An intelligent self-cleaning system for an air purifier filter screen

Abstract

This invention provides an intelligent self-cleaning system for air purifier filters. The system includes a filter status module for collecting status data characterizing the current working state of the air purifier filter; a cleaning control module connected to the filter status module for receiving the status data and generating an initial cleaning control strategy for the filter self-cleaning process based on the status data; and a cleaning feedback module for collecting process feedback data characterizing the current cleaning progress during the filter self-cleaning process. By setting up a filter status module, a cleaning control module, a cleaning feedback module, and a cleaning adjustment module, this invention first generates an initial cleaning control strategy based on the current working state of the filter, and then adjusts this initial cleaning control strategy based on the process feedback data during the self-cleaning process, establishing a dynamic control relationship in the filter self-cleaning process. This solves the problem in existing technologies where the cleaning process is executed according to a fixed strategy and is difficult to correct in a timely manner according to actual changes.

Description

Technical Field

[0001] This invention relates to the field of air filtration technology, and in particular to an intelligent self-cleaning system for air purifier filters. Background Technology

[0002] During long-term operation, the air purifier's filter continuously intercepts dust, particulate matter, and other pollutants in the air. As pollutants accumulate, the filter's flow resistance gradually increases, its airflow capacity gradually decreases, and its air purification efficiency weakens. To maintain the air purifier's continuous purification capacity, existing equipment typically involves periodically replacing the filter, manually disassembling and cleaning it, or setting up a simple self-cleaning program to process the filter.

[0003] The filter cleaning control methods of existing air purifiers are mostly fixed. The common method is to trigger cleaning based on the cumulative running time, preset cycle or single detection parameter, and then perform a complete cleaning process according to the preset duration or fixed parameters after the cleaning starts. Although it can achieve filter maintenance to a certain extent, its cleaning control is usually based on fixed rules, lacking continuous identification of the actual working status of the filter, and also lacking timely response to changes in the status during the cleaning process.

[0004] Therefore, when there are differences in the degree of filter contamination, the distribution of blockages, or the progress of recovery, the existing control methods are unable to dynamically adjust the current cleaning strategy according to the actual situation. Especially during the filter self-cleaning process, the recovery of filter resistance, the recovery of airflow, and the changes in residual contaminants will continuously change with the cleaning progress. If the system continues to execute according to the initially set fixed control strategy, it is easy to have problems with insufficient or excessive cleaning. The former will lead to unsatisfactory filter recovery and affect the subsequent operating performance of the air purifier; the latter will prolong the cleaning time, increase energy consumption, and may cause unnecessary filter wear.

[0005] To address this, an intelligent self-cleaning system for air purifier filters is proposed. Summary of the Invention

[0006] In view of this, the present invention provides an intelligent self-cleaning system for air purifier filters to solve or alleviate the technical problems existing in the prior art, and at least provides a beneficial alternative.

[0007] The technical solution of the present invention is implemented as follows: an intelligent self-cleaning system for air purifier filters, comprising a filter status module, a cleaning control module, a cleaning feedback module, and a cleaning adjustment module.

[0008] The filter status module is used to collect status data characterizing the current working status of the air purifier filter; the cleaning control module is connected to the filter status module, used to receive the status data, and generate an initial cleaning control strategy for the filter self-cleaning process based on the status data; the cleaning feedback module is used to collect process feedback data characterizing the current cleaning progress during the filter self-cleaning process; the cleaning adjustment module is connected to both the cleaning control module and the cleaning feedback module, used to receive the process feedback data, and adjust the initial cleaning control strategy based on the process feedback data to form an adjusted cleaning control command.

[0009] More preferably, the present invention further includes a cleaning start-up module, which is connected to the filter status module and the cleaning control module, and is used to determine whether the filter meets the self-cleaning trigger condition based on the status data, and to trigger the cleaning control module to generate the initial cleaning control strategy when the self-cleaning trigger condition is met;

[0010] By setting a cleaning start module, the self-cleaning start time can be intelligently determined based on the actual operating status of the filter, avoiding cleaning delays or ineffective cleaning caused by fixed-time triggering or triggering under a single condition.

[0011] More preferably, the status data includes at least two of the following: filter resistance data, air inlet data, air outlet data, air quality data, filter image data, and equipment operating load data;

[0012] By jointly characterizing the actual working state of the filter using multidimensional state data, it is beneficial to improve the accuracy of identifying the degree of filter contamination and provide a more reliable data foundation for subsequent cleaning control.

[0013] More preferably, the initial cleaning control strategy includes at least one or more of the following: cleaning start timing, cleaning execution duration, cleaning intensity level, cleaning rhythm parameters, cleaning phase switching conditions, and cleaning termination conditions;

[0014] By setting multiple parameters for the initial cleaning control strategy, the system can match the corresponding cleaning scheme according to different filter conditions, thereby improving the pertinence and adaptability of self-cleaning control.

[0015] More preferably, the cleaning feedback module is used to continuously acquire the process feedback data according to a preset sampling period; the cleaning adjustment module is used to adjust at least one of the cleaning intensity, cleaning execution duration, cleaning rhythm parameters and cleaning stage switching conditions in the initial cleaning control strategy in real time based on the deviation between the process feedback data and the preset cleaning target.

[0016] By continuously acquiring feedback data and revising cleaning control strategies in real time during the cleaning process, the system can dynamically adjust cleaning behavior based on the current cleaning progress, avoiding problems such as insufficient or excessive cleaning caused by using fixed cleaning parameters.

[0017] More preferably, the present invention also includes a cleaning judgment module, which is connected to the filter status module, the cleaning feedback module and the cleaning adjustment module respectively, and is used to judge the current cleaning effect based on the state changes before and after cleaning and the process feedback data, and output a cleaning completion signal when the judgment result reaches the target cleaning condition;

[0018] By setting up a cleaning judgment module, the results of this self-cleaning can be judged, so that the cleaning end condition is based on the actual cleaning effect, rather than relying solely on a single duration or a single trigger signal, thus improving the accuracy of the cleaning completion judgment.

[0019] More preferably, the cleaning judgment module is used to determine the target cleaning conditions based on at least two of the following: the degree of filter resistance recovery, the degree of ventilation recovery, the degree of pollution residue, and the degree of air quality improvement.

[0020] Determining the target cleaning conditions by combining multiple indicators can avoid errors caused by judging with a single indicator and help to reflect the filter cleaning effect more comprehensively.

[0021] More preferably, the present invention also includes a cleaning optimization module, which is connected to the cleaning control module and the cleaning adjustment module respectively, and is used to update at least one of the initial cleaning control strategy and adjustment rules based on the status data, process feedback data and adjustment results of each self-cleaning process;

[0022] The cleaning optimization module can correct and optimize subsequent cleaning control based on historical cleaning results, thereby improving the system's adaptability under different operating conditions.

[0023] More preferably, the present invention also includes a life management module, which is connected to the filter status module and is used to determine whether the filter is in a recoverable state, a decay state or a replacement state based on the filter's recovery rate after self-cleaning, the cumulative number of self-cleaning times and the degree of residual clogging, and to output a filter replacement warning signal when it is determined to be in a replacement state.

[0024] By setting up a lifespan management module, not only can the self-cleaning control of the filter be realized, but the lifespan status of the filter can also be managed to prevent the filter from continuing to be used after its performance has obviously deteriorated, thus ensuring the overall purification performance of the air purifier.

[0025] Furthermore, the present invention further includes a collaborative control module, which is connected to both the cleaning start-up module and the cleaning control module. This collaborative control module is used to modify the self-cleaning trigger condition and the initial cleaning control strategy based on at least one of the air purifier's operating mode, purification load, quiet period, and user-defined strategies. By setting the collaborative control module, the filter self-cleaning process can be coordinated with the overall operating state of the air purifier, improving the rationality of system operation and the user experience.

[0026] The embodiments of the present invention have the following advantages due to the adoption of the above technical solutions:

[0027] I. This invention establishes a dynamic control relationship in the filter self-cleaning process by setting up a filter status module, a cleaning control module, a cleaning feedback module, and a cleaning adjustment module. It first generates an initial cleaning control strategy based on the current working state of the filter, and then adjusts the initial cleaning control strategy in combination with process feedback data during the self-cleaning process. This solves the problem in the prior art that the cleaning process is executed according to a fixed strategy and it is difficult to correct it in a timely manner according to actual changes.

[0028] Second, this invention collects status data representing the current working state of the filter before cleaning begins, continuously acquires process feedback data representing the current cleaning progress during cleaning, and adjusts the initial cleaning control strategy based on the process feedback data, so that the cleaning control process can change synchronously with the actual recovery of the filter, thereby improving the targeting of the filter self-cleaning control and avoiding insufficient or excessive cleaning.

[0029] The above overview is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the invention will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a schematic diagram of the intelligent self-cleaning system module architecture of the present invention. Detailed Implementation

[0032] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0033] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0034] like Figure 1 As shown, this embodiment of the invention provides an intelligent self-cleaning system for air purifier filters, including a filter status module, a cleaning control module, a cleaning feedback module, and a cleaning adjustment module; the system also includes a cleaning start-up module, a cleaning judgment module, a cleaning optimization module, a lifespan management module, and a collaborative control module. Each of these modules is jointly implemented by a main control unit, a data acquisition unit, an image acquisition unit, a storage unit, and a control program.

[0035] During system operation, the filter status module obtains the current working status of the filter;

[0036] The current operating status of the filter is characterized by status data, including filter resistance data, inlet air data, outlet air data, air quality data, filter image data, and equipment operating load data. Filter resistance data is obtained through differential pressure detection units located on both sides of the filter and is used to reflect the degree of filter clogging. Inlet and outlet air data are obtained through airflow detection units and are used to reflect the airflow capacity before and after the filter. Air quality data is obtained through particulate matter detection units and is used to reflect changes in purification capacity. Filter image data is obtained through image acquisition devices and is used to reflect the distribution and residue of contaminants on the filter surface. Equipment operating load data is obtained through fan speed, current, power, and operating level information and is used to reflect the current operating status of the entire machine.

[0037] In this embodiment, the filter status module simultaneously collects filter resistance data, airflow data, and air quality data. The system uses filter resistance data to determine the degree of filter blockage, airflow data to determine changes in filter ventilation capacity, and air quality data to determine changes in purification efficiency. These three types of data together form the basis for determining the current status of the filter.

[0038] After obtaining the above status data, the cleaning start module determines whether the filter meets the self-cleaning trigger conditions.

[0039] The cleaning start-up module is connected to the filter status module and the cleaning control module. Its function is to determine whether to enter the self-cleaning process based on status data. The self-cleaning trigger conditions consist of a filter resistance threshold, an air outlet threshold, an air quality improvement threshold, a pollution coverage threshold, and a cumulative running time threshold. The system uses the filter resistance reaching a preset resistance threshold and the air outlet data being lower than a preset air outlet threshold as the start-up conditions in this embodiment. When the above conditions are met, the cleaning start-up module outputs a start signal, triggering the cleaning control module to generate the initial cleaning control strategy for this filter self-cleaning process.

[0040] The cleaning control module receives status data output from the filter status module and generates an initial cleaning control strategy based on this data. The initial cleaning control strategy includes cleaning start timing, cleaning execution duration, cleaning intensity level, cleaning rhythm parameters, cleaning stage switching conditions, and cleaning termination conditions. The cleaning control module first classifies the current level of filter contamination and then matches the corresponding initial cleaning control strategy based on the level of contamination.

[0041] In this embodiment, the degree of contamination is divided into three levels: light contamination, moderate contamination, and heavy contamination. Light contamination corresponds to short-duration, low-intensity cleaning; moderate contamination corresponds to medium-duration, medium-intensity cleaning; and heavy contamination corresponds to long-duration, high-intensity cleaning, with an enhanced cleaning phase included. In this way, the system determines a matching control scheme based on the actual condition of the filter before cleaning begins.

[0042] After cleaning begins, the cleaning feedback module continuously tracks the cleaning process.

[0043] The cleaning feedback module is used to acquire process feedback data characterizing the current cleaning progress. This process feedback data includes changes in filter resistance, airflow recovery, changes in residual contaminants on the filter surface, and changes in air quality improvement. The cleaning feedback module continuously samples at a fixed sampling period; in this embodiment, the sampling period is set to 10 seconds. The system updates the process feedback data once at the end of each sampling period, ensuring the control system always has a grasp of the current cleaning progress.

[0044] The process feedback data collected by the cleaning feedback module is directly input into the cleaning adjustment module;

[0045] The cleaning adjustment module is connected to the cleaning control module and the cleaning feedback module respectively. Its function is to correct the initial cleaning control strategy based on the current feedback results. The cleaning adjustment module adjusts the cleaning intensity, cleaning execution duration, cleaning rhythm parameters and cleaning stage switching conditions in real time based on the deviation between the process feedback data and the preset cleaning target. The preset cleaning target consists of the filter resistance recovery target, the air outlet recovery target and the pollution residue control target.

[0046] In this embodiment, the system restores the filter resistance to 85% of the reference state, restores the airflow to 90% of the rated level, and sets the residual contamination area on the filter surface below a preset ratio as the control target for the cleaning process.

[0047] When the system detects that the rate of decrease in filter resistance is lower than the set requirement, the cleaning adjustment module increases the current cleaning intensity and extends the execution time of the current stage; when the system detects that the airflow recovery speed reaches the set requirement, the cleaning adjustment module shortens the subsequent execution time; when the system detects that the change in filter surface residue is slow, the cleaning adjustment module maintains the current stage without switching and extends the duration of the current stage.

[0048] Therefore, the system continuously adjusts its control strategy based on real-time feedback during the cleaning process to ensure that the current cleaning status is consistent with the actual recovery status of the filter.

[0049] In one embodiment, the present invention includes a cleaning judgment module, which is connected to a filter status module, a cleaning feedback module, and a cleaning adjustment module. This module judges the current cleaning effect based on changes in the filter's state before and after cleaning and process feedback data, and outputs a cleaning completion signal when the judgment result meets the target cleaning conditions. The target cleaning conditions are jointly determined by the degree of filter resistance recovery, the degree of ventilation recovery, the degree of residual pollution, and the degree of air quality improvement.

[0050] In this embodiment, the system uses the following criteria to determine if cleaning is complete: the filter resistance has returned to the reference range, the airflow has returned to the rated level, and the residual contaminant area has decreased to below a set percentage. When two or more of the above indicators are met, the cleaning judgment module outputs a cleaning completion signal, ending the cleaning process.

[0051] After this cleaning is completed, the cleaning optimization module records and organizes the cleaning process.

[0052] The cleaning optimization module is connected to both the cleaning control module and the cleaning adjustment module. It updates the initial cleaning control strategy and adjustment rules based on status data, process feedback data, and adjustment results from each self-cleaning process. The cleaning optimization module records the initial contamination level, total cleaning time, number of adjustments, resistance recovery rate, and exhaust air recovery rate, using this data as the basis for subsequent strategy adjustments. When the system continuously records that the actual cleaning time for the same contamination level exceeds the original set value, the initial cleaning execution time for the corresponding state is automatically increased; conversely, when the system continuously records that the target cleaning condition is reached ahead of schedule for the same contamination level, the initial cleaning execution time for the corresponding state is automatically decreased. In this way, the system develops cleaning control parameters that match the machine model and the current operating environment after multiple runs.

[0053] The present invention also includes a life management module, which is connected to the filter status module. The life management module is used to determine whether the filter is in a recoverable state, a decay state, or a replacement state based on the filter's recovery rate after self-cleaning, the cumulative number of self-cleaning cycles, and the degree of residual clogging. When the filter is determined to be in a replacement state, a filter replacement warning signal is output.

[0054] In this embodiment, when the filter maintains a high recovery rate and low residual clogging after multiple self-cleaning cycles, the system determines that the filter is in a recoverable state; when the recovery rate continues to decline but still meets the minimum usage requirements, the system determines that the filter is in a deterioration state; when the recovery rate falls below the minimum threshold, or the cumulative number of self-cleaning cycles reaches the set upper limit, the system determines that the filter is in a replacement state and outputs a replacement warning message. Through the lifespan management module, the system not only controls the current cleaning process of the filter but also continuously manages the overall lifespan of the filter.

[0055] This invention also includes a collaborative control module, which is connected to both the cleaning start-up module and the cleaning control module. This module is used to modify the self-cleaning trigger conditions and the initial cleaning control strategy based on the air purifier's operating mode, purification load, quiet period, and user-defined strategies.

[0056] In this embodiment, when the air purifier is in nighttime silent mode, the collaborative control module raises the start conditions for high-intensity cleaning and switches the current cleaning strategy to a low-interference cleaning strategy; when the air purifier is in high-load purification mode, the collaborative control module delays the execution time of the complete cleaning process to prioritize the purification of the entire machine; when the air purifier is in a user-defined maintenance period, the collaborative control module relaxes the execution time limit to ensure that the filter completes a complete cleaning within the maintenance period. Through the collaborative control module, the filter cleaning behavior and the overall machine operating status form a unified and coordinated relationship.

[0057] When the air purifier is operating normally, the filter status module continuously collects filter pressure difference, airflow data, and air quality data. When the system detects that the filter resistance is gradually increasing and the airflow capacity is gradually decreasing, and the set start-up threshold is reached, the cleaning start-up module determines that the filter meets the self-cleaning trigger conditions and sends a start signal to the cleaning control module. The cleaning control module generates an initial cleaning control strategy for this cleaning based on the current filter contamination level, and determines the basic execution duration, cleaning intensity level, and stage switching arrangement.

[0058] After the cleaning process begins, the cleaning feedback module continuously acquires the filter resistance recovery, airflow recovery, and changes in residual contaminants at a 10-second sampling cycle. Upon receiving these feedback results, the cleaning adjustment module adjusts the current strategy against the preset cleaning target. When the system detects that the resistance reduction rate is lower than required, it increases the cleaning intensity and extends the current phase. When the system detects that the airflow recovery is close to the target, it shortens the subsequent execution time. When the system detects that the residual contaminant area is changing slowly, it postpones the phase switch and continues to maintain the current cleaning state.

[0059] In the later stages of cleaning, the cleaning judgment module comprehensively analyzes the degree of filter resistance recovery, ventilation recovery, and residual pollution. When the judgment results meet the target cleaning conditions, the system outputs a cleaning completion signal and ends the cleaning cycle. After cleaning, the cleaning optimization module records the status data, feedback data, and adjustment results to correct the initial cleaning control strategy for similar conditions in the future. The lifespan management module updates the filter lifespan status based on the recovery rate and cumulative self-cleaning count after this cleaning cycle. When the filter reaches the replacement threshold, the system outputs a replacement warning. The collaborative control module makes corresponding adjustments to the trigger conditions and execution strategy for the next self-cleaning cycle based on the current operating mode of the entire machine.

[0060] As can be seen from the above process, the present invention, through the coordinated efforts of filter status recognition, cleaning start determination, process feedback collection, real-time process adjustment, cleaning effect judgment, continuous strategy optimization, and lifespan status management, ensures that the air purifier filter self-cleaning process is consistent with the actual state of the filter, thereby improving the filter cleaning effect and enhancing the overall stability and user experience of the air purifier.

[0061] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in the present invention, and these should all be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An intelligent self-cleaning system for air purifier filters, characterized in that, include: The filter status module is used to collect status data that characterizes the current working status of the air purifier filter; A cleaning control module, connected to the filter status module, is used to receive the status data and generate an initial cleaning control strategy for the filter self-cleaning process based on the status data. The cleaning feedback module is used to collect process feedback data that characterizes the current cleaning progress during the filter self-cleaning process; The cleaning adjustment module is connected to both the cleaning control module and the cleaning feedback module. It is used to receive the process feedback data and adjust the initial cleaning control strategy based on the process feedback data to form an adjusted cleaning control command.

2. The intelligent self-cleaning system for air purifier filters according to claim 1, characterized in that: It also includes a cleaning start-up module, which is connected to the filter status module and the cleaning control module. The cleaning start-up module is used to determine whether the filter meets the self-cleaning trigger condition based on the status data, and to trigger the cleaning control module to generate the initial cleaning control strategy when the self-cleaning trigger condition is met.

3. The intelligent self-cleaning system for air purifier filters according to claim 1, characterized in that: The status data includes at least two of the following: filter resistance data, air intake data, air outlet data, air quality data, filter image data, and equipment operating load data.

4. The intelligent self-cleaning system for air purifier filters according to claim 1, characterized in that: The initial cleaning control strategy includes at least one or more of the following: cleaning start timing, cleaning execution duration, cleaning intensity level, cleaning rhythm parameters, cleaning phase switching conditions, and cleaning termination conditions.

5. The intelligent self-cleaning system for air purifier filters according to claim 1, characterized in that: The cleaning feedback module is used to continuously acquire the process feedback data according to a preset sampling period; The cleaning adjustment module is used to adjust at least one of the cleaning intensity, cleaning execution duration, cleaning rhythm parameters, and cleaning stage switching conditions in the initial cleaning control strategy in real time based on the deviation between the process feedback data and the preset cleaning target.

6. The intelligent self-cleaning system for air purifier filters according to claim 1, characterized in that: It also includes a cleaning judgment module, which is connected to the filter status module, the cleaning feedback module and the cleaning adjustment module respectively. It is used to judge the current cleaning effect based on the state changes before and after cleaning and the process feedback data, and output a cleaning completion signal when the judgment result reaches the target cleaning condition.

7. The intelligent self-cleaning system for air purifier filters according to claim 6, characterized in that: The cleaning judgment module is used to determine the target cleaning conditions based on at least two of the following: the degree of filter resistance recovery, the degree of ventilation recovery, the degree of pollution residue, and the degree of air quality improvement.

8. The intelligent self-cleaning system for air purifier filters according to claim 1, characterized in that: It also includes a cleaning optimization module, which is connected to the cleaning control module and the cleaning adjustment module respectively, and is used to update at least one of the initial cleaning control strategy and adjustment rules based on the status data, process feedback data and adjustment results of each self-cleaning process.

9. The intelligent self-cleaning system for air purifier filters according to claim 1, characterized in that: It also includes a life management module, which is connected to the filter status module. The life management module is used to determine whether the filter is in a recoverable state, a decay state or a replacement state based on the filter's recovery rate after self-cleaning, the cumulative number of self-cleaning times and the degree of residual clogging. When the filter is determined to be in a replacement state, a filter replacement warning signal is output.

10. The intelligent self-cleaning system for air purifier filters according to claim 2, characterized in that: It also includes a collaborative control module, which is connected to the cleaning start-up module and the cleaning control module respectively, and is used to modify the self-cleaning trigger condition and the initial cleaning control strategy by combining at least one of the air purifier's operating mode, purification load, quiet period and user-set strategy.

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