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

Abstract

The embodiment of the invention provides a method and device for determining an atmospheric particulate monitoring abnormal area and computing equipment. The method for determining the abnormal monitoring area of the atmospheric particulates comprises the following steps: determining a target monitoring area and a directly adjacent monitoring area, and judging whether a pollution transmission area is formed according to topographic feature data between the target monitoring area and the directly adjacent monitoring area; under the condition that the pollution transmission area is determined to be formed, 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; and under the condition that the concentration relative deviation is greater than a preset deviation threshold value, determining that the target monitoring area is an abnormal monitoring area. Compared with an existing scheme in which the adjacent region is determined only based on the proximity relation and then the monitoring data of the target monitoring region is judged by using the adjacent region data, the scheme of the embodiment of the invention considers the influence of the terrain on the diffusion of the atmospheric particulates, so that the identification of the obtained abnormal monitoring region is more accurate.

Description

Technical Field

[0001] The present disclosure relates to the technical field of environmental concentration monitoring data processing, and in particular to a method, device and computing equipment for determining an abnormal area for atmospheric particulate matter monitoring. Background Art

[0002] In order to achieve overall atmospheric particulate matter concentration monitoring in a larger area, the larger area is divided into multiple smaller monitoring areas (in actual implementation, the larger area and the smaller area may be divided in an administrative or non-administrative manner), and each smaller area is equipped with a particle concentration monitoring device. The aforementioned particle monitoring devices monitor the atmospheric particulate matter concentration in real time to obtain real-time atmospheric particulate matter concentration monitoring data. The concentration monitoring data of a large number of smaller areas are collected to form a massive concentration monitoring data set.

[0003] In actual applications, due to failures of the particulate matter concentration monitoring equipment itself, human interference and other reasons, the concentration monitoring data of certain monitoring areas have abnormally low values. When judging the mass concentration of atmospheric particulate matter and the pollution process in the entire area, it is necessary to exclude the aforementioned abnormal low-value data. In the prior art, in order to identify and exclude abnormal low-value data, each monitoring area is first screened to determine the low-value area, and then the concentration monitoring data of the low-value area is verified using the concentration monitoring data of the adjacent area of ​​the low-value area to identify whether the concentration monitoring data of the aforementioned low-value area is credible. When it is determined that the deviation ratio between the concentration monitoring data of the low-value area and the concentration monitoring data of the adjacent area is significantly larger, the low-value area is identified as a monitoring abnormality area. However, through actual verification, the monitoring abnormality area identified by the aforementioned method has a large error ratio, that is, the monitoring abnormality area identified by the aforementioned method is not reliable. Summary of the invention

[0004] In order to solve the problem that the existing methods identify abnormal monitoring areas with many errors, the present disclosure provides a new method, device and computing device for determining abnormal atmospheric particulate matter monitoring areas.

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

[0006] Conduct a 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;

[0007] Determine a directly adjacent monitoring area according to the position 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 an area that has no intermediate monitoring area between it and the target monitoring area and is less than a preset distance from the target monitoring area;

[0008] 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 in which atmospheric particulate matter can be transmitted and diffused; if the terrain feature data indicates that the target monitoring area and the directly adjacent monitoring area do not have a terrain that blocks the transmission of pollutants, the two constitute a pollution transmission area;

[0009] In the case where 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 used as the selected 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;

[0010] When the relative concentration deviation is greater than a preset deviation threshold, the target monitoring area is determined to be a monitoring abnormality area.

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

[0012] The determining, based on the terrain feature data, whether the directly adjacent monitoring area and the target monitoring area constitute a pollution transmission area includes: when the rain and snow meteorological data is data representing non-rain and snow weather, determining, based on the terrain feature data, whether the directly adjacent monitoring area and the target monitoring area constitute a pollution transmission area.

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

[0014] 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 in at least one time period, the method further includes:

[0015] Determine an upwind monitoring area and a downwind monitoring area for each time period in the selected adjacent monitoring area 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 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: calculating the relative concentration 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 within the at least one time period.

[0017] Optionally, calculating the relative concentration 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 within the at least one time period includes:

[0018] For each time period within the at least one time period, determining a first corresponding time period of an upwind monitoring area and a second corresponding time period of a downwind monitoring area according to the wind speed;

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

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

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

[0022] Optionally, when the wind speed is less than a preset threshold wind speed, the calculating the relative concentration deviation based on the atmospheric concentration monitoring data of the selected adjacent monitoring area and the target monitoring area includes:

[0023] Based on each time period within the at least one time period, calculating the mean value of the monitoring data of the corresponding selected adjacent monitoring area in multiple monitoring time periods;

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

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

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

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

[0028] Obtaining baseline concentration monitoring data obtained by a baseline monitoring device monitoring atmospheric particulate matter in the target monitoring area, and obtaining verification concentration monitoring data obtained by an automatic monitoring device located at the monitoring point monitoring atmospheric particulate matter during the same period;

[0029] 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.

[0030] Optionally, conduct a preliminary screening of the concentration monitoring data of each atmospheric particulate matter monitoring area to determine the target monitoring area, including:

[0031] The mean of the concentration monitoring data of each atmospheric particulate matter monitoring area under non-rain and snow meteorological conditions and for multiple consecutive periods is calculated to obtain the sliding window mean;

[0032] When the sliding window mean 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; and the method further includes:

[0034] Obtain the equipment status identification of the monitoring equipment in each atmospheric particulate matter monitoring area;

[0035] When the sliding window mean is less than a preset screening value, determining the corresponding atmospheric particulate matter monitoring area as the target monitoring area includes: when the sliding window mean is less than a preset screening value and the corresponding monitoring equipment identifier is a normal operating identifier, determining the corresponding atmospheric particulate matter monitoring area as the target monitoring area.

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

[0037] The area screening unit is used to preliminarily screen the concentration monitoring data of each atmospheric particulate matter monitoring area and determine the target monitoring area; the target monitoring area is a low-value area where the concentration monitoring data of multiple consecutive time periods is lower than the screening value;

[0038] a terrain data determination unit, configured to determine a directly adjacent monitoring area according to the position coordinates of the target monitoring area, and to obtain terrain feature data between the target monitoring area and the directly adjacent monitoring area; the directly adjacent monitoring area is an area having no intermediate monitoring area between the target monitoring area and the target monitoring area, and having a distance from the target monitoring area less than a preset distance;

[0039] an adjacent area selection unit, for determining, 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 in which atmospheric particulate matter can be transmitted and diffused; if the terrain feature data indicates that the target monitoring area and the directly adjacent monitoring area do not have a terrain that blocks the transmission of pollutants, the two constitute a pollution transmission area;

[0040] a deviation calculation unit, for, when it is determined that the directly adjacent monitoring area and the target monitoring area constitute a pollution transmission area, taking the directly adjacent monitoring area as a selected adjacent monitoring area, and calculating a relative concentration deviation based on concentration monitoring data of the selected adjacent monitoring area and the target monitoring area in at least one time period;

[0041] The result determination unit is used to determine that the target monitoring area is a monitoring abnormality area when the relative concentration deviation is greater than a preset deviation threshold.

[0042] In a third aspect, an embodiment of the present disclosure provides a computing device, including a processor and a memory, wherein the memory is used to store a computer program; when the computer program is loaded by the processor, the processor executes the method for determining an abnormal area of ​​atmospheric particulate matter monitoring as described above.

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

[0044] The accompanying drawings, which are incorporated in and constitute a part of this 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 following briefly introduces the drawings required for use in the embodiments or the prior art description. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor, including

[0046] Figure 1 is a flow chart of a method for determining an abnormal area of ​​atmospheric particulate matter monitoring provided by an embodiment of the present disclosure;

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

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

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

[0050] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of 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 "including" and its variations used in this document are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions of other terms will be given in the description below. In this document, relational terms such as "first" and "second" are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

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

[0053] Figure 1 : is a flow chart of a method for determining an abnormal area of ​​atmospheric particulate matter monitoring provided by an embodiment of the present disclosure. Figure 1 As shown, the method for determining an abnormal area of ​​atmospheric particulate matter monitoring includes S110-S150.

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

[0055] The atmospheric particulate matter monitoring area is a pre-determined area used to monitor the atmospheric particulate matter concentration by zoning. In specific implementations, the atmospheric particulate matter monitoring area can be an area determined by a specific monitoring radius with the monitoring point as the center, or it can be a monitoring area formed by combining the areas corresponding to multiple adjacent monitoring points. In the case where the atmospheric particulate matter monitoring area is a monitoring area formed by combining the areas corresponding to multiple adjacent monitoring points, the atmospheric particulate matter monitoring area is mostly divided according to administrative divisions.

[0056] In the disclosed embodiment, the computing device can obtain the concentration monitoring data of the atmospheric particulate matter monitoring area. The concentration monitoring data of each atmospheric particulate matter monitoring area is the data determined by data sampling according to the set sampling period. In other words, the concentration monitoring data of each atmospheric particulate matter is the data obtained in a continuous period.

[0057] The target monitoring area is the area where the concentration monitoring data for multiple consecutive time periods are all lower than the screening value. The above-mentioned use of concentration monitoring data for multiple consecutive time periods to determine the target monitoring area is to eliminate the problem of low credibility of the determined target monitoring area due to small data in a single time period.

[0058] In some embodiments, the computing device may use the following method S111 - S112 to determine the target monitoring area.

[0059] S111: Calculate the mean of the concentration monitoring data of each atmospheric particulate matter monitoring area under non-rain and snow meteorological conditions and for multiple consecutive time periods to obtain a sliding window mean.

[0060] Under rainfall conditions, atmospheric particulate matter will settle due to the adsorption of rainwater, and the concentration monitoring data of the corresponding area will be significantly reduced to low-value data. The aforementioned low-value data has no value for evaluation, so it is necessary to exclude the atmospheric particulate matter concentration monitoring data under rain and snow meteorological conditions.

[0061] After obtaining the concentration monitoring data of multiple consecutive periods under non-rain and snow conditions, the computing device will average the concentration monitoring data of the aforementioned multiple periods to obtain the sliding window mean. The sliding window mean represents the average situation of atmospheric particulate matter in the corresponding monitoring area in multiple consecutive periods.

[0062] S112: When the sliding window mean is less than the screening value, the corresponding atmospheric particulate matter monitoring area is determined as the target monitoring area.

[0063] In some embodiments of the present disclosure, the screening value is a predetermined smaller particle concentration value. If the sliding window mean is less than the aforementioned screening value, the data in the aforementioned monitoring area is likely to be unreliable low-value data, so this monitoring area is used as the target monitoring area.

[0064] In some further embodiments, the computing device can determine the adjacent monitoring area based on the position coordinates of each atmospheric particulate matter monitoring area, and make a preliminary comparison between the concentration monitoring data of the aforementioned monitoring area and the adjacent monitoring area. If it is determined that the concentration monitoring data of a certain monitoring area is significantly less than the data mean determined by the adjacent concentration monitoring data, the aforementioned monitoring area is determined to be the target monitoring area. With this method, there is no need to determine the screening value as in S112 above, and the screening criteria can be determined according to the actual situation, so that the target monitoring area can be determined more reasonably.

[0065] In some embodiments, the atmospheric particulate matter monitoring area is a monitoring point. When the computing device obtains the monitoring data of each atmospheric particulate matter monitoring area, it also obtains the device status identification of the monitoring equipment in each atmospheric particulate matter monitoring area. Accordingly, the aforementioned S112 specifically determines that the corresponding atmospheric particulate matter monitoring area is the target monitoring area when the sliding window mean is less than the preset screening value and the corresponding monitoring equipment identification is a normal operation identification.

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

[0067] The directly adjacent monitoring area is an area with no intermediate monitoring area between it and the target monitoring area. In other words, the target monitoring area and the directly adjacent monitoring area are directly adjacent to each other, and there is no other monitoring area in between as a barrier.

[0068] In a specific implementation, the computing device calculates the distance and direction based on the location coordinates of the target monitoring area and the coordinates of all other monitoring areas, and determines the directly adjacent monitoring areas by means of data comparison.

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

[0070] S130: Determine whether the directly adjacent monitoring area and the target monitoring area constitute a pollution transmission area based on the terrain feature data; if so, S140.

[0071] The pollution transmission area is an area where atmospheric particulate matter can be transmitted and diffused internally, and atmospheric particulate matter can be transmitted and diffused from one monitoring area to the next adjacent monitoring area.

[0072] In a specific implementation, it can be determined whether there is terrain that blocks the transmission of pollutants between the target monitoring area and the directly adjacent monitoring area based on the terrain feature data. If there is terrain that blocks the transmission of pollutants between the target monitoring area and the directly adjacent monitoring area, it is determined that the directly adjacent monitoring area and the target monitoring area constitute a pollution transmission area.

[0073] For example, if there are continuous mountains between the directly adjacent area and the target monitoring area whose relative altitude is significantly higher than the atmospheric particulate matter layer height, it is determined that the directly adjacent area and the target monitoring area cannot constitute a pollution transmission area. For another example, if there are no such continuous mountains between the directly adjacent area and the target monitoring area, or if there are mountains but they are actually low-altitude hills, it is determined that the directly adjacent area and the target monitoring area constitute a pollution transmission area. In practical applications, the knowledge of topography can be combined 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 constitute a pollution transmission area. No specific examples are given here.

[0074] It should be noted here that the aforementioned pollution transmission area is determined with the target monitoring area as the center, and it does not necessarily guarantee that the area between two directly adjacent monitoring areas is a pollution transmission area (in actual applications, directly adjacent areas are likely to constitute pollution transmission areas). In actual applications, when the terrain area where some monitoring areas are located is a plain area or a plateau area, the aforementioned area can be directly determined as a pollution transmission area.

[0075] S140: The directly adjacent monitoring area is taken as the selected adjacent monitoring area, and a 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.

[0076] When it is determined that the directly adjacent monitoring area and the target monitoring area constitute a pollution transmission area, that is, when pollutants may be transmitted between the directly adjacent monitoring area and the target monitoring area, the aforementioned directly adjacent monitoring area is determined as the selected adjacent monitoring area.

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

[0078] The relative deviation of concentration is the deviation of the concentration of the particulate matter displayed in percentage. In a specific implementation, the computing device may calculate the concentration deviation of the particulate matter by using the existing method of calculating the relative deviation.

[0079] S150: When the relative concentration deviation is greater than a preset deviation threshold, the target monitoring area is determined to be a monitoring abnormality area.

[0080] After determining the relative concentration deviation, the computing device determines whether the relative concentration deviation is greater than a preset deviation threshold. If the relative concentration deviation is greater than the preset deviation threshold, it is determined that the concentration monitoring data of the target monitoring area is unreasonable, and the target monitoring area is correspondingly determined to be a monitoring abnormal area.

[0081] According to the scheme of the embodiment of the present disclosure, before determining whether a target monitoring area is an abnormal monitoring area, first determine which directly adjacent areas are selected adjacent monitoring areas based on the terrain feature data, and only use the monitoring data of the selected adjacent monitoring areas to evaluate the monitoring data of the target monitoring area and obtain the relative concentration deviation. Compared with the existing scheme that only determines the adjacent areas based on the proximity relationship (distance relationship) and then uses the adjacent area data to determine the monitoring data of the target monitoring area, the scheme of the embodiment of the present disclosure takes into account the influence of the terrain on the diffusion of atmospheric particulate matter, so that the identification of the abnormal monitoring area is more accurate.

[0082] As analyzed above, in rainy and snowy weather, the effect of rain and snow will cause the particles to settle, making the concentration monitoring data unavailable. Based on this, in the specific implementation of the present disclosure, it is also necessary to consider whether the diffusion path of the corresponding particles between the directly adjacent area and the target monitoring area is blocked due to rain and snow, making the corresponding data have no reference value. Specifically, in the game embodiment, before executing the aforementioned S130, the following S160 can also be executed.

[0083] S160: Acquire rain and snow meteorological data between the target monitoring area and the directly adjacent monitoring area or the directly adjacent monitoring area.

[0084] Specifically, obtaining the rain and snow meteorological data between the target monitoring area and the directly adjacent monitoring area is to obtain the meteorological characteristic data of all meteorological monitoring stations between the two, and determine whether there is rain and snow meteorological weather at the two times. Similarly, obtaining the rain and snow meteorological data of the directly adjacent area is to obtain the meteorological characteristic data of the directly adjacent area and determine the rain and snow meteorological weather in the directly adjacent area.

[0085] On the premise of executing the aforementioned S160, the aforementioned S130 is specifically: when the rain and snow meteorological data is data representing non-rain and snow weather, determine whether the directly adjacent monitoring area and the target monitoring area constitute a pollution transmission area based on the terrain feature data.

[0086] According to existing experience, when it is not rainy or snowy, the transmission mode of atmospheric particulate matter will be significantly different due to different atmospheric meteorological conditions. For example, when the meteorological conditions are strong winds, the transmission mode of atmospheric particulate matter is mostly through wind blowing; while when the meteorological conditions are windless, the transmission mode of atmospheric particulate matter is mostly through diffusion. Under different meteorological conditions, different data sources and calculation methods are needed to determine the relative deviation of concentration.

[0087] Figure 2 1 is a flow chart of a method for determining an abnormal area of ​​atmospheric particulate matter monitoring provided by some other embodiments of the present disclosure. Figure 2 As shown, in some embodiments, the method for determining an abnormal area for atmospheric particulate matter monitoring includes S210-S300.

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

[0089] S220: Determine the directly adjacent monitoring area according to the position coordinates of the target monitoring area, and obtain terrain feature data between the target monitoring area and the directly adjacent monitoring area.

[0090] S230: Determine whether the directly adjacent monitoring area and the target monitoring area constitute a pollution transmission area based on the terrain feature data; if so, execute S240.

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

[0092] S240: Obtain the wind speed and wind direction in the pollution transmission area in at least one time period, and determine whether the wind speed is greater than a threshold wind speed; if so, execute S250; if not, execute S270.

[0093] S250: Determine concentration monitoring data of the upwind monitoring area and concentration monitoring data of the downwind monitoring area in each time period in the selected adjacent monitoring area 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 in the pollution transmission area within at least one period of time by obtaining meteorological data in the pollution transmission area (specifically obtaining meteorological data in the target detection area).

[0095] The threshold wind speed may be determined based on terrain characteristic data (topographic features) of the pollutant transmission area, or may be predetermined, and the present disclosure embodiment does not limit this.

[0096] When the wind speed is greater than the threshold wind speed, it is determined that the atmospheric particulate matter is diffused by wind blowing. The main factor affecting the mass concentration of atmospheric particulate matter in the target monitoring area is the mass concentration of atmospheric particulate matter in the upwind monitoring area (the target monitoring area is no longer treated as a pollution source here, and it cannot be treated as a pollution source through monitoring data). The mass concentration of atmospheric particulate matter in the target monitoring area mainly affects the mass concentration of atmospheric particulate matter in the downwind monitoring area. Therefore, when the wind speed is greater than the threshold wind speed, the upwind monitoring area and downwind monitoring area are determined for each time period.

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

[0098] S260: Calculate the relative concentration 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 at least one time period.

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

[0100] S261: For each time period within at least one time period, determine a first corresponding time period of an upwind monitoring area and a second corresponding time period of a 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 in the target monitoring area of ​​this period. Because it takes time for pollutants to diffuse with the wind, the monitoring data of the upwind monitoring area of ​​a period may not be the monitoring data obtained in the upwind monitoring area of ​​this period, but the monitoring data of the previous period, that is, the monitoring data of the first corresponding period mentioned above.

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

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

[0104] The particle diffusion model is a predetermined model that simulates the concentration variation of particles in different regions due to the diffusion of particles with wind. In a specific implementation, the particle diffusion model can be a Gaussian mist model or a model optimized based on the Gaussian mist model.

[0105] In one specific implementation, the Gaussian plume model is Where c(x, y, z) is the particle concentration at a certain point downwind, q is the particle concentration of the pollution source (specifically the upwind monitoring area or the target monitoring area), x is the downwind distance, y is the cross-wind distance, and z is the height of the observation point. x and y can be determined based on the position coordinates, wind speed and direction of the upwind and downwind points, z is predetermined, v is the wind speed, and δ y and δ z are the diffusion coefficients in the y and z directions respectively, and H is the effective height of the upwind pollution source. y ,δ z Both γ and H can adopt empirical values.

[0106] In the aforementioned S262, the first prediction data is calculated according to the particle diffusion model based on the concentration monitoring data, position coordinates, wind speed and wind direction of the upwind monitoring area in the first corresponding period, and the position coordinates of the target monitoring area, and the aforementioned data is brought into the particle diffusion model to obtain the first prediction data of the corresponding target monitoring area. In the aforementioned S262, the second prediction data is calculated according to the particle diffusion model based on the concentration monitoring data and position coordinates, wind speed and direction of the downwind monitoring area in the second corresponding period, and the position coordinates of the target monitoring area, and the inverse model of the particle diffusion model is used for calculation to obtain the second prediction data of the corresponding target monitoring area.

[0107] S263: Calculate the 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 in each time period.

[0108] After obtaining the first prediction data and the second prediction data for each time period, the computing device can then calculate the average of the first prediction data and the second prediction data as the average prediction data for the corresponding time period. Then, the relative deviation of a single time period can be calculated using the concentration monitoring data and the average prediction data of the target monitoring area. Specifically, the computing device can use the following The relative deviation of a single period is calculated. Among them, pi is the monitoring data of the target monitoring area in a period, pf is the first prediction data, and p b is the second prediction data.

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

[0110] After obtaining the single-time period relative deviations of the aforementioned various time periods, the average of the relative deviations of each single time period will be calculated, that is, the concentration relative deviation can be obtained.

[0111] S270: Based on each time period within at least one time period, calculate the average value of the concentration monitoring data of the corresponding selected adjacent monitoring area in multiple time periods.

[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 concentration monitoring data mean.

[0113] As analyzed above, when the meteorological condition is windless, the transmission mode of atmospheric particulate matter is mostly through diffusion. Based on this, in the embodiment of the present disclosure, when it is determined that the wind speed of the meteorological condition is less than the threshold wind speed, the mean value of the concentration monitoring data of the adjacent monitoring area in each time period is first calculated, and then the relative deviation of the single time period is calculated based on the mean value of the concentration monitoring data and the mean value of the concentration monitoring data of the target monitoring area in the corresponding time period, and then the relative deviation of the single time period is calculated based on the above mean value and the concentration monitoring data of the target monitoring area.

[0114] Specific computing equipment, using The relative deviation of a single period is calculated. Where pi is the monitoring data of the target monitoring area in a period, p n is the monitoring data of the directly adjacent monitoring area, and n is the number of directly adjacent monitoring areas.

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

[0116] After obtaining the single-time period relative deviations of the aforementioned various time periods, the average of the relative deviations of each single time period will be calculated, that is, the concentration relative deviation can be obtained.

[0117] S200: When the relative concentration deviation is greater than a preset deviation threshold, determining the target monitoring area as a monitoring abnormality area.

[0118] The aforementioned method is only used to determine whether the target monitoring area is a monitoring abnormality area from the perspective of data analysis. To truly determine whether the target monitoring area is a monitoring abnormality area, field verification may be required. In some embodiments, after determining that a certain target monitoring area is an abnormal area, the corresponding staff will also carry the benchmark monitoring equipment to the target monitoring area to conduct field data measurement to obtain the benchmark concentration monitoring data. At this time, the monitoring equipment and process system of the target monitoring area continue to operate to generate verification concentration monitoring data for comparison with the benchmark concentration monitoring data.

[0119] The aforementioned S210-S200 does not consider the impact of rain and snow on the determination of pollution transmission areas. In practical applications, when there is more rain and snow in some areas, the aforementioned impact should be considered. In inland arid and rainless areas in winter, the problem of rain and snow can be ignored.

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

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

[0122] In specific implementations, if the verification concentration monitoring data is significantly lower than the benchmark concentration monitoring data, or if data statistical analysis shows that the mean characteristics of the verification concentration monitoring data are significantly lower than the mean characteristics of the benchmark concentration monitoring data, the target monitoring area can be determined as an abnormal monitoring area.

[0123] In addition to providing the aforementioned method for determining an abnormal area for monitoring atmospheric particulate matter, an embodiment of the present disclosure further provides a method for determining an abnormal area for monitoring atmospheric particulate matter. Figure 3 3 is a schematic diagram of the structure of the apparatus 300 for determining an abnormal area of ​​atmospheric particulate matter monitoring provided by an embodiment of the present disclosure. Figure 3 As shown, the apparatus 300 for determining an abnormal area for monitoring atmospheric particulate matter includes an area screening unit 301 , a terrain data determining unit 302 , an adjacent area selecting unit 303 , a deviation calculating unit 304 and a result determining unit 305 .

[0124] The area screening unit 301 is used to preliminarily screen 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 of multiple consecutive time periods is lower than the screening value.

[0125] The terrain data determination unit 302 is used to determine the directly adjacent monitoring area according to the location coordinates of the target monitoring area, and obtain the 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.

[0126] The adjacent area selection unit 303 is used to determine whether the directly adjacent monitoring area and the target monitoring area constitute a pollution transmission area based on the terrain feature data. The pollution transmission area is an area where atmospheric particulate matter can be transmitted and diffused internally; when the terrain feature data indicates that the target monitoring area and the directly adjacent monitoring area do not have a terrain that blocks the transmission of pollutants, the two constitute a pollution transmission area.

[0127] The deviation calculation unit 304 is used to, when 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 the selected adjacent monitoring area, and 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.

[0128] The result determination unit 305 is used to determine that the target monitoring area is a monitoring abnormality area when the relative concentration deviation is greater than a preset deviation threshold.

[0129] In some embodiments, the apparatus 300 for determining an abnormal area for atmospheric particulate matter monitoring further includes a meteorological data acquisition unit. The meteorological data acquisition unit acquires rain and snow meteorological data between the target monitoring area and the directly adjacent monitoring area or the directly adjacent monitoring area; when the rain and snow meteorological data is data representing non-rain and snow weather, the adjacent area selection unit 303 determines whether the directly adjacent monitoring area and the target monitoring area constitute a pollution transmission area according to the terrain feature data.

[0130] In some embodiments, the meteorological data acquisition unit is also used to obtain the wind speed and wind direction in the pollution transmission area within at least one time period. When the wind speed is greater than the 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 in at least one time period, the adjacent area selection unit 303 determines the upwind monitoring area and the downwind monitoring area in each time period in the selected adjacent monitoring area according to the wind direction, the position coordinates of the target monitoring area and the position coordinates of the selected adjacent monitoring area; correspondingly, the deviation calculation unit 304 calculates the relative concentration 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 within at least one time period.

[0131] In some embodiments, the deviation calculation unit 304 determines, for each time period within 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 particle diffusion model based on the concentration monitoring data and position coordinates, wind speed and wind direction of the upwind monitoring area, and the position coordinates of the target monitoring area in the first corresponding time period, and calculates second prediction data according to the particle diffusion model based on the concentration monitoring data and position coordinates, wind speed and direction, and the position coordinates of the downwind monitoring area in the second corresponding time period; 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; and then calculates the concentration relative deviation based on the single time period relative deviation 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 the mean of the monitoring data of the corresponding selected adjacent monitoring area in multiple monitoring time periods based on each time period within at least one time period; calculates the single-time period relative deviation based on the monitoring data of the target monitoring area in each time period and the corresponding monitoring data mean; calculates the concentration relative deviation based on the single-time period relative deviation 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 the baseline concentration monitoring data obtained by the baseline monitoring equipment for atmospheric particulate matter monitoring in the target monitoring area, and obtains the verification concentration monitoring data obtained by the automatic monitoring equipment located at the monitoring point for atmospheric particulate matter monitoring during the same period; then, based on the baseline concentration monitoring data and the verification concentration monitoring data, it is verified whether the target monitoring area is an abnormal monitoring area.

[0134] In some embodiments, the area screening unit 301 calculates the mean of the concentration monitoring data of each atmospheric particulate matter monitoring area under non-rain and snow meteorological conditions and for multiple consecutive time periods to obtain a sliding window mean; when the sliding window mean is less than the screening value, the corresponding atmospheric particulate matter monitoring area is determined as the target monitoring area.

[0135] In some embodiments, the atmospheric particulate matter monitoring area is a monitoring point; the area screening unit 301 obtains the equipment status identification of the monitoring equipment in each atmospheric particulate matter monitoring area; when the sliding window mean is less than the preset screening value and the corresponding monitoring equipment identification is a normal operating identification, the area screening unit 301 determines that the corresponding atmospheric particulate matter monitoring area is the target monitoring area.

[0136] The embodiment of the present disclosure also provides a computing device for implementing the aforementioned method. Figure 4 Schematic diagram of the structure of the computing device provided by the embodiment of the present disclosure. Figure 4 , which shows a schematic diagram of the structure of a computing device 400 suitable for implementing the embodiments of the present disclosure. Figure 4 The computing device shown is only an example and should not bring any limitation to the functionality and scope of use of the embodiments of the present disclosure.

[0137] like Figure 4As shown, the computing device 400 may include a processing device (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 to a random access memory RAM 403. Various programs and data required for the operation of the computing device 400 are also stored in the RAM 403. The processing device 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output I / O interface 405 is also connected to the bus 404.

[0138] Typically, the following devices may be connected to the I / O interface 405: input devices 405 including, for example, a touch screen, a touch pad, a camera, a microphone, etc.; output devices 407 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 408 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 409. The communication devices 409 may allow the computing device 400 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 4 The computing device 400 is shown with various devices, 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 instead.

[0139] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 409, or installed from the storage device 408, or installed from the ROM 402. When the computer program is executed by the processing device 401, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0140] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable storage medium, a computer-readable signal medium, or any combination of the above two.

[0141] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0142] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer readable program codes. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than a computer readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0143] In some embodiments, the client and computing device can communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., communication network). Examples of communication networks include local area networks ("LAN"), wide area networks ("WAN"), internets (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 may be included in the computing device, or may exist independently without being incorporated into the computing device.

[0145] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the tester computer, partially on the tester computer, as a separate software package, partially on the tester computer and partially on a remote computer, or entirely on a remote computer or computing device. In cases involving a remote computer, the remote computer may be connected to the tester computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0146] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated system based on hardware that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0147] The units involved in the embodiments described in the present disclosure may be implemented by software or by hardware. The name of the unit does not, in some cases, constitute a limitation on the unit itself. The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0148] The above are only specific embodiments of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for determining an abnormal area of ​​atmospheric particulate matter monitoring, characterized in that: include: Conduct a 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; Determine a directly adjacent monitoring area according to the position 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 an area that has no intermediate monitoring area between it and the target monitoring area and is less than a preset distance from the target monitoring area; 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 in which atmospheric particulate matter can be transmitted and diffused; In the case where the terrain characteristic data indicates that the target monitoring area and the directly adjacent monitoring area do not have terrain that blocks pollutant transmission, the two constitute a pollution transmission area; In the case where 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 used as the selected 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; When the relative concentration deviation is greater than a preset deviation threshold, the target monitoring area is determined to be a monitoring abnormality area.

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

3. The determination method according to claim 2, characterized in that: The method further comprises: obtaining the wind speed and wind direction in the pollution transmission area within 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 in at least one time period, the method further includes: Determine an upwind monitoring area and a downwind monitoring area for each time period in the selected adjacent monitoring area according to the wind direction, the position coordinates of the target monitoring area and the position coordinates of the selected adjacent monitoring area; 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: calculating the relative concentration 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 within the at least one time period.

4. The determination method according to claim 3, characterized in that: Calculating the relative concentration 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 includes: For each time period within the at least one time period, determining a first corresponding time period of an upwind monitoring area and a second corresponding time period of a downwind monitoring area according to the wind speed; The first prediction data is calculated according to the particle diffusion model based on the concentration monitoring data and position coordinates, wind speed and wind direction of the upwind monitoring area in the first corresponding period, and the position coordinates of the target monitoring area; the second prediction data is calculated according to the particle diffusion model based on the concentration monitoring data and position coordinates, wind speed and direction of the downwind monitoring area in the second corresponding period, and the position coordinates of the target monitoring area; Calculate the single-period relative deviation based on the first prediction data, the second prediction data of 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 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, calculating the mean value of the monitoring data of the corresponding selected adjacent monitoring area in multiple monitoring time periods; Calculate the single-period relative deviation based on the monitoring data of the target monitoring area in each time period and the corresponding mean value of the monitoring data; The relative concentration deviation is calculated based on the single-period relative deviation of each period.

6. The determination method according to any one of claims 1 to 5, characterized in that: The target monitoring area is a monitoring point; After determining that the target monitoring area is a monitoring abnormality area, the method further includes: Obtaining baseline concentration monitoring data obtained by a baseline monitoring device monitoring atmospheric particulate matter in the target monitoring area, and obtaining verification concentration monitoring data obtained by an automatic monitoring device located at the monitoring point monitoring atmospheric particulate matter during the same period; 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.

7. The determination method according to any one of claims 1 to 5, characterized in that: Conduct a preliminary screening of the concentration monitoring data of each atmospheric particulate matter monitoring area to determine the target monitoring area, including: The mean of the concentration monitoring data of each atmospheric particulate matter monitoring area under non-rain and snow meteorological conditions and for multiple consecutive periods is calculated to obtain the sliding window mean; When the sliding window mean is less than the screening value, the corresponding atmospheric particulate matter monitoring area is determined as the target monitoring area.

8. The determination method according to claim 7, characterized in that: The atmospheric particulate matter monitoring area is a monitoring point; the method further includes: Obtain the equipment status identification of the monitoring equipment in each atmospheric particulate matter monitoring area; When the sliding window mean is less than a preset screening value, determining the corresponding atmospheric particulate matter monitoring area as the target monitoring area includes: when the sliding window mean is less than a preset screening value and the corresponding monitoring equipment identifier is a normal operating identifier, determining the corresponding atmospheric particulate matter monitoring area as the target monitoring area.

9. A device for determining abnormal areas of atmospheric particulate matter monitoring, characterized in that: include: The regional screening unit is used to preliminarily screen the concentration monitoring data of each atmospheric particulate matter monitoring area and 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; a terrain data determination unit, configured to determine a directly adjacent monitoring area according to the position coordinates of the target monitoring area, and to obtain terrain feature data between the target monitoring area and the directly adjacent monitoring area; the directly adjacent monitoring area is an area having no intermediate monitoring area between the target monitoring area and the target monitoring area, and having a distance from the target monitoring area less than a preset distance; an adjacent area selection unit, configured 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 in which atmospheric particulate matter can be transmitted and diffused; In the case where the terrain characteristic data indicates that the target monitoring area and the directly adjacent monitoring area do not have terrain that blocks pollutant transmission, the two constitute a pollution transmission area; a deviation calculation unit, for, when it is determined that the directly adjacent monitoring area and the target monitoring area constitute a pollution transmission area, taking the directly adjacent monitoring area as a selected adjacent monitoring area, and calculating a relative concentration deviation based on 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 that the target monitoring area is a monitoring abnormality area when the relative concentration deviation is greater than a preset deviation threshold.

10. A computing device, characterized in that It comprises a processor and a memory, wherein the memory is used to store a computer program; when the computer program is loaded by the processor, the processor executes the method for determining an abnormal area of ​​atmospheric particulate matter monitoring as claimed in any one of claims 1 to 8.

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