Atmospheric environment monitoring system and method

Abstract

The invention provides an atmospheric environment monitoring system and method. The method comprises the following steps: measuring the concentration of small particles in each detection period through an oscillating balance; determining the local difference degree of the small particle concentration change information entropy at each moment in the atmosphere based on the concentration of all the small particles, and determining the sudden change moment when the small particles are agglomerated based on all the local difference degrees; determining the concentration of large particles in each detection period through an oscillating balance; determining a positive trend and a negative trend of the change of the concentration of the large particles in the atmosphere based on the concentration of all the large particles, and determining a sudden change moment when the large particles are subjected to temperature inversion settlement based on the positive trend and the negative trend; and according to the sudden change moment when the small particles are agglomerated and the sudden change moment when the large particles are subjected to temperature reversal sedimentation, determining the starting moment when the concentration of the particulate matters in the atmosphere is subjected to the upside-down abnormity. By adopting the scheme of the invention, the starting moment of the upside-down phenomenon can be detected, so that the upside-down phenomenon can be handled in time, and the accuracy of environment monitoring is improved.

Description

Technical Field

[0001] The present application relates to the technical field of environmental monitoring, and more specifically, to an atmospheric environment monitoring system and method. Background Art

[0002] Environmental monitoring refers to the activities of collecting, analyzing and evaluating environmental factors regularly or in real time through scientific means, in order to understand the current status and changing trends of environmental quality, and to provide data support for environmental management, protection and decision-making. The goal of environmental monitoring is to timely discover the source of environmental pollution, assess the degree and impact of pollution, and provide a basis for the formulation of relevant policies and measures. It covers different environmental elements such as air, water, soil, etc., and uses a variety of technical means for monitoring.

[0003] Atmospheric environmental monitoring mainly detects and evaluates pollutants in the atmosphere and their sources, distribution, and changing trends. Its purpose is to understand the current status of air quality, predict possible pollution trends, and provide a scientific basis for controlling air pollution, protecting public health, and formulating environmental protection policies. Existing atmospheric environment monitoring usually detects the concentration of particulate matter in the atmosphere through experimental methods (including weight method and dynamic sedimentation method, etc.). According to the experimental principle, the content of PM10 in the atmosphere should theoretically always be greater than the content of PM2.5. However, through the results of atmospheric environment monitoring in multiple regions, it is known that the concentration of particulate matter in the atmosphere often shows an inverted phenomenon (that is, the content of PM10 in the atmosphere is less than the content of PM2.5). The occurrence of this inverted phenomenon usually means that the sampling instrument has failed or the meteorological parameters in the environment (temperature, humidity and air pressure, etc.) have changed abnormally. In the existing technology, corresponding processing is usually only performed after the concentration of particulate matter in the atmosphere shows an inverted phenomenon. However, there is a period of time between the abnormal change of meteorological parameters in the environment and the inverted phenomenon of the concentration of particulate matter in the atmosphere, resulting in a lag in the processing of the inverted phenomenon, which will affect the accuracy of environmental monitoring. Therefore, how to detect the starting time of the inverted phenomenon so as to deal with the inverted phenomenon in a timely manner, thereby improving the accuracy of environmental monitoring, has become a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides an atmospheric environment monitoring system and method, which can detect the starting time of the inversion phenomenon so as to handle the inversion phenomenon in a timely manner, thereby improving the accuracy of environmental monitoring.

[0005] In a first aspect, the present application provides a method for determining anomalies in atmospheric particulate matter concentration, comprising: Pre-treating the filter membrane required for the test and preparing the test equipment required for the test, wherein the test equipment includes an oscillating balance and an intelligent sensor; The small particle concentration corresponding to each detection period is obtained by measuring the small particle concentration of the target location atmosphere in multiple detection periods by using the oscillating balance, the pretreated filter membrane and the intelligent sensor; Based on all the small particle concentrations, the local differences of the information entropy of the change of the small particle concentration at each moment in the atmosphere are determined, and based on all the local differences, the mutation moment when the small particles agglomerate is determined; The large particle concentration corresponding to each detection period is obtained by measuring the large particles in the atmosphere of the target location in multiple detection periods by using the oscillating balance, the pretreated filter membrane and the intelligent sensor; Determine the positive trend and negative trend of the change of the concentration of large particles in the atmosphere based on all the concentrations of large particles, and determine the mutation moment when the large particles undergo temperature inversion precipitation based on the positive trend and the negative trend; The starting time of the inverted abnormal concentration of atmospheric particulate matter is determined based on the mutation time when small particles agglomerate and the mutation time when large particles undergo temperature inversion sedimentation.

[0006] In some embodiments, the small particle concentration corresponding to each detection period is obtained by measuring the small particle concentration of the target location atmosphere in multiple detection periods by using the oscillating balance, the pretreated filter membrane and the smart sensor. Specifically, the concentration includes: Obtaining the filter membrane weight of the filter membrane after pretreatment; Place the pretreated filter membrane on top of an oscillating tube in an oscillating balance; The atmospheric gas stream which has passed through the small particle cutter is pumped downwardly into the oscillating tube from the top of the oscillating tube; Starting the oscillating balance to apply a periodic simple harmonic excitation force to the oscillating balance; Preset multiple detection time periods, and measure a set of oscillation frequencies in each detection time period by the intelligent sensor; The concentration of small particles in the detection period corresponding to each group of oscillation frequencies is determined according to each group of oscillation frequencies and the weight of the filter membrane.

[0007] In some embodiments, determining the local difference of the information entropy of the change of the small particle concentration at each moment in the atmosphere based on all the small particle concentrations specifically includes: Convert all small particle concentrations into a small particle concentration sequence; Selecting a small particle concentration in the small particle concentration sequence as a selected small particle concentration, and dividing the small particle concentration sequence into a forward sequence and a backward sequence according to the selected small particle concentration; Determining a forward information entropy of the forward sequence and a backward information entropy of the backward sequence; Determine the local difference of the information entropy of the change of the small particle concentration at the time corresponding to the selected small particle concentration according to the forward information entropy and the backward information entropy; Continue to determine the local difference of the information entropy of the change in the small particle concentration at the corresponding moment of each remaining small particle concentration.

[0008] In some embodiments, determining the mutation time when small particles agglomerate based on all local differences specifically includes: Convert all local differences into difference sequences; Dividing the difference degree sequence into a plurality of difference subsequences; Determine the trend index for each difference subseries; The mutation moment when small particles agglomerate is determined based on all trend indices.

[0009] In some embodiments, the large particle concentration corresponding to each detection period is obtained by measuring the large particle concentration of the target location atmosphere in multiple detection periods by using the oscillating balance, the pretreated filter membrane and the smart sensor. Specifically, the concentration includes: Obtaining the filter membrane weight of the filter membrane after pretreatment; Place the pretreated filter membrane on top of an oscillating tube in an oscillating balance; The atmospheric gas stream which has passed through the large particle cutter is pumped downwardly into the oscillating tube from the top of the oscillating tube; Starting the oscillating balance to apply a periodic simple harmonic excitation force to the oscillating balance; Preset multiple detection time periods, and measure a set of oscillation frequencies in each detection time period by the intelligent sensor; The concentration of large particles in the detection period corresponding to each group of oscillation frequencies is determined according to each group of oscillation frequencies and the weight of the filter membrane.

[0010] In some embodiments, determining the mutation moment when large particles undergo temperature inversion settling based on the positive trend and the negative trend specifically includes: Determine confidence intervals for all large particle concentration distributions; determining a plurality of trend turning points according to the positive trend and the negative trend; The mutation moment when large particles undergo temperature inversion sedimentation is determined based on all trend turning points and the confidence intervals.

[0011] In some embodiments, the smart sensor is an integrated sensor for detecting the oscillation frequency of the oscillation balance, the gas flow through the oscillation tube, the external temperature and the external atmospheric pressure.

[0012] In a second aspect, the present application provides an atmospheric environment monitoring system, the atmospheric environment monitoring system comprising an abnormality determination unit, the abnormality determination unit comprising: The first test module is used to instruct the oscillating balance, the pretreated filter membrane and the intelligent sensor to measure the small particles in the atmosphere of the target location for multiple detection periods after pre-treating the filter membrane required for the test and preparing the test equipment required for the test, so as to obtain the small particle concentration corresponding to each detection period; The first processing module is used to determine the local difference of the information entropy of the change of the small particle concentration at each moment in the atmosphere based on all the small particle concentrations, and determine the mutation moment when the small particles agglomerate based on all the local differences; The second test module is used to instruct the oscillating balance, the pretreated filter membrane and the intelligent sensor to measure the large particles in the atmosphere of the target location in multiple detection periods to obtain the large particle concentration corresponding to each detection period; A second processing module is used to determine the positive trend and negative trend of the change of the concentration of large particles in the atmosphere based on all the concentrations of large particles, and determine the mutation moment when the large particles undergo temperature inversion precipitation based on the positive trend and the negative trend; The execution module is used to determine the starting time of the inverted abnormal concentration of particulate matter in the atmosphere according to the sudden change time when small particles agglomerate and the sudden change time when large particles undergo temperature inversion sedimentation.

[0013] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned method for determining abnormal atmospheric particulate matter concentration.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned method for determining anomaly of atmospheric particulate matter concentration is implemented.

[0015] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects: In the atmospheric environment monitoring system and method provided by the present application, firstly, the filter membrane required for the test is pretreated, and the test equipment required for the test is prepared, and the test equipment includes an oscillating balance and an intelligent sensor; the atmosphere of the target location is measured for small particles in multiple detection periods by using the oscillating balance and the pretreated filter membrane, and the small particle concentration corresponding to each detection period during the small particle detection is determined based on the intelligent sensor; the local difference of the information entropy of the small particle concentration change at each time in the atmosphere is determined based on all the small particle concentrations, and the mutation time when the small particles agglomerate is determined based on all the local differences; the atmosphere of the target location is measured for large particles in multiple detection periods by using the oscillating balance, the pretreated filter membrane and the intelligent sensor, and the large particle concentration corresponding to each detection period is obtained; the positive trend and negative trend of the large particle concentration change in the atmosphere are determined based on all the large particle concentrations, and the mutation time when the large particles undergo inversion sedimentation is determined based on the positive trend and the negative trend; the starting time of the inverted abnormality of the particle concentration in the atmosphere is determined according to the mutation time when the small particles agglomerate and the mutation time when the large particles undergo inversion sedimentation.

[0016] It can be seen that in this application, by detecting the concentration of small particles in the atmosphere, and identifying the time when small particles agglomerate through the local difference of the information entropy of the change of the small particle concentration, that is, determining the time when the small particle concentration decreases abnormally, then, by detecting the concentration of large particles in the atmosphere, and identifying the time when the large particles invert and settle through the trend characteristics of the change of the large particle concentration (i.e., the positive trend and the reverse trend), that is, determining the time when the large particle concentration increases abnormally, and finally accurately identifying the starting time of the abnormal inversion of the particle concentration in the atmosphere through the time when the small particles agglomerate and the time when the large particles invert and settle. In summary, the scheme of this application can detect the starting time of the inversion phenomenon, so as to facilitate the timely processing of the inversion phenomenon, thereby improving the accuracy of environmental monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is an exemplary flow chart of a method for determining anomaly of atmospheric particulate matter concentration according to some embodiments of the present application; Figure 2 is a schematic structural diagram of an oscillating balance according to some embodiments of the present application; Figure 3 is an exemplary flow chart of determining a local difference degree according to some embodiments of the present application; Figure 4 is a schematic structural diagram of an abnormality determination unit according to some embodiments of the present application; Figure 5It is a structural schematic diagram of a computer device for implementing a method for determining anomalies in atmospheric particulate matter concentration according to some embodiments of the present application. DETAILED DESCRIPTION

[0018] In order to better understand the technical solution of the present application, the technical solution of the present application will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0019] refer to Figure 1 , which is an exemplary flow chart of a method for determining an abnormality in atmospheric particulate matter concentration according to some embodiments of the present application. The method 100 for determining an abnormality in atmospheric particulate matter concentration mainly includes the following steps: In step 101, the filter membrane required for the test is pretreated, and the test equipment required for the test is prepared, wherein the test equipment includes an oscillating balance and an intelligent sensor.

[0020] In specific implementation, the pretreatment of the filter membrane required for the test can be achieved in the following manner, namely: first, the filter membrane is placed in a drying oven and dried for 24 hours, the dried filter membrane is taken out and weighed with an analytical balance with a sensitivity of 0.1 mg, and the weighing result is recorded as m1; then, the same filter membrane is placed in the drying oven again and dried for 1 hour, the dried filter membrane is taken out and weighed with an analytical balance with a sensitivity of 0.1 mg, and the weighing result is recorded as m2; if the difference between m1 and m2 is less than 0.5 mg, the pretreatment is completed, and the average value of m1 and m2 is recorded as the filter membrane weight; if the difference between m1 and m2 is greater than or equal to 0.5 mg, the same filter membrane is placed in the drying oven again and dried for 1 hour and weighed with an analytical balance with a sensitivity of 0.1 mg, until the weight after the last drying differs from the weight before the last drying by less than 0.5 mg, and the average value of the weight after the last drying and the weight before the last drying is recorded as the filter membrane weight.

[0021] It should be noted that the filter membrane in the present application refers to an ultra-fine glass fiber filter membrane. In other embodiments, other organic filter membranes such as quartz filter membranes or mixed cellulose may also be used, which is not limited here.

[0022] In specific implementation, the test equipment required for the test can be prepared in the following manner, namely: the test equipment includes an oscillating balance and an intelligent sensor, the intelligent sensor is zero-calibrated, and the oscillating balance is started for testing to ensure normal operation.

[0023] It should be noted that the smart sensor in the present application is an integrated sensor used to detect the oscillation frequency of the oscillation balance, the gas flow through the oscillation tube, the external temperature and the external atmospheric pressure, etc., which are not specifically limited here.

[0024] In some embodiments, reference Figure 2, which is a schematic diagram of the structure of an oscillating balance according to some embodiments of the present application, and is specifically described as follows: Filter screen, used to place the pre-treated filter membrane; The oscillating tube is used to carry and support the filter screen and the filter membrane, and allows the atmospheric air flow to pass through the oscillating tube.

[0025] The magnet is used to respond to the current change of the inductor drive module and then apply a periodic simple harmonic excitation force to the oscillating tube; The inductor drive module is used to respond to the control program to adjust the current change, thereby controlling the magnet to apply a periodic simple harmonic excitation force to the oscillating tube; The smart sensor is used to detect the oscillation frequency of the oscillating balance during the test, and can also detect the gas flow rate through the oscillating tube, the external temperature and the external atmospheric pressure, etc., which are not specifically limited here. In specific implementation, sensors with corresponding functions can be integrated into the smart sensor according to the actual detection scenario; Base for fixing the oscillating balance.

[0026] In step 102, the atmosphere of the target location is subjected to small particle measurement in multiple detection periods by using the oscillating balance, the pre-treated filter membrane and the intelligent sensor to obtain the small particle concentration corresponding to each detection period.

[0027] In some embodiments, the oscillating balance, the pretreated filter membrane and the smart sensor are used to measure the small particles in the atmosphere of the target location for multiple detection periods, and the small particle concentration corresponding to each detection period can be obtained by the following steps: Obtaining the filter membrane weight of the filter membrane after pretreatment; Place the pretreated filter membrane on top of an oscillating tube in an oscillating balance; The atmospheric gas stream which has passed through the small particle cutter is pumped downwardly into the oscillating tube from the top of the oscillating tube; Starting the oscillating balance to apply a periodic simple harmonic excitation force to the oscillating balance; Preset multiple detection time periods, and measure a set of oscillation frequencies in each detection time period by the intelligent sensor; The concentration of small particles in the detection period corresponding to each group of oscillation frequencies is determined according to each group of oscillation frequencies and the weight of the filter membrane.

[0028] It should be noted that the small particles in this application refer to PM2.5 in the atmosphere.

[0029] In specific implementation, placing the pretreated filter membrane on the top of the oscillation tube in the oscillation balance can be achieved in the following manner: use tweezers to place the filter membrane on the filter screen on the top of the oscillation tube in the oscillation balance, with the hairy side of the filter membrane facing up, and firmly press the filter membrane until it is leak-proof.

[0030] It should be noted that the small particle cutter in the present application refers to a PM2.5 cutter. In other embodiments, other devices capable of separating particles with smaller aerodynamic equivalent diameters in the air may also be selected, which is not limited here.

[0031] In specific implementation, the atmospheric air flow passing through the small particle cutter can be pumped downward into the oscillation tube from the top of the oscillation tube by using a vacuum pump.

[0032] In specific implementation, the oscillation balance is started, and a periodic simple harmonic excitation force is applied to the oscillation balance. This can be achieved in the following manner, namely: a periodic simple harmonic excitation force is provided by an inductive drive module. The simple harmonic excitation force can be preset according to actual needs. For example, in the present application, the simple harmonic excitation force is preset to a sinusoidal function with an amplitude of 10 and a frequency of 0.5. In other embodiments, the simple harmonic excitation force can also be preset to other periodic functions with constant frequencies, which is not limited here.

[0033] It should be noted that the detection time periods in the present application are multiple time periods preset according to actual needs. For example, 100 detection time periods can be preset in the present application, the duration of each detection time period is one hour, and the interval between two adjacent detection time periods is one hour. In other embodiments, the detection time periods can also be preset as other time periods, which are not limited here.

[0034] In specific implementation, measuring a group of oscillation frequencies in each detection period by the intelligent sensor can be achieved in the following manner, namely: for each detection period, first, at the beginning of the detection period, the oscillation frequency of the oscillation tube is measured by the intelligent sensor to obtain a first oscillation frequency, and then, at the end of the detection period, the oscillation frequency of the oscillation tube is measured by the intelligent sensor to obtain a second oscillation frequency, and the first oscillation frequency and the second oscillation frequency are used as a group of oscillation frequencies in the detection period, thereby obtaining a group of oscillation frequencies in each detection period.

[0035] In specific implementation, the following method can be used to determine the concentration of small particles in the detection period corresponding to each group of oscillation frequencies according to each group of oscillation frequencies and the weight of the filter membrane, namely: first, the average value of the external temperature and the average value of the air pressure in each detection period are collected by an intelligent sensor, and the volume of gas passing through the oscillation tube in each detection period is collected by an intelligent sensor, and the standard volume in each detection period is calculated according to the average value of the external temperature, the average value of the air pressure and the gas volume, and then, the weight of the filter membrane after pretreatment and the weight of the oscillation balance are obtained, and the elastic coefficient of each detection period is determined according to the weight of the filter membrane after pretreatment and the weight of the oscillation balance, and then, for each detection period, the squares of two oscillation frequencies in a group of oscillation frequencies in the detection period are inversely calculated and then subtracted, and the obtained value is multiplied by the elastic coefficient of the detection period, and the obtained value is used as the mass of small particles in the detection period, and then the mass of small particles is divided by the standard volume in the detection period, and finally, The obtained value is used as the small particle concentration in the detection period, and then the small particle concentration in each detection period is obtained, wherein, calculating the standard volume in each detection period according to the average value of the external temperature, the average value of the air pressure and the gas volume means taking the average value of the external temperature and the average value of the air pressure as the temperature value and the air pressure value, converting the gas volume to the volume under the standard state (temperature is 273K Kelvin, air pressure is 101.325 kilopascals) according to the national standard, and taking the volume under the standard state as the standard volume, and determining the elastic coefficient of each detection period according to the weight of the filter membrane after pretreatment and the weight of the oscillating balance means adding the weight of the filter membrane after pretreatment and the weight of the oscillating balance to obtain the system mass, and for each detection period, adding the system mass to the mass of small particles in the previous detection period to obtain the value as the elastic coefficient of the detection period, and in particular, for the first detection period, directly taking the system mass as the elastic coefficient of the first detection period.

[0036] In step 103, the local differences of the information entropy of the change of the small particle concentration at each moment in the atmosphere are determined based on all the small particle concentrations, and the mutation moment when the small particles agglomerate is determined based on all the local differences.

[0037] In some embodiments, reference Figure 3 , which is an exemplary flow chart of determining the local difference degree according to some embodiments of the present application. In the present application, the local difference degree of the information entropy of the change of the small particle concentration at each moment in the atmosphere based on all the small particle concentrations can be achieved by the following steps: In step 1031, all small particle concentrations are converted into a small particle concentration sequence; In step 1032, a small particle concentration in the small particle concentration sequence is selected as a selected small particle concentration, and the small particle concentration sequence is divided into a forward sequence and a backward sequence according to the selected small particle concentration; In step 1033, the forward information entropy of the forward sequence and the backward information entropy of the backward sequence are determined; In step 1034, the local difference of the information entropy of the change of the small particle concentration at the time corresponding to the selected small particle concentration is determined according to the forward information entropy and the backward information entropy; In step 1035, the local difference of the small particle concentration change information entropy at each remaining small particle concentration corresponding to the moment is continued to be determined.

[0038] In specific implementation, converting all small particle concentrations into a small particle concentration sequence can be achieved in the following manner, namely: sorting all small particle concentrations in chronological order of the detection time periods, and using the obtained sequence as the small particle concentration sequence.

[0039] In specific implementation, the small particle concentration sequence is divided into a forward sequence and a backward sequence according to the selected small particle concentration, which can be achieved in the following manner, namely: the sequence after the selected small particle concentration in the small particle concentration sequence is used as the backward sequence, and then the remaining sequence in the small particle concentration sequence is used as the forward sequence.

[0040] It should be noted that the forward sequence and the backward sequence in the present application are sequences obtained by dividing all small particle concentrations, wherein the forward sequence is a sequence earlier in time, and the backward sequence is a sequence later in time.

[0041] In a specific implementation, determining the forward information entropy of the forward sequence and the backward information entropy of the backward sequence may be achieved in the following manner, namely: using the information entropy of the forward sequence as the forward information entropy, and using the information entropy of the backward sequence as the backward information entropy.

[0042] It should be noted that in the present application, the forward information entropy is the information entropy of the forward sequence, and the backward information entropy is the information entropy of the backward sequence.

[0043] In specific implementation, determining the local difference of the information entropy of the change of small particle concentration at the moment corresponding to the selected small particle concentration based on the forward information entropy and the backward information entropy can be achieved in the following manner, namely: first, calculating the information entropy of the small particle concentration sequence, then adding the forward information entropy and the backward information entropy, then subtracting the information entropy of the small particle concentration sequence from the obtained value, and finally, using the obtained value as the local difference of the information entropy of the change of small particle concentration at the moment corresponding to the selected small particle concentration.

[0044] It should be noted that the local difference in the present application is a parameter used to indicate the degree of difference in the change in the concentration of small particles in the atmosphere before and after the corresponding moment. The larger the local difference, the greater the difference in the change in the concentration of small particles in the atmosphere before and after the corresponding moment. The smaller the local difference, the smaller the difference in the change in the concentration of small particles in the atmosphere before and after the corresponding moment.

[0045] In some embodiments, determining the mutation moment when small particles agglomerate based on all local differences can be achieved by using the following steps: Convert all local differences into difference sequences; Dividing the difference degree sequence into a plurality of difference subsequences; Determine the trend index for each difference subseries; The mutation moment when small particles agglomerate is determined based on all trend indices.

[0046] In specific implementation, converting all local differences into a difference sequence can be achieved in the following manner, namely: sorting all local differences in the chronological order of corresponding moments, and using the obtained sequence as the difference sequence.

[0047] In a specific implementation, the difference degree sequence can be divided into multiple difference subsequences in the following manner, namely: first, the 1st to nth local differences in the difference degree sequence are taken as the difference subsequence, and then the 2nd to n+1th local differences in the difference degree sequence are taken as the difference subsequence, and so on, until the nth to last local difference in the difference degree sequence is taken as the difference subsequence, wherein n is a value preset according to the length of the difference degree sequence. For example, when the length of the difference degree sequence is 100, n can be preset to 10.

[0048] In specific implementation, the trend index of each difference subsequence can be determined in the following manner: first, for each difference subsequence, the Hurst index of the difference subsequence is calculated, and the Hurst index of the difference subsequence is subtracted by 0.5; finally, the obtained value is used as the trend index of the difference subsequence, thereby obtaining the trend index of each difference subsequence.

[0049] It should be noted that the trend index in the present application is a parameter used to represent the trend of changes in all local differences in the difference subsequence. If the trend index is greater than zero, the trend of changes in all local differences in the difference subsequence is a positive trend. If the trend index is less than zero, the trend of changes in all local differences in the difference subsequence is a negative trend.

[0050] In specific implementation, the mutation moment when small particles agglomerate can be determined based on all trend indices in the following manner, namely: first, the trend indices of all adjacent difference subsequences are multiplied, and multiple pairs of difference subsequences whose multiplication results are less than or equal to zero are extracted; then, for each pair of extracted difference subsequences, the moment corresponding to the maximum local difference value is used as the mutation moment when small particles agglomerate, thereby obtaining the mutation moment when multiple small particles agglomerate.

[0051] It should be noted that the mutation moment when small particles in the present application agglomerate refers to the moment when small particles in the atmosphere agglomerate due to environmental changes (usually changes in humidity in the atmosphere). After the small particles in the atmosphere agglomerate, the small particles in the atmosphere will agglomerate to form particles with a larger aerodynamic equivalent diameter.

[0052] In step 104, the large particle concentration of the atmosphere of the target location is measured in multiple detection periods by using the oscillating balance, the pre-treated filter membrane and the intelligent sensor to obtain the large particle concentration corresponding to each detection period.

[0053] In some embodiments, the large particle concentration corresponding to each detection period is obtained by measuring the large particle concentration of the target location atmosphere in multiple detection periods by using the oscillating balance, the pretreated filter membrane and the smart sensor. The following steps can be used: Obtaining the filter membrane weight of the filter membrane after pretreatment; Place the pretreated filter membrane on top of an oscillating tube in an oscillating balance; The atmospheric gas stream which has passed through the large particle cutter is pumped downwardly into the oscillating tube from the top of the oscillating tube; Starting the oscillating balance to apply a periodic simple harmonic excitation force to the oscillating balance; Preset multiple detection time periods, and measure a set of oscillation frequencies in each detection time period by the intelligent sensor; The concentration of large particles in the detection period corresponding to each group of oscillation frequencies is determined according to each group of oscillation frequencies and the weight of the filter membrane.

[0054] It should be noted that large particles in this application refer to PM10 in the atmosphere.

[0055] In specific implementation, placing the pretreated filter membrane on the top of the oscillation tube in the oscillation balance can be achieved in the following manner: use tweezers to place the filter membrane on the filter screen on the top of the oscillation tube in the oscillation balance, with the hairy side of the filter membrane facing up, and firmly press the filter membrane until it is leak-proof.

[0056] It should be noted that the large particle cutter in the present application refers to a PM10 cutter. In other embodiments, other devices capable of separating particles with larger aerodynamic equivalent diameters in the air may also be selected, which is not limited here.

[0057] In specific implementation, the atmospheric air flow that has passed through the large particle cutter can be pumped downward into the oscillation tube from the top of the oscillation tube by means of a vacuum pump.

[0058] In specific implementation, the oscillation balance is started, and a periodic simple harmonic excitation force is applied to the oscillation balance. This can be achieved in the following manner, namely: a periodic simple harmonic excitation force is provided by an inductive drive module. The simple harmonic excitation force can be preset according to actual needs. For example, in the present application, the simple harmonic excitation force is preset to a sinusoidal function with an amplitude of 10 and a frequency of 0.5. In other embodiments, the simple harmonic excitation force can also be preset to other periodic functions with constant frequencies, which is not limited here.

[0059] It should be noted that the detection time periods in the present application are multiple time periods preset according to actual needs. For example, 100 detection time periods can be preset in the present application, the duration of each detection time period is one hour, and the interval between two adjacent detection time periods is one hour. In other embodiments, the detection time periods can also be preset as other time periods, which are not limited here.

[0060] In specific implementation, measuring a group of oscillation frequencies in each detection period by the intelligent sensor can be achieved in the following manner, namely: for each detection period, first, at the beginning of the detection period, the oscillation frequency of the oscillation tube is measured by the intelligent sensor to obtain a first oscillation frequency, and then, at the end of the detection period, the oscillation frequency of the oscillation tube is measured by the intelligent sensor to obtain a second oscillation frequency, and the first oscillation frequency and the second oscillation frequency are used as a group of oscillation frequencies in the detection period, thereby obtaining a group of oscillation frequencies in each detection period.

[0061] In specific implementation, the following method can be used to determine the large particle concentration in the detection period corresponding to each group of oscillation frequencies according to each group of oscillation frequencies and the weight of the filter membrane, namely: first, the average value of the external temperature and the average value of the air pressure in each detection period are collected by an intelligent sensor, and the volume of gas passing through the oscillation tube in each detection period is collected by an intelligent sensor, and the standard volume in each detection period is calculated according to the average value of the external temperature, the average value of the air pressure and the gas volume, and then, the weight of the filter membrane after pretreatment and the weight of the oscillation balance are obtained, and the elastic coefficient of each detection period is determined according to the weight of the filter membrane after pretreatment and the weight of the oscillation balance, and then, for each detection period, the squares of two oscillation frequencies in a group of oscillation frequencies in the detection period are inversely calculated and then subtracted, and the obtained value is multiplied by the elastic coefficient of the detection period, and the obtained value is used as the large particle mass in the detection period, and then the large particle mass is divided by the standard volume in the detection period, and finally, The obtained value is used as the large particle concentration in the detection period, and then the large particle concentration in each detection period is obtained, wherein, calculating the standard volume in each detection period according to the average value of the external temperature, the average value of the air pressure and the gas volume means taking the average value of the external temperature and the average value of the air pressure as the temperature value and the air pressure value, converting the gas volume to the volume under the standard state (temperature is 273K Kelvin, air pressure is 101.325 kilopascals) according to the national standard, and taking the volume under the standard state as the standard volume, and determining the elastic coefficient of each detection period according to the weight of the filter membrane after pretreatment and the weight of the oscillating balance means adding the weight of the filter membrane after pretreatment and the weight of the oscillating balance to obtain the system mass, and for each detection period, the value obtained by adding the system mass and the mass of large particles in the previous detection period is used as the elastic coefficient of the detection period, and in particular, for the first detection period, the system mass is directly used as the elastic coefficient of the first detection period.

[0062] In step 105, the positive trend and negative trend of the change in the concentration of large particles in the atmosphere are determined based on all the large particle concentrations, and the sudden change moment when the large particles undergo temperature inversion precipitation is determined based on the positive trend and the negative trend.

[0063] In some embodiments, determining the positive trend and negative trend of the change in the concentration of large particles in the atmosphere based on all large particle concentrations can be achieved by the following steps: Convert all large particle concentrations into a large particle concentration sequence; The positive trend and negative trend of the change of large particle concentration in the atmosphere are determined according to the large particle concentration sequence.

[0064] In specific implementation, converting all large particle concentrations into a large particle concentration sequence can be achieved in the following manner, namely: sorting all large particle concentrations in chronological order of the detection time periods, and using the obtained sequence as the large particle concentration sequence.

[0065] In specific implementation, the positive trend and negative trend of the change of large particle concentration in the atmosphere can be determined according to the large particle concentration sequence in the following manner, namely: a large particle concentration in the large particle concentration sequence is selected as the selected large particle concentration, the number of large particle concentrations that are less than or equal to the selected large particle concentration before the selected large particle concentration is counted, the number is used as the positive trend value of the selected large particle concentration, the positive trend values ​​of the remaining large particle concentrations in the large particle concentration sequence are continued to be determined, and all positive trend values ​​are arranged according to the order of the large particle concentrations in the large particle concentration sequence, and the obtained sequence is used as the large particle concentration. The positive trend of the change of large particle concentration in the atmosphere is determined. Subsequently, the large particle concentration sequence is sorted in reverse order, and the sequence after reverse order is used as a negative sequence. A large particle concentration in the negative sequence is selected as the selected large particle concentration. The number of large particle concentrations that are less than or equal to the selected large particle concentration before the selected large particle concentration is counted, and the number is used as the negative trend value of the selected large particle concentration. The negative trend values ​​of the remaining large particle concentrations in the negative sequence are continued to be determined, and all negative trend values ​​are arranged according to the order of the large particle concentrations in the negative sequence. The obtained sequence is used as the negative trend of the change of large particle concentration in the atmosphere.

[0066] It should be noted that the positive trend in the present application refers to the trend of increasing concentration of large particles in the atmosphere when the concentration changes positively over time, and the negative trend refers to the trend of increasing concentration of large particles in the atmosphere when the concentration changes negatively over time.

[0067] In some embodiments, determining the mutation moment when large particles undergo temperature inversion settling based on the positive trend and the negative trend can be achieved by using the following steps: Determine confidence intervals for all large particle concentration distributions; determining a plurality of trend turning points according to the positive trend and the negative trend; The mutation moment when large particles undergo temperature inversion sedimentation is determined based on all trend turning points and the confidence intervals.

[0068] In specific implementation, the confidence interval of all large particle concentration distributions can be determined in the following manner, that is, the confidence level interval of 0.05 significance in the normal distribution can be used as the confidence interval of all large particle concentration distributions.

[0069] In a specific implementation, determining multiple trend transition points based on the positive trend and the negative trend can be achieved in the following manner, namely: first, fitting the sequence of the positive trend into a curve by the Lagrange interpolation method in the prior art, then fitting the sequence of the negative trend into a curve by the Lagrange interpolation method in the prior art, and then, taking all points where the two curves intersect as trend transition points.

[0070] It should be noted that the trend turning point in the present application refers to the point where the trend of the change in the concentration of large particles in the atmosphere changes.

[0071] In specific implementation, the mutation moment when large particles undergo temperature inversion sedimentation can be determined based on all trend transition points and the confidence interval in the following manner, namely: the horizontal coordinates corresponding to all trend transition points whose vertical coordinates are in the confidence interval are used as the mutation moment when large particles undergo temperature inversion sedimentation.

[0072] It should be noted that the sudden change moment when large particles undergo temperature inversion sedimentation in the present application is the moment when the large particles in the atmosphere settle due to environmental changes (usually temperature changes in the atmosphere). When the large particles in the atmosphere settle, it will make it difficult for the test equipment to capture the large particles in the atmosphere.

[0073] In step 106, the starting time of the inverted abnormal concentration of the atmospheric particulate matter is determined according to the sudden change time when the small particles agglomerate and the sudden change time when the large particles undergo temperature inversion precipitation.

[0074] In some embodiments, the following steps can be used to determine the starting time of the inverted abnormal concentration of particulate matter in the atmosphere according to the mutation time when small particles agglomerate and the mutation time when large particles undergo temperature inversion precipitation: Determine multiple groups of mutation time groups according to the mutation time when all small particles agglomerate and the mutation time when all large particles undergo temperature inversion sedimentation; The starting time of the inverted abnormality of the particle matter concentration in the atmosphere is determined according to each group of mutation moments.

[0075] In specific implementation, multiple groups of mutation moment groups can be determined based on the mutation moment when all small particles agglomerate and the mutation moment when all large particles undergo temperature inversion sedimentation. This can be achieved in the following manner, namely: first, the mutation moment when all small particles agglomerate and the mutation moment when all large particles undergo temperature inversion sedimentation are subtracted, and the mutation moment when small particles agglomerate and the mutation moment when large particles undergo temperature inversion sedimentation with a difference of less than or equal to one hour are taken as a group of mutation moment groups.

[0076] In addition, in the specific implementation, the starting time of the inverted abnormality of the atmospheric particulate matter concentration corresponding to each group of mutation time groups can be determined in the following way, that is: for each group of mutation time groups, the average value of the two mutation time groups in the mutation time group can be used as the starting time of the inverted abnormality of the atmospheric particulate matter concentration.

[0077] It should be noted that the inverted anomaly in this application refers to the abnormal phenomenon that the PM2.5 concentration in the atmosphere is greater than the PM10 concentration. The inverted anomaly is usually caused by environmental factors and sensor errors. The starting time of the inverted anomaly in this application refers to the time when the environmental factors change. This change in environmental factors means that the concentration of particulate matter in the atmosphere is about to invert.

[0078] In addition, in another aspect of the present application, in some embodiments, the present application provides an atmospheric environment monitoring system, which includes an abnormality determination unit. When it is implemented, the filter membrane required for the test is pretreated first, and the test equipment required for the test is prepared. The test equipment includes an oscillating balance and an intelligent sensor, and the test equipment is controlled by the abnormality determination unit to perform the test. Figure 4 , which is a schematic diagram of the structure of an abnormality determination unit according to some embodiments of the present application, the abnormality determination unit 400 includes: a collection module 401, a processing module 402 and an execution module 403, which are described as follows: The first test module 401 in the present application is mainly used to instruct the oscillating balance, the pretreated filter membrane and the smart sensor to measure the small particles in the atmosphere of the target location for multiple detection periods after pre-treating the filter membrane required for the test and preparing the test equipment required for the test, so as to obtain the small particle concentration corresponding to each detection period; The first processing module 402, in the present application, is mainly used to determine the local difference of the information entropy of the change of the concentration of small particles in the atmosphere at each moment based on all the concentrations of small particles, and to determine the mutation moment when the small particles agglomerate based on all the local differences; The second test module 403 in the present application is mainly used to instruct the oscillating balance, the pretreated filter membrane and the smart sensor to measure the large particles in the atmosphere of the target location in multiple detection periods to obtain the large particle concentration corresponding to each detection period; The second processing module 404 in the present application is mainly used to determine the positive trend and negative trend of the change of the concentration of large particles in the atmosphere based on all the concentrations of large particles, and determine the mutation moment when the large particles undergo temperature inversion precipitation based on the positive trend and the negative trend; Execution module 405, in the present application, execution module 405 is mainly used to determine the starting time of the inverted abnormal concentration of particulate matter in the atmosphere according to the mutation time when small particles agglomerate and the mutation time when large particles undergo temperature inversion sedimentation.

[0079] In addition, the present application also provides a computer device, which includes a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute the above-mentioned method for determining abnormal atmospheric particulate matter concentration.

[0080] In some embodiments, reference Figure 5 , which is a schematic diagram of the structure of a computer device for implementing the method for determining the abnormal concentration of atmospheric particulate matter according to some embodiments of the present application. The method for determining the abnormal concentration of atmospheric particulate matter in the above embodiment can be performed by Figure 5 The computer device 500 shown in the figure is implemented, and the computer device 500 includes at least one processor 501, a communication bus 502, a memory 503 and at least one communication interface 504.

[0081] The processor 501 may be a general-purpose central processing unit (CPU) or an application specific integrated circuit (ASIC).

[0082] The communication bus 502 may be used to transmit information between the above-mentioned components.

[0083] The memory 503 may be a read only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read only memory (EEPROM), a compact disc read only memory (CD ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 503 may exist independently and be connected to the processor 501 via the communication bus 502. The memory 503 may also be integrated with the processor 501.

[0084] The memory 503 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 501. The processor 501 is used to execute the program code stored in the memory 503. The program code may include one or more software modules. The method for determining the abnormal concentration of atmospheric particulate matter in the above embodiment can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0085] The communication interface 504 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0086] In a specific implementation, as an embodiment, a computer device may include multiple processors, each of which may be a single-core (single CPU) processor or a multi-core (multi CPU) processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0087] The above-mentioned computer device may be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device may be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of computer device.

[0088] In addition, the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned method for determining abnormal atmospheric particulate matter concentration is implemented.

[0089] In summary, in the atmospheric environment monitoring system and method disclosed in the embodiment of the present application, first, the filter membrane required for the test is pretreated, and the test equipment required for the test is prepared, and the test equipment includes an oscillating balance and an intelligent sensor; the atmosphere of the target location is measured for small particles in multiple detection periods by the oscillating balance and the pretreated filter membrane, and the small particle concentration corresponding to each detection period during the small particle detection is determined based on the intelligent sensor; the local difference of the information entropy of the change of the small particle concentration at each moment in the atmosphere is determined based on all the small particle concentrations, and the mutation time when the small particles agglomerate is determined based on all the local differences; the atmosphere of the target location is measured for large particles in multiple detection periods by the oscillating balance, the pretreated filter membrane and the intelligent sensor, and the large particle concentration corresponding to each detection period is obtained; the positive trend and negative trend of the change of the large particle concentration in the atmosphere are determined based on all the large particle concentrations, and the mutation time when the large particles undergo inversion sedimentation is determined based on the positive trend and the negative trend; the starting time of the inverted abnormality of the particle concentration in the atmosphere is determined according to the mutation time when the small particles agglomerate and the mutation time when the large particles undergo inversion sedimentation.

[0090] It can be seen that in this application, by detecting the concentration of small particles in the atmosphere, and identifying the time when small particles agglomerate through the local difference of the information entropy of the change of the small particle concentration, that is, determining the time when the small particle concentration decreases abnormally, then, by detecting the concentration of large particles in the atmosphere, and identifying the time when the large particles invert and settle through the trend characteristics of the change of the large particle concentration (i.e., the positive trend and the reverse trend), that is, determining the time when the large particle concentration increases abnormally, and finally accurately identifying the starting time of the abnormal inversion of the particle concentration in the atmosphere through the time when the small particles agglomerate and the time when the large particles invert and settle. In summary, the scheme of this application can detect the starting time of the inversion phenomenon, so as to facilitate the timely processing of the inversion phenomenon, thereby improving the accuracy of environmental monitoring.

[0091] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0092] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for measuring abnormal concentration of atmospheric particulate matter, used in an atmospheric environment monitoring system to measure abnormal concentration of atmospheric particulate matter, characterized in that: include: Pre-treating the filter membrane required for the test and preparing the test equipment required for the test, wherein the test equipment includes an oscillating balance and an intelligent sensor; The small particle concentration corresponding to each detection period is obtained by measuring the small particle concentration of the target location atmosphere in multiple detection periods by using the oscillating balance, the pretreated filter membrane and the intelligent sensor; Based on all the small particle concentrations, the local differences of the information entropy of the change of the small particle concentration at each moment in the atmosphere are determined, and based on all the local differences, the mutation moment when the small particles agglomerate is determined; The large particle concentration corresponding to each detection period is obtained by measuring the large particles in the atmosphere of the target location in multiple detection periods by using the oscillating balance, the pretreated filter membrane and the intelligent sensor; Determine the positive trend and negative trend of the change of the concentration of large particles in the atmosphere based on all the concentrations of large particles, and determine the mutation moment when the large particles undergo temperature inversion precipitation based on the positive trend and the negative trend; The starting time of the inverted abnormal concentration of atmospheric particulate matter is determined based on the mutation time when small particles agglomerate and the mutation time when large particles undergo temperature inversion sedimentation.

2. The method according to claim 1, characterized in that The oscillating balance, the pretreated filter membrane and the intelligent sensor are used to measure the small particles in the atmosphere of the target location in multiple detection periods, and the small particle concentration corresponding to each detection period is obtained, which specifically includes: Obtaining the filter membrane weight of the filter membrane after pretreatment; Place the pretreated filter membrane on top of an oscillating tube in an oscillating balance; The atmospheric gas stream which has passed through the small particle cutter is pumped downwardly into the oscillating tube from the top of the oscillating tube; Starting the oscillating balance to apply a periodic simple harmonic excitation force to the oscillating balance; Preset multiple detection time periods, and measure a set of oscillation frequencies in each detection time period by the intelligent sensor; The concentration of small particles in the detection period corresponding to each group of oscillation frequencies is determined according to each group of oscillation frequencies and the weight of the filter membrane.

3. The method according to claim 1, characterized in that The local difference of the information entropy of the change of small particle concentration at each moment in the atmosphere based on all small particle concentrations is specifically determined as follows: Convert all small particle concentrations into a small particle concentration sequence; Selecting a small particle concentration in the small particle concentration sequence as a selected small particle concentration, and dividing the small particle concentration sequence into a forward sequence and a backward sequence according to the selected small particle concentration; Determining a forward information entropy of the forward sequence and a backward information entropy of the backward sequence; Determine the local difference of the information entropy of the change of the small particle concentration at the time corresponding to the selected small particle concentration according to the forward information entropy and the backward information entropy; Continue to determine the local difference of the information entropy of the change in the small particle concentration at the corresponding moment of each remaining small particle concentration.

4. The method according to claim 1, characterized in that Determining the mutation moment when small particles agglomerate based on all local differences specifically includes: Convert all local differences into difference sequences; Dividing the difference degree sequence into a plurality of difference subsequences; Determine the trend index for each difference subseries; The mutation moment when small particles agglomerate is determined based on all trend indices.

5. The method according to claim 1, characterized in that The large particle concentration corresponding to each detection period is obtained by measuring the large particles in the atmosphere of the target location by using the oscillating balance, the pretreated filter membrane and the intelligent sensor. Specifically, the concentration includes: Obtaining the filter membrane weight of the filter membrane after pretreatment; Place the pretreated filter membrane on top of an oscillating tube in an oscillating balance; The atmospheric gas stream which has passed through the large particle cutter is pumped downwardly into the oscillating tube from the top of the oscillating tube; Starting the oscillating balance to apply a periodic simple harmonic excitation force to the oscillating balance; Preset multiple detection time periods, and measure a set of oscillation frequencies in each detection time period by the intelligent sensor; The concentration of large particles in the detection period corresponding to each group of oscillation frequencies is determined according to each group of oscillation frequencies and the weight of the filter membrane.

6. The method according to claim 1, characterized in that Determining the mutation moment when large particles undergo temperature inversion sedimentation based on the positive trend and the negative trend specifically includes: Determine confidence intervals for all large particle concentration distributions; determining a plurality of trend turning points according to the positive trend and the negative trend; The mutation moment when large particles undergo temperature inversion sedimentation is determined based on all trend turning points and the confidence intervals.

7. The method according to claim 1, characterized in that The intelligent sensor is an integrated sensor used to detect the oscillation frequency of the oscillation balance, the gas flow through the oscillation tube, the external temperature and the external atmospheric pressure.

8. An atmospheric environment monitoring system, comprising an abnormality determination unit, characterized in that: The abnormality determination unit comprises: The first test module is used to instruct the oscillating balance, the pretreated filter membrane and the intelligent sensor to measure the small particles in the atmosphere of the target location for multiple detection periods after pre-treating the filter membrane required for the test and preparing the test equipment required for the test, so as to obtain the small particle concentration corresponding to each detection period; The first processing module is used to determine the local difference of the information entropy of the change of the small particle concentration at each moment in the atmosphere based on all the small particle concentrations, and determine the mutation moment when the small particles agglomerate based on all the local differences; The second test module is used to instruct the oscillating balance, the pretreated filter membrane and the intelligent sensor to measure the large particles in the atmosphere of the target location in multiple detection periods to obtain the large particle concentration corresponding to each detection period; A second processing module is used to determine the positive trend and negative trend of the change of the concentration of large particles in the atmosphere based on all the concentrations of large particles, and determine the mutation moment when the large particles undergo temperature inversion precipitation based on the positive trend and the negative trend; The execution module is used to determine the starting time of the inverted abnormal concentration of particulate matter in the atmosphere according to the sudden change time when small particles agglomerate and the sudden change time when large particles undergo temperature inversion sedimentation.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores codes, and the processor is configured to obtain the codes and execute the method for determining anomaly of atmospheric particulate matter concentration according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for determining anomaly of atmospheric particulate matter concentration according to any one of claims 1 to 7 is implemented.

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