Sensor control method for air purification system

Abstract

The application relates to a sensor control method of an air purification system; in combination with an initial distribution of sensors, a pollutant concentration gradient is monitored in real time, a pollutant diffusion path is simulated by using a fluid dynamics model; a pollutant source is deduced by using sensor data, and a collection range of the sensors is dynamically changed; multi-dimensional data are acquired by using physical sensors including particulate matters, temperature and humidity, and chemical sensors including VOC and harmful gases, pollutant characteristics are extracted by using principal component analysis PCA, a hazard index of the pollutants including AQI grading standards is constructed, and priority of the pollutants is dynamically evaluated in combination with real-time monitoring data of the sensors; resources of the air purification system are allocated to the pollutants with different priorities, including adjustment of filter air volume, activated carbon adsorption strength and ultraviolet sterilization lamp opening time; an adaptive calibration algorithm is introduced, sensor output including interference correction of temperature and humidity on particulate matter sensors is adjusted in real time according to environmental benchmark values; and a sensor maintenance and replacement cycle is dynamically adjusted.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of air purification, in particular to a sensor control method of an air purification system. BACKGROUND

[0002] The current sensor control method for air purification systems can provide environmental monitoring and air purification solutions to some extent, but still has some obvious shortcomings and disadvantages, which restrict its effect in practical application and widespread promotion. First, most traditional air purification system sensors use static deployment, that is, a fixed number and type of sensors are placed at a specific location. This static deployment cannot dynamically respond to environmental changes and is prone to monitoring blind spots, especially when the diffusion path of air pollution sources or the concentration of pollutants changes, the system has difficulty in making timely response. For example, when the diffusion path of air pollution sources changes, fixed sensors cannot adjust their position and density in real time, resulting in incomplete monitoring of pollutant concentration and affecting the precise control of air purification systems.

[0003] Secondly, the existing sensor control method generally lacks sufficient intelligence and self-adaptive ability. Most systems simply control the operation of purification equipment according to the concentration data of certain specific pollutants in the air, ignoring the diffusion path of pollutants, the rate of concentration change, and the relative harm degree between different pollutants. In this way, the system will start high-intensity purification equipment when the concentration of pollutants is low or changes slowly, while in the case of high concentration and rapid diffusion of pollutants, the response of the equipment is not timely or sufficient, resulting in reduced purification efficiency and waste of resources. Furthermore, current air purification sensors mostly rely on the monitoring of a single pollutant, ignoring the interaction of various pollutants in air quality. Pollutants in the air are usually diverse, such as PM2.5, nitrogen dioxide (NO2), carbon monoxide (CO), volatile organic compounds (VOCs), etc., and different pollutants have different harmful degrees to health, and their diffusion characteristics and interactions also have great differences. However, the existing sensor control method fails to consider these diversified pollutants and their interaction with environmental factors (such as wind speed, air temperature, humidity, etc.), resulting in the system's inability to comprehensively assess the real state of air quality, and a lack of scientificity in the scheduling and use of purification resources, ultimately affecting the purification effect and overall performance of the system. In addition, most current sensor control methods rely on simple monitoring data to control the on-off of air purification equipment, but lack in-depth analysis based on fluid dynamics or pollutant diffusion models. The diffusion process of pollutants is not only affected by air flow, but also closely related to temperature, humidity, etc., and existing systems often ignore the interaction of these complex factors, making it impossible to accurately predict the distribution changes of pollutants under different environments.

[0004] Even some systems can monitor and predict according to the location of the pollution source, but it usually relies on static pollutant diffusion model, ignoring the dynamic characteristics of air quality changes, unable to make timely adjustments when the sudden pollution source or pollutant concentration fluctuates greatly. In addition, the sensor control in the existing air purification system cannot achieve intelligent resource allocation. Air purification equipment usually has multiple working parameters, such as air volume, filter performance, activated carbon adsorption strength, ultraviolet lamp switching time, etc., which have important influence on the purification effect of different pollutants. However, most of the current air purification systems fail to dynamically adjust these working parameters based on the concentration, change rate, diffusion path and priority of pollutants. In this way, when the concentration of some pollutants is high, the system adjusts multiple parameters, resulting in waste of resources. When the concentration of pollutants is low, the system ignores the purification needs of some key pollutants, and cannot guarantee good air quality. SUMMARY

[0005] The purpose of the present application is to provide a sensor control method for air purification system, so as to solve some of the problems and deficiencies pointed out in the background art; the technical scheme adopted by the present application to solve the above technical problems is as follows: a sensor control method for air purification system, comprising: S1, dynamic deployment of sensors based on pollutant diffusion law:

[0006] S1.1, combined with the initial distribution of the sensor, real-time monitoring of the pollutant concentration gradient, and using fluid dynamics model to simulate the pollutant diffusion path;

[0007] S1.2, deduce the source of pollutants through sensor data, dynamically change the collection range of the sensor;

[0008] S2, multi-dimensional modeling of pollutant characteristics and decoupling of sensor signals:

[0009] S2.1, obtain multi-dimensional data through physical sensors including particulate matter, temperature and humidity, and chemical sensors including VOC and harmful gas, and extract pollutant characteristics by principal component analysis PCA;

[0010] S2.2, design a decoupling model, and use blind source separation technology to separate the characteristics of each pollutant from the composite signal of the sensor;

[0011] S3, multi-level response control driven by pollutant priority:

[0012] S3.1, construct a hazard index of pollutants including AQI classification standard, and dynamically evaluate the priority of pollutants combined with real-time monitoring data of the sensor;

[0013] S3.2, allocate resources of the air purification system for different priority pollutants, including adjusting filter air volume, activated carbon adsorption strength and ultraviolet sterilization lamp opening time;

[0014] S4. Data-driven sensor health management:

[0015] S4.1. Build a sensor data anomaly detection mechanism and analyze sensor operating status through machine learning algorithms;

[0016] S4.2. Introduce an adaptive calibration algorithm to adjust the sensor output in real time based on the environmental baseline value, including the correction of temperature and humidity interference on the particulate matter sensor;

[0017] S4.3. Dynamically adjust sensor maintenance and replacement cycles based on sensor health status prediction results;

[0018] S5. Sensor energy consumption management based on multi-objective collaborative optimization:

[0019] S5.1. Record the energy consumption characteristics of the sensor at different sampling frequencies and establish a mapping model between energy consumption and monitoring accuracy.

[0020] S5.2. Dynamically adjust the sampling frequency and operating mode based on pollutant priority, monitoring range, and sensor health status.

[0021] Furthermore, the dynamic deployment method of sensors based on pollutant diffusion law includes:

[0022] Each sensor collects real-time data on pollutant concentrations in the air and transmits the data to the control center. Based on the real-time sensor data and environmental conditions including temperature, humidity, and wind speed, the control center updates the fluid dynamics model of pollutant diffusion and calculates the diffusion path and concentration changes of pollutants in the air. The fluid dynamics model establishes an equation based on the time-varying pollutant concentration and diffusion rate, which is described by the following control equation:

[0023]

[0024] Where ΔC(x, t) represents the rate of change of pollutant concentration at position x and time t, reflecting the change of pollutant concentration over time; C(x, t) is the pollutant concentration, describing the pollutant concentration value at position x and time t; D(x) is the diffusion coefficient of the pollutant, indicating the ability of the pollutant to diffuse in the air. Its value is usually related to ambient temperature and humidity factors, reflecting the rate at which the pollutant diffuses from a high-concentration area to a low-concentration area. is the diffusion term, where represents the spatial gradient operator, represents the divergence operator, which calculates the spatial change rate of pollutant concentration; S(x,t) is the pollution source term, which represents the amount of pollutants released by the pollution source per unit time.

[0025] Further, the sensor dynamic deployment method based on pollutant diffusion law includes:

[0026] By calculating the pollutant concentration gradient, the diffusion direction of the pollutant and the location of the pollution source are evaluated; the pollutant concentration gradient calculation is based on the output of the fluid dynamics model, combined with the actual data collected by the sensor, to predict and evaluate the pollution source and the diffusion path; and the following integral formula is used to calculate the pollutant concentration gradient:

[0027]

[0028] wherein, represents the gradient of the pollutant concentration at position x and time t, reflecting the degree of change of the pollutant concentration with the spatial position, i.e. the concentration difference of the pollutant diffusing from a certain position to an adjacent position in space; represents the spatial first-order derivative of the pollutant concentration C at position x, reflecting the rate of change of the concentration at different positions; is an integral operation, representing the cumulative effect of the concentration change over time.

[0029] Further, the sensor dynamic deployment method based on pollutant diffusion law includes:

[0030] According to the real-time calculated pollutant concentration gradient and diffusion path, the position and number of sensors are dynamically adjusted, and the density distribution of the sensors is optimized; the process of dynamically optimizing the sensor deployment is adjusted by the following formula:

[0031]

[0032] wherein, N(x, t) represents the sensor density at position x and time t, the number or deployment frequency of the sensors at the position; ΔC(x, t) is the concentration change of the pollutant, representing the concentration change amount of the pollutant at region x and time t; ΔC max is the system preset concentration change limit, used to standardize the influence of concentration change; is the integral operation in space, reflecting the cumulative effect of the concentration change of the pollutant in the region [x0, x].

[0033] Further, the pollutant priority-driven multi-level response control method includes:

[0034] By monitoring the concentration change, diffusion path, and harm degree to health of different pollutants, a priority coefficient is assigned to each pollutant; the priority of the pollutant changes dynamically over time, and the priority evaluation formula is based on the comprehensive consideration of the pollutant concentration and environmental factors:

[0035]

[0036] The priority evaluation formula is used to evaluate the priority P of the pollutant i at time t i (t); the priority of the pollutant is calculated by weighted integration of the pollutant concentration, concentration change rate and diffusion characteristics; C i (t') represents the concentration of the pollutant t at time t', reflecting the current pollution level of the pollutant; R i (t') is the concentration change rate of the pollutant i , indicating the speed of concentration change, reflecting the diffusion rate of the pollutant; D i (t') is the diffusion characteristics of the pollutant; λ1, λ2, λ3 are weight coefficients, used to adjust the influence degree of concentration, change rate and diffusion characteristics on the priority; a, b, c are exponents, controlling the nonlinear influence of concentration, change rate and diffusion characteristics on the priority.

[0037] Further, the pollutant priority driven multi-stage response control method comprises:

[0038] A multi-dimensional mapping control model between the pollutant concentration and the treatment efficiency of the purification equipment is introduced; the multi-dimensional mapping control model comprises the concentration of the pollutant, the treatment capacity of the purification equipment and environmental factors, and the working state of the purification equipment is adaptively adjusted in different pollutant concentration intervals; the multi-dimensional mapping control formula:

[0039]

[0040] The multi-dimensional mapping control formula is used to calculate the treatment efficiency E i (t) of the pollutant i at time t, and the relationship between the pollutant concentration and the treatment efficiency of the purification equipment is established by integration; wherein, C i (t) is the concentration of the pollutant i at time t, indicating the current concentration level of the pollutant; α and β are parameters, respectively controlling the amplitude and decay rate of the exponential decay part; γ and δ are parameters, controlling the amplitude and periodic change of the cosine function part, representing the periodic adjustment of the treatment efficiency in the concentration change interval; the upper and lower limits of the integral are 0 and C i (t), the treatment efficiency in the pollutant concentration range is calculated by integration.

[0041] Further, the pollutant priority driven multi-stage response control method comprises:

[0042] A system based on the fluid dynamics model and the pollutant diffusion prediction algorithm is introduced, which is used to simulate the diffusion path and concentration change of the pollutant; by considering the distribution and intensity of the pollution source and the meteorological conditions including wind speed, air temperature and humidity, the diffusion process of the pollutant in the air is dynamically simulated, and the future distribution of the pollutant is predicted; the diffusion simulation and prediction formula:

[0043]

[0044] The diffusion simulation and prediction formula describes the diffusion of pollutants in space and time t, simulating the diffusion path and concentration change of pollutants combined with fluid dynamics principles; is the concentration of pollutants at position and time t, reflecting the distribution of pollutants in space; is the diffusion coefficient of pollutants at position and time t, reflecting the diffusion ability of pollutants; is the air flow vector, indicating the migration rate of pollutants with air flow; is the intensity of the pollution source, describing the generation rate of pollutants; is the gradient operator, used to calculate the rate of change of concentration in space, denotes the divergence operation of the diffusion flow, representing the diffusion trend of pollutants.

[0045] Further, the pollutant priority-driven multi-level response control method comprises:

[0046] An adaptive resource allocation mechanism is adopted to dynamically adjust the working parameters of the air purification system according to the priority, concentration and diffusion prediction of pollutants; the filter air volume, activated carbon adsorption strength and ultraviolet lamp switching time resources are dynamically adjusted according to the concentration, health impact and diffusion trend of pollutants, and the resource allocation model formula is:

[0047]

[0048] The resource allocation model formula is used to calculate the required resource allocation amount R i of pollutant i at time t, based on the priority of pollutants to adjust the resource allocation of the purification equipment; wherein P j (t) is the priority of pollutant j at time t, reflecting the processing urgency of pollutants; α j is the weighting coefficient, indicating the contribution of pollutant j to the resource allocation of the purification equipment; β is the overall resource adjustment coefficient, used to control the total amount of resource allocation; n represents the number of pollutants, and the model considers the weighted sum of the priorities of all pollutants.

[0049] The sensor control method of the air purification system of the present application combines pollutant concentration monitoring, dynamic resource allocation, pollutant diffusion prediction and priority-driven multi-level response control, effectively improving the intelligence and adaptive ability of the air purification system. The beneficial effects can be summarized as follows:

[0050] Through real-time monitoring and diffusion prediction based on pollutant concentrations, the system can promptly detect changes in air pollutant concentrations and diffusion paths, dynamically adjusting sensor locations and quantity. This adaptive deployment approach ensures adequate coverage of areas experiencing fluctuating pollutant concentrations, thereby improving monitoring accuracy and response speed.

[0051] By assessing pollutant concentration, rate of change, and diffusion characteristics, the control method of this invention can assign different priorities to different pollutants. Combining pollutant concentration, priority, and diffusion predictions, the system can intelligently adjust resource allocation and optimize air purification equipment operating parameters (such as filter air volume, activated carbon adsorption intensity, and UV lamp on / off time). BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a flow chart of the sensor control method of the air purification system of the present invention.

[0053] Figure 2 This is a flow chart of the sensor dynamic deployment method based on the pollutant diffusion law of the present invention.

[0054] Figure 3 This is a flow chart of the pollutant priority-driven multi-level response control method of the present invention. DETAILED DESCRIPTION

[0055] The following is a detailed description of the specific embodiments of the present invention with reference to the accompanying drawings.

[0056] Combined with attachment Figure 1 The sensor control method for an air purification system of the present invention first optimizes the system's responsiveness through dynamic sensor deployment based on pollutant diffusion patterns. Step S1 aims to simulate pollutant diffusion paths by monitoring pollutant concentrations in real time using a fluid dynamics model. This allows for dynamic adjustments to the sensor distribution, ensuring the system operates efficiently under varying environmental conditions.

[0057] S1.1, in combination with the initial distribution of sensors, real-time monitoring of pollutant concentration gradient, using fluid dynamics model to simulate the diffusion path of pollutants. The distribution of pollutants in the air is constantly changing, usually affected by wind speed, temperature, humidity and other factors, so the concentration of pollutants forms a gradient in space. When the sensor is initially deployed, it will be laid out according to the expected location of the pollutant source and the diffusion characteristics, but as the air flow and weather conditions change, the distribution of pollutants will also change. In order to adapt to this dynamic change, this method uses real-time monitoring of pollutant concentration gradient, the sensor can real-time sense the change of pollutant concentration in the surrounding environment, and use this data to simulate the diffusion path of pollutants. Fluid dynamics model plays an important role in this, which can simulate the characteristics of air flow, the diffusion speed of pollutants, and how pollutants flow with the wind and diffuse to different areas. This model usually includes the diffusion coefficient of pollutants in air flow, wind speed and other parameters, through the calculation of these parameters, the diffusion path of pollutants from the source to each area can be predicted, providing scientific basis for adjusting the sensor layout. Through this dynamic deployment, the sensor can more accurately capture the distribution of pollutants in space, ensuring that the sensor can collect data in the area where the concentration of pollutants is the most concentrated at each moment, improving the sensitivity and response speed of detection.

[0058] S1.2, by sensor data backtracking the source of pollutants, dynamically changing the collection range of sensors. The data collected by the sensor not only reflects the concentration of pollutants in the current area, but also provides time series information of pollutant diffusion. Through analysis of sensor data, the source of pollutants can be further backtracked. Specifically, the concentration change, diffusion rate and time difference of pollutants can provide basis for locating the source of pollution. Assuming that the diffusion of pollutants presents certain regularity, the sensor can calculate the point of initial release of pollutants and the intensity of pollution source according to the concentration data collected. This process usually combines reverse diffusion model and time series analysis method, by simulating the propagation process of pollutants from the source to the sensor, gradually determining the location and intensity of the pollution source. Once the source of pollution is determined, the system can dynamically adjust the collection range of the sensor according to the location of the source of pollutants, so that the sensor can more concentratedly monitor the area around the source of pollutants. This dynamic adjustment process not only optimizes the use of resources, but also improves the accuracy and response speed of the system. For example, if the sensor detects that the pollution concentration in a certain area rises rapidly, the system can quickly calculate the source of pollutants and intensively monitor the source area, ensuring timely capture of changes. This way makes the air purification system able to adjust the position and collection range of the sensor in time according to the change of pollutant diffusion, thereby improving the effect and efficiency of air purification.

[0059] The S2 step aims to further improve the system's accuracy in monitoring and processing pollutants by decoupling the multi-dimensional modeling of pollutant characteristics and sensor signals. In the air purification process, the sensor usually collects composite signals from multiple sources of pollutants, which include multi-dimensional data measured by physical sensors and chemical sensors. Therefore, how to extract the independent characteristics of pollutants and accurately reflect their concentration and type is the key to improving the system's accuracy and response speed. The S2.1 step uses different types of sensors to obtain multi-dimensional data of pollutants, including physical sensor data of particulate matter, temperature and humidity, and chemical sensor data of volatile organic compounds (VOC) and harmful gases. In order to extract the most representative pollutant characteristics from these multi-dimensional data, the invention uses the principal component analysis (PCA) method. Principal component analysis is a data processing technique commonly used for dimensionality reduction. Through linear transformation of multi-dimensional data, redundant information in the original data can be removed, and the principal component that best represents the data characteristics can be extracted, thereby reducing the complexity of the data and preserving important information. Specifically, in the air purification system, PCA can convert multi-dimensional sensor data into fewer principal components, effectively focusing on the main influencing factors of pollutants such as particulate matter concentration, volatile organic compound concentration, and air humidity and temperature, thereby improving the accuracy and efficiency of the system in data analysis and decision-making. The S2.2 step designs a decoupling model and uses blind source separation technology to process the composite signals collected by the sensor. In applications, the signals collected by the sensor are usually the superimposed signals of multiple pollutants, and there is mutual interference and overlap between these signals. Traditional signal processing methods are difficult to effectively separate the independent signal characteristics of each pollutant. To solve this problem, the invention introduces blind source separation (BSS) technology, especially the independent component analysis (ICA) or non-negative matrix factorization (NMF) method, which can separate the independent signals of each pollutant from the composite signals of the sensor without knowing the source signals of the pollutants by using the statistical properties of the signals, such as independence, non-gaussianity, etc. Through blind source separation technology, the system can disassemble the multiple signals collected by the sensor into independent pollutant concentration signals, and then accurately reflect the concentration and trend of each pollutant. The innovation of this technology lies in that it can extract effective pollutant information from complex signals without prior modeling of pollutant sources and sensor response functions, thereby improving the monitoring accuracy and adaptability of the system, efficiently separating different pollutant signals, and improving the real-time response ability and purification efficiency of the system.

[0060] S3 step aims to achieve dynamic adjustment of resource allocation and operation of the purification system according to the degree of harm and concentration changes of pollutants through a multi-level response control driven by pollutant priority. S3.1 step first builds a pollutant harm index and evaluates different pollutants in combination with air quality index (AQI) classification standards. Specifically, the harm index not only considers the concentration of pollutants, but also comprehensively evaluates factors such as the type of pollutants, exposure time, and harm to human health. The system dynamically calculates the harm index of pollutants according to real-time monitoring data of pollutants by sensors, and evaluates the priority of various pollutants according to the index. Pollutants with high priority usually refer to those that are more harmful to human health, or have high current concentration and rapid diffusion, such as PM2.5, nitrogen dioxide (NO2), ozone (O3), etc. These pollutants will be given higher priority for processing first. Traditional air purification systems generally operate according to pre-set standards or fixed thresholds, while the present invention dynamically adjusts the priority by real-time monitoring of pollutant data, so that the system can flexibly adjust the purification strategy according to the actual pollution situation, thereby improving the purification efficiency and response speed. S3.2 step dynamically adjusts the resource allocation of the air purification system according to the priority of pollutants to ensure that high-priority pollutants can be processed in time and effectively. Specifically, the system can optimize resource allocation by adjusting the air volume of the filter, the adsorption strength of activated carbon, and the opening time of the ultraviolet sterilization lamp. For high-priority pollutants, the system can increase the air volume of the filter to increase air flow and further improve the removal efficiency of pollutants in the air; at the same time, it can enhance the adsorption strength of activated carbon to improve its adsorption capacity for harmful gases, especially for the removal of volatile organic compounds (VOC) and other pollutants. The opening time of the ultraviolet sterilization lamp can also be adjusted according to the type and concentration of pollutants, and the irradiation time of the ultraviolet lamp can be extended for high-concentration microbial pollution to enhance the sterilization effect.

[0061] S4 aims to ensure the continuous and accurate operation of sensors in the system through data-driven sensor health management, optimizing their performance and service life. S4.1 first builds a sensor data anomaly detection mechanism, which relies on machine learning algorithms to analyze the operating status of the sensor. As a core component of the air purification system, the sensor may experience performance degradation or data anomalies due to environmental factors, aging, damage, etc. To detect these problems in a timely manner, the invention uses machine learning techniques such as classification models in supervised learning to train a model that can identify abnormal data patterns based on historical data and real-time monitoring data. When the output value of the sensor deviates from the normal range, the model can identify and issue an alarm signal in a timely manner, prompting system operators to check the health of the sensor, thereby avoiding air quality monitoring errors caused by sensor failure or inaccurate data. S4.2 introduces an adaptive calibration algorithm to eliminate the interference of environmental factors on sensor performance by adjusting the output value of the sensor in real time. For example, changes in temperature and humidity can affect the sensitivity of particulate matter sensors, so the system will adjust the calibration parameters of the sensor in real time based on environmental reference values such as the current air temperature and humidity to ensure that the sensor output value corresponds to the actual pollutant concentration. This process is completed through an adaptive algorithm without human intervention, allowing the system to operate stably under different environmental conditions and improving the accuracy and reliability of the sensor. S4.3 dynamically adjusts the maintenance and replacement cycle of the sensor based on the health status prediction results of the sensor. By continuously tracking and analyzing the historical operating data of the sensor and combining machine learning algorithms to predict the health status of the sensor, the system can predict the failure trend and life cycle of the sensor. In this way, the system can reasonably arrange the regular maintenance, calibration and replacement of the sensor according to the actual state of the sensor, avoiding data loss or accuracy degradation caused by sensor failure and ensuring the stability and accuracy of the system in long-term operation.

[0062] S5.1 step first requires recording the energy consumption characteristics of the sensor at different sampling frequencies. The energy consumption of the sensor is usually proportional to its sampling frequency, that is, the higher the sampling frequency, the greater the power consumption of the sensor, but at the same time, the increase of sampling frequency will improve the accuracy and timeliness of monitoring data. In order to effectively manage the energy consumption of the sensor, the present application designs a mapping model between energy consumption and monitoring accuracy, through which the system can quantify the impact of different sampling frequencies on energy consumption and data accuracy. This model relates the energy consumption of the sensor to its working mode, sampling frequency and monitoring accuracy, etc. Using statistical analysis methods or machine learning algorithms, an accurate energy-accuracy model is established according to the actual energy consumption and accuracy data of the sensor at different sampling frequencies. The establishment of this model enables the system to better understand how to adjust the sampling frequency under different working conditions to achieve the best energy efficiency ratio, both to maintain a certain monitoring accuracy and to reduce unnecessary energy consumption. S5.2 step is to dynamically adjust the sampling frequency and working mode of the sensor based on considering factors such as pollutant priority, monitoring range, sensor health status, etc. Specifically, the system will adjust the working mode of the sensor according to the priority of the pollutant. For example, in the case of high concentration of pollutants, the system will increase the sampling frequency of the sensor to ensure real-time tracking of the changes in pollutants; while in the case of low concentration of pollutants, the system will reduce the sampling frequency, thereby saving energy. At the same time, the system will also optimize the adjustment according to the health status of the sensor. If some sensors fail or performance declines, the system will reduce their workload, adjust their sampling frequency or temporarily switch to low-power mode, thereby extending their service life and reducing energy consumption.

[0063] Embodiment 1:

[0064] Referring to Figure 2 The flow, in this embodiment, in the air purification system of the present application, the sensor control method optimizes the monitoring effect and response speed of the system through dynamic deployment based on the diffusion law of pollutants. Taking an application scenario as an example, it is assumed that a series of sensors are deployed in an office building to monitor indoor air quality. These sensors can collect real-time concentration data of various pollutants (such as PM2.5, CO2, TVOCs, etc.) in the air and transmit these data to the central control center. The control center updates the fluid dynamics model of pollutant diffusion according to real-time sensor data and external environmental conditions (including temperature, humidity and wind speed, etc.), calculates the diffusion path and concentration change of pollutants in the air. This process is described by a mathematical model, and the specific mathematical expression is:

[0065]

[0066] In this equation, ΔC(x,t) represents the rate of change of pollutant concentration at position x and time t, reflecting the dynamic change of pollutant concentration over time; C(x,t) is the pollutant concentration, describing the pollutant concentration at a specific position x and time t; D(x) is the diffusion coefficient of the pollutant, indicating the diffusion ability of the pollutant in the air; and the diffusion term By calculating the spatial rate of change of pollutant concentration through the gradient operator, finally, S(x,t) is the source term, representing the amount of pollutant released per unit time.

[0067] Suppose in an office with a pollution source, the source is a printer that is emitting harmful gases and particulate matter. There are multiple sensors in the office, distributed in different areas, and they will dynamically adjust their deployment positions based on real-time monitoring data to optimize monitoring coverage.

[0068] After the sensor collects data, the control center first calculates the diffusion coefficient D(x) of the pollutant according to the environmental conditions in the formula (such as temperature 25℃, humidity 60%, wind speed 1.5m / s). In this example, the diffusion coefficient is set to be linearly related to environmental humidity and temperature, and the specific formula is:

[0069] D(x) = D0·(1 + α·T - β·H)

[0070] Where D0 is the baseline diffusion coefficient (for example, 1.0m 2 / s), T is the temperature (unit ℃), H is the humidity (unit %), α and β are the influence factors of temperature and humidity on the diffusion coefficient, respectively, and α = 0.01 and β = 0.005 are set.

[0071] In the application, the sensor collects pollutant concentration data such as PM2.5 concentration of 80μg / m 3 , CO2 concentration of 400ppm, and TVOCs concentration of 150ppb, combined with temperature, humidity and wind speed, the control center will update the diffusion path of the pollutant in the air according to the formula. Through calculation, the diffusion trend and concentration change of the pollutant in the next hour can be obtained, which will help the control center dynamically adjust the position of the sensor. For example, when the PM2.5 concentration in a certain area continues to rise, while the concentration in another area is lower, the system will instruct the sensor in that area to increase the sampling frequency or increase the coverage range to ensure that the trend of pollutant change is accurately captured.

[0072] At the same time, the control center also uses the pollutant source term S(x, t) to analyze the intensity of the pollution source. In this example, the printer is the pollution source, and its pollutant emission can be estimated by factors such as the use time of the device, emission characteristics, etc. Assuming that the printer emits 5 μg of PM2.5 and 20 ppm of CO2 per minute, the system can input these data into the source term equation to determine the range of the pollution source's impact on the ambient air quality.

[0073] Through this system, the dynamic deployment of sensors can respond to changes in air quality in real time, flexibly adjusting the position and working state of sensors to adapt to changing environmental conditions and fluctuations in pollution sources. The control center continuously optimizes the monitoring layout based on the diffusion model to ensure that the concentration distribution of pollutants can be accurately monitored, thereby achieving optimal allocation of system resources.

[0074] For example, if the pollution source in a certain area of the office suddenly increases, causing the PM2.5 concentration in that area to rise sharply, the system will increase the sampling frequency of the sensors in that area in a short period of time, and deploy new sensors on the pollution diffusion path to ensure more comprehensive air quality monitoring. In areas with lower pollution concentrations, the system will reduce the sampling frequency to save energy and reduce the workload of the sensors.

[0075] Specifically, in a certain office building environment, the air purification system is equipped with multiple sensors distributed on different floors and areas, which collect real-time concentration data of different types of pollutants (such as PM2.5, CO2, TVOCs, etc.) in the air. Assuming that we are now dealing with the diffusion of PM2.5, a printer in the office is continuously emitting PM2.5 particles as a pollution source.

[0076] In order to evaluate the diffusion path of the pollutant and predict the location of the pollution source in real time, the control center calculates the concentration distribution of the pollutant in the air through a fluid dynamics model, obtains the concentration gradient of the pollutant, and dynamically adjusts it in combination with the actual data collected by the sensors. The following calculus formula is used to calculate the concentration gradient of the pollutant:

[0077]

[0078] where, represents the gradient of the pollutant concentration at position x and time t, reflecting the degree of change in the concentration of the pollutant in space, i.e. the concentration difference of the pollutant in space from a certain position to the adjacent position; represents the spatial first-order derivative of the pollutant concentration C at position x, reflecting the rate of change of the concentration at different positions; is the integral operation, representing the cumulative effect of the concentration change over time.

[0079] Set the printer to release 5 μg of PM2.5 particles per minute, and the pollution source is located in the central area of the office. The PM2.5 concentration data collected by the sensor in real time is: the concentration of the sensor near the printer is 80 μg / m 3 , and the concentration of the sensor in the area far from the printer is 40 μg / m 3 . The control center first calculates the spatial gradient of PM2.5 concentration:

[0080]

[0081] This indicates that within a 1-meter range around the printer, the rate of change of PM2.5 concentration is 20 μg / m 3 per meter. Next, the system substitutes this gradient value into the integral formula of the concentration gradient to calculate the accumulated change in the concentration gradient over time:

[0082]

[0083] Assuming the system calculates the pollutant diffusion process within 1 hour, then:

[0084]

[0085] This shows that within 1 hour, the cumulative change in pollutant concentration in the air from the pollution source (printer) to the surrounding area is 1200 μg / m 3 . Through this calculation, the control center can assess the spatial diffusion of pollutants and predict the location of the pollution source and the diffusion path of the pollutants based on real-time data.

[0086] After the system identifies the location of the pollution source, it can dynamically adjust the position of the sensor based on the calculation of the pollutant concentration gradient and the diffusion path. For example, if the pollutant concentration in a certain area is too high, the control center can instruct to increase the sampling frequency of the sensor in that area, or even temporarily deploy more sensors; in areas with lower pollution concentration, the system can reduce the sampling frequency, thereby saving energy and improving the overall efficiency of the system.

[0087] After the sensor on a certain floor calculates the pollutant concentration gradient, it decides to adjust its working mode based on the gradient change. For example, in areas with high pollution concentration, the sensor will increase the sampling frequency from every 10 minutes to every minute. In this way, the system can more accurately capture the changes in pollutant concentration, and thus more timely adjust the purification strategy.

[0088] In addition, the system also adjusts the operation mode of the air purification equipment according to the direction of pollutant diffusion. For example, based on the calculation of the pollutant concentration gradient, the system increases the air volume and the working time of the ultraviolet lamp in some areas to accelerate the removal of pollutants; while in areas with lower pollutant concentration, the system reduces the working load of the purification equipment to achieve energy-saving effect.

[0089] The sensor dynamic deployment method based on pollutant diffusion law optimizes the layout and density of sensors by real-time calculation of pollutant concentration gradient and diffusion path, to improve the accuracy of air quality monitoring and ensure the efficient operation of the purification system. In order to achieve this goal, the system dynamically adjusts the number and deployment frequency of sensors according to the spatial and temporal changes of pollutants, to ensure that it can respond to changes in pollutant concentration in different areas.

[0090] In the case of an office building, there is a PM2.5 pollution source - a printer, which emits 5μg of PM2.5 particles per minute, located in the central area of the office building. In order to achieve accurate monitoring, the air purification system uses multiple sensors arranged in different floors and areas to monitor the pollutant concentration in real time. The distribution of these sensors will be dynamically adjusted according to the pollutant diffusion law, to ensure that areas with higher pollutant concentration can be paid more attention. At this time, the calculation of pollutant diffusion path and concentration gradient becomes a key step. The system first calculates the concentration distribution of PM2.5 in the air based on the fluid dynamics model, and combines the actual data of the sensors to evaluate the diffusion of pollutants.

[0091] In this process, the system uses the following calculus formula to calculate the density distribution and deployment frequency of sensors:

[0092]

[0093] Where N(x,t) represents the sensor density at location x and time t, reflecting the number or sampling frequency of sensors in that area; ΔC(x,t) is the change in pollutant concentration, representing the change in pollutant concentration in area x and time t; ΔC max is the system's preset concentration change limit, used to standardize the impact of concentration change; is the spatial integral operation, representing the cumulative effect of pollutant concentration change in the area [x0,x].

[0094] Set the system's preset concentration change limit ΔC max to 100μg / m 3, the system will adjust based on this concentration change range when deploying sensors. According to the actual data collected by the sensor, the control center can calculate the concentration change near the pollution source, and set the sensor concentration within 1 meter around the printer to 80 μg / m 3 , and the sensor concentration 2 meters away to 40 μg / m 3 . Therefore, the concentration change ΔC(x, t) is 40 μg / m 3 . According to the formula for calculating the sensor density, set the length of the area to 2 meters, and calculate:

[0095]

[0096] This means that within this area, the sensor density per meter is 0.8, and the system will determine the arrangement density and sampling frequency of the sensors in this area based on this density.

[0097] In addition, the control system is also set to adjust for different time periods in the area. Specifically, during high pollution periods (such as when the printer is working), the system will increase the sensor density in this area by increasing the sampling frequency to more accurately monitor the concentration of pollutants. For example, during the printer's working period, the system will increase the sampling frequency from every 10 minutes to every minute to capture the concentration change of PM2.5 in real time. During periods of less pollution, the system reduces the sensor sampling frequency in this area to reduce unnecessary energy consumption.

[0098] Based on the gradient of pollutant concentration and the dynamically adjusted sensor layout, the air purification system can effectively identify and respond to different pollution situations in different areas. In a certain floor, the density and deployment frequency of the sensors will be dynamically optimized according to the direction of pollutant diffusion and the concentration change. The system can timely identify changes in pollution sources and reasonably deploy sensors according to the predicted diffusion path, ensuring the accuracy of air quality monitoring while avoiding excessive deployment and resource waste. For example, if the printer malfunctions and causes an abnormal increase in pollutant concentration, the system can immediately identify and respond by increasing the density of sensors in the area for emergency monitoring. This flexible adjustment not only improves the accuracy of pollutant monitoring, but also enables the air purification system to quickly respond to real-time changes in pollutants and optimize purification efficiency.

[0099] Example 2:

[0100] In combination Figure 3In this embodiment, the pollutant priority-driven multi-level response control method assigns a dynamic priority to each pollutant by comprehensively evaluating the concentration changes, diffusion paths, and harmfulness levels of different pollutants. The core of this method is to dynamically adjust the priority of pollutants by real-time monitoring of factors such as pollutant concentration, concentration change rate, and diffusion characteristics, and adjusting the response strategy of the air purification system through weighted integration. In a typical office building environment, the system first monitors the concentration of pollutants in the air, including PM2.5, PM10, VOC (volatile organic compounds), and NO2 (nitrogen oxides), through a set of sensors installed in each area, and then performs priority evaluation based on these data.

[0101] For example, in an office area, the sensor collects PM2.5 concentration of 150 μg / m 3 , VOC concentration of 100 μg / m 3 , and NO2 concentration of 40 μg / m 3 . The air purification system assigns a priority coefficient to each pollutant based on the concentration, concentration change rate, and diffusion characteristics of the pollutant. The priority of the pollutant is dynamically adjusted over time, and the system comprehensively evaluates the harmfulness and priority of the pollutant through the following priority evaluation formula:

[0102]

[0103] In this formula, P i (t) represents the priority of pollutant i at time t; C i (t') represents the concentration of pollutant i at time t', reflecting the current pollution level of the pollutant; R i (t') is the concentration change rate of pollutant i, reflecting the speed of change of the pollutant concentration, representing the diffusion rate of the pollutant; D i (t') is the diffusion characteristics of the pollutant, describing the ability of the pollutant to diffuse; λ1, λ2, λ3 are weight coefficients, used to adjust the influence of concentration, concentration change rate, and diffusion characteristics on priority; a, b, c are exponents, controlling the non-linear influence of concentration, change rate, and diffusion characteristics on priority.

[0104] Suppose at a certain time, the concentration of PM2.5 is 150 μg / m 3 , the concentration change rate is 0.5 μg / m 3 / min, and the diffusion coefficient is 0.02 m 2 / s; the concentration of VOC is 100 μg / m 3 , the concentration change rate is 0.2 μg / m 3 / min, and the diffusion coefficient is 0.05 m 2The concentration of / s;NO2 is 40 μg / m 3 The rate of concentration change is 0.1 μg / m 3 / min, and the diffusion coefficient is 0.1 m 2 / s. At the same time, the weight coefficients are set as λ1=0.4, λ2=0.3, and λ3=0.3, and the exponential parameters are set as a=1, b=2, and c=1.

[0105] Substitute these data into the formula to calculate the priority of PM2.5:

[0106]

[0107] The calculation result is:

[0108]

[0109]

[0110] Set the evaluation time t as 10 minutes (i.e. 600 seconds), and the priority is:

[0111] P PM2.5 (600) = 60.081 x 600 = 36048.6

[0112] Next, calculate the priority of VOC:

[0113]

[0114] The calculation result is:

[0115]

[0116]

[0117] Similarly, set the evaluation time t as 10 minutes, and the priority is:

[0118] P VOC (600) = 40.027 x 600 = 24016.2

[0119] Finally, calculate the priority of NO2:

[0120]

[0121] The calculation result is:

[0122]

[0123] The priority is:

[0124] P NO2 (600) = 16.033 x 600 = 9618

[0125] According to the evaluation results of the priorities, the priority of PM2.5 is 36048.6, the priority of VOC is 24016.2, and the priority of NO2 is 9618. According to these priority values, the system will adopt different response strategies for the pollutants. Since PM2.5 has the highest priority, the system will increase the working intensity of the air purifier in this area to enhance the filtering effect of PM2.5. Since the priorities of VOC and NO2 are lower, the system will choose to maintain the normal working mode to reduce unnecessary energy consumption.

[0126] The multi-level response control method driven by the priority of pollutants further introduces a multi-dimensional mapping control model between the concentration of pollutants and the treatment efficiency of the purification equipment, and combines the concentration of pollutants, the treatment capacity of the purification equipment, and environmental factors to adaptively adjust the working state of the purification equipment. Specifically, based on the change of the concentration of pollutants, the treatment efficiency of the purification equipment is adjusted through the following multi-dimensional mapping control formula:

[0127]

[0128] In this formula, E i (t) represents the treatment efficiency of pollutant i at time t; C i (t) is the concentration of pollutant i at time t, which represents the current concentration level of the pollutant; α and β are parameters that control the amplitude and decay rate of the exponential decay part; γ and δ are parameters that control the amplitude and periodicity of the cosine function part, representing the periodic adjustment of the treatment efficiency within the concentration change interval; the integral upper and lower limits are 0 and C i (t), and the treatment efficiency within the concentration range is calculated by integration.

[0129] Suppose that in a certain office environment, the concentration of PM2.5 is 200 μg / m 3 at a certain time. The system finds that the concentration of PM2.5 has reached a high level according to real-time monitoring data, so it will adjust the working efficiency of the purification equipment. It is assumed that environmental factors have a greater impact on the treatment efficiency, and the treatment efficiency of the purification equipment is affected by both concentration and environmental changes. The following parameters are selected for calculation: α = 0.5, β = 0.01, γ = 0.3, and δ = 2, which determine the influence degree of the exponential decay part and the periodic adjustment part on the treatment efficiency. According to these parameters, the formula is calculated as follows:

[0130]

[0131] First, calculate the integral of the exponential decay part:

[0132]

[0133] Next, the integral of the cosine function part is calculated:

[0134]

[0135] Since sin(400) is periodic, it can be directly calculated that:

[0136] sin(400)≈-0.7457

[0137] Therefore, the integral of the cosine function part is:

[0138] 0.15·(-0.7457-0)=-0.111855

[0139] Adding the two parts together gives the treatment efficiency:

[0140] E PM2.5 (t)=0.43235+(-0.111855)=0.320495

[0141] This means that when the PM2.5 concentration is 200 μg / m 3 , the treatment efficiency of the air purification system is 0.320495. According to this treatment efficiency, the system will dynamically adjust the operating state of the purification equipment according to the preset working mode. For example, when the PM2.5 concentration is high, the purification equipment will increase the air flow and filtration capacity to cope with the higher pollution level; when the concentration is low, the purification equipment will appropriately reduce power consumption according to the multi-dimensional mapping control model to maintain optimal energy efficiency.

[0142] In addition, the multi-dimensional mapping control model can also adapt to environmental changes. For example, when the environmental temperature is high or the humidity is large, the purification equipment needs more processing capacity to cope with the diffusion of pollutants. Therefore, environmental factors will further adjust the operating efficiency of the purification equipment, and through this model, the air purification system can accurately match the processing needs of different pollutant concentration intervals, ensuring that the system operates in the most suitable way, improving the efficiency and energy saving of air purification.

[0143] In order to improve the purification efficiency and reduce energy consumption, a multi-level response control method driven by pollutant priority is adopted, and a system based on fluid dynamics model and pollutant diffusion prediction algorithm is introduced. The system can dynamically adjust the working state of the air purification equipment by simulating the diffusion path and concentration change of pollutants, thereby improving air quality and optimizing equipment operating efficiency. In-depth analysis of the diffusion simulation and prediction formula.

[0144] The basic form of the diffusion simulation and prediction formula is:

[0145]

[0146] where, C(x, y, z, t) represents the concentration of pollutants at position and time t, reflecting the distribution of pollutants in space; D is the diffusion coefficient of pollutants, representing the diffusion ability of pollutants at that position and time; V is the air flow velocity vector, representing the speed and direction of air flow, thus affecting the migration of pollutants; Q is the intensity of the pollution source, describing the generation rate of pollutants. This formula is based on fluid dynamics principles, considering the diffusion path of pollutants, wind speed, air temperature and humidity, etc. environmental factors to simulate the diffusion process of pollutants.

[0147] Suppose in a certain city, the PM2.5 concentration is high, and the environmental conditions in this area are changing. Through real-time data obtained by sensors, the pollutant concentration is 200 μg / m 3 . The system will simulate the diffusion path of pollutants dynamically based on this data, combined with fluid dynamics model. Set the following known environmental parameters:

[0148] Air flow rate 10 m / s (representing wind speed);

[0149] Diffusion coefficient 0.01 m 2 / s (representing the diffusion ability of pollutants);

[0150] Pollution source intensity 50 μg / m 3 ·s (representing the intensity of the pollution source).

[0151] In addition, the environmental temperature is 25℃ and the humidity is 60%, which will affect the adjustment of the diffusion coefficient and air flow rate . Therefore, the system will dynamically simulate the diffusion process of pollutants in space and predict future pollutant concentrations.

[0152] According to these data, the system will calculate the diffusion trend of pollutants in the next few hours through the fluid dynamics model. Set the pollutant concentration at a specific location to be 200 μg / m 3 at the initial time, and the pollution source intensity is 50 μg / m 3 ·s, through model calculation, it can be predicted that the pollutant concentration in this area will increase to 250 μg / m 3 after 1 hour.

[0153] Next, the system dynamically adjusts the working state of the air purification equipment according to the pollutant concentration prediction results. For this purpose, a multi-dimensional mapping control model between pollutant concentration and purification equipment treatment efficiency is adopted. It is set that the PM2.5 concentration in this area is predicted to be 250 μg / m 3 3 after 1 hour, and the purification equipment adjusts its treatment efficiency according to this data. It is set to use the following parameters to calculate the treatment efficiency:

[0154] α = 0.5 (controls the amplitude of the exponential decay part);

[0155] β = 0.01 (controls the decay rate of the exponential decay part);

[0156] γ = 0.3 (controls the amplitude of the cosine function part);

[0157] δ = 2 (controls the periodic change of the cosine function part).

[0158] Based on these parameters, the treatment efficiency of the purification equipment will be calculated according to the formula:

[0159]

[0160] First, calculate the integral of the exponential decay part:

[0161]

[0162] Next, calculate the integral of the cosine function part:

[0163]

[0164] Since sin(500) is periodic, it can be calculated that:

[0165] sin(500) ≈ 0.3491

[0166] Therefore, the integral of the cosine function part is:

[0167] 0.15 · (0.3491 - 0) = 0.052365

[0168] Add the two parts to get the treatment efficiency:

[0169] E PM2.5 (t) = 0.45895 + 0.052365 = 0.511315

[0170] This means that when the PM2.5 concentration is 250 μg / m 3 3, the treatment efficiency of the purification equipment is 0.511315. The system will adjust the operation mode of the purification equipment according to this treatment efficiency, increase the air flow and filtration capacity, and more effectively treat pollutants.

[0171] Optimize air quality through a multi-level response control method driven by pollutant priority, and dynamically adjust the working parameters of the system to achieve more efficient purification.

[0172] First, use a resource allocation model to dynamically adjust the working parameters of the air purification system. The specific formula is:

[0173]

[0174] Where:

[0175] R i (t) represents the required resource allocation of pollutant i at time t;

[0176] P j (t) is the priority of pollutant j at time t, reflecting the urgency of pollutant treatment;

[0177] α j is the weighting coefficient, indicating the contribution of pollutant j to the resource allocation of the purification equipment;

[0178] β is the overall resource adjustment coefficient, used to control the total amount of resource allocation;

[0179] n represents the number of pollutants, and the model considers the weighted sum of the priorities of all pollutants.

[0180] Assume that the main pollutants in the air of a certain area are PM2.5, real-time monitoring data shows that the current PM2.5 concentration is 200 μg / m 3 . In order to effectively control the operation of the air purifier, the system needs to first analyze the priority of the pollutants and dynamically adjust the resource allocation of the air purification equipment according to the concentration, health impact and diffusion trend.

[0181] Assume that there are three main pollutants: PM2.5, NO2 and CO, and their real-time concentrations and priorities are as follows:

[0182] PM2.5 concentration: 200 μg / m 3 , priority P1(t) = 3

[0183] NO2 concentration: 50 μg / m 3 , priority P2(t) = 2

[0184] CO concentration: 30 μg / m 3 , priority P3(t) = 1

[0185] The system allocates corresponding resources according to these priorities. Set the weighting coefficients α1=0.5, α2=0.3, α3=0.2, and the overall resource adjustment coefficient β=1, then the resource allocation amount R1(t) required for the pollutant PM2.5 is:

[0186] R1(t) = (0.5·3 + 0.3·2 + 0.2·1)·1 = (1.5 + 0.6 + 0.2) = 2.3

[0187] This means that the air purification system needs to allocate 2.3 units of resources for PM2.5.

[0188] Next, according to the changes in pollutant concentration, the working state of the air purification equipment will be dynamically adjusted. The working parameters of the purification equipment include: filter air volume, activated carbon adsorption intensity, and ultraviolet lamp switching time. Through the resource allocation model, these parameters can be adjusted according to the priority and concentration prediction of pollutants.

[0189] Set the concentration of PM2.5 pollutant to increase to 250 μg / m 3 in the next 1 hour, the system will calculate the required treatment efficiency according to the fluid dynamics model and pollutant diffusion prediction. Specifically, the treatment efficiency E PM2.5 (t) of the purification equipment can be calculated by the following model:

[0190]

[0191] Where x represents the pollutant concentration, and the integral interval is 0 to 250 μg / m 3 . First, calculate the exponential decay part:

[0192]

[0193] Then calculate the integral of the cosine part:

[0194]

[0195] Therefore, the treatment efficiency of the purification equipment is:

[0196] E PM2.5 (t) = 0.45895 + 0.052365 = 0.511315

[0197] This treatment efficiency value E PM2.5 (t) = 0.511315 indicates that the efficiency of the purification equipment in handling PM2.5 pollutants is 51.13%. The system adjusts the air flow and filtration capacity according to this treatment efficiency, thereby more effectively handling pollutants.

[0198] The core of the pollution priority-driven multi-stage response control method is to adjust the working state of the equipment in real time according to the changes in the concentration of pollutants and the priority. Through an adaptive resource allocation mechanism, the system can optimize the purification process according to environmental changes. For example, if the PM2.5 concentration is expected to reach 250 μg / m 3 within 1 hour in the future, and the concentrations of other pollutants remain unchanged, the system will prioritize PM2.5 and increase the processing efficiency of the purification equipment to achieve optimal purification results.

[0199] In addition, as the concentration of pollutants and priority changes, the system will continuously adjust the filter air volume, activated carbon adsorption intensity, and ultraviolet lamp switching time and other working parameters to ensure that the air purification equipment is always in the best working state.

[0200] The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above examples, and the above examples and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A sensor control method of an air purification system, characterized by Comprise the following steps: S1, sensor dynamic deployment based on pollutant diffusion law: S1.1, combined with the initial distribution of the sensor, real-time monitoring of pollutant concentration gradient, using fluid dynamics model to simulate the diffusion path of pollutants; S1.2, by sensor data backtracking pollutant source, dynamic change the collection range of sensor; S2, pollutant characteristic multi-dimensional modeling and sensor signal decoupling: S2.1, through the physical sensor including particulate matter, temperature and humidity, chemical sensor including VOC, harmful gas to obtain multi-dimensional data, using principal component analysis PCA to extract pollutant characteristics; S2.2, design decoupling model, using blind source separation technology to separate each pollutant characteristics from the sensor composite signal; S3, pollutant priority driven multi-level response control: S3.1, build pollutant including AQI grading standard hazard index, combined with real-time monitoring data of sensor dynamic evaluation of pollutant priority; S3.2, for different priority of pollutants, allocate air purification system resources, including adjusting filter air volume, activated carbon adsorption intensity and ultraviolet sterilization lamp opening time; S4, data-driven sensor health management: S4.1, build sensor data anomaly detection mechanism, through machine learning algorithm analysis sensor running state; S4.2, introduce adaptive calibration algorithm, according to the environmental benchmark value real-time adjustment including temperature and humidity on particulate matter sensor interference correction sensor output; S4.3, according to the sensor health state prediction results, dynamic adjustment of sensor maintenance and replacement cycle; S5, multi-objective collaborative optimization of sensor energy consumption management: S5.1, record the energy consumption characteristics of sensor under different sampling frequency, establish the mapping model of energy consumption and monitoring accuracy; S5.2, combined with the priority of pollutants, monitoring range, sensor health status, dynamic adjustment of sampling frequency and working mode.

2. The sensor control method of the air purification system according to claim 1, characterized by The sensor dynamic deployment method based on pollutant diffusion law comprises: Each sensor real-time acquisition of the concentration of pollutants in the air data, and the data transmission to the control center; control center based on real-time sensor data and including temperature, humidity, wind speed of environmental conditions update pollutant diffusion fluid dynamics model, calculate the diffusion path and concentration change of pollutants in the air; fluid dynamics model according to the time variation of pollutant concentration and diffusion velocity equation, through the following control equation is described: Wherein, AC(x, t) represents the change rate of pollutant concentration at position x and time t, reflecting the change of pollutant concentration with time; C(x, t) is the pollutant concentration, which describes the pollutant concentration value at position x and time t; D(x) is the diffusion coefficient of the pollutant, which represents the ability of the pollutant to diffuse in the air, and the value is usually related to environmental temperature, humidity factors, reflecting the diffusion rate of the pollutant from the high concentration area to the low concentration area; is the diffusion term, wherein represents the spatial gradient operator, represents the divergence operator, which calculates the spatial variation rate of the pollutant concentration; S(x, t) is the pollutant source term, which represents the pollutant release amount of the pollutant source per unit time.

3. The sensor control method of the air purification system according to claim 2, characterized by The sensor dynamic deployment method based on pollutant diffusion law comprises: Through the calculation of pollutant concentration gradient, evaluate the diffusion direction of pollutants and the location of pollution source; pollutant concentration gradient calculation based on the output of fluid dynamics model, combined with the actual acquisition of sensor data, to predict and evaluate the pollution source and diffusion path.

4. The sensor control method of the air purification system according to claim 3, characterized by The sensor dynamic deployment method based on pollutant diffusion law comprises: According to the real-time calculation of pollutant concentration gradient and diffusion path, dynamic adjustment of the position and quantity of sensor, optimization of sensor density distribution; dynamic optimization of sensor deployment process is adjusted by the following formula: where N(x, t) represents the sensor density, number of sensors or deployment frequency at location x and time t; AC(x, t) is the pollutant concentration change, representing the amount of change in the concentration of the pollutant at region x and time t; AC max is the system preset concentration change limit, used to standardize the impact of concentration change; is the spatial integral operation, reflecting the cumulative effect of the pollutant concentration change in the region [x0, x].

5. The sensor control method of the air purification system according to claim 1, characterized by The pollutant priority driven multi-level response control method comprises: A priority coefficient is assigned to each pollutant by monitoring the concentration change, diffusion path, and health hazard degree of different pollutants; the priority of the pollutants dynamically changes over time, and the priority evaluation formula is based on the comprehensive consideration of pollutant concentration and environmental factors: The priority evaluation formula is used to evaluate the priority P of the pollutant i at time t i (t); the priority of the pollutant is calculated by weighted integration of the pollutant concentration, concentration change rate and diffusion characteristics; C i (t') represents the concentration of the pollutant i at time t', reflecting the current pollution level of the pollutant; R i (t') is the concentration change rate of the pollutant i, indicating the speed of concentration change, reflecting the diffusion rate of the pollutant; D i (t') is the diffusion characteristics of the pollutant; λ1, λ2, λ3 are weight coefficients, used to adjust the influence degree of concentration, change rate and diffusion characteristics on the priority; a, b, c are exponents, controlling the nonlinear influence of concentration, change rate and diffusion characteristics on the priority.

6. The sensor control method of the air purification system according to claim 5, characterized by The pollutant priority-driven multi-level response control method comprises: A multi-dimensional mapping control model between pollutant concentration and purification equipment processing efficiency is introduced; the multi-dimensional mapping control model comprises the concentration of the pollutant, the processing capacity of the purification equipment, and environmental factors, and the working state of the purification equipment is adaptively adjusted in different pollutant concentration intervals.

7. The sensor control method of the air purification system according to claim 6, characterized by The pollutant priority-driven multi-level response control method comprises: A system based on a fluid dynamics model and a pollutant diffusion prediction algorithm is introduced to simulate the diffusion path and concentration change of the pollutant; by considering the distribution and intensity of the pollution source and meteorological conditions including wind speed, air temperature, and humidity, the diffusion process of the pollutant in the air is dynamically simulated, and the future distribution of the pollutant is predicted; the diffusion simulation and prediction formula: The diffusion simulation and prediction formula describes the diffusion of pollutants at position and time t, simulating the diffusion path and concentration change of pollutants in combination with fluid dynamics principles; is the concentration of pollutants at position and time t, reflecting the distribution of pollutants in space; is the diffusion coefficient of pollutants at position and time t, reflecting the diffusion ability of pollutants; is the air flow velocity vector, indicating the migration rate of pollutants with air flow; is the intensity of the pollution source, describing the generation rate of pollutants; is the gradient operator, used to calculate the rate of change of concentration in space, represents the divergence operation of the diffusion flow, representing the diffusion trend of pollutants.

8. The sensor control method of the air purification system according to claim 7, characterized by The pollutant priority-driven multi-level response control method comprises: An adaptive resource allocation mechanism is adopted to dynamically adjust the working parameters of the air purification system according to the priority, concentration, and diffusion prediction of the pollutant; the filter air volume, activated carbon adsorption strength, and ultraviolet lamp switching time resources are dynamically adjusted in combination with the concentration, health impact, and diffusion trend of the pollutant.

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