Method and device for determining abnormal area of atmospheric particulate monitoring and computing equipment

Abstract

The embodiment of the present disclosure provides a method and device for determining an abnormal monitoring area of atmospheric particulate matter and a computing device. The method for determining an abnormal monitoring area of atmospheric particulate matter comprises: determining a target monitoring area and a directly adjacent monitoring area, and determining whether a pollution transmission area is formed according to topographic feature data between the two; in the case of determining that the pollution transmission area is formed, calculating a concentration relative deviation based on concentration monitoring data of the selected adjacent monitoring area and the target monitoring area in at least one period; and in the case of the concentration relative deviation being greater than a preset deviation threshold, determining that the target monitoring area is an abnormal monitoring area. Compared with the prior art scheme of determining an adjacent area based on only the adjacent relationship and then determining the monitoring data of the target monitoring area by using the adjacent area data, the embodiment of the present disclosure considers the influence of the topography on the diffusion of atmospheric particulate matter, so that the identification of the obtained abnormal monitoring area is more accurate.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of environmental concentration monitoring data processing, and particularly relates to a method and device for determining an abnormal atmospheric particulate monitoring area and a computing device. BACKGROUND

[0002] In order to realize overall atmospheric particulate concentration monitoring in a large area, the large area is divided into a plurality of small monitoring areas (in actual implementation, the large area and the small area can be divided in the manner of administrative division or in the manner of non-administrative division), and a particulate concentration monitoring device is arranged in each small monitoring area. Each particulate monitoring device performs real-time atmospheric particulate concentration monitoring to obtain real-time atmospheric particulate concentration monitoring data. A large amount of concentration monitoring data of the small areas is collected to form a large amount of concentration monitoring data set.

[0003] In actual application, due to faults of the particulate concentration monitoring device itself, human interference, etc., the concentration monitoring data of some monitoring areas appears abnormal low values. When the atmospheric particulate mass concentration and pollution progress of the whole area are analyzed, the foregoing abnormal low value data needs to be excluded. In the prior art, in order to identify and exclude the abnormal low value data, first, each monitoring area is screened to determine a low value area, and then the concentration monitoring data of the low value area is verified by using the concentration monitoring data of the adjacent area of the low value area to identify whether the concentration monitoring data of the low value area is reliable. When it is determined that the deviation of the concentration monitoring data of the low value area from the concentration monitoring data of the adjacent area is obviously large, the low value area is identified as a monitoring abnormal area. However, through actual verification, the error rate of the monitoring abnormal area identified by the foregoing method is large, that is, the monitoring abnormal area identified by the foregoing method is unreliable. SUMMARY

[0004] In order to solve the problem that the monitoring abnormal area identified by the prior method has a large error rate, the present disclosure provides a new method, device and computing device for determining an atmospheric particulate monitoring abnormal area.

[0005] In a first aspect, the present disclosure provides a method for determining an atmospheric particulate monitoring abnormal area, comprising:

[0006] performing preliminary screening on the concentration monitoring data of each atmospheric particulate monitoring area to determine a target monitoring area; the target monitoring area is a low value area whose concentration monitoring data in a plurality of continuous time periods is lower than a screening value;

[0007] determine a directly adjacent monitoring area according to position coordinates of the target monitoring area, and acquire terrain feature data between the target monitoring area and the directly adjacent monitoring area; the directly adjacent monitoring area is an area without an intermediate monitoring area between the target monitoring area and the directly adjacent monitoring area, and the distance between the target monitoring area and the directly adjacent monitoring area is less than a preset distance;

[0008] determine whether the directly adjacent monitoring area and the target monitoring area constitute a pollution transmission area according to the terrain feature data; the pollution transmission area is an area in which atmospheric particulate matter can be transmitted and diffused inside; in a case where the terrain feature data indicates that the target monitoring area and the directly adjacent monitoring area do not have terrain that blocks the transmission of pollutants, the target monitoring area and the directly adjacent monitoring area constitute a pollution transmission area;

[0009] in a case where it is determined that the directly adjacent monitoring area and the target monitoring area constitute a pollution transmission area, take the directly adjacent monitoring area as a selected adjacent monitoring area, and calculate a concentration relative deviation based on concentration monitoring data of the selected adjacent monitoring area and the target monitoring area in at least one time period;

[0010] in a case where the concentration relative deviation is greater than a preset deviation threshold, determine that the target monitoring area is a monitoring abnormal area.

[0011] Optionally, the method further comprises: acquiring rain and snow weather data between the target monitoring area and the directly adjacent monitoring area or of the directly adjacent monitoring area;

[0012] the determining whether the directly adjacent monitoring area and the target monitoring area constitute a pollution transmission area according to the terrain feature data comprises: in a case where the rain and snow weather data is data representing non-rain and non-snow weather, determining whether the directly adjacent monitoring area and the target monitoring area constitute a pollution transmission area according to the terrain feature data.

[0013] Optionally, the method further comprises: acquiring wind speed and wind direction in the pollution transmission area in the at least one time period;

[0014] in a case where the wind speed is greater than a preset threshold wind speed, before calculating the concentration relative deviation based on the concentration monitoring data of the selected adjacent monitoring area and the target monitoring area in the at least one time period, the method further comprises:

[0015] determining, in the selected adjacent monitoring area, an upwind monitoring area and a downwind monitoring area of each time period according to the wind direction, the position coordinates of the target monitoring area, and the position coordinates of the selected adjacent monitoring area;

[0016] The concentration relative deviation is calculated based on the atmospheric concentration monitoring data of the selected adjacent monitoring area and the target monitoring area, including: calculating the concentration relative deviation based on the concentration monitoring data of the upwind monitoring area, the downwind monitoring area and the target monitoring area determined in each time period of the at least one time period.

[0017] Optionally, the concentration relative deviation is calculated based on the concentration monitoring data of the upwind monitoring area, the downwind monitoring area and the target monitoring area determined in each time period of the at least one time period, including:

[0018] For each time period in the at least one time period, the first corresponding time period of the upwind monitoring area and the second corresponding time period of the downwind monitoring area are determined according to the wind speed;

[0019] The first prediction data is calculated according to the concentration monitoring data and position coordinates of the upwind monitoring area in the first corresponding time period, the wind speed and direction, and the position coordinates of the target monitoring area according to the particulate matter diffusion model, and the second prediction data is calculated according to the concentration monitoring data and position coordinates of the downwind monitoring area in the second corresponding time period, the wind speed and direction, and the position coordinates of the target monitoring area according to the particulate matter diffusion model;

[0020] The single-time-period relative deviation is calculated based on the first prediction data, the second prediction data and the concentration monitoring data of the target monitoring area in each time period;

[0021] The concentration relative deviation is calculated based on the single-time-period relative deviation in each time period.

[0022] Optionally, in the case where the wind speed is less than a preset threshold wind speed, the concentration relative deviation is calculated based on the atmospheric concentration monitoring data of the selected adjacent monitoring area and the target monitoring area, including:

[0023] The monitoring data mean value of the corresponding selected adjacent monitoring area in each monitoring time period is calculated based on each time period in the at least one time period;

[0024] The single-time-period relative deviation is calculated based on the monitoring data of the target monitoring area in each time period and the corresponding monitoring data mean value;

[0025] The concentration relative deviation is calculated based on the single-time-period relative deviation in each time period.

[0026] Optionally, the target monitoring area is a monitoring point;

[0027] After determining that the target monitoring area is a monitoring abnormal area, the method further includes:

[0028] obtaining reference concentration monitoring data obtained by a reference monitoring device in the target monitoring area, and obtaining verification concentration monitoring data obtained by an automatic monitoring device in the monitoring point at the same period;

[0029] verifying whether the target monitoring area is an abnormal monitoring area based on the reference concentration monitoring data and the verification concentration monitoring data.

[0030] Optionally, the concentration monitoring data of each atmospheric particulate matter monitoring area is preliminarily screened to determine the target monitoring area, comprising:

[0031] respectively calculating the mean value of the concentration monitoring data of each atmospheric particulate matter monitoring area under non-rain and snow weather conditions and in continuous multiple time periods to obtain a sliding window mean value;

[0032] In the case where the sliding window mean value is less than the screening value, the corresponding atmospheric particulate matter monitoring area is determined as the target monitoring area.

[0033] Optionally, the atmospheric particulate matter monitoring area is a monitoring point; the method further comprises:

[0034] obtaining the device state identifier of the monitoring device of each atmospheric particulate matter monitoring area;

[0035] In the case where the sliding window mean value is less than the preset screening value, the corresponding atmospheric particulate matter monitoring area is determined as the target monitoring area, comprising: in the case where the sliding window mean value is less than the preset screening value and the corresponding monitoring device identifier is a normal operation identifier, the corresponding atmospheric particulate matter monitoring area is determined as the target monitoring area.

[0036] In a second aspect, the embodiments of the present disclosure provide a device for determining an abnormal atmospheric particulate matter monitoring area, comprising:

[0037] a region screening unit configured to preliminarily screen the concentration monitoring data of each atmospheric particulate matter monitoring area to determine a target monitoring area; the target monitoring area is a low-value area with concentration monitoring data lower than a screening value in continuous multiple time periods;

[0038] a terrain data determination unit configured to determine a directly adjacent monitoring area according to the location coordinates of the target monitoring area, and obtain terrain feature data between the target monitoring area and the directly adjacent monitoring area; the directly adjacent monitoring area is a region without an intermediate monitoring area between the target monitoring area and the target monitoring area, and the distance between the target monitoring area and the target monitoring area is less than a preset distance;

[0039] a nearby region selection unit configured to determine, according to the terrain feature data, whether the directly adjacent monitoring region and the target monitoring region constitute a pollution transmission region, the pollution transmission region being a region in which atmospheric particulate matter can be transmitted and diffused internally; and in a case where the terrain feature data indicates that the target monitoring region and the directly adjacent monitoring region do not have terrain that blocks the transmission of pollutants, the two regions constitute a pollution transmission region;

[0040] a deviation calculation unit configured to, in a case where it is determined that the directly adjacent monitoring region and the target monitoring region constitute a pollution transmission region, take the directly adjacent monitoring region as a selected adjacent monitoring region, and calculate a concentration relative deviation based on concentration monitoring data of the selected adjacent monitoring region and the target monitoring region in at least one time period;

[0041] a result determination unit configured to, in a case where the concentration relative deviation is greater than a preset deviation threshold, determine that the target monitoring region is a monitoring abnormal region.

[0042] In a third aspect, an embodiment of the present disclosure provides a computing device, comprising a processor and a memory, the memory being configured to store a computer program; the computer program, when loaded by the processor, causes the processor to execute the method for determining an atmospheric particulate monitoring abnormal region as described above.

[0043] By using the scheme of the embodiment of the present disclosure, before determining whether a target monitoring region is an abnormal monitoring region, first, according to terrain feature data, determine which directly adjacent regions are selected adjacent monitoring regions, and only use the monitoring data of the selected adjacent monitoring regions to evaluate the monitoring data of the target monitoring region to obtain a concentration relative deviation. Compared with the scheme in which the prior art determines adjacent regions based on a proximity relationship (distance relationship) and then uses the adjacent region data to determine the monitoring data of the target monitoring region, the scheme of the embodiment of the present disclosure takes into account the influence of terrain on the diffusion of atmospheric particulate matter, so that the identification of the monitoring abnormal region is more accurate. BRIEF DESCRIPTION OF DRAWINGS

[0044] The accompanying drawings, which are incorporated herein and constitute part of the specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0046] Figure 1 is a flowchart of a method for determining an atmospheric particulate monitoring abnormal region provided by the embodiment of the present disclosure;

[0047] Figure 2 is a flowchart of a method for determining an abnormal area of atmospheric particulate matter monitoring provided by some embodiments of the present disclosure;

[0048] Figure 3 is a structural schematic diagram of a device for determining an abnormal area of atmospheric particulate matter monitoring provided by an embodiment of the present disclosure;

[0049] Figure 4 is a structural schematic diagram of a computing device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0050] Embodiments of the present disclosure will be described in more detail with reference to the drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms, and should not be interpreted as being limited to the embodiments set forth herein, but rather, these embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes, and are not intended to limit the scope of protection of the present disclosure.

[0051] The term "comprising" and variations thereof as used herein are open-ended, that is "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Related terms such as "one embodiment", "another embodiment", "some embodiments" and "at least one embodiment" will be understood explicitly as "one of at least one embodiment", "another of at least one embodiment", "some of at least one embodiment", and "at least one of at least one embodiment". Other terms of relation such as "first" and "second" are used herein only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between such entities or operations.

[0052] To solve the problem that the existing method may not be normal to determine a low-value area as an abnormal detection area, embodiments of the present disclosure provide a new method for determining an abnormal area of atmospheric particulate matter monitoring. The method for determining an abnormal area of atmospheric particulate matter monitoring provided by the embodiments of the present disclosure is executed by a computing device.

[0053] Figure 1 is a flowchart of a method for determining an abnormal area of atmospheric particulate matter monitoring provided by some embodiments of the present disclosure. As shown in Figure 1 the method for determining an abnormal area of atmospheric particulate matter monitoring includes S110-S150.

[0054] S110: preliminary screening of concentration monitoring data of each atmospheric particulate matter monitoring area is performed to determine a target monitoring area.

[0055] The atmospheric particulate monitoring area is a predetermined area for zoned monitoring of atmospheric particulate concentration. In specific implementation, the atmospheric particulate monitoring area can be an area determined with a specific monitoring radius centered on a monitoring point, or a monitoring area formed by combining areas corresponding to multiple adjacent monitoring points. In the case where the atmospheric particulate monitoring area is a monitoring area formed by combining areas corresponding to multiple adjacent monitoring points, the atmospheric particulate monitoring area is usually divided according to administrative division.

[0056] In the embodiments of the present disclosure, the computing device can obtain concentration monitoring data of the atmospheric particulate monitoring area. The concentration monitoring data of each atmospheric particulate monitoring area is determined by data sampling according to a set sampling period. That is, the atmospheric particulate concentration monitoring data of each atmospheric particulate monitoring area is obtained in a continuous time period.

[0057] The target monitoring area is an area where the concentration monitoring data of a continuous plurality of time periods is lower than the screening value. The foregoing determination of the target monitoring area using the concentration monitoring data of a continuous plurality of time periods is to exclude the problem that the target monitoring area determined due to small data of a single time period has low reliability.

[0058] In some embodiments, the computing device can determine the target monitoring area using the method of S111-S112 as follows.

[0059] S111: respectively performing mean value calculation on the concentration monitoring data of each atmospheric particulate monitoring area in a non-rain and snow weather condition and for a continuous plurality of time periods to obtain a sliding window mean value.

[0060] Under the condition of rainfall, atmospheric particulate matter will settle due to the adsorption of rainwater, and the concentration monitoring data of the corresponding area will obviously decrease to low value data. The foregoing low value data has no value for judgment, and therefore the atmospheric particulate concentration monitoring data under the condition of rain and snow weather needs to be excluded.

[0061] After obtaining the concentration monitoring data of a continuous plurality of time periods under the condition of non-rain and snow, the computing device will perform mean value calculation on the concentration monitoring data of the plurality of time periods to obtain a sliding window mean value. The sliding window mean value represents the average situation of atmospheric particulate matter in the corresponding monitoring area for a continuous plurality of time periods.

[0062] S112: in the case where the sliding window mean value is less than the screening value, determining the corresponding atmospheric particulate monitoring area as a target monitoring area.

[0063] In some embodiments of the present disclosure, the screening value is a predetermined small particulate concentration value. If the sliding window mean value is less than the foregoing screening value, the data of the monitoring area has a high probability of being unreliable low value data, and therefore this monitoring area is taken as a target monitoring area.

[0064] In some embodiments, the computing device can determine the adjacent monitoring areas according to the location coordinates of the various atmospheric particulate monitoring areas, and preliminarily compare the concentration monitoring data of the aforementioned monitoring areas and the adjacent monitoring areas. If it is determined that the concentration monitoring data of a monitoring area is significantly less than the data mean determined by the adjacent concentration monitoring data, the aforementioned monitoring area is determined as the target monitoring area. In this way, the screening value determined in S112 need not be determined, and the screening criteria can be determined according to the actual situation, and the target monitoring area can be more reasonably determined.

[0065] In some embodiments, the atmospheric particulate monitoring area is a monitoring point. In addition to obtaining the monitoring data of each atmospheric particulate monitoring area, the computing device also obtains the device state identifier of the monitoring device of each atmospheric particulate monitoring area. Accordingly, S112 is specifically that, in the case where the sliding window mean is less than the preset screening value and the corresponding monitoring device identifier is a normal operation identifier, the corresponding atmospheric particulate monitoring area is determined as the target monitoring area.

[0066] S120: Determine the directly adjacent monitoring areas according to the location coordinates of the target monitoring area, and obtain the topographic feature data between the target monitoring area and the directly adjacent monitoring areas.

[0067] The directly adjacent monitoring area is an area that has no intermediate monitoring area between the target monitoring area. That is, the target monitoring area and the directly adjacent monitoring area are directly adjacent, and there is no other monitoring area as a barrier in between.

[0068] In a specific implementation, the computing device calculates the distance and orientation according to the location coordinates of the target monitoring area and the coordinates of all other monitoring areas, and determines the directly adjacent monitoring areas through data comparison.

[0069] After obtaining the target monitoring area and the directly adjacent monitoring areas (specifically, obtaining the location coordinates of the two), the computing device can determine the positions of the two on the map according to the aforementioned monitoring areas, and determine the topographic feature data between the two through the topographic layer information in the map. In a specific implementation, the topographic feature data includes elevation, landform type, slope characteristics, etc.

[0070] S130: Determine whether the directly adjacent monitoring areas and the target monitoring area constitute a pollution transmission area according to the topographic feature data; if so, S140.

[0071] The pollution transmission area is an area in which atmospheric particulate matter can be transmitted and diffused inside. Atmospheric particulate matter can be transmitted and diffused from one monitoring area to the next adjacent monitoring area in it.

[0072] In a specific implementation, the topographical feature data can be used to determine whether the target monitoring area and the directly adjacent monitoring area are separated by a topography that blocks the transmission of pollutants. If the target monitoring area and the directly adjacent monitoring area are separated by a topography that blocks the transmission of pollutants, it is determined that the directly adjacent monitoring area and the target monitoring area form a pollution transmission area.

[0073] For example, if there is a continuous mountain range between the directly adjacent area and the target monitoring area, and the relative altitude of the mountain range is significantly higher than the height of the atmospheric particulate matter layer, it is determined that the directly adjacent area and the target monitoring area cannot form a pollution transmission area. For another example, if there is no such continuous mountain range between the directly adjacent area and the target monitoring area, or if there is a mountain range but it is essentially a low-altitude hill, it is determined that the directly adjacent monitoring area and the target monitoring area form a pollution transmission area. In practical applications, the knowledge of topography can be used in combination with the diffusion height characteristics of atmospheric particulate matter in a certain area to determine whether the directly adjacent area and the target monitoring area form a pollution transmission area, and specific examples are not provided here.

[0074] It should be noted here that the pollution transmission area is determined with the target monitoring area as the center, and it is not necessarily guaranteed that the two directly adjacent monitoring areas are pollution transmission areas (in practical applications, the directly adjacent areas are likely to form pollution transmission areas). In practical applications, in the case of a topographical area where some monitoring areas are located in a plain or plateau region, the aforementioned area can be directly determined as a pollution transmission area.

[0075] S140: The directly adjacent monitoring area is selected as the selected adjacent monitoring area, and the concentration relative deviation is calculated based on the concentration monitoring data of the selected adjacent monitoring area and the target monitoring area in at least one time period.

[0076] In the case where it is determined that the directly adjacent monitoring area and the target monitoring area form a pollution transmission area, i.e., pollutants can be transmitted between the directly adjacent monitoring area and the target monitoring area, the directly adjacent monitoring area is determined as the selected adjacent monitoring area.

[0077] After the selected adjacent monitoring area is determined, the concentration monitoring data of the selected adjacent monitoring area in at least one time period can be determined. Based on the concentration monitoring data of the selected monitoring area and the target monitoring area in at least one time period, the concentration relative deviation can be calculated.

[0078] The concentration relative deviation is the deviation of the particulate matter concentration displayed in percentage. In a specific implementation, the computing device can use existing methods to calculate the relative deviation of the particulate matter concentration.

[0079] S150: In the case where the concentration relative deviation is greater than a preset deviation threshold, the target monitoring area is determined as a monitoring abnormal area.

[0080] After determining the concentration relative deviation, the computing device determines whether the concentration relative deviation is greater than a preset deviation threshold. If the concentration relative deviation is greater than the preset deviation threshold, it is determined that the concentration monitoring data of the target monitoring area is not reasonable, and accordingly the target monitoring area is determined as a monitoring abnormal area.

[0081] By using the scheme of the embodiments of the present disclosure, before determining whether a target monitoring area is an abnormal monitoring area, first, according to the terrain feature data, it is determined which directly adjacent areas are selected adjacent monitoring areas, and only the monitoring data of the selected adjacent monitoring areas is used to evaluate the monitoring data of the target monitoring area to obtain the concentration relative deviation. Compared with the prior art scheme of determining the adjacent areas only based on the adjacent relationship (proximity relationship) and then using the adjacent area data to determine the monitoring data of the target monitoring area, the scheme of the embodiments of the present disclosure considers the influence of the terrain on the diffusion of atmospheric particulate matters, so that the identification of the monitoring abnormal area is more accurate.

[0082] As analyzed before, in rainy and snowy weather, the effect of rain and snow will cause the particulate matters to settle, and thus the concentration monitoring data has no actual availability. Based on this, in the specific implementation of the present disclosure, it is also necessary to consider whether the corresponding particulate matter diffusion path between the directly adjacent area and the target monitoring area is blocked due to the rainy and snowy weather, and thus the corresponding data has no reference value. Specifically, in the embodiments of the present disclosure, before performing the aforementioned S130, the following S160 can also be performed.

[0083] S160: Obtain the rainy and snowy weather data between the target monitoring area and the directly adjacent monitoring area or the directly adjacent monitoring area.

[0084] Specifically, obtaining the rainy and snowy weather data between the target monitoring area and the directly adjacent monitoring area is to obtain the meteorological feature data of all meteorological monitoring stations between the two areas, and to determine whether there is rainy and snowy weather between the two areas. Similarly, obtaining the rainy and snowy weather data of the directly adjacent area is to obtain the meteorological feature data of the directly adjacent area, and to determine the rainy and snowy weather of the directly adjacent area.

[0085] On the premise of performing the aforementioned S160, the aforementioned S130 is specifically: in the case that the rainy and snowy weather data is data representing non-rainy and non-snowy weather, determining whether the directly adjacent monitoring area and the target monitoring area constitute a pollution transmission area according to the terrain feature data.

[0086] According to the existing experience surface, in the case of non-rain and snow weather, the transmission mode of atmospheric particulate matters will be significantly different due to different atmospheric meteorological conditions. For example, in the case of strong wind conditions, the transmission mode of atmospheric particulate matters is mostly diffusion by wind blowing; and in the case of no wind conditions, the transmission mode of atmospheric particulate matters is mostly diffusion by diffusion. In different meteorological conditions, different data sources and calculation methods need to be used to determine the concentration relative deviation.

[0087] Figure 2 is a flow chart of a method for determining an abnormal area of atmospheric particulate matter monitoring according to some embodiments of the present disclosure. As shown in some embodiments, the method for determining an abnormal area of atmospheric particulate matter monitoring includes S210-S300. Figure 2

[0088] S210: preliminary screening of the concentration monitoring data of each atmospheric particulate matter monitoring area is performed to determine a target monitoring area.

[0089] S220: direct adjacent monitoring areas are determined according to the position coordinates of the target monitoring area, and topographic feature data between the target monitoring area and the direct adjacent monitoring areas is obtained.

[0090] S230: it is determined whether the direct adjacent monitoring areas and the target monitoring area constitute a pollution transmission area according to the topographic feature data; if yes, S240 is performed.

[0091] The execution process of S210-S230 is the same as that of the previous embodiments, which will not be repeated here. For details, please refer to the previous description.

[0092] S240: wind speed and wind direction in the pollution transmission area in at least one period are obtained, and it is determined whether the wind speed is greater than a threshold wind speed; if yes, S250 is performed; if no, S270 is performed.

[0093] S250: concentration monitoring data of the upwind monitoring area and concentration monitoring data of the downwind monitoring area of each period in the selected adjacent monitoring area are determined according to the wind direction, the position coordinates of the target monitoring area, and the position coordinates of the selected adjacent monitoring area.

[0094] In a specific implementation, the computing device can determine the wind speed and wind direction of the pollution transmission area in at least one period by obtaining meteorological data in the pollution transmission area (specifically, obtaining meteorological data of the target detection area).

[0095] The threshold wind speed can be determined according to the topographic feature data (topographic features) of the pollution transmission area, or can be determined in advance, which is not limited in the embodiments of the present disclosure.

[0096] ​In the case that the wind speed is greater than the threshold wind speed, it is determined that the atmospheric particulate matter is diffused by wind blowing, and the main factor affecting the atmospheric particulate matter concentration of the target monitoring area is the atmospheric particulate matter concentration of the monitoring area in the upwind direction (here, the target monitoring area is not treated as a pollution source, and it is also impossible to treat it as a pollution source through monitoring data), and the atmospheric particulate matter concentration of the target monitoring area mainly affects the atmospheric particulate matter concentration of the monitoring area in the downwind direction. Therefore, in the case that the wind speed is greater than the threshold wind speed, the upwind monitoring area and the downwind monitoring area of each period are determined.

[0097] It should be noted here that the wind speed and the wind direction of the pollution transmission area in the at least one period mentioned above can be different. In particular, the wind direction can be greatly reversed in different periods. For example, it is a south wind in the previous period, and it quickly switches to a north wind in the next period.

[0098] S260: Calculate the concentration relative deviation based on the concentration monitoring data of the target monitoring area, the upwind monitoring area and the downwind monitoring area determined in each period of the at least one period.

[0099] In a specific implementation, when the computing device performs the aforementioned S260, it can include the following S261-S264.

[0100] S261: For each period in the at least one period, determine the first corresponding period of the upwind monitoring area and the second corresponding period of the downwind monitoring area according to the wind speed.

[0101] It should be noted here that the upwind monitoring area of a period is the upwind monitoring area of the period that causes the particulate matter concentration of the target monitoring area of the period. Because the pollution diffuses with the wind and needs time, the monitoring data of the upwind monitoring area of a period can not be the monitoring data obtained in the upwind monitoring area of the period, but the monitoring data of the previous period, i.e., the monitoring data of the aforementioned first corresponding period.

[0102] Specifically, the first corresponding period corresponding to a period can be determined according to the distance between the target monitoring area and the upwind monitoring area, and the wind speed. Similarly, the second corresponding period corresponding to a period can also be calculated by using the aforementioned method.

[0103] S262: Calculate the first prediction data according to the concentration monitoring data and the position coordinates of the upwind monitoring area of the first corresponding period, the wind speed and the wind direction, and the position coordinates of the target monitoring area according to the particulate matter diffusion model, and calculate the second prediction data according to the concentration monitoring data and the position coordinates of the downwind monitoring area of the second corresponding period, the wind speed and the direction, and the position coordinates of the target monitoring area according to the particulate matter diffusion model.

[0104] The particulate matter diffusion model is a model that simulates the variation of the concentration of particulate matter in different areas as the particulate matter diffuses with the wind. In a specific implementation, the particulate matter diffusion model can be a Gaussian plume model or a model obtained by re-optimizing the Gaussian plume model.

[0105] In a specific implementation, the Gaussian plume model is wherein is the concentration of particulate matter at a certain point downwind, q is the concentration of particulate matter of the pollution source (specifically, the monitoring area upwind or the target monitoring area), is the distance downwind, is the crosswind distance, is the height of the observation point, wherein and can be determined according to the position coordinates of the upwind point and the downwind point, the wind speed, and the wind direction, is predetermined, is the wind speed, and are the diffusion coefficients in the and directions, respectively, is the effective height of the pollution source upwind. The foregoing , , and can all use empirical values.

[0106] In the foregoing S262, the first prediction data is calculated according to the concentration monitoring data of the monitoring area upwind in the first corresponding period, the position coordinates, the wind speed and the wind direction, and the position coordinates of the target monitoring area, according to the particulate matter diffusion model. The foregoing data is brought into the particulate matter diffusion model, and then the first prediction data corresponding to the target monitoring area is obtained. In the foregoing S262, the second prediction data is calculated according to the concentration monitoring data of the monitoring area downwind in the second corresponding period and the position coordinates, the wind speed and the direction, and the position coordinates of the target monitoring area, according to the particulate matter diffusion model. The inverse model of the particulate matter diffusion model is used to calculate, and the second prediction data corresponding to the target monitoring area is obtained.

[0107] S263: Calculate the single-period relative deviation based on the first prediction data, the second prediction data of each period, and the concentration monitoring data of the target monitoring area.

[0108] After obtaining the first prediction data and the second prediction data of each period, the subsequent computing device can calculate the mean of the first prediction data and the second prediction data as the prediction data mean of the corresponding period. Then, the single-period relative deviation can be calculated using the concentration monitoring data of the target monitoring area and the prediction data mean. Specifically, the computing device can calculate the single-period relative deviation as follows . Wherein, monitoring data of the target monitoring area in a time period,

[0109] S264: calculate the concentration relative deviation based on the single-time-period relative deviation of each time period.

[0110] After obtaining the single-time-period relative deviation of each time period, the mean of each single-time-period relative deviation is then calculated, i.e., the concentration relative deviation can be obtained.

[0111] S270: calculate the mean of the concentration monitoring data of the selected adjacent monitoring area in each time period within at least one time period.

[0112] S280: calculate the single-time-period relative deviation based on the concentration monitoring data of the target monitoring area in each time period and the corresponding mean of the concentration monitoring data.

[0113] As analyzed above, under the condition of no wind, the transmission mode of atmospheric particulate matter is mainly diffusion. Based on this, in the embodiment of the present disclosure, when the wind speed of the meteorological condition is less than the threshold wind speed, the mean of the concentration monitoring data of the adjacent monitoring area in each time period is first calculated, and then the mean of the concentration monitoring data and the mean of the concentration monitoring data of the target monitoring area in the corresponding time period are used to calculate the single-time-period relative deviation.

[0114] Specifically, the computing device adopts to calculate the single-time-period relative deviation. Wherein monitoring data of the target monitoring area in a time period, monitoring data of the directly adjacent monitoring area, the number of directly adjacent monitoring areas.

[0115] S290: calculate the concentration relative deviation based on the single-time-period relative deviation of each time period.

[0116] After obtaining the single-time-period relative deviation of each time period, the mean of each single-time-period relative deviation is then calculated, i.e., the concentration relative deviation can be obtained.

[0117] S300: determine that the target monitoring area is a monitoring abnormal area when the concentration relative deviation is greater than a preset deviation threshold.

[0118] ​​The aforementioned methods only determine whether a target monitoring area is an anomaly area from a data analysis perspective. However, truly confirming whether a target monitoring area is an anomaly area may require on-site verification. In some embodiments, after identifying a target monitoring area as an anomaly area, relevant personnel will bring benchmark monitoring equipment to the target monitoring area to conduct on-site data measurements and obtain benchmark concentration monitoring data. At this time, the monitoring equipment and process system in the target monitoring area continue to operate, generating verification concentration monitoring data for comparison with the benchmark concentration monitoring data.

[0119] The aforementioned S210-S300 do not consider the impact of rain and snow on determining pollution transport areas. In practical applications, these impacts should be considered in areas with frequent rain and snow. However, in arid inland areas with little rainfall during winter, the impact of rain and snow can be ignored.

[0120] After the corresponding computing device obtains the aforementioned baseline concentration monitoring data and verification concentration monitoring data, it can also execute the following S310.

[0121] S310: Verify whether the target monitoring area is an abnormal monitoring area based on baseline concentration monitoring data and verification concentration monitoring data.

[0122] In practice, if the verification concentration monitoring data is significantly lower than the baseline concentration monitoring data, or if statistical analysis of the data reveals that the mean characteristics of the verification concentration monitoring data are significantly lower than the mean characteristics of the baseline concentration monitoring data, the target monitoring area can be identified as an abnormal monitoring area.

[0123] In addition to providing the aforementioned method for determining abnormal areas of atmospheric particulate matter monitoring, this disclosure also provides a method for determining abnormal areas of atmospheric particulate matter monitoring. Figure 3 This is a schematic diagram of the structure of the atmospheric particulate matter monitoring anomaly detection device 300 provided in this embodiment of the disclosure. Figure 3 As shown, the device 300 for determining abnormal areas of atmospheric particulate matter monitoring includes a region screening unit 301, a terrain data determination unit 302, a neighboring region selection unit 303, a deviation calculation unit 304, and a result determination unit 305.

[0124] The area screening unit 301 is used to perform preliminary screening of the concentration monitoring data of each atmospheric particulate matter monitoring area to determine the target monitoring area; the target monitoring area is a low-value area where the concentration monitoring data for multiple consecutive time periods are lower than the screening value.

[0125] The terrain data determining unit 302 is configured to determine a directly adjacent monitoring region according to the position coordinates of the target monitoring region, and obtain terrain feature data between the target monitoring region and the directly adjacent monitoring region. The directly adjacent monitoring region is a region that has no intermediate monitoring region between the target monitoring region and the directly adjacent monitoring region, and has a distance less than a preset distance from the target monitoring region.

[0126] The adjacent region selecting unit 303 is configured to determine whether the directly adjacent monitoring region and the target monitoring region constitute a pollution transmission region according to the terrain feature data. The pollution transmission region is a region in which atmospheric particulate matter can be transmitted and diffused inside. In a case where the terrain feature data indicates that the target monitoring region and the directly adjacent monitoring region have no terrain that blocks the transmission of pollutants, the two regions constitute a pollution transmission region.

[0127] The bias calculating unit 304 is configured to, in a case where it is determined that the directly adjacent monitoring region and the target monitoring region constitute a pollution transmission region, select the directly adjacent monitoring region as a selected adjacent monitoring region, and calculate a concentration relative bias based on the concentration monitoring data of the selected adjacent monitoring region and the target monitoring region in at least one time period.

[0128] The result determining unit 305 is configured to, in a case where the concentration relative bias is greater than a preset bias threshold, determine that the target monitoring region is a monitoring abnormal region.

[0129] In some embodiments, the device 300 for determining an atmospheric particulate matter monitoring abnormal region further comprises a meteorological data obtaining unit. The meteorological data obtaining unit obtains rain and snow meteorological data between the target monitoring region and the directly adjacent monitoring region or of the directly adjacent monitoring region. In a case where the rain and snow meteorological data is data representing non-rain and non-snow weather, the adjacent region selecting unit 303 determines whether the directly adjacent monitoring region and the target monitoring region constitute a pollution transmission region according to the terrain feature data.

[0130] In some embodiments, the meteorological data obtaining unit is further configured to obtain wind speed and wind direction in the pollution transmission region in at least one time period. In a case where the wind speed is greater than a preset threshold wind speed, before calculating the concentration relative bias based on the concentration monitoring data of the selected adjacent monitoring region and the target monitoring region in at least one time period, the adjacent region selecting unit 303 determines, according to the wind direction, the position coordinates of the target monitoring region and the position coordinates of the selected adjacent monitoring region, an upwind monitoring region and a downwind monitoring region in each time period in the selected adjacent monitoring region. Correspondingly, the bias calculating unit 304 calculates the concentration relative bias based on the concentration monitoring data of the target monitoring region and the upwind monitoring region and the downwind monitoring region determined in each time period in the at least one time period.

[0131] In some embodiments, the deviation calculation unit 304 determines, for each time period in the at least one time period, a first corresponding time period of the upwind monitoring area and a second corresponding time period of the downwind monitoring area according to the wind speed; calculates first prediction data according to the concentration monitoring data and the position coordinates of the upwind monitoring area in the first corresponding time period, the wind speed and the wind direction, and the position coordinates of the target monitoring area according to the particle diffusion model, calculates second prediction data according to the concentration monitoring data and the position coordinates of the downwind monitoring area in the second corresponding time period, the wind speed and the direction, and the position coordinates of the target monitoring area according to the particle diffusion model; calculates a single-time-period relative deviation based on the first prediction data, the second prediction data, and the concentration monitoring data of the target monitoring area of each time period; and then calculates the concentration relative deviation based on the single-time-period relative deviations of each time period.

[0132] In some embodiments, when the wind speed is less than a preset threshold wind speed, the deviation calculation unit 304 calculates, for each time period in the at least one time period, a monitoring data average of the selected adjacent monitoring area in the plurality of monitoring time periods; calculates a single-time-period relative deviation based on the monitoring data of the target monitoring area in each time period and the corresponding monitoring data average; and calculates the concentration relative deviation based on the single-time-period relative deviations of each time period.

[0133] In some embodiments, the target monitoring area is a monitoring point; after determining that the target monitoring area is an abnormal monitoring area, the result determination unit 305 obtains reference concentration monitoring data obtained by a reference monitoring device in the target monitoring area for atmospheric particulate matter monitoring, and obtains verification concentration monitoring data obtained by an automatic monitoring device located at the monitoring point in the same time period for atmospheric particulate matter monitoring; then, whether the target monitoring area is an abnormal monitoring area is verified based on the reference concentration monitoring data and the verification concentration monitoring data.

[0134] In some embodiments, the area screening unit 301 calculates a sliding window average by performing average calculation on the concentration monitoring data of each atmospheric particulate matter monitoring area in a plurality of continuous time periods under non-rain and snow weather conditions; and determines the corresponding atmospheric particulate matter monitoring area as the target monitoring area when the sliding window average is less than a screening value.

[0135] In some embodiments, the atmospheric particulate matter monitoring area is a monitoring point; the area screening unit 301 obtains a device state identifier of a monitoring device of each atmospheric particulate matter monitoring area; and determines the corresponding atmospheric particulate matter monitoring area as the target monitoring area when the sliding window average is less than a preset screening value and the corresponding monitoring device identifier is a normal operation identifier.

[0136] The disclosure embodiments also provide a computing device for implementing the foregoing method. Figure 4 is a structural schematic diagram of the computing device provided by the disclosure embodiments. The following will be specifically referred toFigure 4 It shows a schematic diagram of a structure suitable for implementing the computing device 400 in the embodiments of this disclosure. Figure 4 The computing device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0137] like Figure 4 As shown, the computing device 400 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory ROM 402 or a program loaded from a storage device 408 into a random access memory RAM 403. The RAM 403 also stores various programs and data required for the operation of the computing device 400. The processing unit 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0138] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, cameras, microphones, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows computing device 400 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 A computing device 400 with various devices is shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively.

[0139] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 409, or installed from storage device 408, or installed from ROM 402. When the computer program is executed by processing device 401, it performs the functions defined in the methods of embodiments of this disclosure.

[0140] It should be noted that the computer-readable medium described above in this disclosure may be a computer-readable storage medium, a computer-readable signal medium, or any combination thereof.

[0141] A computer readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a computer readable storage medium can include, but are not limited to, the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium can be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0142] A computer readable signal medium can include a propagated data signal with computer readable program code embodied therein, for use by or in connection with an instruction execution system, apparatus, or device. Examples of a computer readable signal medium include but are not limited to a propagated data signal with computer readable program code embodied therein, for use by or in connection with an instruction execution system, apparatus, or device. A computer readable signal medium can be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable signal medium can be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0143] In some embodiments, the client, computing device can communicate using any known or future developed network protocols, such as HTTP (HyperText Transfer Protocol), and can be interconnected with digital data communications (e.g., a communications network) of any form or medium, including the Internet, local area networks, wide area networks, etc. Examples of communications networks include local area networks ("LAN"), wide area networks ("WAN"), internetworks (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.

[0144] The computer readable medium described above can be included within the computing device described above; or can exist exclusively on the outside of the computing device.

[0145] Computer program code for carrying out operations of the present disclosure can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the testee's computer, partly on the testee's computer, as a stand-alone software package, partly on the testee's computer and partly on a remote computer or entirely on the remote computer or computing device. In the latter scenario, the remote computer can be connected to the testee's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0146] The flow diagrams and the block diagrams in the drawings are illustrations of architectures, functionalities, and operations of possible implementations of systems, methods, and computer program products according to various embodiments of present disclosure. In this regard, each block in the flow diagrams or block diagrams can represent a module, a procedure, or a part of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or in the reverse order, depending on the functionality involved. It is also noted that each block in the block diagrams and / or flow diagrams and combinations of blocks in the block diagrams and / or flow diagrams can be implemented by special-purpose hardware-based systems that perform the specified functions or operations, or combinations of special-purpose hardware and computer instructions.

[0147] The units described in the embodiments of the present disclosure can be implemented by software, or by hardware. In some cases, the names of the units do not constitute a limitation on the units themselves. The functions described above can be performed at least in part by one or more hardware logic components. For example, non-limiting examples of hardware logic components that can be used include field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SOCs), complex programmable logic devices (CPLDs), etc.

[0148] The foregoing is merely illustrative of the various ways and specific embodiments in which the disclosure can be carried out. Numerous modifications can be made to these embodiments without departing from the spirit and scope of the disclosure. Therefore, the disclosure is not limited to the specific embodiments described herein, but rather the scope of the disclosure is to be accorded the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for determining anomaly areas in atmospheric particulate matter monitoring, characterized in that, include: Preliminary screening of concentration monitoring data from various atmospheric particulate matter monitoring areas is conducted to determine target monitoring areas; the target monitoring areas are low-value areas where the concentration monitoring data for multiple consecutive time periods are lower than the screened values. The directly adjacent monitoring area is determined based on the location coordinates of the target monitoring area, and the terrain feature data between the target monitoring area and the directly adjacent monitoring area is obtained; the directly adjacent monitoring area is an area that has no intermediate monitoring area between it and the target monitoring area, and the distance between it and the target monitoring area is less than a preset distance; Based on the terrain feature data, it is determined whether the directly adjacent monitoring area and the target monitoring area constitute a pollution transmission area, where the pollution transmission area is a region where atmospheric particulate matter can be transmitted and diffused internally. When the terrain feature data indicates that there is no terrain barrier to pollutant transmission between the target monitoring area and the directly adjacent monitoring area, the two constitute a pollution transmission area. If it is determined that the directly adjacent monitoring area and the target monitoring area constitute a pollution transmission area, the directly adjacent monitoring area is selected as the adjacent monitoring area, and the relative concentration deviation is calculated based on the concentration monitoring data of the selected adjacent monitoring area and the target monitoring area in at least one time period. If the relative concentration deviation is greater than a preset deviation threshold, the target monitoring area is determined as an abnormal monitoring area, and the atmospheric particulate matter monitoring area is determined as a monitoring point; the method further includes: obtaining the equipment status identifier of the monitoring equipment in each atmospheric particulate matter monitoring area; Preliminary screening of concentration monitoring data from various atmospheric particulate matter monitoring areas was conducted to determine target monitoring areas, including: The average concentration monitoring data of each atmospheric particulate matter monitoring area under non-rainy / snowy weather conditions and for multiple consecutive time periods are calculated to obtain the sliding window average value. If the sliding window average value is less than the preset screening value and the corresponding monitoring equipment is marked as operating normally, the corresponding atmospheric particulate matter monitoring area is determined as the target monitoring area.

2. The determination method according to claim 1, characterized in that, The method further includes: Acquire rain and snow meteorological data between the target monitoring area and the directly adjacent monitoring area, or between the directly adjacent monitoring areas; The step of determining whether the directly adjacent monitoring area and the target monitoring area constitute a pollution transmission area based on the terrain feature data includes: when the rain and snow meteorological data is data representing non-rain and snow weather, determining whether the directly adjacent monitoring area and the target monitoring area constitute a pollution transmission area based on the terrain feature data.

3. The determination method according to claim 2, characterized in that, The method further includes: obtaining the wind speed and wind direction within the pollution transmission area during the at least one time period; When the wind speed is greater than a preset threshold wind speed, before calculating the relative concentration deviation based on the concentration monitoring data of the selected adjacent monitoring area and the target monitoring area over at least one time period, the method further includes: Based on the wind direction, the location coordinates of the target monitoring area, and the location coordinates of the selected adjacent monitoring area, the upwind monitoring area and the downwind monitoring area for each time period are determined in the selected adjacent monitoring area; The calculation of relative concentration deviation based on atmospheric concentration monitoring data of the selected adjacent monitoring area and the target monitoring area includes: calculating the relative concentration deviation based on concentration monitoring data of the upwind monitoring area, the downwind monitoring area and the target monitoring area determined in each time period within the at least one time period.

4. The determination method according to claim 3, characterized in that, The relative concentration deviation is calculated based on the concentration monitoring data of the upwind monitoring area, the downwind monitoring area, and the target monitoring area determined within each of the at least one time period, including: For each time period within the at least one time period, a first corresponding time period for the upwind monitoring area and a second corresponding time period for the downwind monitoring area are determined based on wind speed; Based on the concentration monitoring data and location coordinates, wind speed and direction of the upwind monitoring area in the first corresponding time period, and the location coordinates of the target monitoring area, the first predicted data is calculated according to the particulate matter diffusion model. Based on the concentration monitoring data and location coordinates, wind speed and direction of the downwind monitoring area in the second corresponding time period, and the location coordinates of the target monitoring area, the second predicted data is calculated according to the particulate matter diffusion model. The relative deviation for a single time period is calculated based on the first and second prediction data for each time period and the concentration monitoring data of the target monitoring area. The relative concentration deviation is calculated based on the single-period relative deviation of each time period.

5. The determination method according to claim 3, characterized in that, When the wind speed is less than a preset threshold wind speed, the calculation of the relative concentration deviation based on the atmospheric concentration monitoring data of the selected adjacent monitoring area and the target monitoring area includes: Based on each time period within the at least one time period, calculate the average monitoring data of the corresponding selected adjacent monitoring area in multiple monitoring time periods; The relative deviation for a single time period is calculated based on the monitoring data of the target monitoring area in each time period and the corresponding average value of the monitoring data; The relative concentration deviation is calculated based on the single-period relative deviation of each time period.

6. The determining method according to any one of claims 1-5, characterized in that, The target monitoring area refers to the monitoring points; After determining that the target monitoring area is a monitoring anomaly area, the method further includes: Acquire the baseline concentration monitoring data obtained by the baseline monitoring equipment in the target monitoring area, and acquire the verification concentration monitoring data obtained by the automatic monitoring equipment located at the monitoring point during the same period. Verify whether the target monitoring area is an abnormal monitoring area based on the baseline concentration monitoring data and the verification concentration monitoring data.

7. A device for determining anomaly areas in atmospheric particulate matter monitoring, characterized in that, include: The regional screening unit is used to perform preliminary screening of the concentration monitoring data of each atmospheric particulate matter monitoring area to determine the target monitoring area. The target monitoring area is a low-value area where the concentration monitoring data for multiple consecutive time periods is lower than the screening value. The atmospheric particulate matter monitoring area is the monitoring point. The regional screening unit obtains the equipment status identifier of the monitoring equipment in each atmospheric particulate matter monitoring area. The regional screening unit calculates the average value of the concentration monitoring data of each atmospheric particulate matter monitoring area under non-rainy / snowy weather conditions for multiple consecutive time periods to obtain the sliding window average value. If the sliding window average value is less than the preset screening value and the corresponding monitoring equipment identifier is in normal operation, the regional screening unit determines the corresponding atmospheric particulate matter monitoring area as the target monitoring area. The terrain data determination unit is used to determine the directly adjacent monitoring area based on the location coordinates of the target monitoring area, and to acquire terrain feature data between the target monitoring area and the directly adjacent monitoring area; the directly adjacent monitoring area is an area that has no intermediate monitoring area between it and the target monitoring area, and whose distance from the target monitoring area is less than a preset distance; The adjacent area selection unit is used to determine, based on the terrain feature data, whether the directly adjacent monitoring area and the target monitoring area constitute a pollution transmission area, wherein the pollution transmission area is an area where atmospheric particulate matter can be transmitted and diffused internally. When the terrain feature data indicates that there is no terrain barrier to pollutant transmission between the target monitoring area and the directly adjacent monitoring area, the two constitute a pollution transmission area. The deviation calculation unit is used to select the directly adjacent monitoring area as the selected adjacent monitoring area when it is determined that the directly adjacent monitoring area and the target monitoring area constitute a pollution transmission area, and to calculate the relative concentration deviation based on the concentration monitoring data of the selected adjacent monitoring area and the target monitoring area in at least one time period. The result determination unit is used to determine the target monitoring area as a monitoring anomaly area when the relative concentration deviation is greater than a preset deviation threshold.

8. A computing device, characterized in that, It includes a processor and a memory, the memory being used to store a computer program; when the computer program is loaded by the processor, it causes the processor to execute the method for determining anomaly areas of atmospheric particulate matter monitoring as described in any one of claims 1-6.

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