Dust particle physical and chemical analysis method for environment detection

Abstract

The embodiment of the invention provides a dust particle physical and chemical analysis method for environment detection. The dust particle physical and chemical analysis method comprises the following steps: collecting dust particle concentration data in an environment and establishing a time sequence model; predicting a dust particle concentration change trend according to the time sequence model; dynamically adjusting a subsequent sampling time interval based on the predicted change trend; and executing a new round of dust particle concentration collection and analysis in the adjusted sampling time interval. According to the scheme of the embodiment of the invention, the sampling time interval can be dynamically adjusted according to the dust particle concentration change trend so as to solve the problems of data redundancy and real-time imbalance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of environmental monitoring, in particular to a dust particle physicochemical analysis method for environmental detection. BACKGROUND

[0002] The dust particle physicochemical analysis for environmental detection is to collect dust particles in the air, analyze their composition, quantity, particle size distribution, etc. by using physical and chemical means, so as to evaluate the environmental quality and pollution status, and provide scientific basis for environmental protection and management. However, in practical application, the problem of how to dynamically adjust the sampling time interval according to the change trend of dust particle concentration is faced. If the sampling is too frequent, it will lead to data redundancy and affect the system efficiency, and if the sampling interval is too long, it may miss the key change and lead to insufficient real-time performance, so an intelligent adjustment mechanism is needed to balance the effectiveness and real-time performance of data. SUMMARY

[0003] Therefore, the present application provides a dust particle physicochemical analysis method for environmental detection, which at least partially solves the problems in the prior art.

[0004] A dust particle physicochemical analysis method for environmental detection, comprising:

[0005] Collecting dust particle concentration data in the environment and establishing a time series model;

[0006] Predicting the change trend of dust particle concentration according to the time series model;

[0007] Dynamically adjusting the subsequent sampling time interval based on the predicted change trend;

[0008] Performing a new round of dust particle concentration collection and analysis in the adjusted sampling time interval.

[0009] According to one embodiment, the dynamically adjusting the subsequent sampling time interval based on the predicted change trend further comprises:

[0010] Obtaining the fluctuation amplitude ΔC of the current predicted change trend;

[0011] Determining the initial sampling interval T0;

[0012] Calculating the dynamically adjusted sampling interval T=T0*(ΔC / ΔC0) based on the following formula, wherein ΔC0 is a preset standard fluctuation threshold;

[0013] If T is less than the minimum allowed interval Tmin, then T is set to Tmin; otherwise, the actual calculated value is kept.

[0014] According to one embodiment, the dynamically adjusting the subsequent sampling time interval based on the predicted change trend further comprises:

[0015] record the timestamp t and the corresponding dust concentration C in the current period;

[0016] build a moving average model MA=(C_n+C_{n1}) / 2, where C_n represents the concentration at the current time point, and C_{n1} represents the concentration in the previous period;

[0017] calculate the time factor a=ΔMA / σ according to the difference ΔMA=MAC_base between MA and the reference concentration C_base, where σ is the standard deviation;

[0018] introduce a into the sampling interval formula T=T0*(1+α) and ensure that T is greater than Tmin.

[0019] According to one embodiment, the dynamically adjusting the subsequent sampling time interval based on the predicted change trend further comprises:

[0020] calculate the fluctuation amplitude VarC=MaxCMinC according to the maximum MaxC and the minimum MinC of the particle concentration within the time window;

[0021] input VarC into the fuzzy controller to generate a control coefficient k, where k∈[5,5], indicating the adjustment coefficient;

[0022] adjust the sampling interval using the formula T=T0*(1+k*(VarC / VarC0)), where VarC0 is the preset reference amplitude;

[0023] When the calculation result exceeds the maximum limit Tmax, it is forced to be set to Tmax to prevent excessive collection.

[0024] According to one embodiment, the dynamically adjusting the subsequent sampling time interval based on the predicted change trend further comprises:

[0025] analyze the change rate dC / dt of the predicted trend;

[0026] Set two reference variables: standard change rate dC0 and tolerance interval ε;

[0027] According to the formula T=T0*(1+(dC / dt) / dC0), where if (dC / dt)>dC0*(1+ε) or <dC0*(1ε), then T is set to Tmin;

[0028] And adjust the sampling frequency in the normal range according to the linear proportion, maintain the system stability.

[0029] According to one embodiment, the dynamically adjusting the subsequent sampling time interval based on the predicted change trend further comprises:

[0030] Sliding window fitting is performed on the prediction curve to extract local maximum and minimum values;

[0031] A trend strength parameter S is constructed, S = (Peak Trough) / (Window Length);

[0032] The sampling time T is updated according to the formula T = T0*e^{a*S}, where a is a decay coefficient;

[0033] If T is less than Tmin, it is set to Tmin to avoid data redundancy caused by too dense sampling.

[0034] According to one embodiment, the dynamically adjusting the subsequent sampling time interval based on the predicted change trend further comprises:

[0035] A regression model is established to predict the change trend of dust particles in the future y(t);

[0036] The model error index E is calculated as E = Σ|y(t) - actual(t)|;

[0037] According to the formula T = T0*(1 + E / E_threshold), E_threshold is the error tolerance;

[0038] High-frequency sampling is triggered when E approaches or exceeds E_threshold, i.e. T = Tmin.

[0039] According to one embodiment, the dynamically adjusting the subsequent sampling time interval based on the predicted change trend further comprises:

[0040] The entropy value H of the historical trend is calculated, H = Σp_i log p_i, p_i is the probability of occurrence of different concentration intervals;

[0041] The sampling sensitivity S is set according to the H value, S = 1 / (1 + e^{k(H - H0)}), where k is an adjustment coefficient and H0 is an equilibrium threshold;

[0042] Then use T = T0*S as the basic adjustment scheme;

[0043] If T is less than Tmin, fixed-frequency sampling is forced.

[0044] According to one embodiment, the dynamically adjusting the subsequent sampling time interval based on the predicted change trend further comprises:

[0045] The predicted trend is divided into three stages: growth, stability and decline;

[0046] Three different strategies are set: fast feedback mechanism (T = T0 / 2) is used in growth phase, default sampling (T = T0) is used in stable phase, and delayed sampling (T = T0*2) is used in decline phase;

[0047] A state transition matrix ψ is used to control the switching between phases.

[0048] A minimum interval Tmin is set to avoid information loss caused by too low sampling.

[0049] According to an embodiment, the dynamically adjusting the subsequent sampling time interval based on the predicted change trend further comprises:

[0050] An exponential decay coefficient β is introduced to measure the speed of trend change, β = 1|dC / dt| / dC_max.

[0051] A sampling factor K = max(β, 5) is calculated based on β to avoid excessive decay.

[0052] The formula T = T0*K is used as the adjustment logic.

[0053] If T is greater than Tmax after calculation, it is limited to Tmax, thereby improving efficiency.

[0054] According to an embodiment, the dynamically adjusting the subsequent sampling time interval based on the predicted change trend further comprises:

[0055] The autocorrelation function AC(k) of the dust particle concentration is calculated to determine the value at time delay k.

[0056] The sampling sensitivity γ = D / D_threshold is determined by combining AC(k) and the historical mean deviation D = |AC(k)AC_avg|.

[0057] The formula T = T0*(1+γ) is used to adapt to different periodic trends.

[0058] If γ is lower than the threshold D_threshold, no adjustment is made and the regular sampling mode is maintained.

[0059] The embodiments of the present disclosure provide a dust particle physicochemical analysis method for environmental detection, comprising: collecting dust particle concentration data in an environment and establishing a time series model; predicting a dust particle concentration change trend according to the time series model; dynamically adjusting a subsequent sampling time interval based on the predicted change trend; and performing a new round of dust particle concentration collection and analysis in the adjusted sampling time interval. Through the scheme of the embodiments of the present disclosure, the problem of how to dynamically adjust the sampling time interval according to the dust particle concentration change trend to solve the imbalance between data redundancy and real-time performance can be solved. BRIEF DESCRIPTION OF DRAWINGS

[0060] In the drawings, like reference numerals refer to like elements throughout the various figures. The drawings are not necessarily to scale, and the emphasis is on the functional relationships between elements. It should be understood that these drawings are merely depictions of some embodiments disclosed herein and that they should not be construed as limiting the scope of the disclosure.

[0061] Figure 1 is a flowchart of a dust particle physicochemical analysis method for environmental detection;

[0062] Figure 2 is a flowchart of dynamically adjusting a subsequent sampling time interval based on a predicted change trend;

[0063] Figure 3 is a flowchart of dynamically adjusting a subsequent sampling time interval based on a predicted change trend;

[0064] Figure 4 is a flowchart of dynamically adjusting a subsequent sampling time interval based on a predicted change trend;

[0065] Figure 5 is a flowchart of dynamically adjusting a subsequent sampling time interval based on a predicted change trend;

[0066] Figure 6 is a flowchart of dynamically adjusting a subsequent sampling time interval based on a predicted change trend;

[0067] Figure 7 is a flowchart of dynamically adjusting a subsequent sampling time interval based on a predicted change trend;

[0068] Figure 8 is a flowchart of dynamically adjusting a subsequent sampling time interval based on a predicted change trend;

[0069] Figure 9 is a flowchart of dynamically adjusting a subsequent sampling time interval based on a predicted change trend;

[0070] Figure 10 is a flowchart of dynamically adjusting a subsequent sampling time interval based on a predicted change trend;

[0071] Figure 11 is a flowchart of dynamically adjusting a subsequent sampling time interval based on a predicted change trend. DETAILED DESCRIPTION

[0072] It is to be understood that the terminology used herein such as first and second, and the like, is only used to distinguish one entity or action from another entity or action, without necessarily requiring or implying any such actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0073] Next, referring to Figure 1 , an environmental detection dust particle physicochemical analysis method of the present application is described, which first includes collecting dust particle concentration data in the environment and establishing a time series model. The specific operation is to monitor the dust particle content in the air in real time through the sensor device installed in the target environment, and record the obtained data in chronological order to form a time series. Then, using mathematical modeling methods, the regularity and periodicity of the data are extracted, and a model that can accurately describe the evolution of dust concentration over time is established. For example, in a factory workshop or urban atmospheric environment, multiple high-precision dust particle sensors can be deployed, which upload measurement values to the central processing system at regular intervals.

[0074] Next, the dust particle concentration change trend is predicted according to the time series model. The specific implementation method is to use statistical and machine learning algorithms such as linear regression, autoregressive model or more complex neural network methods to predict the dust particle concentration change in a certain period of time in the future based on historical data. This prediction not only considers the current time point state, but also analyzes the past change law and its influencing factors. For example, in one embodiment, the system can show according to the historical data that the particle concentration rises in the morning due to frequent personnel flow, so as to make a higher estimate of the concentration in this period when predicting.

[0075] Then, the subsequent sampling time interval is dynamically adjusted based on the predicted change trend. The specific method of this step is to set different threshold intervals to judge the stability of the dust particle concentration, and if the prediction result shows that the concentration is relatively stable, the sampling time interval is increased to reduce data redundancy, and conversely if there is a large fluctuation, the interval is shortened to ensure real-time. For example, in a certain industrial plant, the system finds that the dust in a certain area does not change much according to the prediction results of several consecutive days, so the sampling time of this area is adjusted from every 5 minutes to every 30 minutes.

[0076] Finally, a new round of dust particle concentration collection and analysis is performed within the adjusted sampling time interval. This means that once the analysis and adjustment of the existing model are completed, a new sampling will be performed immediately according to the new interval, and the latest data will be collected again for model updating, and so on. Specifically, after the automatic monitoring system in a certain laboratory is adjusted, the next sampling process will be started according to the newly set time interval, and the latest data will be processed for subsequent use.

[0077] The present application has the advantage of effectively solving the problem of data redundancy and real-time imbalance. Through intelligent analysis of the change trend of dust particle concentration, automatic adjustment of the sampling frequency is realized, thereby improving the efficiency and accuracy of the environmental monitoring system. In addition, such a dynamic adaptation mechanism helps to save resources while ensuring that critical data is not missed due to too long sampling intervals.

[0078] Next, referring to Figure 2 , the process of dynamically adjusting the subsequent sampling time interval based on the predicted change trend of the present application is described. First, the fluctuation amplitude ΔC of the current predicted change trend is obtained, which represents the quantitative value of the change in dust particle concentration or physical properties, usually the absolute value of the difference between the measured value and the average value within a certain time range, the range is set according to the complexity of the environment, generally between 0.1 and 5, and the optimal value is about 1.5.

[0079] Next, the initial sampling interval T0 is determined, which is the basic sampling frequency set according to historical data and regular detection requirements, such as once every hour.

[0080] Then, based on the formula T = T0*(ΔC / ΔC0), where ΔC0 is a preset standard fluctuation threshold used as a reference point for comparison, the value is usually 1.0, used to judge whether the current fluctuation is an abnormal situation. Through this formula, the dynamically adjusted sampling interval can be calculated. When ΔC is small, it means that the change trend is stable, and the sampling interval can be extended; otherwise, it is shortened to improve the response speed.

[0081] If the calculated T is less than the minimum allowed interval Tmin, it is set to Tmin to prevent the sampling from being too frequent to affect the equipment operation or data redundancy; otherwise, the actual calculated value is maintained to ensure reasonable adjustment.

[0082] For example, in one embodiment, assume T0 is set to 60 minutes, ΔC0 is 1.0, and the current ΔC is measured to be 2.0, the calculated T is 120 minutes. If the dust particle concentration in the environment starts to change dramatically, increasing the sampling interval can avoid missing critical information. In another scenario, if ΔC continues to be below 1.0, reducing the sampling interval can improve analysis efficiency. This method helps to reduce resource waste while ensuring data accuracy, improving system adaptability.

[0083] Next, referring to Figure 3 , the flow of dynamically adjusting the subsequent sampling time interval based on the predicted change trend of the present application is described. First, record the timestamp t and the corresponding dust particle concentration C in the current period. This step is used to collect actual detection data as the basis for subsequent calculation. The timestamp t can be in millisecond level to ensure the continuity of the time series; the unit of dust particle concentration C is usually per cubic meter, and the numerical range depends on the specific environmental situation. For example, in one embodiment, it is assumed that the dust concentration in a certain industrial area is 1200 per cubic meter.

[0084] Next, a moving average model MA=(C_n+C_{n-1}) / 2 is constructed, where C_n represents the concentration at the current time point and C_{n-1} represents the concentration at the previous time period. This model is used to smooth short-term fluctuations and reflect the overall trend of particle concentration. Moving average helps to reduce noise interference and make it closer to the real change law. For example, in a certain detection, the dust particle concentration at the previous time is 1100 per cubic meter, and the current time is 1200 per cubic meter, and the MA value is 1150 per cubic meter.

[0085] According to the difference ΔMA=MA-C_base between MA and the reference concentration C_base, the time factor α=ΔMA / σ is calculated, where σ is the standard deviation. Here, C_base represents the expected normal concentration level, and σ is used to measure the dispersion degree of dust particle concentration change. This formula compares the fluctuation range with the standard deviation to obtain the relative change measure. When μ is equal to 0.5, it may indicate that the change trend is more obvious.

[0086] Introduce α into the sampling interval formula T=T0*(1+α), and ensure that T is greater than Tmin. Where T0 is the initial sampling interval, Tmin is the minimum allowed interval, which is used to prevent too dense sampling. By adjusting α, the sampling frequency is adapted to the actual environmental change, thereby improving data utilization and response speed. For example, in the case of sudden increase of dust concentration, T can be increased to reduce the sampling frequency; when the concentration is stable, T can be reduced to improve the sensitivity.

[0087] This technical solution has multiple benefits. By dynamically adjusting the sampling interval, the effectiveness of data acquisition is improved, and resource waste is avoided. At the same time, it can timely capture the changing trend and improve the response capability to abnormal state, thereby providing more accurate data support for environmental monitoring.

[0088] Next, with reference to Figure 4 , the process of dynamically adjusting the subsequent sampling time interval based on the predicted changing trend of the present application is described. First, the fluctuation amplitude VarC = MaxC - MinC is calculated according to the maximum MaxC and minimum MinC of the particle concentration in the time window, which is used to measure the change amplitude of the dust particle concentration in a certain time period. MaxC and MinC represent the maximum and minimum measurement values in the time window, respectively. The larger the value of VarC, the more intense the data fluctuation. For example, in a heavily polluted area, MaxC may reach 1000 particles / cubic foot, while MinC is only 500 particles / cubic foot, and VarC is 500 at this time.

[0089] Next, VarC is input into the fuzzy controller to generate a control coefficient k, where k ∈ [5, 1.5], representing the adjustment coefficient. The value of k decreases as VarC increases, to ensure that it can respond faster when the fluctuation is intense. For example, in the case of high dust particle concentration and rapid change, the value of k may be low, so that the sampling is more intensive.

[0090] The sampling interval is adjusted using the formula T = T0 * (1 + k * (VarC / VarC0)), where T0 represents the initial sampling interval, VarC0 is the preset reference amplitude, usually taking a stable background noise level such as 100. T represents the final sampling interval after adjustment. The meaning of the formula is to adjust the sampling frequency by multiplying the ratio of the amplitude to the reference amplitude by the control coefficient k. When the calculation result exceeds the maximum limit Tmax, it is forced to be set to Tmax to prevent excessive collection and improve the service life of the equipment and the efficiency of data processing.

[0091] This technical solution can flexibly adjust the sampling rhythm according to the actual environmental changes, improve the adaptability of the system to sudden environmental changes and the rationality of data collection, while reducing resource waste and enhancing the reliability and real-time performance of the analysis results. For example, in an industrial production area, when the dust particle concentration in the air is detected to rise rapidly, the system will automatically reduce the sampling interval to monitor the air quality changes more timely and accurately.

[0092] Next, with reference to Figure 5, describes the process of dynamically adjusting the subsequent sampling time interval based on the predicted change trend of the application. First, analyze the change rate dC / dt of the predicted trend, which represents the speed of the change of the environmental dust particle concentration over time. The range of dC / dt depends on the monitored environment and the performance of the sampling equipment, usually in the small value interval, such as 0.1 to 5.0 mg / m3·min. The optimal value should be set according to the specific scene to improve the response sensitivity.

[0093] Two reference variables are set: the standard change rate dC0 and the tolerance interval ε, which are used to define the normal and abnormal change range. The standard change rate dC0 reflects the benchmark value of the change trend in the normal environment, and the common setting range is 0.5 to 2.0 mg / m3·min, and the tolerance interval ε is usually 0.1 to 0.3, representing the upper and lower limits of the allowed deviation. This setting helps to determine whether the data enters the abnormal fluctuation area.

[0094] According to the formula T=T0*(1+(dC / dt) / dC0), when dC / dt is greater than dC0*(1+ε) or less than dC0*(1-ε), the time interval T is set to the minimum value Tmin to avoid missing data due to rapid changes. In the normal range, the sampling frequency is adjusted in proportion to the linear, maintaining system stability. This formula compares the current change speed with the preset benchmark to reasonably adjust the sampling frequency, improving efficiency while reducing redundant data collection.

[0095] For example, in one embodiment, if dC / dt is 1.2 mg / m3·min, dC0 is set to 1.0 mg / m3·min, and ε is set to 0.2, then 1.2 is in the normal range, and T is linearly expanded according to 1.2 / 1.0; if dC / dt rises to 1.4, exceeding 1.0*1.2, then Tmin is triggered to obtain high-frequency data. This mechanism can accurately capture pollution mutations and maintain the continuity and real-time response of the system, thereby improving the accuracy and efficiency of environmental monitoring.

[0096] Next, refer to Figure 6 , describes the process of dynamically adjusting the subsequent sampling time interval based on the predicted change trend of the application. First, a sliding window is used to fit the predicted curve, and the local maximum and minimum values are extracted. This step is used to analyze the fluctuations in the predicted data by identifying peak and valley values to provide a basis for subsequent trend judgment. For example, in environmental detection, if the predicted value of the dust particle concentration has multiple peaks and valleys in a certain time period, it indicates that the air quality changes rapidly. Specifically, a sliding window is used to fit the continuous data points and calculate their local extreme values.

[0097] Then, a trend strength parameter S is constructed, which is equal to the difference between the peak and the valley divided by the window length. This parameter reflects the degree of change, with a larger S value indicating a more pronounced trend. Generally, S values range between 0 and 1, with a value greater than 0.5 indicating a strong variability. The optimal value is determined by the complexity of the actual environment, typically between 0.3 and 0.7.

[0098] Next, the sampling time T is updated according to the formula T = T0 * e^(a * S^b), where a is a decay coefficient that adjusts the sensitivity to changes. The purpose of this formula is to shorten the sampling time as S increases, allowing the system to capture trends more quickly. A reasonable range for a is typically between 0.1 and 1, with a value that is too large causing the sampling to be too frequent, and a value that is too small making the system unable to respond to changes in a timely manner.

[0099] Finally, if the calculated T is less than Tmin, it is set to Tmin to avoid data redundancy and maintain a reasonable sampling frequency. This setting ensures that the system can quickly respond to sudden changes while avoiding resource waste. For example, during a stable phase of dust particle concentration, T may be limited to a longer time, while during periods of increased pollution, T is automatically shortened to allow more intensive sampling, improving the real-time and accuracy of monitoring. This technical solution helps to improve the efficiency and accuracy of environmental detection.

[0100] Next, referring to Figure 7 , the process of dynamically adjusting the subsequent sampling time interval based on the predicted change trend of the present invention is described. First, a regression model is established to predict the dust particle change trend y(t) in the future. This step uses historical data and known variables to construct a model through a regression algorithm, outputting the predicted value. y(t) represents the predicted dust particle concentration value at a specific time point t, typically a floating-point number ranging from 0 to 1000, with the optimal value depending on the type of environment and the accuracy requirements of detection. By establishing a regression model, a theoretical basis is provided for subsequent dynamic adjustment.

[0101] Second, the model error indicator E = Σ|y(t) - actual(t)| is calculated. E is the sum of the absolute values of the difference between the model predicted value y(t) and the actual measured value actual(t), used to quantify the accuracy of the prediction. The parameter Σ represents the sum of all prediction time periods, with a smaller E indicating a more accurate model. The reasonable range is generally between 0 and 500, with an optimal value close to zero indicating good prediction results.

[0102] Subsequently, the subsequent sampling interval T is adjusted according to the formula T = T0 * (1 + E / E_threshold), where E_threshold is the error tolerance. T0 represents the initial default sampling interval, commonly set to 60 seconds. E_threshold is the error threshold, typically set to 10, to determine whether the current model error exceeds expectations. As E increases, T also increases, indicating the need to reduce sampling frequency to reduce unnecessary resource consumption; when E approaches or exceeds E_threshold, the high-frequency sampling mechanism is triggered, i.e. T = Tmin, where Tmin is the minimum allowed sampling interval, usually set to 10 seconds, ensuring that the rapidly changing particle concentration can still be captured in extreme cases.

[0103] Finally, when E approaches or exceeds E_threshold, high-frequency sampling is triggered to improve sampling accuracy and avoid missing important data. This design can dynamically adjust the sampling rhythm according to the prediction error, improving system efficiency while ensuring the timeliness and accuracy of environmental data collection.

[0104] For example, in an environmental detection scenario in a factory workshop, the dust particle concentration may suddenly change due to the start of production equipment. Through this technical solution, the system can automatically adjust the sampling frequency according to the model prediction error, such as maintaining high-frequency sampling in the high-precision control area and using low-frequency sampling in the stable area, thereby optimizing monitoring efficiency and cost.

[0105] Next, referring to Figure 8 , the process of dynamically adjusting the subsequent sampling interval based on the predicted change trend of the present invention is described. First, calculate the entropy value H of the historical trend, H represents the uncertainty of the current environmental data change, H = Σp_i logp_i, where p_i is the probability of dust particle concentration in different intervals. The value of p_i ranges from 0 to 1, and the sum of all p_i is 1. The higher the entropy value, the more unstable the system changes, requiring more frequent sampling. This step can quantify the randomness of the system, facilitating subsequent analysis.

[0106] Next, set the sampling sensitivity S = 1 / (1 + e^{k(H-H0)}), where k is the adjustment coefficient, used to control the slope of the curve, usually taking a value in the range of 5 to 10; H0 is the equilibrium threshold, representing the entropy value limit of the stable state, commonly set to 1.5 to 2.0. This formula maps the H value to the S value between 0 and 1 through the sigmoid function, so that as H increases, S changes gradually, preventing false positives due to fluctuations.

[0107] Then use T = T0 * S as the basis adjustment scheme, T0 is the basic sampling time interval, generally set to a fixed sampling period, such as 60 seconds. The higher S is, the smaller T is, which means that in the case of large changes, the sampling frequency is increased. For example, in an embodiment, when H is 1.8, k is set to 8, H0 is 1.7, S is about equal to 0.64, assuming T0 is 60 seconds, then T is 38.4 seconds, achieving higher frequency monitoring.

[0108] If T is less than Tmin (minimum time interval, such as 10 seconds), then fixed frequency sampling is forced to avoid excessive burden on the system. This method helps to improve sampling efficiency and accuracy, adapt to different trends, and reduce resource waste while ensuring data quality.

[0109] Next, referring to Figure 9 , the process of dynamically adjusting the subsequent sampling time interval based on the predicted trend of the present application is described. The predicted trend is divided into three stages: growth, stability, and decline. This step aims to identify the direction of data changes through classification, so as to develop corresponding coping strategies. For example, when the concentration of particulate matter in the detection environment gradually rises, the system enters the growth stage, and a faster response mechanism is adopted. This classification helps to improve the efficiency and relevance of data analysis.

[0110] Three different strategies are set: fast feedback mechanism (T = T0 / 2) for the growth stage, default sampling (T = T0) for the stability stage, and delayed sampling (T = T0*2) for the decline stage. This setting adjusts the sampling frequency according to the severity of the trend. When the concentration of particulate matter continues to rise, T0 represents the initial sampling time interval, generally ranging from 1 minute to 5 minutes, with an optimal value of 3 minutes, at which the sampling frequency is relatively high to ensure timely response. In an embodiment, when the growth of PM2.5 values is detected, the system will automatically shorten the sampling period to 1.5 minutes. In the case of stable concentration, the original sampling time is maintained. If the data shows a downward trend, the sampling interval is expanded to avoid resource waste.

[0111] A state transition matrix ψ is used to control the switching between stages. The elements in the matrix represent the transition probabilities between different stages, which helps to achieve smooth transition and prevent sudden changes. ψ can be derived from historical data analysis to ensure that the decision logic conforms to the actual rules. In an embodiment, if the particle concentration has decreased continuously for three times, the system will transfer from the decline stage to the stability stage.

[0112] And set the minimum interval Tmin to avoid too low sampling leading to information loss. Tmin generally takes a value of 30 seconds, which is used to limit the lower limit of the sampling period, thereby ensuring sufficient monitoring resolution. In an embodiment, even in the growth stage, the sampling period will not be lower than 30 seconds, preventing the collection of information from being too dense and causing data redundancy.

[0113] The technical solution helps to improve the adaptability and efficiency of the monitoring system, saves computing and storage resources, and enhances real-time performance and accuracy.

[0114] Next, with reference to Figure 10 , the process of dynamically adjusting the subsequent sampling time interval based on the predicted trend of change is described. An exponential decay coefficient β is introduced to measure the speed of trend change, β is equal to 1 divided by the ratio of |dC / dt| to dC_max, where |dC / dt| represents the absolute value of the rate of change of dust particle concentration over time, and dC_max represents the possible maximum concentration change in the current time window. The value range of β is theoretically positive, and the optimal value tends to 1, indicating that the change speed is relatively stable; if |dC / dt| approaches zero, β will become very large, indicating that the concentration change tends to be stable. Through the parameter β, the severity of the trend can be quantified, thereby guiding the subsequent steps.

[0115] Based on β, the sampling factor K is calculated, K is defined as max(β,5), which can avoid the problem of excessive shortening of the sampling interval due to too small β, leading to resource waste or excessive equipment load. For example, in a certain environmental detection system, if the dust particle concentration in the air tends to be stable, the β value is low, and by setting a minimum threshold of 5, the sampling interval will not be too short, ensuring the sampling efficiency and data reliability.

[0116] The formula T=T0×K is used as the adjustment logic, where T0 is the initial sampling interval, and T is the adjusted sampling time interval. Through the multiplier effect of K, the sampling frequency can be flexibly adjusted according to the change trend. For example, when a sudden pollution event occurs in the environment, causing the dust particle concentration to rise sharply, the β value increases rapidly, K increases, and T also increases, thereby optimizing the use of sampling resources while ensuring timely monitoring.

[0117] If T is greater than Tmax after calculation, it is limited to Tmax, Tmax is the maximum allowed sampling interval, to prevent the problem of data loss or response delay due to long-term non-sampling. Such design improves the adaptability and efficiency of the system, while also ensuring the real-time performance and accuracy of the detection results.

[0118] Next, with reference to Figure 11, describes the process of dynamically adjusting the subsequent sampling time interval based on the predicted change trend of the invention. The first step of the method is to calculate the autocorrelation function AC(k) of the dust particle concentration at time delay k, which reflects the degree of correlation between different time points, for identifying the change pattern of dust particle concentration. Wherein, k is the length of the settable time interval, generally selected a reasonable value to capture the main trend change. The range of AC(k) is usually between -1 and 1, the closer to 1 indicates the higher the repeatability of the time series. Through this step, the fluctuation pattern of the data can be preliminarily understood.

[0119] The second step is to determine the sampling sensitivity γ = D / D_threshold by combining AC(k) and the historical mean deviation D = |AC(k) - AC_avg|. Here, D_threshold is a judgment threshold set according to historical data, when the deviation exceeds this value, it indicates that the abnormal change is large. γ as a sensitivity coefficient, its value range is usually in the interval of 0 to a positive number, used to quantify whether the current fluctuation needs to adjust the sampling frequency. For example, in an embodiment, when the measured AC(k) value is lower than the average value more, the D value will be larger, resulting in γ becoming larger, prompting that data should be collected more frequently.

[0120] The third step is to calculate the new sampling time interval using the formula T = T0 * (1 + γ), T0 is the default initial time interval, usually set to a shorter and stable value. The larger γ is, the more T grows, indicating that in a faster changing environment, the collection interval needs to be shortened. For example, if γ is 0.5, the new period is 1.5T0, which improves the sampling density to ensure data continuity and timeliness. The purpose of this formula design is to flexibly adapt to different periodic data characteristics.

[0121] The fourth step is not to adjust if γ is lower than D_threshold, maintaining the regular sampling mode. This setting prevents unnecessary calculation and energy consumption due to small perturbations, improving system efficiency. This scheme helps to improve environmental monitoring accuracy, while saving resources, reducing energy consumption, and improving the response speed to sudden pollution events.

[0122] The dust particle physical and chemical analysis method for environment detection provided by the application comprises the following steps: first, collecting dust particle concentration data in the environment through a special device and constructing a time sequence model; then, using a time sequence analysis algorithm to model and predict the obtained data, and further obtaining the change trend of the dust particle concentration; subsequently, dynamically adjusting the subsequent sampling time interval according to the predicted change trend to cope with the actual situation of dust particle concentration fluctuation in different time periods; and collecting and analyzing the dust particle concentration again in the optimized sampling time interval, so as to ensure real-time performance while avoiding redundant data caused by repeated sampling. The method can effectively capture key data points by establishing a mathematical model to predict environmental changes and flexibly adjusting the sampling frequency according to the change trend, and can increase the sampling density during a high concentration fluctuation period, and appropriately prolong the sampling interval during a low fluctuation period, thereby saving resources and reducing data redundancy, so as to solve the problem of data redundancy or missed detection caused by fixed sampling period in the traditional method, and achieve a balance between data quality and collection efficiency. The dynamic adjustment mechanism not only improves the accuracy and reliability of environmental detection, but also enhances the intelligent level of the system, so that the monitoring of dust particles is more suitable for complex scenes in actual applications.

[0123] In the above embodiments, all or part can be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another.

[0124] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, the skilled in the art can combine and combine the different embodiments or examples described in the specification and the features of the different embodiments or examples without contradiction.

[0125] In addition, the terms "first", "second", etc. are used herein only to describe different instances, and cannot be construed as indicating or implying relative importance or an indicated number of the technical features indicated. Thus, the features defined with "first", "second", etc. can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise specifically limited.

[0126] Any process or method descriptions or descriptions of the flow diagrams described herein can be understood as representing code modules, segments, or portions of code which include one or more executable instructions for implementing specific logic functions (or steps) in the process. And the scope of preferred embodiments of the present application includes additional implementation in which the functions can be performed in different orders, in substantially simultaneous fashion, or in reverse order, depending on the functionality involved.

[0127] The logic and / or steps represented in the flow diagrams described herein, for example, can be considered as a list of executable instructions for implementing the logic function, which can be specifically embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions, or in conjunction with these instructions.

[0128] It should be understood that parts of the present application can be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the above-described embodiment method can be instructed by a program to complete the relevant hardware, which can be stored in a computer-readable storage medium, and the program includes one or a combination of the steps of the method embodiment when executed.

[0129] In addition, each functional unit in each embodiment of the present application can be integrated in one processing module, or each unit can be physically present separately, or two or more units can be integrated in one module. The above-mentioned integrated module can be realized in the form of hardware or in the form of a software functional module. The above-mentioned integrated module, if realized in the form of a software functional module and sold or used as an independent product, can also be stored in a computer-readable storage medium. The storage medium can be a read-only memory, a magnetic disk or an optical disk, etc.

[0130] The above merely provides the specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of various changes or replacements within the technical range disclosed by the present application, and these should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for physicochemical analysis of dust particles for environmental monitoring, characterized in that, Including: Collecting dust particle concentration data in the environment and establishing a time series model; Predicting the change trend of dust particle concentration according to the time series model; Dynamically adjusting the subsequent sampling time interval based on the predicted change trend; Performing a new round of dust particle concentration collection and analysis within the adjusted sampling time interval.

2. The method for physicochemical analysis of dust particles for environmental monitoring according to claim 1, characterized in that, The dynamically adjusting the subsequent sampling time interval based on the predicted change trend further includes: Obtaining the fluctuation amplitude ΔC of the current predicted change trend; Determining the initial sampling interval T0; Calculating the dynamically adjusted sampling interval T = T0 * (ΔC / ΔC0) based on the following formula, where ΔC0 is a preset standard fluctuation threshold; If T is less than the minimum allowable interval Tmin, then T is set to Tmin; otherwise, the actual calculated value is maintained.

3. The method for physicochemical analysis of dust particles for environmental monitoring according to claim 1, characterized in that, The dynamically adjusting the subsequent sampling time interval based on the predicted change trend further includes: Recording the time stamp t and the corresponding dust particle concentration C within the current cycle; Constructing a moving average model MA = (C_n + C_{n1}) / 2, where C_n represents the concentration at the current time point and C_{n1} represents the concentration in the previous period; Calculating the time factor α = ΔMA / σ according to the difference ΔMA = MA - C_base between MA and the reference concentration C_base, where σ is the standard deviation; Introducing α into the sampling interval formula T = T0 * (1 + α) and ensuring that T is greater than Tmin.

4. The method for physicochemical analysis of dust particles for environmental monitoring according to claim 2, characterized in that, The dynamically adjusting the subsequent sampling time interval based on the predicted change trend further includes: Calculating the fluctuation amplitude VarC = MaxC - MinC according to the maximum value MaxC and the minimum value MinC of the particle concentration within the time window; Inputting VarC into a fuzzy controller to generate a control coefficient k, where k ∈ [5, 1.5], representing the adjustment coefficient; Adjusting the sampling interval using the formula T = T0 * (1 + k * (VarC / VarC0)), where VarC0 is a preset reference amplitude; When the calculation result exceeds the maximum limit Tmax, it is forced to be set to Tmax to prevent over-collection.

5. The method for physicochemical analysis of dust particles for environmental monitoring according to claim 3, characterized in that, The dynamically adjusting the subsequent sampling time interval based on the predicted change trend further includes: Analyzing the change rate dC / dt of the predicted trend; Setting two reference variables: the standard change rate dC0 and the tolerance interval ε; According to the formula T = T0 * (1 + (dC / dt) / dC0), where if (dC / dt) > dC0 * (1 + ε) or < dC0 * (1 - ε), then T is set to Tmin; And adjusting the sampling frequency in a linear proportion within the normal range to maintain the system stability.

6. The method for physicochemical analysis of dust particles for environmental monitoring according to claim 4, characterized in that, The dynamically adjusting the subsequent sampling time interval based on the predicted change trend further includes: Performing a sliding window fitting on the predicted curve to extract the local maximum and minimum values; Constructing a trend intensity parameter S, S = (Peak - Trough) / (Window Length); Updating the sampling time T = T0 * e^{α * S} according to the following formula, where α is the attenuation coefficient; If T is less than Tmin, it is set to Tmin to avoid data redundancy caused by over-dense sampling.

7. The method for physicochemical analysis of dust particles for environmental monitoring according to claim 5, characterized in that, The dynamic adjustment of subsequent sampling time intervals based on the predicted trend of change further includes: Establish a regression model to predict the trend of dust particle changes y(t) over a future period; Calculate the model error index E = Σ|y(t)actual(t)|; According to the formula T=T0*(1+E / E_threshold), E_threshold is the error tolerance; High-frequency sampling is triggered when E approaches or exceeds E_threshold, i.e., T = Tmin.

8. The method for physicochemical analysis of dust particles for environmental monitoring according to claim 6, characterized in that, The dynamic adjustment of subsequent sampling time intervals based on the predicted trend of change further includes: Calculate the entropy value H of the historical trend, H = Σp_i log p_i, where p_i is the probability of occurrence of different concentration intervals; The sampling sensitivity is set according to the H value: S = 1 / (1 + e^{k(H H0)}), where k is the adjustment coefficient and H0 is the balance threshold. Then, T = T0 * S is used as the basic adjustment scheme; If T is less than Tmin, then fixed frequency sampling is forced.

9. The method for physicochemical analysis of dust particles for environmental monitoring according to claim 7, characterized in that, The dynamic adjustment of subsequent sampling time intervals based on the predicted trend of change further includes: The predicted trend is divided into three phases: growth, stability, and decline. Three different strategies are set: a fast feedback mechanism is used during the growth phase (T = T0 / 2), default sampling is used during the stable phase (T = T0), and delayed sampling is used during the decline phase (T = T0 * 2). The state transition matrix ψ is used to control the switching between stages; A minimum interval Tmin is set to avoid information loss due to excessively low sampling.

10. The method for physicochemical analysis of dust particles for environmental monitoring according to claim 8, characterized in that, The dynamic adjustment of subsequent sampling time intervals based on the predicted trend of change further includes: An exponential decay coefficient β is introduced to measure the speed of trend change, β = 1|dC / dt / dC_max; The sampling factor K = max(β,5) is calculated based on β to avoid excessive attenuation; The formula T = T0 * K is used as the adjustment logic; If the calculated T is greater than Tmax, then it is limited to Tmax, thereby improving efficiency.

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