Multi-filtering system

Abstract

The invention relates to the technical field of fresh air ventilator filtering, and discloses a multiple filtering system which comprises a multi-layer composite filtering unit, the multi-layer composite filtering unit is composed of a primary filtering layer, an electrostatic adsorption layer, a catalytic decomposition layer and an activated carbon adsorption layer which are sequentially arranged in the air flow direction, the primary filtering layer is made of gradient density melt-blown fiber materials, and the electrostatic adsorption layer is made of an electrostatic adsorption layer; the porosity range is 30%-50%, particulate matter with the particle size larger than or equal to 5 microns is intercepted, the electrostatic adsorption layer is integrated with a nanoscale titanium dioxide electrode array, the applied voltage range is 5-15 kV, PM2.5-level particles are captured through electrostatic force, the catalytic decomposition layer is loaded with a noble metal catalyst (such as Pt / TiO2), volatile organic compounds (VOCs) are decomposed under ultraviolet excitation, the activated carbon adsorption layer is filled with modified bamboo charcoal particles, and the particle size of the particulate matter is larger than or equal to 5 microns. According to the multi-filtration system, the technical breakthrough of efficient purification, precise regulation and control and long-term operation and maintenance is realized by constructing an organic cooperation system of the four-layer composite filtration unit, the distributed sensor network and the intelligent control center.

Description

Technical Field

[0001] The present invention relates to the technical field of fresh air filter, and specifically to a multi - layer filtration system. Background Art

[0002] A fresh air machine is an effective air purification device that can circulate indoor air. On the one hand, it discharges the dirty indoor air outdoors. On the other hand, it inputs the fresh outdoor air into the room after measures such as sterilization, disinfection, and filtration, so that the air in the room is fresh and clean at all times.

[0003] Existing air purification systems generally face the technical contradiction between high - efficiency purification and low - energy - consumption operation. Traditional equipment relies on a single - layer filtration structure, making it difficult to simultaneously remove pollutants such as particulate matter, VOCs, and formaldehyde. Moreover, the air resistance increases rapidly with the aging of the filter material, resulting in a sharp increase in energy consumption (the annual operating cost can reach 200 - 300 yuan per square meter). Therefore, there is an urgent need for a multi - layer filtration system. Summary of the Invention

[0004] Aiming at the above - mentioned technical deficiencies, the purpose of the present invention is to provide a multi - layer filtration system to solve the problems raised in the above background art.

[0005] To solve the above - mentioned technical problems, the present invention provides the following technical solutions: A multi - layer filtration system, including:

[0006] A multi - layer composite filtration unit: It is composed of a primary filtration layer, an electrostatic adsorption layer, a catalytic decomposition layer, and an activated carbon adsorption layer arranged in sequence along the air flow direction, where:

[0007] The primary filtration layer uses a gradient - density melt - blown fiber material with a porosity range of 30% - 50% to intercept particulate matter with a particle size ≥ 5μm;

[0008] The electrostatic adsorption layer integrates a nano - scale titanium dioxide electrode array with an applied voltage range of 5kV - 15kV to capture PM2.5 - level particles through electrostatic force;

[0009] The catalytic decomposition layer is loaded with a noble metal catalyst (such as Pt / TiO 2 ) to decompose volatile organic compounds (VOCs) under ultraviolet light excitation;

[0010] The activated carbon adsorption layer is filled with modified bamboo charcoal particles with a specific surface area ≥ 1200m 2 / g for physical adsorption of polar pollutants such as formaldehyde;

[0011] A distributed sensing network, at least including:

[0012] A PM2.5 / PM10 sensor with an accuracy of ± 3μg / m 3 ;

[0013] VOCs sensor, detection limit ≤ 0.1 ppm;

[0014] Temperature and humidity sensor, sampling frequency 1 Hz;

[0015] Differential pressure sensor, measuring range 0 - 500 Pa, resolution 0.1 Pa;

[0016] Intelligent control center: configured with an embedded processor and a machine learning model, receiving sensing data and outputting control instructions. The model is built based on the LSTM neural network, and the training dataset contains at least 100,000 groups of pollutant concentration before and after filtration, energy consumption parameters, and filter material aging characteristics;

[0017] Self - cleaning actuator: includes a reverse pulse generator, a UV - C light source, and an ultrasonic transducer. Among them, the adjustable range of the reverse pulse air flow intensity is 50 - 200 Pa, the UV - C wavelength is 254 nm, and the power density ≥ 5 W / cm 2 , and the ultrasonic frequency is 28 kHz - 40 kHz.

[0018] Preferably, in the multi - layer composite filtration unit, a gas distribution baffle is arranged between the catalytic decomposition layer and the activated carbon adsorption layer. The baffle is configured with an electric slide rail and can switch the series or parallel working mode of the two according to the control instruction.

[0019] Preferably, the intelligent control center includes the following functional modules:

[0020] Real - time data processing module: performing Kalman filter denoising and normalization processing on the sensing data;

[0021] Dynamic air resistance adjustment algorithm: based on the fuzzy PID control theory, using the data of the differential pressure sensor as the input, outputting the opening instruction of the variable - aperture deflector to keep the system differential pressure stable within the range of ± 5 Pa;

[0022] Filter material life prediction module: establishing a filter material attenuation curve using the random forest algorithm, with a prediction error ≤ 8%;

[0023] Safety interlock mechanism: when the data of any sensor exceeds the threshold (such as PM2.5 concentration > 35 μg / m 3 or VOCs > 0.5 ppm), automatically cut off the power supply and push an alarm message.

[0024] Preferably, the self - cleaning actuator includes a self - cleaning priority determination logic:

[0025] When the PM2.5 cumulative amount reaches 80% of the initial capacity, start the reverse pulse cleaning;

[0026] When the VOCs decomposition efficiency drops below 70%, activate the UV-C photocatalytic module;

[0027] If the TVOC adsorption capacity of the activated carbon adsorption layer reaches the saturation value, trigger the ultrasonic oscillation regeneration program.

[0028] Preferably, the intelligent control center includes an energy consumption optimization module: The energy consumption optimization module includes:

[0029] Establish a wind resistance-power relationship model: P = k·Q2·ΔP, where Q is the air volume, ΔP is the pressure difference, and k is the hydrodynamic coefficient;

[0030] Dynamically adjust the target air volume: According to the air quality standard set by the user and the outdoor meteorological data, use the genetic algorithm to solve the optimal air volume combination to minimize the comprehensive energy consumption;

[0031] Night mode: When it is detected that the indoor personnel activities have stopped, automatically switch to the low-power operation state, the air volume drops to 30% of the rated value, and the power consumption drops by 65%.

[0032] Preferably, each layer of filter material of the multi-layer composite filter unit is equipped with an RFID electronic tag to record the material batch, installation date and cumulative treatment gas volume. The intelligent control center regularly reads the tag information through the wireless communication module and generates a life cycle report.

[0033] Preferably, the intelligent control center supports the following remote operation functions:

[0034] The user-side APP displays the real-time air quality index (AQI) and the filter material health dashboard;

[0035] The cloud server receives the historical operation data and generates an energy efficiency analysis report;

[0036] Multi-device linkage: Share environmental data with other smart home devices such as air conditioners and fresh air hosts, and automatically coordinate the operation strategy.

[0037] Preferably, an ozone concentration monitoring module is added between the catalytic decomposition layer and the activated carbon adsorption layer. When the detected ozone concentration exceeds 0.05 ppm, immediately turn off the UV-C light source and start the fresh air bypass mode.

[0038] Preferably, the self-cleaning actuator includes a modular design. The reverse pulse generator, UV-C light source and ultrasonic transducer can all be individually disassembled and replaced, and the replacement process does not require tool assistance, and the operation time < 30 seconds

[0039] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0040] First, the present invention achieves gradient purification through a four-layer composite filtration unit. The primary effect layer uses gradient density meltblown fibers (porosity 30%-50%) to intercept particulate matter with a particle size ≥ 5 μm, significantly reducing the load on subsequent layers; the electrostatic adsorption layer integrates a nanoscale TiO 2 electrode array (applying a high-voltage electric field of 5-15 kV), and captures PM2.5-level fine particles through electrostatic force (efficiency ≥ 99.5%); the catalytic decomposition layer is loaded with a Pt / TiO 2 catalyst, and decomposes VOCs under the excitation of 254 nm ultraviolet light (reaction formula: CxHy + O 2 → CO 2 + H 2 O), achieving efficient oxidation of organic substances; the activated carbon adsorption layer is filled with modified bamboo charcoal (specific surface area ≥ 1200 m 2 / g), and specifically removes polar pollutants such as formaldehyde through physical adsorption (removal rate ≥ 98%). The distributed sensing network real-time monitors PM2.5 / VOCs / temperature and humidity / differential pressure data (accuracy ± 3 μg / m 3 ), the intelligent control center dynamically adjusts the operating parameters based on a machine learning model, eliminates sensor noise through Kalman filtering and Min-Max normalization, uses a fuzzy PID algorithm to maintain the system differential pressure stable within the range of ± 5 Pa, combines an LSTM neural network to predict the filter material life (error ≤ 8%) and generates a life cycle report, and finally achieves a comprehensive purification efficiency of PM2.5 removal rate ≥ 99.5%, formaldehyde removal rate ≥ 98%, VOCs removal rate ≥ 99%, the energy consumption is reduced by 40% compared with the traditional system, and the filter material life is extended by 30%.

[0041] Second, the present invention adds a UVA-LED ozone sensor (detection limit 0.01 ppm) at the outlet of the catalytic decomposition layer and interlocks with the UV-C light source for control: when the ozone concentration > 0.05 ppm, the light source is immediately turned off and the fresh air bypass valve is started (switching time < 50 ms), and at the same time, a fault code (0x08) is sent to the control center and a self-cleaning restart instruction is pushed. This design ensures zero ozone leakage through a dual-redundancy safety mechanism, maintains a 70% purification efficiency in the bypass mode, the fault response time < 100 ms, the maintenance cost is reduced by 30%, the system complies with the WHO air quality standard (ozone concentration ≤ 0.05 ppm), and the comprehensive reliability reaches MTBF > 50,000 hours.

[0042] Thirdly, the self-cleaning mechanism of the present invention adopts a modular quick-release design: the reverse pulse generator can be replaced without tools through a magnetic interface (N52 neodymium iron boron magnet), the UV-C light source is installed in an inserted manner (guide groove + anti-misinsertion buckle), the ultrasonic transducer can be unlocked and replaced by rotating the base 180°. The replacement process is standardized (<30 seconds), and the parameters are automatically calibrated after completion. This design simplifies the maintenance process (the time is shortened to 1 / 6 of the traditional method), the spare parts cost is reduced by 60%, the MTBF is increased to >50,000 hours, which is suitable for large-scale operation and maintenance. At the same time, it is compatible with the edge computing gateway (response time <50ms) to achieve fast status feedback. Description of the Drawings

[0043] Figure 1 It is the flowchart of the multi-layer composite filtration unit of the present invention;

[0044] Figure 2 It is the flowchart of the distributed sensing network structure of the present invention;

[0045] Figure 3 It is the flowchart of the intelligent control center of the present invention;

[0046] Figure 4 It is the flowchart of the self-cleaning actuator of the present invention;

[0047] Figure 5 It is the flowchart of the energy consumption optimization algorithm of the present invention;

[0048] Figure 6 It is the flowchart of the remote operation function of the present invention;

[0049] Figure 7 It is the flowchart of the ozone concentration monitoring module of the present invention. Detailed Embodiments

[0050] The following will further describe the detailed embodiments of the present invention in conjunction with the drawings. Detailed Embodiment 1

[0052] The following is a detailed embodiment of a multi-filter system.

[0053] Please refer to Figure 1-7 , a multi-filter system, comprising:

[0054] Multi-layer composite filtration unit: It is composed of a primary filtration layer, an electrostatic adsorption layer, a catalytic decomposition layer and an activated carbon adsorption layer arranged in sequence along the air flow direction, where:

[0055] The primary filtration layer adopts a gradient density meltblown fiber material with a porosity range of 30%-50% for intercepting particulate matter with a particle size ≥5μm;

[0056] The electrostatic adsorption layer integrates a nanoscale titanium dioxide electrode array, with an applied voltage range of 5 kV - 15 kV, and captures PM2.5-level particles through electrostatic force;

[0057] The catalytic decomposition layer is loaded with a noble metal catalyst (such as Pt / TiO 2 ), and decomposes volatile organic compounds (VOCs) under ultraviolet light excitation;

[0058] The activated carbon adsorption layer is filled with modified bamboo charcoal particles, with a specific surface area ≥ 1200 m 2 / g, and physically adsorbs polar pollutants such as formaldehyde;

[0059] Distributed sensing network, including at least:

[0060] PM2.5 / PM10 sensor, with an accuracy of ±3 μg / m 3 ;

[0061] VOCs sensor, with a detection limit ≤ 0.1 ppm;

[0062] Temperature and humidity sensor, with a sampling frequency of 1 Hz;

[0063] Differential pressure sensor, with a measurement range of 0 - 500 Pa and a resolution of 0.1 Pa;

[0064] Intelligent control center: configured with an embedded processor and a machine learning model, receives sensing data and outputs control instructions, the model is built based on the LSTM neural network, and the training dataset contains at least 100,000 groups of pollutant concentration before and after filtration, energy consumption parameters, and filter material aging characteristics;

[0065] Self-cleaning actuator: includes a reverse pulse generator, a UV-C light source, and an ultrasonic transducer, where the adjustable range of the reverse pulse air flow intensity is 50 - 200 Pa, the UV-C wavelength is 254 nm, and the power density ≥ 5 W / cm 2 , and the ultrasonic frequency is 28 kHz - 40 kHz.

[0066] This system achieves gradient purification through a four-layer composite filtration unit. The primary effect layer uses gradient density meltblown fibers (porosity 30% - 50%) to intercept particles with a particle size ≥ 5 μm, significantly reducing the load on the subsequent layers; the electrostatic adsorption layer integrates a nanoscale TiO 2 electrode array (applying a 5 - 15 kV high-voltage electric field), and captures PM2.5-level particles through electrostatic force (efficiency ≥ 99.5%); the catalytic decomposition layer is loaded with Pt / TiO 2 catalyst, and decomposes VOCs under 254 nm ultraviolet light excitation (reaction formula: CxHy + O 2 → CO 2 + H 2O), achieving efficient oxidation of organic substances; the activated carbon adsorption layer is filled with modified bamboo charcoal (specific surface area ≥ 1200 m 2 / g), specifically removing polar pollutants such as formaldehyde through physical adsorption (removal rate ≥ 98%), and the distributed sensing network real-time monitors PM2.5 / VOCs / temperature and humidity / differential pressure data (accuracy ± 3 μg / m 3 ), the intelligent control center dynamically adjusts the operating parameters based on the machine learning model, eliminates sensor noise through Kalman filtering and Min-Max normalization, uses the fuzzy PID algorithm to maintain the system differential pressure stable within the range of ± 5 Pa, combines the LSTM neural network to predict the filter material life (error ≤ 8%) and generates a life cycle report, and finally realizes the technical effect that the comprehensive purification efficiency reaches PM2.5 removal rate ≥ 99.5%, formaldehyde removal rate ≥ 98%, VOCs removal rate ≥ 99%, the energy consumption is reduced by 40% compared with the traditional system, and the filter material life is extended by 30%.

[0067] Specifically, in the multi-layer composite filtration unit, a gas distribution baffle is arranged between the catalytic decomposition layer and the activated carbon adsorption layer, and this baffle is configured with an electric slide rail, which can switch the series or parallel working mode of the two according to the control instruction.

[0068] By adding an electric slide rail baffle between the catalytic decomposition layer and the activated carbon adsorption layer, the series / parallel mode switching is realized through the instruction of the intelligent control center. In the series mode, the air flow passes through the catalytic layer and the activated carbon layer in sequence, which is suitable for low-pollution environments to extend the life of the activated carbon (the loss is reduced by 30%); in the parallel mode, the air flow passes through in two paths, and the VOCs removal rate is increased to 99%. The response time of the slide rail is < 1 second and there is no pressure drop fluctuation during the switching process. This design smoothly transitions through the PID algorithm, avoids filter material damage caused by air flow impact, and combines with the ozone concentration monitoring module (detection limit 0.01 ppm) for real-time interlocking protection to ensure the efficient operation of the system in high-pollution scenarios while maintaining safety standards. Finally, the technical synergy effect of doubling the purification efficiency, extending the maintenance cycle to 2.5 times that of the traditional system, and reducing the comprehensive operation and maintenance cost by 40% is realized.

[0069] Specifically, the intelligent control center includes the following functional modules:

[0070] Real-time data processing module: performs Kalman filtering denoising and normalization processing on the sensing data;

[0071] Dynamic air resistance adjustment algorithm: based on the fuzzy PID control theory, takes the differential pressure sensor data as the input, and outputs the opening instruction of the variable aperture deflector to make the system differential pressure stable within the range of ± 5 Pa;

[0072] Filter material life prediction module: uses the random forest algorithm to establish the filter material attenuation curve, and the prediction error ≤ 8%;

[0073] Safety interlock mechanism: When the data of any sensor exceeds the threshold (such as PM2.5 concentration > 35 μg / m 3 or VOCs > 0.5 ppm), the power supply is automatically cut off and an alarm message is pushed.

[0074] The intelligent control center integrates four modules: data processing, wind resistance regulation, life prediction, and safety interlock. Through Kalman filtering (cut-off frequency 0.5 Hz) and Min-Max normalization preprocessing, sensor noise is eliminated and data is standardized. Based on the fuzzy PID algorithm (Kp = 0.8, Ki = 0.05, Kd = 0.1), the opening of the variable-aperture deflector is dynamically adjusted to stabilize the system pressure difference within ±5 Pa to ensure the filtration efficiency; the random forest model (training data ≥ 100,000 groups) is used to predict the filter media attenuation curve (error ≤ 8%), and a replacement warning is pushed 7 days in advance; the safety interlock module triggers an emergency shutdown and alarm push through multi-sensor data fusion (PM2.5 > 35 μg / m 3 and VOCs > 0.5 ppm), with a response time < 100 ms. This architecture achieves millisecond-level pressure difference control (fluctuation < ±5 Pa), a 92% accuracy rate for filter media life prediction, and a 99.99% system reliability, reducing the comprehensive operation and maintenance cost by 40%, meeting the GB / T 18883-2022 safety standard.

[0075] Specifically, the self-cleaning actuator includes a self-cleaning priority determination logic:

[0076] When the PM2.5 accumulation reaches 80% of the initial capacity, reverse pulse cleaning is started;

[0077] When the VOCs decomposition efficiency drops below 70%, the UV-C photocatalytic module is activated;

[0078] If the TVOC adsorption amount of the activated carbon adsorption layer reaches the saturation value, an ultrasonic oscillation regeneration program is triggered.

[0079] The self-cleaning actuator triggers a three-level cleaning program according to the priority: when the PM2.5 accumulation reaches 80% of the initial capacity, reverse pulse cleaning (airflow intensity 150 Pa, pulse width 200 ms) is started to strip surface particles; when the VOCs decomposition efficiency < 70%, the UV-C photocatalytic module (power density 8 W / cm 2, irradiate for 15 minutes) to decompose residual organic matter; when the activated carbon is saturated with TVOC adsorption, trigger ultrasonic oscillation (frequency 40 kHz, generating 20 μm microbubbles) to regenerate the microporous structure, and the ozone concentration monitoring module (detection limit 0.01 ppm) is interlocked in real time. When the standard is exceeded, the UV-C light source is immediately turned off and the fresh air bypass mode is started (switching time < 50 ms). At the same time, a self-cleaning restart instruction is pushed. This design extends the filter material life to 3 times that of the traditional system through a three-level cleaning strategy, reduces the comprehensive energy consumption by 60%, the ozone leakage rate < 0.01 ppm, far lower than the national standard limit, reduces the maintenance frequency by 50%, and saves 60% of the spare parts cost.

[0080] Specifically, the intelligent control center includes an energy consumption optimization module: The energy consumption optimization module includes:

[0081] Establish a wind resistance-power relationship model: P = k·Q2·ΔP, where Q is the air volume, ΔP is the pressure difference, and k is the hydrodynamic coefficient;

[0082] Dynamically adjust the target air volume: According to the air quality standard set by the user and the outdoor meteorological data, use the genetic algorithm to solve the optimal air volume combination to minimize the comprehensive energy consumption;

[0083] Night mode: When it is detected that the indoor personnel activities have stopped, automatically switch to the low-power operation state, the air volume drops to 30% of the rated value, and the power consumption drops by 65%.

[0084] The energy consumption optimization module realizes dynamic air volume regulation through mathematical modeling and intelligent algorithms, establishes the wind resistance-power relationship formula P = k·Q2·ΔP (k = 0.01) to quantify the energy consumption characteristics, and combines the genetic algorithm (population size 50, iteration times 200) to solve the multi-objective optimal air volume combination (air quality compliance rate ≥ 95%, energy consumption ≤ 0.8 kWh / h). In the night mode, after detecting the activity stop signal through the human infrared sensor, the air volume is automatically reduced to 30% (the power consumption drops by 65%), and the meteorological data interface (API) dynamically corrects the air volume strategy to adapt to temperature and humidity changes (when the summer humidity > 60%, the air volume increases by 15%). Finally, the comprehensive energy efficiency index of reducing the annual operation cost by 50% and the dynamic response time < 5 seconds is achieved, and at the same time, it is compatible with smart home protocols (such as HomeKit / Alexa) to achieve cross-scene collaborative control.

[0085] Specifically, each layer of filter material in the multi-layer composite filter unit is equipped with an RFID electronic tag to record the material batch, installation date and cumulative treatment gas volume. The intelligent control center regularly reads the tag information through the wireless communication module and generates a life cycle report.

[0086] Embed RFID tags (ISO 14443 protocol) in each layer of filter media to store batch number, installation date, and cumulative processed air volume (accuracy ±1%). The intelligent control center scans the data daily through the BLE 5.0 module and generates a lifecycle report (including carbon footprint accounting and maintenance warning). The cloud server receives historical data and analyzes energy efficiency. The edge computing gateway (response time <50ms) optimizes local data processing. The user-side APP displays a 3D air quality map and a filter media health dashboard. This design realizes zero-contact management through passive RFID technology. The lifecycle prediction model (formula: Lremain = L0·e-λt, λ = 0.05 / month) has an accuracy rate of ≥90%, the maintenance cost is reduced by 40%, the system MTBF reaches >50,000 hours, and it supports green building certification and carbon emission quantification analysis.

[0087] Specifically, the intelligent control center supports the following remote operation functions:

[0088] The user-side APP displays the real-time air quality index (AQI) and the filter media health dashboard;

[0089] The cloud server receives historical operation data and generates an energy efficiency analysis report;

[0090] Multi-device linkage: Share environmental data with other smart home devices such as air conditioners and fresh air hosts, and automatically coordinate operation strategies.

[0091] Build a three-tier architecture through the Internet of Things platform: The edge layer uploads sensor data to the cloud through the MQTT protocol (delay <50ms). The application layer user-side APP displays the real-time air quality index (AQI) and the filter media health dashboard (color grading: green / yellow / red). The cloud layer generates an energy efficiency report based on the LSTM model and controls the linkage with devices such as air conditioners (such as automatically reducing the wind speed when PM2.5 > 75μg / m 3 ). The edge computing ensures real-time interaction performance (response time <50ms), the cloud analysis provides long-term decision-making support, and the multi-device collaboration improves the comprehensive energy savings by 15%. This architecture realizes a user remote operation response time <2 seconds, cross-scenario collaborative noise reduction (dynamic adjustment of air volume), and the cloud energy efficiency analysis accuracy rate ≥90%.

[0092] Specifically, add an ozone concentration monitoring module between the catalytic decomposition layer and the activated carbon adsorption layer. When the detected ozone concentration exceeds 0.05ppm, immediately turn off the UV-C light source and start the fresh air bypass mode.

[0093] By adding a UVA-LED ozone sensor (detection limit 0.01 ppm) at the outlet of the catalytic decomposition layer and interlocking it with the UV-C light source: when the ozone concentration > 0.05 ppm, the light source is immediately turned off and the fresh air bypass valve is activated (switching time < 50 ms). At the same time, a fault code (0x08) is sent to the control center and a self-cleaning restart instruction is pushed. This design ensures zero ozone leakage through a dual-redundancy safety mechanism, maintains a 70% purification efficiency in bypass mode, has a fault response time < 100 ms, reduces maintenance costs by 30%, the system meets the WHO air quality standard (ozone concentration ≤ 0.05 ppm), and the comprehensive reliability reaches MTBF > 50,000 hours.

[0094] Specifically, the self-cleaning actuator includes a modular design. The reverse pulse generator, UV-C light source, and ultrasonic transducer can all be individually disassembled and replaced without the assistance of tools, and the replacement process takes < 30 seconds.

[0095] With a modular quick-disassembly design for the self-cleaning mechanism: the reverse pulse generator is replaced without tools through a magnetic interface (N52 neodymium iron boron magnet), the UV-C light source is installed in a plug-in manner (guide groove + anti-misinsertion buckle), the ultrasonic transducer is unlocked and replaced by rotating the base 180°. The replacement process is standardized ( < 30 seconds), and the parameters are automatically calibrated after completion. This design simplifies the maintenance process (the time is shortened to 1 / 6 of the traditional method), reduces spare parts costs by 60%, increases MTBF to > 50,000 hours, is suitable for large-scale operation and maintenance, and is also compatible with an edge computing gateway (response time < 50 ms) to achieve rapid status feedback.

[0096] Working principle: This system realizes a technological breakthrough in efficient purification, precise regulation, and long-term operation and maintenance by constructing an organic cooperation system of a four-layer composite filtration unit, a distributed sensing network, and an intelligent control center. The multi-layer composite filtration unit uses gradient density meltblown fibers (porosity 30% - 50%) to intercept particles ≥ 5 μm, and the nano-TiO 2 electrode array (5 - 15 kV high-voltage electric field) captures PM2.5-level particles (efficiency ≥ 99.5%). The Pt / TiO 2 catalyst combines with a 254 nm UV-C light source to decompose VOCs (reaction formula: CxHy + O 2 → CO 2 + H 2 O). The modified bamboo charcoal layer (specific surface area ≥ 1200 m 2 / g) Adsorb polar pollutants such as formaldehyde (removal rate ≥ 98%). The step-by-step purification strategy significantly improves the comprehensive efficiency. The intelligent control center integrates a Kalman filter and a Min-Max normalization data processing module to eliminate sensor noise and optimize the quality of data input. Based on the fuzzy PID algorithm, it dynamically adjusts the opening degree of the variable-aperture deflector (pressure difference stable at ±5 Pa), combines with the LSTM neural network to predict the filter material life (error ≤ 8%) and uses the genetic algorithm to solve the optimal air volume combination (energy consumption reduced by 40%). The night mode detects the activity stop signal through a human infrared sensor and automatically reduces the air volume to 30% (power consumption reduced by 65%) to achieve dynamic energy consumption optimization. The self-cleaning actuator adopts a three-level priority determination logic: when the PM2.5 accumulation reaches 80%, it starts reverse pulse cleaning (airflow intensity 150 Pa); when the VOCs decomposition efficiency < 70%, it activates UV-C regeneration (power density 8 W / cm 2 ), when the activated carbon adsorption is saturated, it triggers ultrasonic oscillation (frequency 40 kHz), supplemented by an ozone concentration monitoring module (detection limit 0.01 ppm) for real-time interlock protection to ensure that the ozone leakage rate < 0.05 ppm. The modular quick-release design (magnetic interface / plug-and-play structure) makes the maintenance time < 30 seconds and reduces the spare part cost by 60%. The Internet of Things integration realizes the full life cycle traceability of the filter material (cumulative treated air volume error ±1%) through BLE5.0 RFID tags. The MQTT protocol cloud server generates an energy efficiency report and a carbon footprint accounting. The user-side APP provides a three-dimensional air quality map and device linkage control (multi-terminal collaborative noise reduction). The edge computing gateway (response time < 50 ms) ensures real-time interaction performance. The technical synergy effect is significant: the four-layer filter unit intercepts pollutants step by step, the intelligent regulation module dynamically matches the working condition requirements, the self-cleaning mechanism extends the filter material life to more than 3 times, the Internet of Things platform realizes full life cycle management, and the comprehensive indicators reach a PM2.5 removal rate of 99.9%, a formaldehyde removal rate of 99.5%, a VOCs removal rate of 99%, a 50% reduction in annual operating costs, and an MTBF > 50,000 hours, forming a closed-loop air purification solution covering "perception - decision - execution - service".

[0097] Although specific embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these specific embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multiple filtration system, characterized in that: include: Multi-layer composite filtration unit: It consists of a primary filtration layer, an electrostatic adsorption layer, a catalytic decomposition layer and an activated carbon adsorption layer arranged in sequence along the airflow direction, wherein: The primary filter layer uses gradient density melt-blown fiber material with a porosity range of 30%-50%, which is used to intercept particles with a particle size of ≥5μm; The electrostatic adsorption layer integrates a nano-scale titanium dioxide electrode array, with an applied voltage range of 5kV-15kV, which captures PM2.5 particles through electrostatic force; The catalytic decomposition layer is loaded with precious metal catalysts (such as Pt / TiO2) to decompose volatile organic compounds (VOCs) under ultraviolet light excitation; The activated carbon adsorption layer is filled with modified bamboo charcoal particles, with a specific surface area of ​​≥1200m 2 / g, for physical adsorption of polar pollutants such as formaldehyde; A distributed sensor network includes at least: PM2.5 / PM10 sensor, accuracy ±3μg / m 3 ; VOCs sensor, detection limit ≤ 0.1ppm; Temperature and humidity sensor, sampling frequency 1Hz; Differential pressure sensor, range 0-500Pa, resolution 0.1Pa; Intelligent control center: equipped with embedded processor and machine learning model, receiving sensor data and outputting control instructions. The model is built based on LSTM neural network, and the training data set contains at least 100,000 sets of pollutant concentrations before and after filtration, energy consumption parameters and filter material aging characteristics; Self-cleaning actuator: includes reverse pulse generator, UV-C light source and ultrasonic transducer, where the reverse pulse airflow intensity can be adjusted in the range of 50-200Pa, UV-C wavelength is 254nm, power density ≥5W / cm 2 , ultrasonic frequency 28kHz-40kHz.

2. A multiple filtration system according to claim 1, characterized in that: In the multi-layer composite filter unit, a gas distribution baffle is arranged between the catalytic decomposition layer and the activated carbon adsorption layer. The baffle is equipped with an electric slide rail and can switch the series or parallel working mode of the two according to control instructions.

3. A multiple filtration system according to claim 1, characterized in that: The intelligent control center includes the following functional modules: Real-time data processing module: Kalman filter denoising and normalization processing for sensor data; Dynamic wind resistance adjustment algorithm: Based on fuzzy PID control theory, it takes the pressure difference sensor data as input and outputs the opening instruction of the variable aperture guide plate to stabilize the system pressure difference within the range of ±5Pa; Filter material life prediction module: Use random forest algorithm to establish filter material attenuation curve, with prediction error ≤8%; Safety interlock mechanism: When any sensor data exceeds the threshold (such as PM2.5 concentration > 35μg / m 3 Or VOCs>0.5ppm), the power supply will be automatically cut off and an alarm message will be sent.

4. A multiple filtration system according to claim 1, characterized in that: The self-cleaning actuator includes a self-cleaning priority determination logic: When the PM2.5 accumulation reaches 80% of the initial capacity, reverse pulse cleaning is started; When the VOCs decomposition efficiency drops below 70%, the UV-C photocatalytic module is activated; If the TVOC adsorption capacity of the activated carbon adsorption layer reaches the saturation value, the ultrasonic oscillation regeneration program is triggered.

5. A multiple filtration system according to claim 1, characterized in that: The intelligent control center includes an energy consumption optimization module: the energy consumption optimization module includes: Establish a wind resistance-power relationship model: P = k·Q2·ΔP, where Q is the air volume, ΔP is the pressure difference, and k is the fluid mechanics coefficient; Dynamically adjust the target air volume: Based on the air quality standards set by the user and outdoor meteorological data, a genetic algorithm is used to solve the optimal air volume combination to minimize the overall energy consumption; Night mode: When it detects that indoor human activity has stopped, it automatically switches to low-power operation, with the air volume reduced to 30% of the rated value and power consumption reduced by 65%.

6. A multiple filtration system according to claim 1, characterized in that: Each layer of filter material of the multi-layer composite filter unit is equipped with an RFID electronic tag to record the material batch, installation date and cumulative gas processing volume. The intelligent control center regularly reads the tag information through the wireless communication module and generates a life cycle report.

7. A multiple filtration system according to claim 1, characterized in that: The intelligent control center supports the following remote operation functions: The user-side APP displays the real-time air quality index (AQI) and filter material health dashboard; The cloud server receives historical operation data and generates energy efficiency analysis reports; Multi-device linkage: Share environmental data with other smart home devices such as air conditioners and fresh air hosts, and automatically coordinate operation strategies.

8. A multiple filtration system according to claim 1, characterized in that: An ozone concentration monitoring module is added between the catalytic decomposition layer and the activated carbon adsorption layer. When the ozone concentration is detected to be above 0.05ppm, the UV-C light source is immediately turned off and the fresh air bypass mode is started.

9. A multiple filtration system according to claim 1, characterized in that: The self-cleaning actuator includes a modular design, and the reverse pulse generator, UV-C light source and ultrasonic transducer can be disassembled and replaced separately, and the replacement process does not require the assistance of tools, and the operation time is less than 30 seconds.

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