Measurement method of dust particle counter with adjustable particle size channel

Abstract

The embodiment of the invention provides a measuring method of a particle size channel-adjustable dust particle counter. The measuring method comprises the following steps: acquiring airflow velocity data and calculating a real-time average airflow velocity; dynamically adjusting the duration of a sampling period based on the real-time average airflow speed; collecting and detecting the particulate matters according to the adjusted sampling period; and correcting and compensating a counting error according to a particle distribution statistical result. According to the scheme of the embodiment of the invention, the sampling period can be dynamically regulated and controlled according to the change of the airflow velocity so as to solve the problem of counting error caused by non-uniform particle distribution.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of particle detection, in particular to a measurement method of a dust particle counter with adjustable particle size channel. BACKGROUND

[0002] The measurement of the dust particle counter with adjustable particle size channel can accurately count and classify dust particles in the air according to different particle size ranges, thereby improving the pertinence and accuracy of the measurement; and the technology can dynamically adjust the sampling period according to the real-time detected airflow speed change to correspondingly optimize the sampling time, so as to ensure that stable and accurate particulate matter counting results can be obtained under different flow rate conditions. SUMMARY

[0003] Therefore, the present application provides a measurement method of a dust particle counter with adjustable particle size channel to at least partially solve the problems in the prior art.

[0004] A measurement method of a dust particle counter with adjustable particle size channel, comprising:

[0005] Obtaining airflow flow rate data and calculating real-time airflow average speed;

[0006] Dynamically adjusting the duration of the sampling period based on the real-time airflow average speed;

[0007] Collecting and detecting particulate matter according to the adjusted sampling period;

[0008] Correcting and compensating the counting error through the particulate matter distribution statistical result.

[0009] Preferably, the dynamically adjusting the duration of the sampling period based on the real-time airflow average speed further comprises:

[0010] Obtaining current airflow flow rate data V_real;

[0011] Calculating historical airflow flow rate average value V_avg;

[0012] Judging the correction coefficient K of the sampling period by the following formula: K = (V_avg / V_real)^2, wherein V_real is the current airflow flow rate and V_avg is the historical airflow flow rate average value;

[0013] If K is less than 1, the sampling period is shortened in proportion; otherwise, it remains unchanged.

[0014] Preferably, the dynamically adjusting the duration of the sampling period based on the real-time airflow average speed further comprises:

[0015] The rate of change of airflow velocity is collected as ΔV = (V_current - V_previous) / Δt, where V_current and V_previous are the current and previous airflow velocities, respectively, and Δt is the time interval.

[0016] A velocity change threshold T_threshold is set.

[0017] If |ΔV| > T_threshold, the fast response mode is adopted to adjust the sampling period.

[0018] The sampling period is dynamically adjusted by the function P = max(P_initial, 5*exp(λ*|ΔV|)), where λ is the decay coefficient.

[0019] Preferably, the dynamically adjusting the duration of the sampling period based on the real-time airflow average velocity further comprises:

[0020] The turbulence intensity of the current airflow flow is analyzed as I = σV 2 / (V_real^2), where σV is the standard deviation of the flow rate.

[0021] A turbulence threshold I_threshold is set.

[0022] If I is greater than I_threshold, the sampling period is shortened by a certain proportion to increase the response speed.

[0023] The condition statement is used to determine whether I ≥ I_threshold, and if so, ΔT = ΔT_base*α1; otherwise, ΔT = ΔT_base, where α1 is the correction coefficient in the case of turbulence.

[0024] Preferably, the dynamically adjusting the duration of the sampling period based on the real-time airflow average velocity further comprises:

[0025] The Reynolds number of the current airflow is calculated as Re = ρV_realD / μ, where ρ is the fluid density, D is the pipe diameter, and μ is the fluid viscosity.

[0026] The Re is compared with a critical value Re_c.

[0027] The sampling period is adjusted according to the formula T_new = T_old*exp(-k*(Re - Re_c)), where k is the adjustment factor.

[0028] If Re < Re_c, the original sampling period is maintained.

[0029] Preferably, the dynamically adjusting the duration of the sampling period based on the real-time airflow average velocity further comprises:

[0030] The fluctuation amplitude A = max(V_real) - min(V_real) of the flow rate is extracted;

[0031] A threshold A_threshold is set for the stability domain;

[0032] The fluctuation intensity percentage B = (A / A_threshold) x 100 is expressed using the formula;

[0033] If the set proportion B > A_threshold, the sampling frequency adaptive strategy is triggered for adjustment.

[0034] Preferably, the dynamic adjustment of the duration of the sampling period based on the real-time average flow rate further comprises:

[0035] The root mean square value V_rms of the flow rate in the historical 10 time windows is counted;

[0036] The current flow stability index S = V_rms_prev / V_rms_current is calculated, where V_rms_prev is the root mean square of the previous period and V_rms_current is the current value;

[0037] A judgment boundary S_boundary is set;

[0038] If S > S_boundary, the sampling period frequency is reduced to reduce the number of repeated measurements.

[0039] Preferably, the dynamic adjustment of the duration of the sampling period based on the real-time average flow rate further comprises:

[0040] The ambient temperature value T_env is obtained;

[0041] The gas density p = P / (R·T_env) is calculated, where P is the gas pressure and R is the gas constant;

[0042] The temperature change rate AT_env = (T_env - T_env_prev) / At is calculated according to the temperature change rate;

[0043] The following conditional logic is applied: if AT_env > AT_threshold, the adaptive control algorithm is enabled to adjust the sampling period.

[0044] Preferably, the dynamic adjustment of the duration of the sampling period based on the real-time average flow rate further comprises:

[0045] The settling velocity v_settle = g(ρ_p / ρ_f) / (9μ) of particles in the pipeline is detected, where g is the gravitational acceleration, ρ_p is the particle density, and ρ_f is the flow rate density;

[0046] calculating the relative relationship R = v_settle / V_real between the air flow and the settling velocity;

[0047] when R is greater than a set value R_limit, starting a short high sampling period strategy;

[0048] controlling the period variation using the formula ΔT = ΔT_initial * e^{k*R}.

[0049] Preferably, the dynamically adjusting the duration of the sampling period based on the real-time air flow average speed further comprises:

[0050] identifying the dust type and its density attribute ρ_particle under the current working condition;

[0051] calculating the diffusion coefficient of the particle in the air D = (k_B*T) / (3πμd), where k_B is the Boltzmann constant and d is the particle size;

[0052] judging the influence degree of the air flow on the particle size using the formula F = 5 * (1 + e^{(ρ_particle / ρ_air)});

[0053] deciding whether to introduce a dynamic weight mechanism to optimize and adjust the sampling period according to the F value.

[0054] Preferably, the dynamically adjusting the duration of the sampling period based on the real-time air flow average speed further comprises:

[0055] analyzing the multi-dimensional correlation of the air flow speed and establishing a Markov chain model;

[0056] estimating the next state probability P(n+1) = ΣP(n,j)*T(j,n+1) through the following formula, where T(j,n+1) is the state transition probability;

[0057] setting a prediction error threshold ε;

[0058] if the prediction error is less than ε, the current period length is used; otherwise, a dynamic compensation method is used to adjust the sampling period.

[0059] The disclosed embodiment provides a measurement method of a dust particle counter with adjustable particle size channels, which comprises: acquiring air flow speed data and calculating a real-time air flow average speed; dynamically adjusting the duration of a sampling period based on the real-time air flow average speed; collecting and detecting particulate matters according to the adjusted sampling period; and correcting and compensating the counting error through particulate matter distribution statistical results. Through the scheme of the disclosed embodiment, the problem of how to dynamically control the sampling period according to the change of the air flow speed to solve the counting error caused by uneven distribution of particulate matters can be solved. BRIEF DESCRIPTION OF DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the exemplary embodiments of the present disclosure, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some of the embodiments of the present disclosure, and therefore should not be considered as a limitation to the scope. Other related drawings can also be obtained by those of ordinary skill in the art without creative effort, based on these drawings.

[0061] Figure 1 is a flow chart of a measurement method of a dust particle counter with adjustable particle size channel;

[0062] Figure 2 is a flow chart of dynamically adjusting the duration of the sampling period based on the real-time average air flow velocity;

[0063] Figure 3 is a flow chart of dynamically adjusting the duration of the sampling period based on the real-time average air flow velocity;

[0064] Figure 4 is a flow chart of dynamically adjusting the duration of the sampling period based on the real-time average air flow velocity;

[0065] Figure 5 is a flow chart of dynamically adjusting the duration of the sampling period based on the real-time average air flow velocity;

[0066] Figure 6 is a flow chart of dynamically adjusting the duration of the sampling period based on the real-time average air flow velocity;

[0067] Figure 7 is a flow chart of dynamically adjusting the duration of the sampling period based on the real-time average air flow velocity;

[0068] Figure 8 is a flow chart of dynamically adjusting the duration of the sampling period based on the real-time average air flow velocity;

[0069] Figure 9 is a flow chart of dynamically adjusting the duration of the sampling period based on the real-time average air flow velocity;

[0070] Figure 10 is a flow chart of dynamically adjusting the duration of the sampling period based on the real-time average air flow velocity;

[0071] Figure 11 is a flow chart of dynamically adjusting the duration of the sampling period based on the real-time average air flow velocity. DETAILED DESCRIPTION

[0072] In the following, certain exemplary embodiments are simply described. As those skilled in the art can recognize, the described embodiments can be modified in various different ways without departing from the spirit or scope of the present application. Therefore, the drawings and description are considered to be exemplary in nature rather than limiting.

[0073] Next, referring to Figure 1 , a measurement method of a particle size channel-adjustable dust particle counter of the present application is described, which includes obtaining airflow velocity data and calculating real-time airflow average speed. Specifically, a flow sensor is integrated inside the device, which continuously monitors the airflow speed in the air and transmits data to the control system. The control system calculates the current real-time airflow average speed by sampling multiple instantaneous flow rate data points combined with mean value calculation within a time window. This step provides the basis for subsequent adjustment of the sampling period. For example, in one embodiment, when the airflow speed is detected to fluctuate within the range of 0.5m / s to 1.5m / s, the system calculates the average value of these data points as the key input of the current airflow condition. This data collection and processing method ensures the intelligent response of the system.

[0074] Next, the duration of the sampling period is dynamically adjusted based on the real-time airflow average speed. Specifically, according to a pre-set algorithm model, the system analyzes the real-time airflow average speed and calculates the appropriate sampling period length combined with historical data and empirical parameters. This model takes into account the characteristics of particle concentration changes under different flow conditions, thereby dynamically adjusting the time length of each detection period. For example, in low flow conditions, the sampling period may be extended to more than 20 seconds to ensure the stability of the sample; while in high flow conditions, the sampling period is shortened to within 10 seconds to prevent counting omission due to rapid passage of particles. This method allows for more balanced particle samples in different environments, reducing the impact of flow rate variations on counting results.

[0075] Then, particle collection and detection are carried out according to the adjusted sampling period. Specifically, the adjusted sampling period controls the direction and speed of gas flow to ensure that enough particles can be captured within this time period. At the same time, sensors or other forms of detection equipment are used to detect these particles and classify and count their particle sizes. In specific implementation, such as using laser scattering principle to detect particles in different particle size ranges and classify them into corresponding channels, thereby realizing multi-channel particle size analysis. This has the advantage of improving detection efficiency and accuracy, while effectively addressing the counting deviation problem caused by uneven airflow.

[0076] Subsequently, the counting error is corrected and compensated by the particle distribution statistics. Specifically, after multiple acquisitions and detections are completed, the system performs statistical analysis on the collected data to generate a particle distribution curve or histogram. Through these data, unstable counting regions existing in certain flow rate intervals can be identified, and algorithms are used to weight or correct these uneven statistical data. For example, if a significant undercounting of particles is detected under a certain flow rate condition, the system will automatically adjust the weight proportion under that condition to reduce the error in the final report. In addition, the system will also optimize the adjustment logic based on long-term accumulated data, so that the entire measurement system can continuously adapt to environmental changes, improving measurement accuracy and robustness.

[0077] Through the above steps, the technical scheme of the present application solves the problem of how to dynamically adjust the sampling period according to the change of air flow rate, thereby effectively alleviating the counting error caused by uneven particle distribution. This method realizes effective capture and accurate statistics of particles in complex gas environments, significantly improves the application range and measurement reliability of the dust particle counter, and is especially suitable for industrial sites that require continuous monitoring and have unstable environmental conditions.

[0078] Next, with reference to Figure 2 , the flow of dynamically adjusting the duration of the sampling period based on the real-time average air flow rate of the present application is described. First, the current air flow rate data V real is obtained, which represents the speed value of the air flow in the current measurement environment, usually in meters per second, and the value range depends on the specific application environment, preferably between 0.5 and 3 meters per second, to ensure the stability and accuracy of the sensor. Second, the historical average air flow rate V avg is calculated, which is the average value obtained by statistically analyzing multiple V real data in a certain time period, used to measure long-term trends and avoid misjudgment caused by short-term fluctuations. The correction coefficient K of the sampling period is determined by the following formula: K = (V avg / V real) 2, where the numerator and denominator in the formula represent the air flow rate, and the units are consistent, so it is dimensionless. When V real is higher than V avg, K is less than 1, indicating that the actual flow rate is high; when V real is lower than V avg, K is greater than 1, indicating that the flow rate is low. If K is less than 1, the sampling period is proportionally shortened to ensure that particles can be effectively captured under high flow rate conditions; otherwise, it remains unchanged to avoid unnecessary repeated sampling and reduce resource waste. For example, in one embodiment, assuming that the historical average air flow rate is 1.2 meters per second and the current flow rate is 1.5 meters per second, then K = (1.2 / 1.5) 2 = 0.64, at this time the sampling period is proportionally reduced, so that the system responds faster to particle changes and improves measurement accuracy. This technical scheme can flexibly adjust the sampling frequency according to the change of air flow, improving the measurement efficiency and accuracy.

[0079] Next, with reference toFigure 3 The flow of dynamically adjusting the duration of the sampling period based on the real-time average airflow velocity is described. First, the airflow velocity change rate ΔV = (V_current - V_previous) / Δt is collected, where V_current and V_previous are the current and previous airflow velocities, respectively, and Δt is the time interval between two adjacent measurement points. This step is used to quantify the change of airflow velocity over time. For example, in an environment with rapid changes in dust concentration, the value of ΔV may increase significantly. The parameter range is usually between 0.1 m / s and 10 m / s, and the optimal value can be set according to the actual application. This formula can capture the instantaneous airflow variation trend.

[0080] Second, set the velocity change threshold T_threshold, which determines when to start the fast response mode. The setting of T_threshold needs to be based on the requirements of environmental stability. For example, in a precision detection environment, T_threshold can be set to 0.5 m / s. The value range is usually between 0.1 m / s and 2 m / s, ensuring that the system can distinguish between normal airflow fluctuations and abnormal changes.

[0081] Third, if |ΔV| is greater than T_threshold, the fast response mode is used to adjust the sampling period. At this time, the system considers that the airflow changes dramatically and needs to speed up the data acquisition frequency to improve the response sensitivity. For example, when the air supply equipment in the laboratory suddenly changes the wind speed, the fast response mode can help capture the changes in dust particles more timely.

[0082] Finally, the sampling period is dynamically adjusted by the function P = max(P_initial, 5 * exp(λ * |ΔV|)), where P_initial is the initial sampling period, λ is the decay coefficient, and |ΔV| represents the absolute value of the airflow velocity change. P represents the final adjusted sampling period. The value of λ is generally between 0.1 and 0.5, and the optimal value can be determined in experiments. The function uses an exponential form to rapidly shorten the sampling period as |ΔV| increases, ensuring that the data accuracy is maintained under high speed changes. This design improves the adaptability of the system to complex working conditions.

[0083] This technical solution helps to improve the accuracy and stability of the dust particle counter in dynamic airflow environments, achieving efficient and accurate real-time monitoring.

[0084] Next, refer to Figure 4 The flow of dynamically adjusting the duration of the sampling period based on the real-time average airflow velocity is described. First, according to the current airflow flow turbulence intensity I, the formula I = σV 2 / V_real 2The calculation is performed, where σV is the standard deviation of the flow rate, V real is the real-time average airflow velocity, and I represents the degree of airflow instability. The range of σV is generally 0.1-5 m / s, V real is usually 0.5-5 m / s, and the optimal value of I is less than 0.2 to represent stable flow. This formula can quantitatively reflect the fluctuation of the airflow, which helps to determine whether the sampling period needs to be adjusted.

[0085] Subsequently, a turbulence threshold I threshold is set, which is generally set between 0.3-0.5, to distinguish between stable and unstable airflow states. The selection of this value needs to be optimized in combination with the specific measurement environment to ensure that the system can accurately identify the turbulence condition. For example, in a clean room environment, when the airflow is disturbed due to equipment operation, the I value will quickly increase.

[0086] Then the turbulence intensity is compared with the set threshold, if I is greater than I threshold, it means that the airflow is unstable, at this time the sampling period ΔT is adjusted, which is shortened by a certain proportion, such as α1 takes 0.6-0.8, to improve the real-time performance of data acquisition; otherwise, the original sampling period ΔT base is maintained to avoid data distortion caused by excessive adjustment. In this way, more stable particle counting accuracy can be achieved under different working conditions.

[0087] In one embodiment, assuming the current measured airflow V real = 2.0 m / s, σV = 0.4 m / s, I = 0.08 is obtained, which does not reach the set I threshold = 0.3, so ΔT base = 1 second is maintained; while in another scenario, if I = 0.5, adjustment is triggered and the sampling period is shortened to 0.7 seconds. This method improves the adaptability of the instrument under dynamic airflow conditions and improves the accuracy and stability of the data.

[0088] Next, referring to Figure 5 , the flow of the present application based on dynamically adjusting the duration of the sampling period according to the real-time average airflow velocity is described. First, the Reynolds number Re = ρV realD / μ is calculated according to the average velocity of the real-time airflow, where ρ is the fluid density, with a unit of kg / m 3 , usually in the range of 1.2 to 1.3 kg / m 3 , V real is the actual airflow velocity, with a unit of m / s, in the range of 0.5 to 5 m / s, D is the pipe diameter, with a unit of m, a typical value is 0.1 m, and μ is the fluid viscosity, with a unit of Pa·s, the viscosity of air is usually about 1.8 × 10 -5 Pa·s. This formula is used to determine whether the airflow flow state is in turbulent or laminar flow.

[0089] Secondly, the calculated Reynolds number Re is compared with the critical value Re c, which is usually set to 2300 and is the key threshold for determining whether the flow state changes. If Re approaches or exceeds this value, it indicates that the airflow may be in an unstable state, which affects the accuracy of particle counting.

[0090] Then the sampling period is adjusted according to the formula T_new = T_old * exp(-k * (Re - Re_c)). Where T_old is the original sampling period, T_new is the new sampling period, k is the adjustment factor, usually taking a value in the range of 0.1 to 0.5, and exp is the natural exponential function to reflect the non-linear relationship between Reynolds number and sampling period. If Re is greater than Re_c, the exponential part will decrease as the Reynolds number increases, resulting in a smaller T_new, making the system more sensitive to changes; otherwise, if it is less than Re_c, it remains unchanged.

[0091] For example, in one embodiment, when the average airflow velocity is 3 m / s, the calculated Re is 2500, which is greater than the critical value 2300. At this time, if k = 0.2, the new sampling period T_new = T_old * exp(-0.2 * 200) = T_old * e^(-40), the sampling period is greatly shortened to improve the measurement accuracy and response speed. Conversely, if the airflow is stable and Re < Re_c, the system does not change the original sampling period, ensuring energy consumption and data continuity.

[0092] This technical solution can automatically adjust the sampling frequency according to different working conditions, improve the measurement accuracy, and effectively avoid data deviation or false positives caused by airflow fluctuations, improving the adaptability and stability of the dust particle counter.

[0093] Next, referring to Figure 6 , the process of dynamically adjusting the duration of the sampling period based on the real-time average airflow velocity of the present application is described. First, the fluctuation amplitude A of the airflow velocity is extracted, which is equal to the maximum velocity minus the minimum velocity, to reflect the degree of airflow instability. For example, in the actual operation of a certain dust particle counter, the airflow velocity data is collected over a period of time, with a maximum value of 2.5 m / s and a minimum value of 1.8 m / s, and A = 0.7 m / s at this time. This parameter can directly represent the fluctuation range of the airflow and guide the subsequent adjustment.

[0094] Then set the stability domain threshold A_threshold to determine whether the airflow is in a stable state, which is generally set to be in the range of 0.5 m / s to 1.0 m / s, preferably 0.8 m / s. The selection of the threshold needs to be combined with experimental data and application environment to ensure that it is neither too sensitive nor loses sensitivity. For example, in a clean room environment, setting the threshold to 0.8 m / s can effectively distinguish between stable and unstable states.

[0095] Subsequently, the formula B = (A / A_threshold) x 100 is used to represent the fluctuation intensity percentage, and the B value ranges from 0% to more than 100%, indicating the relative intensity of the fluctuation relative to the stable domain. If B is greater than the set proportion of A_threshold, such as a set proportion of 80%, the sampling frequency adaptive strategy is triggered for adjustment. This formula converts the absolute fluctuation value into a proportional value, facilitating comparison and decision-making, and improving the consistency and accuracy of judgment.

[0096] In one embodiment, if A_threshold is 0.8 m / s and the current measurement obtains A = 0.9 m / s, then B = 112.5%, triggering the adjustment mechanism to shorten the sampling period to ensure the accuracy of particle counting. This technical solution can flexibly adjust the measurement method according to the actual air flow changes, improve the accuracy of measurement results and system stability, and has important practical value.

[0097] Next, referring to Figure 7 , the flow of dynamically adjusting the duration of the sampling period based on the real-time air flow average speed of the present application is described. First, the root mean square value V_rms of the air flow speed in the historical 10 time windows is counted, which reflects the fluctuation of the air flow speed. The root mean square value calculation formula is V_rms = √(Σv 2 / n), where v is the measured air flow speed in each time window, and n is the sample number, usually n is greater than or equal to 5. The optimal value of V_rms depends on the system set standard, generally controlled within the range of 0.1 m / s to 0.5 m / s to ensure the accuracy and stability of the data. For example, in one embodiment, when the air flow is relatively stable, V_rms may be about 0.2 m / s.

[0098] Then calculate the current air flow stability index S = V_rms_prev / V_rms_current, where V_rms_prev is the root mean square of the previous period, and V_rms_current is the root mean square of the current period. This ratio reflects the trend of air flow changes, and the larger the value, the more unstable the air flow. The value range of S is usually 0 to 2, and if S approaches or exceeds 1, it indicates that the air flow has a large fluctuation. In a specific implementation, if V_rms_prev is 0.3 m / s and V_rms_current is 0.25 m / s, then S is 1.2, indicating that the air flow is becoming unstable.

[0099] Set the judgment boundary S_boundary to determine whether the sampling period needs to be adjusted. Usually set the boundary to 1.2, which is higher than this value indicating that the air flow fluctuates violently. For example, when the S value reaches or exceeds 1.2, it indicates that the current air flow is unstable and the measurement strategy should be adjusted.

[0100] If S > S_boundary, reduce the sampling period frequency to reduce the number of repeated measurements. This approach can avoid invalid data caused by unstable airflow, while improving the efficiency of the device operation. By dynamically adjusting the sampling period, it can ensure that the measurement accuracy and data reliability are maintained under different working conditions.

[0101] The technical scheme has the beneficial effects of effectively dealing with airflow fluctuations, improving the adaptability and measurement accuracy of the dust particle counter in complex environments, prolonging the service life of the device, and improving the data acquisition efficiency.

[0102] Next, with reference to Figure 8 , the flow of dynamically adjusting the duration of the sampling period based on the real-time average airflow velocity of the present application is described. First, the environmental temperature value T_env is obtained, which is used to represent the physical state of the current airflow, and the value range is usually between 0℃ and 50℃, and the most suitable measurement range is 20℃ to 30℃, to reduce the influence of errors on the system. Then, the gas density ρ = P / (R·T_env) is calculated, where P represents the air pressure, the unit is Pascal (Pa), R is the ideal gas constant, the value is 287 J / (kg·K), and T_env is the absolute temperature, the unit is Kelvin (K). The formula is used to estimate the air density, because the gas density changes with temperature, affecting the movement speed of the particles and the detection accuracy. Then, the temperature change rate ΔT_env = (T_env - T_env_prev) / Δt is calculated according to the temperature change rate, where Δt is the time interval between two measurements, the unit is second (s). This parameter reflects the stability of the environmental temperature, if the temperature fluctuation is too large, it may affect the uniformity of the airflow and the measurement accuracy. The following conditional logic is applied: if ΔT_env > ΔT_threshold, the adaptive control algorithm is enabled to adjust the sampling period. ΔT_threshold is a set threshold, usually set to 0.1℃ / s to 0.5℃ / s, to determine whether the temperature is in a state of rapid change. When the threshold is exceeded, it indicates that the airflow may be disturbed due to thermodynamic instability, and the measurement stability needs to be improved by adjusting the sampling period. For example, in an embodiment, when the air conditioner in the laboratory is suddenly turned off, causing the environmental temperature to rise by 0.3℃ / s, the system automatically extends the sampling period from 10 seconds to 15 seconds, ensuring that the collected data is more accurate and reliable. Through this technical scheme, the sampling efficiency can be flexibly optimized according to the actual environmental changes, improving the measurement accuracy and adaptability of the dust particle counter in dynamic environments.

[0103] Next, with reference to Figure 9, describes the flow of the present application based on real-time airflow average speed dynamically adjusting the duration of the sampling period. First, the settling velocity of particles in the pipeline is detected v_settle = g (ρ_p - ρ_f) / (9μ), where g is the acceleration of gravity, about 9.8 m / s 2 , ρ_p is the particle density, usually between 1000-3000 kg / m 3 , ρ_f is the gas flow density, generally about 1.2 kg / m 3 , μ is the dynamic viscosity of the gas flow, about 1.8 × 10 -5 Pa·s. The formula is used to calculate the settling velocity of particles in the gas flow due to gravity, which is the basis for determining whether particles are easy to gather or settle in the pipeline.

[0104] Second, the relative relationship between the airflow and the settling velocity R = v_settle / V_real is calculated, where V_real is the actual measured average speed of the airflow, usually in m / s. R reflects the proportional relationship between the airflow speed and the settling velocity. When R is greater than the set value R_limit (usually set to between 0.05 and 0.2), it indicates that the airflow speed is not sufficient to stably transport particles to the sensor, and particles are prone to deposit.

[0105] When R is greater than the set value R_limit, the short-time high-sampling period strategy is started. This strategy aims to reduce measurement errors caused by particle deposition and improve data accuracy. For example, in one embodiment, if the measurement environment is a high dust concentration area, and the system detects that the R value rises to 0.15, it indicates that some larger particles may be depositing in the current environment, and the sampling interval should be reduced to collect data more densely to avoid missing critical information.

[0106] Finally, the formula ΔT = ΔT_initial × e^{k*R} is used to control the period variation, where ΔT_initial represents the initial sampling period, usually in seconds, and is usually set to 1-10 s; k is the adjustment coefficient, usually between 0.1 and 0.5, used to control the adjustment rate. The exponential growth form of e^{k*R} makes the decay of ΔT more significant when R is larger, thus achieving precise adjustment of the sampling frequency.

[0107] This technical solution helps to improve the response ability and accuracy of the dust particle counter in complex flow conditions, while taking into account system resource consumption, ensuring efficient and reliable acquisition of air quality data.

[0108] Next, refer to Figure 10, describes the flow of the present application based on real-time airflow average speed dynamically adjusting the duration of the sampling period. First, identify the current working condition of the dust species and its density attribute ρ particle, which is used to judge the mass characteristics of the particles. The range of ρ particle depends on the specific dust type, such as the density of cement powder is about 2,300 kg / m 3 , and organic dust may be as low as 1,000 kg / m 3 , its optimal value should match the actual dust of the measured environment to ensure data accuracy. Second, calculate the diffusion coefficient of the particle in the air D = (k_B*T) / (3πμd), where k_B is the Boltzmann constant (about 1.38 x 10 - 23 J / K), T is the environmental temperature (generally taken as 293 K), μ is the gas viscosity (1.81 x 10 -5 Pa·s for air), and d is the particle size (range generally in 0.1-10 μm). The larger the value of D, the more intense the particle motion, and the more significant the impact on sampling accuracy. Then, use the formula F = 5*(1+e^{(ρ_particle / ρ_air)}) to judge the influence of airflow on particle size, where ρ_air is the air density (about 1.225 kg / m 3 ). This formula is designed to rapidly increase the value of F as the particle density increases, reflecting the impact of airflow disturbance. Finally, according to the value of F, decide whether to introduce a dynamic weight mechanism to optimize and adjust the sampling period. When F is large, it means that the particle is easily disturbed by the airflow, and the sampling period needs to be extended to improve the stability of the data. For example, when measuring cement dust in an industrial plant, if ρ particle is high and F is large, the system automatically extends the sampling period to obtain more accurate particle size distribution results. This technical solution improves the reliability and accuracy of dust particle counters in collecting data in different dust environments through real-time monitoring and analysis.

[0109] Next, refer to Figure 11 , describes the flow of the present application based on real-time airflow average speed dynamically adjusting the duration of the sampling period. First, analyze the multi-dimensional correlation of airflow velocity and establish a Markov chain model. This step captures the velocity change trend of the airflow at different time points to build a probability model reflecting state evolution. Among them, P(n,j) represents the joint probability of the current state being n and the previous state being j, and T(j,n+1) is the state transition probability, representing the probability of transitioning from j to n+1. The range of parameters is generally [0,1], and the optimal value depends on the stability of the actual test data. This model helps to predict the future state of the airflow.

[0110] Then, the next state probability P(n+1) =∑P(n,j)*T(j,n+1) is estimated by the following formula, which is used to calculate the probability of the next state. ∑ represents the summation of all possible previous states, ensuring that all possible states are considered, thereby improving the accuracy of the prediction. A prediction error threshold ε is set, which is determined according to the system stability and measurement accuracy, and is generally set to a value between 0.01 and 0.1, with the optimal value depending on the specific application.

[0111] If the prediction error is less than ε, the current cycle length is maintained. This means that the system considers the current airflow to be relatively stable and does not need to frequently adjust the sampling period to reduce resource waste. Otherwise, the sampling period is adjusted using dynamic compensation. For example, in one test, the airflow speed suddenly increases, causing the prediction error to exceed the set threshold, at which point the sampling period is shortened, making the device more sensitive to capturing particle changes and improving the real-time and accuracy of the measurement.

[0112] The technical scheme has the beneficial effects of improving the adaptability of the dust particle counter to complex environmental changes, optimizing the efficiency and accuracy of data collection, and ensuring reliable measurement results under different working conditions.

[0113] The measurement method of the dust particle counter with adjustable particle size channels of the present application includes: first, acquiring airflow velocity data and calculating the real-time airflow average speed; then, dynamically adjusting the duration of the sampling period based on the real-time airflow average speed; then, collecting and detecting particulate matter according to the adjusted sampling period; finally, correcting and compensating for the counting error through the particulate matter distribution statistical results. The entire process is realized through real-time monitoring and response to changes in airflow velocity, achieving precise control of the sampling process.

[0114] Specifically, the system first acquires airflow velocity data in real time through a sensor and calculates the average speed of the current airflow based on known airflow dynamics models. Based on this, the system automatically adjusts the time length of the sampling period according to the airflow velocity. For example, when the airflow velocity is high, the sampling period is shortened appropriately to prevent high flow rates from causing insufficient residence time of particulate matter in the sampling area, thereby causing counting omission or distortion; conversely, when the airflow velocity is low, the sampling period is extended to ensure that enough particulate matter is captured and accurately recorded. This not only improves the counting accuracy, but also enhances the adaptability of the instrument. At the same time, after the collection is completed, the system can further intelligently correct the measurement results by analyzing the obtained particulate matter distribution data, to compensate for the counting error caused by flow rate fluctuations, particle diffusion or other non-stable factors. Therefore, the present application effectively solves the problem of counting error caused by uneven distribution of particulate matter due to changes in airflow velocity, and achieves a more stable and accurate dust monitoring effect.

[0115] The methods, programs, systems, apparatuses, etc. of embodiments of the present application can be implemented in a single or multiple computing devices or processing units, or portions thereof, in a distributed computing environment. In embodiments of the present application, various tasks performed by the computing devices can be performed by remote processing devices that are connected through a communication network.

[0116] Those skilled in the art will appreciate that embodiments of the present application can be provided as methods, systems or computer program products. Accordingly, the means for performing the functions illustrated by the functional modules / units or controllers and related method steps set forth in the above-described embodiments can be realized using software, hardware or a combination of both.

[0117] Unless specifically stated otherwise, the acts or steps of the methods recited in the embodiments of the present application need not be performed in the order in which they are recited, and / or need not be performed in the order in which they are illustrated in the figures. In certain embodiments, multiple acts or steps can be performed at the same time, or in different orders.

[0118] In the present document, for the sake of brevity, the description of each embodiment of the application is not exhaustive, and similar or equivalent features or parts of different embodiments can be omitted. In the present document, "one embodiment," "some embodiments," "an example," "a specific example," or "some examples" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application, but not necessarily in all embodiments, and is included in at least one example of the application, but not necessarily in all examples. The aforementioned terms do not necessarily refer to the same embodiment or example. Embodiments or examples of the present application described in the present document can be combined and / or integrated with each other without departing from the scope of the present application, which is defined in the appended claims.

[0119] The exemplary systems and methods of this application have been described with reference to the specific embodiments and implementations thereof. It should be understood, however, that the systems and methods described herein can be practiced with modification and alteration, and within the scope of the claims. Accordingly, the disclosure of this patent specification is to be considered exemplary and not restrictive; and the scope of the application is to be determined by the following claims.

Claims

1. A measurement method of a dust particle counter with adjustable particle size channels, characterized by, The method comprises: acquiring airflow velocity data and calculating real-time airflow average velocity; dynamically adjusting the duration of the sampling period based on the real-time airflow average velocity; collecting and detecting particulate matter according to the adjusted sampling period; correcting and compensating the counting error through particulate matter distribution statistics.

2. The measurement method of a dust particle counter with adjustable particle size channels according to claim 1, wherein, The method further comprises: acquiring current airflow velocity data V_real; calculating historical airflow velocity average V_avg; determining the correction coefficient K of the sampling period through the following formula: K = (V_avg / V_real)^2, wherein V_real is the current airflow velocity and V_avg is the historical airflow velocity average; if K is less than 1, the sampling period is proportionally shortened; otherwise, it remains unchanged.

3. The measurement method of a dust particle counter with adjustable particle size channels according to claim 1, wherein, The method further comprises: collecting airflow velocity change rate AV = (V_current-V_previous) / At, wherein V_current and V_previous are the current and previous airflow velocities respectively, and At is the time interval; setting a velocity change threshold T_threshold; if |AV|>T_threshold, the fast response mode is adopted to adjust the sampling period; dynamically adjusting the sampling period through the function P = max(P_initial, 5*exp(λ*|AV|)), wherein λ is the attenuation coefficient.

4. The measurement method of a dust particle counter with adjustable particle size channels according to claim 3, wherein, The method further comprises: analyzing the turbulence intensity I = σV of the current airflow flow 2 (V_real^2), where σV is the flow velocity standard deviation; setting a turbulence threshold I_threshold; if I is greater than I_threshold, the sampling period is shortened by a certain proportion to improve the response speed; determining through a conditional statement: if I≥I_threshold, ΔT = ΔT_base*α1; otherwise, ΔT = ΔT_base, wherein α1 is the correction coefficient in the turbulence state.

5. The measurement method of a dust particle counter with adjustable particle size channels according to claim 4, wherein, The method further comprises: calculating the Reynolds number Re of the current airflow: Re = ρV_realD / μ, wherein ρ is the fluid density, D is the pipe diameter, and μ is the fluid viscosity; comparing Re with the critical value Re_c; adjusting the sampling period according to the following formula: T_new = T_old*exp(-k*(Re-Re_c)), wherein k is the adjustment factor; if Re<Re_c, the original sampling period remains unchanged.

6. The measurement method of a dust particle counter with adjustable particle size channels according to claim 5, wherein, The method further comprises: extracting the fluctuation amplitude A of the airflow velocity: A = max(V_real)-min(V_real); setting a stable domain threshold A_threshold; using the formula B = (A / A_threshold)*100 to represent the fluctuation intensity percentage; if the set proportion of B>A_threshold, the sampling frequency adaptive strategy is triggered to adjust.

7. The measurement method of a dust particle counter with adjustable particle size channels according to claim 6, wherein, The method further comprises: Statistical history of 10 time windows of airflow velocity root mean square value V_rms; Calculate the current airflow stability index S = V_rms_prev / V_rms_current, where V_rms_prev is the root mean square of the previous period, and V_rms_current is the current value; Set the judgment boundary S_boundary; If S > S_boundary, reduce the sampling period frequency to reduce the number of repeated measurements.

8. The measurement method of a dust particle counter with adjustable particle size channels according to claim 7, wherein, The dynamic adjustment of the duration of the sampling period based on the real-time airflow average speed further comprises: Obtain the ambient temperature value T_env; Calculate the gas density ρ = P / (R * T_env), where P is the air pressure and R is the gas constant; According to the temperature change rate ΔT_env = (T_env - T_env_prev) / Δt; Apply the following conditional logic: if ΔT_env > ΔT_threshold, enable the adaptive control algorithm to adjust the sampling period.

9. The measurement method of a dust particle counter with adjustable particle size channels according to claim 8, wherein, The dynamic adjustment of the duration of the sampling period based on the real-time airflow average speed further comprises: Detect the settling velocity of particles in the pipeline v_settle = g (ρ_p - ρ_f) / (9 μ), where g is the acceleration of gravity, ρ_p is the particle density, and ρ_f is the airflow density; Calculate the relative relationship between airflow and settling velocity R = v_settle / V_real; When R is greater than the set value R_limit, start the short-time high-sampling period strategy; Use the formula ΔT = ΔT_initial * e^{k*R} to control the period variation.

10. The measurement method of a dust particle counter with adjustable particle size channels according to claim 9, wherein, The dynamic adjustment of the duration of the sampling period based on the real-time airflow average speed further comprises: Identify the type of dust and its density attribute ρ_particle under the current working condition; Calculate the diffusion coefficient of particles in air D = (k_B * T) / (3 π μ d), where k_B is the Boltzmann constant and d is the particle size; Use the formula F = 5 * (1 + e^{(ρ_particle / ρ_air)}) to judge the influence degree of airflow on particle size; According to the F value, decide whether to introduce a dynamic weight mechanism to optimize and adjust the sampling period.

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