Quantitative calculation method for service life of filter element and air purifier

Abstract

The invention relates to a quantitative calculation method for the service life of a filter element. The quantitative calculation method comprises the following steps: detecting content data of different particulate matters in current air through an air quality sensor of an air purifier; acquiring air volume data of the current air purification system in unit time; according to the air volume data of the unit time and the data of the contents of the different particles, the absorption amount of the accumulated different particles is calculated; acquiring total adsorption quantity data of different adsorption layers of the multi-layer filter element and weight data in a use scene; calculating the residual life of each adsorption layer based on the total adsorption quantity data of different adsorption layers of the current filter element, the weight data in the use scene and the accumulated absorption quantity of different particulate matters; according to the weight data of each layer and the residual life of each layer, the residual life of the whole filter element is obtained. The embodiment of the invention further provides the air purifier.

Description

Technical Field

[0001] The present invention relates to the technical field of air purification, and in particular to a method for quantitatively calculating the life of a filter element and an air purifier. Background Art

[0002] As environmental pollution becomes increasingly serious, air purifiers are increasingly used in homes, offices, and various public places. The performance and lifespan of the core component of an air purifier, the filter, are directly related to the air purification effect and usage cost. Traditional filter replacement is mostly based on preset time or usage time. However, this method ignores the impact of actual usage environment differences on filter life, which may lead to premature filter replacement, increased usage costs, or overuse of the filter, affecting the purification effect. Summary of the Invention

[0003] The present invention provides a method for quantitatively calculating the life of an air purifier filter element and an air purifier, which can calculate the life of the filter element and facilitate replacement.

[0004] An embodiment of the present invention provides a method for quantitatively calculating the life of a filter element, comprising:

[0005] The air quality sensor of the air purifier detects the content data of different particulate matter in the current air;

[0006] Get the air volume data per unit time of the current air purification system;

[0007] Calculate the cumulative absorption of different particles based on the wind volume data per unit time and the data of different particle content;

[0008] Obtain the total adsorption capacity data of different adsorption layers of the multi-layer filter element and the weight data under the usage scenario;

[0009] Calculate the remaining life of each adsorption layer based on the total adsorption capacity data of different adsorption layers of the current filter element, the weight data of the usage scenario, and the cumulative absorption of different particles;

[0010] The remaining life of the entire filter element is obtained by combining the weight data of each layer and the remaining life of each layer.

[0011] Further, obtaining data on the number and / or types of pets in the current usage scenario;

[0012] The weight data for the current usage scenario is determined based on the pet quantity and / or type data.

[0013] Furthermore, thermal imaging sensors are used to obtain thermal map data of pet activities;

[0014] Adjust the weight data of each filter layer according to the distribution of high temperature areas in the thermal map.

[0015] Furthermore, the pet's activity level and status are analyzed based on the pet's activity heat map;

[0016] The weight data of each filter layer is determined according to the activity status of the pet.

[0017] Furthermore, the pet's heart rate and activity frequency can be monitored through smart devices worn by the pet;

[0018] The air volume level and corresponding air volume data of the air purifier are adjusted according to the pet's heart rate and activity frequency.

[0019] Furthermore, the air volume data adjustment formula is:

[0020] Q′=Q×(1+k×Δf / f0)

[0021] Where Q′ is the adjusted air volume, Q is the original air volume, Δf is the heart rate change, f0 is the baseline heart rate, and k is the adjustment coefficient.

[0022] Furthermore, the method further comprises:

[0023] Get current season data and pet type data;

[0024] Determine the weight data of each filter layer according to different seasons.

[0025] Furthermore, the method further comprises:

[0026] The particle settling velocity v is calculated based on the real-time detected particle concentration data. The calculation formula is:

[0027] v=k×C×(ρ p -ρ a )×g×d 2 / (μ)

[0028] in:

[0029] Where k is the shape correction coefficient, C is the current particle concentration, ρ p is the particle density, ρ a is the air density, g is the acceleration of gravity, d is the average diameter of the particles, and μ is the air dynamic viscosity;

[0030] Calculate the sedimentation coefficient based on the sedimentation velocity v

[0031] α=1-e (-v×t / h)

[0032] Where t is time and h is the room height;

[0033] Adjust the remaining service life ratio of the filter element according to the sedimentation coefficient α:

[0034] L′=L×(1+0.5α).

[0035] Furthermore, the method further comprises:

[0036] A local settlement enhancement model is established for pet activity areas:

[0037] When the pet is detected to be in a resting state, an enhanced sedimentation coefficient α′=1.2α is applied within the set radius;

[0038] Dynamically adjust the filter layer weight according to local settlement conditions:

[0039] w′ j =w j ×(1-0.3α′),

[0040] where w j ′ is the adjusted weight, w j is the original weight;

[0041] The remaining life of the filter element is recalculated based on the adjusted weight.

[0042] An embodiment of the present invention further provides an air purifier, characterized by comprising:

[0043] The data module is used to detect the content of different particulate matter in the current air through the air quality sensor of the air purifier;

[0044] The first acquisition module is used to obtain the air volume data per unit time of the current air purification system;

[0045] The first calculation module is used to calculate the cumulative absorption amount of different particulate matter based on the wind volume data per unit time and the data of different particulate matter contents;

[0046] The second acquisition module is used to obtain the total adsorption capacity data of different adsorption layers of the multi-layer filter element and the weight data under the usage scenario;

[0047] The second calculation module is used to calculate the remaining life of each adsorption layer based on the total adsorption capacity data of different adsorption layers of the current filter element, the weight data of the usage scenario, and the cumulative absorption of different particulate matter;

[0048] The third calculation module is used to calculate the remaining life of the entire filter element by combining the weight data of each layer and the remaining life of each layer.

[0049] The embodiments of the present invention provide a method for quantitatively calculating the life of an air purifier filter element and an air purifier, which can calculate the life of the filter element and facilitate replacement. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the application, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the application. Those skilled in the art can also derive other drawings based on these drawings without inventive work, among which:

[0051] Figure 1 is a flow chart of an exemplary method for calculating filter element life provided by an embodiment of the present invention;

[0052] Figure 2 is another partial flow chart of an exemplary filter element life calculation method provided by an embodiment of the present invention;

[0053] Figure 3 is another partial flow chart of an exemplary filter element life calculation method provided by an embodiment of the present invention;

[0054] Figure 4 is another partial flow chart of an exemplary filter element life calculation method provided by an embodiment of the present invention;

[0055] Figure 5 is another partial flow chart of an exemplary filter element life calculation method provided by an embodiment of the present invention;

[0056] Figure 6 This is another partial flow chart of an exemplary filter element life calculation method provided in an embodiment of the present invention. Specific embodiments

[0057] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0058] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indications will also change accordingly.

[0059] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0060] Example 1

[0061] like Figure 1 FIG. 1 is a flow chart of a method for quantitatively calculating the life of a filter element according to an embodiment of the present invention.

[0062] The air quality sensor of the air purifier detects the content data of different particulate matter in the current air.

[0063] Air purifiers are equipped with high-precision air quality sensors that monitor the concentrations of different types of particulate matter in the air in real time. Common particulate matter includes PM2.5, PM10, dust, pollen, and smoke. The sensors sample the air at regular intervals (e.g., every minute) and transmit the detected data on the concentrations of different particulate matter to the air purifier's control system.

[0064] Assume that the type of different particles is represented by i (i = 1, 2, ..., n), and the content of the i-th type of particles detected at time t is recorded as C i (t), usually expressed in μg / m 3 .

[0065] Get the air volume data per unit time of the current air purification system.

[0066] Air purification systems are equipped with air volume measurement devices, such as wind speed sensors, that combine with duct cross-sectional area calculations to determine air volume per unit time. The control system records this air volume data at intervals consistent with the particle count.

[0067] Assume that the air volume per unit time of the air purification system at time t is V(t), and the unit is m 3 / h.

[0068] Based on the wind volume data per unit time and the data on different particle content, the cumulative absorption of different particles is calculated.

[0069] In a very small time period Δt (unit: h), the amount of particle matter absorbed by the air purifier ΔM is i(t) can be calculated by the following formula:

[0070] ΔM i (t) = C i (t)×V(t)×Δt

[0071] In order to obtain the cumulative absorption amount M of the i-th particle in the time period [0, T] i , it is necessary to accumulate the absorption of each small time period:

[0072]

[0073] In actual calculations, if the time interval Δt is small enough, the numerical integration method can be used to perform the cumulative calculation approximately.

[0074] Obtain the total adsorption capacity data of different adsorption layers of the multi-layer filter element and the weight data under the usage scenario.

[0075] Total adsorption capacity data for different adsorption layers of multi-layer filter elements. Air purifier filter elements typically utilize a multi-layer structure, with different adsorption layers exhibiting varying adsorption capacities for different particles. During filter element development and production, experiments are conducted to determine the total adsorption capacity of each adsorption layer for each particle type.

[0076] Assume that the filter element has j layers (j = 1, 2, ..., m), and the total adsorption capacity of the jth adsorption layer on the i-th particle is recorded as Aji , unit is μg.

[0077] Weight data for usage scenarios.

[0078] Different types of particulate matter have varying degrees of impact on filter life in different usage scenarios. For example, in industrially polluted areas, high concentrations of large particles like dust and smoke have a greater impact on the filter. In residential areas during pollen season, pollen particles are more critical to filter wear.

[0079] Through a large number of actual tests and data analysis, the weight coefficient of each particle can be determined for different usage scenarios. Let the weight coefficient of the i-th particle in the usage scenario be w i , and satisfies

[0080]

[0081] Based on the total adsorption capacity data of different adsorption layers of the current filter element, the weight data under the usage scenario and the cumulative absorption of different particulate matter, the remaining life of each adsorption layer is calculated.

[0082] First, calculate the comprehensive adsorption consumption ratio p of the jth adsorption layer to all particles j :

[0083]

[0084] Assume that the service life of the jth adsorption layer in a new state is L 0j (Unit: hours), then the remaining life of the jth adsorption layer L j It can be calculated by the following formula:

[0085] L j =(1-p j )×L 0j

[0086] The remaining life of the entire filter element is obtained by combining the weight data of each layer and the remaining life of each layer.

[0087] Each adsorption layer plays a different role in the purification process of the entire filter element, so it is necessary to assign a weight coefficient to each adsorption layer. Let the weight coefficient of the jth adsorption layer be q j , and satisfies

[0088]

[0089] The remaining life L of the entire filter element can be calculated by the weighted average of the remaining life of each adsorption layer:

[0090]

[0091] Through the above steps and formulas, the remaining life of the air purifier filter can be calculated more accurately, helping users to understand the usage of the filter in a timely manner and reasonably arrange the replacement time of the filter.

[0092] Example 2

[0093] Based on Example 1, in the operating environment of the air purifier, pets are an important environmental factor, and their relevant information plays a key role in accurately evaluating the life of the filter element. In order to obtain data on the number and / or type of pets, various methods can be used:

[0094] Get the number and / or type of pets in the current usage scenario.

[0095] One feasible implementation involves equipping air purifiers with cameras that use image recognition algorithms to identify moving objects within their monitoring range. By training a deep learning model based on pets' physical features and movement patterns, the system can accurately identify pet species (such as dogs, cats, and birds) and count their numbers. When the camera detects a change in the image, the recognition process is immediately activated, and the data is updated at regular intervals (for example, every five minutes) to ensure timely information.

[0096] Combined with specially designed sensors, such as weight sensors and infrared sensors, the weight sensor can be installed at the bottom of the air purifier or below the surface where the purifier is placed. If a pet approaches or lies on the purifier, the weight variation range can be used to preliminarily determine the presence and approximate size of the pet, and then infer the pet's type. The infrared sensor can assist in identifying the presence and movement of pets by sensing the heat emission characteristics of surrounding objects. Combined with image recognition, it improves detection accuracy, compensates for the shortcomings of a single detection method, and collaboratively obtains more accurate pet information.

[0097] Considering that technical detection may have errors or be inconvenient in some scenarios, a manual input interface is also provided. When using the air purifier for the first time or adding a new pet, users can directly enter information such as the number and type of pets in their home through the purifier's accompanying mobile app or the operation interface on the control panel. This is convenient and can be updated at any time as the information changes, ensuring that the data is consistent with the actual usage scenario.

[0098] The weight data for the current usage scenario is determined based on the pet quantity and / or type data.

[0099] Different pets have different impacts on air purification needs and filter wear due to factors such as their living habits, hair loss, dander production, and exhaled gas composition. Based on the number and type of pets obtained, weighted data is determined in the following way:

[0100] A database covering a large number of common pet types and their characteristics related to filter life is pre-built. In the database, for each pet, its average daily hair loss (in grams), dandruff production rate (mg / hour), the content of odor components in the exhaled gas (such as ppm value of ammonia, hydrogen sulfide, etc.) and the loss coefficient of each adsorption layer of a typical filter when each of them exists alone are recorded in detail. For example, an adult golden retriever sheds about 30 grams of hair per day on average, produces dandruff at a rate of about 50 mg / hour, and has an ammonia content of about 2 ppm in its exhaled gas. After a large number of experimental tests, it is determined that its loss coefficient for the first coarse filter layer is 0.05 / day, and the loss coefficient for the second HEPA filter layer is 0.03 / day, and so on. When multiple pets are detected in the current scene, the corresponding loss coefficients are superimposed according to the number ratio to obtain the comprehensive loss situation, and then the weight data of each particulate matter relative to the pet factor in this scene is determined.

[0101] Weighting data is not static but is dynamically adjusted based on factors such as the pet's growth stage and health status. For example, hair shedding and dandruff production are relatively low in young pets, but gradually increase with age. Sick pets may also exhale more odorous components, which can affect filter lifespan. By regularly reminding users to update their pet information (e.g., monthly), combined with cloud-based big data analysis and user feedback, weighting data is promptly optimized to make filter life calculations more relevant to actual usage scenarios and provide users with accurate filter replacement prompts.

[0102] Example 3

[0103] In conjunction with the above embodiment, an infrared thermal imaging sensor installed on an air purifier can be used to obtain thermographic data on pet activity in the environment. The sensor converts infrared radiation emitted by received objects into electrical signals, which are then processed to generate a thermographic image reflecting temperature distribution. To ensure timely capture of changes in pet activity, data is typically collected once per second and pre-processed to remove interference from other heat sources in the environment.

[0104] Analyze the distribution of high-temperature areas (representing pet activity areas) in the heat map. If pets frequently move in front of the air purifier, this will increase the burden on the pre-coarse and medium-efficiency filter layers, and the weights of these layers can be increased accordingly. If pets move farther away, this will significantly impact the subsequent high-efficiency filter layers, and their weights can be increased accordingly.

[0105] Example 4

[0106] In combination with the above embodiment, the pet activity thermogram is further analyzed in depth, and the activity level and status of the pet, such as active, resting, sleeping, etc., are judged by observing the frequency of changes, range size and other characteristics of the high temperature area.

[0107] The weighting of each filter layer is determined based on the pet's different activity states. For example, when a pet is active, more pollutants are generated, which puts more pressure on each filter layer, so the overall weight of each filter layer can be increased. When the pet is resting or sleeping, the weight of some filter layers can be appropriately reduced.

[0108] Example 5

[0109] In conjunction with the above embodiment, a pet can be equipped with a smart device to monitor its heart rate and activity level in real time. The smart device transmits the collected data to the air purifier control system. The air purifier's air volume level and corresponding air volume data are adjusted based on the pet's heart rate and activity level.

[0110] Use the formula:

[0111] Q′=Q×(1+k×Δf / f0)

[0112] Calculate this using the formula, where Q' is the adjusted air volume, Q is the original air volume, Δf is the heart rate change, f0 is the baseline heart rate, and k is the adjustment factor. When a pet's heart rate increases and their activity increases, pollutants may be generated. This formula increases the purifier's air volume; otherwise, decreases it.

[0113] Example 6

[0114] In combination with the above embodiment, further, the current season data and pet type data are obtained. The season data can be obtained by obtaining local weather information through a network interface, and the pet type data can be manually input by the user or determined by image recognition.

[0115] Pets' hair loss and dander production vary in different seasons, impacting each filter layer in the air purifier differently. For example, during shedding season, when pets shed more, the weight of the pre-coarse filter layer can be increased; during dry seasons, when dander increases, the weight of the medium-efficiency filter layer can be appropriately increased.

[0116] According to the real-time detected particle concentration data, use the formula:

[0117] υ=k×C×(ρ p -ρ a )×g×d 2 / (μ)

[0118] Calculate the particle settling velocity v. Where k is the shape correction coefficient, C is the current particle concentration, ρ p is the particle density, ρ a is the air density, g is the acceleration due to gravity, d is the average diameter of the particles, and μ is the air dynamic viscosity.

[0119] Calculation of sedimentation coefficient based on sedimentation velocity v

[0120] α=1-e (-υ×t / h)

[0121] Where t is time and h is the room height. The sedimentation coefficient reflects the degree to which particles settle within a certain period of time.

[0122] Adjust the remaining service life ratio of the filter element according to the sedimentation coefficient α, the formula is:

[0123] L′=L×(1+0.5α)

[0124] When the sedimentation coefficient increases, it means that the sedimentation of particulate matter increases and the impact on the filter element is relatively reduced, and the estimation of the remaining service life of the filter element can be appropriately extended.

[0125] A local sedimentation enhancement model is established for pet activity areas. When a pet is detected to be in a resting state, an enhanced sedimentation coefficient α′=1.2α is applied within a set radius.

[0126] Then the filter layer weight is dynamically adjusted according to the local settlement situation. The formula is:

[0127] w′ j =w j ×(1-0.3α′)

[0128] where w j ′ is the adjusted weight, w j The remaining life of the filter element is recalculated based on the adjusted weight, so that the calculation of the filter element life can more accurately reflect the actual situation.

[0129] Although the present invention has been described with reference to several typical embodiments, it should be understood that the terms used are descriptive and exemplary, rather than restrictive. Since the present invention can be embodied in many forms without departing from the spirit or essence of the invention, it should be understood that the above-mentioned embodiments are not limited to any of the foregoing details, but should be broadly interpreted within the spirit and scope defined by the appended claims. Therefore, all changes and modifications that fall within the scope of the claims or their equivalents should be covered by the appended claims.

Claims

1. A method for quantitatively calculating the life of a filter element, characterized in that: include: The air quality sensor of the air purifier detects the content data of different particulate matter in the current air; Get the air volume data per unit time of the current air purification system; Calculate the cumulative absorption of different particles based on the wind volume data per unit time and the data of different particle content; Obtain the total adsorption capacity data of different adsorption layers of the multi-layer filter element and the weight data under the usage scenario; Calculate the remaining life of each adsorption layer based on the total adsorption capacity data of different adsorption layers of the current filter element, the weight data of the usage scenario, and the cumulative absorption of different particles; The remaining life of the entire filter element is obtained by combining the weight data of each layer and the remaining life of each layer.

2. The method for quantitatively calculating the filter element life according to claim 1, wherein: Also includes: Obtain data on the number and / or types of pets in the current usage scenario; The weight data for the current usage scenario is determined based on the pet quantity and / or type data.

3. The method for quantitatively calculating the filter element life according to claim 2, wherein: Also includes: Obtain pet activity thermal map data through infrared thermal imaging sensors; Adjust the weight data of each filter layer according to the distribution of high temperature areas in the thermal map.

4. The method for quantitatively calculating the filter element life according to claim 3, wherein: Also includes: Analyze pet activity and status based on pet activity heat maps; The weight data of each filter layer is determined according to the activity status of the pet.

5. The method for quantitatively calculating the filter element life according to claim 1, wherein: Also includes: Monitor your pet's heart rate and activity frequency through smart devices worn by your pet; The air volume level and corresponding air volume data of the air purifier are adjusted according to the pet's heart rate and activity frequency.

6. The method for quantitatively calculating the filter element life according to claim 5, wherein: The air volume data adjustment formula is: Q'=Q×(1+k×Δf / f0) Where Q′ is the adjusted air volume, Q is the original air volume, Δf is the heart rate change, f0 is the baseline heart rate, and k is the adjustment coefficient.

7. The method for quantitatively calculating the filter element life according to claim 2, wherein: Also includes: Get current season data and pet type data; Determine the weight data of each filter layer according to different seasons.

8. The method for quantitatively calculating the filter element life according to claim 1, wherein: The particle settling velocity v is calculated based on the real-time detected particle concentration data. The calculation formula is: v=k×C×(ρ p -r a )×g×d 2 / (m) in: Where k is the shape correction coefficient, C is the current particle concentration, ρ p is the particle density, ρ a is the air density, g is the acceleration of gravity, d is the average diameter of the particles, and μ is the air dynamic viscosity; Calculate the sedimentation coefficient based on the sedimentation velocity v α＝1-e (-v×t / h) Where t is time and h is the room height; Adjust the remaining service life ratio of the filter element according to the sedimentation coefficient α: L′=L×(1+0.5α).

9. The method for quantitatively calculating the filter element life according to claim 1, wherein: Also includes: A local settlement enhancement model is established for pet activity areas: When the pet is detected to be in a resting state, an enhanced sedimentation coefficient α′=1.2α is applied within the set radius; Dynamically adjust the filter layer weight according to local settlement conditions: In' j =in j ×(1-0.3α′), where w j ′ is the adjusted weight, w j is the original weight; The remaining life of the filter element is recalculated based on the adjusted weight.

10. An air purifier, characterized in that: include: The data module is used to detect the content of different particulate matter in the current air through the air quality sensor of the air purifier; The first acquisition module is used to obtain the air volume data per unit time of the current air purification system; The first calculation module is used to calculate the cumulative absorption amount of different particulate matter based on the wind volume data per unit time and the data of different particulate matter contents; The second acquisition module is used to obtain the total adsorption capacity data of different adsorption layers of the multi-layer filter element and the weight data under the usage scenario; The second calculation module is used to calculate the remaining life of each adsorption layer based on the total adsorption capacity data of different adsorption layers of the current filter element, the weight data of the usage scenario, and the cumulative absorption of different particulate matter; The third calculation module is used to calculate the remaining life of the entire filter element by combining the weight data of each layer and the remaining life of each layer.

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