A multi-scenario adaptive air purification system
Patent Information
- Application Number
- CN202611375122.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Priority Date
- 2026-05-25
- Filing Date
- 2026-09-07
- Publication Date
- 2026-10-09
AI Technical Summary
[0005]针对现有技术的不足，本发明提供了一种多场景自适应空气净化系统，解决了以下问题：
[0017]本发明提供了一种多场景自适应空气净化系统。具备以下有益效果：
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Figure CN122881165A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the fields of air purification, indoor air transmission control, environmental sensing, edge intelligent control, and purification medium path scheduling, specifically a multi-scenario adaptive air purification system. Background Technology
[0002] Existing air purification equipment typically relies on single-point air quality detection and fixed filter paths, adjusting fan speed only when particulate matter or gaseous pollutant concentrations increase. This type of equipment treats the space to be purified as a uniformly mixed environment and simplifies the treatment objective to a decrease in average indoor concentration, failing to establish a control model for the migration process of pollutants from the pollution source area to the human breathing zone. In open kitchens, children's playrooms, bedrooms, conference rooms, nursing homes, vehicle spaces, and underground connecting spaces, pollutants are not instantaneously uniformly distributed but rather form transmission paths along doors and windows, air conditioning airflow, occupant movement trajectories, furniture boundaries, and local temperature and humidity gradients. Single-point concentration detection cannot reflect the migration state of pollutants before they reach the human breathing zone.
[0003] The aforementioned patent documents and prior art have the following technical problems when used: Problem 1: Existing air purification equipment generally uses a fixed series filter path, causing particulate pollution, molecular pollution, pollution released from human respiration, surface re-release pollution, and externally infiltrated pollution to pass through the same media combination. This treatment method results in a lack of differentiation in the consumption of media load by different pollutants. Particulate matter clogs the molecular adsorption medium, and pressure loss occurs in the high-volume particulate filtration path in scenarios with volatile pollutants. Purification paths containing ozone risks or catalytic temperature rise risks lack hard constraints. Some equipment uses electrostatic, photocatalytic, strong oxidizing, or catalytic media to treat pollutants, but lacks closed-loop constraints on ozone, intermediate aldehydes, desorption of adsorbent materials, and catalytic temperature rise. This makes it difficult to meet the purification safety requirements of children, the elderly, bedridden persons, and vehicle occupants. Therefore, air purification systems need to shift from concentration-responsive control to propagation process control. The system needs to obtain the current real air propagation relationship in the space, determine the estimated arrival time of pollutants from the pollution source area to the human breathing zone, and construct a dynamic air boundary before the pollution arrives, so that pollutants enter the purification path rather than the human breathing zone. At the same time, it is necessary to dynamically program the purification media path according to the pollution phase and continuously constrain the media load and by-product risks. Summary of the Invention
[0004] Technical problems to be solved
[0005] To address the shortcomings of existing technologies, this invention provides a multi-scenario adaptive air purification system, which solves the following problems: 1. Existing equipment often only activates adjustments after pollution levels have risen, making it easy for pollutants to reach the human breathing zone before they do. This solution calculates the time margin of pollution arrival, predicts in advance whether pollution will reach the breathing zone, and establishes a virtual purification boundary to intercept it before arrival. Traditional purification equipment typically allows particulate matter, gaseous pollutants, odors, ozone, etc., to follow the same treatment path, easily leading to low efficiency, high pressure loss, and filter waste. This solution uses pollution phase coding and a programmable purification media array to dynamically select different purification branches according to the type of pollution. Technical solution
[0006] To achieve the above objectives, the present invention provides the following technical solution: a multi-scenario adaptive air purification system, comprising a main purification device, a first air state node, a second air state node, a third air state node, a fourth air state node, an active airflow pulse execution component, a steerable inhalation component, a low-speed clean air replenishment component, a programmable purification media array, an edge controller, and a local memory, characterized in that: the first air state node is arranged in the pollution source identification area, the second air state node is arranged in the personnel breathing zone area, the third air state node is arranged in the external air exchange area, and the fourth air state node is arranged in the spatial stagnation area; the active airflow pulse execution component, the steerable inhalation component, the low-speed clean air replenishment component, and the programmable purification media array are all disposed within the main purification device; the edge controller is connected to the first air state node, the second air state node, the third air state node, and the fourth air state node respectively. The edge controller is electrically connected to the following components: a first air state node, a fourth air state node, an active airflow pulse execution component, a steerable intake component, a low-speed clean air replenishment component, a programmable purification media array, and a local memory. The edge controller incorporates a spatial transfer matrix data structure, a pollution phase encoding data structure, a pollution arrival time margin data structure, a virtual purification boundary control quantity data structure, a media load ledger data structure, and a byproduct safety index data structure. The spatial transfer matrix data structure consists of pulse input data from the active airflow pulse execution component, response data from the first air state node, response data from the second air state node, response data from the third air state node, and response data from the fourth air state node. The virtual purification boundary control quantity data structure includes inlet direction parameters, intake airflow parameters, clean air replenishment direction parameters, clean air replenishment airflow parameters, local negative pressure parameters, duct diversion ratio parameters, and purification media opening / closing parameters.
[0007] Preferably, the active airflow pulse execution component includes a micro-pulse fan, a clean airflow buffer chamber, a pulse control valve, a first guide vane group, and a pulse outlet group; the air outlet end of the micro-pulse fan is connected to the clean airflow buffer chamber, the clean airflow buffer chamber is connected to the pulse outlet group via the pulse control valve, and the first guide vane group is disposed within the pulse outlet group; the pulse outlet group includes a first pulse outlet, a second pulse outlet, a third pulse outlet, and a fourth pulse outlet, the first pulse outlet facing the front of the main purification equipment, the second pulse outlet facing the left side of the main purification equipment, the third pulse outlet facing the right side of the main purification equipment, and the fourth pulse outlet facing the upper side of the main purification equipment.
[0008] Preferably, the first, second, third, and fourth air state nodes each include a particulate matter detector, a gas detector, a temperature and humidity detector, a differential pressure detector, a low-velocity flow detector, a node control board, and a wireless communication device; the particulate matter detector, gas detector, temperature and humidity detector, differential pressure detector, and low-velocity flow detector are all electrically connected to the node control board, and the node control board is electrically connected to the wireless communication device; the spatial transfer matrix data structure is recorded as follows: in, For a moment The spatial transfer matrix, For the first The pulse output to the first Each air state node has a propagation lag time The propagation function under, This is the sequence number of the air state node. This is the pulse output sequence number.
[0009] Preferably, the pollution arrival time margin data structure is recorded as follows: in, For the first Pollutants are generated from pollution source nodes. To the respiratory zone node Arrival time margin For the first Pollutants are generated from pollution source nodes. To the respiratory zone node The estimated arrival time, To determine the execution response time for forming a virtual cleanup boundary, For the first The processing time of pollutants after they enter the purification medium path.
[0010] Preferably, the steerable suction assembly includes a first suction inlet, a second suction inlet, a third suction inlet, a suction guide vane assembly, a suction airflow regulating valve, and a local negative pressure chamber; the first, second, and third suction inlets are all connected to the local negative pressure chamber through the suction airflow regulating valve, and the suction guide vane assembly is disposed within the first, second, and third suction inlets; the low-speed clean air replenishment assembly includes a first air replenishment port, a second air replenishment port, a third air replenishment port, a replenishment guide vane assembly, and a replenishment airflow regulating valve, the first, second, and third air replenishment ports are all connected to the clean air output chamber of the main purification equipment through the replenishment airflow regulating valve; the pollution flux recorded in the virtual purification boundary control data structure is: in, For the first Pollutants at any time Through candidate edges pollution flux, Candidate edges The converted cross-sectional area, Candidate edges normal air velocity, Candidate edges First Concentration of pollutants, Candidate edges The pollution penetration coefficient.
[0011] Preferably, the programmable purification media array includes a pressure balancing chamber, a particle pretreatment branch, a particle capture branch, a molecular adsorption branch, a formaldehyde capture branch, an odor suppression branch, an ozone decomposition branch, a safety bypass branch, a first electrically controlled damper group, a second electrically controlled damper group, a branch differential pressure detection port, and a media identification contact. The pressure balancing chamber is connected to the particle pretreatment branch, the particle capture branch, the molecular adsorption branch, the formaldehyde capture branch, the odor suppression branch, the ozone decomposition branch, and the safety bypass branch, respectively. The first electrically controlled damper group is located at the inlet end of each branch, the second electrically controlled damper group is located at the outlet end of each branch, the branch differential pressure detection port is located between the inlet end and the outlet end of each branch, and the media identification contact is located in the media mounting base of each branch.
[0012] Preferably, the pollution phase state coding data structure is recorded as follows: in, For a moment The pollution phase state encoding vector, The degree to which particulate pollution is dominant. The degree to which molecular-state contamination is dominant. The degree to which pollutants are released through human respiration. The degree to which surface re-emission of pollutants is dominant. The degree to which externally infiltrated pollution is dominant. To determine the degree of dominance of compound pollution, the local memory contains a path mapping table between the pollution phase state encoding vector and the particle pretreatment branch, particle capture branch, molecular adsorption branch, formaldehyde capture branch, odor suppression branch, ozone decomposition branch, and safety bypass branch.
[0013] Preferably, the media load ledger data structure is recorded as follows: in, For the first Each purification medium at time For the first The cumulative load of pollutants, For the first Each purification medium at time For the first The cumulative load of pollutants, To calculate the time interval, For time Through the first Airflow rate of each purification medium For time Entering the The first purification medium before Concentration of pollutants, For the first Each purification medium in time For the first Removal efficiency of pollutants, For the first Each purification medium in time Activation coefficient, For the first Each purification medium at time The resulting load correction amount.
[0014] Preferably, the by-product safety index data structure is recorded as follows: in, For a moment The safety index of by-products For ozone risk weights, This refers to ozone concentration. Risk weights for aldehyde intermediates. Risk concentration of aldehyde intermediates. To catalyze the risk weighting of temperature rise, The surface temperature of the catalytic medium. The safe temperature threshold for the catalytic medium, This represents the risk weight for desorption of the adsorption medium. This represents the risk value of desorption of the adsorption medium. As the risk weight for electrical purification, The value represents the electrical purification risk; the local memory contains a built-in byproduct safety index threshold table, which is associated with the path mapping table of the programmable purification media array.
[0015] Preferably, it includes the following steps: Sp1: The edge controller reads the installation location of the main purification equipment, the address of the first air status node, the address of the second air status node, the address of the third air status node, the address of the fourth air status node, the purification medium identification data, the air duct branch data, and the personnel breathing zone area data; Sp2: The active airflow pulse execution component releases clean airflow pulses in the order of the first pulse outlet, the second pulse outlet, the third pulse outlet, and the fourth pulse outlet. The first air state node, the second air state node, the third air state node, and the fourth air state node record the response time, response amplitude, and response attenuation data. The edge controller forms a spatial transfer matrix. Sp3: The first air state node, the second air state node, the third air state node and the fourth air state node collect particulate matter concentration, gas concentration, temperature and humidity, micro pressure difference and low velocity flow direction data, and the edge controller forms a pollution phase state encoding vector; Sp4: The edge controller calculates the pollution arrival time margin based on the spatial transfer matrix, pollution phase encoding vector, pollution source nodes, and breathing zone nodes; Sp5: The edge controller calculates the virtual cleanup boundary control quantity based on the pollution arrival time margin and pollution flux; SP6: The edge controller adjusts the inlet direction, intake air volume, clean air supply direction, clean air supply air volume, local negative pressure, air duct diversion ratio, and clean media opening and closing status according to the virtual clean boundary control amount. Sp7: The edge controller updates the cumulative load of each purification medium on various pollutants based on the media load ledger data structure; Sp8: The edge controller generates purification path constraint data based on the by-product safety index data structure; SP9: Local memory records contamination event data, which includes spatial transfer matrix, contamination phase encoding vector, contamination arrival time margin, virtual cleanup boundary control quantity, media load ledger data, and by-product safety index data.
[0016] Beneficial effects
[0017] This invention provides a multi-scenario adaptive air purification system. It has the following beneficial effects: This invention no longer relies solely on indoor average concentration. Instead, it establishes a spatial transfer matrix through active airflow pulse identification and combines this with pollution arrival time margin to determine whether pollution will reach the human breathing zone. This creates a virtual purification boundary before pollution spreads to the human body, enabling feedforward and interceptive purification control. By using pollution phase state encoding vectors to distinguish particulate, molecular, respiratory release, surface re-release, external infiltration, and composite pollution, it links a programmable purification media array to switch between different branches. This avoids the efficiency reduction, increased pressure loss, and filter material waste caused by particulate matter, gaseous pollutants, and odors sharing a fixed filter path. A media load ledger records the cumulative load of each purification medium on different pollutants, and a byproduct safety index constrains the risks of ozone, aldehyde intermediates, catalytic temperature rise, adsorption-desorption, and electro-purification, preventing secondary pollution or safety hazards during purification. This enhances the system's applicability in scenarios such as children, the elderly, bedrooms, and vehicles. Attached Figure Description
[0018] Figure 1 This is a schematic diagram of the overall structure of the multi-scenario adaptive air purification system of the present invention; Figure 2 This is a schematic diagram of the active airflow pulse identification structure of the present invention; Figure 3 This is a schematic diagram of the virtual purification boundary generation structure of the present invention; Figure 4 This is a schematic diagram of the programmable purification media array structure of the present invention; Figure 5 This is a schematic diagram of the data processing flow of the edge controller of the present invention; Figure 6 This is a schematic diagram illustrating the multi-scenario application layout of the present invention. Detailed Implementation
[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Specific Implementation Example 1: like Figures 1 to 6As shown, a multi-scenario adaptive air purification system is described. During installation, the main purification device is placed in a representative location for air exchange within the space to be purified. A first air status node is set in the pollution source identification area, a second air status node in the human breathing zone, a third air status node in the external air exchange area, and a fourth air status node in the spatial stagnation area. The pollution source identification area corresponds to the area near the kitchen exit in an open-plan kitchen, the near-ground activity area in a children's playroom, the area around the nursing bed in a nursing care room, the center of the conference table in a conference room, and the area near the external air intake in a vehicle. The human breathing zone is set according to the usage scenario: corresponding to the breathing height of a child standing or sitting in a children's activity scenario, the breathing height above the bed in a bedroom sleeping scenario, the breathing height of an adult sitting in a conference scenario, the breathing height of a bedridden person in a nursing care scenario, and the area near the heads of front and rear passengers in a vehicle scenario.
[0021] After the system is powered on, the edge controller reads the installation location of the main purification equipment, the addresses of each air status node, the purification medium identification data, the duct branch data, the user-defined sensitive area data, and the initial media load data. The edge controller performs zero-point drift correction on the particulate matter detector, temperature and humidity compensation on the gas detector, reference pressure calibration on the micro-differential pressure detector, and directional reference calibration on the low-velocity flow detector. After calibration, the system enters the active identification phase. The micro-pulse fan generates low-disturbance clean airflow, which enters the clean airflow buffer chamber. The pulse control valve outputs airflow pulses in the order of the first pulse outlet, the second pulse outlet, the third pulse outlet, and the fourth pulse outlet. The first, second, third, and fourth air status nodes record the changes in local flow direction disturbance, micro-differential pressure disturbance, temperature and humidity disturbance, and particulate background disturbance after each pulse.
[0022] Active airflow pulse identification uses the following propagation model:
[0023] in, Indicates the first Each air state node at time... The detected response signal is formed by one or more of the following: local flow disturbance, micro-pressure difference disturbance, temperature and humidity micro-disturbance, and particulate background disturbance, after being unified by the node control board with a timestamp. Indicates the number of pulse outputs; Indicates the first Each pulse output at time The emitted airflow pulse input; Indicates the first The pulse output to the first Spatial propagation function between air state nodes; Indicates the propagation lag time; Indicates the maximum observation window for propagation; Indicates the first Measurement error term for each air state node. Specific Implementation Example 2: like Figures 1 to 6 As shown, the edge controller forms a spatial transfer matrix based on this propagation model: in, Indicates time The spatial transfer matrix; Indicates time Next The pulse output to the first Each air state node has a propagation lag time The propagation function under; Indicates the sequence number of the air state node; Indicates the pulse output sequence number; This indicates the propagation lag time. The spatial transfer matrix is written to local memory and updated by a new round of active airflow pulse identification data after changes occur in the status of doors and windows, air conditioning status, personnel distribution, or furniture obstruction. Specific Implementation Example 3: like Figures 1 to 6 As shown, in order to achieve real-time computation on the edge controller, the continuous propagation model is discretized into a finite impulse response model: in, Indicates the first The air state node is at the _ The response value at each sampling time; Indicates the first The pulse output at the first The airflow pulse input value at each sampling time; Indicates the first The pulse output to the first The air state node is at the _ The propagation coefficients under discrete delay steps; R represents the maximum delay order of the discrete propagation model; Indicates the first The air state node is at the _ Error terms at each sampling time. The edge controller updates recursively. When scene boundary data changes, the edge controller increases the weight of the new sampled data and decreases the weight of the old sampled data, so that the spatial transfer matrix is consistent with the current spatial propagation state. Specific Implementation Example 4: like Figures 1 to 6 As shown, during normal operation, each air state node collects particulate matter concentration, particle size distribution, gas concentration, temperature and humidity, micro-pressure difference, and low-velocity flow data according to the control cycle. The edge controller maps the data from each node to the spatial air node and forms a pollution phase encoding vector based on the pollutant change rate, node response sequence, scene boundary data, and spatial transfer matrix. in, Indicates time The pollution phase state encoding vector; Indicates the degree to which particulate pollution is dominant; Indicates the degree of dominance of molecular-state contamination; This indicates the predominance of contaminants released through human respiration; Indicates the predominance of surface re-emission contamination; Indicates the degree to which externally infiltrated pollution is dominant; Indicates the degree of dominance of compound pollution; superscript This represents the vector transpose. The dominance of particulate pollution is determined by the rising rates and particle size distribution data of PM1, PM2.5, and PM10; the dominance of molecular pollution is determined by the rising rates of formaldehyde, total volatile organic compounds, ammonia, and hydrogen sulfide; the dominance of pollution released from human respiration is determined by the rising rate of carbon dioxide, the number of people, and the area where people reside; the dominance of surface re-release pollution is determined by temperature, humidity, the response of nodes near materials, and the slow release characteristics in an unoccupied state; the dominance of externally infiltrated pollution is determined by the leader response of nodes in the external exchange area, the status of doors and windows, and changes in micro-pressure difference; and the dominance of complex pollution is determined when multiple pollutants rise simultaneously and no single phase component becomes dominant.
[0027] like Figures 1 to 6 As shown, after the edge controller determines the pollution source node s and the breathing zone node b, it calculates the estimated arrival time of the i-th type of pollutant from the pollution source node to the breathing zone node based on the spatial transfer matrix and the pollution phase encoding vector: in, Indicates time No. Pollutants are generated from pollution source nodes. To the respiratory zone node The estimated arrival time; This represents the minimum time required to satisfy the conditions within the parentheses. Indicates the propagation time variable; Indicates the first In the current scenario, these pollutants originate from pollution source nodes. Spread to respiratory zone nodes The propagation kernel function; Represents the integral variable; Indicates the first The cumulative transmission threshold at which pollutants of this type have an effective impact on the respiratory zone. Specific Implementation Example Six: like Figures 1 to 6 As shown, the pollution arrival time margin is calculated using the following formula: in, Indicates the first Pollutants are generated from pollution source nodes. To the respiratory zone node Arrival time margin; Indicates the estimated arrival time of the pollution; This indicates the response time required for the actuator to form a virtual clean boundary from receiving a control command; Indicates the first The processing time of pollutants after they enter the purification medium path. The edge controller writes this margin into the pollutant arrival time margin data structure and uses it as input for subsequent boundary control quantity calculations. Specific Implementation Example 7: like Figures 1 to 6 As shown, the virtual purification boundary is formed by a steerable inhalation component, a low-speed clean air replenishment component, a local negative pressure chamber, and a programmable purification media array. The edge controller delineates a set of candidate edges between the pollution source region and the breathing zone region, and calculates the first edge according to the following formula. Pollutants pass through candidate edges Pollution flux: in, Indicates the first Pollutants at any time Through candidate edges Pollution flux; Indicate candidate edges The equivalent cross-sectional area; Indicates time Through candidate edges Normal air velocity; Indicates time Candidate edges First Concentration of pollutants; Indicate candidate edges The pollution penetration coefficient. The edge controller forms virtual purification boundary control quantities based on pollution arrival time margin, pollution flux, noise, energy consumption, media load and by-product safety index. The control quantities include inlet direction, intake air volume, clean air supply direction, clean air supply air volume, local negative pressure, duct diversion ratio and purification media opening and closing status. Specific Implementation Example 8: like Figures 1 to 6 As shown, the objective function of the virtual purification boundary control variable is recorded as follows: in, Indicates time The optimal control variable; This represents the control variable to be solved; This represents the set of control variables consisting of equipment structure limitations, safety threshold limitations, noise limitations, power limitations, and media state limitations; This represents the control variable that minimizes the objective function. Indicates the exposure weight of the breathing zone; This indicates the predicted human respiratory zone pollution exposure within the time domain; Indicates the weight of cross-border pollution fluxes; Indicates the number of pollutant categories; Represents the set of candidate edges; Indicates the first Pollutants pass through candidate edges Pollution flux; Indicates energy consumption weight; Indicates the system's operating power; Indicates noise weight; This indicates the system's operating noise level; Indicates the weight of purification media consumption; This indicates the overall load consumption index of the purification medium; Indicates the risk weight of by-products; Indicates the safety index of by-products; Indicates the weight of airflow comfort; It represents an index of airflow disturbance formed by direct wind speed, turbulence intensity, and wind speed variation frequency. Specific Implementation Example Nine: like Figures 1 to 6As shown, the programmable purification media array adjusts the branches according to the pollution phase encoding vector and path mapping table. When particulate pollution is dominant, the first electrically controlled damper group opens the particulate pretreatment branch and the particulate capture branch, while the second electrically controlled damper group keeps the corresponding outlet connected. When molecular pollution is dominant, the first electrically controlled damper group opens the molecular adsorption branch, the formaldehyde capture branch, and the odor suppression branch. When pollution released from human respiration is dominant, the system links the upper return path with the particulate capture branch, the molecular adsorption branch, and the external air supply branch. When surface re-release pollution is dominant, the system reduces the instantaneous airflow and increases the continuous treatment ratio of the molecular adsorption branch. When externally infiltrated pollution is dominant, the system increases the inhalation ratio near the external exchange area and introduces the incoming air into the particulate capture branch, the molecular adsorption branch, and the ozone decomposition branch. When combined pollution is dominant, the system forms a combined treatment path in the order of particulate pretreatment branch, particulate capture branch, molecular adsorption branch, formaldehyde capture branch, and ozone decomposition branch. Specific Implementation Example 10: like Figures 1 to 6 As shown, each purification media unit has an independent load ledger, and the media load ledger is updated according to the following formula: in, Indicates the first Each purification medium at time For the first The cumulative load of pollutants; Indicates the first Each purification medium at time For the first The cumulative load of pollutants; Indicates the calculation time interval; , Indicates time Through the first Airflow rate of each purification medium; Indicates time Entering the The first purification medium before Concentration of pollutants; Indicates the first Each purification medium in time For the first Removal efficiency of pollutants; Indicates the first Each purification medium in time Activation coefficient; Indicates the first Each purification medium at time The resulting load correction. This load ledger records the loads of particulate matter, formaldehyde, total volatile organic compounds, ammonia, hydrogen sulfide, ozone, and complex odors according to pollutant categories, and correlates them with branch differential pressure detection data and media identification data. Specific Implementation Example Eleven: like Figures 1 to 6 As shown, the by-product safety index is calculated according to the following formula: in, Indicates time The safety index of by-products; Indicates ozone risk weights; Indicates ozone concentration; Indicates the risk weight of aldehyde intermediates; This indicates the risk concentration of aldehyde intermediates obtained by fusing the data from a formaldehyde sensor, a total volatile organic compound sensor, and the state of the catalytic medium. Indicates the risk weight of catalytic temperature rise; Indicates the surface temperature of the catalytic medium; Indicates the safe temperature threshold of the catalytic medium; This indicates the temperature difference between the catalytic medium and the safe temperature threshold. Indicates the risk weight of desorption in the adsorption medium; This indicates the desorption risk value of the adsorption medium under the current temperature, humidity, and load conditions; Indicates the risk weight of electrical purification; This represents the electrical purification risk value. The edge controller compares the by-product safety index with the by-product safety index threshold table and converts the comparison result into purification path constraint data, which is then associated with the path mapping table of the programmable purification media array. Specific Implementation Example Twelve: like Figures 1 to 6As shown, in an implementation scenario where an open kitchen connects to the living room, the first air state node is positioned near the kitchen exit, the second air state node is positioned in the living room sofa breathing zone area, the third air state node is positioned near a window or fresh air inlet, and the fourth air state node is positioned in a corner of the living room. After cooking begins, the first air state node detects a simultaneous increase in PM2.5, PM10, and total volatile organic compounds. The edge controller, combined with the spatial transfer matrix, determines the propagation kernel function from the pollution source node to the sofa breathing zone node and calculates the pollution arrival time margin. The edge controller controls the opening of the inlet near the kitchen exit, controls the low-speed clean air supply inlet to output low-speed clean air to the living room side, and controls the connection of the particulate pretreatment branch, particulate capture branch, molecular adsorption branch, and ozone decomposition branch, creating a virtual purification boundary between the kitchen exit and the living room. After the pollution event ends, the local memory records the spatial transfer matrix, pollution phase encoding vector, virtual purification boundary control quantity, and media load ledger corresponding to the kitchen scenario.
[0035] In a bedroom sleep scenario, the first air status node is positioned near the bedside table or wardrobe, the second air status node is positioned in the breathing zone above the bed, the third air status node is positioned near the window, and the fourth air status node is positioned in a corner of the room. During nighttime operation, the second air status node detects a slow rise in carbon dioxide, while the first air status node detects a low-intensity release of formaldehyde and total volatile organic compounds (TVOCs) with temperature changes. The edge controller calculates the pollution arrival time margin from the furniture area to the bed's breathing zone based on the spatial transfer matrix and controls the wall-mounted air intake and side intake to form a low-disturbance virtual purification boundary. The programmable purification media array activates the molecular adsorption branch, the formaldehyde capture branch, and the ozone decomposition branch. The edge controller limits the fan speed variation based on noise and airflow disturbance indicators.
[0036] In the conference room scenario, the first air state node is positioned in the center of the conference table, the second in the seating breathing height area, the third near the door or fresh air inlet, and the fourth in the upper return air area. After the meeting begins, the carbon dioxide rise rate and aerosol particle fluctuations increase simultaneously. The edge controller generates a pollution phase encoding vector based on the predominance of pollution released through respiration. The edge controller controls the upper intake inlet to enhance recovery, controls the lateral air supply inlet to output low-speed clean air to the seating area, and introduces the intake air into the particle capture and molecular adsorption branches. In this scenario, the virtual purification boundary is set above the center of the conference table and between the seating breathing zone. The system incorporates operating noise into the objective function, controlling the fan speed and guide vane angle.
[0037] In the implementation scenario of a nursing home, the first air status node is located on one side of the nursing bed, the second air status node is located in the breathing zone of the bedridden person, the third air status node is located near the door or exhaust vent, and the fourth air status node is located in the nursing staff's operating area. During nursing operations, ammonia, hydrogen sulfide, aerosol particles, and humidity change synchronously. The edge controller generates a composite pollution phase encoding vector and calculates the pollution arrival time margin from the nursing bed area to the bedridden person's breathing zone. The edge controller controls the inlet on the pollution source side of the nursing bed to form a local negative pressure, controls the upstream air supply port of the breathing zone to form a low-speed clean air supply, and guides the inhaled air into the molecular adsorption branch, odor suppression branch, particle capture branch, and ozone decomposition branch. When the byproduct safety index exceeds the threshold in the nursing scenario threshold table, the edge controller shuts down the path corresponding to the electro-purification risk while keeping the ozone decomposition branch connected.
[0038] In the vehicle-mounted scenario, the first air state node is positioned near the external air intake, the second air state node is positioned in the front passenger breathing zone, the third air state node is positioned in the rear passenger breathing zone, and the fourth air state node is positioned in the stagnant area inside the vehicle. When the vehicle is in congested traffic or an underground parking area, the first air state node detects the input of external particulate matter and gaseous pollution. The edge controller generates a pollution phase encoding vector indicating that externally infiltrated pollution is dominant, and forms an inlet-type virtual purification boundary between the external air intake and the passenger breathing zone. The programmable purification media array guides the incoming external air into the particulate pretreatment branch, particulate capture branch, molecular adsorption branch, and ozone decomposition branch. After the vehicle has been exposed to sunlight, when the total volatile organic compounds (TVOCs) increase, the edge controller adjusts the pollution phase encoding vector to indicate that surface re-release pollution is dominant, and increases the processing ratio of the molecular adsorption branch and the formaldehyde capture branch.
[0039] After a pollution event concludes, the edge controller generates a pollution event record. This record includes the event start time, spatial transfer matrix, pollution phase encoding vector, pollution source node, breathing zone node, pollution arrival time margin, virtual purification boundary control variable, purification media path, media load ledger change, byproduct safety index, and equipment operating data. The pollution event record is written to local memory, and a local policy library is established based on the scene identifier, pollution phase identifier, and spatial transfer matrix index. When the same scene boundary state and similar spatial transfer matrix occur subsequently, the edge controller uses historical control variables from the local policy library as initial control variables and corrects them based on real-time sensor data.
[0040] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising a reference structure" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.
[0041] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. A multi-scenario adaptive air purification system, comprising a main purification device, a first air state node, a second air state node, a third air state node, a fourth air state node, an active airflow pulse execution component, a steerable suction component, a low-speed clean air replenishment component, a programmable purification media array, an edge controller, and a local memory, characterized in that: The first air status node is arranged in the pollution source identification area, the second air status node is arranged in the personnel breathing zone area, the third air status node is arranged in the external air exchange area, and the fourth air status node is arranged in the spatial stagnation area. The active airflow pulse actuator, steerable suction assembly, low-speed clean air replenishment assembly, and programmable purification media array are all housed within the main purification equipment. The edge controller is electrically connected to the first air state node, the second air state node, the third air state node, the fourth air state node, the active airflow pulse actuator, the steerable suction assembly, the low-speed clean air replenishment assembly, the programmable purification media array, and the local memory. The edge controller incorporates a spatial transfer matrix data structure, a pollution phase encoding data structure, a pollution arrival time margin data structure, a virtual purification boundary control quantity data structure, a media load ledger data structure, and a byproduct safety index data structure. The spatial transfer matrix data structure consists of the pulse input data of the active airflow pulse actuator, the response data of the first air state node, the response data of the second air state node, the response data of the third air state node, and the response data of the fourth air state node. The virtual purification boundary control quantity data structure includes inlet direction parameters, intake air volume parameters, clean air replenishment direction parameters, clean air replenishment air volume parameters, local negative pressure parameters, duct diversion ratio parameters, and purification media opening and closing parameters.
2. The multi-scenario adaptive air purification system according to claim 1, characterized in that: The active airflow pulse execution component includes a micro-pulse fan, a clean airflow buffer chamber, a pulse control valve, a first guide vane group, and a pulse outlet group; the air outlet of the micro-pulse fan is connected to the clean airflow buffer chamber, the clean airflow buffer chamber is connected to the pulse outlet group via the pulse control valve, and the first guide vane group is installed inside the pulse outlet group. The pulse outlet group includes a first pulse outlet, a second pulse outlet, a third pulse outlet, and a fourth pulse outlet. The first pulse outlet faces the front of the main purification equipment, the second pulse outlet faces the left side of the main purification equipment, the third pulse outlet faces the right side of the main purification equipment, and the fourth pulse outlet faces the top of the main purification equipment.
3. The multi-scenario adaptive air purification system according to claim 1, characterized in that: The first, second, third, and fourth air state nodes each include a particulate matter detector, a gas detector, a temperature and humidity detector, a differential pressure detector, a low-velocity flow detector, a node control board, and a wireless communication device. The particulate matter detector, gas detector, temperature and humidity detector, differential pressure detector, and low-velocity flow detector are all electrically connected to the node control board, and the node control board is electrically connected to the wireless communication device. The spatial transfer matrix data structure is recorded as follows: in, Let be the spatial transfer matrix at time t. For the first The pulse output to the first Each air state node has a propagation lag time The propagation function under, This is the sequence number of the air state node. This is the pulse output sequence number.
4. The multi-scenario adaptive air purification system according to claim 1, characterized in that: The pollution arrival time margin data structure is recorded as follows: in, For pollutant of type i, from the pollution source node To the respiratory zone node Arrival time margin For the first Pollutants are generated from pollution source nodes. To the respiratory zone node The estimated arrival time, To determine the execution response time for forming a virtual cleanup boundary, For the first The processing time of pollutants after they enter the purification medium path.
5. The multi-scenario adaptive air purification system according to claim 1, characterized in that: The steerable suction assembly includes a first suction inlet, a second suction inlet, a third suction inlet, a suction guide vane assembly, a suction airflow regulating valve, and a local negative pressure chamber; the first, second, and third suction inlets are all connected to the local negative pressure chamber via the suction airflow regulating valve, and the suction guide vane assembly is disposed within the first, second, and third suction inlets; the low-speed clean air supply assembly includes a first air supply port, a second air supply port, a third air supply port, an air supply guide vane assembly, and an air supply airflow regulating valve, the first, second, and third air supply ports are all connected to the clean air output chamber of the main purification equipment via the air supply airflow regulating valve; the pollution flux recorded in the virtual purification boundary control data structure is: in, For the first Pollutants at any time Through candidate edges pollution flux, Candidate edges The converted cross-sectional area, Candidate edges normal air velocity, Candidate edges First Concentration of pollutants, Candidate edges The pollution penetration coefficient.
6. The multi-scenario adaptive air purification system according to claim 1, characterized in that: The programmable purification media array includes a pressure balancing chamber, a particle pretreatment branch, a particle capture branch, a molecular adsorption branch, a formaldehyde capture branch, an odor suppression branch, an ozone decomposition branch, a safety bypass branch, a first electrically controlled damper group, a second electrically controlled damper group, branch differential pressure detection ports, and media identification contacts. The pressure balancing chamber is connected to the particle pretreatment branch, particle capture branch, molecular adsorption branch, formaldehyde capture branch, odor suppression branch, ozone decomposition branch, and safety bypass branch, respectively. The first electrically controlled damper group is located at the inlet end of each branch, the second electrically controlled damper group is located at the outlet end of each branch, the branch differential pressure detection port is located between the inlet end and the outlet end of each branch, and the media identification contacts are located in the media mounting base of each branch.
7. The multi-scenario adaptive air purification system according to claim 6, characterized in that: The pollution phase coding data structure is recorded as follows: in, For a moment The pollution phase state encoding vector, The degree to which particulate pollution is dominant. The degree to which molecular-state contamination is dominant. The degree to which pollutants are released through human respiration. The degree to which surface re-emission of pollutants is dominant. The degree to which externally infiltrated pollution is dominant. To determine the degree of dominance of compound pollution, the local memory contains a path mapping table between the pollution phase state encoding vector and the particle pretreatment branch, particle capture branch, molecular adsorption branch, formaldehyde capture branch, odor suppression branch, ozone decomposition branch, and safety bypass branch.
8. The multi-scenario adaptive air purification system according to claim 1, characterized in that: The media load ledger data structure is recorded as follows: in, For the first Each purification medium at time The cumulative load of pollutants of type i, For the first Each purification medium at time For the first The cumulative load of pollutants, To calculate the time interval, For time Through the first Airflow rate of each purification medium For time Entering the The first purification medium before Concentration of pollutants, For the first Each purification medium in time For the first Removal efficiency of pollutants, For the first Each purification medium in time Activation coefficient, For the first Each purification medium at time The resulting load correction amount.
9. A multi-scenario adaptive air purification system according to claim 1, characterized in that: The by-product safety index data structure is recorded as follows: in, For a moment The safety index of by-products For ozone risk weights, This refers to ozone concentration. Risk weights for aldehyde intermediates. Risk concentration of aldehyde intermediates. To catalyze the risk weighting of temperature rise, The surface temperature of the catalytic medium. The safe temperature threshold for the catalytic medium, This represents the risk weight for desorption of the adsorption medium. This represents the risk value of desorption of the adsorption medium. As the risk weight for electrical purification, The value represents the electrical purification risk; the local memory contains a built-in byproduct safety index threshold table, which is associated with the path mapping table of the programmable purification media array.
10. An implementation of a multi-scenario adaptive air purification system according to any one of claims 1 to 9, characterized in that, Includes the following steps: Sp1: The edge controller reads the installation location of the main purification equipment, the address of the first air status node, the address of the second air status node, the address of the third air status node, the address of the fourth air status node, the purification medium identification data, the air duct branch data, and the personnel breathing zone area data; Sp2: The active airflow pulse execution component releases clean airflow pulses in the order of the first pulse outlet, the second pulse outlet, the third pulse outlet, and the fourth pulse outlet. The first air state node, the second air state node, the third air state node, and the fourth air state node record the response time, response amplitude, and response attenuation data. The edge controller forms a spatial transfer matrix. Sp3: The first air state node, the second air state node, the third air state node and the fourth air state node collect particulate matter concentration, gas concentration, temperature and humidity, micro pressure difference and low velocity flow direction data, and the edge controller forms a pollution phase state encoding vector; Sp4: The edge controller calculates the pollution arrival time margin based on the spatial transfer matrix, pollution phase encoding vector, pollution source nodes, and breathing zone nodes; Sp5: The edge controller calculates the virtual cleanup boundary control quantity based on the pollution arrival time margin and pollution flux; SP6: The edge controller adjusts the inlet direction, intake air volume, clean air supply direction, clean air supply air volume, local negative pressure, air duct diversion ratio, and clean media opening and closing status according to the virtual clean boundary control amount. Sp7: The edge controller updates the cumulative load of each purification medium on various pollutants based on the media load ledger data structure; Sp8: The edge controller generates purification path constraint data based on the by-product safety index data structure; SP9: Local memory records contamination event data, which includes spatial transfer matrix, contamination phase encoding vector, contamination arrival time margin, virtual cleanup boundary control quantity, media load ledger data, and by-product safety index data.