Air purification method and system based on ion air supply technology

Abstract

The present invention discloses an air purification method and system based on ion air supply technology, which relates to the field of air purification technology. The method comprises: firstly, generating a spatial model of a target air purification area and performing block division, obtaining an ion air supply module including a high-voltage generating circuit, etc., and performing layout analysis on the modules based on a sub-block set to obtain a deployment scheme set. Then, the modules are deployed according to the scheme set, and a feedback parameter set is obtained through purification control analysis feedback of the modules. Finally, the control parameters are adjusted based on the feedback parameters, a fine-tuning control parameter set is obtained, and air purification coordinated control is performed. The technical problem that the existing air purification technology is difficult to achieve accurate, efficient, and adaptive air purification in complex spaces, resulting in uneven purification effects, unreasonable resource utilization, and inability to optimize in real time is solved, and the technical effect of providing accurate, uniform, efficient, and real-time adaptive high-quality air purification in complex spaces is achieved.

Description

Technical Field

[0001] The present application relates to the field of air purification technology, and in particular to an air purification method and system based on ion air supply technology. Background Art

[0002] In the field of air purification, the demand for efficient and uniform purification of complex spaces has become increasingly prominent, and the contradiction of reasonable allocation of purification resources has become increasingly prominent. Achieving accurate and adaptive air purification to better meet the purification requirements of different spaces has become a crucial link in improving the air purification effect. Traditional air purification technology is often extensive and one-sided, relying only on simple filtering equipment or local ventilation systems, and has insufficient control over the purification effect of the overall space. It lacks in-depth analysis and utilization of spatial environmental data, making it difficult to comprehensively and accurately evaluate the air quality status of each area. There are unreasonable situations in the allocation of purification resources, resulting in poor purification effects in some key areas and waste of resources in some areas. The formulation of purification plans is relatively rigid and fixed, and cannot cope well with the complex and changeable actual air quality conditions.

[0003] Among the current related technologies, there are technical problems that air purification technology is difficult to achieve accurate, efficient and adaptive air purification in complex spaces, resulting in uneven purification effects, unreasonable resource utilization and inability to optimize in real time. Summary of the invention

[0004] The present application provides an air purification method and system based on ion air supply technology. First, a spatial model of the target air purification area is generated and divided into blocks, and an ion air supply module including a high-voltage generating circuit is obtained. The module is laid out and analyzed based on a sub-block set to obtain a deployment scheme set. Then, the modules are deployed according to the scheme set, and the feedback of purification control analysis is performed through the modules to obtain a feedback parameter set. Finally, the control parameters are adjusted based on the feedback parameters to obtain a fine-tuning control parameter set and coordinate air purification control, thereby achieving the technical effect of providing accurate, uniform, efficient and real-time adaptive high-quality air purification in complex spaces.

[0005] The present application provides an air purification method based on ion air supply technology, including:

[0006] According to the target air purification area, an air purification area space model is generated, and the air purification area space model is divided into blocks according to the air purification requirements to obtain an air purification space sub-block set; an ion air supply module is obtained, and the ion air supply module includes a high-voltage generating circuit, an ion generator, a micro fan and a control module, and the ion air supply module is laid out and analyzed based on the air purification space sub-block set to obtain a module deployment scheme set; according to the module deployment scheme set, the ion air supply modules are deployed on the air purification space sub-block set in sequence, and the air purification space sub-block set is subjected to purification control analysis and feedback through the ion air supply module to obtain a block air purification feedback parameter set; based on the block air purification feedback parameter set, the control parameters of the ion air supply module are adjusted to obtain a block air supply fine-tuning control parameter set, and the air purification coordination control of the air purification space sub-block set is performed based on the block air supply fine-tuning control parameter set.

[0007] The present application also provides an air purification system based on ion air supply technology, including:

[0008] A regional space model generation module, the regional space model generation module is used to generate an air purification regional space model according to the target air purification area, and divide the air purification regional space model into blocks according to the air purification requirements to obtain an air purification space sub-block set; a deployment plan set acquisition module, the deployment plan set acquisition module is used to acquire an ion air supply module, the ion air supply module includes a high voltage generating circuit, an ion generator, a micro fan and a control module, and the ion air supply module is laid out and analyzed based on the air purification space sub-block set to obtain a module deployment plan set; a feedback parameter set acquisition module, the feedback parameter set acquisition module The feedback parameter set acquisition module is used to deploy the ion air supply module on the air purification space sub-block set in sequence according to the module deployment scheme set, and perform purification control analysis feedback on the air purification space sub-block set through the ion air supply module to obtain a block air purification feedback parameter set; the purification coordination control module is used to adjust the control parameters of the ion air supply module based on the block air purification feedback parameter set to obtain a block air supply fine-tuning control parameter set, and perform air purification coordination control on the air purification space sub-block set based on the block air supply fine-tuning control parameter set.

[0009] The air purification method and system based on ion air supply technology proposed in this application first generates a spatial model of the target air purification area and divides it into blocks, obtains an ion air supply module including a high-voltage generating circuit, and analyzes the module layout based on the sub-block set to obtain a deployment plan set. Then, the modules are deployed according to the plan set, and the feedback parameter set is obtained through purification control analysis feedback of the modules. Finally, the control parameters are adjusted based on the feedback parameters to obtain a fine-tuning control parameter set and coordinate air purification control, achieving the technical effect of providing accurate, uniform, efficient and real-time adaptive high-quality air purification in complex spaces. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0011] Figure 1 A schematic flow chart of an air purification method based on ion air supply technology provided in an embodiment of the present application.

[0012] Figure 2 A schematic diagram of the structure of an air purification system based on ion air supply technology provided in an embodiment of the present application.

[0013] Description of reference numerals: regional space model generation module 10 , deployment plan set acquisition module 20 , feedback parameter set acquisition module 30 , purification coordination control module 40 . DETAILED DESCRIPTION

[0014] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0015] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0016] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0017] The present application provides an air purification method based on ion air supply technology, such as Figure 1 As shown, the method includes:

[0018] Step S100, generate an air purification area space model according to the target air purification area, divide the air purification area space model into blocks according to the air purification needs, and obtain an air purification space sub-block set. Specifically, clarify the scope and boundaries of the target air purification area, and determine it through field surveys, checking architectural drawings or using professional measuring equipment. For example, for a large shopping mall, it is necessary to determine the overall scope including each floor and different functional areas (such as shopping areas, dining areas, rest areas, etc.). Use three-dimensional modeling software, geographic information system and other spatial modeling tools to generate an accurate air purification area space model based on the collected detailed information of the target air purification area. The model will include an accurate description of the area's geometry, spatial dimensions, internal structure and layout, etc. The space model is divided into blocks according to specific air purification needs. The determination of air purification needs usually takes into account multiple factors, such as the density of people in different areas, the type of activities (for example, the air circulation needs in the sports area) and the demand for air purification. When dividing the blocks, the grid division method is used to evenly divide the space into blocks of a certain size; it can also be divided irregularly according to functional zoning to better meet actual needs. For example, in a school's air purification project, the classroom area may be divided according to the number of classes and the layout of the classroom; and the playground area will be divided according to different activity areas (such as running areas, ball sports areas, etc.). Finally, a set of air purification space sub-blocks covering various characteristics and needs is obtained. Each sub-block has its specific purification requirements and focus, which provides a precise foundation for subsequent air purification work.

[0019] Step S200, obtain an ion air supply module, the ion air supply module includes a high voltage generating circuit, an ion generator, a micro fan and a control module, and the ion air supply module is laid out and analyzed based on the air purification space sub-block set to obtain a module deployment solution set. Specifically, a complete ion air supply module is obtained, and the module contains multiple key components. The high voltage generating circuit is responsible for stably providing DC negative high voltage to the negative ion generator so that it can normally ionize the air to produce negative ions. The negative ion generator uses high voltage to ionize the air, thereby releasing a large amount of negative ions to achieve the purpose of purifying the air. The micro fan is driven by an advanced brushless DC motor, which has the characteristics of low noise and efficient air supply, and can quickly and evenly diffuse the generated negative ions into the air. The control module integrates a variety of sensors, such as air quality sensors, temperature and humidity sensors, and negative ion concentration sensors, which can monitor key parameters such as indoor air quality, temperature, humidity, and negative ion concentration in real time. The ion air supply module is laid out and analyzed based on the obtained air purification space sub-block set, taking into account the specific conditions of each sub-block, such as the size of the sub-block, the shape of the space, the activities of personnel, the distribution of pollution sources, the ventilation conditions, and the initial state of air quality. For sub-blocks with larger areas, it is necessary to deploy multiple ion air supply modules or select modules with higher power to ensure that the entire area can be covered and effective air purification can be achieved. In sub-blocks with frequent personnel activities and more pollution sources, it is necessary to increase the amount of negative ions generated and the air supply volume to quickly improve the air quality. For sub-blocks with good ventilation, the number of modules deployed or their work intensity can be appropriately reduced. Mathematical modeling and simulation technology are used to establish a mathematical model to calculate the effect of ion air supply modules on air purification under different layout schemes, including the distribution of negative ions, air flow, pollutant removal efficiency, etc. The simulation software is used to intuitively display the dynamic process of air purification under different schemes, help evaluate the feasibility and effectiveness of the scheme, and generate a series of feasible module deployment schemes. The schemes will differ in the number, location, working parameters, etc. of modules to form a set of module deployment schemes. For example, for the air purification of a large conference room, the effect of deploying ion air supply modules in different positions will be analyzed based on the seating layout of the conference room, the personnel activity area, and the location of the vents.A solution may be generated to deploy a high-power module in each of the four corners of the conference room, or to evenly distribute multiple low-power modules above the area where people gather, and so on, eventually forming a set of module deployment solutions with multiple options. Among them, the layout plan is calculated. First, the system analyzes the sub-block set of the air purification space, taking into account the specific conditions of each sub-block (such as area, pollution source, air flow path and quality requirements, etc.). Based on this information, the system simulates and calculates the layout plan for each sub-block. The calculation process uses mathematical modeling and simulation technology. By establishing an air flow model, a negative ion diffusion model and a pollutant removal model, the purification effect under different layout plans is calculated for each sub-block according to different needs. For example, for a sub-block with a larger area, the system may calculate multiple layout plans, including different combinations of high-power modules and low-power modules, as well as layout effects at different locations. Each plan takes into account the air flow path, the distribution of pollution sources and air quality requirements. For areas with more pollution sources, the system increases the release of negative ions and air supply by adjusting the working parameters of the modules (such as fan speed, ion generation, etc.) to improve the air purification effect. Based on the above calculation results, the system generates multiple module deployment plans, each with different layout positions, quantities and densities. For example, for a large conference room, the system can generate multiple feasible plans, including: 4 high-power modules are laid out in the four corners of the conference room, with the wind speed set to 1200 RPM and the ion concentration of 30,000 ions / s. Multiple low-power modules are evenly laid out above the area where people gather, with the wind speed set to 1000 RPM and the ion concentration of 25,000 ions / s. The system will further optimize each plan, adjust the number, location and working parameters of the modules based on the simulation results, and finally determine the layout plan that best suits the air purification space through the optimization process, ensuring that energy efficiency and cost-effectiveness are improved while meeting air quality requirements.

[0020] In a possible implementation, an ion air supply module is obtained, the ion air supply module includes a high voltage generating circuit, an ion generator, a micro fan and a control module, and the ion air supply module is laid out and analyzed based on the air purification space sub-block set to obtain a module deployment scheme set. Step S200 further includes step S210, obtaining regional air purification factor information, the regional air purification factor information includes regional functional dimensions, pollution source attributes, air flow path and air quality requirements. Specifically, comprehensive information is collected. For regional functional dimensions, the length, width, height and other dimensional data of different spaces are measured on site to understand their spatial size and shape. For pollution source attributes, the type of pollution source (such as chemical pollution, dust pollution, etc.), intensity (rate of pollution emission) and location are determined. The air flow path is determined through smoke experiments, fluid mechanics simulations and other methods to understand the natural flow direction and speed of air in the space. According to relevant standards and actual needs, the air quality requirements of each area are clarified, such as restrictions on particulate matter concentration and harmful gas content.

[0021] Step S220, based on the regional air purification factor information, the air purification space sub-block set is attribute-classified and marked to obtain a block purification factor attribute set. Specifically, according to the acquired regional air purification factor information, each sub-block in the air purification space sub-block set is carefully evaluated and classified. For example, sub-blocks with similar regional functional dimensions, pollution source attributes, air flow paths, and air quality requirements are classified into one category, and the corresponding attribute categories are marked for them. Through classification and marking, a clear block purification factor attribute set is formed, so that the characteristics and requirements of each sub-block can be clarified.

[0022] Step S230, based on the block purification factor attribute set, the air purification space sub-block set is analyzed for layout scheme, and an initial layout scheme set is determined, wherein the initial layout scheme set includes layout position, layout quantity and density, and module performance parameters. Specifically, based on the block purification factor attribute set, the characteristics and requirements of each sub-block are deeply analyzed to determine the initial layout scheme of the ion air supply module. For sub-blocks with large space, many pollution sources and high air quality requirements, a large number of ion air supply modules will be selected to be laid out in the center, and the performance parameters of the modules (such as fan speed, ion generation, etc.) will also be set higher. For sub-blocks with small space, few pollution sources and relatively low air quality requirements, a small number of modules will be laid out in the corners or edge positions, and the performance parameters are relatively low. Through analysis, the layout position, layout quantity and density of the ion air supply module in each sub-block and the module performance parameters are determined to form an initial layout scheme set. When formulating the layout scheme, historical data and experience will be combined, and the layout effects in similar areas or projects will be referred to to further optimize the module layout scheme. Historical data includes the characteristics of pollution sources in similar areas in previous projects, air flow paths, and the actual effects of different module layout methods. The system will conduct comparative analysis based on the data to optimize the preliminary plan and ensure the purification effect under various environmental conditions. Based on the above factors, the system can dynamically adjust the layout plan so that it can meet the current air purification needs and adapt to possible changes and uncertainties in the future. The final set of initial layout plans combines multi-dimensional data analysis and historical experience, thereby improving the stability of the system and the air purification effect.

[0023] Step S240, performing simulation verification on the initial layout solution set to obtain module layout simulation results, optimizing the initial layout solution set based on the module layout simulation results, and obtaining the module deployment solution set. Specifically, professional simulation software is used to input various parameters in the initial layout scheme set to simulate the effect of the ion air supply module in actual operation. Through simulation, the effect of air purification can be intuitively seen, such as the removal efficiency of pollutants, the uniformity of air flow, etc. According to the simulation effect of the module layout obtained by simulation, it is evaluated whether it meets the expected purification requirements. If there are deficiencies, such as the purification effect of some areas is not up to standard or resources are wasted, the initial layout scheme is adjusted and optimized, such as changing the layout position of the module, increasing or decreasing the number, adjusting the performance parameters, etc. After multiple simulations and optimizations, a complete module deployment scheme set that can meet the air purification needs is finally obtained. For example, in an air purification project in a factory workshop, the functional dimensions of different areas of the workshop (such as the size of the processing area, storage area, etc.), pollution source attributes (such as dust generated by welding, chemical volatilization, etc.), air flow path (affected by ventilation equipment and workshop layout) and air quality requirements (in accordance with relevant industry standards) are first obtained. Then, each sub-block is marked by attribute classification, such as marking the processing area with severe pollution as one category and the storage area as another category. Then, we determined the initial layout of ion air supply modules with a large number and high performance for the processing area, and a relatively small number and low performance for the storage area. After simulating the initial plan, we found that the purification effect was not good in some corners of the processing area, so we optimized the plan by increasing the number of modules or adjusting their positions. After repeated optimization, we obtained the final set of module deployment plans to ensure that the air in the entire workshop is effectively purified.

[0024] Step S300, deploy the ion air supply module on the air purification space sub-block set in sequence according to the module deployment scheme set, perform purification control analysis and feedback on the air purification space sub-block set through the ion air supply module, and obtain a block air purification feedback parameter set. Specifically, according to the module deployment scheme set determined previously, the ion air supply modules are installed and deployed one by one on the corresponding air purification space sub-blocks. During the deployment process, the operation is strictly carried out in accordance with the position, quantity and density requirements specified in the scheme. For example, for a specific sub-block, if the scheme requires the installation of three ion air supply modules at its center position, then this deployment will be accurately executed. After the deployment is completed, the ion air supply module is started to work and the air purification space sub-block set is purified. During the purification process, various sensors and monitoring devices inside the module will collect relevant data in real time. For the high-voltage generating circuit, the voltage stability and current size of its output will be monitored. The ion generator monitors the number and quality of the generated ions. The micro fan will record its rotation speed and air supply effect. The air quality sensor in the control module will continuously detect the concentration of pollutants in the air, such as Changes in the content of particulate matter, harmful gases, etc.; temperature and humidity sensors will record the numerical changes in temperature and humidity; negative ion concentration sensors will monitor the concentration of negative ions. The real-time collected data will be summarized and analyzed to evaluate the purification effect of the ion air supply module on each sub-block. For example, by comparing the changes in the concentration of pollutants in the air before and after purification, it is judged whether the purification effect has reached the expected level. At the same time, considering the impact of factors such as temperature, humidity and negative ion concentration on the overall air quality, after operation and monitoring, the collected data will be sorted and analyzed to form a set of purification feedback parameters for different sub-blocks. The parameter set includes purification effect evaluation indicators for each sub-block, such as pollutant removal rate, air quality improvement degree, energy consumption and other specific values. For example, in an air purification project in a large office building, ion air supply modules are deployed in various office areas and public areas according to the module deployment plan. After running for a period of time, air quality data of various areas are collected. For example, the concentration of particulate matter in a certain office area has dropped from the initial high value to a value that meets the standard. At the same time, the temperature and humidity are also kept within a suitable range, and the negative ion concentration has reached the expected level. These data together constitute the air purification feedback parameters of the area. By summarizing and analyzing the feedback parameters of all sub-blocks, the block air purification feedback parameter set of the entire office building is obtained.

[0025] In a possible implementation, the ion air supply module is sequentially deployed on the air purification space sub-block set according to the module deployment scheme set, and the air purification space sub-block set is subjected to purification control analysis feedback by the ion air supply module to obtain a block air purification feedback parameter set. Step S300 further includes step S310, constructing an ion air supply control space according to the ion air supply module, and performing attribute matching division on the ion air supply control space based on the block purification factor attribute set to obtain a block control parameter calibration library set. Specifically, based on the working principle and performance characteristics of the ion air supply module, a virtual ion air supply control space is constructed, and the control space contains various possible working states and control parameter combinations of the ion air supply module. According to the previously obtained block purification factor attribute set, the control space is divided into detailed attribute matching divisions. For example, according to the pollution source intensity, space size, air quality requirements and other attributes of different sub-blocks, the control space is divided into parts corresponding to each sub-block. Through division, a block control parameter calibration library set covering various control parameter possibilities is established, providing a basis for subsequent optimization selection.

[0026] Step S320, obtain an air purification effect index set, which includes purification volume, clean air transmission rate and purification energy efficiency. Specifically, the key indicators used to evaluate the air purification effect are clarified. The purification volume refers to the total amount of pollutants that can be removed by the ion air supply module per unit time; the clean air transmission rate indicates the propagation and distribution efficiency of clean air in the space; the purification energy efficiency reflects the efficiency of energy utilization in the process of achieving the purification effect; by obtaining specific indicators, a quantitative standard is provided for measuring and comparing the purification effects under different control parameters.

[0027] Step S330, based on the air purification effect index set, the control parameters of the ion air supply control space are associated and fitted to generate an air purification effect reward function. Specifically, the various control parameters in the ion air supply control space are analyzed and associated using the acquired air purification effect index set, and the intrinsic relationship between the control parameters and the purification effect index is found through mathematical modeling and data analysis, and this relationship is fitted into a function, namely, the air purification effect reward function, which can calculate the quality of the corresponding purification effect according to the given control parameters, and provide an evaluation basis for the subsequent optimization process.

[0028] Step S340, using the air purification effect reward function to compare and optimize in the block control parameter calibration library set respectively, to obtain the block air supply control parameter set. Specifically, using the generated air purification effect reward function, one by one calculation and comparison are performed in the previously established block control parameter calibration library set, and by evaluating the reward of different control parameter combinations, the control parameter combination that can achieve the optimal or near-optimal purification effect is screened out, thereby obtaining the block air supply control parameter set for each sub-block.

[0029] Step S350, the ion air supply module performs air purification control monitoring on the air purification space sub-block set based on the block air supply control parameter set, and obtains the block air purification feedback parameter set. Specifically, the ion air supply module adjusts the working state according to the obtained block air supply control parameter set, and performs actual purification operations on the air purification space sub-block set. During the purification process, various relevant data are monitored in real time, such as changes in the concentration of pollutants in the air, the flow rate of the air, temperature and humidity, etc. The monitoring data is sorted and analyzed to obtain a block air purification feedback parameter set reflecting the purification effect of each sub-block, which is used to evaluate the actual purification effect and provide a basis for further optimization and adjustment. For example, in a workshop air purification project in a large factory, the ion air supply control space is first constructed and attribute matching is performed to obtain an indicator set including purification amount, clean air transfer rate and purification energy efficiency. Then a reward function is generated, and the control parameters suitable for different workshop sub-blocks are optimized in the calibration library. The ion air supply module works and monitors according to these parameters, and obtains feedback parameters such as the significant reduction in pollutant concentration in a sub-block after purification and the improvement of air transfer rate, so as to determine whether the purification effect meets expectations and provide a reference for subsequent adjustments.

[0030] In a possible implementation, the air purification effect reward function is used to compare and optimize in the block control parameter calibration library set to obtain the block air supply control parameter set, and step S340 further includes step S341, performing omnidirectional parameter search in the block control parameter calibration library set according to a preset step length to obtain multiple calibration control parameter sets. Specifically, a fixed step length value is set, starting from the starting point of the block control parameter calibration library set, and searching step by step along each possible direction with a set step length. Each search will obtain a set of different control parameter combinations. After multiple such omnidirectional search operations, multiple calibration control parameter sets are finally obtained.

[0031] Step S342, the multiple calibration control parameter sets are evaluated and calculated by the air purification effect reward function to obtain multiple control parameter reward sets. Specifically, the air purification effect reward function is obtained by comprehensive calculation based on indicators such as purification volume, clean air delivery rate and purification energy efficiency. The voltage, speed and current in the first parameter combination are input into the function to calculate the corresponding reward value. The reward value of the second parameter combination is calculated in the same way, and finally multiple control parameter reward sets are obtained.

[0032] Step S343, based on the multiple control parameter reward sets, the multiple calibration control parameter sets are compared by gradient ascent calculation to determine the calibration control parameter optimization direction. Specifically, the reward increase of each calibration control parameter set relative to the starting point is calculated. For example, the reward increase of the first parameter combination relative to the starting point is 75 minus the starting point reward, assuming it is 50, to get 25; the reward increase of the second parameter combination relative to the starting point is 80 minus 50, to get 30; the reward increase of the third parameter combination relative to the starting point is 70 minus 50, to get 20, and the increase is compared. The direction with the largest increase is the calibration control parameter optimization direction.

[0033] Step S344, the block control parameter calibration library set is optimized and compared according to the optimization direction of the calibration control parameter to obtain the block air supply control parameter set. Specifically, along the direction of increasing speed, continue to search and compare with the previous step size, for example, starting from a speed of 900 rpm, each time increasing by 100 rpm, obtain a new parameter combination, calculate the reward of the new parameter combination, and continuously adjust and screen until the control parameter combination with the highest or close to the highest reward is found, which is the block air supply control parameter set. For example, in a complex indoor air purification scene, a full-direction search is performed in the calibration library according to the preset step size to obtain multiple calibration control parameter sets such as parameter combinations A, B, and C. Through the calculation of the reward function, it is found that the reward of A is 80, B is 70, C is 90, etc., forming multiple control parameter reward sets. After gradient ascent calculation and comparison, it is found that the reward rises the most in the direction of parameter combination C, which is determined as the optimization direction. Then, we continue to search for the best solution and compare in the calibration library in this direction, and finally obtain a set of block air supply control parameters such as parameter combination D to achieve better air purification effect.

[0034] In a possible implementation, the block control parameter calibration library set is optimized and compared according to the calibration control parameter optimization direction to obtain the block air supply control parameter set, and step S344 further includes step S3441, dividing the block control parameter calibration library set according to the calibration control parameter optimization direction to obtain a block calibration direction control parameter set. Specifically, according to the calibration control parameter optimization direction determined previously, the parameters in the block control parameter calibration library set are classified. For example, if the optimization direction is to increase the voltage of the ion generator, then those parameters with higher voltage values ​​are divided out. Through targeted division, a set of control parameters in a specific direction is obtained, namely, the block calibration direction control parameter set.

[0035] Step S3442, evaluate the block calibration direction control parameter set through the air purification effect reward function to obtain a block calibration parameter reward set. Specifically, each group of parameters in the block calibration direction control parameter set just obtained is sequentially input into the air purification effect reward function, and the corresponding reward value is calculated according to the input parameters. After all parameter groups are evaluated in this way, a set consisting of these reward values ​​is obtained, that is, the block calibration parameter reward set.

[0036] Step S3443, based on the block calibration parameter reward set, the block calibration direction control parameter set is clustered and updated to generate a block calibration control update parameter cluster. Specifically, according to the values ​​in the block calibration parameter reward set, the block calibration direction control parameter set is clustered and analyzed, and parameter groups with similar rewards are classified into one category to form a parameter cluster, and the parameter cluster is updated, for example, the range or value of the parameter is adjusted to generate a new parameter combination, so as to obtain a block calibration control update parameter cluster.

[0037] Step S3444, a particle swarm algorithm is used to perform global optimization of parameters in the block calibration control update parameter cluster to obtain the block air supply control parameter set. Specifically, the particle swarm algorithm is used to perform extensive and in-depth search and optimization in the generated block calibration control update parameter cluster. The particle swarm algorithm simulates the movement of a group of particles in the search space, and continuously updates the position and speed of the particles to find the optimal parameter combination. After a series of calculations and iterations, the optimal or near-optimal parameter combination is finally found from this parameter cluster. This is the block air supply control parameter set we need. For example, assuming that in an air purification scene in an office area, a series of parameters are divided according to the calibration control parameter optimization direction, and after evaluation by the reward function, clustering updates are performed. Then, the particle swarm algorithm is used to optimize the updated parameter cluster, and finally the optimal block air supply control parameter set suitable for this office area is found, achieving efficient air purification.

[0038] In a possible implementation, the block calibration direction control parameter set is clustered and updated based on the block calibration parameter reward set to generate a block calibration control update parameter cluster, and step S3443 further includes step S34431, based on the block calibration parameter reward set, the block calibration direction control parameter set is clustered to determine a block calibration control parameter cluster set, and the block calibration control parameter cluster set uses the control parameter with the maximum reward as the cluster center. Specifically, according to the values ​​in the block calibration parameter reward set, a clustering algorithm is used to group the parameters in the block calibration direction control parameter set, for example, parameters with similar reward characteristics are grouped into one cluster. In each cluster, the control parameter with the maximum reward is determined as the center of the cluster. In this way, multiple block calibration control parameter cluster sets are formed.

[0039] Step S34432, taking the cluster center as the teacher solution, and the remaining parameter solutions in the block calibration control parameter cluster set as the student solutions to learn and optimize the teacher solution, and obtaining the block learning control parameter cluster set. Specifically, the central parameter in each cluster is regarded as the teacher solution, and the other parameter solutions in the cluster except the central parameter are regarded as the student solutions. The student solution will learn and optimize in the direction of the teacher solution, which means that the student solution will adjust its own parameter value according to the difference with the teacher solution to approach the excellent characteristics of the teacher solution. After the learning process, the updated block learning control parameter cluster set is obtained.

[0040] Step S34433, perform parameter cross-update of a preset span threshold in the block learning control parameter cluster set to generate the block calibration control update parameter cluster. Specifically, for each control parameter in the block learning control parameter cluster set, set a preset span threshold. Within the threshold range, each control parameter will cross-learn and update with other control parameters. For example, a control parameter may exchange part of its numerical value with other control parameters within its allowed span threshold or perform weighted averaging and other operations. Through cross-update, the parameter cluster is further expanded and optimized, and finally a new block calibration control update parameter cluster is generated. For example, assume that there is a parameter set including ion generator voltage, micro fan speed and high voltage generation circuit current. After clustering, the central parameters of a cluster are voltage 100V, speed 1500 rpm, and current 2A. Other parameters such as voltage 90V, speed 1400 rpm, and current 1.8A are learned as student solutions to the central parameters. Then, within the preset span threshold, such as the voltage threshold is ±10V, the speed threshold is ±200 rpm, and the current threshold is ±0.5A, these parameters are cross-updated to finally generate a new optimized parameter cluster.

[0041] In a possible implementation, the block control parameter calibration library set is optimized and compared according to the optimization direction of the calibration control parameter to obtain the block air supply control parameter set, and step S344 further includes step S3445, performing air flow simulation analysis on the air purification space sub-block set to obtain the block air flow direction and block flow diffusion coefficient. Specifically, using fluid mechanics simulation software, inputting relevant parameters such as the geometry, ventilation conditions, and temperature differences of the air purification space sub-block set, through simulation calculation, the direction of air flow in each sub-block is obtained, such as from left to right, from top to bottom, etc., and the flow diffusion coefficient of air in each sub-block is obtained, and the coefficient reflects the difficulty of air diffusion in the block.

[0042] Step S3446, based on the air flow direction of the block and the diffusion coefficient of the block flow, the influence degree of pollutants is accumulated for each sub-block in the air purification space sub-block set in turn to obtain a set of block pollutant influence factors. Specifically, according to the acquired air flow direction and diffusion coefficient, the propagation and accumulation of pollutants in each sub-block are analyzed. It is assumed that the air flow is from sub-block A to sub-block B, and there is a certain amount of pollutants in sub-block A. Due to the flow of air, the pollutants will migrate to sub-block B and gradually accumulate in B. For each sub-block, the pollutant influence brought by its upstream sub-block is considered in turn, and cumulative calculation is performed. Finally, a value reflecting the degree of influence of pollutants on each sub-block is determined to form a set of block pollutant influence factors.

[0043] Step S3447, based on the block pollutant impact factor set, the block air supply control parameter set is corrected for gain impact. Specifically, according to the values ​​in the block pollutant impact factor set, the block air supply control parameter set is adjusted. If the pollutant impact factor of a sub-block is large, it means that it is seriously affected by the pollutants, then it is necessary to correct the air supply control parameters corresponding to the sub-block, for example, increase the concentration of negative ions generated by the ion generator to enhance the ability to remove pollutants; or increase the speed of the micro fan to speed up the circulation and purification of the air. Through the correction, the air purification system can more effectively cope with the pollution conditions of different sub-blocks, thereby achieving a more optimized air purification effect. For example, in a multi-story office building, the air flow simulation analysis is performed on the sub-blocks of each floor. It is found that the air flow of the sub-blocks on the bottom floor is relatively complex due to their proximity to the entrance and exit, and the degree of influence of pollutants is relatively high. According to this situation, the air supply control parameters of the sub-blocks on the bottom floor are corrected for gain impact, and the concentration of negative ions and the speed of the micro fan are increased, thereby effectively improving the air quality of the bottom floor.

[0044] Step S400, based on the block air purification feedback parameter set, the control parameters of the ion air supply module are adjusted to obtain the block air supply fine-tuning control parameter set, and the air purification coordination control of the air purification space sub-block set is performed based on the block air supply fine-tuning control parameter set. Specifically, the various data in the block air purification feedback parameter set, including specific indicators of air quality, such as the concentration of different pollutants, humidity, temperature and negative ion concentration, etc., are analyzed, and the actual feedback data is compared with the preset ideal purification target. For example, if the feedback parameters of a sub-block show that the pollutant concentration is still higher than expected, it may be considered to increase the working intensity of the ion generator in the ion air supply module, or to increase the speed of the micro fan. By fine-tuning, calculating and adjusting these control parameters, a new and more optimized set of control parameters is determined for each sub-block, thereby forming a block air supply fine-tuning control parameter set. The obtained block air supply fine-tuning control parameter set is used to accurately coordinate and control each sub-block in the air purification space sub-block set, which means that each ion air supply module will operate according to the fine-tuning control parameters of its sub-block. For example, for sub-blocks with more serious pollution and requiring stronger purification capabilities, the corresponding ion air supply module will operate at higher power and efficiency; and for sub-blocks that have achieved better purification effects, the module will appropriately reduce the working intensity to achieve energy-saving and balanced purification effects. Through coordinated control, the entire air purification space can achieve efficient, uniform and energy-saving air purification to meet the specific purification needs of different sub-blocks. For example, in the air purification system of a large shopping mall, the control parameters of the ion air supply module are adjusted according to the feedback parameters of different areas (such as clothing areas, dining areas, rest areas, etc.). In the dining area where there are dense crowds and poor ventilation, the purification intensity of the module is increased; while in the relatively open and well-ventilated rest area, the working intensity of the module is appropriately reduced, achieving effective and coordinated control of the air quality of the entire shopping mall.

[0045] The embodiment of the present application first generates a spatial model of the target air purification area and divides it into blocks, obtains an ion air supply module including a high-voltage generating circuit, etc., and analyzes the module layout based on the sub-block set to obtain a deployment plan set. Then, the module is deployed according to the plan set, and the feedback of the purification control analysis and feedback of the module is obtained. Finally, the control parameters are adjusted based on the feedback parameters, and a fine-tuning control parameter set is obtained to coordinate air purification control, thereby achieving the technical effect of providing accurate, uniform, efficient and real-time adaptive high-quality air purification in a complex space.

[0046] In the above, refer to Figure 1 The air purification method based on ion air supply technology according to an embodiment of the present invention is described in detail. Figure 2 An air purification system based on ion air supply technology according to an embodiment of the present invention is described.

[0047] The air purification system based on ion air supply technology according to the embodiment of the present invention is used to solve the technical problems that the existing air purification technology is difficult to achieve accurate, efficient and adaptive air purification in complex spaces, resulting in uneven purification effects, unreasonable resource utilization and inability to optimize in real time, so as to achieve the technical effect of providing accurate, uniform, efficient and real-time adaptive high-quality air purification in complex spaces. The air purification system based on ion air supply technology includes: a regional space model generation module 10, a deployment plan set acquisition module 20, a feedback parameter set acquisition module 30, and a purification coordination control module 40.

[0048] The regional space model generation module 10 is used to generate an air purification regional space model according to the target air purification area, and divide the air purification regional space model into blocks according to the air purification requirements to obtain an air purification space sub-block set;

[0049] The deployment scheme set acquisition module 20 is used to acquire an ion air supply module, which includes a high voltage generating circuit, an ion generator, a micro fan and a control module, and to perform layout analysis on the ion air supply module based on the air purification space sub-block set to obtain a module deployment scheme set;

[0050] The feedback parameter set acquisition module 30 is used to deploy the ion air supply module on the air purification space sub-block set in sequence according to the module deployment scheme set, and perform purification control analysis and feedback on the air purification space sub-block set through the ion air supply module to obtain a block air purification feedback parameter set;

[0051] The purification coordination control module 40 is used to adjust the control parameters of the ion air supply module based on the block air purification feedback parameter set, obtain the block air supply fine-tuning control parameter set, and perform air purification coordination control on the air purification space sub-block set based on the block air supply fine-tuning control parameter set.

[0052] The specific configuration of the deployment scheme set acquisition module 20 will be described in detail below. As described above, an ion air supply module is acquired, and the ion air supply module includes a high voltage generating circuit, an ion generator, a micro fan and a control module. The ion air supply module is laid out and analyzed based on the air purification space sub-block set to obtain a module deployment scheme set. The deployment scheme set acquisition module 20 may further include: a purification factor information acquisition unit for acquiring regional air purification factor information, and the regional air purification factor information includes regional functional dimensions, pollution source attributes, air flow paths and air quality requirements; a purification factor attribute set acquisition unit for analysing the regional air purification factor information based on the regional air purification factor information. The air purification space sub-block set is attribute-classified and marked to obtain a block purification factor attribute set; the layout plan set determination unit is used to analyze the layout plan of the air purification space sub-block set based on the block purification factor attribute set to determine an initial layout plan set, wherein the initial layout plan set includes layout positions, layout quantities and densities, and module performance parameters; the module deployment plan set acquisition unit is used to simulate and verify the initial layout plan set to obtain a module layout simulation effect, and optimize the initial layout plan set based on the module layout simulation effect to obtain the module deployment plan set.

[0053] The specific configuration of the feedback parameter set acquisition module 30 will be described in detail below. As described above, the ion air supply module is deployed on the air purification space sub-block set in sequence according to the module deployment scheme set, and the air purification space sub-block set is subjected to purification control analysis feedback by the ion air supply module to obtain a block air purification feedback parameter set. The feedback parameter set acquisition module 30 may further include: a control parameter calibration library set acquisition unit for constructing an ion air supply control space according to the ion air supply module, and performing attribute matching division on the ion air supply control space based on the block purification factor attribute set to obtain a block control parameter calibration library set; an effect indicator set acquisition unit for obtaining an air purification effect indicator set, the air The purification effect index set includes purification amount, clean air transfer rate and purification energy efficiency; the reward function generation unit is used to perform control parameter association fitting on the ion air supply control space based on the air purification effect index set to generate an air purification effect reward function; the air supply control parameter set acquisition unit is used to use the air purification effect reward function to perform comparison and optimization in the block control parameter calibration library set respectively to obtain the block air supply control parameter set; the feedback parameter set acquisition unit is used for the ion air supply module to perform air purification control monitoring on the air purification space sub-block set based on the block air supply control parameter set to obtain the block air purification feedback parameter set.

[0054] Among them, the air purification effect reward function is used to perform comparison and optimization in the block control parameter calibration library set respectively to obtain the block air supply control parameter set, and the air supply control parameter set acquisition unit may further include: a calibration control parameter set acquisition subunit is used to perform full-directional parameter search in the block control parameter calibration library set according to a preset step size to obtain multiple calibration control parameter sets; a control parameter reward set acquisition subunit is used to evaluate and calculate the multiple calibration control parameter sets through the air purification effect reward function to obtain multiple control parameter reward sets; an optimization direction determination subunit is used to perform gradient ascent calculation and comparison on the multiple calibration control parameter sets based on the multiple control parameter reward sets to determine the calibration control parameter optimization direction; the air supply control parameter set subunit is used to perform optimization comparison on the block control parameter calibration library set according to the calibration control parameter optimization direction to obtain the block air supply control parameter set.

[0055] Among them, the block control parameter calibration library set is optimized and compared according to the calibration control parameter optimization direction to obtain the block air supply control parameter set, and the air supply control parameter set subunit may further include: a calibration direction control parameter set acquisition microunit is used to perform parameter division on the block control parameter calibration library set according to the calibration control parameter optimization direction to obtain the block calibration direction control parameter set; a block calibration parameter reward set acquisition microunit is used to evaluate the block calibration direction control parameter set through the air purification effect reward function to obtain the block calibration parameter reward set; a control update parameter cluster generation microunit is used to perform parameter cluster clustering update on the block calibration direction control parameter set based on the block calibration parameter reward set to generate a block calibration control update parameter cluster; a parameter global optimization microunit is used to perform parameter global optimization in the block calibration control update parameter cluster using a particle swarm algorithm to obtain the block air supply control parameter set.

[0056] Among them, based on the block calibration parameter reward set, the block calibration direction control parameter set is clustered and updated to generate a block calibration control update parameter cluster, and the control update parameter cluster generation micro unit may further include: a cluster center unit is used to cluster the block calibration direction control parameter set based on the block calibration parameter reward set to determine a block calibration control parameter cluster set, wherein the block calibration control parameter cluster set uses the control parameter with the maximum reward as the cluster center; a block learning control parameter cluster set unit is used to use the cluster center as the teacher solution, and the remaining parameter solutions in the block calibration control parameter cluster set as student solutions to perform learning optimization towards the teacher solution to obtain a block learning control parameter cluster set; a calibration control update parameter cluster unit is used to perform parameter cross-update of a preset span threshold within the block learning control parameter cluster set to generate the block calibration control update parameter cluster.

[0057] Among them, the block control parameter calibration library set is optimized and compared according to the optimization direction of the calibration control parameters to obtain the block air supply control parameter set, and the air supply control parameter set sub-unit may further include: a simulation analysis micro-unit is used to perform air flow simulation analysis on the air purification space sub-block set to obtain the block air flow direction and block flow diffusion coefficient; a pollutant influence factor set acquisition micro-unit is used to randomly perturb the preset number of individuals with larger fitness several times according to the target direction of the selected population with the smallest individual fitness to generate the expanded population D; a gain influence correction micro-unit is used to perform gain influence correction on the block air supply control parameter set based on the block pollutant influence factor set.

[0058] The air purification system based on ion air supply technology provided in the embodiment of the present invention can execute the air purification method based on ion air supply technology provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0059] The present invention also provides an air purification system utilizing the above-mentioned air purification method based on ion air supply technology, which uses ion air supply technology to purify the air in a specified area, and arranges ion air supply modules according to the air purification requirements of each sub-block. The system will adjust the parameters of the module based on feedback.

[0060] The method may include the following steps:

[0061] Step 1: Generate a spatial model of the target air purification area (such as a large shopping mall). Input the size and functional area of ​​the shopping mall, 500 meters long, 300 meters wide, and 100 meters high, and create a 3D model based on the structure and purification needs of the area. Create an air purification area model that includes the length, width, height, and functional areas (such as shopping areas, dining areas, rest areas, etc.) of the area, and divide it into multiple purification sub-blocks.

[0062] Step 2: Divide the air purification area into multiple smaller sub-blocks, and the purification requirements of each sub-block will be different. The size of each sub-block is determined by the purification requirements. For example, the size of each sub-block can be 10 meters × 10 meters × 10 meters. The size and air quality requirements of each sub-block will affect the deployment of the ion air supply module, where sub-area 1: area 1000 square meters, the pollution source is dust, the air flow path is short, and the air quality requirements are high; sub-area 2: area 1500 square meters, the pollution source is smoke, the air flow path is medium, and the air quality requirements are medium.

[0063] Step 3: The ion air supply module includes a high-voltage circuit, an ion generator, a micro fan, and a control module. Based on the properties of the sub-blocks (such as pollution source, area, air flow path, etc.), the control parameters of the ion air supply module are initialized as follows: the high-voltage generation circuit provides a negative 5000V voltage to the ion generator, the ion generator is set to "high" output ion concentration, the micro fan is set to "high efficiency" working mode to ensure air circulation, and the control module integrates air quality, temperature and humidity, and negative ion concentration sensors to monitor air parameters in real time.

[0064] Step 4: Based on the area, pollution source, air flow path and other factors of each sub-block, the system automatically analyzes the module layout plan. In sub-area 1 (1000 square meters, the pollution source is dust, and the air quality requirement is high), three ion air supply modules are selected for layout according to the area size and pollution source intensity, and the module working parameters (such as fan speed and ion concentration) are set to higher values ​​to meet the air purification needs. Sub-area 2 (1500 square meters, the pollution source is smoke, and the air quality requirement is medium) deploys 2 modules according to the area and pollution source intensity, and sets the module working parameters to moderate values ​​to achieve medium air purification effect.

[0065] Step 5: After the module is deployed, the system obtains the air purification effect of each module through control analysis feedback. The feedback parameters include: pollutant concentration change (pollutant concentration change of each module before and after operation), air flow uniformity change (uniformity of module air supply), temperature and humidity change (data change of temperature and humidity sensor feedback). Among them, the pollutant concentration of sub-block 1 dropped from 50 to 30, and the air flow uniformity was 0.85. The pollutant concentration of sub-area 2 dropped from 40 to 25, and the air flow uniformity was 0.80.

[0066] Step 6: The reward function evaluates the air purification effect based on the control parameters (fan speed, filtration efficiency, vent opening, etc.). Among them, in sub-area 1, the control parameters initially set by the system are: fan speed is 2, filtration efficiency is 0.9, vent opening is 0.6, purification amount = fan speed × filtration efficiency × vent opening = 2 × 0.9 × 0.6 = 1.08, reward = purification amount + clean air transmission rate + energy efficiency = 1.08 + 0.8 + (1.08 / (2 × 0.6)) = 1.08 + 0.8 + 1.8 = 3.68. Multiple iterations are performed through methods such as particle swarm algorithm (PSO), and the fan speed, filtration efficiency and vent opening are gradually adjusted to calculate the reward for each control parameter combination. In each iteration, PSO evaluates multiple parameter combinations and selects the best solution based on the reward of each combination. Among them, the particle swarm is initialized: each particle represents a set of control parameters (fan speed, filtration efficiency, vent opening), and the position of the particle is the specific value of these parameters. For example, the initial value of particle 1 may be (2, 0.9, 0.6), the initial value of particle 2 may be (2.5, 0.8, 0.5), and so on. For each particle, the reward corresponding to its control parameter combination is calculated and compared with the fitness values ​​of other particles. Based on the fitness of the current particle, the particle will adjust its speed and position, i.e., the control parameters, according to a certain formula. This is the key step of PSO, through which the particle swarm continuously searches for the optimal solution. After multiple iterations, the particle swarm finally finds a set of optimal control parameters to maximize the reward. The purification effect of each module is maximized. Finally, the optimal parameters of sub-area 1 are calculated as fan speed 2, filtration efficiency 0.9, and vent opening 0.6. The optimal parameters of sub-area 2 are fan speed 3, filtration efficiency 0.7, and vent opening 0.5. The most suitable air purification control parameter combination is automatically found for each sub-block, thereby maximizing the air purification effect of each area, while also ensuring efficient operation and energy saving of the system.

[0067] By modeling the air purification area and dividing it into multiple sub-blocks, according to the needs and characteristics of each sub-block, by analyzing the air quality and pollution source distribution, deploying an appropriate number of ion air supply modules, real-time monitoring and feedback adjustment of module control parameters, ensuring the best air purification effect in the entire area.

[0068] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0069] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. An air purification method based on ion air supply technology, characterized in that: The method comprises: Generate an air purification area space model according to the target air purification area, divide the air purification area space model into blocks according to the air purification requirements, and obtain an air purification space sub-block set; Acquire an ion air supply module, the ion air supply module comprising a high voltage generating circuit, an ion generator, a micro fan and a control module, perform layout analysis on the ion air supply module based on the air purification space sub-block set, and obtain a module deployment solution set; Deploy the ion air supply modules on the air purification space sub-block set in sequence according to the module deployment scheme set, and perform purification control analysis and feedback on the air purification space sub-block set through the ion air supply modules to obtain a block air purification feedback parameter set; Based on the block air purification feedback parameter set, the control parameters of the ion air supply module are adjusted to obtain a block air supply fine-tuning control parameter set, and based on the block air supply fine-tuning control parameter set, air purification coordination control is performed on the air purification space sub-block set.

2. The method according to claim 1, characterized in that The obtaining of a module deployment solution set includes: Acquiring regional air purification factor information, wherein the regional air purification factor information includes regional functional size, pollution source attributes, air flow path, and air quality requirements; Based on the regional air purification factor information, attribute classification and labeling are performed on the air purification space sub-block set to obtain a block purification factor attribute set; Based on the block purification factor attribute set, a layout scheme analysis is performed on the air purification space sub-block set to determine an initial layout scheme set, wherein the initial layout scheme set includes a layout position, a layout quantity and density, and module performance parameters; The initial layout solution set is simulated and verified to obtain a module layout simulation effect, and the initial layout solution set is optimized based on the module layout simulation effect to obtain the module deployment solution set.

3. The method according to claim 2, characterized in that The obtaining of the block air purification feedback parameter set includes: Constructing an ion air supply control space according to the ion air supply module, performing attribute matching division on the ion air supply control space based on the block purification factor attribute set, and obtaining a block control parameter calibration library set; Acquire an air purification effect index set, wherein the air purification effect index set includes purification amount, clean air delivery rate and purification energy efficiency; Based on the air purification effect index set, control parameter correlation fitting is performed on the ion air supply control space to generate an air purification effect reward function; Using the air purification effect reward function, respectively compare and optimize in the block control parameter calibration library set to obtain a block air supply control parameter set; The ion air supply module performs air purification control monitoring on the air purification space sub-block set based on the block air supply control parameter set to obtain the block air purification feedback parameter set.

4. The method according to claim 3, characterized in that The obtaining of a block air supply control parameter set includes: Performing a full-directional search for parameters in the block control parameter calibration library set according to a preset step length to obtain multiple calibration control parameter sets; Evaluate and calculate the multiple calibration control parameter sets by using the air purification effect reward function to obtain multiple control parameter reward sets; Performing gradient ascent calculation and comparison on the multiple calibration control parameter sets based on the multiple control parameter reward degree sets to determine the calibration control parameter optimization direction; The block control parameter calibration library set is optimized and compared according to the optimization direction of the calibration control parameters to obtain the block air supply control parameter set.

5. The method according to claim 4, characterized in that The obtaining of the block air supply control parameter set includes: According to the calibration control parameter optimization direction, the block control parameter calibration library set is divided into parameters to obtain a block calibration direction control parameter set; Evaluating the block calibration direction control parameter set by using the air purification effect reward function to obtain a block calibration parameter reward set; Based on the block calibration parameter reward set, the block calibration direction control parameter set is clustered and updated to generate a block calibration control update parameter cluster; A particle swarm algorithm is used to perform global parameter optimization within the block calibration control update parameter cluster to obtain the block air supply control parameter set.

6. The method according to claim 5, characterized in that The generating block calibration control updating parameter cluster includes: Performing parameter clustering on the block calibration direction control parameter set based on the block calibration parameter reward set to determine a block calibration control parameter cluster set, wherein the block calibration control parameter cluster set uses the control parameter with the maximum reward as the cluster center; The cluster center is used as the teacher solution, and the remaining parameter solutions in the block calibration control parameter cluster set are used as student solutions to perform learning optimization on the teacher solution to obtain a block learning control parameter cluster set; A parameter cross-update of a preset span threshold is performed within the block learning control parameter cluster set to generate the block calibration control update parameter cluster.

7. The method according to claim 4, characterized in that The method comprises: Performing air flow simulation analysis on the air purification space sub-block set to obtain the block air flow direction and block flow diffusion coefficient; Based on the air flow direction of the block and the block flow diffusion coefficient, the pollutant impact degree of each sub-block in the air purification space sub-block set is accumulated in turn to obtain a block pollutant impact factor set; The block air supply control parameter set is subjected to gain impact correction based on the block pollutant impact factor set.

8. The air purification system based on ion air supply technology is characterized by: The system is used to implement the air purification method based on ion air supply technology according to any one of claims 1 to 7, and the system comprises: A regional space model generation module, the regional space model generation module is used to generate an air purification regional space model according to the target air purification area, divide the air purification regional space model into blocks according to the air purification requirements, and obtain an air purification space sub-block set; A deployment scheme set acquisition module, the deployment scheme set acquisition module is used to acquire an ion air supply module, the ion air supply module includes a high voltage generating circuit, an ion generator, a micro fan and a control module, and the ion air supply module is laid out and analyzed based on the air purification space sub-block set to obtain a module deployment scheme set; A feedback parameter set acquisition module, the feedback parameter set acquisition module is used to deploy the ion air supply module on the air purification space sub-block set in sequence according to the module deployment scheme set, and perform purification control analysis and feedback on the air purification space sub-block set through the ion air supply module to obtain a block air purification feedback parameter set; A purification coordination control module, the purification coordination control module is used to adjust the control parameters of the ion air supply module based on the block air purification feedback parameter set, obtain the block air supply fine-tuning control parameter set, and perform air purification coordination control on the air purification space sub-block set based on the block air supply fine-tuning control parameter set.

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