Filter screen self-cleaning method, device and air purification equipment

Abstract

The application relates to the technical field of electrical equipment, and discloses a filter screen self-cleaning method and device and air purification equipment, wherein a current filter screen pressure difference between an air inlet side and an air outlet side of a filter screen in a current period is obtained, a predicted air quality index corresponding to a future period and a historical air quality index corresponding to a historical period at the same time in the current period are determined; based on the predicted air quality index and the historical air quality index, a filter screen pollution index corresponding to the current period is calculated; based on the filter screen pollution index, a target filter screen pressure difference threshold of the filter screen in the current period is determined, and when it is detected that the current filter screen pressure difference reaches the target filter screen pressure difference threshold, the filter screen is controlled to perform self-cleaning; the filter screen is moderately cleaned according to the influence of the environmental characteristics of the environment where the filter screen is located on the cleanliness of the filter screen, the filter screen self-cleaning efficiency is improved, the service life of the filter screen is prolonged, and the overall energy consumption is reduced.

Description

Technical Field

[0001] This invention relates to the field of electrical equipment technology, specifically to a method, apparatus, and air purification equipment for self-cleaning filters. Background Technology

[0002] Air purification or ventilation system filters often use fixed cycles or simple differential pressure triggering methods for self-cleaning. These methods cannot distinguish between the actual degree of dust accumulation and mechanical wear caused by ineffective dust cleaning. The cleaning process relies solely on real-time sensor feedback, which is lagging and cannot proactively prevent severe dust accumulation. Furthermore, it cannot perform appropriate cleaning based on the environmental characteristics of the filter's environment, resulting in incomplete or over-cleaning, which leads to shortened filter life and increased energy consumption. Summary of the Invention

[0003] This invention provides a filter self-cleaning method, apparatus, and air purification equipment to solve the problem of not being able to properly clean the filter based on the influence of the environmental characteristics of the environment in which the filter is located on the filter's cleanliness.

[0004] In a first aspect, the present invention provides a filter self-cleaning method, the method comprising: Obtain the current filter pressure difference between the filter inlet and outlet sides during the current period, and determine the predicted air quality index for future periods and the historical air quality index for the same period in history during the current period. Calculate the filter pollution index for the current period based on predicted and historical air quality indicators. Based on the filter contamination index, the target filter pressure difference threshold for the current period is determined, and when the current filter pressure difference is detected to reach the target filter pressure difference threshold, the filter is controlled to perform self-cleaning.

[0005] This invention obtains the current filter pressure difference between the inlet and outlet sides of the filter during the current period, determines the predicted air quality index for future periods and the historical air quality index for the same period, and calculates the filter contamination index for the current period, reflecting the degree of filter contamination. Based on the filter contamination index, a target filter pressure difference threshold is dynamically set for the current period. This allows for adaptive determination of the timing and frequency of filter self-cleaning through the fusion of historical big data and weather forecast data, without relying on additional sensors to detect contamination levels. This reduces ineffective cleaning wear and avoids severe dust accumulation and blockage. The dynamically adaptively adjusted target filter pressure difference threshold can adapt to differences in contamination at different times, making the self-cleaning trigger timing more closely match the actual dust accumulation on the filter, reducing unnecessary frequent or improper cleaning. Combining historical benchmarks helps distinguish seasonal fluctuations and improves the robustness of decision-making. This achieves appropriate filter cleaning based on the environmental characteristics of the filter's environment, improving filter self-cleaning efficiency, extending filter life, and reducing overall energy consumption.

[0006] In one optional implementation, determining the predicted air quality index for a future time period and the historical air quality index for the same historical period in the current time period includes: Obtain several atmospheric particulate matter concentration values ​​for future periods, and calculate the average of these atmospheric particulate matter concentration values ​​to obtain the predicted air quality index. Determine a preset number of consecutive historical years earlier than the current time period, and then determine the historical time periods that include the corresponding period of the current time period in each historical year to obtain the historical contemporaneous period; Historical air quality indicators are determined based on air quality indicators for each historical sub-cycle during the same historical period.

[0007] This invention obtains several atmospheric particulate matter concentration values ​​for future periods and calculates their average value as a predicted air quality indicator. Multi-value averaging improves data reliability and reduces the impact of instantaneous fluctuations. Simultaneously, it identifies a predetermined number of consecutive historical years earlier than the current period and extracts historical periods corresponding to the current period from each year, combining them into a historical contemporaneous period. Based on the air quality indicators of each historical sub-period within this historical contemporaneous period, a comprehensive historical air quality indicator is calculated. Multi-concentration averaging makes the predicted indicator more stable and representative, reducing the interference of single outliers on the calculation results. Using contemporaneous data from multiple consecutive historical years effectively captures the climate and pollution patterns of that period, avoiding deviations caused by special events in a single historical year, making the historical benchmark more reliable. Furthermore, dividing historical sub-periods and synthesizing their indicators more precisely reflects the phased changes within the same period, providing a more solid and seasonally consistent reference for subsequent filter pollution index calculations, thereby improving the rationality and adaptability of the self-cleaning threshold setting.

[0008] In one optional implementation, historical air quality indicators are determined based on air quality indicators for each historical sub-cycle within the same historical period, including: Obtain several historical atmospheric particulate matter concentration values ​​and weighting coefficients corresponding to each historical sub-cycle. The smaller the year difference between the historical year and the current period, the larger the weighting coefficient corresponding to the historical sub-cycle. Based on several historical atmospheric particulate matter concentration values ​​and weighting coefficients, the baseline pollution value for each historical sub-cycle is calculated. The historical air quality index is obtained by calculating the average of the baseline pollution values ​​for each historical sub-cycle.

[0009] This invention obtains several historical atmospheric particulate matter concentration values ​​and weighting coefficients corresponding to each historical sub-cycle. The smaller the time difference between the historical year and the current period, the larger the weighting coefficient corresponding to that historical sub-cycle, thus reflecting the priority of recent historical data for current reference value. Based on several concentration values ​​and their weighting coefficients for each sub-cycle, a baseline pollution value for each historical sub-cycle is calculated by weighting. The average of the baseline pollution values ​​of all sub-cycles is used as the final historical air quality index. By assigning weights in descending order of time difference, more recent historical years have a higher proportion in the baseline, which helps to capture the pollution evolution trend in recent years and the potential impact of climate change, making the baseline pollution value of each sub-cycle more stable. Finally, by averaging across multiple sub-cycles, the levels of different stages within the same period are comprehensively reflected, thereby constructing a historical reference value that takes into account both timeliness and statistical robustness, providing a more realistic comparison basis for the subsequent calculation of the filter pollution index.

[0010] In one optional implementation, the filter pollution index for the current time period is calculated based on predicted air quality indicators and historical air quality indicators, including: Get the current air humidity value for the current time period and the corresponding historical air humidity value for the same period in history; Humidity deviation is determined based on the difference between the current air humidity value and the historical air humidity value; Based on a pre-defined correspondence, the first target weight coefficient for future time periods, the second target weight coefficient for historical contemporaneous periods, and the third target weight coefficient for the time periods corresponding to humidity deviations are determined; wherein, the pre-defined correspondence is used to characterize the mapping relationship between time periods and time period weights; The predicted index is obtained based on the predicted air quality index and the first target weighting coefficient; The historical index is obtained based on historical air quality indicators and the weighting coefficient of the second objective. The humidity index is obtained based on the humidity deviation and the third objective weighting coefficient; The filter contamination index is obtained based on the predicted index, historical index, and humidity index.

[0011] This invention obtains the current air humidity value and the corresponding historical air humidity value for the same period in the current time period, and calculates the difference between the two to obtain the humidity deviation. Based on the mapping relationship used to characterize the time period and time period weights, it determines the first target weight coefficient for the future time period, the second target weight coefficient for the historical same period, and the third target weight coefficient for the time period corresponding to the humidity deviation. A predicted index is obtained by multiplying the predicted air quality index by the first target weight coefficient, a historical index is obtained by multiplying the historical air quality index by the second target weight coefficient, and a humidity index is obtained by multiplying the humidity deviation by the third target weight coefficient. Finally, the predicted index, historical index, and humidity index are weighted and calculated. The system calculates the filter contamination index. By incorporating the difference between current and historical humidity, it reflects the impact of humidity changes on particulate matter adhesion and pressure differential, making the contamination index more closely reflect the actual operating environment. Through preset correspondence and dynamic allocation of three weights, the contribution of each component can be flexibly adjusted by comprehensively considering future contamination risk, historical reference reliability, and humidity deviation, avoiding the incompatibility caused by fixed weights. This improves the estimation accuracy of the actual dust accumulation. At the same time, the time-period mapping mechanism of the weight coefficients ensures the adaptability under different seasons or climate conditions, providing a more reliable basis for setting a more reasonable target filter pressure differential threshold, thereby optimizing the accuracy of self-cleaning triggering and the stability of system operation.

[0012] In one optional implementation, determining the target filter differential pressure threshold for the current time period based on the filter contamination index includes: Determine the preset filter differential pressure threshold corresponding to the current filter mode; Based on the comparison between the filter contamination index and the contamination index threshold, the target contamination index range of the filter contamination index is determined. The target filter pressure difference threshold is obtained by adjusting the preset filter pressure difference threshold based on the target pollution index range.

[0013] This invention determines a preset filter pressure difference threshold corresponding to the current filtration mode as a benchmark value; based on the comparison between the filter contamination index and the preset contamination index threshold, it determines the target contamination index range into which the contamination index falls; then, it adjusts the preset filter pressure difference threshold accordingly based on the target contamination index range to obtain the target filter pressure difference threshold for the current period. By comparing indices in different ranges, continuous contamination indices are transformed into discrete ranges, making the adjustment operation simple and with clear judgment boundaries, avoiding the uncertainty caused by complex nonlinear calculations; at the same time, combined with the preset value of the current filtration mode, the original setting of the equipment is preserved, making the adjustment range controlled and reasonable; dynamically correcting the threshold according to the degree of contamination allows the self-cleaning trigger to better match the actual dust accumulation rate, helping to reduce the risk of a sharp increase in ventilation resistance under excessively high contamination, and also avoiding unnecessary airflow disturbance and energy consumption caused by prematurely starting cleaning during low-contamination periods, thereby achieving a better cleaning effect between filter maintenance cycle, system energy efficiency and operational stability.

[0014] In one optional implementation, adjusting a preset filter differential pressure threshold based on a target pollution index range to obtain the target filter differential pressure threshold includes: When the target pollution index range is determined to be the first pollution index range, the current filter pressure difference threshold is increased to obtain the target filter pressure difference threshold. When the target pollution index range is determined to be the second pollution index range, the current filter pressure differential threshold is determined to be the target filter pressure differential threshold. When the target pollution index range is determined to be the third pollution index range, the current filter pressure difference threshold is reduced to obtain the target filter pressure difference threshold. Among them, the pollution index threshold of the first pollution index range is less than the pollution index threshold of the second pollution index range, and the pollution index threshold of the second pollution index range is less than the pollution index threshold of the third pollution index range.

[0015] This invention determines whether the target pollution index range falls within a first, second, or third pollution index range, where the pollution index threshold of the first range is lower than that of the second range, and the second range is lower than that of the third range. When the target range is determined to be the first range, the current filter pressure difference threshold is increased to obtain the target filter pressure difference threshold, enabling cleaning to be triggered at a higher pressure difference. When the target range is determined to be the second range, the current filter pressure difference threshold is directly determined as the target filter pressure difference threshold. When the target range is determined to be the third range, the current filter pressure difference threshold is decreased to obtain the target filter pressure difference threshold, enabling cleaning to be performed earlier under high pollution conditions. By classifying the pollution level, the threshold adjustment rules are clear and highly operable, avoiding the uncertainty caused by complex calculations. Increasing the threshold at low pollution levels helps reduce unnecessary self-cleaning frequency, extends the effective working time of the filter, and reduces airflow disturbance. Decreasing the threshold at high pollution levels allows for a more timely response to increased dust accumulation, helping to avoid energy efficiency degradation caused by a rapid increase in ventilation resistance. Maintaining the original threshold at medium pollution levels ensures stable system operation, achieving a more reasonable balance between maintenance cycle, energy consumption control, and filtration efficiency.

[0016] In one alternative implementation, the method further includes: Obtain predicted rainfall for future periods; When the predicted rainfall exceeds the preset rainfall cleaning threshold, the filter is controlled to perform self-cleaning during the current period.

[0017] This invention, based on adaptive triggering of self-cleaning timing according to the filter contamination index, can also obtain predicted rainfall for future periods. When the predicted rainfall exceeds a preset rainfall cleaning threshold, the filter is controlled to self-clean during the current period, without waiting for the pressure difference to reach the target threshold. By introducing rainfall prediction as a pre-triggered condition, the natural loosening effect of rainwater or high humidity on filter surface particles can be utilized to proactively complete cleaning before rainfall. This reduces the risk of dust adhering tightly to the filter surface due to moisture during subsequent operation, thus simplifying cleaning. Simultaneously, this strategy incorporates weather forecast information as data, enabling early intervention under favorable rainfall conditions. This helps reduce energy consumption increases caused by continuously rising pressure differences and may extend the effective service life of the filter after rainfall. Therefore, it enhances the system's flexibility in responding to contamination accumulation under variable weather conditions.

[0018] In an alternative implementation, after determining the target filter differential pressure threshold for the current time period based on the filter contamination index, the method further includes: Based on the target filter differential pressure threshold, the current filter differential pressure is detected according to a preset detection cycle; When the current filter pressure difference is detected to be below the target filter pressure difference threshold, the process returns to the steps of obtaining the current filter pressure difference between the filter inlet side and the filter outlet side for the current time period, and determining the predicted air quality index for the future time period and the historical air quality index for the same period in history for the current time period.

[0019] After determining the target filter differential pressure threshold, this invention performs real-time detection of the current filter differential pressure according to a preset detection cycle. If the current differential pressure has not yet reached the target threshold, the system returns to the initial step to reacquire the current differential pressure, predicted air quality indicators, and historical air quality indicators. The filter pollution index is then recalculated, and the target threshold is updated. This cycle repeats until the differential pressure reaches the target, triggering self-cleaning. Through periodic detection and feedback loops, the system ensures continuous tracking of differential pressure changes, preventing monitoring from ceasing due to a single judgment. Each recalculation incorporates the latest meteorological and pollution data, dynamically updating the target threshold with the actual environment and preventing fixed thresholds from losing their reference value during sudden environmental changes. This mechanism helps to continuously optimize the threshold setting during periods when the differential pressure is below the target, thus more flexibly capturing changes in dust accumulation rates and reducing the possibility of triggering cleaning too early or too late. While ensuring stability, this enhances the system's responsiveness to dynamic operating conditions and the timeliness of decision-making.

[0020] In one alternative implementation, the method further includes: Get the filter's cumulative runtime; When the cumulative running time reaches a preset running time threshold, the control filter performs self-cleaning.

[0021] This invention acquires the cumulative runtime of the filter and, upon determining that the cumulative runtime has reached a preset runtime threshold, directly controls the filter to self-clean without waiting for the differential pressure to reach the target threshold. By introducing a time dimension as an independent trigger condition, a time-based protection mechanism is provided to prevent the filter from continuously operating due to long-term dust accumulation, even when the differential pressure signal may drift, lag, or fail to trigger. Simultaneously, this runtime threshold can be set according to the filter material and normal usage cycle, helping to prevent irreversible performance degradation caused by excessive dust accumulation. This measure, in parallel with dynamic differential pressure control, forms a dual criterion, taking into account both immediate contamination and long-term wear and tear, thereby improving the system's fault tolerance and reliability under different operating conditions.

[0022] Secondly, the present invention provides a filter self-cleaning device, the device comprising: The acquisition module is used to acquire the current filter pressure difference between the filter inlet side and the filter outlet side during the current period, and to determine the predicted air quality index for the future period and the historical air quality index for the same period in history during the current period. The calculation module is used to calculate the filter pollution index for the current period based on predicted air quality indicators and historical air quality indicators; The control module is used to determine the target filter pressure difference threshold for the current period based on the filter contamination index, and to control the filter to perform self-cleaning when the current filter pressure difference is detected to reach the target filter pressure difference threshold.

[0023] Thirdly, the present invention provides an air purification device, the air purification device including a controller, the controller including a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the method of the first aspect or any corresponding embodiment described above.

[0024] In one alternative implementation, the air purification device is an air purifier.

[0025] This invention's air purifier dynamically adjusts the target filter differential pressure threshold by combining predicted air quality, historical benchmarks, real-time humidity deviations, and rainfall forecasts. It also utilizes timed triggering based on accumulated runtime to improve the accuracy of filter self-cleaning timing. This avoids the problems of delayed or excessively frequent cleaning caused by fixed thresholds, making the triggering timing more closely match the actual dust accumulation rate and changes in the external environment. This helps stabilize airflow and energy efficiency, reducing unnecessary energy consumption and airflow disturbances. Furthermore, the combination of historical references and forward-looking forecasts effectively distinguishes between seasonal fluctuations and abnormal pollution, enhancing the robustness of decision-making. The integration of rainfall pre-cleaning and runtime further enhances the system's adaptability to complex weather conditions and long-term operation, extending filter lifespan. Attached Figure Description

[0026] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0027] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the first process of a filter self-cleaning method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a second process of a filter self-cleaning method according to an embodiment of the present invention; Figure 4 This is a flowchart of the filter self-cleaning process according to an embodiment of the present invention; Figure 5This is a block diagram of a filter self-cleaning system according to an embodiment of the present invention; Figure 6 This is a structural block diagram of a filter self-cleaning device according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the hardware structure of the controller of the air purification device according to an embodiment of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0030] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0031] As an optional application scenario of this invention, such as Figure 1 As shown, the air purification device includes a filter 101 and a controller 102. The controller 102 is used to perform a filter self-cleaning method. The overall process of the controller 102 performing the filter self-cleaning method is detailed in the relevant description of the method embodiment below, and will not be repeated here.

[0032] In related technologies, the self-cleaning of filters in air purification or ventilation systems, such as pulse backflushing, scraping, and ultrasonic vibration, often adopts fixed cycles or simple differential pressure triggering methods. These methods cannot distinguish between the actual degree of dust accumulation and mechanical wear caused by ineffective cleaning. In particular, frequent cleaning is still performed when the filter is in a low-load environment, resulting in shortened filter life and increased energy consumption. For example, frequent cleaning is performed in environments with low humidity and low PM2.5 concentration. Other solutions rely solely on real-time sensor feedback, which has a lag and cannot proactively prevent severe dust accumulation, leading to incomplete cleaning or over-cleaning that causes excessive wear.

[0033] The relevant technologies have the following specific drawbacks: Fixed cycle mode: Regardless of the actual level of pollution, cleaning is forcibly performed at preset time intervals, which can cause unnecessary mechanical fatigue in low-pollution seasons. According to statistics, this can shorten the lifespan of the filter by 20% to 30%.

[0034] Differential pressure trigger mode: Cleaning is only triggered when the filter becomes clogged to a certain extent. It is a passive response and cannot cope with the rapid dust accumulation caused by sudden pollution weather (such as sandstorms or smog).

[0035] Single sensor solution: Relying on a high-precision particulate matter concentration sensor will significantly increase hardware costs, and the sensor itself is susceptible to contamination drift, resulting in insufficient long-term reliability.

[0036] Humidity factor not considered: In high humidity environments, particulate matter is prone to caking on the filter surface. In this case, the cleaning effect of backflushing will be greatly reduced, and there is no predictive mechanism.

[0037] The above deficiencies mainly stem from the lack of comprehensive prediction of historical pollution patterns and future weather trends, failing to upgrade the control strategy from passive response to proactive prevention.

[0038] To address the aforementioned problems, this embodiment provides a filter self-cleaning method that solves the problem of how to adaptively determine the timing and frequency of filter self-cleaning by fusing historical big data and weather forecast data without relying on additional high-cost sensors (such as particulate matter concentration meters). This reduces ineffective cleaning wear, avoids severe dust accumulation and clogging, extends filter life, and reduces overall energy consumption.

[0039] This embodiment separates historical patterns from future changes, and by acquiring historical data and weather forecast data, introduces a composite triggering mechanism for real-time wind resistance monitoring of the filter. This compensates for the lag and blind spots in macro-meteorological observations in the face of sudden local micro-pollution, ensuring that the filter remains unobstructed even in extreme emergencies and improving the system's emergency response capability. It resolves the contradiction of time delay or over-response caused by relying solely on single feedback, avoiding ineffective mechanical wear on the filter structure during low-pollution periods and enabling early deep cleaning in high-pollution or high-humidity scenarios. This extends filter lifespan, reduces energy consumption, and ensures cleaning effectiveness, achieving a system evolution from passive response to proactive prevention.

[0040] According to an embodiment of the present invention, a filter self-cleaning method embodiment is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0041] This embodiment provides a filter self-cleaning method, which can be used in the controller of the aforementioned air purification equipment. Figure 2 This is a schematic diagram of the first process of the filter self-cleaning method according to an embodiment of the present invention, as shown below. Figure 2 As shown, the process includes the following steps: Step S201: Obtain the current filter pressure difference between the filter inlet side and the filter outlet side during the current period, and determine the predicted air quality index for the future period and the historical air quality index for the same period in history during the current period.

[0042] It should be noted that the current time period can be the time period of filter operation in hours or days. Filter operation refers to the entire dynamic physical filtration process in which the fan drives air through the filter to filter pollutants, and generates pressure difference in real time and gradually accumulates resistance.

[0043] The current filter pressure difference refers to the pressure difference between the air inlet and outlet sides when air passes through the filter, measured in Pa. For example, the cleaner the filter, the smoother the airflow and the smaller the pressure difference; the more dust accumulates on the filter, the greater the resistance and the larger the pressure difference. In other words, the current filter pressure difference reflects the current degree of filter blockage.

[0044] In this context, "future period" refers to a point in time or a period of time following the current period. "Historical period" refers to a point in time or a period of time preceding the current period, and these historical periods strictly correspond to the current period in terms of cyclical patterns. For example, if the current period is a specific month and day, then the historical period is the period of at least one year preceding the year corresponding to the current period, and then the week encompassing the specific month and day of the current period.

[0045] Air quality indicators (AQIs) are quantitative values ​​that measure the concentration of pollutants in the air. The specific AQI values ​​will vary depending on the operating environment of the filter. AQIs can include atmospheric particulate matter concentrations, chemical gaseous substances, etc.

[0046] Step S202: Calculate the filter pollution index for the current period based on the predicted air quality index and historical air quality index.

[0047] It should be noted that the filter contamination index is a comprehensive dynamic score calculated to quantify the degree of filter contamination in the current period after taking into account air quality indicators from the same historical period and future periods. The filter contamination index can reflect the growth rate of dust load or the degree of wear and tear on the remaining lifespan of the filter. The higher the value, the more serious the degree of filter contamination.

[0048] Step S203: Based on the filter contamination index, determine the target filter pressure difference threshold for the current time period, and when the current filter pressure difference is detected to reach the target filter pressure difference threshold, control the filter to perform self-cleaning.

[0049] It should be noted that the target filter differential pressure threshold is determined by the filter material and structural characteristics. The target filter differential pressure threshold represents the upper limit of dust accumulation allowed by the system under the current filter contamination level. For example, if the pollution index is high, it means that the filter is facing severe air pollution at the current time. In this case, the target filter differential pressure threshold should be appropriately lowered so that cleaning can be triggered in advance before the filter is completely clogged. Conversely, if the pollution index is low, the target filter differential pressure threshold should be appropriately lowered to make full use of the filter's dust holding capacity and avoid energy waste and mechanical wear of the filter caused by excessively frequent cleaning.

[0050] Specifically, by combining future air quality indicators with the current level of dust accumulation in real time, the system can trigger filter self-cleaning, thereby achieving on-demand cleaning. It can proactively lower the threshold to enhance defense before the filter becomes heavily polluted, and extend the self-cleaning cycle when the filter is lightly polluted, thus avoiding over-cleaning and extending the filter's lifespan while ensuring filtration efficiency.

[0051] The filter self-cleaning method provided in this embodiment obtains the current filter pressure difference between the air inlet and outlet sides of the filter during the current period, determines the predicted air quality index for future periods and the historical air quality index for the same period in history, and calculates the filter pollution index for the current period, reflecting the degree of filter pollution in the current period. Based on the filter pollution index, a target filter pressure difference threshold is dynamically set for the current period. This allows for adaptive determination of the timing and frequency of filter self-cleaning through the fusion of historical big data and weather forecast data, without relying on additional sensors to detect pollution levels. This reduces ineffective cleaning wear and avoids severe dust accumulation and blockage. The dynamically adaptively adjusted target filter pressure difference threshold can adapt to pollution differences at different times, making the self-cleaning trigger timing more closely match the actual dust accumulation on the filter, reducing unnecessary frequent or improper cleaning. Combining historical benchmarks helps distinguish seasonal fluctuations and improves the robustness of decision-making. This method achieves appropriate cleaning of the filter based on the environmental characteristics of the filter's environment, improving filter self-cleaning efficiency, extending filter life, and reducing overall energy consumption.

[0052] This embodiment provides a filter self-cleaning method, which can be used in the controller of the aforementioned air purification equipment. Figure 3 This is a schematic diagram of a second process of the filter self-cleaning method according to an embodiment of the present invention, as shown below. Figure 3 As shown, the process includes the following steps: Step S301: Obtain the current filter pressure difference between the filter inlet side and the filter outlet side during the current period, and determine the predicted air quality index for the future period and the historical air quality index for the same period in history during the current period.

[0053] Specifically, step S301 includes: Step S3011: Obtain several atmospheric particulate matter concentration values ​​for future periods, and calculate the average value of several atmospheric particulate matter concentration values ​​to obtain the predicted air quality index.

[0054] Among them, the concentration values ​​of certain atmospheric particulate matter in the future period can be obtained from weather forecast data; the concentration value of atmospheric particulate matter is PM2.5; the future period can be within the next 72 hours.

[0055] It should be noted that the predicted air quality index is determined based on the normalized value of the average PM2.5 for future periods.

[0056] For example, several atmospheric particulate matter concentration values ​​for future periods are obtained, such as the PM2.5 forecast values ​​for the next 72 hours, and their arithmetic mean is calculated to characterize the overall concentration level for that period. This average value is then normalized, for example, by dividing it by the local air quality standard or the equipment's preset reference concentration upper limit (e.g., 200 μg / m³). 3 Alternatively, a linear or sigmoid mapping can be used to map the data to the [0,1] interval to obtain a dimensionless predicted air quality index. In this embodiment, the normalization process is performed at 200 μg / m³. 3 The upper limit is used to map the concentration value to the interval [0, 1]; this allows the index to be calculated in a unified manner with historical indicators and weighting coefficients, and at the same time, it reflects the relative degree of pollution more intuitively, providing a standardized and easily comparable input basis for the subsequent weighted fusion of filter pollution index.

[0057] Step S3012: Determine a preset number of consecutive historical years earlier than the current time period, and determine the historical time periods that include the corresponding period of the current time period in each historical year to obtain the historical contemporaneous period.

[0058] It should be noted that the historical years preceding the current time period, which are consecutively preset number of years, refer to the number of historical years traced back from the current time period. The preset number can be 3 to 5 years; in this embodiment, 3 is used as an example.

[0059] For example, historical data from the same period is extracted using a "weekly alignment" method. That is, based on the current date, the complete 7×24-hour data of the corresponding calendar week in the past 3 years are traced back, and a baseline pollution curve is generated by weighted averaging. The closer the year is to the present, the higher the weight, so as to reflect the year-by-year change trend of pollution level.

[0060] The baseline pollution curve has time on the horizontal axis, which can be in hours; the range is from 0:00 Monday to 23:00 Sunday of the current calendar week, with a total of 168 discrete time points; the vertical axis is the weighted average PM2.5 concentration (μg / m³). 3 This refers to the weighted average of data from the same time point in three years, weighted by 0.5 / 0.3 / 0.2. The specific weighting method is as follows: for each time point on the horizontal axis, the PM2.5 concentration values ​​from the corresponding times in the three years are taken, and a weighted average is calculated to obtain the baseline pollution value. Historical air quality indicators are then calculated based on this baseline pollution value.

[0061] Step S3013: Determine historical air quality indicators based on air quality indicators for each historical sub-cycle during the same historical period.

[0062] It should be noted that each historical sub-cycle refers to each day in the aforementioned week-aligned manner. By calculating the air quality index for the same day of each year in the same historical period, seven air quality indices are obtained for the seven days of the week, and historical air quality indices are calculated based on these seven air quality indices.

[0063] The filter self-cleaning method provided in this embodiment obtains several atmospheric particulate matter concentration values ​​for future periods and calculates their average value as a predicted air quality indicator. This multi-value averaging improves data reliability and reduces the impact of instantaneous fluctuations. Simultaneously, it identifies a preset number of consecutive historical years earlier than the current period and extracts historical periods corresponding to the current period from each year, combining them into a historical contemporaneous period. Based on the air quality indicators of each historical sub-period within this historical contemporaneous period, a comprehensive calculation of the historical air quality indicator is obtained. The multi-concentration value averaging makes the predicted indicator more stable and representative, reducing the interference of single outliers on the calculation results. Using contemporaneous data from multiple consecutive historical years effectively captures the climate and pollution patterns of that period, avoiding deviations caused by special events in a single historical year, making the historical benchmark more reliable. Furthermore, dividing historical sub-periods and integrating their indicators more meticulously reflects the phased changes within the same period, providing a more solid and seasonally consistent reference for subsequent filter pollution index calculations, thereby improving the rationality and adaptability of the self-cleaning threshold setting.

[0064] In some optional implementations, step S3013 above includes: Step a1: Obtain several historical atmospheric particulate matter concentration values ​​and weighting coefficients corresponding to each historical sub-cycle. The smaller the time difference between the historical year and the current period, the larger the weighting coefficient corresponding to the historical sub-cycle.

[0065] For example, the weight of the most recent year is 0.5, the weight of the year before last is 0.3, and the weight of the year before that is 0.2.

[0066] Step a2: Based on several historical atmospheric particulate matter concentration values ​​and weighting coefficients, calculate the baseline pollution value for each historical sub-cycle.

[0067] Wherein, the baseline pollution value (t) = 0.5 × C1(t) + 0.3 × C2(t) + 0.2 × C3(t); C1(t), C2(t), and C3(t) represent the PM2.5 concentration values ​​at the same time t in the most recent year, the year before last, and the year before that, respectively. This generates a baseline pollution curve containing 168 points (7 days × 24 hours).

[0068] Step a3: Calculate the average of the baseline pollution values ​​for each historical sub-cycle to obtain the historical air quality index.

[0069] The historical air quality index is calculated by summing the baseline pollution values ​​for each historical sub-cycle and dividing by the number of sub-cycles. This average value integrates data from multiple years of the same period, effectively smoothing out interannual fluctuations caused by occasional meteorological events or sudden changes in local emissions, thus obtaining a more representative and stable historical baseline. This allows the historical index to more objectively reflect the pollution situation during that period, providing a reliable reference baseline for subsequent comparisons with predicted indicators.

[0070] The filter self-cleaning method provided in this embodiment obtains several historical atmospheric particulate matter concentration values ​​and weighting coefficients corresponding to each historical sub-cycle. The smaller the time difference between the historical year and the current period, the larger the weighting coefficient corresponding to that historical sub-cycle, thus reflecting the priority of recent historical data for current reference value. Based on several concentration values ​​and their weighting coefficients for each sub-cycle, a baseline pollution value for each historical sub-cycle is calculated by weighting. The average of the baseline pollution values ​​for all sub-cycles is used as the final historical air quality index. By assigning weights in descending order of time difference, more recent historical years have a higher proportion in the baseline, which helps to capture the pollution evolution trend in recent years and the potential impact of climate change, making the baseline pollution value for each sub-cycle more stable. Then, by averaging across multiple sub-cycles, the level of different stages within the same period is comprehensively reflected, thereby constructing a historical reference value that takes into account both timeliness and statistical robustness, providing a more realistic comparison basis for the subsequent calculation of the filter pollution index.

[0071] Step S302: Calculate the filter pollution index for the current period based on the predicted air quality index and historical air quality index.

[0072] Specifically, step S302 includes: Step S3021: Obtain the current air humidity value for the current time period and the corresponding historical air humidity value for the same period in history.

[0073] In this embodiment, an air purifier with a self-cleaning function and an air volume of 900m³ is provided. 3 / h, equipped with a primary and secondary filter, and features built-in memory and WiFi wireless communication module.

[0074] The memory pre-stores hourly PM2.5, temperature, and humidity data for the same season each year for the past three years. The data comes from publicly available data from the meteorological monitoring station where the device is installed. Initialization is completed via cloud synchronization during the initial installation, and local historical data is automatically recorded and updated during subsequent operation. The wireless communication module obtains the 72-hour weather forecast (including PM2.5 forecast, precipitation probability, and relative humidity) issued by the local meteorological bureau via the Internet, with data updated every 6 hours.

[0075] Step S3022: Determine the humidity deviation based on the difference between the current air humidity value and the historical air humidity value.

[0076] The difference between the current air humidity value and the historical air humidity value is normalized to obtain the humidity deviation.

[0077] For example, the difference between the current air humidity value and the historical air humidity value can be normalized using a linear mapping method: a reasonable range for humidity deviation is preset (e.g., -40% to +40% relative humidity), and the actual difference is linearly scaled to the range of [-1, 1] or [0, 1]. If the difference exceeds the range, it is truncated to the boundary value. Alternatively, an S-shaped function can be used to map the difference to (-1, 1) to suppress the influence of extreme values. Normalization ensures that the humidity deviation is on the same comparable scale as the predicted air quality index and the historical air quality index, which facilitates subsequent weighted fusion after assigning weight coefficients. At the same time, it avoids the dominance of excessively large or small humidity values ​​in the pollution index calculation results, ensuring that the contribution of each influencing factor to the filter pollution index is more balanced and reasonable.

[0078] Step S3023: Based on the preset correspondence, determine the first target weight coefficient for the future time period, the second target weight coefficient for the same historical period, and the third target weight coefficient for the time period corresponding to the humidity deviation; wherein, the preset correspondence is used to characterize the mapping relationship between the time period and the time period weight.

[0079] The weight coefficients for the first target (α), the second target (β), and the third target (γ) are empirical weight coefficients, which automatically switch according to the month: March-May: α=0.35, β=0.55, γ=0.1; June-August: α=0.3, β=0.5, γ=0.2; September-November: α=0.45, β=0.45, γ=0.1; December-February: α=0.4, β=0.5, γ=0.1.

[0080] Step S3024: Based on the predicted air quality index and the first target weighting coefficient, the predicted index is obtained.

[0081] Specifically, the predicted index is obtained by multiplying the predicted air quality index and the first target weighting coefficient.

[0082] Step S3025: Based on historical air quality indicators and the second target weighting coefficient, obtain the historical index.

[0083] Specifically, the historical index is obtained by multiplying the historical air quality index and the weighting coefficient of the second target.

[0084] Step S3026: Obtain the humidity index based on the humidity deviation and the third target weighting coefficient.

[0085] Specifically, the humidity index is obtained by multiplying the humidity deviation and the third objective weighting coefficient.

[0086] Step S3027: Based on the predicted index, historical index and humidity index, obtain the filter contamination index.

[0087] Specifically, the filter contamination index = predicted index + historical index - humidity index.

[0088] For example, the filter contamination index is calculated by adding a predicted index to a historical index and then subtracting a humidity index. The predicted index reflects the anticipated contribution of future pollution load, while the historical index represents the baseline of normal dust accumulation during the same period. The sum of the two reflects the positive cumulative effect of pollution sources on filter dust accumulation. The humidity index is based on the deviation between the current humidity and historical humidity. When humidity is high, particulate matter is prone to moisture absorption and expansion, and the pressure difference may be artificially high, but the actual amount of solid dust accumulation may not increase proportionally. Therefore, this index is subtracted from the sum to correct for the interference caused by humidity, making the final index closer to the actual dust accumulation on the filter. All three indices are balanced with weighting coefficients and dimensionless, allowing for direct addition and subtraction. This approach integrates forward-looking and historical information while dynamically eliminating the additional influence of humidity deviation, thereby improving the accuracy of the filter contamination index in representing actual cleaning needs.

[0089] The filter self-cleaning method provided in this embodiment obtains the current air humidity value and the corresponding historical air humidity value for the same period in the current time period, and calculates the difference between the two to obtain the humidity deviation; based on the mapping relationship used to characterize the time period and time period weight, it determines the first target weight coefficient for the future time period, the second target weight coefficient for the historical same period, and the third target weight coefficient for the time period corresponding to the humidity deviation; it obtains the predicted index by multiplying the predicted air quality index by the first target weight coefficient, the historical index by multiplying the historical air quality index by the second target weight coefficient, and the humidity index by multiplying the humidity deviation by the third target weight coefficient; finally, it combines the predicted index, the historical index, and the humidity index. The filter contamination index is calculated by weighting the data. By incorporating the difference between current and historical humidity, the impact of humidity changes on particulate matter adhesion and pressure differential is reflected, making the contamination index closer to the actual operating environment. By dynamically allocating the three weights through preset correspondences, the contribution of each component can be flexibly adjusted by comprehensively considering future contamination risk, historical reference reliability, and humidity deviation, avoiding the incompatibility caused by fixed weights. This improves the estimation accuracy of the actual dust accumulation. At the same time, the time-period mapping mechanism of the weight coefficients ensures the adaptability under different seasons or climate conditions, providing a more reliable basis for setting a more reasonable target filter pressure differential threshold, thereby optimizing the accuracy of self-cleaning triggering and the stability of system operation.

[0090] Step S303: Based on the filter fouling index, determine the target filter differential pressure threshold for the current time period, and when the current filter differential pressure reaches the target filter differential pressure threshold, control the filter to perform self-cleaning. For details, please refer to... Figure 1 Step S103 of the illustrated embodiment will not be described again here.

[0091] Specifically, step S303 includes: Step S3031: Determine the preset filter pressure difference threshold corresponding to the current filter mode.

[0092] Specifically, when the filter is a pre-filter (G3 / G4 grade), the preset filter pressure difference threshold is 100Pa; when the filter is a medium-efficiency filter (F7 / F8 grade), the preset filter pressure difference threshold is 150Pa; and when the filter is a high-efficiency filter (H13 grade), the preset filter pressure difference threshold is 250Pa.

[0093] Step S3032: Based on the comparison results between the filter contamination index and the contamination index threshold, determine the target contamination index range of the filter contamination index.

[0094] Specifically, when it is determined that the filter contamination index is less than the first index threshold, the target contamination index range of the filter contamination index is determined to be the first contamination index range; When the filter contamination index is determined to be greater than or equal to the first index threshold and less than the second index threshold, the target contamination index range of the filter contamination index is determined to be the second contamination index range; When the filter contamination index is determined to be greater than or equal to the second index threshold, the target contamination index range of the filter contamination index is determined to be the third contamination index range, wherein the first index threshold is less than the second index threshold.

[0095] For example, the first index threshold is 0.3 to 0.5, and in this embodiment, the first index threshold is 0.3; the second index threshold is 0.7 to 1, and in this embodiment, the second index threshold is 0.7.

[0096] Step S3033: Adjust the preset filter pressure difference threshold based on the target pollution index range to obtain the target filter pressure difference threshold.

[0097] The filter self-cleaning method provided in this embodiment determines a preset filter pressure difference threshold corresponding to the current filtration mode as a benchmark value; based on the comparison between the filter contamination index and the preset contamination index threshold, it determines the target contamination index range into which the contamination index falls; then, it adjusts the preset filter pressure difference threshold accordingly based on the target contamination index range to obtain the target filter pressure difference threshold for the current period. By comparing in stages, the continuous contamination index is transformed into a discrete range, making the adjustment operation simple and with clear judgment boundaries, avoiding the uncertainty caused by complex nonlinear calculations; at the same time, combined with the preset value of the current filtration mode, the original setting basis of the equipment is retained, making the adjustment range controlled and reasonable; dynamically correcting the threshold according to the degree of contamination makes the self-cleaning trigger more consistent with the actual dust accumulation rate, which helps to reduce the risk of a sharp increase in ventilation resistance under excessively high contamination, and also avoids unnecessary airflow disturbance and energy consumption caused by prematurely starting cleaning during low-contamination periods, thereby achieving a better cleaning effect between filter maintenance cycle, system energy efficiency and operational stability.

[0098] In some optional implementations, step S3033 above includes: Step b1: When the target pollution index range is determined to be the first pollution index range, the current filter pressure difference threshold is increased to obtain the target filter pressure difference threshold.

[0099] Specifically, the pre-filter (G3 / G4 grade): the pressure difference threshold is increased from the preset filter pressure difference threshold of 100Pa to 120Pa; Medium-efficiency filter (F7 / F8 grade): Increased from the preset filter differential pressure threshold of 150Pa to 180Pa; High-efficiency filter (H13 grade): The preset filter differential pressure threshold of 250Pa is increased to 300Pa.

[0100] Step b2: When the target pollution index range is determined to be the second pollution index range, the current filter pressure differential threshold is determined to be the target filter pressure differential threshold.

[0101] It should be noted that when the filter contamination index falls within the second contamination index range (i.e., the contamination level is moderate), the current filter differential pressure threshold is not changed and is used as the target filter differential pressure threshold. The second range represents a contamination level within a normal fluctuation range. Maintaining the original threshold avoids frequent adjustments that could cause system oscillations, thus preserving the stability and predictability of the self-cleaning trigger conditions. Simultaneously, under moderate contamination conditions, cleaning is neither premature nor delayed, which helps to fully utilize the filter's dust-holding capacity and balance cleaning frequency with filtration efficiency. This makes the overall threshold decision-making mechanism more reasonable and easier to implement.

[0102] Step b3: When the target pollution index range is determined to be the third pollution index range, the current filter pressure difference threshold is reduced to obtain the target filter pressure difference threshold. The pollution index threshold of the first pollution index range is less than the pollution index threshold of the second pollution index range, and the pollution index threshold of the second pollution index range is less than the pollution index threshold of the third pollution index range.

[0103] Specifically, the pre-filter (G3 / G4 grade): the preset filter differential pressure threshold of 100Pa has been reduced to 80Pa; Medium efficiency filter (F7 / F8 grade): Reduce the preset filter differential pressure threshold from 150Pa to 120Pa; High-efficiency filter (H13 grade): Reduced from the preset filter differential pressure threshold of 250Pa to 200Pa.

[0104] The filter self-cleaning method provided in this embodiment determines whether the target pollution index range belongs to a first, second, or third pollution index range, where the pollution index threshold of the first range is lower than that of the second range, and the second range is lower than that of the third range. When it is determined to be in the first range, the current filter pressure difference threshold is increased to obtain the target filter pressure difference threshold, so as to trigger cleaning at a higher pressure difference. When it is determined to be in the second range, the current filter pressure difference threshold is directly determined as the target filter pressure difference threshold. When it is determined to be in the third range, the current filter pressure difference threshold is decreased to obtain the target filter pressure difference threshold, so as to achieve early cleaning under high pollution conditions. By classifying the pollution level, the threshold adjustment rules are clear and highly operable, avoiding the uncertainty caused by complex calculations. Increasing the threshold when the pollution level is low helps to reduce unnecessary self-cleaning frequency, extend the effective working time of the filter, and reduce airflow disturbance. Decreasing the threshold when the pollution level is high allows for a more timely response to the situation of increased dust accumulation, which helps to avoid the energy efficiency decline caused by the rapid increase in ventilation resistance. Maintaining the original threshold when the pollution level is moderate keeps the system operating stably, achieving a more reasonable balance between maintenance cycle, energy consumption control, and filtration efficiency.

[0105] Step S304: Obtain the predicted rainfall for the future period.

[0106] It should be noted that forecasted rainfall for future periods is typically obtained by accessing third-party weather forecast data interfaces or local weather sensor prediction models.

[0107] Step S305: When it is determined that the predicted rainfall exceeds the preset rainfall cleaning threshold, the filter is controlled to perform self-cleaning during the current time period.

[0108] For example, the predicted rainfall is obtained in the form of hourly or daily cumulative rainfall (in mm); this predicted rainfall can be selected as the cumulative rainfall or the maximum hourly rainfall intensity within a preset future time period (such as the next 6 hours or 24 hours) as a representative value. After obtaining it, it is directly compared with a preset rainfall cleaning threshold. If it exceeds the threshold, the filter self-cleaning is triggered.

[0109] For example, when the predicted probability of precipitation is greater than 80% and the relative humidity is greater than 80% within the next 6 hours, the system will force a pre-cleaning before the rain to prevent the wet dust from hardening and becoming difficult to clean.

[0110] The filter self-cleaning method provided in this embodiment, in addition to adaptively triggering the self-cleaning timing based on the filter contamination index, can also obtain the predicted rainfall for future periods. When the predicted rainfall exceeds a preset rainfall cleaning threshold, the filter is controlled to self-clean in the current period, without waiting for the pressure difference to reach the target threshold. By introducing rainfall prediction as a pre-triggered condition, the natural loosening effect of rainwater scouring or high humidity on the filter surface particles can be utilized to proactively complete cleaning before rainfall arrives. This helps reduce the risk of dust adhering tightly to the filter surface due to moisture during subsequent operation, thereby reducing cleaning difficulty. At the same time, this strategy incorporates meteorological forecast information as a data basis, enabling early intervention under favorable rainfall conditions. This helps reduce the increase in energy consumption caused by a continuous increase in pressure difference and may extend the effective service life of the filter after rainfall. Thus, it improves the system's flexibility in dealing with contamination accumulation under variable weather conditions.

[0111] Step S306: Based on the target filter pressure difference threshold, detect the current filter pressure difference according to the preset detection cycle.

[0112] The preset detection cycle is at least 6 hours; in this embodiment, a preset detection cycle of 6 hours is used as an example. After the target filter differential pressure threshold is adjusted, the current state is maintained for at least 6 hours. After the period expires, the filter contamination index is recalculated, and a new target filter differential pressure threshold is obtained based on the new filter contamination index.

[0113] Each time the filter contamination index is calculated, the controller adjusts the target filter differential pressure threshold in real time based on the current filter contamination index.

[0114] Step S307: When it is detected that the current filter pressure difference has not reached the target filter pressure difference threshold, return to the step of obtaining the current filter pressure difference between the filter inlet side and the filter outlet side during the current period, and determining the predicted air quality index for the future period and the historical air quality index for the same period in history during the current period.

[0115] For example, when the filter contamination index rises from less than 0.3 to greater than or equal to 0.3, the target filter index threshold is restored to the standard value of 100 Pa or 150 Pa; when the filter contamination index falls from greater than 0.7 to less than or equal to 0.7, the target filter differential pressure threshold is restored to the standard value. That is, the threshold is dynamically adjusted with each index calculation, rather than being locked once.

[0116] The filter self-cleaning method provided in this embodiment, after determining the target filter differential pressure threshold, performs real-time detection of the current filter differential pressure according to a preset detection cycle. If the current differential pressure has not yet reached the target threshold, it returns to the initial step, re-acquires the current differential pressure, predicted air quality indicators, and historical air quality indicators, and recalculates the filter pollution index and updates the target threshold. This cycle repeats until the differential pressure reaches the target to trigger self-cleaning. Through periodic detection and feedback loops, the system ensures continuous tracking of differential pressure changes and does not stop monitoring due to a single judgment. Each return and recalculation incorporates the latest meteorological and pollution data for the current period, allowing the target threshold to be dynamically updated with the actual environment, avoiding the loss of reference value of fixed thresholds during sudden environmental changes. This mechanism helps to continuously optimize the threshold setting during periods when the differential pressure does not reach the target, thereby more flexibly capturing changes in dust accumulation rate, reducing the possibility of triggering cleaning too early or too late, and enhancing the system's responsiveness to dynamic operating conditions and decision-making timeliness while ensuring stability.

[0117] Step S308: Obtain the cumulative runtime of the filter.

[0118] Specifically, the timer starts from the last time the self-cleaning was completed, and the cumulative running time of the filter is obtained.

[0119] Step S309: When the cumulative running time reaches the preset running time threshold, control the filter to perform self-cleaning.

[0120] It should be noted that regardless of the filter contamination index, a standard filter self-cleaning should be performed once the filter has accumulated 72 hours of operation to prevent excessive dust accumulation in extreme cases.

[0121] The filter self-cleaning method provided in this embodiment acquires the cumulative runtime of the filter and directly controls the filter to self-clean when the cumulative runtime reaches a preset runtime threshold, without waiting for the differential pressure to reach the target threshold. By introducing a time dimension as an independent triggering condition, a time-based protection mechanism is provided in cases where the differential pressure signal may drift, lag, or not be triggered, preventing the filter from continuously operating due to long-term dust accumulation. At the same time, this duration threshold can be set according to the filter material and normal usage cycle, helping to prevent irreversible performance degradation caused by excessive dust accumulation. This measure runs in parallel with dynamic differential pressure control, forming a dual criterion that takes into account both immediate contamination and long-term wear and tear, thereby improving the system's fault tolerance and reliability under different operating conditions.

[0122] Combination Figure 4 and Figure 5 This embodiment describes an optional application example of a filter self-cleaning method provided in this example.

[0123] A filter self-cleaning method includes the following steps: Step A: Obtain historical data for the same period, which includes hourly PM2.5 concentration, temperature, and humidity data for the same calendar week over the past 3 years, and establish a baseline curve characterizing the pollution trend during that period.

[0124] Step B: Access real-time weather forecast data for the next 72 hours, including at least PM2.5 concentration levels, precipitation probability, and relative humidity changes.

[0125] Step C: The baseline curve from Step A and the predicted data from Step B are weighted and fused to generate a comprehensive pollution potential index. This index, combined with the current filter pressure difference or cumulative running time, defines the recommended cleaning time for the next cleaning cycle.

[0126] Step D: The controller adjusts the self-cleaning trigger threshold according to the comprehensive pollution potential index: when the comprehensive pollution potential index is lower than the first threshold, the cleaning is postponed or skipped; when the index is higher than the second threshold, the deep cleaning program is executed in advance; when the index is in the middle range, standard cleaning is performed according to the default cycle.

[0127] Furthermore, the weighting coefficients of the weighted fusion in step C are dynamically adjusted according to the season and region. For example, during the rainy season when humidity is high, the weight of historical data is reduced and the weight of weather forecasts is increased in advance to prevent the phenomenon of dust accumulation and hardening due to high humidity.

[0128] By separating the dimensions of "historical patterns" and "future changes," the system eliminates the contradictions of time delays or over-response caused by relying solely on a single feedback mechanism. This avoids ineffective mechanical wear on the filter structure during low-pollution periods and enables pre-emptive deep cleaning in high-pollution or high-humidity scenarios. This extends filter lifespan, reduces energy consumption, and ensures cleaning effectiveness, achieving a system evolution from "passive response" to "proactive prevention." Specifically, this includes: Extended filter lifespan: By reducing ineffective cleaning cycles, mechanical wear on the filter can be reduced by 25%-40%, extending its lifespan by approximately 30%; Reduced energy consumption: Reduced start-stop cycles of the self-cleaning mechanism reduce overall energy consumption by 15%-25%; Improved cleaning effectiveness: The pre-cleaning mechanism before high-humidity weather prevents dust accumulation and improves cleaning efficiency by approximately 20%; Low-cost implementation: No additional high-precision particulate matter sensors are required; existing differential pressure sensors and publicly available meteorological data are sufficient, resulting in almost no increase in hardware costs; Strong adaptability: Automatic switching of seasonal weights and configurable regional parameters adapt to different climate regions and usage scenarios.

[0129] In this embodiment, a filter self-cleaning frequency control system is also provided, including: a data acquisition module, which includes a historical database, a meteorological API interface, a differential pressure sensor, and a running timer; a data processing module, which includes a historical curve generation unit, a predicted data weighting unit, and a comprehensive index calculation unit; a decision execution module, which includes a threshold comparator, a cleaning mode selector, and a self-cleaning controller; and a parameter storage module, which stores threshold parameters, seasonal weight coefficients, and regional correction factors for different filter types.

[0130] The air purifier provided in this embodiment dynamically adjusts the target filter differential pressure threshold by combining predicted air quality, historical benchmarks, real-time humidity deviation, and rainfall forecasts. It also utilizes timed triggering based on accumulated runtime to improve the accuracy of filter self-cleaning timing. This avoids the cleaning lag or excessive frequency issues caused by fixed thresholds, making the triggering timing more closely match the actual dust accumulation rate and changes in the external environment. This helps stabilize airflow and energy efficiency, reducing unnecessary energy consumption and airflow disturbance. Furthermore, the combination of historical reference and forward-looking forecasts effectively distinguishes between seasonal fluctuations and abnormal pollution, enhancing the robustness of decision-making. The integration of rainfall pre-cleaning and runtime further enhances the system's adaptability to complex weather conditions and long-term operation, extending filter lifespan.

[0131] This embodiment also provides a filter self-cleaning device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0132] This embodiment provides a filter self-cleaning device, such as... Figure 6 As shown, it includes: The acquisition module 601 is used to acquire the current filter pressure difference between the filter inlet side and the filter outlet side during the current period, and to determine the predicted air quality index for the future period and the historical air quality index for the same period in history during the current period. Calculation module 602 is used to calculate the filter pollution index for the current period based on predicted air quality indicators and historical air quality indicators; The control module 603 is used to determine the target filter pressure difference threshold for the current period based on the filter contamination index, and to control the filter to perform self-cleaning when the current filter pressure difference is detected to reach the target filter pressure difference threshold.

[0133] In some alternative implementations, the acquisition module 601 includes: The acquisition unit is used to acquire several atmospheric particulate matter concentration values ​​for future periods and calculate the average value of these atmospheric particulate matter concentration values ​​to obtain the predicted air quality index.

[0134] The first determining unit is used to determine a preset number of consecutive historical years earlier than the current time period, and to determine the historical time periods that include the corresponding period of the current time period in each historical year, so as to obtain the historical contemporaneous period.

[0135] The second determining unit is used to determine historical air quality indicators based on the air quality indicators of each historical sub-cycle in the same historical period.

[0136] In some optional implementations, the second determining unit includes: The acquisition sub-unit is used to obtain several historical atmospheric particulate matter concentration values ​​and weighting coefficients corresponding to each historical sub-cycle. The smaller the year difference between the historical year and the current period, the larger the weighting coefficient corresponding to the historical sub-cycle. The baseline pollution value calculation subunit is used to calculate the baseline pollution value for each historical sub-cycle based on several historical atmospheric particulate matter concentration values ​​and weighting coefficients. The historical air quality index calculation sub-unit is used to calculate the average value of the baseline pollution value for each historical sub-cycle to obtain the historical air quality index.

[0137] In some alternative implementations, the computing module 602 includes: The humidity acquisition unit is used to acquire the current air humidity value for the current time period and the corresponding historical air humidity value for the same period in history. The humidity deviation calculation unit is used to determine the humidity deviation based on the difference between the current air humidity value and the historical air humidity value; The weight coefficient determination unit is used to determine the first target weight coefficient for a future time period, the second target weight coefficient for the same historical period, and the third target weight coefficient for the time period corresponding to the humidity deviation, based on a preset correspondence relationship; wherein, the preset correspondence relationship is used to characterize the mapping relationship between time periods and time period weights; The prediction index determination unit is used to obtain the prediction index based on the predicted air quality index and the first target weighting coefficient; The historical index determination unit is used to obtain the historical index based on historical air quality indicators and the second target weighting coefficient. The humidity index determination unit is used to obtain the humidity index based on the humidity deviation and the third target weighting coefficient; The filter contamination index determination unit is used to obtain the filter contamination index based on the predicted index, historical index, and humidity index.

[0138] In some alternative implementations, the control module 603 includes: The preset filter pressure differential threshold determination unit is used to determine the preset filter pressure differential threshold corresponding to the current filtration mode of the filter. The target pollution index range determination unit is used to determine the target pollution index range of the filter screen pollution index based on the comparison results between the filter screen pollution index and the pollution index threshold. The target filter pressure differential threshold determination unit is used to adjust the preset filter pressure differential threshold based on the target pollution index range to obtain the target filter pressure differential threshold.

[0139] In some optional implementations, the target filter differential pressure threshold determination unit includes: The first adjustment subunit is used to increase the current filter pressure difference threshold to obtain the target filter pressure difference threshold when the target pollution index range is determined to be the first pollution index range. The second adjustment subunit is used to determine the current filter pressure difference threshold as the target filter pressure difference threshold when the target pollution index range is determined to be the second pollution index range. The third adjustment subunit is used to reduce the current filter pressure difference threshold to obtain the target filter pressure difference threshold when the target pollution index range is determined to be the third pollution index range. The pollution index threshold of the first pollution index range is less than the pollution index threshold of the second pollution index range, and the pollution index threshold of the second pollution index range is less than the pollution index threshold of the third pollution index range.

[0140] The rainfall acquisition module is used to obtain the predicted rainfall for future periods; The first self-cleaning module is used to control the filter to perform self-cleaning during the current period when the predicted rainfall exceeds the preset rainfall cleaning threshold.

[0141] The first detection module is used to detect the current filter pressure difference of the filter according to a preset detection cycle based on the target filter pressure difference threshold. The second detection module is used to return to the steps of obtaining the current filter pressure difference between the filter inlet side and the filter outlet side during the current period when the current filter pressure difference is not detected to reach the target filter pressure difference threshold, and to determine the predicted air quality index for the future period and the historical air quality index for the same period in history.

[0142] The runtime acquisition module is used to obtain the cumulative runtime of the filter. The second self-cleaning module is used to control the filter to self-clean when the cumulative running time reaches a preset running time threshold.

[0143] The filter self-cleaning device provided in this embodiment of the invention can execute the filter self-cleaning method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the various modules and units described above are the same as in the corresponding embodiments described above, and will not be repeated here.

[0144] Figure 7 This is a schematic diagram of the controller structure of an air purification device provided in an embodiment of the present invention.

[0145] The following is a detailed reference. Figure 7 The diagram illustrates a structural schematic suitable for implementing a controller in an embodiment of the present invention. The controller may include a processor (e.g., a central processing unit, graphics processing unit, etc.) 701, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 702 or a program loaded from memory 708 into random access memory (RAM) 703. RAM 703 also stores various programs and data required for controller operation. The processor 701, ROM 702, and RAM 703 are interconnected via bus 704. Input / output (I / O) interface 705 is also connected to bus 704.

[0146] Typically, the following devices can be connected to I / O interface 705: input devices 706 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 707 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 708 including, for example, magnetic tapes, hard disks, etc.; and communication devices 709. Communication device 709 allows the controller to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 7 A controller with various devices is shown, but it should be understood that it is not required to implement or have all of the devices shown, and may alternatively implement or have more or fewer devices.

[0147] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 709, or installed from a memory 708, or installed from a ROM 702. When the computer program is executed by the processor 701, it performs the functions defined in the filter self-cleaning method of the embodiments of the present invention.

[0148] Figure 7 The controller shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0149] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the filter self-cleaning method shown in the above embodiments is implemented.

[0150] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0151] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for self-cleaning a filter screen, characterized in that, The method includes: Obtain the current filter pressure difference between the filter inlet side and the filter outlet side during the current time period, and determine the predicted air quality index for the future time period and the historical air quality index for the same historical period of the current time period. Based on the predicted air quality index and the historical air quality index, calculate the filter pollution index for the current time period; Based on the filter contamination index, a target filter pressure difference threshold is determined for the filter in the current time period, and when the current filter pressure difference is detected to reach the target filter pressure difference threshold, the filter is controlled to perform self-cleaning.

2. The method according to claim 1, characterized in that, Determining the predicted air quality index for the future period and the historical air quality index for the same historical period of the current period includes: The predicted air quality index is obtained by acquiring several atmospheric particulate matter concentration values ​​for future periods and calculating the average of the several atmospheric particulate matter concentration values. Determine a preset number of consecutive historical years earlier than the current time period, and determine the historical time periods that include the corresponding period of the current time period in each historical year to obtain the historical contemporaneity; The historical air quality index is determined based on the air quality index of each historical sub-cycle in the same historical period.

3. The method according to claim 2, characterized in that, The determination of the historical air quality index based on the air quality index of each historical sub-cycle in the same historical period includes: Obtain several historical atmospheric particulate matter concentration values ​​and weighting coefficients corresponding to each historical sub-cycle, wherein the smaller the year time difference between the historical year and the current period, the larger the weighting coefficient corresponding to the historical sub-cycle; Based on the aforementioned historical atmospheric particulate matter concentration values ​​and the aforementioned weighting coefficients, the baseline pollution value for each historical sub-cycle is calculated respectively. The historical air quality index is obtained by calculating the average of the baseline pollution values ​​for each historical sub-cycle.

4. The method according to claim 1, characterized in that, The step of calculating the filter pollution index for the current period based on the predicted air quality index and the historical air quality index includes: Obtain the current air humidity value for the current time period and the historical air humidity value for the same historical period; The humidity deviation is determined based on the difference between the current air humidity value and the historical air humidity value; Based on a preset correspondence, a first target weight coefficient for the future time period, a second target weight coefficient for the historical same period, and a third target weight coefficient for the time period corresponding to the humidity deviation are determined; wherein, the preset correspondence is used to characterize the mapping relationship between time periods and time period weights; Based on the predicted air quality index and the first target weighting coefficient, a predicted index is obtained; Based on the historical air quality index and the second target weighting coefficient, the historical index is obtained; Based on the humidity deviation and the third target weighting coefficient, the humidity index is obtained; The filter contamination index is obtained based on the predicted index, the historical index, and the humidity index.

5. The method according to claim 1, characterized in that, The step of determining the target filter pressure difference threshold for the filter in the current time period based on the filter contamination index includes: Determine the preset filter differential pressure threshold corresponding to the current filter mode; Based on the comparison between the filter contamination index and the contamination index threshold, the target contamination index range of the filter contamination index is determined; The preset filter pressure difference threshold is adjusted based on the target pollution index range to obtain the target filter pressure difference threshold.

6. The method according to claim 5, characterized in that, The step of adjusting the preset filter pressure difference threshold based on the target pollution index range to obtain the target filter pressure difference threshold includes: When the target pollution index range is determined to be the first pollution index range, the current filter pressure difference threshold is increased to obtain the target filter pressure difference threshold. When the target pollution index range is determined to be the second pollution index range, the current filter pressure difference threshold is determined to be the target filter pressure difference threshold. When the target pollution index range is determined to be the third pollution index range, the current filter pressure difference threshold is reduced to obtain the target filter pressure difference threshold, wherein the pollution index threshold of the first pollution index range is less than the pollution index threshold of the second pollution index range, and the pollution index threshold of the second pollution index range is less than the pollution index threshold of the third pollution index range.

7. The method according to any one of claims 1 to 6, characterized in that, The method further includes: Obtain the predicted rainfall for the future time period; When the predicted rainfall exceeds a preset rainfall cleaning threshold, the filter is controlled to perform self-cleaning during the current time period.

8. The method according to any one of claims 1 to 6, characterized in that, After determining the target filter differential pressure threshold for the filter in the current time period based on the filter contamination index, the method further includes: Based on the target filter pressure difference threshold, the current filter pressure difference is detected according to a preset detection cycle; When it is detected that the current filter pressure difference has not reached the target filter pressure difference threshold, the process returns to the steps of obtaining the current filter pressure difference between the filter inlet side and the filter outlet side during the current time period, and determining the predicted air quality index for the future time period and the historical air quality index for the same historical period during the current time period.

9. The method according to any one of claims 1 to 6, characterized in that, The method further includes: Obtain the cumulative runtime of the filter; When the cumulative runtime reaches a preset runtime threshold, the filter is controlled to perform self-cleaning.

10. A filter self-cleaning device, characterized in that, The device includes: The acquisition module is used to acquire the current filter pressure difference between the filter inlet side and the filter outlet side during the current period, and to determine the predicted air quality index for the future period and the historical air quality index for the same period in history during the current period. The calculation module is used to calculate the filter pollution index corresponding to the current time period based on the predicted air quality index and the historical air quality index. The control module is used to determine the target filter pressure difference threshold of the filter in the current time period based on the filter contamination index, and to control the filter to perform self-cleaning when the current filter pressure difference is detected to reach the target filter pressure difference threshold.

11. An air purification device, characterized in that, The air purification device includes a controller, the controller comprising: A memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the method of any one of claims 1 to 9.

12. The air purification device according to claim 11, characterized in that, The air purification device is an air purifier.

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