Air purification method and system based on ion air supply technology

Abstract

The present application relates to the technical field of air purification, and particularly relates to an air purification method and system based on ion air supply technology. The method comprises the following steps: obtaining target area data and reconstructing a space model, performing block division, determining virtual layout nodes and performing layout rehearsal, obtaining a unit layout topology through space node dynamic clustering and redundancy elimination, performing ion air supply scope superposition simulation, analyzing scope collaborative empowerment parameters, monitoring ion transmission in real time, performing diffusion path tracking and blind area correction, realizing dynamic coupling of heterogeneous response units and purification intensity boundary migration, adjusting simulation based on adjusted purification intensity, collecting feedback data, coordinating control through an air purification threshold, and realizing intelligent air purification. The present application realizes precise control and optimization of the air purification process, improves purification efficiency, reduces energy consumption, and provides technical support for efficient and intelligent air purification.

Description

Technical Field

[0001] This invention relates to the field of air purification technology, and in particular to an air purification method and system based on ion delivery technology. Background Technology

[0002] Existing air purification technologies face numerous challenges in addressing air pollution in complex spatial environments. Traditional air purification methods often employ single purification techniques, making it difficult to adapt to varying pollution levels and spatial structures, resulting in poor purification effects. This is particularly true in large or structurally complex spaces, where purification blind spots are prone to occur, hindering comprehensive and effective air purification. Furthermore, existing technologies have shortcomings in the layout and control of purification units, lacking refined modeling and optimization algorithms for the spatial environment, leading to low purification efficiency and excessive energy consumption. Air purification areas often contain multiple pollution sources and complex airflow patterns, making it difficult for existing technologies to accurately predict the effective range and synergistic effects of purification units, resulting in unstable purification effects. In addition, existing technologies have limitations in real-time monitoring and adjustment of the purification process, failing to dynamically adjust purification strategies according to actual conditions, making it difficult to achieve optimal purification results. These shortcomings are even more pronounced in densely populated areas or places with high air quality requirements. Summary of the Invention

[0003] Therefore, it is necessary to provide an air purification method and system based on ion air delivery technology to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, an air purification method based on ion delivery technology includes the following steps:

[0005] Step S1: Obtain the target air purification area and data of each block unit; reconstruct the target area spatial model based on the target air purification area; divide the target area spatial model into independent blocks based on the data of each block unit, thereby obtaining a set of split sub-blocks;

[0006] Step S2: Determine virtual deployment nodes through the data of each block unit, and perform deployment pre-simulation based on the virtual deployment nodes and the set of split sub-blocks to generate deployment pre-simulation data; perform dynamic clustering of spatial nodes based on the deployment pre-simulation data, and perform redundancy removal based on the clustered spatial nodes to obtain the unit deployment topology.

[0007] Step S3: Based on the preset ion air supply data and the unit layout topology, perform ion air supply action domain superposition simulation, and analyze the action domain synergistic empowerment parameters based on the simulated superposition action domain; perform real-time ion transport monitoring based on the action domain synergistic empowerment parameters to obtain ion monitoring data;

[0008] Step S4: Track the diffusion path based on the ion monitoring data, and correct the diffusion blind zone based on the diffusion path to generate blind zone correction data; perform dynamic coupling of heterogeneous response units based on the blind zone correction data, and perform purification intensity boundary migration based on the coupled heterogeneous response units to obtain the adjusted purification intensity.

[0009] Step S5: Based on the adjustment of the purification intensity, simulate the adjustment of the target air purification area and collect feedback data; coordinate and control the air purification through the preset air purification threshold and the feedback data.

[0010] This invention acquires data on the target air purification area and its various unit blocks to comprehensively understand the area's structural information, laying the foundation for subsequent precise purification. It reconstructs the spatial model of the target area, achieving digital simulation of the purification zone. Independent block division allows for differentiated processing of different areas. Virtual deployment nodes are determined and deployment simulations are performed, generating deployment simulation data to support node layout optimization. Dynamic clustering of spatial nodes enables automatic node optimization. Redundancy removal reduces resource waste. The unit deployment topology provides clear guidance for actual deployment. Ion air delivery domain superposition simulation accurately predicts purification effects. Analysis of domain synergistic empowerment parameters provides a basis for optimizing air delivery strategies. Real-time ion transport monitoring provides real-time feedback on the purification process. Ion monitoring data provides data support for evaluating purification effects. The diffusion path... Path tracking accurately identifies purification blind spots, and blind spot correction data provides guidance for eliminating purification dead zones. Dynamic coupling of heterogeneous response units enables the synergistic effect of multiple purification technologies. Purification intensity boundary migration makes the purification effect more uniform. Adjusting the purification intensity provides a means to achieve precise control. Simulating adjustments to the target air purification area can predict the effect after adjustment. Collecting feedback data provides a basis for evaluating the adjustment effect. Coordinated control of air purification through air purification thresholds and feedback data achieves intelligent control of the purification process, ensuring that the purification effect meets expectations. The comprehensive application of this series of measures enables precise control and optimization of the air purification process, improves purification efficiency, reduces energy consumption, provides technical support for achieving efficient and intelligent air purification, and ultimately provides strong support for improving air quality and achieving effective control of air pollution.

[0011] The present invention also provides an air purification system based on ion delivery technology for performing the air purification method based on ion delivery technology as described above. The air purification system based on ion delivery technology includes:

[0012] The spatial modeling module is used to acquire data on the target air purification area and each block unit; reconstruct the target area spatial model based on the target air purification area; and divide the target area spatial model into independent blocks based on the data of each block unit, thereby obtaining a set of sub-blocks.

[0013] The topology deployment module is used to determine virtual deployment nodes through the data of each block unit, and to perform deployment simulation based on the virtual deployment nodes and the set of split sub-blocks to generate deployment simulation data; based on the deployment simulation data, the spatial nodes are dynamically clustered, and redundancy removal is performed based on the clustered spatial nodes to obtain the unit deployment topology structure.

[0014] The domain monitoring module is used to simulate the superposition of ion air supply domains based on preset ion air supply data and unit layout topology, and analyze the domain synergistic empowerment parameters based on the simulated superposition domains; and to perform real-time ion transport monitoring based on the domain synergistic empowerment parameters, thereby obtaining ion monitoring data.

[0015] The diffusion blind zone correction module is used to track the diffusion path based on ion monitoring data and correct the diffusion blind zone based on the diffusion path to generate blind zone correction data; based on the blind zone correction data, the heterogeneous response unit is dynamically coupled, and the purification intensity boundary is migrated based on the coupled heterogeneous response unit to obtain the adjusted purification intensity.

[0016] The purification coordination module is used to simulate the adjustment of the target air purification area based on the adjustment of purification intensity and to collect feedback data; it performs air purification coordination control through preset air purification thresholds and feedback data.

[0017] This invention, through a spatial modeling module, can comprehensively acquire data on the target air purification area and each block unit, providing a precise data foundation for subsequent air purification. It reconstructs the spatial model of the target area, achieving a digital representation of the purification area, facilitating simulation and analysis. Independent block division allows the system to differentiate processing based on the characteristics of different areas. The topology deployment module, by determining virtual deployment nodes and performing deployment simulations, generates deployment simulation data, providing data support for optimizing the layout of purification units. Dynamic clustering of spatial nodes enables automatic optimization of the purification unit layout, improving purification efficiency. Redundancy removal reduces unnecessary resource waste and lowers system operating costs. The unit deployment topology provides a clear guidance for actual deployment. The domain monitoring module, through ion air delivery domain superposition simulation, can accurately predict purification effects, providing a basis for optimizing air delivery strategies. Analysis of domain synergistic empowerment parameters optimizes the synergistic effects between purification units. Real-time ion transmission monitoring provides real-time feedback on the purification process. Ion monitoring data provides data for evaluation. The system provides data support for estimating purification effectiveness. The diffusion blind zone correction module accurately identifies purification blind zones through diffusion path tracking. Blind zone correction data provides guidance for eliminating purification dead zones. The dynamic coupling of heterogeneous response units enables the synergistic effect of multiple purification technologies, improving purification effectiveness. Purification intensity boundary migration makes the purification effect more uniform, avoiding local over- or under-purification. Adjusting purification intensity provides a means to achieve precise control. The purification coordination module can predict the effect after adjustment by simulating the adjustment of the target air purification area, providing a basis for further optimization. The collection of feedback data provides a basis for evaluating the adjustment effect. By using air purification thresholds and feedback data for coordinated air purification control, intelligent control of the purification process is achieved, ensuring that the purification effect reaches the expected level. The collaborative work of this series of modules enables precise control and optimization of the air purification process, improves purification efficiency, reduces energy consumption, provides technical support for achieving efficient and intelligent air purification, and ultimately provides strong support for improving air quality and achieving effective control of air pollution. Attached Figure Description

[0018] Fig. 1 This is a schematic diagram of the steps in an air purification method based on ion delivery technology.

[0019] Fig. 2 This is a detailed flowchart illustrating the implementation steps of step S5.

[0020] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0022] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0023] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0024] To achieve the above objectives, please refer to Figs. 1-2 An air purification method based on ion delivery technology includes the following steps:

[0025] Step S1: Obtain the target air purification area and data of each block unit; reconstruct the target area spatial model based on the target air purification area; divide the target area spatial model into independent blocks based on the data of each block unit, thereby obtaining a set of split sub-blocks;

[0026] Step S2: Determine virtual deployment nodes through the data of each block unit, and perform deployment pre-simulation based on the virtual deployment nodes and the set of split sub-blocks to generate deployment pre-simulation data; perform dynamic clustering of spatial nodes based on the deployment pre-simulation data, and perform redundancy removal based on the clustered spatial nodes to obtain the unit deployment topology.

[0027] Step S3: Based on the preset ion air supply data and the unit layout topology, perform ion air supply action domain superposition simulation, and analyze the action domain synergistic empowerment parameters based on the simulated superposition action domain; perform real-time ion transport monitoring based on the action domain synergistic empowerment parameters to obtain ion monitoring data;

[0028] Step S4: Track the diffusion path based on the ion monitoring data, and correct the diffusion blind zone based on the diffusion path to generate blind zone correction data; perform dynamic coupling of heterogeneous response units based on the blind zone correction data, and perform purification intensity boundary migration based on the coupled heterogeneous response units to obtain the adjusted purification intensity.

[0029] Step S5: Based on the adjustment of the purification intensity, simulate the adjustment of the target air purification area and collect feedback data; coordinate and control the air purification through the preset air purification threshold and the feedback data.

[0030] This invention acquires data on the target air purification area and its various unit blocks to comprehensively understand the area's structural information, laying the foundation for subsequent precise purification. It reconstructs the spatial model of the target area, achieving digital simulation of the purification zone. Independent block division allows for differentiated processing of different areas. Virtual deployment nodes are determined and deployment simulations are performed, generating deployment simulation data to support node layout optimization. Dynamic clustering of spatial nodes enables automatic node optimization. Redundancy removal reduces resource waste. The unit deployment topology provides clear guidance for actual deployment. Ion air delivery domain superposition simulation accurately predicts purification effects. Analysis of domain synergistic empowerment parameters provides a basis for optimizing air delivery strategies. Real-time ion transport monitoring provides real-time feedback on the purification process. Ion monitoring data provides data support for evaluating purification effects. The diffusion path... Path tracking accurately identifies purification blind spots, and blind spot correction data provides guidance for eliminating purification dead zones. Dynamic coupling of heterogeneous response units enables the synergistic effect of multiple purification technologies. Purification intensity boundary migration makes the purification effect more uniform. Adjusting the purification intensity provides a means to achieve precise control. Simulating adjustments to the target air purification area can predict the effect after adjustment. Collecting feedback data provides a basis for evaluating the adjustment effect. Coordinated control of air purification through air purification thresholds and feedback data achieves intelligent control of the purification process, ensuring that the purification effect meets expectations. The comprehensive application of this series of measures enables precise control and optimization of the air purification process, improves purification efficiency, reduces energy consumption, provides technical support for achieving efficient and intelligent air purification, and ultimately provides strong support for improving air quality and achieving effective control of air pollution.

[0031] In this embodiment of the invention, the air purification method based on ion air delivery technology includes the following steps:

[0032] Step S1: Obtain the target air purification area and data of each block unit; reconstruct the target area spatial model based on the target air purification area; divide the target area spatial model into independent blocks based on the data of each block unit, thereby obtaining a set of split sub-blocks;

[0033] In this embodiment, building structure data of the target air purification area is collected, including floor plans, elevations, and functional zoning information. Three-dimensional point cloud data is acquired using a laser scanner, and the point cloud data is converted into a digital three-dimensional model using Building Information Modeling (BIM) technology. Then, based on the functional zoning and air circulation characteristics of the area, the entire area is divided into several independent block units. The division of each block unit is based on factors such as space use, personnel density, and ventilation conditions. The three-dimensional model is meshed using spatial analysis tools to generate a set of sub-blocks. The size and shape of each sub-block are adjusted according to the actual building structure to ensure that the air purification equipment can cover the entire area.

[0034] Step S2: Determine virtual deployment nodes through the data of each block unit, and perform deployment pre-simulation based on the virtual deployment nodes and the set of split sub-blocks to generate deployment pre-simulation data; perform dynamic clustering of spatial nodes based on the deployment pre-simulation data, and perform redundancy removal based on the clustered spatial nodes to obtain the unit deployment topology.

[0035] In this embodiment, a virtual deployment node is set at the center of each sub-block, representing a potential installation point for air purification equipment. Computational Fluid Dynamics (CFD) simulation software is used to simulate the airflow at each virtual deployment node, generating deployment simulation data, including information such as airflow velocity, flow direction, and pollutant concentration distribution. Subsequently, clustering algorithms (such as K-means or DBSCAN) are used to analyze the deployment simulation data, grouping nodes with similar airflow characteristics into one category to form a spatial node dynamic cluster. Then, redundancy removal is performed on the nodes in each cluster, retaining key nodes and removing redundant nodes, ultimately forming a unit deployment topology to guide the actual equipment installation layout.

[0036] Step S3: Based on the preset ion air supply data and the unit layout topology, perform ion air supply action domain superposition simulation, and analyze the action domain synergistic empowerment parameters based on the simulated superposition action domain; perform real-time ion transport monitoring based on the action domain synergistic empowerment parameters to obtain ion monitoring data;

[0037] In this embodiment, based on preset ion air supply parameters (such as ion concentration, wind speed, and air supply angle) and combined with the unit layout topology, CFD simulation software is used to perform superimposed simulation of the ion air supply action domain. The air supply range and overlapping area of ​​each device are analyzed, and the action domain synergistic empowerment parameters are calculated, including indicators such as ion coverage, ion concentration uniformity, and synergistic purification efficiency. In actual operation, a sensor network is installed to monitor ion concentration and air quality in real time, collect ion monitoring data, and use it to evaluate the purification effect and adjust the equipment operating parameters.

[0038] Step S4: Track the diffusion path based on the ion monitoring data, and correct the diffusion blind zone based on the diffusion path to generate blind zone correction data; perform dynamic coupling of heterogeneous response units based on the blind zone correction data, and perform purification intensity boundary migration based on the coupled heterogeneous response units to obtain the adjusted purification intensity.

[0039] In this embodiment, ion monitoring data is used to track the diffusion path of pollutants in the air, identify purification blind spots, and use reverse modeling technology to analyze the causes of blind spot formation, such as unreasonable equipment layout or improper air supply direction. Blind spot correction data is generated to guide the re-layout of equipment or adjustment of air supply parameters. Subsequently, for different types of air purification equipment (such as electrostatic precipitators, activated carbon filters, etc.), heterogeneous response units are dynamically coupled to coordinate the operating parameters of each device, realize the migration of purification intensity boundary, optimize the overall purification effect, and finally obtain the adjusted purification intensity parameters.

[0040] Step S5: Based on the adjustment of the purification intensity, simulate the adjustment of the target air purification area and collect feedback data; coordinate and control the air purification through the preset air purification threshold and the feedback data.

[0041] In this embodiment, based on the adjusted purification intensity parameters, CFD simulation software is used to simulate the target air purification area, evaluate the purification effect, collect feedback data, including air quality indicators and equipment operating status, set air purification thresholds, such as PM2.5 concentration, CO2 concentration, and VOC concentration, compare the feedback data with the preset thresholds, determine whether the purification effect meets the standard, and if it does not meet the standard, adjust the equipment operating parameters or deployment plan, and carry out coordinated air purification control to ensure that the air quality meets the expected standard.

[0042] Preferably, step S1 includes the following steps:

[0043] Step S11: Obtain data of the target air purification area and each block unit; perform boundary contour recognition on the target air purification area to obtain the boundary of the target area, wherein the minimum contour closure length for boundary recognition is set to 150 meters;

[0044] Step S12: Reconstruct the spatial skeleton based on the boundary of the target region to generate a spatial model of the target region. During the reconstruction process, the main skeleton extension angle threshold is set to continuously expand within 30°.

[0045] Step S13: Anchor the cell position of each block cell data to obtain the block anchor coordinate data, wherein the anchor tolerance is limited to ±5 meters.

[0046] Step S14: Perform block nesting mapping between the block anchoring coordinate data and the target area spatial model to obtain the initial spatial nesting layer data, wherein the upper limit of layer overlay is set to no more than 3 overlapping units at each location;

[0047] Step S15: Perform independent unit decoupling processing based on the initial spatial nested layer data to obtain the split sub-block set data, where the minimum side length of the sub-block is limited to 20 meters and the area range is set to 400-900 square meters.

[0048] In this embodiment, when acquiring data on the target air purification area and each block unit, a UAV equipped with a high-precision LiDAR (Light Detection and Range) device is used to scan the outer contour of the building to obtain three-dimensional point cloud data. Then, a point cloud data processing engine is used to extract the contour of the outer envelope of the point cloud through a boundary extraction algorithm. The minimum contour closure length is set to 150 meters. A contour simplification method based on the Ramer-Douglas-Peucker algorithm is used to control the continuity and geometric closure of the extracted boundary. After extraction, a two-dimensional vector format boundary line file is generated and used as the basic boundary input for subsequent spatial reconstruction. When reconstructing the spatial skeleton based on the boundary of the target area, the main skeleton extraction rule is used with boundary line segments as input. First, uniform sampling of the boundary is performed, and a boundary control is extracted every 2 meters. First, control points are established, and then equidistant radial lines are generated from each control point into the region. The growth direction of the line segments is controlled based on the normal angle difference between adjacent control points. The extension angle threshold of the main skeleton is set to within 30 degrees, limiting the angle between each main skeleton line and the previous segment to no more than 30 degrees. A graph structure model is used to determine the connectivity of the generated skeleton line segments, removing disconnected branches and redundant loop segments. Finally, the set of skeleton center lines is output and embedded into the regional three-dimensional coordinate framework using three-dimensional vector representation to form the spatial model skeleton structure. When anchoring the location of each block unit data, initial positioning is performed based on the building level, functional attributes, and existing spatial coordinate records corresponding to each unit. Using GPS and GIS coordinate alignment technology, the original positioning information of each block is uniformly converted into a unified coordinate system (such as WGS) through coordinate projection transformation functions. For the planar coordinate data under 84), the minimum Euclidean distance between the center point of each block and the target spatial model skeleton is calculated. If this distance exceeds the preset ±5-meter anchoring tolerance, the unit is marked as anchoring failed; otherwise, the current coordinates are retained as anchoring coordinates. When performing nested mapping between the block anchoring coordinate data and the target area spatial model, the basic layer structure of the target area spatial model is constructed. Spatial units are divided according to the skeleton centerline grid. Each layer represents a spatial unit within a height range. Anchor blocks are superimposed in the layers, with a maximum of three layers of units superimposed at each spatial location. That is, if more than three sets of block coordinates are superimposed at any location, a limit judgment is triggered. The system executes conflict elimination logic, prioritizing the embedding of data with the latest upload time, and indexes and marks the retained units according to their functional labels. The layers are marked as "embedding successful" and an initial spatial nested layer dataset is generated for the next stage of sub-block processing. When performing independent unit decoupling processing based on the initial spatial nested layer data...The spatial Boolean operation module performs difference set partitioning on overlapping blocks in each layer, precisely cutting the boundaries between blocks. A vector segmentation algorithm is used to decompose overlapping areas into non-intersecting sub-regions. The minimum side length of each sub-region is set to 20 meters to ensure accessibility and stability of the spatial entity during actual equipment deployment. The sub-block area is set to range from 400 to 900 square meters. An area detection module calculates the area of ​​each sub-block. If the area is less than 400 square meters, it is merged into the adjacent sub-block with the longest shared boundary. If the area is greater than 900 square meters, it is preferentially divided into equal-distance sub-blocks according to the side length of the smallest bounding rectangle. The final result is a set of split sub-blocks that satisfy the spatial boundary constraints.

[0049] Preferably, step S2, which involves determining virtual deployment nodes using data from each block unit and performing deployment rehearsals based on the virtual deployment nodes and the set of sub-blocks, includes:

[0050] Based on the data of each block unit, available nodes are filtered to obtain candidate node data;

[0051] Virtual feature mapping is performed on the candidate node data to generate virtual feature data;

[0052] Virtual deployment nodes are established based on virtual feature data;

[0053] Node block matching is performed based on virtual node deployment and splitting sub-block sets to obtain node block adaptation data;

[0054] Deployment path simulation data is obtained by using node block adaptation data;

[0055] The deployment path simulation data is fused with multi-scenario pre-simulation data to generate deployment pre-simulation data.

[0056] In this embodiment, when filtering available nodes based on the data of each block unit, accessibility detection is first performed on each sub-block in the regional coordinate model. An obstacle determination matrix is ​​used to identify structural beams, columns, electrical conduits, and fixed equipment occupancy areas by comparing the entity model data exported from the Building Information Modeling (BIM) system. After eliminating overlapping spaces, the remaining area is spatially divided into grids. A square grid with a side length of 2 meters is used to divide the sub-block space, and a minimum node spacing of 5 meters is set. A set of original coordinates for candidate nodes is generated according to the center position of each grid. Then, the airflow model analysis interface is called to obtain historical ventilation data for the sub-blocks. Coordinates with wind speeds below 0.1 meters per second are filtered out, and the nodes are retained as preliminary candidate node data. When performing virtual feature mapping on the candidate node data, a three-dimensional feature vector is constructed, containing six basic parameters: spatial coordinate values, spatial permeability index, historical airflow turbulence coefficient, accessibility score, equipment interference probability, and environmental background noise value. Each parameter is normalized and concatenated into a 1×6 dimension node vector data. Principal component analysis is then used to perform the final analysis. Component Analysis (PCA) performs dimensionality reduction on the node vector set, compressing the six-dimensional data into projection points in a three-dimensional virtual feature space as virtual feature data. During the projection point generation stage, over 95% of the information variance is retained to ensure the distinguishability and accurate positioning of different nodes in the three-dimensional feature space. When establishing the virtual node deployment based on the virtual feature data, a density-based spatial clustering method is used. DBSCAN (withNoise) performs node clustering in the 3D feature space, setting a feature space distance threshold of 0.35 and a minimum clustering unit of 4 nodes. It extracts the center points of each cluster as representative virtual nodes and further calculates node stability weights based on the fluctuation range of environmental parameters within each cluster. Cluster center points with stability weights greater than 0.8 are designated as the final virtual nodes, and their coordinate lists are output, along with their respective feature vector indices. When performing node-block matching based on the virtual nodes and the set of sub-blocks, a matching algorithm based on minimum assignment cost is used. Each virtual node is bidirectionally mapped to a sub-block. The cost matrix is ​​constructed considering three indicators: the Euclidean distance between the virtual node and the sub-block centroid, the angle error between the node's direction and the prevailing wind direction, and the closest distance from the node's location to the sub-block boundary. The weights for each indicator are set to 0.5, 0.3, and 0, respectively.2. The Hungarian algorithm is used to extract the minimum matching result from the cost matrix, outputting the optimal sub-block number and adaptation cost corresponding to each deployment node, forming node block adaptation data. When performing deployment path deduction using the node block adaptation data, a deployment path diagram model is constructed based on the spatial relationship of each node block combination. The connection between nodes is based on the distribution of building obstacles in the BIM model and the shortest accessible path for the cables of the deployed equipment. The path planning uses the A* algorithm to construct the path priority. In the heuristic function, g is set as the geometric length of the path, and h is set as the straight-line distance between the node and the initial inlet of the air source multiplied by the obstacle coefficient. The obstacle coefficient is derived from the degree of area closure and limited to between 1.0 and 2.5. Finally, the set of shortest actual paths from the air supply source to each deployment node is generated, and the path is... Path length, number of direction changes, and number of obstacle avoidances are output as path evaluation indicators to form deployment path simulation data. When performing multi-scenario pre-simulation fusion of the deployment path simulation data, firstly, air supply environment data are constructed, including four typical air disturbance scenarios: morning peak, noon, low pedestrian traffic, and evening peak. Based on actual air disturbance sensor data from a large commercial building in a city throughout 2023, an air disturbance model is constructed. The simulated environment includes an environmental disturbance factor model with wind speed varying between 0.2 m / s and 0.8 m / s, and temperature and humidity combinations varying between 18-28 degrees Celsius and 30%-60% relative humidity. In each disturbance scenario, deployment path simulation data is loaded to simulate ionized airflow. The simulation engine uses an air supply particle simulation module based on the Lattice Boltzmann Method (LBM) to record the particle coverage and path interference rate of each node in each scenario. Finally, all scenario data are overlaid and fused to output deployment performance evaluation indicators for each node in each environmental disturbance scenario and a unified deployment pre-simulation dataset.

[0057] Preferably, step S2, which involves dynamically clustering spatial nodes based on deployment simulation data and performing redundancy removal based on the clustered spatial nodes, includes:

[0058] Spatial attribute injection processing is performed based on deployment simulation data to generate deployment spatial attribute data;

[0059] Node feature codes are obtained by performing multi-dimensional feature encoding on the spatial attribute data of the layout.

[0060] A dynamic clustering index is constructed based on node feature encoding;

[0061] Based on dynamic clustering index mapping of cluster space node data;

[0062] Node correlation degree screening is performed on the clustered spatial node data to obtain correlation degree data;

[0063] Redundant connections are identified based on correlation data, and redundant connection data is generated.

[0064] Redundant connection data is processed to remove redundancy, resulting in the removed data.

[0065] The data to be removed is mapped to an entity structure to generate a cell layout topology.

[0066] In this embodiment, after acquiring the deployment simulation data, spatial attribute injection is performed based on numerical indicators such as the three-dimensional coordinate position of each virtual deployment node in space, the simulation length of the path between nodes, the ion transport capacity of the path, the coverage radius of the node, the coverage overlap coefficient of the sub-block, and the connection strength between nodes. During the spatial injection stage, a spatial attribute fusion model based on coordinates and diffusion radius is used to construct a six-dimensional spatial attribute vector for all nodes. The model inputs are the node's three-dimensional coordinates X, Y, and Z, the simulation length of the deployment path L, the ion wind outlet dynamic pressure P, and the node coverage radius R. The model uses the attribute fusion function F(x) = [X,Y,Z,L,P,R] for unified dimension normalization. Linear normalization is used to stretch all values ​​to between 0 and 1, suppressing extreme value interference and maintaining relative gradients. All nodes are assigned this vector for subsequent feature analysis. After obtaining node data containing unified attribute vectors, multi-dimensional feature encoding of the nodes is performed using principal component analysis (PCA). Component Analysis performs dimensionality reduction on the six-dimensional attribute vectors, selecting the top three principal components with a cumulative explained variance exceeding 95% to form the final three-dimensional feature encoding vector. This dimensionality reduction operation uses a standard orthogonal transformation matrix to linearly map all vectors. The weight distribution of each principal component is determined by its eigenvalue λ. Feature compression is achieved through the dot product of the feature vector matrix and the original data matrix. The dimensionality reduction result retains the main variation structures in the node space and physical diffusion characteristics, used to construct a discriminative clustering model. After dimensionality reduction, a dynamic clustering index is constructed based on the three-dimensional feature vectors of all nodes. The Mini-Batch K-Means algorithm is used to perform dynamic clustering. The number of cluster centers K is automatically calculated using the elbow method. This method extracts 10 node feature samples and updates the centroids in each iteration, with a maximum of 100 iterations per round. It automatically terminates when the cluster center movement is less than 0.001. Euclidean distance is used as the distance metric between nodes during the calculation. All nodes are assigned to clusters, forming a preliminary cluster structure. Based on the clustering results, nodes are further grouped from the original deployment set according to Cluster. The nodes are grouped by ID, and the Euclidean distance d and the difference in ion air delivery path length ΔL between all pairs of nodes in each cluster are extracted. These two values ​​are then combined into a single correlation value using a weighted ratio, with the formula A = α·d + β·ΔL, where α = 0.6 and β = 0.4. The weights are derived from previous statistical experiments on node distribution errors under a 5×5 meter spatial grid, and the ratio is determined through the process of minimizing the error variance. Finally, the correlation index is calculated for each pair of nodes for redundancy judgment. Based on the correlation index A of all node pairs calculated in the previous step, the correlation threshold T is set to 1 of the experimental average.Five times, all node pairs with a correlation value less than T are identified as redundant connections because they do not exhibit high coupling in terms of spatial distance and path transmission. These node pairs are marked as redundant connections. This process is repeated sequentially in each cluster until all node pairs have been screened. For node pairs marked as redundant connections, a redundancy removal process is performed using a connection edge breaking operation. This removes the physical connection corresponding to the redundant connection from the ion wind path simulation model. Simultaneously, the set of direct adjacent nodes for each node is recalculated and the connection status is updated. Each time a removal operation is completed, it is immediately updated in the same... Under each cluster, the correlation of the remaining connections that were not removed is reassessed, and the above judgment is repeated until no new redundancy is added. After all redundant nodes in the clusters are removed, a cell layout topology is generated based on all removed and retained connections, and a node-connection graph structure is constructed. Starting from each node, a node-by-node scanning method is used to recursively traverse all its remaining connected nodes, and a topology graph is generated using a depth-first path approach. The connections between nodes are the final retained physical connection paths. The node locations are directly mapped into the 3D layout space based on the X, Y, and Z coordinates of the previously pre-designed spatial layout.

[0067] Preferably, step S3 involves performing a superimposed simulation of the ion air supply domain based on preset ion air supply data and unit layout topology, and analyzing the synergistic empowerment parameters of the domain based on the simulated superimposed domain, including:

[0068] The preset ion air supply data is decomposed into scope primitives to obtain scope primitive data.

[0069] Mapping the scope primitive data onto the cell layout topology generates mapped and fused data.

[0070] Simulate multiple ion delivery paths based on mapping and fusion data;

[0071] A superimposed action domain is constructed based on multiple ion delivery paths;

[0072] Divide the superimposed scope into multiple independent scope blocks;

[0073] Local scope boundaries are identified for independent scope blocks to obtain the scope block boundaries;

[0074] Interaction node data is generated by mining interaction nodes based on the boundaries of the scope blocks.

[0075] The scope-based primitive data is used to inject collaborative empowerment parameters into the data of the interactive nodes to obtain the scope collaborative empowerment parameters.

[0076] In this embodiment, the operation of decomposing the preset ion air supply data into domain primitives is based on an ion density distribution model. First, static air is ionized using a high-density corona electrode assembly under a fixed voltage. Ion concentration data are collected at air supply distances of 1 meter, 2 meters, 3 meters, and 4 meters, with the unit being ions / cm³. 3Five detection angles were set at each distance point: 0°, 30°, 60°, 90°, and 120°, resulting in 20 sets of ion-driven airflow intensity data. This ion data was then discretized based on a spatial gradient function. Each domain primitive was set to a volume of 10cm × 10cm × 10cm. The spatial gradient variation of the airflow field intensity was processed using a three-dimensional rasterization. The maximum ion density of each raster cell was used as the representative value of that domain primitive, generating a domain primitive dataset containing the intensity, direction vector, and attenuation coefficient. All sampled primitive data were recorded in vector form, showing their ion direction, density, and local turbulence interference parameters. The mapping of the domain primitive data to the cell layout topology was performed using a node-vector matching mechanism. First, data from the layout topology... The spatial coordinates and orientation vectors of all ion generator nodes are extracted. The Euclidean distance between the center point of each primitive in the domain and the nearest deployed node is calculated, with a distance threshold of 15cm. If the distance between the primitive center and the deployed node is less than this threshold, the primitive is considered to be affected by that node, and the node number is recorded in the primitive attribute. After completing one round of initial mapping, further analysis of the direction vector angle is performed. If the angle between the direction vector of the deployed node and the primitive's action vector is less than 30°, it is considered an efficient mapping; otherwise, it is marked as an inefficient mapping. Based on this, mapping fusion data is generated, recording the mapping set and efficiency level of each node to multiple primitives. The operation of multiple ion delivery paths is simulated based on the mapping fusion data using a particle tracking algorithm, employing an improved Monte Carlo method. The Carlo particle motion model predicts the ion emission path for each node. The initial ion emission velocity is set to 2.4 m / s. The airflow vector field is constructed into a three-dimensional velocity vector matrix based on the flow field data obtained from the experimental wind tunnel. The simulated particles are emitted with a unit charge in the initial vector direction, and the trajectory is corrected by superimposing the local wind speed vector and the spatial turbulence disturbance function. Each simulated particle is at 0.Within a 0.1s time step, path changes are calculated, simulating 300 particle motion paths per node. The primitive numbers and path lengths traversed by particles on all paths are recorded, resulting in a complete set of ion airflow paths. This forms a mapping matrix between ion paths and interacting primitives. The operation of constructing superimposed domains based on multiple ion airflow paths is performed using a spatial overlap superposition strategy. First, the primitive numbers on all paths are counted, and the number of times each primitive is covered by a path is counted. A superposition threshold of 2 is set; if a primitive is covered by two or more paths simultaneously, path superposition is considered to have occurred. Based on this, a three-dimensional Boolean volume matrix is ​​constructed, encoding each superposition region as... Connecting regions and calculating their center point, boundary voxels, and path contributions generates ion superposition domain model data. The data records the boundary coordinates, path number set, and path overlap density of each connected superposition region. The operation of dividing the superposition domain into multiple independent domain blocks relies on a density-based hierarchical segmentation algorithm. First, based on the path overlap density distribution in the superposition region, the region is split according to the direction of the maximum density gradient. The maximum overlap density difference within each block is set to not exceed 25%. The three-dimensional mean drift method is used for spatial clustering to obtain multiple spatially uniform action blocks. The structural information of each action block is recorded with the center voxel coordinates, spatial dimensions, and bounding box. During the processing, it is ensured that the minimum volume of each action block is not less than 1000 cm³. 3The operation of identifying local scope boundaries for independent scope blocks is implemented through a boundary gradient detector. A 3×3×3 voxel window convolution operation is performed on the bounding box of each block to calculate the gradient intensity and direction of the boundary voxels. Voxels with boundary gradient values ​​higher than a set threshold of 0.5 are marked as boundaries. These marked boundaries are clustered based on directional consistency, forming polygonal boundary surfaces for each cluster. The normal vector, rate of curvature change, and boundary distribution density of the space containing these boundary surfaces are recorded to constitute scope block boundary data. The operation of mining interactive nodes based on scope block boundaries is implemented through boundary overlap calculation. All scope block boundary surfaces are spatially compared. If the distance between two boundary surfaces is less than 10cm and the angle between their normal vectors is less than 20°, they are considered to have boundary interaction. The center coordinates of the overlapping area are extracted from the interaction region as candidate points for interactive nodes. A set of adjacent paths is constructed for each candidate point. Points with more than 2 paths are defined as... For each interactive node, a structured record is created using the interaction point ID, coordinates, number of adjacent paths, and adjacent action block identifier. The operation of injecting collaborative empowerment parameters into the interactive node data through domain primitive data employs an attribute mapping and function fusion processing mechanism. For each interactive node, the average direction vector density, action duration interval, and path penetration level are extracted from the associated action primitive data in its adjacent path set. These three sets of parameters are mapped to collaborative empowerment factors A, B, and C. A represents ion direction consistency, calculated as the average cosine of the angle between the directions of the action vectors in the adjacent path; B represents action duration, taken as the ratio of the maximum to minimum action duration in the path; and C represents path penetration level, defined as the ratio of the number of penetrating grids in the action path to the total path length. Finally, the three factors A, B, and C are fused into a collaborative empowerment parameter vector, which is used to label the corresponding interactive node, forming the domain collaborative empowerment parameter dataset.

[0077] Of particular importance is the division of the overlay scope into multiple independent scope blocks, including:

[0078] Analyze the node density distribution within the superimposed domain;

[0079] Extract the superposition boundary region of the superposition domain based on the node action density distribution;

[0080] The regions are segmented based on the overlapping boundary areas to obtain the segmented region data;

[0081] Identify local peak overlap nodes based on data from the cut region;

[0082] The attribution determination data is determined based on the local peak overlap nodes and the data of the cut area;

[0083] The superimposed scope is divided into independent scope regions by using the attribution determination data, and multiple independent scope block structures are output, thereby generating independent scope blocks.

[0084] In this embodiment, when specifically implementing the operation of dividing the superimposed domain into multiple independent domain blocks in the air purification method based on ion air delivery technology, it is first necessary to perform statistical analysis on the node density distribution within the superimposed domain. Specifically, the superimposed area is traversed in a three-dimensional spatial grid in units of 5cm×5cm×5cm voxels, and the number of ion path nodes at the center coordinates of each voxel is counted. The statistical results are stored as a three-dimensional node density matrix. The node density is represented by the number of path intersection points contained in each voxel, and the node density value ranges from 1 to an upper limit of 25. Then, based on this density matrix, an isodense cross-sectional volume dataset is constructed, and local density gradient direction is used as a vector to generate local density cross-sections. An isosurface volume is generated, outputting a node density distribution model and recording the spatial density value, density change rate, and directional gradient information of each voxel. Based on the obtained node density distribution, the overlapping boundary regions are extracted. The process involves inputting the node density matrix into a 3D edge detection operator and using a Laplacian-Gaussian edge extraction method. Regions with node density changes exceeding a set threshold are labeled. The threshold is set to a density change of at least 4 nodes per centimeter. All voxels meeting this condition are recorded as candidate boundary regions. Then, cluster analysis is performed on the candidate boundary voxels, and a 3D mean shift algorithm is used to separate adjacent boundary regions. Boundary points are merged and clustered to form multiple closed boundary block sets. The outermost boundary point sequence of each set constitutes the superimposed boundary region data. All boundary regions are recorded in a structured manner, including their 3D boundary surfaces, areas, maximum density change rates, and center coordinates. In the region segmentation operation based on the superimposed boundary regions, the boundary surface data of all boundary regions are first read, dividing the 3D space into multiple closed surface-enclosed regions. The boundary of each region is composed of multiple interfaces. Based on the principle of spatial voxel connectivity, starting from each high-density node not within a boundary region, a six-neighbor voxel filling expansion is performed until a boundary voxel is encountered. All filled regions are marked as a cutting block. After each filling operation is completed... A closed cutting region is formed by filling in the gaps. This process is repeated for all starting points until all spatial voxels are covered, resulting in a cutting region dataset. The boundary point sequence, region volume, average node density, number of path intersections, and shortest path length to the boundary are recorded for each cutting region. After obtaining the cutting region data, local peak overlap nodes within each region are identified. The process involves extracting the density values ​​of all internal path nodes in each cutting region, sorting them from largest to smallest, and selecting the top 5% of high-density nodes as peak candidate points. Then, the density difference between adjacent nodes is detected for each candidate point. If the average density value of the six neighboring voxels around a point is less than 80% of the density of the current point, that point is identified as a local peak overlap node.All nodes meeting the criteria are individually labeled and their coordinates, density values, number of adjacent paths, and the cut region number are recorded to form a set of locally overlapping peak nodes. The operation of determining the attribution data based on the locally overlapping peak nodes and cut region data is performed using a spatial weighted aggregation model. First, the voxels in the cut region where each locally overlapping peak node is located are numbered and indexed. The spatial Euclidean distance from all voxels in that region to the peak node is calculated. Using the reciprocal of the distance as a weighting factor, the attribution probability of all voxels in the entire region is calculated according to this weight. If the attribution probability of a voxel to multiple peak nodes differs by no more than 10%, it is set as a fuzzy attribution voxel and the fuzzy boundary label is recorded. Finally, each voxel is labeled with its optimal attribution peak node number, and... The attribution determination data includes voxel numbers, optimal peak numbers, attribution confidence levels, and fuzzy labeling states. The operation of dividing the overlay scope into independent action regions using this data is performed via a clustering pruning mechanism. After reading the attribution determination results for all voxels, connectivity analysis is conducted on voxel sets with the same attribution number. A breadth-first search algorithm is used to label all connected voxel groups with the same attribution, with each group forming an independent action block. Then, fuzzy-attributed voxels undergo nearest-neighbor assignment, being assigned to the nearest high-confidence region number. For each independent action block, its voxel set, boundary coordinate range, internal average density, number of action paths, and maximum internal path penetration depth are recorded, ultimately generating multiple independent action block structures.

[0085] Preferably, the real-time ion transport monitoring based on the domain synergistic empowerment parameters in step S3 includes:

[0086] Multi-dimensional parameter tuning is performed based on the scope-based collaborative empowerment parameters to generate tuning parameters, where the collaborative dimension is controlled between 3 and 7 dimensions, and the empowerment weight value ranges from 0.2 to 0.85.

[0087] Ion air supply simulation is performed based on the optimized parameters to generate simulated optimized ion air supply conditions.

[0088] Real-time response monitoring of the scope collaborative empowerment parameters is performed to generate real-time response monitoring data, with a monitoring period of 10 seconds and a response latency controlled within 200ms.

[0089] Ion monitoring data was filtered based on real-time response monitoring data, with only ion concentration values ​​whose data drift was within ±5% retained.

[0090] In this embodiment, the synergistic empowerment parameters of the action domain are optimized in multiple dimensions. Specifically, the synergistic dimension range for optimization is first determined to be 3-7 dimensions. Through detailed analysis of the parameter space of each dimension, the key factors affecting the ion air delivery effect are selected as the optimization dimensions. Each dimension of the optimization parameters is adjusted by setting a certain initial value and step size. The initial empowerment weight value is set to 0.5, and the empowerment weight value ranges from 0.2 to 0.85. Based on the optimization of different dimensions, the contribution of each dimension is calculated and adjusted. During the optimization process, an incremental optimization method is adopted. The new values ​​after each adjustment are verified experimentally and the system response effect is calculated. Finally, a set of optimized optimization parameters is obtained. The generated optimization parameters will be recorded in the optimization parameter dataset, which includes the optimization step size, optimal value, and corresponding system response data for each dimension. After obtaining the optimization parameters, ion air supply simulation is carried out based on the optimization parameters. During the simulation, a numerical calculation model is used to input the optimization parameters to simulate the ion air supply process. The ion air supply model simulates factors such as ion diffusion, collision, and reaction in space. The simulation calculates and outputs information such as ion concentration distribution, wind speed changes, and air volume in each area. Optimization parameters control the operating mode, power, and output frequency of each ion generator in the air supply system. The simulation process adjusts the operating status of the air supply system through multi-dimensional data input, adjusting parameters such as actual ion concentration and wind speed to ensure the simulation results match the actual operating conditions. After the simulation, an optimized ion air supply status report is generated based on the simulation data. The report records information such as simulated wind speed, ion concentration, and air supply path, and performs real-time adjustments to the ion air supply equipment based on the simulation results. Based on the optimization data from the ion air supply simulation, the parameters for domain-specific collaborative empowerment are implemented. Real-time response monitoring is implemented. During operation, monitoring equipment tracks changes in ion concentration within the air supply area in real time. The monitoring system measures the ion concentration every 10 seconds and compares each measurement with the previous one to calculate the magnitude of the change. The system's response delay is controlled to not exceed 200 milliseconds. All monitoring data is recorded in the response monitoring database. Response data for each monitoring cycle includes ion concentration, response time, and system stability. The monitoring system utilizes high-frequency sampling technology to ensure the real-time accuracy of ion concentration measurements. Data is processed using time-series analysis methods to track the immediate performance changes of the ion supply system. The ion monitoring data is filtered based on real-time response monitoring data. First, all ion concentration values ​​in the real-time monitoring data are sampled. The filtering condition is to retain only ion concentration values ​​with a drift range within ±5%. Specifically, for all data points within the monitoring period, their relative change range is calculated. If the ion concentration change range of a data point exceeds 5%, it is marked as abnormal data and discarded. Ion concentration data within the fluctuation range are retained, stored, and further processed. The filtered data is used for fine-tuning of the ion air supply system to ensure the stability and efficiency of the air supply system during operation. The filtering results are saved to the ion monitoring data storage system.

[0091] Preferably, step S4, which involves tracing the diffusion path based on ion monitoring data and correcting the diffusion blind zone based on the diffusion path, includes:

[0092] Construct a path node sequence based on ion monitoring data;

[0093] The path node sequence is reorganized into diffusion trajectory data to generate diffusion trajectory data.

[0094] Spatial folding and aggregation of diffusion trajectory data are performed to generate folded and aggregated trajectory data;

[0095] Ion diffusion path coverage is determined based on folded aggregation trajectory data;

[0096] The ion diffusion blind zone is obtained by projecting the reverse concealed domain through the coverage of the ion diffusion path and identifying the blind zone based on the concealed domain.

[0097] The ion diffusion blind region is profiled and then the diffusion blind region is corrected based on the profile of the blind region to generate blind region correction data.

[0098] In this embodiment, during the process of tracing diffusion paths based on ion monitoring data and correcting diffusion blind zones based on the diffusion paths, it is first necessary to construct a path node sequence based on the ion monitoring data. Specifically, a three-dimensional ion concentration detection array is used to collect the ion concentration value at each coordinate point in the space. Each sampling unit is set as a 50 mm × 50 mm × 50 mm spatial cube, and the ion concentration at each time point is recorded to be greater than or equal to 10. 3 ions / cm 3The cell coordinates are used to link adjacent cells whose concentration remains above a certain threshold at consecutive time points, generating a directed graph-structured path node sequence. Each node contains three-dimensional coordinates, a timestamp, an ion concentration value, and the index information of its adjacent nodes. The path relationships between nodes are established according to the rule that the spatial distance is less than or equal to 150 mm and the time interval is no more than 2 seconds. All constructed path nodes are uniformly stored in the path node sequence database. After constructing the path node sequence, the path node sequence is reconstructed using diffusion trajectories. A time-series-based trajectory backtracking algorithm is used to reverse the aggregation of high-concentration ion sequences appearing in the path in chronological order, calculate the ion concentration change trend in each path segment, delete low-concentration fluctuating paths by setting a threshold, retain the main path sequence, and use a dynamic time warping algorithm. Time Warping (Dynamic Time Warping) aligns paths, merges similar trajectories, and ultimately outputs diffusion trajectory data. This trajectory data includes the time series, spatial coordinate series, concentration peaks, and total path length for each path. The output trajectory data serves as the input data for the next step of spatial aggregation. In the specific operation of spatial folding and aggregation of the diffusion trajectory data, voxel aggregation is used to remap all trajectory paths to a unified 3D spatial mesh. The voxel unit side length is set to 100 mm. The spatial coordinates of all trajectory points are rounded down and mapped to the voxel coordinate system. The occurrence frequency of trajectory points in each voxel is counted, and this frequency is used as the voxel density value. The density value is then smoothed using Gaussian blur to eliminate sharp abrupt changes and obtain a more continuous aggregation pattern. The aggregation result generates spatially folded aggregated trajectory data according to the trajectory density value corresponding to each voxel. The data structure includes three dimensions. After obtaining the folded aggregation trajectory data, information such as voxel index, aggregation density value, and corresponding time series average and peak values ​​are used to determine the ion diffusion path coverage. Specifically, the entire purification space is divided into a three-dimensional spatial grid, with each grid cell having a side length of 100 mm. For each grid cell, the number of times it is traversed by at least one folded trajectory during the monitoring period is calculated, and the coverage value is obtained by dividing the number of traversals by the total number of time steps in the monitoring cycle. The coverage value ranges from 0 to 1, representing the degree to which diffusion in that spatial area is covered by the trajectory during the ion air delivery process. Areas with a coverage value less than 0.15 are marked as low-coverage areas. All coverage data is written into a coverage matrix file, with each matrix element corresponding to the ion diffusion coverage of a spatial cell. Based on the coverage matrix, a reverse concealment domain projection is performed to identify ion diffusion blind spots. A spatial inversion method is used to identify blind spots with a coverage value less than 0.15.Spatial clustering was performed on 15 low-coverage units, with a spatial connectivity threshold of 200 mm. DBSCAN (density clustering algorithm) was used to cluster the low-coverage units, and each clustered spatial cluster represents a potential diffusion blind zone. Through inverse trajectory projection, spatial regions not reached by the current diffusion trajectory but theoretically accessible by a path were marked as hidden regions. Intersection calculations were then performed with the clustered blind zone regions to obtain the location data of the actual diffusion blind zone. The blind zone coordinates were uniformly saved as a blind zone point cloud set, where each point contains its 3D coordinates, cluster number, and coverage value. After obtaining the blind zone point cloud set, the ion diffusion blind zone was contoured. 3D morphological processing was used for boundary extraction and contour subdivision. A dilation operation was used to expand the envelope of the blind zone voxel region, and then a morphological boundary detection algorithm was used to extract its 3D boundary. Next, a boundary subdivision algorithm based on normal vector estimation was used to equally divide the boundary region. Each sub-contour block recorded its vertices in the form of a polygon mesh. The process involves indexing the surfaces and calculating the area and volume of each sub-contour for subsequent correction analysis. Contour data is stored in the blind zone contour structure array. After contour splitting, the specific process of diffusion blind zone correction based on the split contours involves re-establishing the ion path simulation of the uncovered area using the ion jet modeling method. The ion jet source is set near the boundary of the blind zone, and the jet intensity is calculated based on the ion concentration value at the boundary point. The simulation simulates the path of ions diffusing into the blind zone under the current airflow conditions. The airflow direction field is introduced into the path simulation, and the wind speed value is taken from the previously established air supply simulation model. After the simulation, the generated new diffusion path is merged with the original diffusion trajectory, and the coverage matrix is ​​recalculated. If the coverage in the updated blind zone area increases to greater than 0.3, the blind zone correction is considered successful. All corrected data is stored in the blind zone correction dataset, which includes the path trajectory before and after correction, the change in coverage value, the jet intensity parameter, and the new path coordinate point sequence. All corrected data is used as input for downstream purification control strategies.

[0099] Of particular importance is the determination of ion diffusion path coverage based on folded polymerization trajectory data, including:

[0100] Extract unstructured folded trajectories from folded aggregated trajectory data;

[0101] Unstructured folding trajectories are mapped onto grid cells to obtain a diffused grid distribution;

[0102] Weights are assigned to the diffusion grid distribution based on the folded aggregate trajectory data, thus obtaining the weighted data.

[0103] Coverage quantification is performed based on weighted data to generate ion diffusion path coverage.

[0104] In this embodiment, the process of determining ion diffusion path coverage based on folded aggregation trajectory data first requires extracting unstructured folded trajectories from the folded aggregation trajectory data. Unstructured folded trajectories refer to the set of trajectory points that have not undergone spatial regularization grid division during trajectory reconstruction. Specifically, trajectory point data without corresponding voxel numbers are screened from the aggregation trajectory database. The extraction conditions include: the spatial coordinates (x, y, z) of the trajectory point have not been rounded down to the nearest integer; they do not match the aggregation voxel index; the trajectory point time series is continuous; and the concentration value is greater than 1000 ions / cm. 3 The set of trajectory points is composed of triples (x, y, z) + t and uniformly stored in an unstructured trajectory buffer. Once the number of data points in the buffer reaches 10,000, the subsequent spatial mapping module is automatically triggered. The operation of mapping the unstructured folded trajectories to grid cells is completed using a 3D regular raster projection algorithm. A uniform grid cell size of 100 mm × 100 mm × 100 mm is set. The coordinate value of each unstructured trajectory point is divided by the grid size and rounded down to obtain its corresponding grid index value (i, j, k). This index value is the grid cell number to which the trajectory point belongs. Trajectory points with the same index are grouped together to form a trajectory cluster within that grid cell. All trajectory cluster numbers constitute a diffusion grid distribution data structure. Each grid cell records the number of trajectory points it contains, the average concentration value of the points, and the maximum concentration value. Concentration values, time span information, and source trajectory numbers are mapped and a sparse matrix structure is constructed to save storage space. All data is written into the diffusion grid mapping table. After obtaining the diffusion grid distribution, a composite weight calculation method based on ion concentration and path residence time is used to assign a diffusion weight value to each grid cell during the weight allocation process based on the folded aggregated trajectory data. Specifically, the calculation method involves traversing the trajectory points in each grid cell, calculating the local residence time of each point (calculated as the average time difference between the trajectory point and its preceding and following time points, in seconds), and then multiplying the ion concentration value of the point by the residence time to obtain the local contribution value of the trajectory point. The local contribution values ​​of all trajectory points are summed in the grid cell to obtain the total diffusion weight value of the grid cell. The weight value is in ions × seconds / cm. 3The diffusion weight values ​​of all grids constitute an assigned weight data structure. This structure records the spatial index, weight value, peak concentration, average residence time, and number of trajectory points for each grid cell. After obtaining the assigned weight data, coverage quantification is performed based on this data. The operation of generating ion diffusion path coverage is implemented through a three-dimensional region normalization algorithm. First, the sum of the weight values ​​of all grids in the entire purification area is calculated and set as W_total. Then, the weight value W_cell of each grid cell is normalized, and the coverage value C_cell = W_cell / W_total of that cell is calculated. The coverage value is a decimal between 0 and 1, representing the relative diffusion degree of the area during the ion air delivery process. Grids with coverage values ​​less than 0.05 are marked as low coverage areas, and areas with coverage values ​​greater than 0.3 are marked as high coverage areas. The coverage values ​​of all grid cells form a coverage matrix structure. This structure contains the spatial grid index and its corresponding normalized diffusion coverage value. The coverage matrix is ​​stored in a sparse structure and output in binary format for subsequent blind zone identification module calls and visualization analysis operations.

[0105] Preferably, step S4, which involves dynamically coupling heterogeneous response units based on blind zone correction data and migrating the purification intensity boundary based on the coupled heterogeneous response units, includes:

[0106] The blind zone correction data is processed by heterogeneous clustering of response units, and the clustering index data is extracted.

[0107] Project clustered index data into a polymorphic cell matrix;

[0108] Response unit sequence concatenation is performed based on polymorphic unit matrix and blind zone correction data to obtain concatenated sequence data;

[0109] Boundary migration simulation is performed by combining sequence data, thereby generating multiple boundary migration data sets;

[0110] Determine the boundary restriction area based on boundary migration data;

[0111] The purification intensity threshold data is reconstructed based on the boundary restriction area, and the purification intensity is optimized based on the reconstructed purification intensity threshold data, thereby obtaining the adjusted purification intensity.

[0112] In this embodiment, structured spatial reconstruction technology is used to normalize the blind zone correction data from the air purifier's built-in sensor array. This data comes from three sources: ion concentration sensors, electric field strength sensors, and particle detectors, sampled at frequencies of 1Hz, 2Hz, and 0.5Hz respectively. The sampling area covers a 50mm × 50mm grid area within the purification cavity. The acquired data is then rescaled between 0 and 1 using a minimum-maximum normalization method to construct an original feature matrix of dimension [m × n], where m is the number of sampling points and n is the number of multidimensional features. Then, a local density peak discrimination algorithm (Density-Peak) is used... Clustering is used to cluster the feature matrix, setting the cutoff distance dc to 0.15 and the weight adjustment coefficient λ to 0.85. After clustering, the index information corresponding to each group of response units is stored in a HashMap structure, with the key being the cluster center index and the values ​​being the numbers of all members within the cluster, thus forming complete clustered index data. During the projection of the clustered index data into a polymorphic unit matrix, the response unit index corresponding to each cluster is mapped to a state vector of length n, with each dimension corresponding to a feature dimension in the original corrected data. All state vectors are combined to construct a three-dimensional polymorphic unit matrix, where the first dimension represents the cluster number, the second dimension represents the response unit number, and the third dimension represents the feature channel number. This matrix is ​​stored using a Compressed Sparse format. The Row format (compressed sparse row format) avoids null value interference caused by missing feature dimensions during construction. A default padding value of 0.01 is set for each dimension without collected data and marked as virtual padding. During the response unit sequence concatenation based on the polymorphic unit matrix and blind zone correction data, the state vector sequence of each polymorphic unit within each cluster is first extracted and time-aligned. Specifically, the state vector of each response unit is sorted in ascending order by time label. A Savitzky-Golay filter is used to denoise each sequence, with a window length of 11 and a polynomial order of 3. After denoising, each sequence is concatenated using a sequence concatenation algorithm. This algorithm is based on the Smith-Waterman local sequence alignment strategy for locally optimal alignment, with an insertion cost of 0.15, a deletion cost of 0.20, and a matching reward of 1.00. The set of sequence data generated by the concatenation operation is called the concatenated sequence data. Its length is not fixed and depends on the number of valid state points in each response unit. In the boundary migration simulation using the concatenated sequence data, the concatenated sequence is first mapped to the physical coordinate system in the three-dimensional cleanroom space. A continuous path distribution model is constructed using the third-order B-spline interpolation method. The spatial position of each concatenation point is obtained by back-calculation using the mesh positioning information of the original response unit. The control point interval during the interpolation process is set to 25 mm, and the number of interpolation points is automatically allocated according to the path length. After the spatial model is constructed, the Lattice Boltzmann algorithm is used... The Lattice Boltzmann Method was used to numerically simulate the migration of ion currents under the control of concatenated paths. The simulation environment was set with an initial ion velocity of 2.4 m / s, a simulation duration of 120 s, and an output interval of 1 s. The boundary point positions recorded at each time point were combined into a set of boundary migration data. Each data point included X, Y, and Z coordinates and the instantaneous ion concentration. In determining the boundary confinement area based on the boundary migration data, trajectory density analysis was first performed on all boundary migration data. The cleanroom space was divided into 100 mm × 100 mm × 100 mm cubic regions, and the number of boundary occurrences within each cube was counted. A density threshold was then set. The value is the global average plus 1.5 times the standard deviation. All cubic regions exceeding this density threshold are marked as boundary restriction regions. The spatial number and coordinate information of this region are stored as a three-dimensional bitmap structure. The region status label within each voxel is recorded as 1 or 0, and a maximum ion concentration value field is attached for subsequent purification intensity control adjustment. In the process of reconstructing the purification intensity threshold data based on the boundary restriction region and optimizing the purification intensity based on the reconstructed purification intensity threshold data, the boundary restriction region bitmap structure is first read, and a purification intensity adjustment coefficient matrix is ​​generated according to the voxel position. The adjustment strategy is that if the maximum ion concentration in each voxel is less than the set target value of 2.0 × 10, the maximum ion concentration is less than the set target value of 2.0 × 10. 6 ion / cm 3 If the enhancement coefficient is 1.25, then the enhancement coefficient is set to 1.0; otherwise, it is set to 1.0. The coefficients of all voxels are combined to form a purification intensity threshold matrix. This matrix is ​​then input into the purification control unit, which is a control platform based on the STM32F767ZIT6 chip with a control frequency of 200Hz. The voltage of the ion wind output electrode is controlled by a PWM signal with a frequency of 25kHz and a duty cycle adjustment range of 10% to 90%. Ultimately, the electrode voltage corresponding to the spatial point located in the boundary restriction area is increased by 20% to 30% to respond to the purification intensity adjustment target required for dynamic boundary migration, thereby generating purification intensity adjustment data and completing the optimization process.

[0113] Preferably, step S5 includes the following steps:

[0114] Step S51: Map the adjusted purification intensity data to purification blocks to obtain block mapping data; inject parameter perturbation into the block mapping data to obtain perturbation injection data;

[0115] Step S52: Perform time-series response deduction based on the perturbation injection data to generate response deduction data;

[0116] Step S53: Locate the mining nodes based on the response simulation data to obtain mining node data; archive the mining node data as feedback signals to obtain feedback mining data;

[0117] Step S54: Apply threshold adaptation filtering to the feedback data to generate threshold adaptation data; reconstruct the coordination matrix based on the threshold adaptation data to obtain coordination control data for air purification coordination control.

[0118] In this embodiment, based on the physical grid structure of the internal space of the purification device, a correspondence is established between the spatial coordinates in the adjusted purification intensity data and the physical area where the ion air outlet is located. The space is divided using a regular cubic grid of 200mm×200mm×200mm, with each cubic block serving as an independent purification block unit. Each block is assigned a unique number and mapped to a set of index vectors. These index vectors contain the block's spatial number, initial ion concentration, adjusted ion intensity target value, and historical purification frequency parameters. The block mapping data formed after mapping is stored in a structured array format, with each record occupying [a certain number of records]. The 32-byte block mapping data is then subjected to parameter perturbation injection. The perturbation injection is constructed using a Gaussian perturbation superposition method, with the perturbation parameters fluctuating within ±7% of the original adjusted value. The perturbation superposition ratio is 0.4, and the perturbation source is a pseudo-random generator with a fixed seed value of 314159 to ensure experimental reproducibility. The purification intensity after each perturbation is written into the original index structure to form perturbation injection data, and a perturbation level field is marked for subsequent time-series processing. During the time-series response extrapolation based on the perturbation injection data, a time-series extrapolation model is first constructed. This model is based on a Long Short-Term Memory (LSTM) network. The network uses a memory structure, with input consisting of a perturbation parameter sequence for each purification block in the perturbation injection data and its corresponding historical purification state sequence. Each sequence is 60 steps long, corresponding to the purification change process within 60 seconds. The network structure includes three LSTM layers, each with 128 hidden units, and the activation function is Tanh. The output is connected to a fully connected layer and outputs the purification response intensity value. The training data comes from a 300-hour dataset of ion output and particle concentration collected during the actual operation of the purification chamber. The training loss function is mean squared error (MSE), the optimizer is Adam, the learning rate is set to 0.001, and the training iterations are 2000 rounds. After the model outputs, the predicted value of each perturbation sequence is subtracted from the state before the perturbation to generate purification response projection data. The projection results for each block are output in time series form, including the predicted ion concentration intensity change and the spatial state change flag value of the purification area per second. During the process of locking back sampling nodes based on the response projection data, the response surge point is first extracted from the response projection data sequence of each purification block. The response surge point refers to the purification response intensity growth rate exceeding 0 per unit time.At time 18, a sliding window method was used to traverse each projection sequence. The window width was set to 5 seconds and the movement step size was 1 second. For each surge point that met the conditions, its corresponding timestamp and spatial index were recorded as candidate sampling nodes. Then, combined with the disturbance level field, a joint screening was performed, and only sampling points with disturbance amplitude greater than 5% were retained as the final locked nodes, generating sampling node data. Each node record includes four fields: spatial location number, disturbance intensity, response enhancement rate, and time label. Feedback signal archiving was performed on the sampling node data. The feedback signal was extracted from a particle sampling detection probe installed on the wall of the purification chamber. The probe has a sampling frequency of 0.5Hz, a sampling aperture of 30mm, and the detection signal includes PM2.5 particle count values. Along with ion decay rate information, feedback data is transmitted to the control host via CAN bus. The data from the data acquisition nodes is matched with the actual detection data based on timestamps, and the matched data is written into an archive table to form feedback data acquisition. The archive structure uses CSV format, with each row recording all feedback fields for one node, arranged by spatial number and accompanied by a disturbance level marker. During the threshold adaptation and filtering process for the feedback data acquisition, all feedback data acquisition data undergoes field normalization processing. The data structure includes five key fields: purification block number, disturbance injection intensity value, acquired particle concentration decay rate, feedback ion response index, and purification response time delay flag value. A static threshold of 2.2 × 10⁻⁶ is set for the feedback ion response index. 6 ion / cm 3A dynamic range threshold is set for the particle concentration decay rate. This threshold is based on the average basic particle concentration in the current region, and the expected decay ratio is calculated with a weighted lower limit of 0.16. In all collected data records, if the particle concentration decay rate is lower than this expected threshold or the feedback ion response index is lower than the static threshold, the record is marked as "L" (Low Level, inefficient response level) in the data label and excluded from the valid feedback set. Furthermore, if the purification response time delay flag value is greater than 120s, it is also marked as an invalid node exceeding the limit and is not adopted. The above filtering operations are performed under the GPU acceleration engine, using a CUDA parallel structure to execute the batch filtering process, outputting a set of feedback records that meet the requirements and numbering them as threshold-adapted data. All filtered field records are stored in the data processing log for subsequent comparison and tracking. This filtered data is used as the input source for subsequent coordination matrix reconstruction to determine the matrix dimensions. The row dimension represents the purification block number, and the column dimension contains four types of response fields: perturbation injection intensity, standardized feedback ion concentration, standardized particle concentration decay rate, and purification response time delay score. The dynamic injection intensity field uses the raw value of the actual perturbation. The standardized feedback ion concentration field is Z-score normalized based on the mean and standard deviation of all nodes in the current region. The standardized particle concentration decay rate is normalized after logarithmic transformation. The purification response time delay score is processed by constructing a normalized time loss function, defined as a standard score between 0 and 1, with lower values ​​indicating faster response. This processing structure is implemented in PyTorch tensor structures. After each block of data is synthesized into tensor entries, it is filled into the corresponding matrix rows. After completing the preliminary matrix, covariance analysis is performed to remove data with a negative correlation coefficient higher than -0.45 with the overall response direction. Then, principal component analysis (PCA) is used. ComponentAnalysis performs dimensionality reduction on the matrix, retaining the first three principal components to explain over 95% of the variance. The resulting principal component vectors are combined to form a purification coordination control matrix. This matrix is ​​named "Coordination Control Data" and exported as a binary HDF5 file. This file is called by the embedded control unit for coordinated scheduling control of the ion air supply equipment group. All parameters are matched and verified against the spatial purification classification strategy table set by the main control unit to ensure regional adaptability and response coverage of the coordination data. The control logic dynamically updates the matrix with a minimum cycle of 0.2 seconds and continuously loads the updated matrix content to adjust the ion output power and directional channel matrix arrangement in real time.

[0119] The present invention also provides an air purification system based on ion delivery technology for performing the air purification method based on ion delivery technology as described above. The air purification system based on ion delivery technology includes:

[0120] The spatial modeling module is used to acquire data on the target air purification area and each block unit; reconstruct the target area spatial model based on the target air purification area; and divide the target area spatial model into independent blocks based on the data of each block unit, thereby obtaining a set of sub-blocks.

[0121] The topology deployment module is used to determine virtual deployment nodes through the data of each block unit, and to perform deployment simulation based on the virtual deployment nodes and the set of split sub-blocks to generate deployment simulation data; based on the deployment simulation data, the spatial nodes are dynamically clustered, and redundancy removal is performed based on the clustered spatial nodes to obtain the unit deployment topology structure.

[0122] The domain monitoring module is used to simulate the superposition of ion air supply domains based on preset ion air supply data and unit layout topology, and analyze the domain synergistic empowerment parameters based on the simulated superposition domains; and to perform real-time ion transport monitoring based on the domain synergistic empowerment parameters, thereby obtaining ion monitoring data.

[0123] The diffusion blind zone correction module is used to track the diffusion path based on ion monitoring data and correct the diffusion blind zone based on the diffusion path to generate blind zone correction data; based on the blind zone correction data, the heterogeneous response unit is dynamically coupled, and the purification intensity boundary is migrated based on the coupled heterogeneous response unit to obtain the adjusted purification intensity.

[0124] The purification coordination module is used to simulate the adjustment of the target air purification area based on the adjustment of purification intensity and to collect feedback data; it performs air purification coordination control through preset air purification thresholds and feedback data.

[0125] This invention, through a spatial modeling module, can comprehensively acquire data on the target air purification area and each block unit, providing a precise data foundation for subsequent air purification. It reconstructs the spatial model of the target area, achieving a digital representation of the purification area, facilitating simulation and analysis. Independent block division allows the system to differentiate processing based on the characteristics of different areas. The topology deployment module, by determining virtual deployment nodes and performing deployment simulations, generates deployment simulation data, providing data support for optimizing the layout of purification units. Dynamic clustering of spatial nodes enables automatic optimization of the purification unit layout, improving purification efficiency. Redundancy removal reduces unnecessary resource waste and lowers system operating costs. The unit deployment topology provides a clear guidance for actual deployment. The domain monitoring module, through ion air delivery domain superposition simulation, can accurately predict purification effects, providing a basis for optimizing air delivery strategies. Analysis of domain synergistic empowerment parameters optimizes the synergistic effects between purification units. Real-time ion transmission monitoring provides real-time feedback on the purification process. Ion monitoring data provides data for evaluation. The system provides data support for estimating purification effectiveness. The diffusion blind zone correction module accurately identifies purification blind zones through diffusion path tracking. Blind zone correction data provides guidance for eliminating purification dead zones. The dynamic coupling of heterogeneous response units enables the synergistic effect of multiple purification technologies, improving purification effectiveness. Purification intensity boundary migration makes the purification effect more uniform, avoiding local over- or under-purification. Adjusting purification intensity provides a means to achieve precise control. The purification coordination module can predict the effect after adjustment by simulating the adjustment of the target air purification area, providing a basis for further optimization. The collection of feedback data provides a basis for evaluating the adjustment effect. By using air purification thresholds and feedback data for coordinated air purification control, intelligent control of the purification process is achieved, ensuring that the purification effect reaches the expected level. The collaborative work of this series of modules enables precise control and optimization of the air purification process, improves purification efficiency, reduces energy consumption, provides technical support for achieving efficient and intelligent air purification, and ultimately provides strong support for improving air quality and achieving effective control of air pollution.

[0126] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0127] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. An air purification method based on ion delivery technology, characterized in that, Includes the following steps: Step S1: Obtain the target air purification area and data of each block unit; reconstruct the target area spatial model based on the target air purification area; divide the target area spatial model into independent blocks based on the data of each block unit, thereby obtaining a set of split sub-blocks; Step S2: Determine virtual deployment nodes through the data of each block unit, and perform deployment rehearsal based on the virtual deployment nodes and the set of split sub-blocks to generate deployment rehearsal data; Based on the deployment simulation data, spatial nodes are dynamically clustered, and redundancy removal is performed based on the clustered spatial nodes to obtain the unit deployment topology. Step S3: Based on the preset ion air supply data and the unit layout topology, perform ion air supply action domain superposition simulation, and analyze the action domain synergistic empowerment parameters based on the simulated superposition action domain; perform real-time ion transport monitoring based on the action domain synergistic empowerment parameters to obtain ion monitoring data; Step S4: Track the diffusion path based on the ion monitoring data, and correct the diffusion blind zone based on the diffusion path to generate blind zone correction data; Based on blind zone correction data, heterogeneous response units are dynamically coupled, and purification intensity boundary migration is performed based on coupled heterogeneous response units, thereby obtaining adjusted purification intensity. Specifically, step S4, which involves tracing the diffusion path based on ion monitoring data and correcting the diffusion blind zone based on the diffusion path, includes: Construct a path node sequence based on ion monitoring data; The path node sequence is reorganized into diffusion trajectory data to generate diffusion trajectory data. Spatial folding and aggregation of diffusion trajectory data are performed to generate folded and aggregated trajectory data; Ion diffusion path coverage is determined based on folded aggregation trajectory data; Among them, determining the ion diffusion path coverage based on folded polymerization trajectory data includes: Extract unstructured folded trajectories from folded aggregated trajectory data; Unstructured folding trajectories are mapped onto grid cells to obtain a diffused grid distribution; Weights are assigned to the diffusion grid distribution based on the folded aggregate trajectory data, thus obtaining the weighted data. Coverage measurement is performed based on weighted data to generate ion diffusion path coverage. The ion diffusion blind zone is obtained by projecting the reverse concealed domain through the coverage of the ion diffusion path and identifying the blind zone based on the concealed domain. The ion diffusion blind region is profiled and then the diffusion blind region is corrected based on the profile of the blind region to generate blind region correction data. Step S5: Based on the adjustment of the purification intensity, simulate the adjustment of the target air purification area and collect feedback data; coordinate and control the air purification through the preset air purification threshold and the feedback data.

2. The air purification method based on ion air delivery technology according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain data of the target air purification area and each block unit; perform boundary contour recognition on the target air purification area to obtain the boundary of the target area, wherein the minimum contour closure length for boundary recognition is set to 150 meters; Step S12: Reconstruct the spatial skeleton based on the boundary of the target region to generate a spatial model of the target region. During the reconstruction process, the main skeleton extension angle threshold is set to continuously expand within 30°. Step S13: Anchor the cell position of each block cell data to obtain the block anchor coordinate data, wherein the anchor tolerance is limited to ±5 meters. Step S14: Perform block nesting mapping between the block anchoring coordinate data and the target area spatial model to obtain the initial spatial nesting layer data, wherein the upper limit of layer overlay is set to no more than 3 overlapping units at each location; Step S15: Perform independent unit decoupling processing based on the initial spatial nested layer data to obtain the split sub-block set data, where the minimum side length of the sub-block is limited to 20 meters and the area range is set to 400-900 square meters.

3. The air purification method based on ion air delivery technology according to claim 1, characterized in that, Step S2, which involves determining virtual deployment nodes using data from each block unit and performing a deployment rehearsal based on the virtual deployment nodes and the set of sub-blocks, includes: Based on the data of each block unit, available nodes are filtered to obtain candidate node data; Virtual feature mapping is performed on the candidate node data to generate virtual feature data; Virtual deployment nodes are established based on virtual feature data; Node block matching is performed based on virtual node deployment and splitting sub-block sets to obtain node block adaptation data; Deployment path simulation data is obtained by using node block adaptation data; The deployment path simulation data is fused with multi-scenario pre-simulation data to generate deployment pre-simulation data.

4. The air purification method based on ion air delivery technology according to claim 1, characterized in that, Step S2, which involves dynamic clustering of spatial nodes based on deployment simulation data and redundancy removal based on the clustered spatial nodes, includes: Spatial attribute injection processing is performed based on deployment simulation data to generate deployment spatial attribute data; Node feature codes are obtained by performing multi-dimensional feature encoding on the spatial attribute data of the layout. A dynamic clustering index is constructed based on node feature encoding; Based on dynamic clustering index mapping of cluster space node data; Node correlation degree screening is performed on the clustered spatial node data to obtain correlation degree data; Redundant connections are identified based on correlation data, and redundant connection data is generated. Redundant connection data is processed to remove redundancy, resulting in the removed data. The data to be removed is mapped to an entity structure to generate a cell layout topology.

5. The air purification method based on ion air delivery technology according to claim 1, characterized in that, Step S3 involves performing a superimposed simulation of the ion air supply domain based on preset ion air supply data and unit layout topology, and analyzing the synergistic empowerment parameters of the domain based on the simulated superimposed domain, including: The preset ion air supply data is decomposed into scope primitives to obtain scope primitive data. Mapping the scope primitive data onto the cell layout topology generates mapped and fused data. Simulate multiple ion delivery paths based on mapping and fusion data; A superimposed action domain is constructed based on multiple ion delivery paths; Divide the superimposed scope into multiple independent scope blocks; Local scope boundaries are identified for independent scope blocks to obtain the scope block boundaries; Interaction node data is generated by mining interaction nodes based on the boundaries of the scope blocks. The scope-based primitive data is used to inject collaborative empowerment parameters into the data of the interactive nodes to obtain the scope collaborative empowerment parameters.

6. The air purification method based on ion air delivery technology according to claim 1, characterized in that, Step S3, which involves real-time ion transport monitoring based on domain-specific synergistic empowerment parameters, includes: Multi-dimensional parameter tuning is performed based on the scope-based collaborative empowerment parameters to generate tuning parameters, where the collaborative dimension is controlled between 3 and 7 dimensions, and the empowerment weight value ranges from 0.2 to 0.

85. Ion air supply simulation is performed based on the optimized parameters to generate simulated optimized ion air supply conditions. Real-time response monitoring of the scope collaborative empowerment parameters is performed to generate real-time response monitoring data, with a monitoring period of 10 seconds and a response latency controlled within 200ms. Ion monitoring data was filtered out based on real-time response monitoring data, with only ion concentration values ​​whose data drift range was within ±5% retained.

7. The air purification method based on ion air delivery technology according to claim 1, characterized in that, Step S4, which involves dynamically coupling heterogeneous response units based on blind zone correction data and migrating the purification intensity boundary based on the coupled heterogeneous response units, includes: The blind zone correction data is processed by heterogeneous clustering of response units, and the clustering index data is extracted. Project clustered index data into a polymorphic cell matrix; Response unit sequence concatenation is performed based on polymorphic unit matrix and blind zone correction data to obtain concatenated sequence data; Boundary migration simulation is performed by combining sequence data, thereby generating multiple boundary migration data sets; Determine the boundary restriction area based on boundary migration data; The purification intensity threshold data is reconstructed based on the boundary restriction area, and the purification intensity is optimized based on the reconstructed purification intensity threshold data, thereby obtaining the adjusted purification intensity.

8. The air purification method based on ion air delivery technology according to claim 1, characterized in that, Step S5 includes the following steps: Step S51: Map the adjusted purification intensity data to purification blocks to obtain block mapping data; inject parameter perturbation into the block mapping data to obtain perturbation injection data; Step S52: Perform time-series response deduction based on the perturbation injection data to generate response deduction data; Step S53: Locate the mining nodes based on the response simulation data to obtain mining node data; archive the mining node data as feedback signals to obtain feedback mining data; Step S54: Apply threshold adaptation filtering to the feedback data to generate threshold adaptation data; reconstruct the coordination matrix based on the threshold adaptation data to obtain coordination control data for air purification coordination control.

9. An air purification system based on ion delivery technology, characterized in that, For performing the air purification method based on ion delivery technology as described in claim 1, the air purification system based on ion delivery technology comprises: The spatial modeling module is used to acquire data on the target air purification area and each block unit; reconstruct the target area spatial model based on the target air purification area; and divide the target area spatial model into independent blocks based on the data of each block unit, thereby obtaining a set of sub-blocks. The topology deployment module is used to determine virtual deployment nodes through the data of each block unit, and to perform deployment simulation based on the virtual deployment nodes and the set of split sub-blocks to generate deployment simulation data; based on the deployment simulation data, the spatial nodes are dynamically clustered, and redundancy removal is performed based on the clustered spatial nodes to obtain the unit deployment topology structure. The domain monitoring module is used to simulate the superposition of ion air supply domains based on preset ion air supply data and unit layout topology, and analyze the domain synergistic empowerment parameters based on the simulated superposition domains; and to perform real-time ion transport monitoring based on the domain synergistic empowerment parameters, thereby obtaining ion monitoring data. The diffusion blind zone correction module is used to track the diffusion path based on ion monitoring data and correct the diffusion blind zone based on the diffusion path to generate blind zone correction data; based on the blind zone correction data, the heterogeneous response unit is dynamically coupled, and the purification intensity boundary is migrated based on the coupled heterogeneous response unit to obtain the adjusted purification intensity. The purification coordination module is used to simulate the adjustment of the target air purification area based on the adjustment of purification intensity and to collect feedback data; it performs air purification coordination control through preset air purification thresholds and feedback data.

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