PM2.5 control method and system based on air purifier, medium and program product

Abstract

The invention discloses a PM2.5 control method and system based on an air purifier, a medium and a program product, and the method comprises the steps: collecting indoor PM2.5 concentration data and indoor temperature field distribution data; on the basis of the concentration data, vortex distribution of the indoor PM2.5 concentration field is calculated; determining a vortex center position according to the vortex degree distribution, and establishing a polar coordinate system taking the vortex center as an original point; decomposing the concentration field into a radial component and a tangential component in the polar coordinate system, and calculating a radial concentration gradient and a tangential concentration gradient; calculating indoor thermal convection intensity according to the temperature field distribution data, and constructing a PM2.5 migration potential energy field in combination with a radial concentration gradient and a tangential concentration gradient; identifying potential energy barriers and potential well positions in the migration potential energy field; calculating an optimal purification channel according to the space distribution characteristics of the potential energy barrier and the potential well position; the mounting position and the air supply angle of the air purifier are determined based on the optimal purification channel; and the air purifier is controlled to supply air in the direction of the optimal purification channel. And the accuracy and the reliability of the purification effect are improved.

Description

Technical Field

[0001] The present application relates to the field of air purification, and in particular to a PM2.5 control method, system, medium and program product based on an air purifier. Background Art

[0002] With the acceleration of urbanization and the development of industrialization, air pollution is becoming increasingly serious, among which PM2.5 has become an important factor affecting air quality. In order to improve indoor air quality, people usually use air purifiers to reduce indoor PM2.5 concentration. However, traditional air purifiers often adopt a fixed operating mode and cannot adaptively adjust the working state according to the actual air quality, resulting in low purification efficiency and energy waste.

[0003] In related technologies, multiple PM2.5 sensors can be arranged at different locations indoors to collect spatial distribution data, and combined with a human activity detection module, a spatial distribution model of indoor PM2.5 concentration can be established. The system dynamically adjusts the air supply direction and purification intensity of the purifier based on the model, achieving directional purification and precise control, and improving purification efficiency.

[0004] However, due to the turbulent characteristics of indoor airflow and the irregularity of room structure, the diffusion path of PM2.5 in space exhibits strong nonlinear characteristics, which makes it difficult for control strategies based on simple spatial distribution models to accurately capture the actual migration laws of pollutants, thereby reducing the accuracy and reliability of the purification effect. Summary of the invention

[0005] The present application provides a PM2.5 control method, system, medium and program product based on an air purifier, which are used to improve the accuracy and reliability of the purification effect.

[0006] In the first aspect, the present application provides a PM2.5 control method based on an air purifier, which collects PM2.5 concentration data at several different locations indoors and obtains indoor temperature field distribution data; The vorticity distribution of the indoor PM2.5 concentration field is calculated based on the concentration data. The vorticity distribution represents the rotation characteristics of the concentration field. The vortex center position of the concentration field is determined according to the vorticity distribution, and a polar coordinate system with the vortex center as the origin is established; Decomposing the concentration field into radial and tangential components in a polar coordinate system, and calculating the radial and tangential concentration gradients; The indoor thermal convection intensity is calculated based on the temperature field distribution data, and the migration potential field of PM2.5 is constructed by combining the radial concentration gradient and the tangential concentration gradient; Identify the locations of potential barriers and wells in the migration potential energy field; The optimal purification channel is calculated according to the spatial distribution characteristics of the potential energy barrier and the potential well position, and the optimal purification channel is the shortest path that does not pass through the potential energy barrier and connects each potential well position; Determine the installation position and air supply angle of the air purifier based on the optimal purification channel, so that the air outlet direction of the air purifier coincides with the optimal purification channel; Control the air purifier to deliver air along the direction of the optimal purification channel.

[0007] By adopting the above technical solution, indoor PM2.5 concentration data and temperature field distribution data are collected, the vorticity distribution of the concentration field is calculated to characterize its rotation characteristics, the radial and tangential components of the concentration field are decomposed in the polar coordinate system, and the migration potential field is constructed in combination with the thermal convection intensity, thereby identifying the potential barrier and potential well position. Based on these physical characteristics, the shortest path that does not pass through the potential barrier and connects the potential well position is calculated as the optimal purification channel, and the installation position and air supply angle of the air purifier are determined accordingly. It can improve the accuracy of grasping the movement law of PM2.5 in indoor space, reduce the energy loss of purified airflow when passing through the high potential barrier area, and realize efficient purification of multiple potential well positions through the optimal path, reduce airflow turbulence loss, and improve purification efficiency. It not only reduces the energy consumption of the purifier, but also shortens the purification time of indoor PM2.5, and realizes energy-saving and efficient precise control.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, determining the vortex center position of the concentration field according to the vorticity distribution specifically includes: The vorticity distribution is converted into a frequency domain signal, and the frequency domain signal is decomposed by wavelet to obtain the vorticity characteristics at different scales; The local maximum points are calculated based on the vorticity characteristics of different scales, and the local maximum points are clustered to obtain the corresponding centroids; The centroid is determined as a candidate point of the vortex center, and the vorticity intensity and duration of each candidate point of the vortex center are calculated; A vortex center candidate point whose vorticity intensity is greater than a first threshold and whose duration is longer than a second threshold is determined as the vortex center.

[0009] By adopting the above technical solution, in the process of converting the vorticity distribution to the frequency domain, the vorticity characteristics at different scales are obtained through wavelet decomposition, the local maximum points are clustered to obtain the center of mass, and the vortex center position is determined based on the dual thresholds of vorticity intensity and duration. It can effectively identify and distinguish vortex structures of different scales, exclude false vortex centers caused by instantaneous disturbances, and retain real vortices with strong persistence. By accurately locating the vortex center position, a stable and reliable polar coordinate reference system is established, the accuracy of concentration field analysis is improved, and the description of the movement law of PM2.5 is more in line with the actual situation, so that the migration path and aggregation area of ​​PM2.5 can be more accurately predicted.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, calculating the indoor thermal convection intensity according to the temperature field distribution data specifically includes: Divide the temperature field distribution data into several temperature layers and calculate the temperature gradient between adjacent temperature layers; Calculate the thermal buoyancy velocity between temperature layers based on the temperature gradient; Construct the indoor air density field according to the thermal buoyancy velocity and calculate the vertical gradient of the air density field; The thermal convection intensity distribution is calculated based on the vertical gradient and gravitational acceleration.

[0011] By adopting the above technical solution, the temperature field is divided into layers and the temperature gradient is calculated. The air density field is constructed by the thermal buoyancy velocity, and the distribution of thermal convection intensity is calculated in combination with the gravity acceleration. This can improve the accuracy of depicting the vertical stratification phenomenon of indoor air and reflect the thermal convection characteristics at different heights. By establishing the relationship between the temperature gradient and the buoyancy velocity, the vertical distribution of the air density field is obtained, making the calculation results of the thermal convection intensity more consistent with the laws of fluid mechanics.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, after identifying the potential energy barriers and potential well positions in the migration potential energy field, the method further includes: Construct equipotential surfaces of the concentration field and calculate the probability of potential energy transition between adjacent equipotential surfaces; A migration channel network is constructed based on the potential energy transition probability. The network nodes in the migration channel network represent the intersection points of each equipotential surface, and the connection weight represents the transition probability between adjacent network nodes. Identify channel clusters with transition probabilities greater than a preset probability in the migration channel network, and calculate the PM2.5 transmission flux of each channel cluster; The channel cluster with the largest PM2.5 transmission flux is taken as the dominant transmission path.

[0013] By adopting the above technical solution, the equipotential surfaces of the concentration field are constructed, the probability of potential energy transition between adjacent equipotential surfaces is calculated, a migration channel network characterized by network nodes and connection weights is established, and the main channel clusters are identified by preset probability thresholds. The complexity of solving the pollutant transport problem is reduced. By calculating the transmission flux of the channel cluster, the importance of different migration paths can be quantitatively evaluated, thereby identifying the transmission channels that play a dominant role in the diffusion of pollutants. This method establishes a networked description model for PM2.5 migration, which can improve the accuracy of predicting the main propagation paths of pollutants.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, calculating the potential energy transition probability between adjacent equipotential surfaces specifically includes: Obtain sampling point pairs on adjacent equipotential surfaces, and calculate the potential energy difference and spatial distance between each sampling point pair; Determine the ground state transition probability based on the potential energy difference and spatial distance; The excited state transition probability is calculated based on the indoor ambient temperature and turbulence intensity; The ground state transition probability and the excited state transition probability are weighted to obtain the comprehensive transition probability.

[0015] By adopting the above technical solution, the potential energy transition probability between adjacent equipotential surfaces is calculated, and the ground state transition probability and the excited state transition probability are weighted to obtain a comprehensive transition probability, which can reflect the difficulty of PM2.5 particles migrating between different potential energy surfaces. The ground state transition probability is calculated based on the potential energy difference and spatial distance, reflecting the basic difficulty of PM2.5 particles to overcome the potential energy difference for spatial displacement, while the excited state transition probability reflects the role of external conditions in promoting PM2.5 migration by considering the influence of ambient temperature and turbulence intensity. The comprehensive transition probability formed by the weighted combination of the two includes both the migration resistance caused by physical potential energy and the migration convenience brought by environmental factors, making the description of PM2.5 migration behavior more accurate. It improves the subsequent accurate identification of PM2.5 transmission channels, thereby improving the accuracy of the layout and control effect of the air purifier, and thus improving the accuracy and reliability of the purification effect.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after calculating the PM2.5 transmission flux of each channel cluster, the method further includes: Construct a state transfer matrix based on the comprehensive transition probability; Determine the characteristic relaxation time according to the eigenvalue spectrum of the state transfer matrix; Time-scale classification of channel clusters based on characteristic relaxation times to identify fast and slow channels; The maximum deployment range of the air purifier is determined. When the air purifier operates within the maximum deployment range, its coverage of the fast channel is greater than its coverage of the slow channel.

[0017] By adopting the above technical solution, constructing a state transfer matrix based on the comprehensive transition probability, and using its eigenvalue spectrum to determine the characteristic relaxation time, the PM2.5 transmission channels can be classified on a time scale. The characteristic relaxation time reflects the transmission rate of PM2.5 in different channels. The fast channels and slow channels identified on this basis can reflect the transmission characteristics of PM2.5 in indoor spaces. By placing the air purifier in a position with a larger coverage of the fast channel, those areas with a higher PM2.5 transmission rate can be processed first, thereby improving the purification efficiency and further improving the air purification effect.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, determining the characteristic relaxation time according to the eigenvalue spectrum of the state transfer matrix specifically includes: Decompose the state transition matrix into a transition rate matrix and a residence time matrix; An eigenvalue equation is constructed based on the transfer rate matrix, and the sub-eigenvalue of the eigenvalue equation is solved by numerical iteration; The characteristic relaxation time is calculated based on the secondary eigenvalue.

[0019] By adopting the above technical solution, the state transfer matrix is ​​decomposed into a transfer rate matrix and a residence time matrix, and the characteristic relaxation time is calculated by solving the eigenvalue equation to obtain the sub-eigenvalue, making the time characteristic analysis of the PM2.5 transmission process more accurate. The transfer rate matrix reflects the probability of PM2.5 switching between different states, and the residence time matrix characterizes the length of time PM2.5 stays in each state, which can decompose the transmission behavior of PM2.5 into two independent dimensions: transfer probability and time. The sub-eigenvalue obtained by numerically iteratively solving the eigenvalue equation can accurately reflect the characteristic time scale of the system reaching a steady state, thereby making the control strategy of the air purifier more reasonable and effective.

[0020] In the second aspect, an embodiment of the present application provides a PM2.5 control system based on an air purifier, and the PM2.5 control system based on an air purifier includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and one or more processors call the computer instructions to enable the system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions, which, when executed on a system, causes the system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer program product, characterized in that when the computer program product runs on a system, the system executes the method described in any possible implementation manner in the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The present application provides a PM2.5 control method based on an air purifier, collects indoor PM2.5 concentration data and temperature field distribution data, calculates the vorticity distribution of the concentration field to characterize its rotational characteristics, decomposes the radial and tangential components of the concentration field in the polar coordinate system, and constructs a migration potential field in combination with the thermal convection intensity, thereby identifying the potential barrier and the potential well position. Based on these physical characteristics, the shortest path that does not pass through the potential barrier and connects the potential well position is calculated as the optimal purification channel, and the installation position and air supply angle of the air purifier are determined accordingly. It can improve the accuracy of grasping the movement law of PM2.5 in indoor space, reduce the energy loss of purified airflow when passing through the high barrier area, and realize efficient purification of multiple potential well positions through the optimal path, reduce airflow turbulence loss, and improve purification efficiency. Not only does it reduce the energy consumption of the purifier, but it can also shorten the purification time of indoor PM2.5, and achieve energy-saving and efficient precise control.

[0024] 2. The present application provides a PM2.5 control method based on an air purifier, constructs equipotential surfaces of the concentration field, calculates the probability of potential energy transition between adjacent equipotential surfaces, establishes a migration channel network characterized by network nodes and connection weights, and identifies the main channel clusters by preset probability thresholds. The complexity of solving the pollutant transport problem is reduced. By calculating the transmission flux of the channel cluster, the importance of different migration paths can be quantitatively evaluated, thereby identifying the transmission channels that play a dominant role in the diffusion of pollutants. This method establishes a networked description model for PM2.5 migration, which can improve the accuracy of predicting the main propagation paths of pollutants.

[0025] 3. The present application provides a PM2.5 control method based on an air purifier, constructs a state transition matrix based on the comprehensive transition probability, and uses its eigenvalue spectrum to determine the characteristic relaxation time, so that the PM2.5 transmission channels can be classified on a time scale. The characteristic relaxation time reflects the transmission rate of PM2.5 in different channels. The fast channels and slow channels identified accordingly can reflect the transmission characteristics of PM2.5 in indoor spaces. By arranging the air purifier in a position with a larger coverage range for the fast channel, those areas with a higher PM2.5 transmission rate can be processed preferentially, thereby improving the purification efficiency and further improving the air purification effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a PM2.5 control method based on an air purifier in an embodiment of the present application.

[0027] Figure 2 This is another flow chart of a PM2.5 control method based on an air purifier in an embodiment of the present application.

[0028] Figure 3It is a schematic diagram of the physical device structure of a PM2.5 control system based on an air purifier provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0030] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0031] The following uses an embodiment and combines Figure 1 , a PM2.5 control method based on an air purifier in an embodiment of the present application is described: See also Figure 1 , which is a flow chart of a PM2.5 control method based on an air purifier in an embodiment of the present application.

[0032] S101, collecting PM2.5 concentration data at several different locations indoors, and obtaining indoor temperature field distribution data; In this step, the system first needs to arrange multiple PM2.5 concentration sensors indoors to collect PM2.5 concentration data at different locations in real time. At the same time, the system also needs to obtain indoor temperature field distribution data for subsequent analysis of PM2.5 migration patterns. Temperature field distribution data can be collected by arranging a temperature sensor array or by non-contact temperature measurement technologies such as infrared thermal imaging.

[0033] Specifically, the system can use PM2.5 sensors based on laser scattering, beta-ray absorption or micro-oscillation balance principles, and arrange multiple sampling points at different heights and different areas indoors to form a three-dimensional concentration monitoring network. The sensor should have high sensitivity and fast response speed to meet the needs of real-time dynamic monitoring. At the same time, the system can also introduce wireless networking technology to realize automatic upload and remote management of sensor data, thereby improving the flexibility and convenience of monitoring.

[0034] For the collection of temperature field distribution, the system can use thermocouples, thermal resistors or semiconductor temperature sensors, etc., and obtain real-time temperature data at different locations in the room through reasonable layout. Considering that the indoor temperature distribution may be affected by equipment such as air conditioning and heating, showing local non-uniformity, the system can also be supplemented by numerical simulation technologies such as computational fluid dynamics (CFD) simulation to predict and calibrate the temperature field and improve the spatial resolution and accuracy of the collected data. In addition, the system should also consider the interference of factors such as the heat of the sensor itself, and take measures such as shielding and compensation to minimize the impact of external factors on temperature measurement.

[0035] S102, calculating the vorticity distribution of the indoor PM2.5 concentration field based on the concentration data; The system calculates the vorticity distribution of the indoor PM2.5 concentration field based on the concentration data. The vorticity distribution represents the rotation characteristics of the concentration field.

[0036] After obtaining the indoor PM2.5 concentration data, the system needs to further calculate the vorticity distribution of the concentration field to characterize the rotational motion characteristics of PM2.5 in space. Vorticity is an important physical quantity in fluid mechanics, which characterizes the rotational angular velocity of fluid particles. Its size and direction determine the local vortex structure of the concentration field.

[0037] Specifically, the system can discretize the continuous concentration field into gridded data points based on numerical discretization methods such as finite differences and finite volumes. At each grid point, the vorticity vector at that point can be obtained by performing a vector cross multiplication operation on the concentration gradients of adjacent points. Repeating the above operation for the entire concentration field can obtain complete vorticity distribution data. It should be noted that since the calculation of vorticity involves the numerical solution of gradients, it has high requirements on the layout density and sampling accuracy of the concentration sensor, and a balance needs to be struck between data quality and computational cost.

[0038] S103, determining the vortex center position of the concentration field according to the vorticity distribution, and establishing a polar coordinate system with the vortex center as the origin; The system determines the vortex center position of the concentration field according to the vorticity distribution, and establishes a polar coordinate system with the vortex center as the origin. The vortex center position of the concentration field is determined according to the vorticity distribution, specifically including: The vorticity distribution is converted into a frequency domain signal, and the frequency domain signal is decomposed by wavelet to obtain the vorticity characteristics at different scales; The local maximum points are calculated based on the vorticity characteristics of different scales, and the local maximum points are clustered to obtain the corresponding centroids; The centroid is determined as a candidate point of the vortex center, and the vorticity intensity and duration of each candidate point of the vortex center are calculated; A vortex center candidate point whose vorticity intensity is greater than a first threshold and whose duration is longer than a second threshold is determined as the vortex center.

[0039] After obtaining the vorticity distribution of the concentration field, the system needs to further determine the center position of the vortex structure and establish a polar coordinate system with the vortex center as the origin, laying the foundation for the subsequent concentration field component decomposition. The vortex center usually corresponds to the area with the highest concentration of PM2.5 in the concentration field and is the key monitoring object for purification control.

[0040] Specifically, the system can use the wavelet transform method based on frequency domain analysis to perform multi-scale decomposition of the vorticity distribution, thereby extracting vortex characteristics at different spatial scales. By performing local maximum detection and cluster analysis on the wavelet coefficients, the system can obtain the energy center of the vorticity distribution and use it as a candidate point for the vortex center. Furthermore, the system can also introduce physical quantity indicators such as vorticity intensity and duration to screen and sort the candidate points, and finally determine the optimal vortex center position.

[0041] In practical applications, due to the complexity of the indoor environment, the concentration field may show the coexistence of multiple vortex centers. In order to deal with this situation, the system can adopt a hierarchical partitioning strategy to independently identify the vortex center and construct a polar coordinate system for different spatial regions. At the same time, the system should also consider the time evolution characteristics of the vortex center position, and realize real-time estimation and prediction of the vortex center trajectory by introducing dynamic tracking algorithms such as Kalman filtering and particle filtering, providing a more accurate decision-making basis for purification control.

[0042] When establishing a polar coordinate system, the system can use the vortex center as the origin and the ray connecting the vortex center and a reference point in the room (such as a door, window, etc.) as the polar axis, thus forming a natural coordinate frame. In the polar coordinate system, the PM2.5 concentration field will be mapped into a binary function of polar diameter and polar angle, which facilitates subsequent component decomposition and gradient calculation.

[0043] S104, decomposing the concentration field into a radial component and a tangential component in a polar coordinate system, and calculating a radial concentration gradient and a tangential concentration gradient; After establishing a polar coordinate system with the vortex center as the origin, the system needs to further decompose the concentration field into two orthogonal components, radial and tangential, and calculate their concentration gradients respectively. The radial component characterizes the radial transport characteristics of PM2.5 between the vortex center and the indoor boundary, while the tangential component describes the tangential migration law of PM2.5 on the concentric circular orbit. The size and direction of the concentration gradient determine the migration trend and rate of PM2.5, which is an important basis for the subsequent construction of the migration potential field.

[0044] Specifically, the system can use numerical methods such as finite differences to discretize the concentration field function in the polar coordinate system. By comparing the radial and tangential concentration differences between adjacent grid points and dividing by the corresponding grid size, the radial concentration gradient and tangential concentration gradient at each grid point can be obtained. During the calculation process, attention should be paid to ensuring the numerical stability and convergence of the discrete format. Techniques such as weighted averaging and high-order interpolation can be used to improve the accuracy of gradient calculation.

[0045] In practical applications, due to the non-uniformity and anisotropy of the PM2.5 concentration field, the contributions of the radial and tangential components may show significant regional differences. In order to better characterize this difference, the system can introduce mathematical tools such as tensor analysis to perform local feature extraction and pattern recognition on the concentration gradient. At the same time, considering the data noise and uncertainty in actual measurements, the system can also use methods such as regularization and Bayesian estimation to optimize and robustly process the gradient calculation process to improve the reliability of the results.

[0046] S105, calculating the indoor thermal convection intensity according to the temperature field distribution data, and constructing the migration potential field of PM2.5 by combining the radial concentration gradient and the tangential concentration gradient; The system calculates the indoor thermal convection intensity based on the temperature field distribution data, and constructs the migration potential field of PM2.5 by combining the radial concentration gradient and the tangential concentration gradient. The indoor thermal convection intensity is calculated based on the temperature field distribution data, specifically including: Divide the temperature field distribution data into several temperature layers and calculate the temperature gradient between adjacent temperature layers; Calculate the thermal buoyancy velocity between temperature layers based on the temperature gradient; Construct the indoor air density field according to the thermal buoyancy velocity and calculate the vertical gradient of the air density field; The thermal convection intensity distribution is calculated based on the vertical gradient and gravitational acceleration.

[0047] On the basis of obtaining radial and tangential concentration gradients, the system also needs to combine the indoor temperature field distribution data, calculate the thermal convection intensity, and construct the migration potential field of PM2.5. Thermal convection is one of the important factors affecting the indoor migration of PM2.5. Its intensity and direction directly determine the transport channel and aggregation area of ​​PM2.5. The migration potential field comprehensively considers the dual effects of concentration gradient and thermal convection, providing an intuitive physical image for the subsequent optimization of purification channels.

[0048] Specifically, the system can calculate the distribution of thermal convection intensity in the room using temperature field distribution data based on the principles of fluid mechanics and thermodynamics. First, the system divides the temperature field into multiple discrete temperature layers and calculates the temperature gradient between adjacent layers. Based on the temperature gradient and the thermal expansion coefficient of the air, the system can estimate the difference in buoyancy velocity between the temperature layers and further calculate the distribution of thermal convection intensity in the entire indoor space. During the calculation process, attention should be paid to the influence of factors such as gravity and viscosity. Dimensionless parameters such as the Boussinesque approximation and the Prandtl number can be introduced to simplify the calculation.

[0049] After obtaining the distribution of thermal convection intensity, the system can vector-synthesize it with the radial and tangential concentration gradients to construct the migration potential field of PM2.5. In the migration potential field, the potential energy value of each location point reflects the migration trend and aggregation degree of PM2.5 at that point. Areas with higher potential energy values ​​often correspond to the aggregation centers of PM2.5 and key areas for purification; while areas with lower potential energy values ​​may be the migration channels of PM2.5 and secondary areas for purification.

[0050] S106, identifying the potential energy barriers and potential well positions in the migration potential energy field, and calculating the optimal purification channel according to the spatial distribution characteristics of the potential energy barriers and potential well positions; The system identifies the potential energy barriers and potential well positions in the migration potential energy field, and calculates the optimal purification channel according to the spatial distribution characteristics of the potential energy barriers and potential well positions. The optimal purification channel is the shortest path that does not pass through the potential energy barriers and connects each potential well position.

[0051] After constructing the migration potential field of PM2.5, the system needs to further identify the potential barriers and potential wells, and calculate the optimal purification channel accordingly. The potential barriers correspond to the local maximum areas in the migration potential field, which often hinder the free migration of PM2.5 in space; while the potential wells correspond to the local minimum areas, where PM2.5 tends to gather and stay. The optimal purification channel should avoid the potential barrier areas and pass through each potential well area as much as possible to achieve efficient migration and capture of PM2.5.

[0052] Specifically, the system can use methods such as numerical optimization and topological data analysis to detect critical points and divide regions of the migration potential energy field. The points where the gradient of the potential energy field is zero often correspond to potential barriers or potential wells, and can be identified and extracted as key feature points. After obtaining the key feature points, the system can further analyze their spatial distribution patterns and use clustering, watershed and other algorithms to divide them into regions, and obtain spatial distribution maps of potential barrier areas and potential well areas.

[0053] Based on the spatial distribution of potential barrier areas and potential well areas, the system can use graph theory algorithms to calculate the optimal purification channel. First, the indoor space is divided into discrete grids, and each grid is regarded as a node of the graph. Directed edges are established between the nodes, and their weights are the migration potential energy differences between the corresponding grids. Next, the nodes in the potential barrier area are regarded as obstacle nodes, and the nodes in the potential well area are regarded as target nodes. By running the shortest path algorithm, the optimal purification channel that avoids potential barriers and connects all potential wells can be obtained. In practical applications, the shortest path algorithm can be improved and optimized according to actual needs, such as introducing multi-objective optimization, robust optimization and other technologies to improve the global optimality and practicality of the purification channel.

[0054] When identifying potential barriers and potential wells, the system should also consider the dynamic evolution characteristics of the migration potential energy field. Due to the real-time changes in indoor environmental parameters, the position and shape of potential barriers and potential wells are often changing dynamically. In order to achieve real-time updates of the optimal purification channel, the system can introduce technologies such as sliding windows and incremental learning to continuously track and analyze the potential energy field and dynamically adjust the purification channel. At the same time, the system can also use intelligent optimization technologies such as heuristic search and reinforcement learning to perform online optimization and adaptive adjustment of the purification channel to cope with the complexity and variability of the environment.

[0055] S107. Determine the installation position and air supply angle of the air purifier based on the optimal purification channel, and control the air purifier to supply air along the direction of the optimal purification channel.

[0056] The system determines the installation position and air supply angle of the air purifier based on the optimal purification channel, so that the air outlet direction of the air purifier coincides with the optimal purification channel, and controls the air purifier to supply air along the direction of the optimal purification channel.

[0057] After obtaining the optimal purification channel, the system needs to further determine the installation position and air supply angle of the air purifier, and control it to supply air along the optimal purification channel to achieve efficient PM2.5 removal and purification. The installation position of the air purifier should be as close to the potential well area as possible to capture the accumulated PM2.5 in time; and the air supply angle should be consistent with the direction of the optimal purification channel to reduce airflow disturbance and energy loss.

[0058] Specifically, the system can use the starting node of the optimal purification channel as the installation position of the air purifier, and the starting direction of the channel as the initial air supply angle of the purifier. When arranging the purifier, factors such as the installation height and floor space of the equipment should also be considered to ensure its coordination and aesthetics with the indoor environment. At the same time, the system can also optimize the number and distribution of purifiers according to the length and complexity of the purification channel to achieve collaborative work and global optimal control of multiple devices.

[0059] When controlling the air supply of the purifier, the system should monitor the changes in the PM2.5 concentration field and the thermal convection field in real time, and dynamically adjust the air supply strategy to adapt to the real-time changes in the optimal purification channel. Specifically, the system can use classic control methods such as PID control and adaptive control to adjust the air volume and direction angle of the purifier in real time to track the optimal purification channel. At the same time, the system can also introduce advanced control technologies such as predictive control and robust control to predict and actively respond to future environmental changes in advance, thereby improving the accuracy and stability of purification control.

[0060] In the above embodiment, indoor PM2.5 concentration data and temperature field distribution data are collected, the vorticity distribution of the concentration field is calculated to characterize its rotational characteristics, the radial and tangential components of the concentration field are decomposed in the polar coordinate system, and the migration potential field is constructed in combination with the thermal convection intensity, thereby identifying the potential barrier and the potential well position. Based on these physical characteristics, the shortest path that does not pass through the potential barrier and connects the potential well position is calculated as the optimal purification channel, and the installation position and air supply angle of the air purifier are determined accordingly. It can improve the accuracy of grasping the movement law of PM2.5 in indoor space, reduce the energy loss when the purified airflow passes through the high barrier area, and realize efficient purification of multiple potential well positions through the optimal path, reduce the airflow turbulence loss, and improve the purification efficiency. Not only reduces the energy consumption of the purifier, but also shortens the purification time of indoor PM2.5, and realizes energy-saving and efficient precise control.

[0061] Based on the first embodiment, this application also provides an optimization scheme, which further improves the control effect of the air purifier by finely analyzing and classifying the PM2.5 migration channel. Starting from the potential energy field, this optimization scheme constructs a complete migration channel network by analyzing the migration characteristics of PM2.5 between different potential energy surfaces, and classifies the channels based on the time scale, thereby providing a more scientific basis for the arrangement of the air purifier. Figure 2 , another PM2.5 control method based on an air purifier in an embodiment of the present application is described: See also Figure 2 , is another flow chart of a PM2.5 control method based on an air purifier in an embodiment of the present application.

[0062] S201, constructing equipotential surfaces of the concentration field, and calculating the probability of potential energy transition between adjacent equipotential surfaces; The system constructs equipotential surfaces of the concentration field and calculates the probability of potential energy transition between adjacent equipotential surfaces. The calculation of the probability of potential energy transition between adjacent equipotential surfaces specifically includes: Obtain sampling point pairs on adjacent equipotential surfaces, and calculate the potential energy difference and spatial distance between each sampling point pair; Determine the ground state transition probability based on the potential energy difference and spatial distance; The excited state transition probability is calculated based on the indoor ambient temperature and turbulence intensity; The ground state transition probability and the excited state transition probability are weighted to obtain the comprehensive transition probability.

[0063] In this step, the system first constructs the equipotential surface of the PM2.5 concentration field. The equipotential surface refers to a collection of points with the same potential energy in the concentration field, which can be understood as the contour lines of PM2.5 concentration. By constructing the equipotential surface of the concentration field, the distribution characteristics of PM2.5 in space can be intuitively reflected. After constructing the equipotential surface, the system calculates the potential energy transition probability between adjacent equipotential surfaces. The potential energy transition probability indicates the probability of PM2.5 particles transitioning from one equipotential surface to an adjacent equipotential surface, reflecting the migration trend of PM2.5 between different concentration areas.

[0064] In specific implementation, the system can use numerical simulation methods to construct equipotential surfaces of concentration fields. First, the system obtains the PM2.5 concentration distribution data of the indoor environment, and preprocesses and interpolates the data to obtain a continuous concentration field. Then, the system selects a suitable potential energy function, such as a Gaussian potential energy function, to map the concentration value to a potential energy value. Through equipotential surface extraction algorithms, such as the March algorithm or the contour line tracking algorithm, the system can extract a series of equipotential surfaces. For the calculation of the potential energy transition probability between adjacent equipotential surfaces, the system can use the Monte Carlo method for simulation. By randomly selecting a large number of sampling point pairs on adjacent equipotential surfaces, the potential energy difference and spatial distance between each sampling point pair are calculated, and the transition probability is determined according to the Boltzmann distribution.

[0065] S202, constructing a migration channel network based on the potential energy transition probability, identifying channel clusters with transition probabilities greater than a preset probability in the migration channel network, and calculating the PM2.5 transmission flux of each channel cluster; In this step, the system constructs a migration channel network based on the potential energy transition probability. The migration channel network is an abstract network model used to describe the migration path of PM2.5 between different areas. The nodes in the network represent the intersection points of equipotential surfaces in the concentration field, and the edges connecting two nodes represent the migration channels between adjacent equipotential surfaces. The weight of the edge is the corresponding transition probability. By constructing the migration channel network, the system can analyze the transmission characteristics of PM2.5 in the indoor environment and identify the main migration paths.

[0066] After the migration channel network is built, the system needs to identify channel clusters with transition probabilities greater than the preset probability. A channel cluster refers to a set of channels that are connected in the network and have a high transition probability, representing the main migration path of PM2.5. By setting a preset probability threshold, the system can screen out channels with a transition probability higher than the threshold and classify them into the same channel cluster. For each channel cluster, the system calculates its PM2.5 transmission flux, that is, the mass of PM2.5 passing through the channel cluster per unit time. The calculation of the transmission flux can be based on the transition probability of each channel in the channel cluster and the concentration difference between the equipotential surfaces.

[0067] In specific implementation, the system can use graph theory algorithms to construct and analyze the migration channel network. For example, the system can use the Dijkstra algorithm or the Floyd algorithm to calculate the shortest path between any two nodes in the network, and use this to characterize the migration characteristics of PM2.5 in the network. For the identification of channel clusters, the system can use community discovery algorithms, such as modularity maximization algorithms or label propagation algorithms, to divide the network into multiple tightly connected subgraphs, each of which is a channel cluster. When calculating the transmission flux, the system can use the finite element method to numerically simulate the PM2.5 concentration distribution in the channel cluster, and calculate the flux based on the simulation results.

[0068] S203, constructing a state transfer matrix based on the comprehensive transition probability; The system constructs a migration channel network based on the potential energy transition probability. The network nodes in the migration channel network represent the intersection points of each equipotential surface, and the connection weights represent the transition probability between adjacent network nodes.

[0069] In this step, the system constructs a state transfer matrix based on the comprehensive transition probability. The state transfer matrix is ​​a matrix that describes the system state transition law, in which each element represents the probability of transitioning from one state to another. In the PM2.5 migration channel network, the state can be understood as the equipotential surface where PM2.5 is located, and the transition probability between states is the comprehensive transition probability between adjacent equipotential surfaces. By constructing the state transfer matrix, the system can quantitatively describe the migration law of PM2.5 between different equipotential surfaces, providing a basis for subsequent time scale analysis.

[0070] Specifically, the construction process of the state transfer matrix is ​​as follows: First, the system determines the dimension of the state transfer matrix, that is, the number of equipotential surfaces. Then, the system calculates each element in the matrix based on the comprehensive transition probability. For the element a_ij in the matrix, its value is the comprehensive transition probability of transitioning from equipotential surface i to equipotential surface j. Since the comprehensive transition probability has taken into account the influence of factors such as temperature and turbulence, the state transfer matrix can more accurately reflect the migration law of PM2.5 in the actual environment.

[0071] It should be noted that the state transition matrix satisfies the property of the probability matrix, that is, all elements in the matrix are non-negative, and the sum of the elements in each row is 1. This means that the sum of the probabilities of transitioning from one state to all other states is 1. In order to ensure that the state transition matrix satisfies this property, the system can normalize the comprehensive transition probability, that is, divide the elements of each row by the sum of the elements in that row, so that the sum of the elements in each row is 1.

[0072] S204, determining a characteristic relaxation time according to an eigenvalue spectrum of a state transfer matrix, classifying the channel clusters by time scale according to the characteristic relaxation time, and identifying fast channels and slow channels; The system determines the characteristic relaxation time based on the eigenvalue spectrum of the state transfer matrix. Specifically: Decompose the state transition matrix into a transition rate matrix and a residence time matrix; An eigenvalue equation is constructed based on the transfer rate matrix, and the sub-eigenvalue of the eigenvalue equation is solved by numerical iteration; The characteristic relaxation time is calculated based on the secondary eigenvalue.

[0073] After this, the system classifies the channel clusters on a time scale based on their characteristic relaxation times, identifying fast and slow channels.

[0074] In this step, the system uses the eigenvalue spectrum of the state transfer matrix to determine the characteristic relaxation time, and classifies the channel clusters on a time scale based on the characteristic relaxation time to identify fast channels and slow channels. The characteristic relaxation time is a physical quantity that describes the time required for a system to transition from one state to another, and can reflect the transition rate between different states. By analyzing the eigenvalue spectrum of the state transfer matrix, the system can obtain a series of characteristic relaxation times and classify the migration channels of PM2.5 on a time scale accordingly.

[0075] Specifically, the process of determining the characteristic relaxation time is as follows: First, the system performs eigenvalue decomposition on the state transfer matrix to obtain eigenvalues ​​and eigenvectors. The eigenvalue describes the time scale of the system state evolution, and the eigenvector describes the transition mode between different states. Then, the system calculates the characteristic relaxation time based on the eigenvalue. Generally speaking, the characteristic relaxation time is proportional to the inverse of the eigenvalue. The larger the eigenvalue, the shorter the corresponding relaxation time, indicating that the state transition is faster. By calculating the relaxation time corresponding to each eigenvalue, the system can obtain a series of relaxation processes on different time scales.

[0076] Based on the characteristic relaxation time, the system classifies channel clusters on a time scale. Specifically, the system identifies channel clusters with relaxation times shorter than a certain threshold as fast channels, and channel clusters with relaxation times longer than the threshold as slow channels. Fast channels represent paths through which PM2.5 can complete migration in a shorter period of time, corresponding to the main migration pathways of PM2.5; while slow channels represent paths through which PM2.5 takes a longer time to migrate, corresponding to secondary migration pathways. Through time scale classification, the system can more clearly characterize the migration characteristics of PM2.5 in indoor environments and identify the main migration pathways and secondary migration pathways.

[0077] In practical applications, the migration process of PM2.5 may be affected by many factors, such as airflow, temperature gradient, etc., resulting in significant differences in the relaxation time of different channel clusters. In order to improve the accuracy of time scale classification, the system can introduce more environmental parameters, such as wind speed, temperature gradient, etc., to correct the relaxation time. For example, the system can establish a quantitative relationship between wind speed and relaxation time, considering the impact of wind speed on the migration rate of PM2.5. In addition, the system can also adopt an adaptive threshold selection strategy to dynamically adjust the division criteria of fast channels and slow channels according to changes in environmental conditions, so that the classification results can better adapt to actual conditions.

[0078] S205: Determine the maximum layout range of the air purifier.

[0079] The system determines the maximum deployment range of the air purifier. When the air purifier operates within the maximum deployment range, the coverage of the fast channel is greater than the coverage of the slow channel.

[0080] In this step, the system determines the maximum layout range of the air purifier based on the migration characteristics of PM2.5, especially the distribution of fast channels and slow channels. The maximum layout range refers to the optimal layout area of ​​the air purifier in the indoor environment, so that the purifier can intercept and purify PM2.5 to the maximum extent and improve the purification efficiency. By comprehensively considering the coverage of fast channels and slow channels, the system can provide scientific guidance for the layout of air purifiers.

[0081] Specifically, the process of determining the maximum layout range is as follows: First, the system analyzes the spatial distribution characteristics of fast channels and slow channels in the indoor environment. Generally speaking, the fast channel corresponds to the main migration path of PM2.5, so the area covering the fast channel should be the key layout area of ​​the air purifier. The slow channel corresponds to the secondary migration path, and the area covering the slow channel can be used as the secondary layout area of ​​the air purifier. Based on this principle, the system determines a maximum layout range so that the air purifiers arranged within this range can cover the fast channel to the maximum extent while taking into account the coverage of the slow channel.

[0082] In the above embodiment, equipotential surfaces of the concentration field are constructed, the probability of potential energy transition between adjacent equipotential surfaces is calculated, a migration channel network characterized by network nodes and connection weights is established, and the main channel clusters are identified by preset probability thresholds. The complexity of solving the pollutant transport problem is reduced. By calculating the transmission flux of the channel cluster, the importance of different migration paths can be quantitatively evaluated, thereby identifying the transmission channels that play a leading role in the diffusion of pollutants. This method establishes a networked description model for PM2.5 migration, which can improve the accuracy of predicting the main propagation paths of pollutants.

[0083] The following describes the system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , which is a schematic diagram of the physical device structure of a PM2.5 control system based on an air purifier provided in an embodiment of the present application.

[0084] It should be noted that Figure 3 The structure of the system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0085] like Figure 3 As shown, the system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through a bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0086] The following components are connected to the I / O interface 305: an input section 306 including a camera, an infrared sensor, etc.; an output section 307 including a liquid crystal display (LCD) and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0087] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0088] It should be noted that the computer-readable medium shown in the embodiment of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.

[0089] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Among them, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0090] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the system described in the above embodiment; or may exist independently without being assembled into the system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of a system, the system implements the method provided in the above embodiment.

[0091] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0092] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0093] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk), etc.

[0094] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A PM2.5 control method based on an air purifier, characterized in that: include: Collect PM2.5 concentration data at several different locations indoors and obtain indoor temperature field distribution data; Calculating the vorticity distribution of the indoor PM2.5 concentration field based on the concentration data, wherein the vorticity distribution represents the rotation characteristics of the concentration field; Determining the vortex center position of the concentration field according to the vorticity distribution, and establishing a polar coordinate system with the vortex center as the origin; Decomposing the concentration field into radial components and tangential components in the polar coordinate system, and calculating radial concentration gradients and tangential concentration gradients; Calculating the indoor thermal convection intensity according to the temperature field distribution data, and constructing the migration potential field of PM2.5 in combination with the radial concentration gradient and the tangential concentration gradient; identifying potential energy barriers and potential well locations in the migration potential energy field; Calculating an optimal purification channel according to the spatial distribution characteristics of the potential energy barrier and the potential well position, wherein the optimal purification channel is the shortest path that does not pass through the potential energy barrier and connects each of the potential well positions; Determine the installation position and air supply angle of the air purifier based on the optimal purification channel, so that the air outlet direction of the air purifier coincides with the optimal purification channel; The air purifier is controlled to supply air along the direction of the optimal purification channel.

2. The method according to claim 1, characterized in that: Determining the vortex center position of the concentration field according to the vorticity distribution specifically includes: Converting the vorticity distribution into a frequency domain signal, and performing wavelet decomposition on the frequency domain signal to obtain vorticity characteristics at different scales; Calculating local maximum points based on the vorticity characteristics of different scales, and clustering the local maximum points to obtain corresponding centroids; Determine the centroid as a vortex center candidate point, and calculate the vorticity intensity and duration of each vortex center candidate point; A vortex center candidate point whose vorticity intensity is greater than a first threshold and whose duration is longer than a second threshold is determined as the vortex center.

3. The method according to claim 1, characterized in that The calculating the indoor thermal convection intensity according to the temperature field distribution data specifically includes: Dividing the temperature field distribution data into a plurality of temperature layers, and calculating the temperature gradient between adjacent temperature layers; Calculating the thermal buoyancy velocity between the temperature layers based on the temperature gradient; Constructing an indoor air density field according to the thermal buoyancy velocity, and calculating a vertical gradient of the air density field; Thermodynamic convection intensity distribution is calculated based on the vertical gradient and gravitational acceleration.

4. The method according to claim 1, characterized in that: After identifying the potential energy barriers and potential well positions in the migration potential energy field, the method further includes: Constructing equipotential surfaces of the concentration field, and calculating the potential energy transition probability between adjacent equipotential surfaces; Constructing a migration channel network based on the potential energy transition probability, wherein the network nodes in the migration channel network represent the intersection points of each of the equipotential surfaces, and the connection weights represent the transition probability between adjacent network nodes; Identifying channel clusters with a transition probability greater than a preset probability in the migration channel network, and calculating the PM2.5 transmission flux of each of the channel clusters; The channel cluster with the largest PM2.5 transmission flux is taken as the dominant transmission path.

5. The method according to claim 4, characterized in that The calculating of the potential energy transition probability between adjacent equipotential surfaces specifically includes: Acquire pairs of sampling points on adjacent equipotential surfaces, and calculate the potential energy difference and spatial distance between each pair of sampling points; determining a ground state transition probability based on the potential energy difference and the spatial distance; Calculating the excited state transition probability based on the ambient temperature and turbulence intensity in the chamber; The ground state transition probability and the excited state transition probability are weightedly calculated to obtain a comprehensive transition probability.

6. The method according to claim 4 or 5, characterized in that: After calculating the PM2.5 transmission flux of each channel cluster, the method further includes: constructing a state transfer matrix based on the comprehensive transition probability; Determining a characteristic relaxation time according to an eigenvalue spectrum of the state transfer matrix; classifying the channel clusters on a time scale according to the characteristic relaxation times to identify fast channels and slow channels; The maximum arrangement range of the air purifier is determined, and when the air purifier operates within the maximum arrangement range, the coverage range of the fast channel is greater than the coverage range of the slow channel.

7. The method according to claim 6, characterized in that Determining the characteristic relaxation time according to the eigenvalue spectrum of the state transfer matrix specifically includes: Decomposing the state transfer matrix into a transfer rate matrix and a residence time matrix; Constructing an eigenvalue equation based on the transfer rate matrix, and solving the sub-eigenvalue of the eigenvalue equation through numerical iteration; The characteristic relaxation time is calculated according to the secondary eigenvalue.

8. A PM2.5 control system based on an air purifier, characterized in that: The system comprises: One or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the system to execute the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a system, the system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a system, the system is caused to execute the method according to any one of claims 1 to 7.

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