A method, system, device and medium for identifying abnormal particulate matter concentration

Abstract

The present invention relates to a method, system, device and medium for identifying abnormal particulate matter concentration, belonging to the technical field of atmospheric environment monitoring. The method includes: obtaining a first particulate matter concentration sequence and a second particulate matter concentration sequence of each station in the area to be identified at the same time period; calculating a correlation matrix according to the first particulate matter concentration sequence, and dividing the area to be identified into multiple blocks; setting the station with the highest first particulate matter concentration in each block as the first station, and calculating the relative deviation of the first particulate matter concentration of the first station from the average value of the first particulate matter concentrations of other stations in the block where it is located at the same moment; preliminarily judging whether the first particulate matter concentration of the first station is abnormal according to the relative deviation; if it is preliminarily judged that the first particulate matter concentration is abnormal, calculating the Pearson correlation coefficient according to the first particulate matter concentration sequence and the second particulate matter concentration sequence of the first station, and finally judging whether the first particulate matter concentration of the first station is abnormal according to the Pearson correlation coefficient.

Description

Technical Field

[0001] The present invention belongs to the technical field of atmospheric environment monitoring, and particularly relates to a method, a system, a device and a medium for identifying abnormal particulate matter concentration. Background Art

[0002] At present, atmospheric particulate matter has become the main factor of air pollution in China. Monitoring data is the key basis for effectively formulating environmental policies and evaluating the effectiveness of policies and accuracy. Therefore, it is crucial to ensure the authenticity and accuracy of atmospheric particulate matter monitoring data. However, in actual monitoring, there will be abnormal events of particulate matter concentration caused by multiple complex factors, such as the intrusion of particles such as catkins and dust into the monitoring equipment or sampling system, or the phenomenon of large particulate matter falling off caused by dust accumulation due to time accumulation during the sampling process, which seriously affects the accuracy of monitoring data and cannot truly reflect the air quality status. It is necessary to detect and maintain the sampling system or monitoring equipment in time.

[0003] At present, there is still a lack of effective methods for identifying abnormal data of atmospheric particulate matter. Usually, it is determined manually or by setting a single concentration threshold, and its timeliness and accuracy are relatively low. Therefore, it is urgent to develop a set of efficient and intelligent methods for identifying abnormal particulate matter concentration to accurately identify abnormal high-value events of particulate matter concentration triggered by multiple complex factors, so as to ensure the timely implementation of manual maintenance measures and reduce the impact on the quality of monitoring data. Summary of the Invention

[0004] In view of the above analysis, the embodiments of the present invention aim to provide a method, a system, a device and a medium for identifying abnormal particulate matter concentration to solve one or more of the above problems existing in the prior art. The object of the present invention is achieved as follows:

[0005] The first aspect of the present invention provides a method for identifying abnormal particulate matter concentration, including:

[0006] Obtain the first particulate matter concentration sequence and the second particulate matter concentration sequence of each station in the area to be identified at the same time period;

[0007] Calculate the correlation matrix according to the first particulate matter concentration sequence, and divide the area to be identified into multiple blocks according to the correlation matrix;

[0008] Set the station with the highest first particulate matter concentration in each block as the first station, and calculate the relative deviation between the first particulate matter concentration of the first station and the average value of the first particulate matter concentrations of other stations in the block at the same moment;

[0009] Preliminarily judge whether the first particulate matter concentration of the first station is abnormal according to the relative deviation;

[0010] If it is initially determined that the first particulate matter concentration is abnormal, calculate the Pearson correlation coefficient based on the first particulate matter concentration sequence and the second particulate matter concentration sequence of the first site, and finally determine whether the first particulate matter concentration of the first site is abnormal according to the Pearson correlation coefficient.

[0011] Further, the first particulate matter concentration and the second particulate matter concentration are the concentrations of different types of particulate matter divided by particle size range.

[0012] Further, calculating the correlation matrix according to the first particulate matter concentration sequence includes: preprocessing the first particulate matter concentration sequences of each site; calculating a difference sequence according to the first particulate matter concentration sequences of two different sites at the same time period; calculating the correlation coefficients between all pairs of sites according to the difference sequence, calculating the grey correlation degree according to the correlation coefficients; calculating the correlation matrix according to the grey correlation degree.

[0013] Further, dividing the area to be identified into multiple blocks according to the correlation matrix includes: drawing a grey correlation degree heat map according to the correlation matrix, and dividing the area to be identified into multiple blocks according to the range of the grey correlation degree.

[0014] Further, initially determining whether the first particulate matter concentration of the first site is abnormal according to the relative deviation includes: if the average value of the first particulate matter concentrations of other sites in the block at the same moment except the first site is greater than the first particulate matter concentration threshold, and the relative deviation is greater than the first threshold, then initially determine that the first particulate matter concentration of the first site is abnormal.

[0015] Further, calculating the Pearson correlation coefficient according to the first particulate matter concentration sequence and the second particulate matter concentration sequence of the first site, and finally determining whether the first particulate matter concentration of the first site is abnormal according to the Pearson correlation coefficient includes: if it is initially determined that the first particulate matter concentration of the first site is abnormal, intercept the parts of the first particulate matter concentration sequence and the second particulate matter concentration sequence of the first site in a specific historical period, and calculate the Pearson correlation coefficient; if the Pearson correlation coefficient is less than the second threshold, then finally determine that the first particulate matter concentration of the first site is abnormal.

[0016] Further, it also includes: obtaining new first particulate matter concentration sequences and second particulate matter concentration sequences at specific time intervals to update the correlation matrix and dynamically divide the area to be identified.

[0017] An embodiment of the second aspect of the present invention provides a system for identifying abnormal particulate matter concentration, including:

[0018] An acquisition module, configured to acquire the first particulate matter concentration sequences and the second particulate matter concentration sequences of each site in the area to be identified at the same time period;

[0019] A dynamic partitioning module, configured to calculate a correlation matrix according to the first particulate matter concentration sequence, and partition the area to be identified into multiple blocks according to the correlation matrix;

[0020] A deviation calculation module, configured to set the station with the highest first particulate matter concentration in each block as the first station, and calculate the relative deviation between the first particulate matter concentration of the first station and the average value of the first particulate matter concentrations of other stations in the block where the first station is located at the same moment;

[0021] A preliminary judgment module, configured to preliminarily judge whether the first particulate matter concentration of the first station is abnormal according to the relative deviation;

[0022] A final judgment module, configured to, if it is preliminarily judged that the first particulate matter concentration is abnormal, calculate the Pearson correlation coefficient according to the first particulate matter concentration sequence and the second particulate matter concentration sequence of the first station, and finally judge whether the first particulate matter concentration of the first station is abnormal according to the Pearson correlation coefficient.

[0023] An embodiment of the third aspect of the present invention provides an electronic device, including a memory and a processor, where the memory stores a computer program, and when the computer program is executed by the processor, the method for identifying abnormal particulate matter concentration according to any embodiment is implemented.

[0024] An embodiment of the fourth aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for identifying abnormal particulate matter concentration according to any embodiment is implemented.

[0025] Compared with the prior art, the present invention can at least achieve one of the following beneficial effects:

[0026] The method for identifying abnormal particulate matter concentration provided by the present invention divides the area to be identified into blocks with smaller scales by calculating the correlation matrix, which is convenient for accurately identifying and maintaining the stations where abnormalities occur subsequently. Then, a preliminary judgment is made through the relative deviation, and then whether the particulate matter concentration is abnormal is further judged by calculating the Pearson correlation coefficient, improving the accuracy of identifying abnormal particulate matter concentration. Description of the Drawings

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present specification or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings described below are only some embodiments recorded in the embodiments of the present specification. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings.

[0028] Figure 1 It is a flowchart of the method for identifying abnormal particulate matter concentration provided by Embodiment 1 of the present invention;

[0029] Figure 2 Schematic diagram of the grey correlation heat map provided by Embodiment 1 of the present invention;

[0030] Figure 3 Schematic diagram of the system for identifying abnormal particulate matter concentration provided by Embodiment 2 of the present invention;

[0031] Figure 4 Schematic diagram of the electronic device architecture provided by Embodiment 3 of the present invention. Detailed implementation manners

[0032] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. It should be noted that, without conflict, the implementation manners and features in the present disclosure can be combined with each other, separated, interchanged and / or rearranged. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0033] Embodiment 1

[0034] A specific embodiment of the present invention, as Figure 1-2 shown, discloses a method for identifying abnormal particulate matter concentration, including the following steps:

[0035] S1. Obtain the first particulate matter concentration sequence and the second particulate matter concentration sequence of each station in the area to be identified at the same time period.

[0036] In this embodiment, the first particulate matter concentration and the second particulate matter concentration are the concentrations of particulate matter divided by different particle size ranges. For example, taking PM2.5 (fine particulate matter, with a diameter not exceeding 2.5 microns) of each monitoring station in the area to be identified as the first particulate matter concentration and PM10 (inhalable particulate matter, with a diameter not exceeding 10 microns) as the second particulate matter concentration, the first particulate matter concentration with a one-month time period and an hour as the unit forms the first particulate matter concentration sequence, and similarly, the second particulate matter concentration forms the second particulate matter concentration sequence.

[0037] S2. Calculate the correlation matrix according to the first particulate matter concentration sequence, and divide the area to be identified into multiple blocks according to the correlation matrix.

[0038] In this embodiment, step S2 specifically includes:

[0039] S201. Preprocess the first particulate matter concentration sequence of each station;

[0040] Specifically, during the process of monitoring the particulate matter concentration at the monitoring site, due to reasons such as equipment failures, human operation errors, and data transmission errors, incorrect or missing data may occur. Therefore, in order to improve the data quality and ensure the accuracy and reliability of the monitoring results, the data should be preliminarily processed. For example, the 3σ rule is selected to clean the first particulate matter concentration sequence, the mean value μ and the standard deviation σ are calculated, and the data outside the interval [μ - 3σ, μ + 3σ] is regarded as an outlier. For outliers or missing values, the mean value μ is used to replace or supplement them.

[0041] S202. Calculate the difference sequence according to the first particulate matter concentration sequences of two different sites in the same time period;

[0042] Exemplarily, use X i and X j to represent the first particulate matter concentration sequences of two different sites preprocessed in the same time period, then the difference sequence is expressed as:

[0043] Δij(k) = |X ik - X jk |, k = 1, 2,...m;

[0044] where, X ik and X jk respectively represent the first particulate matter concentration values monitored at two different sites at the k-th moment.

[0045] S203. Calculate the correlation coefficients between all pairs of sites according to the difference sequence, and calculate the grey correlation degree according to the correlation coefficients;

[0046] Specifically, the correlation coefficient between site i and site j at the k-th moment is expressed as follows:

[0047]

[0048] where, ρ represents the resolution coefficient, which is set manually and is usually set to 0.5;

[0049] X i and X j The grey correlation degree is expressed as follows:

[0050]

[0051] where, m represents the maximum moment of the first particulate matter concentration sequence and the second particulate matter concentration sequence. When i = j, r ij = 1.

[0052] S204. Calculate the correlation matrix according to the grey correlation degree;

[0053] Specifically, the correlation matrix of the entire area to be recognized is expressed as follows:

[0054]

[0055] S205. Draw a grey correlation heat map according to the correlation matrix, and divide the area to be recognized into multiple blocks according to the range of the grey correlation.

[0056] Exemplarily, use a drawing tool to draw the grey correlation heat map as shown in Figure 2 . The zoning principle can be set according to the actual situation. For example, divide 12 stations into three blocks according to three ranges of [-1, -0.3], (-0.3, 0.5], and (0.5, 1] to facilitate subsequent identification of anomalies.

[0057] S3. Set the station with the highest concentration of the first particulate matter in each block as the first station, and calculate the relative deviation of the concentration of the first particulate matter at the first station from the average value of the concentrations of the first particulate matter at other stations in the same block at the same moment.

[0058] Specifically, after obtaining multiple blocks in step S205, for each block, find the station with the highest concentration of the first particulate matter within the 1 - m time period, set it as the first station, and record the moment when the concentration of the first particulate matter is the highest. Then calculate the relative deviation of the concentration of the first particulate matter at the first station from the average value of the concentrations of the first particulate matter at other stations in the same block at the same moment, which is expressed as follows:

[0059]

[0060] Among them, X a represents the concentration of the first particulate matter at the first station, represents the average value of the concentrations of the first particulate matter at other stations in the same block at the same moment except the first station; the relative deviation helps to quantify the degree of difference between a single station and the overall block.

[0061] S4. Initially judge whether the concentration of the first particulate matter at the first station is abnormal according to the relative deviation.

[0062] In this embodiment, step S4 specifically includes:

[0063] If the average value of the concentrations of the first particulate matter at other stations in the same block at the same moment except the first station is greater than the first particulate matter concentration threshold, and the relative deviation is greater than the first threshold, then initially judge that the concentration of the first particulate matter at the first station is abnormal.

[0064] Specifically, the first particulate matter concentration threshold can be adjusted according to the actual situation. When the first particulate matter concentration is PM2.5, the first particulate matter concentration threshold should be set to 35 μg / m 3, the first threshold is set to 0.5. When ε > 0.5, it indicates that the concentration of the first particulate matter at the first site in the block is higher than that at other sites. Then, it should be preliminarily determined that this concentration is abnormal.

[0065] S5. If it is preliminarily determined that the concentration of the first particulate matter is abnormal, then calculate the Pearson correlation coefficient based on the concentration sequence of the first particulate matter and the concentration sequence of the second particulate matter at the first site, and finally determine whether the concentration of the first particulate matter at the first site is abnormal according to the Pearson correlation coefficient.

[0066] Specifically, after step S4 preliminarily determines that the concentration of the first particulate matter at the first site is abnormal, in order to accurately identify whether it is abnormal, further judgment should be carried out, including: if it is preliminarily determined that the concentration of the first particulate matter at the first site is abnormal, then intercept a part of the concentration sequence of the first particulate matter and the concentration sequence of the second particulate matter at the first site in a specific historical period, and calculate the Pearson correlation coefficient; if the Pearson correlation coefficient is less than the second threshold, then finally determine that the concentration of the first particulate matter at the first site is abnormal.

[0067] Exemplarily, assume that the first particulate matter concentration is the highest at time q at the first site. Then, intercept the concentration sequence of the first particulate matter and the concentration sequence of the second particulate matter in the time period [q - 5, q]. The Pearson correlation coefficient of the two is calculated as follows:

[0068]

[0069] Among them, X i represents the hourly value of the first particulate matter concentration, and Y i represents the hourly value of the second particulate matter concentration. represents the average value of X i ; represents the average value of Y i ;

[0070] The Pearson correlation coefficient can reflect the linear correlation degree between the first concentration sequence and the second concentration sequence. When the Pearson correlation coefficient is less than the second threshold, it indicates that the correlation between the concentration of the first particulate matter and the concentration of the second particulate matter at the first site in this time period is poor. Then, it is finally determined that the concentration of the first particulate matter at the first site is indeed abnormal.

[0071] It should be noted that the second threshold is between 0 and 1. In this embodiment, it is set to 0.7 and can be adjusted according to the actual situation.

[0072] In some embodiments, the method for identifying abnormal particulate matter concentration further includes: obtaining new concentration sequences of the first particulate matter and the second particulate matter at specific time intervals to update the correlation matrix and dynamically divide the area to be identified.

[0073] Exemplarily, the first particulate matter concentration sequence and the second particulate matter concentration sequence of each site in the area to be identified are retrieved monthly, the correlation matrix is recalculated, the area to be identified is repartitioned, and whether an anomaly occurs is rejudged to enhance the dynamics of the identification process.

[0074] In some embodiments, after it is finally determined in step S5 that the first particulate matter concentration of the first site is abnormal, an alarm work order is further formed and pushed to the operation and maintenance personnel and relevant personnel of the monitoring station; the operation and maintenance personnel promptly go to the site for investigation, determine the cause and handle it in a timely manner until the alarm is lifted, and fill in the operation and maintenance work order for this alarm handling; the relevant personnel of the monitoring station promptly check whether the alarm is lifted and make relevant records.

[0075] Compared with the prior art, the method for identifying abnormal particulate matter concentration provided in this embodiment divides the area to be identified into smaller-scale blocks by calculating the correlation matrix, which is convenient for subsequent accurate identification and maintenance of the sites where anomalies occur. Then, a preliminary judgment is made through the relative deviation, and then the Pearson correlation coefficient is calculated to further judge whether the particulate matter concentration is abnormal, improving the accuracy of identifying abnormal particulate matter concentration.

[0076] Embodiment 2

[0077] This embodiment provides a system for identifying abnormal particulate matter concentration, as Figure 3 shown, including:

[0078] An acquisition module, configured to acquire the first particulate matter concentration sequence and the second particulate matter concentration sequence of each site in the area to be identified at the same time period;

[0079] A dynamic partitioning module, configured to calculate a correlation matrix according to the first particulate matter concentration sequence, and partition the area to be identified into multiple blocks according to the correlation matrix;

[0080] A deviation calculation module, configured to set the site with the highest first particulate matter concentration in each block as the first site, and calculate the relative deviation between the first particulate matter concentration of the first site and the average value of the first particulate matter concentrations of other sites in the block where it is located at the same moment;

[0081] A preliminary judgment module, configured to preliminarily judge whether the first particulate matter concentration of the first site is abnormal according to the relative deviation;

[0082] A final judgment module, configured to, if it is preliminarily judged that the first particulate matter concentration is abnormal, calculate the Pearson correlation coefficient according to the first particulate matter concentration sequence and the second particulate matter concentration sequence of the first site, and finally judge whether the first particulate matter concentration of the first site is abnormal according to the Pearson correlation coefficient.

[0083] Embodiment 3

[0084] This embodiment provides an electronic device, such as Figure 4 shown, which includes a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, it implements the method for identifying the concentration of abnormal particulate matter as described in any of the foregoing embodiments.

[0085] Embodiment 4

[0086] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the method for identifying the concentration of abnormal particulate matter as described in any of the foregoing embodiments.

[0087] Computer-readable storage media include both permanent and non-permanent, removable and non-removable media and can be implemented by any method or technology for storing information. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0088] Those skilled in the art should also be able to further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described according to their functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Skilled professionals can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present invention.

[0089] The steps of the methods or algorithms described in connection with the embodiments disclosed herein may be implemented in hardware, software modules executed by a processor, or a combination of both. The software modules may be placed in a random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0090] The specific embodiments described above have further elaborated on the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above description is only for the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for identifying abnormal particle concentration, characterized in that: include: Obtain a first particle concentration sequence and a second particle concentration sequence at the same time period for each station in the area to be identified; Calculating a correlation matrix according to the first particle concentration sequence, and dividing the area to be identified into a plurality of blocks according to the correlation matrix, including: preprocessing the first particle concentration sequence of each station; calculating a difference sequence according to the first particle concentration sequences of two different stations in the same period; calculating the correlation coefficients between all stations according to the difference sequence, and calculating the gray correlation according to the correlation coefficients; calculating the correlation matrix according to the gray correlation; drawing a gray correlation heat map according to the correlation matrix, and dividing the area to be identified into a plurality of blocks according to the range of the gray correlation; The station with the highest first particle concentration in each block is set as the first station, and the relative deviation of the first particle concentration of the first station and the average first particle concentration of other stations in the block at the same time is calculated; Preliminarily determine whether the first particle concentration at the first station is abnormal according to the relative deviation; If it is preliminarily determined that the first particle concentration is abnormal, the Pearson correlation coefficient is calculated based on the first particle concentration sequence and the second particle concentration sequence of the first station, and whether the first particle concentration of the first station is abnormal is finally determined based on the Pearson correlation coefficient.

2. The method for identifying abnormal particle concentration according to claim 1, characterized in that: The first particle concentration and the second particle concentration are concentrations of different types of particles divided by particle size range.

3. The method for identifying abnormal particle concentration according to claim 1, characterized in that: The preliminarily determining whether the first particle concentration at the first station is abnormal according to the relative deviation includes: If the average first particle concentration of other sites in the block except the first site at the same time is greater than the first particle concentration threshold, and the relative deviation is greater than the first threshold, it is preliminarily determined that the first particle concentration of the first site is abnormal.

4. The method for identifying abnormal particle concentration according to claim 3, characterized in that: The calculating of the Pearson correlation coefficient according to the first particle concentration sequence and the second particle concentration sequence of the first station, and finally judging whether the first particle concentration of the first station is abnormal according to the Pearson correlation coefficient, comprises: If it is preliminarily determined that the first particle concentration at the first station is abnormal, the first particle concentration sequence and the second particle concentration sequence at the first station are intercepted for a specific historical period, and the Pearson correlation coefficient is calculated; If the Pearson correlation coefficient is less than the second threshold, it is ultimately determined that the first particle concentration at the first station is abnormal.

5. The method for identifying abnormal particle concentration according to any one of claims 1 to 4, characterized in that: Also includes: A new first particle concentration sequence and a new second particle concentration sequence are acquired at specific time intervals to update the correlation matrix and dynamically divide the area to be identified.

6. A system for identifying abnormal particle concentration, characterized in that: The system comprises: An acquisition module, used to acquire a first particle concentration sequence and a second particle concentration sequence at the same time period at each station in the area to be identified; A dynamic division module, used to calculate a correlation matrix according to the first particle concentration sequence, and dynamically divide the area to be identified into multiple blocks according to the correlation matrix, including: preprocessing the first particle concentration sequence of each station; calculating a difference sequence according to the first particle concentration sequences of two different stations in the same period; calculating the correlation coefficients between all stations according to the difference sequence, and calculating the gray correlation according to the correlation coefficient; calculating the correlation matrix according to the gray correlation; drawing a gray correlation heat map according to the correlation matrix, and dividing the area to be identified into multiple blocks according to the range of the gray correlation; A deviation calculation module, used to set the station with the highest first particle concentration in each block as the first station, and calculate the relative deviation of the first particle concentration of the first station and the average first particle concentration of other stations in the block at the same time; A preliminary judgment module, used for preliminarily judging whether the first particle concentration at the first station is abnormal according to the relative deviation; The final judgment module is used to calculate the Pearson correlation coefficient according to the first particle concentration sequence and the second particle concentration sequence of the first station if the first particle concentration is initially judged to be abnormal, and finally judge whether the first particle concentration of the first station is abnormal according to the Pearson correlation coefficient.

7. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method for identifying abnormal particle concentration according to any one of claims 1 to 5 is implemented.

8. A storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the method for identifying abnormal particle concentration according to any one of claims 1 to 5 is implemented.

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