically includes sub-steps S4031-S4036, wherein, S4031. If the first comparison result is that the estimated cumulative dry weight of dust on the filter screen is less than the light dust accumulation threshold... If the second comparison result shows that the attenuation rate is less than the attenuation threshold, then the current filter dust accumulation level is determined to be the micro dust accumulation level. In this step, combined with the comparison results, it can be seen that the filter is very clean or has only a very small amount of dust. The theoretical dust accumulation amount has not reached the mild threshold, and the actual purification efficiency has almost no attenuation (still close to the baseline efficiency). The filter is in the best working state.

[0049] S4032, if the first comparison result is that the estimated cumulative dry weight of dust on the filter is less than the light dust accumulation threshold. If the second comparison result shows that the attenuation rate is greater than or equal to the attenuation threshold, then the current filter dust accumulation level is determined to be a moderate dust accumulation level. In this step, combined with the comparison results, it can be seen that the model-predicted cumulative dry weight of dust on the filter indicates that the filter is still very clean (not reaching the light dust accumulation threshold), but the actual purification efficiency has significantly decreased. This phenomenon usually occurs in scenarios such as: highly viscous dust (such as kitchen fumes) clogging the filter pores, resulting in a sharp drop in efficiency despite its light weight; the filter being damp or partially damaged; and the PM2.5 sensor at the air inlet drifting, leading to an underestimation of the cumulative dust flux C. At this time, the effect of the dual threshold correlation mechanism can be demonstrated, that is, using actual performance as an auxiliary judgment, when the model prediction fails, the attenuation rate condition automatically upgrades the filter dust accumulation level, forcibly triggering the dust cleaning operation to avoid long-term inefficient operation of the equipment.

[0050] S4033, if the first comparison result is that the estimated cumulative dry weight of dust on the filter is greater than the light dust accumulation threshold. And less than or equal to the moderate dust accumulation threshold. If the second comparison result shows that the attenuation rate is less than the attenuation threshold, then the current filter dust accumulation level is determined to be light dust accumulation. In this step, based on the comparison results, it can be seen that a certain amount of dust has accumulated on the filter (reaching the light threshold but not the moderate level), and the actual purification efficiency has not yet decreased significantly. This indicates that the dust mainly remains on the filter surface and has not yet blocked the deep pores, thus having a small impact on performance. At this time, dust cleaning is not triggered; only data is recorded to avoid premature intervention.

[0051] S4034, if the first comparison result is that the estimated cumulative dry weight of dust on the filter is greater than the light dust accumulation threshold. And less than or equal to the moderate dust accumulation threshold. If the second comparison result is that the attenuation rate is greater than or equal to the attenuation threshold, then the current dust accumulation level of the filter screen is determined to be a medium dust accumulation level. In this step, based on the comparison results, it can be seen that although the estimated cumulative dry weight of dust on the filter screen is still within the range of light dust accumulation threshold and medium dust accumulation threshold, its attenuation rate is greater than or equal to the attenuation threshold, indicating that its actual purification efficiency has also decreased significantly. At this time, timely intervention is required to forcibly trigger the dust removal operation to avoid long-term inefficient operation of the equipment.

[0052] S4035, If the first comparison result is that the estimated cumulative dry weight of dust on the filter is greater than the moderate dust accumulation threshold... And less than or equal to the heavy dust accumulation threshold. If the current filter dust accumulation level is determined to be moderate, then the model directly determines that the estimated cumulative dry weight of dust on the filter is at the moderate dust accumulation threshold. In this step, based on the comparison results, it can be seen that the model-predicted cumulative dry weight of dust on the filter is clearly at the moderate dust accumulation threshold. With heavy dust threshold Between these two points, the amount of dust accumulation is relatively large, and the confidence level of the model prediction is high. The attenuation rate is no longer needed to assist in determining the dust accumulation level, which can directly simplify the logic and improve stability.

[0053] S4036. If the first comparison result is that the estimated cumulative dry weight of dust on the filter is greater than the heavy dust accumulation threshold... If the filter dust accumulation level is determined to be heavy dust accumulation, then the current filter dust accumulation level is directly identified as heavy dust accumulation. In this step, based on the comparison results, it can be seen that the model-predicted cumulative dry weight of dust on the filter has reached or exceeded the heavy dust threshold, indicating that the filter dust accumulation is very serious, and it has most likely formed a dense dust cake, significantly increasing air resistance, raising energy consumption, and severely reducing efficiency. Heavy dust accumulation is an emergency situation that requires immediate deep cleaning. At this point, the attenuation rate is no longer relied upon for judgment, because the estimated cumulative dry weight of dust on the filter is sufficient to make the determination.

[0054] In this embodiment, steps S4031-S4036 constitute a complete, non-overlapping, and comprehensive dust accumulation level determination system, which takes into account both the accuracy of the prediction model and the reliability of actual performance. It is the core decision-making basis for realizing graded intelligent dust removal control.

[0055] In some implementations, step S500 includes the following sub-steps: S501. When the dust accumulation level of the filter screen is micro dust accumulation level or light dust accumulation level, the strategy of not triggering dust cleaning operation is to not perform dust cleaning operation. S502. When the dust accumulation level of the filter screen is medium dust accumulation level, a light dust removal operation strategy is executed. The light dust removal operation strategy is as follows: control the filter screen to rotate 1 revolution, the brush sweeping structure lightly touches the filter screen with 30-80% of the preset pressure value, and the dust suction structure maintains low power operation at 30-80% of the rated power. S503. When the dust accumulation level of the filter is heavy dust accumulation level, a deep dust cleaning operation strategy is executed. The deep dust cleaning operation strategy is as follows: control the filter to rotate at least 2 times, the brush structure adheres to the filter with a preset pressure value, and the suction structure maintains full power operation at 100% rated power.

[0056] This embodiment matches three differentiated cleaning strategies based on the determined dust accumulation level of the filter (micro dust accumulation, light dust accumulation, medium dust accumulation, and heavy dust accumulation), and provides specific mechanical action parameters for light cleaning and deep cleaning, including the number of rotations, brush pressure percentage, and suction power percentage. This embodiment breaks away from the traditional single, coarse mode of cleaning at the threshold, and achieves a refined, graded response to the filter state. Specifically, no action is taken for micro / light dust accumulation, completely avoiding ineffective cleaning and mechanical wear on the filter fibers and cleaning structure, thereby extending the service life of the filter and the cleaning mechanism; for medium dust accumulation, low-intensity light cleaning is used to remove surface dust with low energy consumption and structural damage, restoring most of the purification efficiency; for heavy dust accumulation, high-intensity deep cleaning is used to thoroughly remove deep dust and dense dust cake through multiple rotations, full-pressure brushing, and full-power suction, ensuring full recovery of equipment performance. Meanwhile, by setting specific pressure percentage and power percentage ranges, the adjustable dust removal effect is ensured, and flexible space is reserved for the adaptation of different models and different filter materials. This achieves the optimal balance between dust removal efficiency and structural protection, and reduces the overall energy consumption and operating noise.

[0057] Specifically, when the dust accumulation level is determined to be micro-dust accumulation or light dust accumulation, it indicates that the filter is in good condition. At this time, no dust cleaning operation is performed to prevent the brush structure from frequently rubbing against the filter fibers, thus delaying filter fatigue, damage, and efficiency decline. The system only maintains normal operation: it continues to accumulate the cumulative dust flux C, and stores the sampling data of this operation (inlet / outlet particulate matter concentration, air volume, running time, humidity, and real-time purification efficiency) into the historical database and continuously monitors it.

[0058] When the dust accumulation level is determined to be moderate, a light dust removal strategy is implemented. This strategy involves controlling the filter to rotate one full revolution, with the brush mechanism lightly touching the filter at 30-80% of a preset pressure, and the suction mechanism operating at 30-80% of its rated power. In this embodiment, one full revolution combined with light brushing is sufficient to remove floating dust and some shallow dust adhering to the filter surface without excessively compressing the filter fibers. The 30%-80% pressure range avoids mechanical damage to the filter from full-pressure brushing, making it particularly suitable for precision filters such as HEPA filters. Controlling the suction power at 30%-80% significantly reduces energy consumption and noise compared to full-power operation, minimizing user interference during the cleaning process. After the light dust removal operation, the purification efficiency recovery rate typically reaches 60%-90%, sufficient to bring the equipment back to its high-efficiency operating range.

[0059] When the dust accumulation level is determined to be heavy, a deep cleaning operation strategy is implemented. This strategy involves controlling the filter to rotate at least two revolutions, ensuring the brush structure adheres to the filter at a preset pressure value, and maintaining full power operation at 100% of rated power. A heavy dust accumulation level indicates that the filter is severely clogged, forming a dense dust cake, significantly increasing wind resistance, raising energy consumption, and severely reducing purification efficiency. This embodiment selects controlling the filter to rotate at least two revolutions, for example, rotating it two revolutions forward and then one revolution backward, but is not limited to this. Bidirectional rotation allows for impacting the dense dust cake within the filter folds and pores from different directions, significantly improving cleaning effectiveness. Full-pressure brushing ensures the bristles penetrate deep into the fibers, breaking up stubborn dust. Full-power suction generates maximum negative pressure, thoroughly removing loose dust particles and deep-seated dust. After deep cleaning, the purification efficiency recovery is typically ≥90%, and the filter performance is close to its initial state.

[0060] In some embodiments, the step of matching and executing a corresponding filter cleaning strategy based on the filter dust accumulation level further includes the following steps: S600: Obtain the updated purification efficiency of the air purifier under fixed operating conditions, and calculate the purification efficiency recovery degree based on the updated purification efficiency. In this step, after each dust removal operation (light dust removal or deep dust removal), an effect verification program is automatically started. The fixed operating conditions are set by fixing the fan speed to a standard setting (e.g., medium speed, corresponding to the rated airflow Q). std The system runs continuously for a preset time (e.g., 5 minutes) to eliminate the impact of fan speed changes on purification efficiency measurement; updated purification efficiency is obtained by collecting the particulate matter concentration at the air outlet and calculating the average purification efficiency during the calibration run. At the same time, the real-time purification efficiency before dust removal was retrieved. This refers to the efficiency value before the last dust removal was triggered; then, the purification efficiency recovery rate is calculated using the following formula: ,in, The baseline purification efficiency is the same as that of a brand new filter. In this embodiment, the purification efficiency recovery rate R quantifies the proportion of filter performance restored to the baseline level after cleaning: R=100% means that it is completely restored to a brand new state, and R=0% means that the cleaning is ineffective.

[0061] S700. Based on the purification efficiency recovery degree, perform one or more of the following operations: reset the cumulative dust flux, reduce the dust accumulation level threshold, and push manual maintenance reminder. At the same time, store the parameter change data before and after dust removal into the historical database.

[0062] This step mainly involves performing differentiated state adjustment operations based on the range of the purification efficiency recovery degree R, which includes sub-steps S701-S703, as shown below: S701. If the purification efficiency recovery degree is greater than or equal to the first recovery threshold, then reset the cumulative dust flux and store the parameter change data before and after dust removal into the historical database. S702. If the purification efficiency recovery degree is less than the first recovery threshold but greater than or equal to the second recovery threshold, the cumulative dust flux is not reset, only the cumulative dust flux increment in this cleaning cycle is cleared, and the dust accumulation level threshold for triggering the next cleaning is lowered based on the purification efficiency recovery degree, and the parameter change data before and after cleaning is stored in the historical database. S703. If the purification efficiency recovery rate is less than the second recovery threshold, it is determined that the filter is irreversibly clogged by deep dust accumulation or the filter life is exhausted, and a prompt message is generated indicating that the filter needs to be manually maintained or replaced.

[0063] In this embodiment, the values ​​of the first recovery threshold and the second recovery threshold can be calibrated at the factory according to the filter type. For example, the first recovery threshold can be a value between 85-95%, and the second recovery threshold can be a value between 50-70%. The following description uses an example of a first recovery threshold of 90% and a second recovery threshold of 60% to illustrate the above sub-steps S701-S703. Figure 2 As shown: When the purification efficiency recovery rate R ≥ 90%, the dust removal strategy is deemed effective, indicating that the filter performance has basically returned to a clean state. At this point, the cumulative dust flux can be reset, that is, the cumulative dust flux C is directly set to 0, indicating that the dust accumulated on the filter has been removed, and the new monitoring cycle starts from zero; at the same time, the cumulative dust flux C before dust removal is recorded. before The purification efficiency recovery rate R after dust removal is recorded and stored in the historical database as samples for subsequent adaptive updates of the model. When 60% ≤ purification efficiency recovery rate R < 90%, the cleaning is deemed effective, but the filter shows irreversible aging or deep dust accumulation has not been completely removed. In this case, the cumulative dust flux C is not reset because a considerable amount of dust remains on the filter, and resetting C would lead to a significantly lower prediction in subsequent cycles. However, the cumulative dust flux increment within this cleaning cycle needs to be zeroed out, meaning the C increment accumulated from the last cleaning to the current cleaning cycle is reset to zero, while the base value of C remains unchanged. For example, before cleaning, C = 5000mg, with a current cycle increment of 2000mg; after zeroing, C = 3000mg. This preserves the historical base value while removing the contribution of the already removed portion. When 60% ≤ purification efficiency recovery rate R < 90%, this embodiment also lowers the dust accumulation level threshold for triggering the next cleaning cycle based on the purification efficiency recovery rate. For example, the original moderate dust accumulation threshold... =4g. If the purification efficiency recovery rate is 60%, then the reduction coefficient α = 0.6, and the new moderate dust accumulation threshold is 2.4g; if the purification efficiency recovery rate is 70%, then the reduction coefficient α = 0.7, and the new moderate dust accumulation threshold is 2.8g. It should be noted that the value of the reduction coefficient is not limited to this, and can also be a linear function related to the purification efficiency recovery rate. In this embodiment, after the threshold is reduced, the system will trigger the next dust cleaning earlier to compensate for the incomplete dust cleaning in this instance.

[0064] When the purification efficiency recovery rate R < 60%, it is determined that the filter is irreversibly clogged due to deep dust accumulation or that the filter life has been exhausted. At this time, continuing to clean the dust will not only fail to improve the performance, but will also aggravate structural wear. Therefore, it is only necessary to generate a prompt message indicating that the filter needs manual maintenance or replacement. For example, the prompt message could be that the filter is severely clogged and you should manually clean or replace the filter.

[0065] This embodiment solves the blind spot problem of traditional dust removal schemes that only remove dust without verification by implementing a closed-loop control mechanism that includes dust removal, effect verification, status correction, and data recording. Specifically, when the recovery rate is high (≥90%), this embodiment resets the cumulative dust flux, allowing the system to restart dust monitoring and avoiding interference from historical data; when the recovery rate is medium (60%-90%), it does not reset the value but lowers the subsequent dust removal threshold, preserving filter aging information while compensating for incomplete dust removal by increasing trigger sensitivity; when the recovery rate is low (<60%), it determines that the filter is irreversibly clogged and promptly sends a manual maintenance reminder to avoid continuous damage to the structure from ineffective dust removal. Simultaneously, all verification data is stored in a historical database, providing valuable true value samples for subsequent adaptive model updates. This closed-loop mechanism enables the system to learn from each dust removal operation, continuously optimizing control parameters and significantly improving long-term reliability and intelligence.

[0066] In some implementations, such as Figure 2 As shown, the air purifier dust removal control method further includes the step of: periodically or event-triggeredly refitting and updating the coefficients of the adaptive nonlinear dust prediction model based on the historical database.

[0067] Specifically, the historical database includes data pairs consisting of cumulative dust flux and corresponding actual dust dry weight. After each number of effective purification operations or after each dust removal operation, the historical database is checked to see if there are m sets of data pairs consisting of cumulative dust flux and corresponding actual dust dry weight. If so, least squares regression is used to apply the adaptive nonlinear dust prediction model. Refit, Update and .

[0068] In this embodiment, the following data is automatically obtained after each dust removal process: The cumulative dust flux C before dust removal and the corresponding actual dust dry weight are given. The actual dust dry weight is obtained through the following steps: conducting a calibration experiment on the same type of filter, measuring the real-time purification efficiency under different dust dry weights, establishing a mapping relationship between dust dry weight and real-time purification efficiency, and forming a calibration fitting function. Using the calibration fitting function, the currently calculated real-time purification efficiency is... Mapped to the corresponding actual dry weight of dust .

[0069] Specifically, take a brand new filter, weigh its initial dry weight m0, install it in the air purifier, and measure its initial purification efficiency. (Fixed airflow, fixed intake dust concentration); run the air purifier in a high-dust environment for a preset time (e.g., 4 hours), then remove the filter after stopping and weigh it to obtain m1, which is the increase in dust accumulation. =m1 m0; Reinstall the same filter in the air purifier and measure the purification efficiency under the same airflow and intake dust concentration. Repeat the above process of increasing weight, weighing, and measuring efficiency to obtain a series of data points. , It covers the entire lifecycle of the filter, from brand new to near clogging (when the purification efficiency drops to less than 50% of the initial value).

[0070] Based on the experimental data, select an appropriate mathematical model to describe and To illustrate this relationship, for example, we can use the rate of decrease in real-time purification efficiency relative to the baseline purification efficiency. As the independent variable, the calibration fitting function can be obtained according to the quadratic polynomial mathematical model. = The coefficients in the function are obtained by fitting using the least squares method. and These coefficients are then written into the non-volatile memory of each purifier of the same model.

[0071] When the air purifier is running normally, it collects the PM2.5 concentration at the air inlet and the air outlet at regular intervals to calculate the real-time purification efficiency. Substituting the calculated real-time purification efficiency into the pre-stored calibration fitting function, the actual dry weight of dust on the filter can be obtained.

[0072] In this embodiment, a new set of data is generated each time an effective purification operation ends or before and after dust removal. , As usage increases, the historical database gradually becomes richer, providing sufficient basis for model optimization.

[0073] In this embodiment, after each number of effective purification runs or after each dust removal operation, the system checks the historical database for m sets of data pairs. An effective purification run refers to a complete cycle in which the user uses the purifier normally and runs continuously for a time ≥ a preset value (e.g., 30 minutes). The system triggers a check every k effective purification runs (e.g., k=10). A check is also triggered immediately after each dust removal operation (light or deep). These two triggering mechanisms complement each other; effective purification runs ensure regular checks, while dust removal operations ensure timely checks after each new sample is obtained, accelerating model convergence. The check in this embodiment involves statistically analyzing the effective data pairs already stored in the historical database. , To avoid overfitting or model bias when the sample size is too small, this embodiment presets a minimum sample size m. Only when the number of data pairs is ≥ m is it considered statistically significant and regression fitting can be performed. The threshold m can be adjusted according to the filter type and actual needs to balance the update frequency and computational cost. For example, m can be 20, 25, 30, etc., but is not limited to these.

[0074] In this embodiment, if there are m sets of data pairs, then least squares regression is used to apply the adaptive nonlinear dust prediction model. Refit, Update and Specifically, in this embodiment, It is usually fixed at 0 or can also participate in the fitting as a bias term, but the main update is... and Least squares regression refers to finding a set of... and Minimize the sum of squared residuals for all data pairs: This is a standard quadratic polynomial regression, which can be solved through numerical optimization to obtain the new result. and Write the values ​​to non-volatile memory, overwriting the old values. The new coefficients will be used in subsequent prediction calculations.

[0075] The least squares method used in this embodiment is statistically the optimal linear unbiased estimator, which maximizes the use of historical data and makes the model most closely resemble the actual dust accumulation pattern. Since the model itself contains quadratic terms, refitting can adjust... and This allows the filter to adapt to changes in deposition characteristics caused by filter aging (e.g., linear deposition coefficient after aging). It will decrease, and the saturation effect will become more pronounced. (The negatives increase); as usage time increases and the number of data pairs increases, the model becomes more and more accurate, eventually converging to the characteristics of the real system.

[0076] This embodiment overcomes the limitations of traditional fixed-coefficient models that cannot adapt to filter aging and sensor drift, achieving online adaptive evolution of model parameters. As usage time increases, the model can learn from each cleaning and each operation, continuously calibrating the linear deposition coefficient and nonlinear saturation coefficient, keeping the prediction error at a low level over the long term. At the same time, this mechanism requires no manual intervention, automatically completing model optimization throughout the entire life cycle of the equipment, significantly improving long-term reliability and prediction accuracy. Furthermore, by setting trigger conditions (several operations or each cleaning) and a data pair quantity threshold m, sufficient basis for model updates is ensured while avoiding the computational overhead caused by frequent fitting, achieving a balance between accuracy and efficiency.

[0077] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0078] Based on the same inventive concept, embodiments of the present invention also provide a control device for implementing the air purifier dust removal control method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more air purifier dust removal control device embodiments provided below can be found in the limitations of the air purifier dust removal control method described above, and will not be repeated here.

[0079] In some embodiments, the present invention also provides an air purifier dust removal control device, such as... Figure 3 As shown, it includes: The acquisition module 10 is used to acquire the operating data of the air purifier during the current purification operation cycle. The operating data includes the inlet particulate matter concentration, the outlet particulate matter concentration, the real-time air volume corresponding to the fan speed, the effective operating time, and the inlet humidity. The calculation module 20 is used to calculate the real-time purification efficiency and the cumulative dust flux at the end of the current operating cycle based on the operating data. The estimation module 30 is used to input the cumulative dust flux into a preset adaptive nonlinear dust prediction model to obtain the estimated cumulative dust dry weight of the filter screen. The expression of the preset adaptive nonlinear dust prediction model is as follows: ,in, To estimate the dry weight of dust on the filter screen, For the calibrated intercept term; The linear deposition coefficient; It is a nonlinear saturation coefficient. This represents the cumulative dust flux at the end of the current operating cycle. The grade judgment module 40 is used to determine the current dust accumulation level of the filter based on the estimated cumulative dry weight of dust on the filter and the decay rate of the real-time purification efficiency relative to the benchmark purification efficiency, wherein the benchmark purification efficiency is the initial purification efficiency of the air purifier filter in a clean state. The execution module 50 is used to match and execute the corresponding filter cleaning strategy according to the filter dust accumulation level. The filter cleaning strategy includes a no-cleaning operation strategy, a light cleaning operation strategy, and a deep cleaning operation strategy.

[0080] In some embodiments, the level determination module 40 includes: The first comparison unit is used to compare the estimated cumulative dry weight of dust on the filter with a plurality of preset dust accumulation level thresholds to obtain a first comparison result. The plurality of dust accumulation level thresholds include a light dust accumulation threshold with progressively increasing values. Moderate dust accumulation threshold Heavy dust threshold ; The second comparison unit is used to compare the decay rate of the real-time purification efficiency relative to the benchmark purification efficiency with a preset decay threshold to obtain a second comparison result. The determination unit is used to determine the current dust accumulation level of the filter screen by combining the first comparison result and the second comparison result. The dust accumulation level of the filter screen includes: micro dust accumulation level, light dust accumulation level, medium dust accumulation level and heavy dust accumulation level.

[0081] In some embodiments, the execution module 50 includes: The first execution unit is used to execute a strategy of not triggering dust removal when the dust accumulation level of the filter screen is micro dust accumulation level or light dust accumulation level. The second execution unit is used to execute a light dust removal operation strategy when the dust accumulation level of the filter screen is medium dust accumulation level. The third execution unit is used to execute a deep cleaning operation strategy when the dust accumulation level of the filter screen is heavy dust accumulation level.

[0082] In some embodiments, the air purifier dust removal control device further includes: The data update module is used to obtain the updated purification efficiency of the air purifier under fixed operating conditions, and calculate the purification efficiency recovery degree based on the updated purification efficiency; and based on the purification efficiency recovery degree, match and execute one or more of the following operations: resetting the cumulative dust flux, reducing the dust accumulation level threshold, and pushing manual maintenance reminders, while storing the parameter change data before and after dust cleaning into the historical database.

[0083] In some embodiments, the air purifier dust removal control device further includes: The model parameter update module is used to refit and update the coefficients of the adaptive nonlinear dust prediction model periodically or triggered by events based on the historical database.

[0084] Figure 3 The air purifier dust removal control device in this embodiment of the invention will be described in detail from the perspective of modular functional entities. The air purifier in this embodiment of the invention will be described in detail from the perspective of hardware processing.

[0085] Figure 4 This is a schematic diagram of an air purifier 100 provided in an embodiment of the present invention. The air purifier 100 can vary significantly due to different configurations or performance characteristics. It may include one or more central processing units (CPUs) 111 (e.g., one or more processors) and a memory 121, and one or more storage media 130 (e.g., one or more mass storage devices) for storing application programs 133 or data 132. The memory 121 and storage media 130 can be temporary or persistent storage. The program stored in the storage media 130 may include one or more modules (not shown in the diagram), each module including a series of instruction operations on the air purifier 100. Furthermore, the processor 111 may be configured to communicate with the storage media 130 and execute the series of instruction operations in the storage media 130 on the air purifier 100.

[0086] The air purifier 100 may also include one or more power supplies 141, one or more wired or wireless network interfaces 151, one or more input / output interfaces 161, and / or one or more operating systems 131, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 4 The device structure shown does not constitute a limitation on the air purifier 100, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0087] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the steps of the air purifier dust removal control method.

[0088] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system, device, or unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0089] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0090] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for controlling dust removal in an air purifier, characterized in that, Including the following steps: The air purifier's operating data during the current purification cycle is obtained. The operating data includes the inlet particulate matter concentration, the outlet particulate matter concentration, the real-time air volume corresponding to the fan speed, the effective operating time, and the inlet humidity. Based on the aforementioned operational data, the real-time purification efficiency and the cumulative dust flux at the end of the current operational cycle are calculated. The cumulative dust flux is input into a preset adaptive nonlinear dust prediction model to obtain the estimated cumulative dust dry weight of the filter screen. The expression of the preset adaptive nonlinear dust prediction model is as follows: ,in, To estimate the dry weight of dust on the filter screen, For the calibrated intercept term; The linear deposition coefficient; It is a nonlinear saturation coefficient. This represents the cumulative dust flux at the end of the current operating cycle. Based on the estimated cumulative dry weight of dust on the filter and the rate of decrease in real-time purification efficiency relative to the baseline purification efficiency, the current dust accumulation level of the filter is determined, wherein the baseline purification efficiency is the initial purification efficiency of the air purifier filter in a clean state. Based on the dust accumulation level of the filter screen, a corresponding filter screen cleaning strategy is matched and executed. The filter screen cleaning strategy includes a no-cleaning operation strategy, a light-cleaning operation strategy, and a deep-cleaning operation strategy.

2. The air purifier dust removal control method according to claim 1, characterized in that, The formula for calculating the real-time purification efficiency based on the aforementioned operational data is as follows: ,in, For real-time purification efficiency, The concentration of particulate matter at the air inlet. This refers to the particulate matter concentration at the air outlet.

3. The air purifier dust removal control method according to claim 1, characterized in that, The formula for calculating the cumulative dust flux at the end of the current operating cycle based on the aforementioned operating data is as follows: ,in, This represents the cumulative dust flux prior to this operation. The concentration of particulate matter at the air inlet; The baseline purification efficiency for the new filter; This is the calibrated humidity correction factor; This represents the real-time airflow corresponding to the fan speed setting. Effective runtime is measured in minutes.

4. The air purifier dust removal control method according to claim 1, characterized in that, The step of determining the current dust accumulation level of the filter based on the estimated cumulative dry weight of dust on the filter and the rate of decrease in real-time purification efficiency relative to the baseline purification efficiency includes the following steps: The estimated cumulative dry weight of dust on the filter screen is compared with multiple preset dust accumulation level thresholds to obtain a first comparison result. The multiple dust accumulation level thresholds include a light dust accumulation threshold with progressively increasing values. Moderate dust accumulation threshold Heavy dust threshold ; The attenuation rate of the real-time purification efficiency relative to the benchmark purification efficiency is compared with a preset attenuation threshold to obtain a second comparison result; The current filter dust accumulation level is determined by combining the first comparison result and the second comparison result. The filter dust accumulation level includes: micro dust accumulation level, light dust accumulation level, medium dust accumulation level and heavy dust accumulation level.

5. The air purifier dust removal control method according to claim 4, characterized in that, The step of determining the current filter dust accumulation level by combining the first comparison result and the second comparison result includes the following steps: If the first comparison result is that the estimated cumulative dry weight of dust on the filter is less than the light dust accumulation threshold... If the second comparison result is that the attenuation rate is less than the attenuation threshold, then the current filter dust accumulation level is determined to be the micro dust accumulation level. If the first comparison result is that the estimated cumulative dry weight of dust on the filter is less than the light dust accumulation threshold... If the second comparison result is that the attenuation rate is greater than or equal to the attenuation threshold, then the current filter dust accumulation level is determined to be a medium dust accumulation level. If the first comparison result is that the estimated cumulative dry weight of dust on the filter is greater than the light dust accumulation threshold... And less than or equal to the moderate dust accumulation threshold. If the second comparison result is that the attenuation rate is less than the attenuation threshold, then the current filter dust accumulation level is determined to be a light dust accumulation level. If the first comparison result is that the estimated cumulative dry weight of dust on the filter is greater than the light dust accumulation threshold... And less than or equal to the moderate dust accumulation threshold. If the second comparison result is that the attenuation rate is greater than or equal to the attenuation threshold, then the current dust accumulation level of the filter screen is determined to be a medium dust accumulation level. If the first comparison result is that the estimated cumulative dry weight of dust on the filter is greater than the moderate dust accumulation threshold... And less than or equal to the heavy dust accumulation threshold. If so, the current dust accumulation level of the filter is directly determined to be medium dust accumulation level; If the first comparison result is that the estimated cumulative dry weight of dust on the filter is greater than the heavy dust accumulation threshold... If the current dust accumulation level is determined to be heavy dust accumulation level, then the current filter dust accumulation level will be directly determined.

6. The air purifier dust removal control method according to claim 5, characterized in that, The step of matching and executing a corresponding filter cleaning strategy based on the filter dust accumulation level includes the following steps: When the dust accumulation level of the filter is micro dust accumulation level or light dust accumulation level, the strategy of not triggering the dust cleaning operation is executed. When the dust accumulation level of the filter is medium, a light dust removal operation strategy is executed. When the filter dust accumulation level is heavy dust accumulation level, a deep cleaning operation strategy is executed.

7. The air purifier dust removal control method according to claim 6, characterized in that, The strategy of not triggering the dust removal operation is to not perform the dust removal operation. The lightweight dust removal operation strategy is as follows: control the filter screen to rotate 1 revolution, the brushing structure lightly touches the filter screen with 30-80% of the preset pressure value, and the dust collection structure maintains low power operation at 30-80% of the rated power. The deep cleaning operation strategy is as follows: control the filter screen to rotate at least 2 times, the brushing structure to adhere to the filter screen with a preset pressure value, and the suction structure to maintain full power operation at 100% rated power.

8. The air purifier dust removal control method according to any one of claims 1-7, characterized in that, The step of matching and executing a corresponding filter cleaning strategy based on the filter dust accumulation level further includes the following steps: Obtain the updated purification efficiency of the air purifier under fixed operating conditions, and calculate the purification efficiency recovery rate based on the updated purification efficiency; Based on the purification efficiency recovery rate, one or more of the following operations are performed: resetting the cumulative dust flux, reducing the dust accumulation level threshold, and pushing manual maintenance reminders. At the same time, the parameter change data before and after dust cleaning are stored in the historical database.

9. The air purifier dust removal control method according to claim 8, characterized in that, The step of performing one or more of the following operations based on the purification efficiency recovery degree: resetting the cumulative dust flux, reducing the dust accumulation level threshold, and sending a manual maintenance reminder, includes the following steps: If the purification efficiency recovery degree is greater than or equal to the first recovery threshold, then the cumulative dust flux is reset, and the parameter change data before and after dust removal is stored in the historical database. If the purification efficiency recovery degree is less than the first recovery threshold but greater than or equal to the second recovery threshold, the cumulative dust flux will not be reset, but only the cumulative dust flux increment within the current cleaning cycle will be cleared. Based on the purification efficiency recovery degree, the dust accumulation level threshold for triggering the next cleaning will be lowered, and the parameter change data before and after cleaning will be stored in the historical database. If the purification efficiency recovery rate is less than the second recovery threshold, it is determined that the filter is irreversibly clogged by deep dust accumulation or the filter life is exhausted, and a prompt message is generated indicating that the filter needs manual maintenance or replacement.

10. The air purifier dust removal control method according to claim 8, characterized in that, It also includes the following steps: Based on the historical database, the coefficients of the adaptive nonlinear dust prediction model are periodically or triggered by events and then refitted and updated.

11. The air purifier dust removal control method according to claim 10, characterized in that, Based on the historical database, the coefficients of the adaptive nonlinear dust prediction model are periodically or event-triggered and refitted, including the following steps: After each effective purification operation or after each dust removal operation, check the historical database to see if there are m sets of data pairs consisting of the cumulative dust flux and the corresponding actual dust dry weight. If it exists, then least squares regression is used to evaluate the adaptive nonlinear dust prediction model. Refit, Update and ,in, To estimate the dry weight of dust on the filter screen, For the calibrated intercept term; The linear deposition coefficient; It is a nonlinear saturation coefficient. This represents the cumulative dust flux at the end of the current operating cycle.

12. The air purifier dust removal control method according to claim 11, characterized in that, The actual dry weight of the dust is obtained through the following steps: A calibration experiment was conducted on the same type of filter in advance to measure the real-time purification efficiency under different dust dry weights, establish the mapping relationship between dust dry weight and real-time purification efficiency, and form a calibration fitting function. Using the calibration fitting function, the currently calculated real-time purification efficiency is... This is mapped to the corresponding actual dry weight of dust.

13. An air purifier dust removal control device, characterized in that, include: The acquisition module is used to acquire the operating data of the air purifier during the current purification operation cycle. The operating data includes the inlet particulate matter concentration, the outlet particulate matter concentration, the real-time air volume corresponding to the fan speed, the effective operating time, and the inlet humidity. The calculation module is used to calculate the real-time purification efficiency and the cumulative dust flux at the end of the current operating cycle based on the operating data. The estimation module is used to input the cumulative dust flux into a preset adaptive nonlinear dust prediction model to obtain the estimated cumulative dust dry weight of the filter screen. The expression of the preset adaptive nonlinear dust prediction model is as follows: ,in, To estimate the dry weight of dust on the filter screen, For the calibrated intercept term; The linear deposition coefficient; It is a nonlinear saturation coefficient. This represents the cumulative dust flux at the end of the current operating cycle. The level determination module is used to determine the current dust accumulation level of the filter based on the estimated cumulative dry weight of dust on the filter and the decay rate of the real-time purification efficiency relative to the benchmark purification efficiency. The benchmark purification efficiency is the initial purification efficiency of the air purifier filter in a clean state. The execution module is used to match and execute the corresponding filter cleaning strategy according to the filter dust accumulation level. The filter cleaning strategy includes a no-cleaning operation strategy, a light cleaning operation strategy, and a deep cleaning operation strategy.

14. An air purifier, characterized in that, It includes a memory and at least one processor, wherein the memory stores computer-readable instructions; The at least one processor invokes the computer-readable instructions in the memory to perform various steps of the air purifier dust removal control method as claimed in any one of claims 1-12.

15. A computer-readable storage medium storing computer-readable instructions thereon, characterized in that, When the computer-readable instructions are executed by a processor, they implement the various steps of the air purifier dust removal control method as described in any one of claims 1-12.

Images