Intelligent electronic cigarette dustproof control method and system

Abstract

The invention relates to the technical field of dustproof control, in particular to an intelligent electronic cigarette dustproof control method and system.The method comprises the following steps that dust and airflow data are collected to eliminate noise, air pressure and flow state are analyzed to generate a characteristic spectrum, concentration change is judged to execute correlation analysis, and a draught fan is adjusted to control pressure feedback to generate response data; and formulating an electronic cigarette intelligent dustproof control scheme according to the efficiency and time ratio. According to the method, airflow behavior characteristics are constructed by introducing differential air pressure gradient and flow state recognition, the dust motion path prediction capacity is improved, a multi-parameter adjusting instruction is generated based on the concentration gradient, the flow state parameters and the pressure difference variable rate, dynamic adaptation of air volume control is achieved, a PWM adjusting and closed-loop feedback mechanism is introduced, the particle backflow risk is reduced, and the dust removal efficiency is improved. A multi-stage control strategy is constructed by taking the sedimentation efficiency and response time ratio as a boundary index, linkage adjustment of the pollution level and the control strength is realized, a full-link linkage system with quick response and efficient adjustment is formed, and the dustproof effect of the electronic cigarette is fully improved.

Description

Technical Field

[0001] The present invention relates to the technical field of dust prevention control, and particularly to an intelligent electronic cigarette dust prevention control method and system. Background Art

[0002] The technical field of dust prevention control includes the identification, classification of dust particles in the air, and their effective isolation and management in different application scenarios. The core content is to sense physical parameters such as the dust particle size and concentration in the environment, combine fluid dynamics characteristics and material surface properties, and design a multi-level physical protection layer and air flow guiding system to dynamically intercept and separate the entry of dust. The overall technical field covers directions such as dust detection, physical isolation, material selection, filter structure design, and air flow dynamics regulation, and is widely applied in multiple fields such as electronic products, air purification, industrial protection, and medical devices.

[0003] Among them, the intelligent electronic cigarette dust prevention control method refers to the method of combining a physical protection layer and an air flow diversion structure in an electronic cigarette device to physically block dust particles in the external air. Specifically, a polymer dust-proof film and a porous isolation net are stacked, combined with an air flow re-guiding catheter system. When air flows in, large particle dust is blocked by the surface protection film, and then fine dust particles are further separated by the middle-layer porous isolation net. At the same time, the air flow is guided to a set ventilation path to reduce the entry of suspended particles in the air into the interior of the electronic cigarette device.

[0004] In the dust prevention treatment of existing electronic cigarettes, mainly rely on a single structure combination for dust interception. During operation, there is no continuous monitoring mechanism for the environmental state, and dynamic factors such as particle size changes and air flow disturbances are difficult to be captured in time, resulting in a risk of lag in the protective effect. The dust prevention design structure is mainly static stacking, and the interception ability is fixed and unchanged, without forming an adjustment strategy in response to environmental changes, resulting in an increase in the particle penetration probability and affecting the cleanliness of the device interior. In terms of air volume control, there is no real-time analysis mechanism, and the ventilation ability cannot be adjusted with the fluctuation of particle concentration. The filtering system is difficult to quickly enhance the processing ability under high dust impact. The response boundary setting depends on the initial engineering parameters and does not have an adaptive correction ability. In the case of system lag or protection failure, it is unable to switch to a higher-level protection state in time. For example, during the short-term high-incidence period of pollution in an open public space, the system is difficult to form an air volume increase or channel pressure difference compensation, resulting in an increase in particle deposition inside the device, causing problems such as an increase in maintenance frequency and a shortening of service life. At the same time, the existing structure does not combine air dynamic characteristics and particle behavior modeling, lacking multi-dimensional parameter adjustment based on the actual operating state, and the flexibility of the protection strategy is insufficient, restricting the intelligent response ability of the system under multi-scenario and multi-intensity working conditions. Summary of the Invention

[0005] The purpose of the present invention is to solve the disadvantages existing in the prior art and propose an intelligent electronic cigarette dust prevention control method.

[0006] To achieve the above object, the present invention adopts the following technical solutions: an intelligent electronic cigarette dust-proof control method, comprising the following steps: S1: Obtain the dust concentration and air flow velocity data collected by the PM2.5 sensor and the thermal anemometer inside the electronic cigarette, eliminate noise through Kalman filtering, and generate an environmental monitoring data set; S2: Based on the wind speed data in the environmental monitoring data set, calculate the differential air pressure gradient and judge the air flow direction, mark the laminar / turbulent state, combine time series analysis to divide the air flow pattern categories, and generate a hydrodynamic characteristic spectrum; S3: Read the dust concentration gradient in adjacent periods, judge whether it exceeds the WHO air quality transition value, and perform multi-parameter correlation analysis in combination with the hydrodynamic characteristic spectrum to generate adaptive air duct adjustment data; S4: According to the adaptive air duct adjustment data, adjust the PWM duty cycle of the brushless DC fan inside the electronic cigarette, maintain the air duct pressure through PID control, and process the differential pressure sensor feedback data using moving average filtering to generate closed-loop control response data; S5: Based on the closed-loop control response data, calculate the ratio of particulate matter sedimentation efficiency to system response time, and execute corresponding control strategies according to the calculation results to generate an intelligent dust-proof control scheme for the electronic cigarette.

[0007] As a further solution of the present invention, the environmental monitoring data set includes the discrete distribution of air flow velocity, the trend characteristics of dust concentration, and the estimated value of noise interference. The hydrodynamic characteristic spectrum includes the laminar flow identification factor, the turbulent flow evolution parameter, and the air flow pattern category label. The multi-parameter correlation analysis includes the concentration-Reynolds mapping relationship, the identification of air pressure change trend, and the determination of the air volume response area. The adaptive air duct adjustment data includes the PWM duty cycle curve, the air flow direction angle distribution, and the dynamic weight distribution matrix. The closed-loop control response data includes the differential pressure filtered feedback, the PID adjustment amplitude, and the air duct pressure balance index. The intelligent dust-proof control scheme for the electronic cigarette includes the air volume adjustment mechanism, the fan mode switching strategy, and the particulate matter sedimentation criterion.

[0008] As a further solution of the present invention, the specific steps for obtaining the environmental monitoring data set are as follows: S111: Obtain the dust concentration data and air flow velocity data collected by the PM2.5 sensor and the thermal anemometer inside the electronic cigarette. Compare the instantaneous change value and the continuous change sequence value of the dust concentration data to judge the mean value. Select the fluctuation data segment where the mean change frequency and the amplitude change frequency both exceed the threshold judgment standard as the noise interference segment, and screen out the interference segment data to obtain the initial noise-screened data set; S112: Based on the initial noise-removed data set, for the time series trends of dust concentration and air flow velocity data, construct a sliding window for continuous data points according to the sampling time interval, calculate the covariance for the data within each group of windows, and make a difference judgment with the historical covariance benchmark value to identify the data points whose variance fluctuations exceed the difference range. Rearrange them in chronological order and fill in the missing values to obtain a covariance-corrected data matrix; S113: Based on the covariance-corrected data matrix, extract the dust concentration change values and air flow velocity change values between adjacent sampling points in the continuous time series, synchronize the mutation positions corresponding to the two groups of change values according to time, and calibrate the particle flow disturbance area by identifying the concentration degree and relative distribution density of the mutation points to obtain an environmental monitoring data set.

[0009] As a further solution of the present invention, the steps for obtaining the hydrodynamic characteristic spectrum are specifically as follows: S211: Based on the wind speed data in the environmental monitoring data set, obtain the wind speed change values and time interval values between adjacent time points, calculate the differential air pressure gradient, and obtain a differential air pressure gradient sequence; S212: According to the differential air pressure gradient sequence, calculate the air flow state change degree for the pressure change trend and direction fluctuation sequence of each continuous wind speed change interval, and compare it with a preset turbulence determination benchmark value. If it is greater than the critical value, mark it as turbulent; otherwise, mark it as laminar to obtain a flow state identification matrix; S213: According to the flow state identification matrix, merge the intervals of adjacent continuous segments with the same state to construct state segments, establish a time series mapping for each state segment in sequence, and obtain the hydrodynamic characteristic spectrum.

[0010] As a further solution of the present invention, the steps for obtaining the adaptive air duct adjustment data are specifically as follows: S311: Obtain the PM2.5 concentration data within adjacent periods, calculate the concentration difference and time difference, and establish a concentration gradient vector in the continuous sequence. If the gradient is positive and exceeds the WHO air quality transition value, mark it as an upward pollution trend; if the gradient is negative and the concentration is lower than the WHO air quality transition value, mark it as a weakening trend to obtain a concentration change trend sequence; S312: Based on the concentration change trend sequence and the hydrodynamic characteristic spectrum, construct a three-dimensional parameter matrix, calculate the adjustment amplitude of the fan PWM duty cycle, and map it to the fan PWM duty cycle and direction angle control quantity to obtain a wind volume adjustment response value; S313: According to the air volume adjustment response value, convert the command of the target area fan control signal, adopt the dual-parameter linkage form of duty cycle and direction angle, combine with the corresponding direction angle, set the control angle change range, control the deflection direction according to the concentration gradient polarity, update the duty cycle adjustment sequence and the direction angle mapping group in units of cycles, and obtain the adaptive air duct adjustment data.

[0011] As a further solution of the present invention, the step of obtaining the closed-loop control response data is specifically as follows: S411: According to the adaptive air duct adjustment data, combine with the brushless DC fan drive interface, update the PWM signal parameters for each cycle, record the stable time of the fan response, synchronously monitor the change trends of the fan speed and pressure after continuously inputting the periodic control signal, and obtain the fan response state data; S412: According to the fan response state data, process the air duct pressure data collected by the differential pressure sensor, calculate the difference between the current cycle and the previous cycle sampling values and evaluate the change trend, and perform deviation correction by adjusting the proportional, integral, and differential response factors to obtain the pressure closed-loop correction value; S413: Based on the pressure closed-loop correction value, perform a summation operation with the PWM control value of the current cycle to obtain the updated PWM signal output value, judge whether it exceeds the effective input upper and lower limits, synchronize with the direction angle of the current cycle to form a control instruction packet, update it to the brushless DC fan driver, and establish the closed-loop control response data.

[0012] As a further solution of the present invention, the step of obtaining the intelligent dust-proof control scheme for electronic cigarettes is specifically as follows: S511: Based on the closed-loop control response data, extract the particulate matter concentration change data and the air duct response time series for each cycle, obtain the time taken for each cycle of particulate matter from the start of decline to the stable concentration stage as the sedimentation time, and at the same time extract the time period from the issuance of the control signal to the stable air duct pressure as the system response time, and obtain the system response time ratio series; S512: According to the system response time ratio series, extract the fluctuation amplitude at the initial stage of concentration, the remaining concentration at the end, and the concentration variation coefficient at the initial stage of sedimentation, use the difference between the initial concentration and the final concentration of particulate matter within the cycle as the basic efficiency calculation item, adjust the actual concentration change intensity with the fluctuation amplitude, and then combine the variation coefficient and the sedimentation time to correct the efficiency estimation value to generate the particulate matter sedimentation efficiency value; S513: According to the particulate matter sedimentation efficiency value and the system response time ratio series, perform rule judgment and strategy matching on the corresponding value combinations of each cycle, execute the discrimination rule on the results of all cycles for classification and generate the corresponding strategy instructions, complete the cycle control adjustment label filing, and establish the intelligent dust-proof control scheme for electronic cigarettes.

[0013] Intelligent electronic cigarette dust-proof control system, comprising: The data acquisition module obtains the PM2.5 sensor concentration value and the thermal wind speed value, calculates the difference vector and performs a moving average process, combines the concentration and the wind speed, and generates an environmental monitoring data set; The airflow characteristic module calculates the pressure gradient based on the wind speed sequence of the environmental monitoring data set and determines the direction, classifies the airflow state and the time frequency, and generates a hydrodynamic characteristic spectrum; The multi-parameter coupling module sets the Re number and the pressure difference change rate as three-dimensional coordinate axes according to the hydrodynamic characteristic spectrum and the concentration gradient value, calculates the PWM value of each section by weighting, and generates adaptive air duct adjustment data; The fan adjustment module sets the PWM parameter according to the adaptive air duct adjustment data, reads the differential pressure value to calculate the PID compensation, and the moving average feedback value, and obtains the closed-loop control response data; The strategy execution module calculates the sedimentation efficiency and the response time ratio based on the closed-loop control response data, compares the set threshold to match the air volume section, and obtains the intelligent dust-proof control scheme for the electronic cigarette.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the present invention, by introducing the differential pressure gradient and the flow state recognition to construct the airflow behavior characteristics, the ability to predict the dust movement path is improved. Based on the concentration gradient, the flow state parameters and the pressure difference change rate, multi-parameter adjustment instructions are generated to realize the dynamic adaptation of the air volume control. The PWM adjustment and the closed-loop feedback mechanism are introduced to stabilize the air duct pressure and reduce the risk of particle backflow. Taking the sedimentation efficiency and the response time ratio as the boundary indexes, a multi-level control strategy is constructed to realize the linkage adjustment of the pollution level and the control strength, forming a full-linkage system with fast response and high efficiency adjustment, and fully improving the dust-proof effect of the electronic cigarette. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is the main process flow chart of the present invention; Figure 2 is the flow chart for obtaining the environmental monitoring data set of the present invention; Figure 3 is the flow chart for obtaining the hydrodynamic characteristic spectrum of the present invention; Figure 4 is the flow chart for obtaining the adaptive air duct adjustment data of the present invention; Figure 5 is the flow chart for obtaining the closed-loop control response data of the present invention; Figure 6 is the flow chart for obtaining the intelligent dust-proof control scheme for the electronic cigarette of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0016] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0017] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0018] Please refer to Figure 1 , an intelligent electronic cigarette dust prevention control method, including the following steps: S1: Obtain the dust concentration and air flow velocity data collected by the PM2.5 sensor (laser dust sensor) and the thermal anemometer inside the electronic cigarette, and exclude noise through the Kalman filter algorithm (a sensor data processing method that conforms to the IEEE1451 standard) to generate an environmental monitoring data set; S2: Based on the wind speed data in the environmental monitoring data set, calculate the differential pressure gradient (the standard air flow change rate in fluid mechanics) and judge the air flow direction, mark the laminar / turbulent state, and combine time series analysis to divide the air flow mode categories to generate a hydrodynamic characteristic spectrum; S3: Read the dust concentration gradient in the adjacent period, judge whether it exceeds the WHO air quality transition value (based on the PM2.5 24-hour average value of 35 μg / m³ released by the World Health Organization in 2021), and perform multi-parameter correlation analysis in combination with the hydrodynamic characteristic spectrum (implemented by constructing a three-dimensional parameter matrix of the ISO14644-1 standard, with the PM2.5 concentration gradient, Reynolds number (Re), and differential pressure change rate as the reference axes, and dividing the positive / negative correlation areas in combination with the ASHRAE55-2020 multi-parameter evaluation method: when the PM2.5 gradient rises and Re>2000 (turbulent state), the air volume is doubled, and when the gradient drops and Re<1000 (laminar state), the basic mode is enabled. The IEC60704-3 dynamic weight system is used to allocate the parameter influence (dust 60%, wind speed 30%, temperature 10%), and control instructions are generated through the ANSI / ASHRAE62.1-2022 ventilation model, mapping the PM gradient and the wind speed change rate to the fan PWM duty cycle and the air flow direction angle to achieve precise adjustment), and generate adaptive air duct adjustment data; S4: Adjust the PWM duty cycle of the brushless DC fan in the e-cigarette according to the adaptive air duct adjustment data, maintain the air duct pressure through PID control (the standard proportional-integral-derivative control method in the field of industrial automation), and use moving average filtering to process the feedback data of the differential pressure sensor to generate closed-loop control response data; S5: Based on the closed-loop control response data, calculate the ratio of the particulate matter sedimentation efficiency to the system response time, and implement a three-level control strategy: If the sedimentation efficiency ≥ 80% (corresponding to the ePM1 standard) and the response time ratio ≤ 0.5 (the system response time ≤ 50% of the particulate concentration change period), then implement the ISO16890 standard air volume adjustment (the international test standard for air filters); If the sedimentation efficiency < 60% (lower than the ePM10 standard) and the response time ratio > 1.2 (the system response lags), then implement the EN1822 emergency pressurization mode (the test specification for high-efficiency air filters); If the sedimentation efficiency is in the range of 65% - 80% and the response time ratio is in the range of 0.8 - 1.0, then implement the ASHRAE52.2 maintenance mode (the filter test standard); Comprehensively generate an intelligent dust-proof control plan for the e-cigarette.

[0019] The environmental monitoring data set includes the discrete distribution of air flow velocity, the trend characteristics of dust concentration, and the estimated value of noise interference. The hydrodynamic characteristic spectrum includes the laminar flow identification factor, the turbulent flow evolution parameter, and the air flow pattern category label. The multi-parameter correlation analysis includes the concentration-Reynolds mapping relationship, the identification of air pressure change trend, and the determination of the air volume response area. The adaptive air duct adjustment data includes the PWM duty cycle curve, the air flow direction angle distribution, and the dynamic weight distribution matrix. The closed-loop control response data includes the differential pressure filtered feedback, the PID adjustment amplitude, and the air duct pressure balance index. The intelligent dust-proof control plan for the e-cigarette includes the air volume adjustment mechanism, the fan mode switching strategy, and the particulate matter sedimentation criterion.

[0020] Please refer to Figure 2 , the steps of S1 are as follows: S111: Obtain the dust concentration data and air flow velocity data collected by the PM2.5 sensor and the thermal anemometer inside the e-cigarette. Compare the instantaneous change value and the continuous change sequence value of the dust concentration data to judge the mean value. Select the fluctuation data segment where both the mean change frequency and the amplitude change frequency exceed the threshold judgment standard as the noise interference segment, and screen out the interference segment data to obtain the initial noise-screened data set; Obtain the dust concentration data and air velocity data collected by the PM2.5 sensor and the thermal anemometer inside the electronic cigarette respectively. In this stage, the sampling frequency of the PM2.5 sensor is set to 1 time per second, and the sampling interval of the thermal anemometer is also set to 1 second. The duration of the initial data collection is 300 seconds, and 300 groups of dust concentration and air velocity data sets are obtained cumulatively. For the instantaneous change values and continuous change sequence values in the dust concentration data, perform a mean comparison and judgment. In this process, each group of data is processed according to a sliding window of 10 seconds. There are 10 data points of dust concentration data in each window, and calculate its mean value Subsequently, slide the window backward by 1 second and repeat the calculation of the next group of mean values By comparing Whether it is greater than the set threshold Judge whether there is fluctuation in this section of data. Set If the difference in mean values of three consecutive groups is greater than this threshold, then this section of data is determined to have a frequency anomaly. Further, perform an amplitude analysis on the difference between the maximum and minimum values in each window. If this amplitude Is greater than the set amplitude determination value Then it is considered that this section of data has an abnormal amplitude fluctuation. Set Combining the dual conditions of frequency and amplitude, if the mean value fluctuation frequency exceeds 3 times and the amplitude fluctuation is greater than Then it is determined that this section of data is a noise interference section, and perform data deletion operations. For example, the sampling data within 25 - 35 seconds are [15.0, 15.8, 16.2, 21.3, 22.0, 23.5, 21.1, 19.8, 18.6, 17.5] respectively, and its mean value is The mean value of the next window is The difference is Exceeds the set threshold, and the difference between the maximum and minimum values is Meets the dual determination conditions, then mark and delete this section of data. Repeat the above steps to complete the fluctuation deletion of all sampling data, and finally obtain the initial noise screening data set

[0021] S112: Based on the initial noise screening data set, for the time series trends of the dust concentration and air velocity data, construct a sliding window for continuous data points according to the sampling time interval, calculate the covariance for the data within each window, and perform a difference judgment with the historical covariance reference value to identify the data points where the variance fluctuation exceeds the difference range, rearrange them in chronological order and fill in the missing values to obtain a covariance correction data matrix Based on the initial noise screening data set, perform covariance operation processing on the time series trends of the dust concentration and air velocity data therein. First, construct a sliding window according to the sampling time interval of 1 second, set the window size to 5 seconds, and take the change value of the dust concentration within every 5 seconds With the corresponding air flow velocity change value Form data pairs, and construct 5 groups of data pairs as an observation unit. For example, in the sampling point, the data from the 5th second to the 9th second are the dust concentration [17.2, 18.4, 16.5, 19.3, 20.1] and the air flow velocity [0.32, 0.35, 0.33, 0.38, 0.40]. According to the covariance formula , where represents the mean value of the dust concentration within the window, represents the mean value of the air flow velocity, represents the number of data points within the window. Calculate the covariance value for this section. Set the historical covariance reference value as , if the difference between the current calculated value and it is greater than , that is, the current covariance value is , then the difference is . If it exceeds the determination range, it is considered that there are covariance fluctuation points within this window. Extract the corresponding data point numbers, and perform interpolation processing in combination with adjacent data points. Use the average value of two adjacent points for interpolation. For example, if point 7 is missing, use the average value of the data of point 6 and point 8 to fill it. After processing, rearrange the group of data points in chronological order to obtain a continuous and non-missing matrix data. Each column of the matrix is the dust concentration and the air flow velocity, and each row is the data of continuous sampling points. Finally, form a complete covariance correction data matrix. The relevant observation data is summarized as follows: Table 1 Covariance analysis data table As shown in Table 1, some covariance values have exceeded the determination threshold, and data correction processing is required.

[0022] S113: Based on the covariance correction data matrix, extract the dust concentration change value and the air flow velocity change value between adjacent sampling points in the continuous time series. Synchronize the two groups of change values according to time to mark the mutation positions. By identifying the concentration degree and relative distribution density of the mutation points, calibrate the particle flow disturbance area to obtain the environmental monitoring data set; Based on the covariance correction data matrix, extract the dust concentration change value and the air flow velocity change value between adjacent sampling points in its continuous time series. Set a group of change pairs to be constructed between every two continuous points. For example, point and form , . Identify mutations by comparing whether the change amplitude value of each item is greater than the set mutation threshold. The basis for setting the mutation threshold is , . If any change value exceeds the corresponding threshold, record this point as a suspected mutation point. During the sampling process, for example, the dust concentration changes from 16.3 to 23.0 at the 60th second, , and at the same time , meeting the mutation determination condition, further calculate the number of mutation points within 5 seconds before and after it. If the mutation density is greater than 2 points / 5 seconds, it is a high-density mutation segment, then mark this time period as the granular flow disturbance area, establish a time identifier for each mutation area, and map it to the original time series of the corrected data matrix. After marking, generate a complete environmental monitoring data set.

[0023] Please refer to Figure 3 , and the S2 step is as follows: S211: Based on the wind speed data in the environmental monitoring data set, obtain the wind speed change value and time interval value between adjacent time points, calculate the differential pressure gradient, and obtain the differential pressure gradient sequence; Based on the wind speed data in the environmental monitoring data set, extract the wind speed values of adjacent time points and calculate their change values, and then combine the known time interval to calculate the pressure difference caused by the wind speed change per unit length. First, collect the wind speed sequence as (unit: ), set the interval between every two points as , calculate the adjacent change values such as , , etc. Subsequently, set the gas density as according to the theoretical relationship of pressure change, and call the wind speed difference of each segment for dynamic pressure calculation, that is , such as . Repeat the operation to obtain multiple pressure differences, and uniformly normalize them with each segment of time difference into the pressure gradient value per unit length. After calculating the pressure differences of all data segments, form a differential sequence, and finally obtain the differential pressure gradient sequence.

[0024] S212: According to the differential pressure gradient sequence, for the pressure change trend and direction fluctuation sequence of each continuous wind speed change interval, use the formula: ; Calculate the degree of airflow state change , compare it with the preset turbulence determination reference value . If it is greater than the critical value, mark it as turbulence, otherwise mark it as laminar flow, and obtain the flow state identification matrix. Among them, represents the air density within the th time period, with the unit of ; , are the wind speed values at the th, th moments respectively, with the unit of ; is the differential pressure gradient corresponding to the th section, with the unit of ; represents the sampling time interval, with the unit of ; Indicates the turbulence disturbance energy correction value, which is converted from the integral intensity of the sensor oscillation signal, and the unit is ; For the The angle of the airflow direction at the point, in degrees; Indicates the average direction angle of this time period; is the angle difference normalization factor, in degrees; is the perturbation background offset constant, set to Used to prevent the denominator from returning to zero, the unit is dimensionless; Indicates the number of sections; Based on the differential pressure gradient sequence, the state change expression is constructed by combining the wind speed difference, pressure change and disturbance factor in the continuous section. The air density needs to be clearly defined during the calculation process. The wind speed change value is derived from the combined data collected by the thermometer and the pressure gauge and estimated by the state equation. The disturbance correction value is directly calculated from the time series difference Derived from the collector's instantaneous vibration response integral conversion, direction angle Collected by wind direction sensor, average direction angle , set the angle difference normalization factor , constant term To prevent the denominator from returning to zero, a disturbance factor is added. Four sets of data are selected. The specific data are shown in the following table: As shown in Table 2, first calculate the numerator item by item: ; ; ; ; ; Then calculate the denominator: Average direction angle ; ; ; Finally substitute into the formula: ; Compare this result with the turbulence critical value Compare and find that if the less than condition is met, it is judged to be a laminar flow section and the flow state identification matrix is obtained.

[0025] The degree of change in airflow state is a numerical index used to measure the comprehensive fluctuation intensity of airflow in multiple dimensions such as velocity, pressure, and direction per unit time. This index is obtained by aggregating the kinetic energy changes brought about by wind speed changes, the response intensity of pressure gradient fluctuations, and the participation degree of environmental disturbance energy, and performing proportional normalization processing with the deviation amplitude of the airflow direction angle. Essentially, it reflects the change trend of airflow in terms of structural stability within a certain time period. The larger the value, the more unstable the airflow state, indicating severe disturbance or sudden direction change. Conversely, it indicates a stable airflow trend, approximate to laminar flow. Therefore, it can be used to distinguish turbulent and laminar regions and assist in dividing the flow field pattern.

[0026] The overall operation logic of the formula is constructed based on the comprehensive influence of kinetic energy changes, pressure fluctuation responses, and direction disturbances of the fluid within a unit time period on the stability of the airflow state. First, in the numerator part, represents the kinetic energy change value brought about by the wind speed change within a unit time period, which reflects the amplitude of gas inertia fluctuation by multiplying the density by the square of the velocity difference. Immediately following, represents the fluctuation energy efficiency of the pressure gradient in the time dimension. Taking the square root is used to weaken the interference of abnormally large values on the overall trend, and multiplying by the time difference reflects its continuous influence on the system. Then, adding represents the local energy injection brought about by environmental oscillations or external disturbances to supplement the existence of nonlinear oscillations in the real system. The above three items together constitute the overall description of the "airflow system fluctuation intensity"; while the denominator part aggregates the direction fluctuation terms in the form of , expresses the instantaneous wind direction deviation amount in a normalized manner, and forms a direction fluctuation integration index after accumulating to a unit constant. This part cooperates with the numerator term to form a ratio relationship between the "airflow interference intensity" and the "direction instability", and taking the absolute value of the whole constructs a unsigned scalar output, which is used to quantify the change degree of the flow state under the influence of multiple physical dimensions in different segments.

[0027] S213: According to the flow state identification matrix, merge the continuous segments of adjacent same states into intervals to construct state segments, establish a time series mapping by sequentially numbering each state segment, and obtain the hydrodynamic characteristic spectrum; According to the flow state identification matrix, extract the information of continuous state label segments, merge and segment them according to the state type. Set the time threshold greater than 5 seconds to determine it as a long state segment, extract the data such as wind speed, direction, and pressure within the segment, calculate their mean value, range, and coefficient of variation, establish a characteristic matrix, and finally form a continuous spectrum structure to generate the hydrodynamic characteristic spectrum.

[0028] Please refer to Figure 4 , the steps of S3 are as follows: S311: Obtain PM2.5 concentration data within adjacent periods, calculate the concentration difference and time difference, establish the concentration gradient vector in the continuous sequence. If the gradient is positive and exceeds the WHO air quality transition value, it is marked as an upward pollution trend. If the gradient is negative and the concentration is lower than the WHO air quality transition value, it is marked as a weakening trend. Obtain the concentration change trend sequence; Obtain PM2.5 concentration data within adjacent periods. Use a 1-minute sampling period to obtain the concentration data with cycle numbers from P1 to P4, which are respectively μg / m³, and calculate the concentration gradient between its adjacent periods in sequence, that is , and correspondingly obtain that P2 is μg / m³ / min, P3 is μg / m³ / min, P4 is μg / m³ / min. Subsequently, determine whether the concentration value of each period exceeds the WHO-set 24-hour average limit of PM2.5, which is 35 μg / m³. When the P3 value is 36.5 μg / m³, it meets the "exceeding limit" state. If the corresponding concentration gradient is positive, it is judged as a pollution intensification trend. If the concentration is lower than the limit and the concentration gradient is negative, it is judged as pollution weakening. For example, when the P4 concentration is 32.4 μg / m³ and the gradient is -4.1 μg / m³ / min, it belongs to the pollution decline section. In this way, extract the pollution change trend of each period, establish a trend marking system based on the dual judgment of concentration amplitude and change direction, and obtain the concentration change trend sequence.

[0029] S312: Based on the concentration change trend sequence and the hydrodynamic characteristic spectrum, construct a three-dimensional parameter matrix. Using the PM2.5 concentration gradient, Reynolds number Re, and pressure difference change rate ΔP / Δt as the three reference axes, mark the positive correlation area and negative correlation area by means of regional division, and then establish a ventilation adjustment area instruction. Define the trigger state rule as: if the PM2.5 gradient rises and Re > 2000, it is determined as the turbulent growth state. Conversely, if the gradient drops and Re < 1000, it is the stable laminar flow state. After setting the air volume doubling threshold and the basic mode enabling benchmark, the parameter items called are respectively the dust concentration gradient G, the wind speed change rate ΔV / Δt, the temperature change ΔT, etc. Use the IEC60704-3 dynamic weight method to construct the air volume adjustment factor, and use the formula: ; Calculate the adjustment amplitude of the fan PWM duty cycle , and map it to the fan PWM duty cycle and the direction angle control amount to obtain the air volume adjustment response value, where is the PM2.5 concentration gradient value within the current period, is the wind speed change rate per unit time, is the temperature change amount, 、 , They are the dynamic weights corresponding to dust, wind speed, and temperature specified in IEC60704-3 respectively. is the temperature coefficient of variation within the current 5 minutes. is the temperature smoothing correction factor. is the system conversion gain. The units of all variables are: μg / m³ / min, m / s², °C, dimensionless. Construct a three-dimensional parameter matrix based on the concentration change trend sequence and the Reynolds number and pressure difference change rate in the hydrodynamic characteristic spectrum obtained previously. Map the PM2.5 concentration gradient G, wind speed change rate ΔV / Δt, temperature change ΔT, and temperature coefficient of variation σT in each cycle, and calculate the air volume adjustment factor based on the IEC60704-3 dynamic weight system. The parameter assignments are as follows: dust concentration weight , wind speed change rate weight , temperature change weight , temperature smoothing correction factor , system conversion gain . Taking P1 as an example, the participating items are shown in the following table: Table 3 Sample Table of Air Volume Adjustment Parameters Taking P1 as an example, substitute it into the formula: ; ; This result indicates that the PWM duty cycle of P1 cycle should be set to 32.3, and the corresponding adjustment amplitude is the air volume adjustment response value.

[0030] The adjustment amplitude of the fan PWM duty cycle refers to the fan output control quantity calculated based on the combined influence of multiple air environment dynamic factors. This value comprehensively reflects the overall influence of pollution intensity, air flow disturbance degree, and thermal environment fluctuation on ventilation demand in the current cycle in a normalized manner. Its calculation result is used to directly drive the setting of the fan PWM duty cycle and the adjustment of the air flow direction. The larger the value, the stronger the required air volume increase, corresponding to a higher wind speed output and a more intense air flow state. While a smaller value indicates that the environment is relatively stable and only basic ventilation is required. This response value participates in the fan operation mode switching as a signal instruction source in the control system and is the key intermediate quantity connecting multi-source environmental monitoring parameters and the air duct actuator, with characteristics such as real-time, differentiability, and controllability.

[0031] The operation logic of the formula is based on the contribution relationship of the combined action intensity of different physical factors in ventilation regulation. First, in the numerator part, the three key influencing factors are weighted and summed. The weighted addition form is adopted because the physical dimensions and influence intensities of the PM2.5 concentration gradient, wind speed change rate, and temperature change are different. Therefore, they need to be unified into an additive form through a weighted ratio. Among them, the concentration gradient directly reflects the change intensity of the pollution source and has the highest weight, which is set to 0.6. The wind speed change rate represents the airflow response rate and is a medium contribution factor with a weight of 0.3. The temperature change although it affects the airflow stability but has relatively weak sensitivity, so it is given the lowest weight of 0.1. To maintain the numerical comparability of the temperature change amount in different intervals, a square root transformation is applied to it, that is , which is used to reduce the interference of its fluctuation on the regulation intensity and at the same time maintain a positive expression. In the denominator part, a normalized correction factor is constructed, and the structure is adopted, where is the temperature variation coefficient, which reflects the instability of the temperature within this period, is a manually set temperature stability regulation intensity constant. The larger the denominator, the more unstable the temperature, thus suppressing the change amplitude of PWM. The whole formula is then multiplied by the system gain , which converts the normalized decimal regulation intensity into the actual PWM signal amplitude, and finally realizes the air volume control output with multi-factor fusion, direction normalization, and dynamic stability.

[0032] S313: According to the air volume regulation response value, the command of the fan control signal in the target area is transformed. The duty cycle and direction angle are used in a two-parameter linkage form. Combined with the corresponding direction angle, the control angle change range is set. The deflection direction is controlled according to the polarity of the concentration gradient, and the duty cycle adjustment sequence and the direction angle mapping group are updated in units of cycles to obtain adaptive air duct regulation data; According to the air volume regulation response value, the result is directly mapped to the fan duty cycle control signal. The continuous cycle adjustment value is converted into a PWM input value in the 0–255 integer interval, and the fan deflection angle is calibrated in combination with the gradient direction. The direction control logic is that when the gradient value is positive, the deflection angle is set to +15 degrees, and when the gradient is negative, it is -15 degrees. For example, if the gradient in the P1 cycle is 4.2, which is positive, the angle control is +15 degrees, the corresponding PWM integer is 32, the fan control signal is the PWM value 32, the duty cycle is 12.5%, and the direction deflection is +15 degrees. Finally, a mapping combination of PWM and direction angle is formed in each cycle to obtain adaptive air duct regulation data.

[0033] Please refer to Figure 5 , and the steps of S4 are as follows: S411: According to the adaptive air duct adjustment data, combined with the brushless DC fan drive interface, update the PWM signal parameters for each cycle, record the stable time of the fan response, synchronously monitor the change trends of the fan speed and pressure after continuously inputting the periodic control signal, and obtain the fan response state data; Based on the PWM duty cycle and direction angle fields extracted from the adaptive air duct adjustment data, first separate each cycle control instruction, read the PWM value and direction deflection amount one by one, then synchronously write them into the brushless DC fan driver, record the fan startup delay and stable time at the same time, and observe the change trend of the fan air volume in the corresponding cycle. Limit the PWM control signal within the range of 0 to 255. For example, if the PWM value of a cycle is 64, it represents a 25% duty cycle. By adjusting the fan power supply cycle through the interface, the fan speed can be controlled to achieve the purpose of variable speed control. The direction angle is deflected by the encoder electronic control drive structure, and the offset range is ±15 degrees. For example, if the PWM of a certain cycle is 48, the duty cycle is 18.8%, and the direction angle is set to -15 degrees, it means that the fan deflects to the left and is running at a low speed in this cycle. During the drive execution process, synchronously record the differential pressure sensor feedback pressure value as an evaluation reference. For example, when the PWM value input in cycle t1 is 48 and the fan output is stable, the measured air duct pressure is 172 Pa, and the corresponding disturbed air volume is 2.5 L / min. The data is shown in Table 5, and then obtain the fan response state data.

[0034] Table 4 Sample Data Table of Pressure Feedback As shown in Table 4, the PWM setting value corresponding to cycle t3 is 80, the duty cycle is 31.4%, the direction angle is +15 degrees, and the measured sampling pressure value is 178 Pa, and the air volume disturbance is 2.7 L / min.

[0035] S412: According to the fan response state data, process the air duct pressure data collected by the differential pressure sensor, calculate the difference between the current cycle and the previous cycle sampling values and evaluate the change trend, and perform deviation correction by adjusting the proportional, integral, and differential response factors to obtain the pressure closed-loop correction value; According to the fan response status data, collect the continuous feedback values of the differential pressure sensor within the sampling period, calculate the instantaneous error value of each period, that is, the difference between the target pressure and the sampled pressure. At the same time, average the error values of the first five periods to form a moving average pressure curve. When the error data such as Ps is 180 Pa, the errors in periods t1 to t5 are 8, 6, 2, 5, and 1 Pa in sequence. On this basis, obtain the change in the period difference. For example, the pressure rises by 4 Pa from t2 to t3. Further compare the cumulative total error, difference change value, and period disturbance air volume magnitude of each period. At the same time, extract the maximum fluctuation range within t1 to t5 as 179 - 172 = 7 Pa, and use this value as the basis for stability evaluation. Then, combine the sampling disturbance air volume intensity of each period for judgment. For example, the disturbance air volume in period t2 is 2.8 L / min, which is 0.3 L / min higher than that in period t1, and it is determined that the current stage is in the trend of increasing load. Then, estimate the required intensity of the adjustment command using the above results, and synchronously calculate the feedback adjustment item. Finally, obtain the pressure closed-loop correction value.

[0036] S413: Based on the pressure closed-loop correction value, perform a summation operation with the PWM control value of the current period to obtain the updated PWM signal output value. Judge whether it exceeds the effective input upper and lower limits, and synchronize with the direction angle of the current period to form a control command packet, which is updated to the brushless DC fan driver to establish closed-loop control response data; According to the pressure closed-loop correction value, superimpose it on the original period PWM duty cycle input value to obtain the PWM control command for the next period. The original duty cycle value is 80. If the correction value is +10, the updated value is 90. If the correction value is -15, the duty cycle becomes 65. Then, judge whether the corrected PWM value exceeds the maximum value of 255 or is less than the minimum value of 0. If there is an out-of-bounds situation, use the boundary value for limit processing. Then, re-integrate the complete control signal packet in combination with the direction angle information corresponding to the current period. For example, if the PWM value is updated to 85 and the direction angle is +10 degrees, the control command is PWM = 85, direction = +10°. This control signal will be written as the input signal for period t6 to the brushless DC fan controller interface. At the same time, record the pressure feedback value in period t6 for the correction reference of the next period to obtain the closed-loop control response data.

[0037] Please refer to Figure 6 , step S5 is as follows: S511: Based on the closed-loop control response data, extract the particulate matter concentration change data and the air duct response time series of each period. Obtain the time taken for the particulate matter to fall from the start to the stable concentration stage in each period as the sedimentation time. At the same time, extract the time period from when the control signal is issued to when the air duct pressure stabilizes as the system response time, and obtain the system response time ratio series; Based on the closed-loop control response data, first extract the complete change trajectory of the particulate matter concentration and the corresponding air duct pressure feedback time data in each cycle. Treat the sampled concentration change records of each cycle as a time series, and determine the start and end points of the particulate matter sedimentation stage within each cycle. The start point refers to the time when the initial concentration reaches the peak, and the end point is the time point when the concentration enters the stable range and the change remains less than 1 μg / m³. Then, based on the time axis data, calculate the total duration of this stage as the sedimentation time. For example, in cycle P2, the particulate matter concentration drops from 96 μg / m³ to the stable concentration of 20 μg / m³, with a time span of 58 seconds. Record this section as the sedimentation time period. Within the same cycle, call the response time feedback by the fan control module, and take the duration from the start of the control signal recording to the air duct pressure stabilizing within the error range of ±1 Pa as the system response time. For example, in cycle P2, it takes 30 seconds from the issuance of the control instruction to the air duct pressure stabilizing. Obtain the cycle response ratio as 30÷58 = 0.517. Process all cycles in sequence and organize them into a complete system response time ratio data column. At the same time, set a judgment criterion. If the sedimentation time in a certain cycle is less than 30 seconds, it is regarded as a fast response section; if it is greater than 60 seconds, it is a delay section; and the intermediate section is set as a normal response section. For the cycles corresponding to the fast response section with a system response time ratio less than 0.5, such as P3, P5, etc., record their ratio results as 0.47, 0.42 respectively. Finally, summarize the response time ratios of all cycles in order to establish a system response time ratio sequence.

[0038] S512: According to the system response time ratio sequence, extract the fluctuation amplitude at the starting stage of the concentration, the remaining amount of the concentration at the end, and the coefficient of variation of the concentration at the starting stage of sedimentation. Use the difference between the initial concentration and the final concentration of the particulate matter within the cycle as the basic efficiency calculation term, and adjust the actual concentration change intensity with the fluctuation amplitude. Then, combine the coefficient of variation and the sedimentation time to correct the efficiency estimate value to generate the particulate matter sedimentation efficiency value; According to the system response time ratio sequence, the initial value and the termination value data of the particulate matter concentration sampled in each cycle are called respectively to calculate the total amplitude of the concentration decrease. At the same time, the sedimentation time used in this process is recorded, and the fluctuation value in the starting stage and the concentration margin in the termination stage during the change process of the particulate matter concentration are collected. Taking cycle P3 as an example, its initial concentration is 95 μg / m³, the termination concentration is 20 μg / m³, the maximum fluctuation amplitude within the first 10 seconds in the concentration change section is 13 μg / m³, the concentration in the final section fluctuates within 20 ± 1 μg / m³, and the set remaining concentration is 3 μg / m³, the total sedimentation time is 60 seconds. In addition, the standard deviation of the initial fluctuation stage is 0.07 obtained from the change data, indicating the stability fluctuation degree of the concentration change in this stage. Combining the above data to construct a sedimentation efficiency index to judge the integrity of the sedimentation process, setting an efficiency reference benchmark. If the total sedimentation amplitude of the particulate matter is not less than 70 μg / m³, the sedimentation time is less than 70 seconds and the end-point concentration fluctuation is less than 5 μg / m³, it is regarded as a qualified efficiency cycle. If the fluctuation standard deviation is lower than 0.08, it is considered that the concentration change process has a stable trend. The sedimentation amplitude of cycle P3 is 75 μg / m³, the sedimentation time is 60 seconds, the terminal fluctuation is 3 μg / m³, and the standard deviation is 0.07, all of which meet the set standards. Record its sedimentation efficiency as 72.8%. And so on, complete the calculation of all cycles and summarize to form the particulate matter sedimentation efficiency value.

[0039] S513: According to the particulate matter sedimentation efficiency value and the system response time ratio sequence, perform rule judgment and strategy matching on the corresponding values of each cycle, execute the discrimination rule on the results of all cycles for classification and generate the corresponding strategy instructions, complete the filing of the cycle control adjustment label, and establish an intelligent dust-proof control scheme for electronic cigarettes; According to the particulate matter sedimentation efficiency value and the system response time ratio sequence, call the results of two groups of indicators in each cycle for combined judgment, and set three groups of strategy criteria as classification benchmarks. Among them, the matching condition of the first strategy is that the sedimentation efficiency is not less than 80% and the system response time ratio is not greater than 0.5. The matching condition of the second strategy is that the sedimentation efficiency is lower than 60% and the system response time ratio is greater than 1.2. The matching condition of the third strategy is that the sedimentation efficiency is between 65% - 80% and the response time ratio is between 0.8 - 1.0. Perform judgments on each cycle respectively. For example, the sedimentation efficiency of cycle P2 is 58.5% and the response ratio is 1.31, corresponding to the second strategy, marked as the EN1822 emergency pressurization mode. The sedimentation efficiency of cycle P3 is 72.8% and the response ratio is 0.47, corresponding to the first strategy but the efficiency does not meet the standard. Check that the response ratio meets the fast response section, then it does not match ISO16890. Further judge whether it meets the ASHRAE52.2 strategy. Since the response ratio is lower than the set interval and misses, it is marked as no match. The efficiency of cycle P4 is 85.4% and the response ratio is 0.42, meeting the matching condition of the first strategy, marked as the ISO16890 mode. The matching results of each cycle are summarized as follows: Table 5 Cycle Control Strategy Judgment Table As shown in Table 5, the period P4 clearly matches the ISO16890 air volume adjustment strategy, the period P2 falls into the EN1822 mode, and finally all the strategy matching results are summarized to generate a control output sequence, establishing an intelligent dust-proof control scheme for electronic cigarettes.

[0040] The intelligent electronic cigarette dust-proof control system includes: The data acquisition module obtains the PM2.5 sensor concentration value and the thermal wind speed value, calculates the difference vector and performs a moving average process, combines the concentration and the wind speed, and generates an environmental monitoring data set; The air flow characteristic module calculates the pressure gradient based on the wind speed sequence of the environmental monitoring data set and judges the direction, classifies the air flow state and the time frequency, and generates a hydrodynamic characteristic spectrum; The multi-parameter coupling module sets the Re number and the pressure difference change rate as the three-dimensional coordinate axes according to the hydrodynamic characteristic spectrum and the concentration gradient value, calculates the PWM value of each section by weighting, and generates adaptive air duct adjustment data; The fan adjustment module sets the PWM parameters according to the adaptive air duct adjustment data, reads the differential pressure value to calculate the PID compensation, and performs a moving average feedback value to obtain the closed-loop control response data; The strategy execution module calculates the sedimentation efficiency and the response time ratio based on the closed-loop control response data, compares the set threshold to match the air volume section, and obtains the intelligent dust-proof control scheme for electronic cigarettes.

[0041] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical content of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. Intelligent e-cigarette dust-proof control method, characterized in that, It includes the following steps: S1: Obtain the dust concentration and air flow velocity data collected by the PM2.5 sensor and the thermal anemometer inside the electronic cigarette, exclude the noise through Kalman filtering, and generate an environmental monitoring data set; S2: Based on the wind speed data in the environmental monitoring data set, calculate the differential air pressure gradient and judge the air flow direction, mark the laminar / turbulent state, combine time series analysis to divide the air flow pattern categories, and generate a hydrodynamic characteristic spectrum; S3: Read the dust concentration gradient in adjacent periods, judge whether it exceeds the WHO air quality transition value, and perform multi-parameter correlation analysis in combination with the hydrodynamic characteristic spectrum to generate adaptive air duct adjustment data; S4: According to the adaptive air duct adjustment data, adjust the PWM duty cycle of the brushless DC fan inside the electronic cigarette, maintain the air duct pressure through PID control, and process the differential pressure sensor feedback data using moving average filtering to generate closed-loop control response data; S5: Based on the closed-loop control response data, calculate the ratio of particulate matter sedimentation efficiency to system response time, and execute the corresponding control strategy according to the calculation result to generate an intelligent dust-proof control scheme for the electronic cigarette.

2. The intelligent electronic cigarette dust-proof control method according to claim 1, wherein The environmental monitoring data set includes the discrete distribution of air flow velocity, the trend characteristics of dust concentration, and the estimated value of noise interference. The hydrodynamic characteristic spectrum includes the laminar flow identification factor, the turbulent flow evolution parameter, and the air flow pattern category label. The multi-parameter correlation analysis includes the concentration-Reynolds mapping relationship, the identification of air pressure change trend, and the determination of the air volume response area. The adaptive air duct adjustment data includes the PWM duty cycle curve, the air flow direction angle distribution, and the dynamic weight distribution matrix. The closed-loop control response data includes the differential pressure filtered feedback, the PID adjustment amplitude, and the air duct pressure balance index. The intelligent dust-proof control scheme for the electronic cigarette includes the air volume adjustment mechanism, the fan mode switching strategy, and the particulate matter sedimentation criterion.

3. The intelligent electronic cigarette dust-proof control method according to claim 1, wherein, The specific steps for obtaining the environmental monitoring data set are as follows: S111: Obtain the dust concentration data and air flow velocity data collected by the PM2.5 sensor and the thermal anemometer inside the electronic cigarette. Compare the instantaneous change value and the continuous change sequence value of the dust concentration data for mean value judgment. Select the fluctuation data segment where both the mean change frequency and the amplitude change frequency exceed the threshold judgment standard as the noise interference segment, and screen out the interference segment data to obtain the initial noise-screened data set; S112: Based on the initial noise-screened data set, for the time series trend of the dust concentration and air flow velocity data, construct a sliding window for continuous data points according to the sampling time interval, calculate the covariance for each group of data within the window, and perform difference judgment with the historical covariance reference value to identify the data points where the variance fluctuation exceeds the difference range. Rearrange them in chronological order and fill in the missing values to obtain the covariance corrected data matrix; S113: Based on the covariance corrected data matrix, extract the dust concentration change value and the air flow velocity change value between adjacent sampling points in the continuous time series, mark the corresponding mutation positions of the two groups of change values according to time synchronization, and calibrate the particle flow disturbance area by identifying the concentration and relative distribution density of the mutation points to obtain the environmental monitoring data set.

4. The intelligent electronic cigarette dust-proof control method according to claim 1, wherein, The specific steps for obtaining the hydrodynamic characteristic spectrum are as follows: S211: Based on the wind speed data in the environmental monitoring dataset, obtain the wind speed change value and time interval value between adjacent time points, calculate the differential pressure gradient, and obtain the differential pressure gradient sequence; S212: According to the differential pressure gradient sequence, calculate the airflow state change degree for the pressure change trend and direction fluctuation sequence of each continuous wind speed change interval, compare it with the preset turbulence determination reference value. If it is greater than the critical value, mark it as turbulent; otherwise, mark it as laminar to obtain the flow state identification matrix; S213: According to the flow state identification matrix, merge the adjacent continuous segments of the same state to construct state segments, establish a time series mapping by sequentially numbering each state segment, and obtain the hydrodynamic characteristic spectrum.

5. The intelligent electronic cigarette dust-proof control method according to claim 1, characterized in that, The specific steps for obtaining the adaptive air duct adjustment data are as follows: S311: Obtain the PM2.5 concentration data within adjacent periods, calculate the concentration difference and time difference, establish the concentration gradient vector in the continuous sequence. If the gradient is positive and exceeds the WHO air quality transition value, mark it as an upward pollution trend; if the gradient is negative and the concentration is lower than the WHO air quality transition value, mark it as a weakening trend to obtain the concentration change trend sequence; S312: Based on the concentration change trend sequence and the hydrodynamic characteristic spectrum, construct a three-dimensional parameter matrix, calculate the adjustment amplitude of the fan PWM duty cycle, and map it to the fan PWM duty cycle and direction angle control amount to obtain the air volume adjustment response value; S313: According to the air volume adjustment response value, perform command conversion on the fan control signal in the target area. Adopt the dual-parameter linkage form of the duty cycle and direction angle, combine it with the corresponding direction angle, set the control angle change range, control the deflection direction according to the polarity of the concentration gradient, and update the duty cycle adjustment sequence and direction angle mapping group in units of cycles to obtain the adaptive air duct adjustment data.

6. The intelligent electronic cigarette dust-proof control method according to claim 1, characterized in that, The specific steps for obtaining the closed-loop control response data are as follows: S411: According to the adaptive air duct adjustment data, combined with the brushless DC fan drive interface, update the PWM signal parameters for each cycle and record the stable time of the fan response. Synchronously monitor the change trends of the fan speed and pressure after continuously inputting the periodic control signal to obtain the fan response state data; S412: According to the fan response state data, process the air duct pressure data collected by the differential pressure sensor, calculate the difference between the current cycle and the previous cycle sampling values and evaluate the change trend, and perform deviation correction by adjusting the proportional, integral, and differential response factors to obtain the pressure closed-loop correction value; S413: Based on the pressure closed-loop correction value, perform a summation operation with the PWM control value of the current cycle to obtain the updated PWM signal output value. Judge whether it exceeds the effective input upper and lower limits, and form a control instruction packet synchronously with the direction angle of the current cycle, update it to the brushless DC fan driver, and establish the closed-loop control response data.

7. The intelligent electronic cigarette dust-proof control method according to claim 1, characterized in that The specific steps for obtaining the intelligent dust-proof control scheme for electronic cigarettes are as follows: S511: Based on the closed-loop control response data, extract the particulate matter concentration change data and the air duct response time series for each cycle. Obtain the time taken for the particulate matter to drop from the start to the stable concentration stage in each cycle as the sedimentation time. At the same time, extract the time period from when the control signal is issued to when the air duct pressure stabilizes as the system response time, and obtain the system response time ratio series. S512: According to the system response time ratio series, extract the fluctuation amplitude at the initial concentration stage, the remaining concentration at the end, and the coefficient of variation of the concentration at the initial sedimentation stage. Use the difference between the initial concentration and the final concentration of the particulate matter within a cycle as the basic efficiency calculation term, and adjust the actual concentration change intensity with the fluctuation amplitude. Then, combine the coefficient of variation and the sedimentation time to correct the efficiency estimate value, and generate the particulate matter sedimentation efficiency value. S513: According to the particulate matter sedimentation efficiency value and the system response time ratio series, perform rule judgment and strategy matching on the combined corresponding values for each cycle. Execute the discrimination rule on the results of all cycles for classification and generate the corresponding strategy instructions, complete the cycle control adjustment label filing, and establish an intelligent e-cigarette dust prevention control plan.

8. Intelligent electronic cigarette dust-proof control system, characterized in that, The system is used to implement the intelligent e-cigarette dust prevention control method according to any one of claims 1-7, and includes: The data acquisition module obtains the PM2.5 sensor concentration value and the thermal anemometer value, calculates the difference vector and performs moving average processing, combines the concentration and the wind speed, and generates an environmental monitoring data set. The airflow characteristics module calculates the pressure gradient based on the wind speed sequence of the environmental monitoring data set and judges the direction, classifies the airflow state and the time frequency, and generates a hydrodynamic characteristics spectrum. The multi-parameter coupling module sets the Re number and the pressure difference change rate as the three-dimensional coordinate axes according to the hydrodynamic characteristics spectrum and the concentration gradient value, calculates the PWM value of each section by weighted averaging, and generates adaptive air duct adjustment data. The fan adjustment module sets the PWM parameter according to the adaptive air duct adjustment data, reads the differential pressure value to calculate the PID compensation, and slides the average feedback value to obtain the closed-loop control response data. The strategy execution module calculates the sedimentation efficiency and the response time ratio based on the closed-loop control response data, compares with the set threshold to match the air volume section, and obtains the intelligent e-cigarette dust prevention control plan.

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