Abnormal emission identification method and system based on upwind monitoring data comparison

Abstract

The present application discloses a method and system for identifying abnormal emissions based on comparison of upwind monitoring data, the method comprising: acquiring emission identification monitoring data, and performing time correspondence thereto to obtain monitoring-related data for different time periods; eliminating all monitoring-related data affected by meteorological factors in the emission identification monitoring data to obtain first monitoring statistical data; eliminating all monitoring-related data with local stable and continuous high values ​​in the first monitoring statistical data based on the concentration comparison results at various directions and at different time periods to obtain second monitoring statistical data; eliminating all monitoring-related data affected by upwind pollution transmission in the second monitoring statistical data based on the concentration comparison results at various directions and at different time periods to obtain third monitoring statistical data; and performing statistical analysis on the proportion of abnormal emissions of various types of pollutants in various time periods under various wind directions at various directions based on the third monitoring statistical data to obtain the abnormal emission direction and concentrated emission time period of each type of pollutant.

Description

Technical Field

[0001] The present application relates to the technical field of atmospheric pollution monitoring, and in particular to a method and system for identifying abnormal emissions based on comparison of upwind monitoring data. Background Art

[0002] At present, in the work of air pollution prevention and control supervision, when the concentration of pollutants reaches a high value, the supervision department mainly uses three methods to identify whether there are abnormal emissions in the local area. The first is to rely on staff to verify the enterprises around the air quality monitoring station, conduct on-site inspections of the production conditions and operation of pollution control facilities of the enterprises, or determine whether the enterprises have abnormal emissions through on-site monitoring. This method has many on-site verification procedures and it is difficult to grasp as many emission sources as possible in a short period of time; the second is to use a cruise monitoring vehicle to conduct cruise monitoring around the station to identify high-emission areas, and its monitoring investment cost is relatively high; the third is to query and analyze the historical data of online monitoring of pollution sources to identify whether there are pollutants that exceed the emission standards, but the installation rate and networking rate of existing online equipment for air pollution sources are not high, and the key pollution sources that are installed and networked are also found to have problems such as irregular operation and maintenance of monitoring equipment and distortion and inaccuracy of online data during daily work inspections. It can be seen that the abnormal emission identification methods in the existing technology have high requirements for on-site manpower and material resources, and the identification efficiency and accuracy are low. Summary of the Invention

[0003] The present application provides a method and system for identifying abnormal emissions based on comparison of upwind monitoring data, which solves the problems in the existing technology of abnormal pollutant emission monitoring methods that have high requirements for manpower, material and financial resources, and are also limited by the current problems of low installation and networking rate of online pollutant emission monitoring equipment in industrial enterprises, irregular operation and maintenance of monitoring equipment, and distortion and inaccuracy of emission monitoring data. It can quickly analyze and identify local abnormal emissions, and identify the direction and concentrated time period of abnormal emissions, so as to facilitate accurate policy implementation.

[0004] The specific technical solutions are as follows:

[0005] In a first aspect, an embodiment of the present application provides a method for identifying abnormal emissions based on comparison of upwind monitoring data, the abnormal emissions identification method comprising:

[0006] Determine a region to be identified and multiple comparison regions of the region to be identified; wherein the multiple comparison regions are regions in different directions relative to the region to be identified;

[0007] Acquiring emission identification monitoring data, the emission identification monitoring data including local hourly meteorological data of the area to be identified, local hourly air quality monitoring data of the area to be identified, and comparative hourly air quality monitoring data of each of the comparison areas, and performing time correspondence on the local hourly meteorological data, the local hourly air quality monitoring data, and the plurality of comparative hourly air quality monitoring data to obtain monitoring correlation data for different time periods;

[0008] Eliminating all monitoring-related data affected by meteorological factors from the emission identification monitoring data to obtain first monitoring statistical data;

[0009] Classify the local hourly air quality monitoring data in the first monitoring statistical data based on wind direction to obtain local pollutant concentration data in the wind direction of each direction; obtain pollutant concentration data in the upwind area of ​​each direction based on the comparative hourly air quality monitoring data of the multiple comparison areas; compare the local pollutant concentration data in the same time period in each direction with the corresponding pollutant concentration data in the upwind area to obtain concentration comparison results in different time periods in each direction;

[0010] According to the concentration comparison results at different locations and time periods, all the monitoring-related data with local stable and continuous high values ​​in the first monitoring statistical data are eliminated to obtain second monitoring statistical data;

[0011] Eliminate all monitoring-related data affected by upwind pollution transmission from the second monitoring statistical data based on the concentration comparison results at various locations and in different time periods to obtain third monitoring statistical data;

[0012] Based on the third monitoring statistical data, a statistical analysis is performed on the proportion of abnormal emissions of various types of pollutants in various wind directions and time periods to obtain the abnormal emission directions and concentrated emission time periods of each type of pollutant.

[0013] In some embodiments of the present application, removing all monitoring-related data affected by meteorological factors from the emission identification monitoring data to obtain first monitoring statistical data specifically includes:

[0014] According to the local hourly meteorological data, all high wind periods with wind force not less than a preset wind force level threshold are obtained, and the monitoring-related data corresponding to all the high wind periods in the emission identification monitoring data are eliminated to obtain the first monitoring statistical data.

[0015] In some embodiments of the present application, the local pollutant concentration data and the corresponding upwind area pollutant concentration data in the same time period at each direction are compared to obtain concentration comparison results at different time periods at each direction, specifically including:

[0016] The local pollutant concentration data in the same time period at each direction and the corresponding upwind area pollutant concentration data are subjected to difference calculation processing to calculate the concentration difference at different time periods at each direction as the concentration comparison result.

[0017] In some embodiments of the present application, the second monitoring statistical data is obtained by eliminating all the monitoring-related data with local stable and continuous high values ​​from the first monitoring statistical data based on the concentration comparison results at various locations and different time periods, specifically including:

[0018] Calculate the average value and standard deviation of the concentration difference between the local pollutant concentration data and the corresponding upwind area pollutant concentration data in the same time period at each direction;

[0019] The monitoring-related data in which all the concentration difference values ​​in the first monitoring statistical data are higher than the sum of the average value and the leveling deviation are obtained as the second monitoring statistical data.

[0020] In some embodiments of the present application, the third monitoring statistical data is obtained by removing all the monitoring-related data affected by upwind pollution transmission from the second monitoring statistical data based on the concentration comparison results at different directions and time periods, specifically including:

[0021] Calculate the Pearson median skewness of the concentration difference between the local pollutant concentration data and the corresponding upwind regional pollutant concentration data in the same time period at each direction;

[0022] The monitoring-related data in which the Pearson median skewness is greater than 0 in the second monitoring statistical data is obtained as the third monitoring statistical data.

[0023] In some embodiments of the present application, the third monitoring statistical data is used to perform a statistical analysis on the proportion of abnormal emissions of various types of pollutants in each direction and at each time period, to obtain the direction and concentrated emission period of each type of pollutant, specifically including:

[0024] According to the third monitoring statistical data, the proportion of abnormal emissions of each type of pollutant in each azimuth wind direction and each time period is obtained, and the proportion of abnormal emissions of each type of pollutant in each azimuth wind direction and each time period is compared, and the azimuth wind direction with a higher proportion of abnormal emissions of each type of pollutant is obtained as the direction of abnormal emissions, and the time period with a higher proportion of abnormal emissions of each type of pollutant is obtained as the concentrated emission period.

[0025] In a second aspect, an embodiment of the present application provides an abnormal emission identification system based on comparison of upwind monitoring data, the abnormal emission identification system comprising:

[0026] An area determination module is used to determine an area to be identified and multiple comparison areas of the area to be identified; wherein the multiple comparison areas are respectively areas in different directions relative to the area to be identified;

[0027] a data acquisition module, configured to acquire emission identification monitoring data, the emission identification monitoring data including local hourly meteorological data of the area to be identified, local hourly air quality monitoring data of the area to be identified, and comparative hourly air quality monitoring data of each of the comparison areas, and to perform time correspondence between the local hourly meteorological data, the local hourly air quality monitoring data, and the plurality of comparative hourly air quality monitoring data to obtain monitoring-related data for different time periods;

[0028] A meteorological factor elimination module is used to eliminate all the monitoring-related data affected by meteorological factors from the emission identification monitoring data to obtain first monitoring statistical data;

[0029] a classification and comparison module for classifying the local hourly air quality monitoring data in the first monitoring statistical data based on wind direction and azimuth to obtain local pollutant concentration data in the wind direction of each azimuth; obtaining pollutant concentration data in the upwind area of ​​each azimuth based on the compared hourly air quality monitoring data of the plurality of comparison areas; and comparing the local pollutant concentration data in the same time period in each azimuth with the corresponding pollutant concentration data in the upwind area to obtain concentration comparison results in different time periods in each azimuth;

[0030] A local high-value screening module is used to eliminate all the monitoring-related data with local stable and continuous high values ​​from the first monitoring statistical data based on the concentration comparison results at various locations and different time periods, to obtain second monitoring statistical data;

[0031] an upwind pollution elimination module, configured to eliminate, from the second monitoring statistical data, all the monitoring-related data affected by upwind pollution transmission based on the concentration comparison results at various directions and in different time periods, to obtain third monitoring statistical data;

[0032] The proportion analysis module is used to perform statistical analysis on the proportion of abnormal emissions of various types of pollutants in various wind directions and time periods based on the third monitoring statistical data, and obtain the abnormal emission direction and concentrated emission period of each type of pollutant.

[0033] In some embodiments of the present application, the classification comparison module compares the local pollutant concentration data in the same time period at each direction and the corresponding upwind area pollutant concentration data to obtain concentration comparison results at different time periods at each direction, specifically for:

[0034] The local pollutant concentration data in the same time period at each direction and the corresponding upwind area pollutant concentration data are subjected to difference calculation processing to calculate the concentration difference at different time periods at each direction as the concentration comparison result.

[0035] In some embodiments of the present application, the local high-value screening module removes all the monitoring-related data with local stable and continuous high values ​​from the first monitoring statistical data based on the concentration comparison results at various locations and different time periods to obtain second monitoring statistical data, which is specifically used to:

[0036] Calculate the average value and standard deviation of the concentration difference between the local pollutant concentration data and the corresponding upwind area pollutant concentration data in the same time period at each direction;

[0037] The monitoring-related data in which all the concentration difference values ​​in the first monitoring statistical data are higher than the sum of the average value and the leveling deviation are obtained as the second monitoring statistical data.

[0038] In some embodiments of the present application, the upwind pollution elimination module removes all the monitoring-related data affected by upwind pollution transmission from the second monitoring statistical data based on the concentration comparison results at various directions and different time periods to obtain third monitoring statistical data, which is specifically used to:

[0039] Calculate the Pearson median skewness of the concentration difference between the local pollutant concentration data and the corresponding upwind regional pollutant concentration data in the same time period at each direction;

[0040] The monitoring-related data in which the Pearson median skewness is greater than 0 in the second monitoring statistical data is obtained as the third monitoring statistical data.

[0041] In a third aspect, an embodiment of the present application provides an abnormal emission identification device based on comparison of upwind monitoring data, comprising: a processor, a memory, and a computer program stored in the memory. When the processor executes the computer program, it executes the abnormal emission identification method based on comparison of upwind monitoring data as described in the first aspect.

[0042] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program, which can be executed by a processor to complete the abnormal emission identification method based on upwind monitoring data comparison as described in the first aspect.

[0043] The innovative features of the embodiments of this application include but are not limited to the following:

[0044] 1. Calculate the mean and standard deviation of the local and upwind pollutant concentration difference samples in all wind directions, and determine that the data with concentration difference values ​​within the sum of the mean and standard deviation are stable and continuously high values, and exclude them from statistics.

[0045] 2. The impact of pollution transmission is initially eliminated by excluding concentration data during periods of wind force 5 or above. Then, the skewness of the difference samples of local and upwind pollutant concentrations under various wind directions is calculated to further eliminate the impact of upwind pollution transmission.

[0046] The beneficial effects of the embodiments of the present application are as follows:

[0047] Focusing on the difference in pollutant concentrations between local and upwind areas, the normal distribution probability density function is used to calculate the mean, standard deviation, skewness and other parameters of the concentration difference samples, and quantitatively evaluate the proportion of abnormal emissions of various pollutants in various wind directions. It can not only monitor and identify local abnormal emissions, but also identify the main pollution transmission directions and concentrated periods of local abnormal emissions of various pollutants. While providing a decision-making basis for formulating the direction of local site control, it can also provide a basis for regional joint prevention and control, thereby more comprehensively reducing the impact of pollutant emissions on local air quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] To more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be derived from these drawings without inventive effort.

[0049] Figure 1 A flow chart of a method for identifying abnormal emissions based on comparison of upwind monitoring data provided in an embodiment of the present application;

[0050] Figure 2 A schematic diagram of the components of an abnormal emission identification system based on upwind monitoring data comparison provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0052] It should be noted that, in the absence of conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The terms "including" and "having" in the embodiments of this application and the accompanying drawings, as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0053] The embodiment of the present application discloses a method for identifying abnormal emissions based on comparison of upwind monitoring data. It is mainly applicable to scenarios where abnormal pollutant emissions are identified and monitored. It is based on the current relatively complete air quality monitoring network and is associated with meteorological data. It performs systematic and scientific logical processing and mathematical operations on local and upwind pollutant concentration monitoring data, excluding stable and continuous high values ​​as much as possible while minimizing the impact of long-distance transmission. It then analyzes and identifies local abnormal pollutant emissions, and identifies the direction of local abnormal emissions and the period of concentrated pollutant emissions. Detailed explanations are given below.

[0054] Figure 1 The following shows an abnormal emission identification method based on upwind monitoring data comparison according to an embodiment of the present application. Figure 1 As shown, the abnormal emission identification method includes the following steps:

[0055] Step S100: determining a region to be identified and a plurality of comparison regions of the region to be identified.

[0056] In the embodiments of this application, the "area to be identified" refers to the local area (i.e., the local site) where abnormal pollutant emissions need to be identified and monitored. This abnormal emissions identification method uses various azimuth regions relative to the area to be identified as comparison regions. Specifically, based on the spatial geographic location, the distribution of other azimuth regions (sites) relative to the area to be identified is determined. The azimuths include east, southeast, south, southwest, west, northwest, north, and northeast.

[0057] Step S200, obtaining emission identification monitoring data, the emission identification monitoring data includes local hourly meteorological data of the area to be identified, local hourly air quality monitoring data of the area to be identified, and comparative hourly air quality monitoring data of each comparison area, and performing time correspondence on the local hourly meteorological data, local hourly air quality monitoring data and multiple comparative hourly air quality monitoring data to obtain monitoring correlation data of different time periods.

[0058] In some embodiments, local hourly air quality monitoring data (i.e., local hourly air quality monitoring data) and hourly air quality monitoring data for each comparison area (i.e., comparative hourly air quality monitoring data) are obtained through an air quality data publishing platform (e.g., the China National Environmental Monitoring Center). The hourly air quality monitoring data includes, but is not limited to, hourly concentrations of SO2, NO2, and CO. Additionally, local hourly meteorological data (i.e., local hourly meteorological data) are obtained through a meteorological data publishing platform (e.g., the China Meteorological Data Network). The hourly meteorological data includes, but is not limited to, hourly wind direction and wind speed data. Furthermore, the hourly wind direction and wind speed data are correlated with the hourly pollutant concentration data to obtain monitoring correlation data for different time periods, facilitating subsequent data processing and analysis. It should be noted and understood that, in this application, the length of each time period can be set according to specific needs and can be one hour or half an hour, and this application does not impose any restrictions on this. Furthermore, the local hourly air quality monitoring data and local hourly meteorological data both refer to relevant data for stations within the area to be identified. Similarly, the multiple comparative hourly air quality monitoring data all refer to relevant data for stations within each azimuth area.

[0059] Step S300: Eliminate all monitoring-related data affected by meteorological factors from the emission identification monitoring data to obtain first monitoring statistical data.

[0060] In an embodiment of the present application, the abnormal emission identification method excludes relevant data affected by meteorological factors from the monitoring and statistical data used for abnormal pollutant emission identification, so as to eliminate the influence of meteorological factors on the statistical probability of pollutant outliers. Specifically, based on the local hourly meteorological data, all windy periods with wind speeds not less than a preset wind level threshold are obtained, and the monitoring-related data corresponding to all windy periods in the emission identification monitoring data are eliminated to obtain the first monitoring statistical data. This eliminates the situation where pollutants from the upwind direction are transmitted to the local area when the wind direction is strong in a certain direction, thereby causing the local hourly pollutant concentration to be high, and eliminates the influence of the occurrence of windy weather under different wind directions on the statistical probability of local pollutant outliers.

[0061] In some specific embodiments, wind speed is significantly negatively correlated with pollutant concentration. The greater the wind speed, the more conducive it is to the diffusion of local pollution, but it also makes the local area susceptible to upwind pollution transmission. Combined with the analysis case of a certain place, primary atmospheric pollutants such as SO2, NO2, and CO have short-term high values ​​when the wind speed is higher than 8m / s (corresponding to wind speed higher than level 5), indicating that the high pollution value at this time is mainly affected by upwind pollution transmission. Therefore, in order to effectively eliminate the transmission effect and ensure the accuracy of the local abnormal emission identification results, the preset wind force level threshold is level 5, that is, the data corresponding to the time period when the wind force reaches level 5 and above (not less than 8m / s) is deleted and not included in the statistical analysis range, thereby preliminarily eliminating the impact of pollution transmission and improving the accuracy of abnormal emission identification monitoring results.

[0062] Step S400, classify and process the local hourly air quality monitoring data in the first monitoring statistical data based on the wind direction, obtain the local pollutant concentration data under the wind direction of each direction, obtain the pollutant concentration data of the upwind area of ​​each direction based on the comparative hourly air quality monitoring data of multiple comparison areas, compare the local pollutant concentration data in the same time period under each direction and its corresponding upwind area pollutant concentration data, and obtain the concentration comparison results under each direction and different time periods.

[0063] In an embodiment of the present application, the abnormal emission identification method classifies the pollutant concentration data for wind speeds below level five (wind speeds less than 8 m / s) retained in step S300 above. Specifically, based on the wind direction, the local pollutant concentration data is divided into eight categories of data corresponding to the eight wind directions of east, southeast, south, southwest, west, northwest, north, and northeast, thereby completing the classification of the first monitoring statistical data initially included in the statistical analysis. Furthermore, the abnormal emission identification method compares the local and upwind pollutant concentration levels to further determine abnormal pollutant emissions based on the concentration comparison results. Specifically, the local pollutant concentration data for the same time period at each direction and the pollutant concentration data for the upwind area (station) corresponding to the wind direction are subjected to a difference operation, that is, the two data are subtracted to calculate the concentration difference at different time periods at each direction, and the concentration difference is used as the concentration comparison result to determine whether the local pollutant concentration is abnormally higher than that of the surrounding area (station).

[0064] Step S500 , based on the concentration comparison results at various locations and in different time periods, all the monitoring-related data with local stable and continuous high values ​​in the first monitoring statistical data are eliminated to obtain the second monitoring statistical data.

[0065] A "stable, persistent local high" refers to a situation where, due to the local industrial structure and coal-based energy mix, pollutant background concentrations are high. Even under normal circumstances, when pollution sources meet emission standards, pollutant concentrations remain elevated for extended periods, exceeding those in surrounding areas (sites). In this embodiment, by excluding stable, persistent local high values, the accuracy of the monitoring results for identifying abnormal local pollutant emissions is further improved.

[0066] Specifically, the mean and standard deviation of the concentration difference between the local pollutant concentration data and the corresponding upwind pollutant concentration data for the same time period at each azimuth are calculated. All monitoring-related data in the first monitoring statistical data whose concentration difference exceeds the sum of the mean and the mean deviation are obtained as the second monitoring statistical data. This method effectively eliminates stable and persistent local high values ​​from the first monitoring statistical data by introducing a mathematical operation on the local and upwind pollutant concentration differences for each azimuth wind direction. The mathematical operation involves calculating the mean (mean) and standard deviation (SD) of the concentration differences. The mean reflects the central tendency of the concentration difference data between the local and upwind pollutant concentrations, while the standard deviation reflects the dispersion (degree of deviation from the mean concentration difference) of the concentration difference data between the local and upwind pollutant concentrations. In this embodiment, data with a concentration difference between the local and upwind pollutant concentrations within the sum of the mean and standard deviation (mean + SD) are determined to be stable and persistent local high values ​​and are excluded from subsequent statistics. Instead, only data with a concentration difference exceeding the mean + SD are counted. The local pollutant concentration data for all corresponding time periods are preliminarily determined to be abnormally high values ​​and recorded as the second monitoring statistical data.

[0067] Step S600 , based on the concentration comparison results at different directions and different time periods, all monitoring-related data affected by upwind pollution transmission in the second monitoring statistical data are eliminated to obtain third monitoring statistical data.

[0068] Because cross-regional transmission of pollutants is an objective reality, during atmospheric pollution transmission, even if pollutant concentrations in upwind areas are lower than in downwind areas, the pollutants emitted will be transported to the downwind areas due to horizontal atmospheric movement. To address this phenomenon, the abnormal emission identification method in the embodiments of this application eliminates the influence of upwind pollution transmission, thereby further improving the accuracy of the local abnormal pollutant emission identification monitoring results.

[0069] Specifically, the Pearson median skewness of the concentration difference between the local pollutant concentration data and the corresponding upwind pollutant concentration data for the same time period at each azimuth is calculated. The monitoring-related data with a Pearson median skewness greater than 0 in the second monitoring statistical data are obtained as the third monitoring statistical data. This means that by introducing the Pearson median skewness calculation for the difference between the local and upwind pollutant concentrations for each azimuth wind direction, the influence of upwind pollution transmission is effectively eliminated. The Pearson median skewness measures the direction and degree of skewness in the statistical data distribution and is a characteristic number used to characterize the degree of asymmetry of the probability distribution density curve relative to the mean. A skewness greater than 0 is positively skewed (right-skewed), while a skewness less than 0 is negatively skewed (left-skewed). In this embodiment, when the skewness is negative, it is determined that the high local pollutant concentration is affected to some extent by upwind pollution transmission. Therefore, it is not determined as abnormal emissions and is eliminated. Only the data with positive skewness are counted as the final statistical analysis data of the abnormal emission identification method, recorded as the third monitoring statistical data.

[0070] Step S700 , performing statistical analysis on the abnormal emission proportions of various types of pollutants in various wind directions and time periods based on the third monitoring statistical data, to obtain the abnormal emission direction and concentrated emission period of each type of pollutant.

[0071] Through the above steps S500 and S600, after excluding the influence of stable and continuous local high values ​​and upwind pollution transmission, the proportion of abnormal high values ​​of emissions under positive deviation is counted and recorded as the abnormal emission proportion of a certain pollutant in the azimuth wind direction, and the proportion of abnormal high values ​​of emissions in each time period is counted and recorded as the abnormal emission proportion of a certain pollutant in different time periods. That is, according to the third monitoring statistical data, the abnormal emission proportion of each type of pollutant in each azimuth wind direction and each time period is obtained. Afterwards, the abnormal emission proportion of each type of pollutant in each azimuth wind direction and each time period is compared to obtain the azimuth wind direction with a higher proportion of abnormal pollutant emissions for each type of pollutant as the direction of abnormal emissions, and at the same time, obtain the time period with a higher proportion of abnormal pollutant emissions for each type of pollutant as the concentrated emission period, thereby identifying the main direction and concentrated period of abnormal emissions of a certain pollutant, and use the obtained abnormal emission direction and concentrated emission period as the key direction and period of local supervision.

[0072] However, it should be noted and understood that "higher" in this application is a relative concept. The number of "abnormal emission directions" and "concentrated emission time periods" selected can be set according to specific needs, and the first few azimuth wind directions and time periods arranged in order from high to low can be directly selected. The abnormal emission proportion range can also be set according to specific needs, and the azimuth wind direction and time period where the proportion of abnormal pollutant emissions exceeds the preset proportion threshold can be selected.

[0073] Corresponding to the above method embodiment, the present application embodiment also provides an abnormal emission identification system based on upwind monitoring data comparison, which is used to execute the abnormal emission identification method steps based on upwind monitoring data comparison in the above embodiment. Figure 2 As shown, the abnormal emission identification system includes: an area determination module 1, a data acquisition module 2, a meteorological factor exclusion module 3, a classification comparison module 4, a local high value screening module 5, an upwind pollution exclusion module 6 and a proportion analysis module 7.

[0074] Specifically, the region determination module 1 is used to determine the region to be identified and multiple comparison regions of the region to be identified, wherein the multiple comparison regions are regions in different directions relative to the region to be identified.

[0075] The data acquisition module 2 is used to obtain emission identification monitoring data, which includes local hourly meteorological data of the area to be identified, local hourly air quality monitoring data of the area to be identified, and comparative hourly air quality monitoring data of each comparison area. The local hourly meteorological data, local hourly air quality monitoring data and multiple comparative hourly air quality monitoring data are time-correlated to obtain monitoring related data of different time periods.

[0076] The meteorological factor elimination module 3 is used to eliminate all monitoring-related data affected by meteorological factors in the emission identification monitoring data to obtain first monitoring statistical data.

[0077] The classification comparison module 4 is used to classify the local hourly air quality monitoring data in the first monitoring statistical data based on wind direction and azimuth, obtain local pollutant concentration data in the wind direction of each azimuth, obtain pollutant concentration data in the upwind area of ​​each azimuth based on the compared hourly air quality monitoring data of multiple comparison areas, compare the local pollutant concentration data in the same time period in each azimuth and its corresponding upwind area pollutant concentration data, and obtain concentration comparison results for different time periods in each azimuth. Further, the classification comparison module 4 is specifically used to perform difference calculation processing on the local pollutant concentration data in the same time period in each azimuth and its corresponding upwind area pollutant concentration data, and calculate the concentration difference in different time periods in each azimuth as the concentration comparison result.

[0078] The local high value screening module 5 is used to eliminate all monitoring-related data with stable and persistent high local values ​​from the first monitoring statistical data based on the concentration comparison results at each location and in different time periods, thereby obtaining second monitoring statistical data. Furthermore, the local high value screening module 5 is specifically used to calculate the average and standard deviation of the concentration difference between the local pollutant concentration data and the corresponding upwind area pollutant concentration data for the same time period at each location, and specifically to obtain all monitoring-related data in the first monitoring statistical data whose concentration difference is greater than the sum of the average value and the mean deviation, as the second monitoring statistical data.

[0079] Upwind pollution elimination module 6 is configured to eliminate all monitoring-related data affected by upwind pollution transmission from the second monitoring statistical data based on the concentration comparison results at each azimuth and at different time periods, thereby obtaining third monitoring statistical data. Furthermore, upwind pollution elimination module 6 is configured to calculate the Pearson median skewness of the concentration difference between the local pollutant concentration data and the corresponding upwind regional pollutant concentration data at each azimuth and within the same time period, and to obtain monitoring-related data from the second monitoring statistical data for which the Pearson median skewness is greater than 0 as the third monitoring statistical data.

[0080] The proportion analysis module 7 is used to perform statistical analysis on the proportion of abnormal emissions of various types of pollutants in various wind directions and time periods based on the third monitoring statistical data, and obtain the abnormal emission direction and concentrated emission period of each type of pollutant.

[0081] It should be noted that the abnormal emission identification system based on upwind monitoring data comparison provided in the embodiment of the present application is based on the same concept as the abnormal emission identification method embodiment based on upwind monitoring data comparison in the present application, and the technical effect it brings is the same as the abnormal emission identification method embodiment based on upwind monitoring data comparison in the present application. For specific contents, please refer to the description in the abnormal emission identification method embodiment based on upwind monitoring data comparison in the present application, and no further details will be given here.

[0082] Embodiments of the present application also provide an apparatus for identifying abnormal emissions based on upwind monitoring data comparison, comprising: a processor, a memory, and a computer program stored in the memory. When the processor executes the computer program, the steps of the method for identifying abnormal emissions based on upwind monitoring data comparison described in the above embodiment are implemented. Alternatively, when the processor executes the computer program, the functions of each module in the system for identifying abnormal emissions based on upwind monitoring data comparison described in the above embodiment are implemented.

[0083] In a specific embodiment, the computer program can be divided into one or more modules, one or more modules are stored in a memory and executed by a processor to complete the embodiment of the present application. One or more modules can be a series of computer program instruction segments that can perform specific functions, and the instruction segments are used to describe the execution process of the computer program in the abnormal emission identification device based on upwind monitoring data comparison. For example, the computer program can be divided into a region determination module, a data acquisition module, a meteorological factor exclusion module, a classification comparison module, a local high value screening module, an upwind pollution exclusion module, and a proportion analysis module. The specific functions of each module are as follows:

[0084] The region determination module is used to determine the region to be identified and multiple comparison regions of the region to be identified; wherein the multiple comparison regions are regions in various directions relative to the region to be identified;

[0085] The data acquisition module is used to obtain emission identification monitoring data, which includes local hourly meteorological data of the area to be identified, local hourly air quality monitoring data of the area to be identified, and comparative hourly air quality monitoring data of each comparison area, and the local hourly meteorological data, local hourly air quality monitoring data and multiple comparative hourly air quality monitoring data are time-correlated to obtain monitoring correlation data of different time periods;

[0086] The meteorological factor exclusion module is used to eliminate all monitoring-related data affected by meteorological factors from the emission identification monitoring data to obtain first monitoring statistical data;

[0087] The classification and comparison module is used to classify the local hourly air quality monitoring data in the first monitoring statistical data based on wind direction and azimuth, obtain local pollutant concentration data under the wind direction of each azimuth, obtain pollutant concentration data of the upwind area of ​​each azimuth based on the compared hourly air quality monitoring data of multiple comparison areas, compare the local pollutant concentration data of each azimuth within the same time period with its corresponding upwind area pollutant concentration data, and obtain concentration comparison results of different time periods at each azimuth;

[0088] The local high-value screening module is used to eliminate all local stable and continuous high-value monitoring-related data in the first monitoring statistical data based on the concentration comparison results in various directions and different time periods, and obtain the second monitoring statistical data;

[0089] The upwind pollution elimination module is used to eliminate all monitoring-related data affected by upwind pollution transmission from the second monitoring statistical data based on the concentration comparison results at various directions and different time periods, thereby obtaining third monitoring statistical data;

[0090] The proportion analysis module is used to perform statistical analysis on the proportion of abnormal emissions of various types of pollutants in various wind directions and time periods based on the third monitoring statistical data, and obtain the abnormal emission direction and concentrated emission period of each type of pollutant.

[0091] The abnormal emission identification device based on upwind monitoring data comparison can be a computing device such as a desktop computer, a laptop, a PDA, or a cloud management server. Those skilled in the art will appreciate that the abnormal emission identification device based on upwind monitoring data comparison can include, but is not limited to, a processor and a memory, and can also include more or fewer components, or a combination of certain components, or different components. For example, the abnormal emission identification device based on upwind monitoring data comparison can also include input and output devices, network access devices, buses, etc.

[0092] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or any conventional processor.

[0093] The memory can be an internal storage unit of the device for identifying abnormal emissions based on upwind monitoring data comparison, for example, the hard disk or memory of the device. The memory can also be an external storage device of the device for identifying abnormal emissions based on upwind monitoring data comparison, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital Card (SD card), a flash memory card, etc. equipped with the device. Furthermore, the memory can include both the internal storage unit of the device for identifying abnormal emissions based on upwind monitoring data comparison and an external storage device. The memory is used to store computer programs and other programs or data required by the device for identifying abnormal emissions based on upwind monitoring data comparison. The memory can also be used to temporarily store data that has been output or is about to be output.

[0094] In the above embodiments, the description of each embodiment has different emphases. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0095] Those skilled in the art will appreciate that the modules and algorithm steps of the various embodiments described in conjunction with the embodiments disclosed in this specification can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0096] In addition, an embodiment of the present invention further provides a computer-readable storage medium, which stores a computer program. The computer program can be executed by a processor to complete the abnormal emission identification method based on upwind monitoring data comparison as described in the above embodiment. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable storage medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), etc.

[0097] In summary, the present application discloses a method, system and device for identifying abnormal emissions based on comparison of upwind monitoring data. It mainly focuses on the difference in pollutant concentrations between local and upwind areas, uses the normal distribution probability density function, calculates the mean, standard deviation, skewness and other parameters of the concentration difference samples, and quantitatively evaluates the proportion of abnormal emissions of various pollutants in various wind directions. It can not only monitor and identify local abnormal emissions, but also identify the main pollution transmission directions and concentrated periods of local abnormal emissions of various pollutants. While providing a decision-making basis for formulating the control direction of local sites, it can also provide a basis for regional joint prevention and control, thereby more comprehensively reducing the impact of pollutant emissions on local air quality.

[0098] Those skilled in the art will appreciate that the accompanying drawings are merely schematic diagrams of an embodiment, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0099] Those skilled in the art will appreciate that the modules in the apparatuses of the embodiments may be distributed in the apparatuses of the embodiments as described in the embodiments, or may be located in one or more apparatuses different from the embodiments with corresponding changes. The modules in the above embodiments may be combined into one module or further divided into multiple sub-modules.

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying abnormal emissions based on comparison of upwind monitoring data, characterized in that: The abnormal emission identification method comprises: Determine a region to be identified and multiple comparison regions of the region to be identified; wherein the multiple comparison regions are regions in different directions relative to the region to be identified; Acquiring emission identification monitoring data, the emission identification monitoring data including local hourly meteorological data of the area to be identified, local hourly air quality monitoring data of the area to be identified, and comparative hourly air quality monitoring data of each of the comparison areas, and performing time correspondence on the local hourly meteorological data, the local hourly air quality monitoring data, and the plurality of comparative hourly air quality monitoring data to obtain monitoring correlation data for different time periods; Eliminating all monitoring-related data affected by meteorological factors from the emission identification monitoring data to obtain first monitoring statistical data; Classify the local hourly air quality monitoring data in the first monitoring statistical data based on wind direction to obtain local pollutant concentration data in the wind direction of each direction; obtain pollutant concentration data in the upwind area of ​​each direction based on the comparative hourly air quality monitoring data of the multiple comparison areas; compare the local pollutant concentration data in the same time period in each direction with the corresponding pollutant concentration data in the upwind area to obtain concentration comparison results in different time periods in each direction; According to the concentration comparison results at different locations and time periods, all the monitoring-related data with local stable and continuous high values ​​in the first monitoring statistical data are eliminated to obtain second monitoring statistical data; Eliminate all monitoring-related data affected by upwind pollution transmission from the second monitoring statistical data based on the concentration comparison results at various locations and in different time periods to obtain third monitoring statistical data; Based on the third monitoring statistical data, a statistical analysis is performed on the proportion of abnormal emissions of various types of pollutants in various wind directions and time periods to obtain the abnormal emission directions and concentrated emission time periods of each type of pollutant.

2. The abnormal emission identification method based on upwind monitoring data comparison according to claim 1 is characterized in that: Eliminating all the monitoring-related data affected by meteorological factors from the emission identification monitoring data to obtain first monitoring statistical data specifically includes: According to the local hourly meteorological data, all high wind periods with wind force not less than a preset wind force level threshold are obtained, and the monitoring-related data corresponding to all the high wind periods in the emission identification monitoring data are eliminated to obtain the first monitoring statistical data.

3. The abnormal emission identification method based on upwind monitoring data comparison according to claim 1 is characterized in that: The comparison of the local pollutant concentration data in the same time period at each direction and the corresponding upwind area pollutant concentration data is performed to obtain concentration comparison results at different time periods at each direction, specifically including: The local pollutant concentration data in the same time period at each direction and the corresponding upwind area pollutant concentration data are subjected to difference calculation processing to calculate the concentration difference at different time periods at each direction as the concentration comparison result.

4. The abnormal emission identification method based on upwind monitoring data comparison according to claim 3 is characterized in that: The second monitoring statistical data is obtained by eliminating all the monitoring-related data of local stable and continuously high values ​​from the first monitoring statistical data according to the concentration comparison results at various locations and different time periods, specifically including: Calculate the average value and standard deviation of the concentration difference between the local pollutant concentration data and the corresponding upwind area pollutant concentration data in the same time period at each direction; The monitoring-related data in which all the concentration difference values ​​in the first monitoring statistical data are higher than the sum of the average value and the standard deviation are obtained as the second monitoring statistical data.

5. The abnormal emission identification method based on upwind monitoring data comparison according to claim 3 is characterized in that: According to the concentration comparison results at different locations and time periods, all the monitoring-related data affected by upwind pollution transmission in the second monitoring statistical data are eliminated to obtain third monitoring statistical data, specifically including: Calculate the Pearson median skewness of the concentration difference between the local pollutant concentration data and the corresponding upwind regional pollutant concentration data in the same time period at each direction; The monitoring-related data in which the Pearson median skewness is greater than 0 in the second monitoring statistical data is obtained as the third monitoring statistical data.

6. The abnormal emission identification method based on upwind monitoring data comparison according to claim 1 is characterized in that: The third monitoring statistical data is used to statistically analyze the proportion of abnormal emissions of various types of pollutants in various wind directions and time periods, and to obtain the direction and concentrated emission period of each type of pollutant, specifically including: According to the third monitoring statistical data, the proportion of abnormal emissions of each type of pollutant in each azimuth wind direction and each time period is obtained, and the proportion of abnormal emissions of each type of pollutant in each azimuth wind direction and each time period is compared, and the azimuth wind direction with a higher proportion of abnormal emissions of each type of pollutant is obtained as the direction of abnormal emissions, and the time period with a higher proportion of abnormal emissions of each type of pollutant is obtained as the concentrated emission period.

7. An abnormal emission identification system based on upwind monitoring data comparison, characterized in that: The abnormal emission identification system includes: An area determination module is used to determine an area to be identified and multiple comparison areas of the area to be identified; wherein the multiple comparison areas are respectively areas in different directions relative to the area to be identified; a data acquisition module, configured to acquire emission identification monitoring data, the emission identification monitoring data including local hourly meteorological data of the area to be identified, local hourly air quality monitoring data of the area to be identified, and comparative hourly air quality monitoring data of each of the comparison areas, and to perform time correspondence between the local hourly meteorological data, the local hourly air quality monitoring data, and the plurality of comparative hourly air quality monitoring data to obtain monitoring-related data for different time periods; A meteorological factor elimination module is used to eliminate all the monitoring-related data affected by meteorological factors from the emission identification monitoring data to obtain first monitoring statistical data; a classification and comparison module for classifying the local hourly air quality monitoring data in the first monitoring statistical data based on wind direction and azimuth to obtain local pollutant concentration data in the wind direction of each azimuth; obtaining pollutant concentration data in the upwind area of ​​each azimuth based on the compared hourly air quality monitoring data of the plurality of comparison areas; and comparing the local pollutant concentration data in the same time period in each azimuth with the corresponding pollutant concentration data in the upwind area to obtain concentration comparison results in different time periods in each azimuth; A local high-value screening module is used to eliminate all the monitoring-related data with local stable and continuous high values ​​from the first monitoring statistical data based on the concentration comparison results at various locations and different time periods, to obtain second monitoring statistical data; an upwind pollution elimination module, configured to eliminate, from the second monitoring statistical data, all the monitoring-related data affected by upwind pollution transmission based on the concentration comparison results at various directions and in different time periods, to obtain third monitoring statistical data; The proportion analysis module is used to perform statistical analysis on the proportion of abnormal emissions of various types of pollutants in various wind directions and time periods based on the third monitoring statistical data, and obtain the abnormal emission direction and concentrated emission period of each type of pollutant.

8. The abnormal emission identification system based on upwind monitoring data comparison according to claim 7 is characterized in that: The classification comparison module compares the local pollutant concentration data in the same time period at each direction with the corresponding upwind area pollutant concentration data to obtain concentration comparison results at different time periods at each direction, specifically for: The local pollutant concentration data in the same time period at each direction and the corresponding upwind area pollutant concentration data are subjected to difference calculation processing to calculate the concentration difference at different time periods at each direction as the concentration comparison result.

9. The abnormal emission identification system based on upwind monitoring data comparison according to claim 8 is characterized in that: The local high-value screening module eliminates all the monitoring-related data of local stable and continuous high values ​​in the first monitoring statistical data based on the concentration comparison results at various locations and different time periods to obtain second monitoring statistical data, which is specifically used to: Calculate the average value and standard deviation of the concentration difference between the local pollutant concentration data and the corresponding upwind area pollutant concentration data in the same time period at each direction; The monitoring-related data in which all the concentration difference values ​​in the first monitoring statistical data are higher than the sum of the average value and the standard deviation are obtained as the second monitoring statistical data.

10. The abnormal emission identification system based on upwind monitoring data comparison according to claim 8, characterized in that: The upwind pollution elimination module removes all the monitoring-related data affected by upwind pollution transmission from the second monitoring statistical data based on the concentration comparison results at various directions and in different time periods to obtain third monitoring statistical data, which is specifically used to: Calculate the Pearson median skewness of the concentration difference between the local pollutant concentration data and the corresponding upwind regional pollutant concentration data in the same time period at each direction; The monitoring-related data in which the Pearson median skewness is greater than 0 in the second monitoring statistical data is obtained as the third monitoring statistical data.

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