Intelligent evaluation method for air organic emission flux

Abstract

The present application relates to the technical field of chemical analysis, in particular to a kind of intelligent evaluation method of organic matter emission flux in air, comprising: according to the difference between data in each cluster and each time period, obtain the isolated coefficient of each time period in relative abundance sequence;According to the difference between the isolated coefficient of all time periods of relative abundance sequence, the initial screening parameter of each time period is corrected, and the screening parameter after correction of each time period is obtained;According to the screening parameter after correction of each time period, obtain the artificial monoterpene concentration of each collection device at each time;According to the artificial monoterpene concentration of each collection device at each time, the flux evaluation of artificial monoterpene emission is carried out.The present application improves the accuracy of the prediction of the emission amount of artificial monoterpene, and improves the accuracy of the flux evaluation of artificial source monoterpene.

Description

Technical Field

[0001] This invention relates to the field of chemical analysis technology, specifically to an intelligent assessment method for the emission flux of organic matter in the air. Background Technology

[0002] Anthropogenic monoterpenes are a class of volatile organic compounds released by human activities. Anthropogenic monoterpenes emissions can impact air quality and ecosystems because they participate in the formation of ozone and particulate matter in the atmosphere, and also have some influence on meteorology and climate. Therefore, flux assessment of anthropogenic monoterpenes emissions is crucial. Flux assessment of anthropogenic monoterpenes emissions is a relatively common intelligent assessment method for evaluating the flux of organic matter emissions into the atmosphere.

[0003] When collecting emissions of anthropogenic monoterpenes, the presence of noise means that the obtained anthropogenic monoterpenes contain other substances. If the emission of monoterpenes is predicted, the accuracy of the emission prediction may be reduced, which in turn reduces the accuracy of flux assessment of anthropogenic monoterpenes. Summary of the Invention

[0004] This invention provides an intelligent assessment method for the emission flux of organic matter in the air to solve existing problems.

[0005] The intelligent assessment method for airborne organic matter emission flux of the present invention adopts the following technical solution:

[0006] One embodiment of the present invention provides a smart assessment method for the emission flux of organic matter in the air, the method comprising the following steps:

[0007] Obtain time-series mass spectra of artificial monoterpenes at several times from all acquisition devices;

[0008] The time-series mass spectra of artificial monoterpenes from each acquisition device at each time step are converted into discrete mass spectra of artificial monoterpenes from each acquisition device at each time step. The data points in the discrete mass spectra of artificial monoterpenes from each acquisition device at each time step are clustered to obtain several clusters. Based on the discrete mass spectra of artificial monoterpenes from each acquisition device at each time step, the relative abundance sequence of each acquisition device at each time step is obtained. The relative abundance sequence is equally divided into several time periods. Based on the differences between the data in each cluster and each time period, the isolation coefficient of each time period in the relative abundance sequence is obtained.

[0009] Based on the differences in isolation coefficients across all time periods of the relative abundance sequence, the initial screening parameters for each time period are adjusted to obtain the adjusted screening parameters for each time period.

[0010] Based on the corrected screening parameters for each time period, the artificial monoterpene concentration of each collection device at each time moment is obtained;

[0011] The flux of anthropogenic monoterpene emissions was assessed based on the anthropogenic monoterpene concentration at each time point for each collection device.

[0012] Furthermore, the specific steps involved in clustering the data points in the mass spectrometry discrete point map of artificial monoterpenes from each acquisition device at each time moment to obtain several clusters are as follows:

[0013] pass The clustering algorithm clusters the data points in the mass spectrometry discrete point map of artificial monoterpenes of each acquisition device at each time step to obtain several clusters.

[0014] Furthermore, the specific steps for obtaining the relative abundance sequence of each acquisition device at each time step based on the mass spectrometry discrete point map of artificial monoterpenes at each time step are as follows:

[0015] The relative abundance at each time point is obtained from the mass spectrometry discrete point map, and the relative abundance at all times is arranged into a relative abundance sequence in chronological order.

[0016] In the mass spectrometry discrete plot, the horizontal axis represents time, and the vertical axis represents relative abundance.

[0017] Furthermore, the specific steps for obtaining the isolation coefficient of each time period in the relative abundance sequence based on the differences between data in each cluster and each time period are as follows:

[0018] Based on the differences between data in each cluster and each time period, obtain the isolation coefficient of each data point in each cluster;

[0019] Based on the isolation coefficient of each data point in each cluster, the isolation coefficient of each data point in the relative abundance sequence is obtained, and based on the isolation coefficient of each data point in the relative abundance sequence, the isolation coefficient of each time period in the relative abundance sequence is obtained.

[0020] Furthermore, the specific steps for obtaining the isolation coefficient of each data point in each cluster based on the differences between data in each cluster and each time period are as follows:

[0021] The difference between the maximum and minimum values ​​in each cluster is recorded as the first value of each cluster. The sum of the maximum and minimum values ​​in each cluster is recorded as the second value of each cluster. The ratio between the first and second values ​​of each cluster is recorded as the third value of each cluster. The result of multiplying the third value of each cluster by the reciprocal of the number of data in each cluster is recorded as the first feature of each cluster.

[0022] The absolute value of the difference between each data point in each cluster and the mean of all data points in that cluster is denoted as the fourth value for each data point in that cluster. ,Will As the fifth value of each data in each cluster, the reciprocal of the number of data in the time period corresponding to each data in each cluster is added to the fifth value of each data in each cluster, and the result is used as the second feature of each data in each cluster;

[0023] The ratio of the number of data points belonging to the same time period in each cluster to the number of data points in each time period corresponding to each data point in each cluster is denoted as the third feature of each data point in each cluster. ,Will As the fourth feature of each data point in each cluster; where, Represents an exponential function with the natural constant as its base;

[0024] Then, the product of the first feature of each cluster, the second feature of each data point in each cluster, and the fourth feature of each data point in each cluster is used as the isolation coefficient of each data point in each cluster.

[0025] Furthermore, the specific steps for obtaining the isolation coefficient for each time period in the relative abundance sequence based on the isolation coefficient of each data point in the relative abundance sequence are as follows:

[0026] The isolation coefficients of all data in each time period of the relative abundance sequence are summed to obtain the isolation coefficient for each time period.

[0027] Furthermore, the specific steps for correcting the initial screening parameters for each time period based on the differences in isolation coefficients across all time periods of the relative abundance sequence, to obtain the corrected screening parameters for each time period, are as follows:

[0028] The first isolated feature of each time period is obtained by subtracting the mean of the isolation coefficients of all time periods other than that time period from the isolation coefficient of each time period.

[0029] When the first isolated feature of each time period is greater than 0, the isolation coefficient of each time period minus the mean of the isolation coefficients of all time periods other than each time period is used as the second isolated feature of each time period. When the first isolated feature of each time period is less than or equal to 0, the negative of the mean of the isolation coefficients of all time periods other than each time period is used as the second isolated feature of each time period.

[0030] The reciprocal of the second isolated feature for each time period is denoted as the third isolated feature for that time period. The standard deviation of the isolated coefficients for all time periods other than each time period is denoted as the fourth isolated feature for that time period. The result of multiplying the third isolated feature of each time period by the fourth isolated feature of each time period is taken as the fifth isolated feature for that time period. ,Will As the sixth isolated feature of each time period ,Will As the adjusted filtering parameters for each time period;

[0031] in, This represents an exponential function with the natural constant as its base. Represents a linear normalization function. This represents the initial filtering parameters for each time period.

[0032] Furthermore, the specific steps for obtaining the artificial monoterpene concentration of each collection device at each time moment based on the corrected screening parameters for each time period are as follows:

[0033] The isolation coefficients for all moments within each time period are sorted in descending order to obtain an isolation coefficient sequence for each time period. The product of the number of data points in the isolation coefficient sequence for each time period and the adjusted filtering parameters for that time period is recorded as the first filtering coefficient. The first filtering coefficient is then rounded up to obtain the second filtering coefficient. The first few isolated coefficient sequences in each time period The time of each data point is recorded as the removal time, thus obtaining all removal times in each time period;

[0034] Based on all the removal times in all time periods, remove all removal times from the time-series mass spectra. Record the time-series mass spectra after removal as the new time-series mass spectra. At this time, the new time-series mass spectra of each acquisition device at each time are obtained.

[0035] Based on the new time-series mass spectra of each acquisition device at each time step, the concentration of artificial monoterpenes of each acquisition device at each time step is obtained using calibration curves.

[0036] Furthermore, the specific steps for assessing the flux of anthropogenic monoterpene emissions based on the anthropogenic monoterpene concentration of each collection device at each time step are as follows:

[0037] The average concentration of artificial monoterpenes at each moment is calculated from all data acquisition devices and recorded as the artificial monoterpenes concentration at each moment. Based on the artificial monoterpenes concentration at all moments, the artificial monoterpenes concentration at each subsequent moment is predicted using an ARMA model. When the predicted artificial monoterpenes concentration at each moment is greater than or equal to a preset threshold U, it is determined that the artificial monoterpenes concentration exceeds the standard and an early warning is required. When the artificial monoterpenes concentration at each moment is less than the preset threshold U, it is determined that the artificial monoterpenes concentration is normal.

[0038] The present invention also provides an intelligent assessment system for airborne organic matter emission flux, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described intelligent assessment methods for airborne organic matter emission flux.

[0039] The beneficial effects of the technical solution of this invention are as follows: This invention converts the time-series mass spectra of anthropogenic monoterpenes from each acquisition device at each time step into discrete mass spectra of anthropogenic monoterpenes from each acquisition device at each time step. Data points in the discrete mass spectra of anthropogenic monoterpenes from each acquisition device at each time step are clustered to obtain several clusters. By obtaining discrete data points and then performing clustering, the isolation status of each data point can be analyzed, improving the accuracy of anomaly data identification. Based on the differences between data in each cluster and each time period, the isolation coefficient of each time period in the relative abundance sequence is obtained. Based on the differences between the isolation coefficients of all time periods in the relative abundance sequence, the corrected screening parameters for each time period are obtained, identifying more accurate anomaly data. Based on the corrected screening parameters for each time period, the concentration of anthropogenic monoterpenes from each acquisition device at each time step is obtained. Based on the concentration of anthropogenic monoterpenes from each acquisition device at each time step, the flux of anthropogenic monoterpenes emissions is assessed, improving the accuracy of predicting anthropogenic monoterpenes emissions and improving the accuracy of flux assessment for anthropogenic monoterpenes. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a flowchart illustrating the steps of an intelligent assessment method for airborne organic matter emission flux according to the present invention.

[0042] Figure 2This is a flowchart illustrating the features of an intelligent assessment method for the emission flux of organic matter in the air according to the present invention. Detailed Implementation

[0043] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an intelligent assessment method for airborne organic matter emission flux proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0045] The following description, in conjunction with the accompanying drawings, details the specific scheme of the intelligent assessment method for airborne organic matter emission flux provided by the present invention.

[0046] Please see Figure 1 The diagram illustrates a flowchart of a method for intelligently assessing the emission flux of organic matter in the air according to an embodiment of the present invention. The method includes the following steps:

[0047] Step S001: Collect time-series mass spectra of artificial monoterpenes.

[0048] It should be noted that in order to detect the emission of anthropogenic monoterpenes in certain areas, it is necessary to set up anthropogenic monoterpenes data acquisition devices centered on a specific location in order to analyze and evaluate the emission situation.

[0049] Specifically, a 102-meter observation platform was selected as the flux observation site. Centered on this site, artificial monoterpene collection devices were deployed at locations with an average building height of 15.3 meters within a 6-kilometer radius. The sampling frequency was 10 Hz, and data was collected over two hours. Time-series mass spectra of monoterpenes were obtained from each collection device at each moment. Here, 10 Hz represents 10 samplings per second. This yielded several time-series mass spectra of artificial monoterpenes at each moment. The sampling frequency was not specifically limited and could be determined by the implementer based on specific circumstances. The collection devices were... .

[0050] Among them, the time-series mass spectra of monoterpenes were obtained through... (Acquired by Proton-Transfer-Reaction Time-of-Flight Mass Spectrometry); where, As this is a well-known technique, it will not be described in detail here. The time-series mass spectrum of the directly obtained artificial monoterpenes is shown as a bar chart.

[0051] Thus, the time-series mass spectra of monoterpenes from each acquisition device at each time step were obtained.

[0052] Step S002: Obtain a mass spectrometry discrete point map based on the time-series mass spectrometry of artificial monoterpenes. Cluster the data points in each mass spectrometry discrete point map to obtain several clusters. Based on each mass spectrometry discrete point map, obtain the relative abundance sequence of each acquisition device at each time step. Divide the relative abundance sequence equally into several time periods. Based on the differences between the data in each cluster and each time period, obtain the isolation coefficient of each data in each cluster. Based on the isolation coefficient of each data in each cluster, obtain the isolation coefficient of each data in the relative abundance sequence.

[0053] It should be noted that in the time-series mass spectrum of each substance, the mass-to-charge ratio of the corresponding ions is relatively concentrated. When the mass-to-charge ratio of ions in the time-series mass spectrum of a substance is relatively dispersed, then the substance may contain other substances. Therefore, the analysis is carried out by the relative abundance of the mass-to-charge ratio of ions in the time-series mass spectrum of the substance.

[0054] It should be further noted that in the time-series mass spectra of artificial monoterpenes, the horizontal axis represents time, and the vertical axis represents the relative abundance of ions with mass-to-charge ratios corresponding to different flight times.

[0055] Specifically, the time-series mass spectra of artificial monoterpenes from each acquisition device at each time step are converted into discrete point plots, and these converted discrete point plots are denoted as the mass spectrometry discrete point plots of artificial monoterpenes. In these discrete point plots, the horizontal axis represents time, and the vertical axis represents the relative abundance of ions with mass-to-charge ratios corresponding to different flight times. The conversion of bar charts into discrete point plots is a well-known technique and will not be elaborated upon here.

[0056] In this embodiment, analysis is performed using the time-series mass spectra of each acquisition device at each time step. The clustering algorithm clusters the data points in the mass spectrometry discrete image of each human monoterpene, obtaining several clusters. The number of clusters is obtained using the elbow method. Clustering algorithms and elbow methods are well-known techniques, and will not be elaborated on here.

[0057] It should be noted that the fewer data points in a cluster, the more abnormal the data in that cluster is, meaning it is more likely to be due to noise interference. Therefore, abnormal data can be analyzed by examining the number of data points between clusters and the differences between the data points.

[0058] Specifically, the relative abundance at each time point is obtained from the mass spectrometry discrete point plot, and the relative abundance at all time points is arranged into a relative abundance sequence in chronological order. A parameter A is preset, and in this embodiment, A = 0.05 seconds is used as an example. This embodiment does not impose specific limitations, and A can be determined according to the specific implementation.

[0059] Based on a preset parameter A, the relative abundance sequence is divided into several time periods, starting from the time of the first data point. The isolation coefficient of each data point in each cluster is obtained based on the differences between data points within each time period.

[0060] As a preferred embodiment, the specific method for obtaining the isolation coefficient is as follows:

[0061] The difference between the maximum and minimum values ​​in each cluster is recorded as the first value of each cluster. The sum of the maximum and minimum values ​​in each cluster is recorded as the second value of each cluster. The ratio between the first and second values ​​of each cluster is recorded as the third value of each cluster. The result of multiplying the third value of each cluster by the reciprocal of the number of data in each cluster is recorded as the first feature of each cluster.

[0062] The absolute value of the difference between each data point in each cluster and the mean of all data points in that cluster is denoted as the fourth value for each data point in that cluster. ,Will As the fifth value of each data in each cluster, the reciprocal of the number of data in the time period corresponding to each data in each cluster is added to the fifth value of each data in each cluster, and the result is used as the second feature of each data in each cluster;

[0063] The ratio of the number of data points belonging to the same time period in each cluster to the number of data points in each time period corresponding to each data point in each cluster is denoted as the third feature of each data point in each cluster. ,Will As the fourth feature of each data point in each cluster; where, Represents an exponential function with the natural constant as its base;

[0064] Then, the product of the first feature of each cluster, the second feature of each data point in each cluster, and the fourth feature of each data point in each cluster is used as the isolation coefficient of each data point in each cluster.

[0065] As an optional embodiment, the specific calculation method is as follows:

[0066]

[0067] in, Indicates the first The first in the cluster The isolation coefficient of each data point Indicates the first The first characteristic of each cluster Indicates the first The first in the cluster The second characteristic of the data, Indicates the first The first in the cluster The fourth feature of the data.

[0068] As one embodiment, the specific calculation method for the first feature is as follows:

[0069]

[0070] in, Indicates the first The first characteristic of each cluster Indicates the first The number of data items in each cluster Indicates the first The maximum value among the clusters, Indicates the first The minimum value among all clusters.

[0071] As one embodiment, the specific calculation method for the second feature is as follows:

[0072]

[0073] in, Indicates the first The first in the cluster The second characteristic of the data, Indicates the first The first in the cluster One data point, Indicates the first The mean of all data in each cluster, Indicates the first The first in the cluster The number of data points within the corresponding time period for each data point. It is the absolute value symbol.

[0074] As one embodiment, the specific calculation method for the fourth feature is as follows:

[0075]

[0076] In the formula, Indicates the first The first in the cluster The fourth characteristic of the data, Indicates the first In the clusters and the th The number of data points belonging to the same time period. This represents an exponential function with the natural constant as its base.

[0077] Among them, the third value This represents the difference between data within each cluster. A larger difference indicates more dispersed data within that cluster, resulting in a higher isolation coefficient. Conversely, a smaller difference indicates more concentrated data within that cluster, resulting in a lower isolation coefficient. Similarly, a larger number of data points in each cluster indicates more concentrated data, resulting in a lower isolation coefficient. Conversely, a smaller number of data points in each cluster indicates more dispersed data, resulting in a higher isolation coefficient. (Fourth numerical value) This represents the absolute value of the difference between each data point in each cluster and the mean of all data points in that cluster. A larger absolute value indicates that the data in that cluster is more dispersed, and thus the isolation coefficient is higher. Conversely, a smaller absolute value indicates that the data in that cluster is more concentrated, and thus the isolation coefficient is lower. (Third feature) , that is It represents the ratio between the number of data points belonging to the same time period in each cluster and the total number of data points in the corresponding time period. The larger the ratio, the smaller the isolation coefficient of the corresponding data; the smaller the ratio, the larger the isolation coefficient of the corresponding data.

[0078] Thus, the isolation coefficients of each data point in each cluster are obtained; since the data in the clusters and the data in the relative abundance sequence are the same, the isolation coefficients of each data point in the relative abundance sequence can also be obtained.

[0079] Step S003: Obtain the isolation coefficient of each time period in the relative abundance sequence based on the isolation coefficient of each data point in the relative abundance sequence. Based on the difference between the isolation coefficients of all time periods in the relative abundance sequence, correct the initial screening parameters for each time period to obtain the corrected screening parameters for each time period. Based on the corrected screening parameters for each time period, obtain the artificial monoterpene concentration of each collection device at each moment.

[0080] It should be noted that the larger the isolation coefficient of each data point, the more isolated the data point. Due to the presence of noise, there are outlier data in each time period. Therefore, we first obtain the isolation coefficient of each time period by using the isolation coefficient of each data point, and then use this to conduct the analysis.

[0081] Specifically, the isolation coefficients of all data in each time period of the relative abundance sequence are summed to obtain the isolation coefficient for each time period; an initial screening parameter is preset. In this embodiment, This example is used for illustration; no specific limitations are set in this embodiment. It depends on the specific implementation situation.

[0082] Based on the differences in isolation coefficients across all time periods of the relative abundance sequence, the initial screening parameters for each time period are adjusted to obtain the adjusted screening parameters for each time period.

[0083] As a preferred embodiment, the method for obtaining the screening parameters includes:

[0084] The first isolated feature of each time period is obtained by subtracting the mean of the isolation coefficients of all time periods other than that time period from the isolation coefficient of each time period.

[0085] When the first isolated feature of each time period is greater than 0, the isolation coefficient of each time period minus the mean of the isolation coefficients of all time periods other than each time period is used as the second isolated feature of each time period. When the first isolated feature of each time period is less than or equal to 0, the negative of the mean of the isolation coefficients of all time periods other than each time period is used as the second isolated feature of each time period.

[0086] The reciprocal of the second isolated feature for each time period is denoted as the third isolated feature for that time period. The standard deviation of the isolated coefficients for all time periods other than each time period is denoted as the fourth isolated feature for that time period. The result of dividing the third isolated feature of each time period by the fourth isolated feature of each time period is taken as the fifth isolated feature for that time period. ,Will As the sixth isolated feature of each time period ,Will As the adjusted filtering parameters for each time period;

[0087] in, This represents an exponential function with the natural constant as its base. Represents a linear normalization function. This represents the initial filtering parameters for each time period.

[0088] At this point, the corrected filtering parameters for each time period are obtained.

[0089] The isolation coefficients for all moments within each time period are sorted in descending order to obtain an isolation coefficient sequence for each time period. The product of the number of data points in the isolation coefficient sequence for each time period and the adjusted filtering parameters for that time period is recorded as the first filtering coefficient. The first filtering coefficient is then rounded up to obtain the second filtering coefficient. The first few isolated coefficient sequences in each time period If the time of each data point is recorded as the removal time, then all removal times in each time period are obtained; at this time, all removal times in all time periods are also obtained.

[0090] Based on all the elimination times in all time periods, all elimination times in the time-series mass spectra are eliminated, and the time-series mass spectra after elimination are recorded as the new time-series mass spectra. Thus, the new time-series mass spectra of each acquisition device at each time are obtained.

[0091] Based on the new time-series mass spectra of each acquisition device at each time step, the concentration of artificial monoterpenes of each acquisition device at each time step is obtained using calibration curves; the calibration curves are a well-known technique and will not be described in detail here.

[0092] Thus, the artificial monoterpene concentration of each collection device at each moment was obtained.

[0093] Step S004: Evaluate the flux of anthropogenic monoterpene emissions based on the anthropogenic monoterpene concentration of each collection device at each time point.

[0094] A threshold U is preset. In this embodiment, U=0.37 is used as an example. This embodiment does not impose specific limitations. U can be determined according to the specific implementation.

[0095] The average anthropogenic monoterpene concentration at each time step is calculated from all data collection devices and denoted as the anthropogenic monoterpene concentration at each time step. Based on the anthropogenic monoterpene concentration at all time steps, the anthropogenic monoterpene concentration at each subsequent time step is predicted using an ARMA model. When the predicted anthropogenic monoterpene concentration at each time step is greater than or equal to a preset threshold U, the anthropogenic monoterpene concentration is considered to be exceeding the limit, and an early warning is required. When the anthropogenic monoterpene concentration at each time step is less than the preset threshold U, the anthropogenic monoterpene concentration is considered to be normal. The flux assessment process for anthropogenic monoterpene emissions is as follows: Figure 2 As shown. The unit for the concentration of artificial monoterpenes is ppb (parts per billion).

[0096] The ARMA model is a well-known technology and will not be described in detail here.

[0097] This concludes the embodiment.

[0098] It should be noted that the embodiments used in this example The model is only used to represent negative correlations and the results of the constraint model output are in Within this range, in specific implementations, other models with the same purpose can be substituted; this embodiment is merely an example. The description will be based on a model, without making specific limitations on it. This refers to the input of the model.

[0099] This embodiment provides an intelligent assessment system for airborne organic matter emission flux, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements an intelligent assessment method for airborne organic matter emission flux in steps S001 to S004.

[0100] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart assessment method for the emission flux of organic matter in the air, characterized in that, The method includes the following steps: Obtain time-series mass spectra of artificial monoterpenes at several times from all acquisition devices; The time-series mass spectra of artificial monoterpenes from each acquisition device at each time step are converted into discrete mass spectra of artificial monoterpenes from each acquisition device at each time step. The data points in the discrete mass spectra of artificial monoterpenes from each acquisition device at each time step are clustered to obtain several clusters. Based on the discrete mass spectra of artificial monoterpenes from each acquisition device at each time step, the relative abundance sequence of each acquisition device at each time step is obtained. The relative abundance sequence is equally divided into several time periods. Based on the differences between the data in each cluster and each time period, the isolation coefficient of each time period in the relative abundance sequence is obtained. Based on the differences in isolation coefficients across all time periods of the relative abundance sequence, the initial screening parameters for each time period are adjusted to obtain the adjusted screening parameters for each time period. Based on the corrected screening parameters for each time period, the artificial monoterpene concentration of each collection device at each time moment is obtained; The flux of anthropogenic monoterpene emissions was assessed based on the anthropogenic monoterpene concentration at each time point for each collection device. The specific steps for obtaining the relative abundance sequence of each acquisition device at each time step based on the mass spectrometry discrete point map of artificial monoterpenes at each time step are as follows: The relative abundance at each time point is obtained from the mass spectrometry discrete point plot, and the relative abundance at all times is arranged into a relative abundance sequence in chronological order. In the mass spectrometry discrete point plot, the horizontal axis represents time, and the vertical axis represents relative abundance. The specific steps for obtaining the isolation coefficient of each time period in the relative abundance sequence based on the differences between data in each cluster and each time period are as follows: Based on the differences between data in each cluster and each time period, obtain the isolation coefficient of each data point in each cluster; Based on the isolation coefficient of each data point in each cluster, the isolation coefficient of each data point in the relative abundance sequence is obtained, and based on the isolation coefficient of each data point in the relative abundance sequence, the isolation coefficient of each time period in the relative abundance sequence is obtained. The specific steps for obtaining the isolation coefficient of each data point in each cluster based on the differences between data in each cluster and each time period are as follows: The difference between the maximum and minimum values ​​in each cluster is recorded as the first value of each cluster. The sum of the maximum and minimum values ​​in each cluster is recorded as the second value of each cluster. The ratio between the first and second values ​​of each cluster is recorded as the third value of each cluster. The result of multiplying the third value of each cluster by the reciprocal of the number of data in each cluster is recorded as the first feature of each cluster. The absolute value of the difference between each data point in each cluster and the mean of all data points in that cluster is denoted as the fourth value for each data point in that cluster. ,Will As the fifth value of each data in each cluster, the reciprocal of the number of data in the time period corresponding to each data in each cluster is added to the fifth value of each data in each cluster, and the result is used as the second feature of each data in each cluster; The ratio of the number of data points belonging to the same time period in each cluster to the number of data points in each time period corresponding to each data point in each cluster is denoted as the third feature of each data point in each cluster. ,Will As the fourth feature of each data point in each cluster; where, Represents an exponential function with the natural constant as its base; Then, the product of the first feature of each cluster, the second feature of each data point in each cluster, and the fourth feature of each data point in each cluster is used as the isolation coefficient of each data point in each cluster; The specific steps for obtaining the isolation coefficient of each time period in the relative abundance sequence based on the isolation coefficient of each data point in the relative abundance sequence are as follows: The isolation coefficients of all data in each time period in the relative abundance sequence are summed to obtain the isolation coefficient for each time period. The specific steps for correcting the initial screening parameters for each time period based on the differences in isolation coefficients across all time periods of the relative abundance sequence, to obtain the corrected screening parameters for each time period, are as follows: The first isolated feature of each time period is obtained by subtracting the mean of the isolation coefficients of all time periods other than that time period from the isolation coefficient of each time period. When the first isolated feature of each time period is greater than 0, the isolation coefficient of each time period minus the mean of the isolation coefficients of all time periods other than each time period is used as the second isolated feature of each time period. When the first isolated feature of each time period is less than or equal to 0, the negative of the mean of the isolation coefficients of all time periods other than each time period is used as the second isolated feature of each time period. The reciprocal of the second isolated feature for each time period is denoted as the third isolated feature for that time period. The standard deviation of the isolated coefficients for all time periods other than each time period is denoted as the fourth isolated feature for that time period. The result of dividing the third isolated feature of each time period by the fourth isolated feature of each time period is taken as the fifth isolated feature for that time period. ,Will As the sixth isolated feature of each time period ,Will As the adjusted filtering parameters for each time period; in, This represents an exponential function with the natural constant as its base. Represents a linear normalization function. This represents the initial filtering parameters for each time period; The specific steps for obtaining the artificial monoterpene concentration of each collection device at each time moment based on the corrected screening parameters for each time period are as follows: The isolation coefficients for all moments within each time period are sorted in descending order to obtain an isolation coefficient sequence for each time period. The product of the number of data points in the isolation coefficient sequence for each time period and the adjusted filtering parameters for that time period is recorded as the first filtering coefficient. The first filtering coefficient is then rounded up to obtain the second filtering coefficient. The first few isolated coefficient sequences in each time period The time of each data point is recorded as the removal time, thus obtaining all removal times in each time period; Based on all the removal times in all time periods, remove all removal times from the time-series mass spectra. Record the time-series mass spectra after removal as the new time-series mass spectra. At this time, the new time-series mass spectra of each acquisition device at each time are obtained. Based on the new time-series mass spectra of each acquisition device at each time step, the concentration of artificial monoterpenes of each acquisition device at each time step is obtained using calibration curves.

2. The intelligent assessment method for airborne organic matter emission flux according to claim 1, characterized in that, The specific steps involved in clustering the data points in the mass spectrometry discrete point map of artificial monoterpenes from each acquisition device at each time moment to obtain several clusters are as follows: pass The clustering algorithm clusters the data points in the mass spectrometry discrete point map of artificial monoterpenes of each acquisition device at each time step to obtain several clusters.

3. The intelligent assessment method for airborne organic matter emission flux according to claim 1, characterized in that, The specific steps for assessing the flux of anthropogenic monoterpene emissions based on the anthropogenic monoterpene concentration of each collection device at each time point are as follows: The average concentration of artificial monoterpenes at each moment is calculated from all data acquisition devices and recorded as the artificial monoterpenes concentration at each moment. Based on the artificial monoterpenes concentration at all moments, the artificial monoterpenes concentration at each subsequent moment is predicted using an ARMA model. When the predicted artificial monoterpenes concentration at each moment is greater than or equal to a preset threshold U, it is determined that the artificial monoterpenes concentration exceeds the standard and an early warning is required. When the artificial monoterpenes concentration at each moment is less than the preset threshold U, it is determined that the artificial monoterpenes concentration is normal.

Images