Atmospheric pollution traceability analysis method, computer storage medium and program product

Abstract

The invention discloses an air pollution traceability analysis method. The method comprises the following steps: acquiring actually measured meteorological data of monitoring points in a target area; meteorological scene data matched with the actually measured meteorological data are determined according to a meteorological scene library, and the meteorological scene library comprises various kinds of meteorological scene data; pollution traceability data matched with the meteorological scene data are determined according to a pollution traceability library, and the pollution traceability library comprises multiple kinds of pollution traceability data in one-to-one correspondence with the multiple kinds of meteorological scene data; and determining an emission source of the matched pollution traceability data and a concentration contribution value corresponding to the emission source according to the CALPUFF mode. The method is suitable for industrial parks with high density, numerous emission sources and complex pollutant components, the specific pollution characteristics of a certain emission source in the industrial parks and the concentration contribution value of pollutants can be accurately measured, and accurate, efficient and reliable traceability is achieved. The invention further discloses a computer storage medium and a computer program product.

Description

Technical Field

[0001] The present invention relates to the technical field of air pollution source tracing, and in particular to an air pollution source tracing analysis method, a computer storage medium and a computer program product. Background Art

[0002] Air pollution prevention and control in industrial parks has always been one of the important tasks for the development of green and low-carbon industries. Enterprises in industrial parks are densely distributed, there are many emission sources, the composition of pollutants is complex, and the emission time and emission space are unevenly distributed. Therefore, when a monitoring point detects abnormal pollutant concentration, it is often difficult to quickly determine the source of pollution, which has caused a certain degree of trouble for the environmental management department in carrying out prevention and control law enforcement work.

[0003] Therefore, in recent years, the research on the source of air pollution in industrial parks has never stopped. At present, the source tracing work in industrial parks usually uses technologies such as cruise monitoring, receptor model method, and source tracing method based on Lagrangian model.

[0004] However, first of all, the existing methods of tracing the source of air pollution are of low accuracy and can no longer be applied to densely populated industrial parks. For example, cruise monitoring refers to the use of vehicle-mounted rapid monitoring equipment to continuously and automatically monitor while moving, combined with fixed-point monitoring, to conduct qualitative and quantitative analysis of pollutants, and to display the continuous spatial distribution of pollutants along the route based on geographic location information. Specifically, cruise monitoring can obtain points with abnormally high pollutant concentrations and roughly determine the source of pollutants based on wind direction. However, the enterprises near the high-value points are densely populated, and the source of pollution cannot be accurately located.

[0005] The receptor model method is a source apportionment method, which usually includes chemical mass balance (CMB) and positive matrix factorization (PMF). It is widely used in particulate matter and O 3 However, the analysis results of the receptor model method can only be assigned to industry sources, but cannot give the contribution value of specific enterprises to the pollutant concentration at each monitoring point.

[0006] The principle of the Lagrangian model tracing method is to trace the movement path of air particles in the atmosphere to semi-quantitatively analyze the grid source of pollutants. Usually, it is necessary to combine the emission inventory with the same model simulation grid to further obtain the grid source contribution. However, in industrial parks, there are many polluting gases, and it is generally difficult to obtain a grid emission inventory that meets the requirements, and it is difficult to achieve accurate tracing from the grid to the enterprise.

[0007] Secondly, the above-mentioned tracing methods such as the receptor model method and the Lagrangian model only use physical diffusion models for tracing, and cannot accurately trace the pollution characteristics of specific emission sources, that is, they cannot accurately trace the specific concentration. Summary of the invention

[0008] The purpose of the present invention is to solve the technical problem that the traditional atmospheric pollution source tracing method has low accuracy and cannot accurately determine the pollution characteristics of specific emission sources. The present invention provides an atmospheric pollution source tracing analysis method, a computer storage medium and a computer program product, which are suitable for industrial parks with high density, a large number of emission sources and complex pollutant components. It can accurately determine the specific pollution characteristics of a certain emission source in the industrial park and the concentration contribution value of the pollutant, and realize accurate, efficient and reliable source tracing.

[0009] In order to solve the above technical problems, the embodiment of the present invention discloses an atmospheric pollution source tracing analysis method, comprising:

[0010] Obtain the measured meteorological data of the monitoring points in the target area;

[0011] Determining meteorological scenario data matching the measured meteorological data according to a meteorological scenario library, wherein the meteorological scenario library includes a plurality of meteorological scenario data;

[0012] Determining pollution source tracing data matching the meteorological scenario data according to a pollution source tracing database, wherein the pollution source tracing database includes a plurality of pollution source tracing data corresponding one-to-one to the plurality of meteorological scenario data;

[0013] The emission sources of the matching pollution source tracing data and the concentration contribution values ​​corresponding to the emission sources are determined according to the CALPUFF model.

[0014] By adopting the above-mentioned technical scheme, the embodiment of the present application adopts the rapid tracing technology means of "matching measured meteorological data-finding pollution source tracing events", which can realize accurate tracing in target areas with multiple emission sources, multiple polluting gases, and multiple monitoring points such as industrial parks. In other words, the atmospheric pollution source tracing analysis method provided in the embodiment of the present application can not only accurately trace the source to the specific emission source, but also can accurately trace the specific pollution characteristics of the emission source and the concentration contribution value of the pollutants, which is something that traditional tracing methods such as the receptor model method and the Lagrangian model tracing method cannot achieve.

[0015] Specifically, in an embodiment of the present application, it is only necessary to obtain the measured meteorological data of a monitoring point in the target area to match the corresponding meteorological scenario data in the meteorological scenario library. The meteorological scenario data can be further matched to the corresponding pollution source tracing data in the pollution source tracing library. Finally, based on the CALPUFF model, the emission source corresponding to the pollution source tracing data and the concentration contribution value of the emission source can be calculated.

[0016] Therefore, the atmospheric pollution source tracing analysis method of the embodiment of the present application has high tracing accuracy and fast tracing speed, and is particularly suitable for industrial parks with high density, a large number of emission sources, and complex pollutant components. At the same time, compared with previous technologies, the tracing process takes less time, effectively improving the execution efficiency of tracing analysis and facilitating environmental law enforcement.

[0017] According to another specific embodiment of the present invention, determining meteorological scenario data matching the measured meteorological data based on a meteorological scenario library includes: determining meteorological scenario data corresponding to the measured meteorological data based on multiple meteorological scenario reference values ​​of the meteorological scenario library.

[0018] According to another specific embodiment of the present invention, the measured meteorological data includes measured wind direction data and measured wind speed data.

[0019] According to another specific implementation of the present invention, the measured wind direction data includes the measured wind direction at the monitoring point within a plurality of hours, and the measured wind speed includes the measured wind speed at the monitoring point within a plurality of hours.

[0020] According to another specific embodiment of the present invention, the atmospheric pollution source tracing analysis method further includes: establishing the meteorological scenario library, wherein the establishing the meteorological scenario library includes:

[0021] Classify the wind direction and wind speed in the target area according to the CALPUFF mode;

[0022] Determine multiple wind direction scenario reference values ​​according to the wind direction level, and determine multiple wind speed scenario reference values ​​according to the wind speed level;

[0023] Based on the three-hour simulation of the CALPUFF model, the multiple wind direction scenario reference values ​​and the multiple wind speed scenario reference values ​​are arranged and combined to obtain the multiple meteorological scenario data.

[0024] By adopting the above-mentioned technical scheme, the meteorological scenario library establishment process of the embodiment of the present application does not need to adopt a large number of parameters such as temperature, low cloud cover, total cloud cover, etc., and only needs to classify and combine the wind direction and wind speed parameters to generate the meteorological scenario library. Therefore, the meteorological scenario library establishment and subsequent meteorological condition matching process of the embodiment of the present application are relatively simple and quick.

[0025] According to another specific embodiment of the present invention, the atmospheric pollution source tracing analysis method further includes: establishing the pollution source tracing library, wherein the establishing the pollution source tracing library includes:

[0026] Acquiring the plurality of meteorological scenario data;

[0027] Pollution source tracing data corresponding one-to-one to the multiple meteorological scenario data are determined according to the CALPUFF model, wherein the CALPUFF model includes chemical component simulations of multiple pollutants.

[0028] Using the above technical solution, compared with the annual emission inventory tracing results based on the traditional physical diffusion model, the embodiment of the present application adopts the physical diffusion model and chemical component joint tracing. That is, in the CALPUFF mode, the diffusion process of each group of chemical components is simulated, which not only considers the impact of objective meteorological factors on pollutants, but also further considers the information of multiple chemical components in each group of chemical components, thereby obtaining the concentration contribution value of each emission source to each pollutant at each monitoring point, effectively improving the accuracy of tracing.

[0029] According to another specific embodiment of the present invention, the pollution source tracing library includes multiple ones, and the multiple pollution source tracing libraries correspond one-to-one to multiple emission intensities of emission sources.

[0030] According to another specific embodiment of the present invention, the multiple emission intensities include the measured emission intensities of the emission source under normal emission conditions and the measured emission intensities of the emission source under abnormal multiple emission conditions.

[0031] Using the above technical solution, compared to the traditional single pollution source tracing library, the embodiment of the present application is based on a variety of different emission intensities in the CALPUFF mode to form a variety of different pollution source tracing libraries. Specifically, that is to say, the measured value of the emission source under normal emission conditions can be used as the basic emission intensity to form a pollution source tracing library under normal emission conditions of the emission source; in some emergencies, the emission source will have abnormal multiple emissions, then multiple pollution source tracing libraries corresponding to the abnormal multiple emission conditions of multiple emission sources will be formed. Therefore, the embodiment of the present application can establish a richer pollution source tracing library to be able to respond to a variety of sudden environmental events and improve the reliability of tracing.

[0032] According to another specific embodiment of the present invention, the CALPUFF model includes basic data in the target area, and the basic data includes multiple chemical components of pollutants in the target area.

[0033] An embodiment of the present invention further discloses a computer storage medium, including a memory and a processor, wherein the memory is suitable for storing computer instructions, and the processor is suitable for executing the air pollution source tracing analysis method described in any of the above embodiments when running the computer instructions.

[0034] An embodiment of the present invention further discloses a computer program product, including a computer program / instruction, which, when executed by a processor, implements the air pollution source tracing analysis method described in any of the above embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 The process of the atmospheric pollution source tracing analysis method according to the embodiment of the present invention is shown as follows Figure 1 .

[0036] Figure 2 An example diagram of a monitoring point alarm page of the air pollution source tracing analysis method according to an embodiment of the present invention is shown.

[0037] Figure 3 An example diagram showing the acquisition of measured meteorological scenario data, determination of matching meteorological scenario data, and determination of matching pollution source tracing data in the atmospheric pollution source tracing analysis method according to an embodiment of the present invention.

[0038] Figure 4 An example diagram showing a meteorological scenario library of the atmospheric pollution source tracing analysis method according to an embodiment of the present invention.

[0039] Figure 5 A schematic diagram showing the correspondence between the meteorological scenario library and the pollution source tracing library of the atmospheric pollution source tracing analysis method according to an embodiment of the present invention.

[0040] Figure 6 An example diagram showing a pollution source tracing library of the atmospheric pollution source tracing analysis method according to an embodiment of the present invention.

[0041] Figure 7 A pollution source tracing result analysis diagram of the atmospheric pollution source tracing analysis method according to an embodiment of the present invention is shown; wherein the diagram shows four emission sources and the concentration contribution values ​​corresponding to the four emission sources.

[0042] Figure 8 A simulation result interface diagram showing emission sources of pollution source tracing data that match the emission sources and concentration contribution values ​​corresponding to the emission sources determined according to the CALPUFF model in the atmospheric pollution source tracing analysis method according to an embodiment of the present invention.

[0043] Fig. 9 The process of the atmospheric pollution source tracing analysis method according to the embodiment of the present invention is shown as follows Figure 2 .

[0044] Fig.10 The process of the atmospheric pollution source tracing analysis method according to the embodiment of the present invention is shown as follows Figure 3 .

[0045] Fig.11 An example diagram showing wind direction levels used in a meteorological scenario library of an atmospheric pollution source tracing analysis method according to an embodiment of the present invention.

[0046] Fig.12 The process of the atmospheric pollution source tracing analysis method according to the embodiment of the present invention is shown as follows Figure 4 .

[0047] Fig.13A block diagram of an electronic device provided by an embodiment of the present invention is shown;

[0048] Fig.14 A block diagram of a system on chip (SoC) provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0049] The following specific embodiments illustrate the implementation of the present invention, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Although the description of the present invention will be introduced in conjunction with the preferred embodiment, this does not mean that the features of this invention are limited to this implementation. On the contrary, the purpose of introducing the invention in conjunction with the implementation is to cover other options or modifications that may extend based on the claims of the present invention. In order to provide a deep understanding of the present invention, the following description will include many specific details. The present invention can also be implemented without using these details. In addition, in order to avoid confusion or blurring the focus of the present invention, some specific details will be omitted in the description. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0050] It should be noted that in this specification, similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0051] In the description of this embodiment, it should be noted that the terms "upper", "lower", "inner", "bottom", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, or are the orientations or positional relationships in which the inventive product is usually placed when used. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0052] The terms “first”, “second”, etc. are only used for distinguishing descriptions and should not be understood as indicating or implying relative importance.

[0053] In the description of this embodiment, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this embodiment can be understood according to specific circumstances.

[0054] To facilitate understanding of the technical solution of the present application, some concepts or terms involved in the present application are first explained.

[0055] (1) CALPUFF mode

[0056] The CALPUFF model (California Puff model, non-steady-state Lagrangian puff model system) can simulate the transport, transformation and removal process of pollutants in the atmospheric environment when the three-dimensional flow field changes with time and space. In the embodiment of the present application, the CALPUFF model is an atmospheric pollution diffusion model.

[0057] (2) Emission sources

[0058] An emission source refers to a place, equipment or device that emits pollutants or releases harmful factors into the environment.

[0059] (3) Monitoring points

[0060] A monitoring point refers to an observation point that is directly or indirectly set on the monitored object and can reflect its changing characteristics.

[0061] (4) Single pollution source contribution tracing method

[0062] A source tracing method based on the CALPUFF model. During the CALPUFF model simulation, this method calculates the concentration contribution value of each emission source to the receptor one by one (i.e., monitoring point) and one by one pollutant.

[0063] (5) Concentration contribution value

[0064] The concentration contribution value refers to the concentration of the pollutant at a specific point. In this embodiment of the application, the unit is μg / m 3 .

[0065] (6) Emission intensity

[0066] Emission intensity refers to the amount of specific pollutant emitted per unit of output.

[0067] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0068] The present application provides an atmospheric pollution source tracing analysis method. The atmospheric pollution source tracing analysis method of the embodiment of the present application can be used for pollution source tracing analysis in any area, for example, in different cities, towns, streets, parks and other areas that have pollutant source tracing and investigation needs. Moreover, the atmospheric pollution source tracing analysis method of the embodiment of the present application is particularly suitable for accurate tracing of industrial parks with a large number of emission sources, a large number of monitoring points and complex pollutants.

[0069] In the embodiment of the present application, for example, a target area B in city A is used as the key area for pollutant source tracing analysis. The target area B is, for example, an industrial park or a key industrial park with relatively dense enterprises (emission sources). At the same time, illustratively, there are 96 emission sources, 35 pollutants and 17 monitoring points in the target area B, but this is only one possible implementation method. The target area B may also have other numbers of emission sources, pollutants and monitoring points, and the embodiment of the present application does not specifically limit this.

[0070] Therefore, the specific implementation of the embodiment of the present application will be described in detail below using the above-mentioned target area B as an example and in combination with the accompanying drawings.

[0071] refer to Figure 1 , Figure 1 The flow chart of the atmospheric pollution source tracing analysis method in the embodiment of the present application is shown.

[0072] like Figure 1 As shown, the air pollution source tracing analysis method provided in the embodiment of the present application includes the following steps:

[0073] S100: Acquire the measured meteorological data of the monitoring points in the target area.

[0074] In this step, the measured meteorological data (hereinafter referred to as measured meteorological data) in the multiple hours before the monitoring point in the target area B can be measured by means of a wind direction and speed meter, a radar (such as a laser wind radar), an ultrasonic wind speed and direction sensor, etc., but the embodiment of the present application does not specifically limit the method for obtaining the measured meteorological data. It should be noted that the measured meteorological data in the embodiment of the present application includes measured wind direction data and measured wind speed data, and the measured wind direction data includes the measured wind direction in the multiple hours before the monitoring point, and the measured wind speed data includes the measured wind speed in the multiple hours before the monitoring point.

[0075] Exemplarily, this solution uses a wind direction and speed meter to measure and collect the actual wind direction and wind speed of 17 monitoring points in real time in the first three hours (i.e., the case time, the previous hour, and the previous two hours described below).

[0076] refer to Figure 2 Before executing this step, when a monitoring point (e.g., monitoring point F) among the 17 monitoring points in target area B detects abnormal pollutant emissions, the monitoring point F will issue an abnormally high pollutant value alarm. Figure 2 As shown, Figure 2 The figure shows 11 abnormally high-value pollutant alarm events that occurred at some monitoring points within a certain period of time. It can be seen that the column No. 2 in the figure represents the abnormally high-value pollutant alarm issued by the monitoring point F (this article uses the third-level alarm as an example for explanation, but is not limited to this).

[0077] Then when the monitoring point F issues a level 3 alarm, the following steps are executed: Figure 3 In step S100 shown in FIG. Figure 3 , we can see that when Figure 3 When the site in is selected as monitoring point F, the measured wind direction and wind speed of monitoring point F at the case time, the measured wind direction and wind speed in the previous hour, and the measured wind direction and wind speed in the previous two hours can be obtained respectively.

[0078] It should be noted that Figure 3 The measured wind direction and wind speed at the time of the case shown refer to the measured wind direction and wind speed at the monitoring point F within the hour before the third-level alarm was issued (for example, if the third-level alarm was issued at 5:10 pm at the monitoring point F, the time of the case is 5 pm). In the embodiment of the present application, the measured wind direction obtained at this time is 227°, and the measured wind speed obtained is 3.8 m / s.

[0079] at the same time, Figure 3 The measured wind direction and wind speed in the previous hour shown refer to the measured wind direction and wind speed in the two hours before the monitoring point F issues the above-mentioned level 3 alarm (for example, if the monitoring point F issues a level 3 alarm at 5:10 p.m., the previous hour is 4 p.m.). In the embodiment of the present application, the measured wind direction obtained at this time is 216°, and the measured wind speed obtained is 2.2 m / s.

[0080] Accordingly, Figure 3 The measured wind direction and wind speed in the previous two hours are: the measured wind direction and wind speed in the three hours before the monitoring point F issues the above-mentioned level 3 alarm (for example, if the monitoring point F issues the level 3 alarm at 5:10 pm, the previous two hours are 3 pm). In the embodiment of the present application, the measured wind direction at this time is 203°, and the measured wind speed is 1.4 m / s.

[0081] Therefore, illustratively, the measured wind direction data obtained is {203°, 216°, 227°}, that is, the measured wind directions in the first three hours of the monitoring point F are {203°, 216°, 227°} respectively. At the same time, the measured wind speed data obtained is {1.4m / s, 2.2m / s, 3.8m / s}, that is, the measured wind speed ws in the first three hours of the monitoring point F is {1.4m / s, 2.2m / s, 3.8m / s} respectively. The execution of step S100 is completed.

[0082] In addition, it should be noted that before executing this step, the monitoring point data in the target area B (such as the number of monitoring points, longitude and latitude positions), emission source data (basic parameters such as the longitude and latitude of the emission source and the height of the outlet from the ground, emission intensity), and pollutant data (the number and type of pollutants) are known, and a pollutant diffusion simulation has been performed, which will be described in detail later.

[0083] Continue to refer Figure 1 After executing step S100, start executing step S200.

[0084] S200: Determine meteorological scenario data that matches the measured meteorological data according to the meteorological scenario library.

[0085] In this step, the measured wind directions {203°, 216°, 227°} (i.e., measured wind direction data) of the monitoring point F within the first three hours (i.e., measured wind direction data) obtained in the above step S100 and the measured wind speeds {1.4m / s, 2.2m / s, 3.8m / s} (i.e., measured wind speed data) within the first three hours (i.e., measured wind speed data) are respectively corresponded to the meteorological scenario library to match them with the meteorological scenario data in the meteorological scenario library, so as to obtain the most matching meteorological scenario data.

[0086] That is to say, reference Figure 3 Based on the above step S100, start clicking Figure 3 , the above measured wind direction data {203°, 216°, 227°} and measured wind speed data {1.4m / s, 2.2m / s, 3.8m / s} can be mapped to the meteorological scenario library to find the meteorological scenario data with the highest matching degree in the meteorological scenario library. For example, the meteorological scenario data with the highest matching degree finally obtained is Figure 3 input_366_28 as shown in column No. 3.

[0087] Further, refer to Figure 4 Combined with Figure 3 The meteorological scenario library of the embodiment of the present application includes a large amount of meteorological scenario data, each meteorological scenario data corresponds to a meteorological scenario further composed of a wind direction combination and a wind speed combination, and the meteorological scenario data is named input_wd_ws.

[0088] Figure 4Some meteorological scenario data in the meteorological scenario library are shown as examples. For example, the first meteorological scenario data is input_001_01, where wd="001" means: the wind direction in the previous two hours was 360° north wind, the wind direction in the previous hour was 360° north wind, and the wind direction at the time of the case was 360° north wind. Wherein, ws="01" means: the wind speed in the previous two hours was 0.1 m / s calm wind, the wind speed in the previous hour was 0.1 m / s calm wind, and the wind speed at the time of the case was 0.1 m / s calm wind.

[0089] For example, Figure 4 The weather scenario data shown in the first row of the fourth column is input_002_15, where wd = "002" means: the wind direction in the previous two hours was 360° north wind, the wind direction in the previous hour was 360° north wind, and the wind direction at the time of the case was 45° northeast wind. Where ws = "15" means: the wind speed in the previous two hours was 0.1 m / s calm, the wind speed in the previous hour was 4.4 m / s breeze, and the wind speed at the time of the case was 2.5 m / s light wind.

[0090] For example, Figure 4 The weather scenario data shown in the fourth row of the sixth column is input_003_06, where wd = "003" means: the wind direction in the previous two hours was 360° north wind, the wind direction in the previous hour was 360° north wind, and the wind direction at the time of the case was 90° east wind. Where ws = "06" means: the wind speed in the previous two hours was 0.1 m / s calm wind, the wind speed in the previous hour was 0.9 m / s soft wind, and the wind speed at the time of the case was 0.9 m / s soft wind.

[0091] For another example, Figure 4 The last weather scenario data shown is input_005_04, where wd="005" means: the wind direction in the previous two hours was 360° north wind, the wind direction in the previous hour was 360° north wind, and the wind direction at the time of the case was 180° south wind. Where ws="04" means the wind speed in the previous two hours was 0.1 m / s calm wind, the wind speed in the previous hour was 0.1 m / s calm wind, and the wind speed at the time of the case was 4.4 m / s breeze.

[0092] For another example, Figure 4 A piece of weather scenario data not shown is input_006_46, where wd="006" means: the wind direction in the previous two hours was 360° north wind, the wind direction in the previous hour was 360° north wind, and the wind direction at the time of the case was 225° southwest wind. Where ws="46" means the wind speed in the previous two hours was 2.5 m / s light wind, the wind speed in the previous hour was 4.4 m / s breeze, and the wind speed at the time of the case was 0.9 m / s soft wind.

[0093] Then, the measured wind directions {203°, 216°, 227°} of the first three hours of the monitoring point F are respectively matched to the meteorological scenario library, and the most similar wind direction scenario value is found to be {225°, 225°, 225°} in the meteorological scenario library. Then, in the meteorological scenario library, the value of the wind direction combination wd corresponding to the wind direction scenario value {225°, 225°, 225°} is, for example, 366.

[0094] Accordingly, the measured wind speeds {1.4m / s, 2.2m / s, 3.8m / s} of the first three hours of the monitoring point F are respectively matched to the meteorological scenario library, and the most similar wind speed scenario value is found to be {0.9m / s, 2.5m / s, 4.4m / s} in the meteorological scenario library. Then, in the meteorological scenario library, the value of the wind direction combination ws corresponding to the wind speed scenario value {0.9m / s, 2.5m / s, 4.4m / s} is, for example, 28.

[0095] Based on the above operation, it can be determined that the meteorological scenario data that best matches the above measured wind direction data and the above measured wind speed data (that is, the meteorological scenario that is most similar) is the above input_366_28, thereby completing the execution of step S200.

[0096] It should be noted that before executing this step, it is necessary to establish a meteorological scenario library in advance (such as step S210 described later), and then correspond the measured wind direction data and measured wind speed data obtained in the above step S100 to the meteorological scenario library. The establishment of the meteorological scenario library is a possible implementation method of the atmospheric pollution source tracing analysis method of the embodiment of the present application, and will be described in detail later.

[0097] refer to Figure 1 After executing step S200, start executing step S300.

[0098] S300: Determine pollution source tracing data that matches the meteorological scenario data according to the pollution source tracing database.

[0099] refer to Figure 1 Combined with Figure 5 In this step, the meteorological scenario data input_366_28 determined in step S200 is matched to the pollution source tracing library to match the pollution source tracing data in the pollution source tracing library, and then the pollution source tracing data with the highest matching degree is determined. Figure 3 Based on the above step S200, start clicking the "View" button to obtain the pollution source tracing data that has the highest degree of matching with the above meteorological scenario data input_366_28.

[0100] In other words, refer to Figure 6The pollution source tracing library of the embodiment of the present application is a data set formed by a variety of pollution source tracing data, and each pollution source tracing data corresponds to a meteorological scenario data.

[0101] Figure 6 Some pollution source tracing data in the pollution source tracing database are shown as an example, for example, Figure 6 The first pollution source data shown is output_001_01, which is Figure 4 The pollution source tracing data output_001_01 corresponds to the meteorological scenario data input_001_01 in the target area B. The pollution source tracing data output_001_01 represents the pollutant diffusion simulation corresponding to the monitoring point F in the target area B monitoring the wind direction of the previous two hours as 360° north wind, the wind direction of the previous hour as 360° north wind, the wind direction of the case time as 360° north wind, the wind speed of the previous two hours as 0.1m / s, the wind speed of the previous hour as 0.1m / s, and the wind speed of the case time as 0.1m / s.

[0102] Then, by matching the meteorological scenario data input_366_28 obtained in the above step S200 to the pollution source tracing library, the matching pollution source tracing data that can be determined is output_366_28.

[0103] It should be noted that before executing this step, it is necessary to establish a pollution source tracing library (such as step S310 described later), and then correspond the meteorological scenario data input_366_28 to the pollution source tracing library. Accordingly, the establishment of the pollution source tracing library is a possible implementation method of the atmospheric pollution source tracing analysis method of the embodiment of the present application, and will be described in detail later.

[0104] In a possible implementation, according to the above description, further referring to Figure 3 Combined with Figure 1 , you can see, Figure 3 An example of the operation of executing the above steps S100 to S300 is shown. In actual application, first manually select the monitoring point and the measured wind direction data (i.e. the measured wind direction at the case time, the previous hour, and the previous two hours), and the measured wind speed data (i.e. the measured wind speed at the case time, the previous hour, and the previous two hours) (step S100). After clicking the "Query" button, it can be matched with the meteorological scenario library (step S200). After obtaining the most similar meteorological scenario data, click the "View" button to match it with the pollution source tracing (step S300). Complete the execution of steps S100 to S300.

[0105] refer to Figure 1 After executing step S300, start executing step S400.

[0106] S400: Determine the emission sources of the matching pollution source tracing data and the concentration contribution values ​​corresponding to the emission sources according to the CALPUFF model.

[0107] In this step, the matching pollution source tracing data output_366_28 determined in the above step S300 is input into the CALPUFF mode, and the pollutant diffusion corresponding to output_366_28 can be calculated through the CALPUFF mode. That is, each pollution source tracing data (such as Figure 6 As shown in the figure, all of them have been pre-simulated in the CALPUFF mode, that is, each piece of pollution source tracing data corresponds to a pollutant diffusion situation.

[0108] Therefore, reference Figure 3 After clicking the "View" button in step S300, the following information can be obtained based on the CALPUFF mode: Figure 7 The emission sources and their concentration contribution values ​​are shown. Figure 7 A concentration contribution percentage table of each emission source obtained by tracing back the source of monitoring point F is shown, which is also the final tracing back analysis result obtained after executing the above step S400.

[0109] In addition, further combining Figure 8 , you can see, Figure 8 The table of percentage contribution of each emission source concentration obtained by tracing the source of other monitoring points and the corresponding dynamic simulation view of tracing the source analysis are shown as examples. Figure 8 The traceability analysis results shown are not obtained based on the above examples in this article, but they can also be used as an example of the embodiments of this application. Figure 7 The concentration contribution percentage table shown also has Figure 8 The dynamic simulation view of the source tracing analysis shown in FIG. 1 , in other words, the source tracing analysis results (i.e., the determined emission sources and the concentration contribution values ​​corresponding to the emission sources) of the embodiment of the present application can be Figure 7 and Figure 8 Two diagrams are shown.

[0110] Specifically, the following Figure 7 To further explain step S400. Figure 7 Based on the CALPUFF model, the emission sources No. 04, No. 19, No. 28, and No. 89 corresponding to the pollution source tracing data output_366_28 can be determined, and the percentage contribution of the four emission sources to the pollutant concentration of the monitoring point F per hour in the first three hours can be determined.

[0111] like Figure 7As shown, it can be seen that within the hour before the monitoring point F issued an abnormally high pollutant alarm signal (i.e., the case time mentioned above), the emission source No. 04 contributed about 22% to the emission concentration of the monitoring point F, the emission source No. 19 contributed about 66% to the emission concentration of the monitoring point F, the emission source No. 28 contributed about 10% to the emission concentration of the monitoring point F, and the emission source No. 89 contributed about 2% to the emission concentration of the monitoring point F. That is, within one hour when the monitoring point F issued an abnormally high pollutant alarm signal, the emission source No. 19 contributed the most to the concentration of the monitoring point F, followed by the emission source No. 04.

[0112] Continue to refer Figure 7 , within the first two hours before the monitoring point F issued an abnormally high pollutant alarm signal (i.e., the first hour mentioned above), it can be seen that the emission source No. 04 contributed about 18% to the emission concentration of the monitoring point F, the emission source No. 19 contributed about 44% to the emission concentration of the monitoring point F, the emission source No. 28 contributed about 28% to the emission concentration of the monitoring point F, and the emission source No. 89 contributed about 10% to the emission concentration of the monitoring point F. That is, within the first two hours before the monitoring point F issued an abnormally high pollutant alarm signal, the emission source No. 19 contributed the most to the concentration of the monitoring point F, followed by the emission source No. 28.

[0113] Continue to refer Figure 7 , within the first three hours before the monitoring point F issued an abnormally high pollutant alarm signal (i.e., the first two hours mentioned above), the emission source No. 04 contributed about 20% to the emission concentration of the monitoring point F, the emission source No. 19 contributed about 70% to the emission concentration of the monitoring point F, the emission source No. 89 contributed about 10% to the emission concentration of the monitoring point F, and the emission source No. 28 did not emit to the monitoring point F. Therefore, within the first three hours before the monitoring point F issued an abnormally high pollutant alarm signal, the emission source No. 19 still contributed the most to the concentration of the monitoring point F, followed by the emission source No. 04.

[0114] That is to say, when the above-mentioned pollution source tracing data output_366_28 is input into the CALPUFF mode, the CALPUFF mode can adopt the single pollution source contribution backtracking method to calculate the concentration contribution value of each emission source to the monitoring point F within the hourly unit, for example, taking the previous hour when the monitoring point F issues an abnormally high pollutant alarm signal as an example.

[0115] Currently, there are the above-mentioned four emission sources: emission source No. 04, emission source No. 19, emission source No. 28, and emission source No. 89. Each emission source emits two pollutants, dichloromethane CH2Cl2 and propylene C3H6. At the same time, the above-mentioned monitoring point F exists.

[0116] In the simulation of the CALPUFF model, the concentration of the pollutant dichloromethane (CH2Cl2) monitored at monitoring point F is 10 μg / m 3 The concentration of the pollutant propylene C3H6 monitored was 15μg / m 3 Using the CALPUFF model single pollution source contribution tracing method, it can be determined that the concentration of the pollutant dichloromethane CH2Cl2 contributed by emission sources 04, 19, 28, and 89 to monitoring point F is 2.5 μg / m 3 , 5μg / m 3 , 2μg / m 3 , 0.5 μg / m 3 , and it can be determined that the concentration of the pollutant propylene C3H6 contributed by emission sources 04, 19, 28, and 89 to monitoring point F is 4 μg / m 3 , 8μg / m 3 , 2μg / m 3 , 1μg / m 3 Similarly, we can also perform similar processing and analysis on other monitoring points except monitoring point F.

[0117] It should be noted that in the embodiment of the present application, the CALPUFF model has performed wind field simulation and pollutant diffusion simulation for the target area B in advance. The establishment and simulation of the CALPUFF model serves as a possible implementation method of the atmospheric pollution source tracing analysis method in the embodiment of the present application, and will be described in detail later together with the establishment of the above-mentioned meteorological scenario library and pollution source tracing library.

[0118] refer to Figures 1 to 8 In summary, according to the above steps S100 to S400, accurate tracing of the target area B with multiple emission sources (e.g., 96 emission sources), multiple pollutants (e.g., 35 pollutants), and multiple monitoring points (e.g., 17 monitoring points) can be achieved. That is, by only inputting the measured wind speed and measured wind direction of a certain monitoring point (e.g., monitoring point F) in the first three full hours, a matching meteorological scenario data can be obtained, and then further matching pollution tracing data can be obtained. The pollution tracing data is input into the CALPUFF mode, and then the specific emission source (e.g., the above-mentioned emission source No. 19) can be quickly and accurately traced to the specific emission source (e.g., the emission source No. 19 mentioned above), and the specific pollution characteristics of the emission source and the concentration contribution value of the pollutants (e.g., the type of pollutants and the emission contribution concentration of pollutants of the emission source No. 19 to the monitoring point F) can be accurately obtained, which is not achievable by traditional tracing methods such as the receptor model method and the Lagrangian model tracing method.

[0119] Therefore, the atmospheric pollution source tracing analysis method of the embodiment of the present application has high tracing accuracy and fast tracing speed, and is particularly suitable for industrial parks with high density, a large number of emission sources, and complex pollutant components. At the same time, compared with previous technologies, the tracing process takes less time, effectively improving the execution efficiency of tracing analysis and facilitating environmental law enforcement.

[0120] The specific implementation method of determining the meteorological scenario data input_366_28 matching the measured meteorological data according to the meteorological scenario library in S200 is described in detail below.

[0121] In a possible implementation, the above step S200 includes: determining meteorological scenario data corresponding to the measured meteorological data according to multiple meteorological scenario reference values ​​in the meteorological scenario library.

[0122] Specifically, in the embodiment of the present application, the meteorological scenario library has a variety of meteorological scenario reference values, and each meteorological scenario reference value includes a wind direction scenario reference value and a wind speed scenario reference value.

[0123] The embodiments of the present application are illustrated by taking the wind direction scenario reference values ​​including {360°, 45°, 90°, 135°, 180°, 225°, 270°, 315°} and the wind speed scenario reference values ​​including {0.1m / s, 0.9m / s, 2.5m / s, 4.4m / s} as examples, but the wind direction scenario reference values ​​and wind speed scenario reference values ​​of the embodiments of the present application are not limited to the above values.

[0124] It should be noted that a large amount of meteorological scenario data in the meteorological scenario library is generated by permuting and combining the above-mentioned wind direction scenario reference values ​​and wind speed scenario reference values. The eight values ​​in the wind direction scenario reference values ​​represent different wind directions in the meteorological scenario library, and the four values ​​in the wind speed scenario reference values ​​represent different wind speeds in the meteorological scenario library.

[0125] First, the measured wind direction data {203°, 216°, 227°} collected in the above step S100 are compared with the wind direction scenario reference values ​​{360°, 45°, 90°, 135°, 180°, 225°, 270°, 315°}, and finally the wind direction scenario value {225°, 225°, 225°} mentioned above is matched. In the meteorological scenario library, this wind direction scenario value corresponds exactly to a wind direction combination wd numbered 366.

[0126] Secondly, the measured wind speed data {1.4m / s, 2.2m / s, 3.8m / s} collected in the above step S100 are compared with the wind speed scenario reference values ​​{0.1m / s, 0.9m / s, 2.5m / s, 4.4m / s}, and finally the wind speed scenario value {0.9m / s, 2.5m / s, 4.4m / s} mentioned above is matched. In the meteorological scenario library, this wind speed scenario value corresponds exactly to a wind direction combination ws numbered 28.

[0127] Therefore, it can be determined that the meteorological scenario data corresponding to the measured wind direction data and the measured wind speed data is input_366_28.

[0128] It should be noted that the matching process between the measured wind direction data and the wind direction scenario reference value is the same as the generation process of the wind direction scenario reference value, and the matching process between the measured wind speed data and the wind speed scenario reference value is the same as the generation process of the wind speed scenario reference value, so it will be explained later when the establishment of the meteorological scenario library is explained.

[0129] The following describes in detail the construction and simulation of the CALPUFF model, the establishment of a meteorological scenario library, and the establishment of a pollution source tracing library in the embodiment of the present application.

[0130] The following first introduces the construction and simulation of the CALPUFF mode.

[0131] First, build the CALPUFF model. First determine a geographical area that contains the intended simulated industrial park. The geographical area is, for example, the target area B mentioned above. The size of the target area B can be determined based on parameters such as the central longitude and latitude, horizontal resolution, and number of grids. In the embodiment of the present application, considering the average wind speed of the target area B, the simulation time is set to 3 hours to ensure that each emission source can affect each monitoring point.

[0132] Secondly, the basic data required for CALPUFF model simulation are obtained, including geographical data in the target area B, ground meteorological observation data and high-altitude detection data in the past three years, emission source data and monitoring point data.

[0133] The CALPUFF mode can read ground meteorological observations and high-altitude detection data written in a certain format. Among these two types of data, reasonable and unchanged settings are made for data other than wind direction and wind speed, and the changing meteorological elements are limited to wind direction and wind speed.

[0134] Exemplarily, geographic data includes terrain elevation data and land use data. The terrain elevation data is the European Space Agency Copernicus terrain elevation data, which comes from the European Space Agency Copernicus Space Data Center. The data actually includes the characteristics of surface man-made buildings, infrastructure and vegetation. The version is 2022_1, and the horizontal resolution is 30 meters; the land use data is the European Space Agency's global land cover data, the version is 2021_v200, and the horizontal resolution is 10 meters.

[0135] Exemplarily, the ground meteorological observation data include hourly temperature, ground air pressure, relative humidity, wind direction and wind speed, etc. Exemplarily, the high-altitude detection data include daily air pressure, height, temperature, wind direction and wind speed of each layer.

[0136] Exemplarily, the emission source data includes the basic parameters and emission intensity of the emission source, such as longitude and latitude, height of the outlet from the ground, pipe diameter, discharge flow rate and flue gas temperature. Exemplarily, the monitoring point data includes the longitude and latitude of the monitoring point.

[0137] In the embodiment of the present application, based on the CALPUFF mode, it is necessary to complete the simulation of the target area B through physical diffusion simulation and chemical component simulation. Fig. 9 , Fig. 9 A flowchart of simulation S500 based on the CALPUFF mode in an embodiment of the present application is shown.

[0138] The first is the physical diffusion simulation, such as Fig. 9 Steps S501, S502 and S503 are shown.

[0139] Step S501: Based on the constructed CALPUFF model, obtain DSM terrain elevation data and process it through the TERREL terrain elevation data preprocessing module; at the same time, obtain LULC land use data and process it through the CTGPROC land use data preprocessing module. Then, the two are combined and processed through the MAKEGEO geographic data synthesis module. That is, the terrain elevation data and land use data generated by the TERREL terrain elevation data preprocessing module and the CTGPROC land use data preprocessing module are read using the MAKEGEO geographic data synthesis module.

[0140] Step S502: Based on the constructed CALPUFF mode, ground meteorological data is preprocessed through the SURFACE module (a module for processing ground data), and high-altitude meteorological data is preprocessed through the UPPER AIR module (a module for high-altitude data). Both process the meteorological scenarios in the meteorological scenario library described later.

[0141] Step S503: Generate geographic data format and meteorological data format that can be recognized by the CALMET module.

[0142] The above steps can complete the physical diffusion simulation in the target area B, thereby obtaining the ground conditions and meteorological conditions of 96 emission sources and 17 monitoring points in the target area B.

[0143] The second is chemical simulation, such as Fig. 9 Step S504 is shown. Step S504: Based on the physical diffusion simulation of steps S501 to S503, the chemical components of different pollutants are input into the CALPUFF model.

[0144] The CALPUFF model can select a reasonable chemical mechanism according to the type of pollutant. In the embodiment of the present application, the pollutants considered are all VOCs and the types reach the maximum number of pollutants allowed by the model. Therefore, only the transmission of pollutants by meteorological conditions is considered, and chemical reactions are not considered.

[0145] For example, in the embodiment of the present application, based on the actual measurement of 96 emission sources, 35 chemical components of pollutants are input: 1,3-butadiene, dichloromethane, 1,2-dichloroethane, trichloroethylene, tetrachloroethylene, chlorobenzene, ethylene, acetylene, ethane, propylene, propane, isobutane, n-butane, trans-2-butene, cis-2-butene, isopentane, n-pentane, isoprene, 2-methylpentane + 2,3-dimethylbutane, n-hexane, benzene, 2,2,4-trimethylpentane, n-heptane, 2,3,4-trimethylpentane, toluene, n-octane, ethylbenzene, p-xylene, styrene, o-xylene, isopropylbenzene, propylbenzene, 1,3,5-trimethylbenzene, 1,2,4-trimethylbenzene, 1,2,3-trimethylbenzene. It should be noted that the embodiment of the present application does not limit the type, quantity and emission intensity of pollutants.

[0146] Finally, the merge simulation is Fig. 9 Steps S505 and S506 are shown.

[0147] Step S505: Use the CALPUFF module to simulate the diffusion of the 35 pollutants released in the above step S504 in the physical environment simulated in steps S501 to S503.

[0148] Step S506: The CALPOST module is used to calculate the contribution of the above 96 emission sources to the pollutant concentration of the 17 monitoring points, so as to determine the emission source with the highest pollutant concentration contribution value.

[0149] The establishment of the meteorological scenario database and the pollution source tracing database is further introduced below.

[0150] In some possible implementations, reference Fig.10Combined with Figure 1 The atmospheric pollution source tracing analysis method provided in the embodiment of the present application further includes step S210: establishing a meteorological scenario library. Further, the meteorological scenario library S210 provided in the embodiment of the present application includes the following steps:

[0151] S2101: Classify the wind direction and wind speed in the target area B according to the CALPUFF mode.

[0152] In this step, based on the CALPUFF mode, the wind direction is first classified into grades, that is, the wind direction grades are set according to the meteorological characteristics of the target area B over the years.

[0153] refer to Fig.10 and Fig.11 For example, the China Meteorological Administration usually divides the wind direction into 8 directions, namely: north wind, northeast wind, east wind, southeast wind, south wind, southwest wind, west wind and northwest wind. According to the simulation analysis of the meteorological observation data of the target area B in the past three years (i.e., the physical diffusion simulation in step S500 above), Fig. 9 As shown), it is found that the wind direction of the target area B is distributed in these 8 directions. Therefore, the wind direction level of the target area B is set to 8 directions.

[0154] Then, based on the CALPUFF model, the wind speed is further divided into grades. That is, the wind speed grade is set according to the meteorological characteristics of the target area B over the years.

[0155] For example, the China Meteorological Administration generally classifies wind speed into dozens of levels: calm wind [0.0m / s, 0.2m / s], soft wind [0.3m / s, 1.5m / s], light wind [1.6m / s, 3.3m / s], breeze [3.4m / s, 5.4m / s], gentle wind, strong wind, gale, gale, gale, hurricane, etc. According to the simulation analysis of the meteorological observation data of the target area B in the past three years (i.e., the physical diffusion simulation of step S500 above), as shown in FIG. Fig. 9 As shown in the figure, it is found that in target area B, the probability of wind speed being in the interval [0.0m / s, 5.4m / s] is 98.1%, that is, the four wind speed levels of no wind, soft wind, light wind, and breeze can cover 98.1% of the situations in target area B. Therefore, the wind speed level of target area B is set to 4 levels: no wind [0.0m / s, 0.2m / s], soft wind [0.3m / s, 1.5m / s], light wind [1.6m / s, 3.3m / s], and breeze [3.4m / s, 5.4m / s].

[0156] S2102: Determine multiple wind direction scenario reference values ​​according to the level of wind direction, and determine multiple wind speed scenario reference values ​​according to the level of wind speed.

[0157] First, multiple wind direction scenario reference values ​​are determined. Based on the above step S2101, it can be known that the wind direction level of target area B for target area B is 8 directions. In this step, it is necessary to further determine the representative value of each wind direction direction as the wind direction scenario reference value.

[0158] Exemplarily, the embodiment of the present application adopts the central value of each wind direction azimuth as the wind direction scenario reference value. For example, the value range of the north wind is (337.5°, 360°) and (0°, 22.5°), so the central value 360° is adopted as the wind direction scenario reference value of the north wind azimuth; for example, the value range of the northeast wind is (22.5°, 67.5°), so the central value 45° is adopted as the wind direction scenario reference value of the northeast wind azimuth.

[0159] For example, the value range of east wind is (67.5°, 112.5°), so the central value 90° is used as the wind direction scenario reference value for the east wind direction; for example, the value range of southeast wind is (112.5°, 157.5°), so the central value 135° is used as the wind direction scenario reference value for the southeast wind direction. For example, the value range of south wind is (157.5°, 202.5°), so the central value 180° is used as the wind direction scenario reference value for the south wind direction.

[0160] For another example, the numerical range of the southwest wind is (202.5°, 247.5°), so the central value 225° is used as the wind direction scenario reference value for the southwest wind direction; for another example, the numerical range of the westerly wind is (247.5°, 292.5°), so the central value 270° is used as the wind direction scenario reference value for the westerly wind direction; finally, the numerical range of the northwest wind is (292.5°, 337.5°), so the central value 315° is used as the wind direction scenario reference value for the northwest wind direction.

[0161] Therefore, the wind direction scenario reference values ​​for 8 wind direction orientations were finally determined as: {360°, 45°, 90°, 135°, 180°, 225°, 270°, 315°}.

[0162] In addition, it should be noted that, as mentioned above, the matching process between the measured wind direction data {203°, 216°, 227°} and the wind direction scenario reference value is the same as the generation process of the wind direction scenario reference value. For example, the measured wind direction 203° falls within the above-mentioned numerical range (202.5°, 247.5°), so the measured wind direction 203° corresponds to the wind direction scenario reference value 225° of the southwest wind; accordingly, the measured wind directions 216° and 227° also fall within (202.5°, 247.5°), so the measured wind directions 216° and 227° also correspond to the wind direction scenario reference value 225° of the southwest wind, so the above-mentioned wind speed scenario value {225°, 225°, 225°} can be matched in the end. The second step is to determine multiple wind speed scenario reference values. Based on the four wind speed levels divided in step S2101 above, the average of the upper and lower wind speed limits of each level is taken as the representative value of the corresponding level, and finally the wind speed scenario reference value is formed. For example, the windless level: (0.0+0.2)m / s / 2=0.1m / s, that is, 0.1m / s is used to represent the entire windless level; for example, the soft wind level: (0.3+1.5)m / s / 2=0.9m / s, that is, 0.9m / s is used to represent the entire soft wind level.

[0163] For another example, the light breeze level: (1.6+3.3)m / s / 2=2.5m / s, that is, 2.5m / s is used to represent the entire light breeze level; for another example, the breeze level: (3.4+5.4)m / s / 2=4.4m / s, that is, 4.4m / s is used to represent the entire breeze level.

[0164] Therefore, the wind speed scenario reference values ​​for the four wind speed levels were finally determined as: {0.1m / s, 0.9m / s, 2.5m / s, 4.4m / s}.

[0165] In addition, it should be noted that, as mentioned above, the matching process between the measured wind speed data {1.4m / s, 2.2m / s, 3.8m / s} and the wind speed scenario reference value is the same as the generation process of the wind speed scenario reference value. For example, the measured wind speed of 1.4m / s falls within the above numerical range [0.3m / s, 1.5m / s], so the measured wind direction of 1.4m / s corresponds to the wind speed scenario reference value of 0.9m / s for soft wind; accordingly, the measured wind speed of 2.2m / s falls within the above numerical range [1.6m / s, 3.3m / s], so the measured wind speed of 2.2m / s corresponds to the wind speed scenario reference value of 2.5m / s for light wind; the measured wind speed of 3.8m / s falls within the above numerical range [3.4m / s, 5.4m / s], so the measured wind speed of 3.8m / s corresponds to the wind speed scenario reference value of 4.4m / s for light wind. Then the above wind speed scenario values ​​{0.9m / s, 2.5m / s, 4.4m / s} can be finally matched.

[0166] S2103: Based on the CALPUFF model, simulate for three hours, arrange and combine multiple wind direction scenario reference values ​​and multiple wind speed scenario reference values ​​to obtain multiple meteorological scenario data.

[0167] First, based on the wind direction scenario reference values ​​obtained in the above step S2102: {360°, 45°, 90°, 135°, 180°, 225°, 270°, 315°}, in this step, continuous simulation is performed for 3 hours, that is, the wind directions of the previous two hours, the previous hour and the case moment are arranged and combined, and the wind direction at each moment is selected independently, then there are a total of 8×8×8=512 wind direction combinations.

[0168] For example, if the wind direction is 360° in the first two hours, 360° in the first hour, and 360° at the time of the case, then the wind direction combination wd=001; if the wind direction is 360° in the first two hours, 360° in the first hour, and 45° at the time of the case, then the wind direction combination wd=002; if the wind direction is 360° in the first two hours, 45° in the first hour, and 360° at the time of the case, then the wind direction combination wd=009... and so on, until: 315° in the first two hours, 315° in the first hour, and 315° at the time of the case, then the last wind direction combination wd=512.

[0169] Secondly, based on the wind speed scenario reference values ​​{0.1m / s, 0.9m / s, 2.5m / s, 4.4m / s} obtained in the above step S2102, in this step, continuous simulation is performed for 3 hours, that is, the wind speeds of the previous two hours, the previous hour and the case moment are arranged and combined, and accordingly, the wind speed at each moment is independently selected, for a total of 4×4×4=64 wind speed combinations.

[0170] For example, if the wind speed is 0.1m / s in the first two hours, 0.1m / s in the first hour, and 0.1m / s at the time of the case, then the wind speed combination ws=01; if the wind speed is 0.1m / s in the first two hours, 0.1m / s in the first hour, and 0.9m / s at the time of the case, then the wind speed combination ws=02; if the wind speed is 0.1m / s in the first two hours, 0.9m / s in the first hour, and 0.1m / s at the time of the case, then the wind speed combination ws=05... and so on, until: 4.4m / s in the first two hours, 4.4m / s in the first hour, and 4.4m / s at the time of the case, then the last wind direction combination ws=64.

[0171] Then, the above 512 wind direction combinations and the above 64 wind speed combinations are combined to obtain 32768 meteorological scenario data, as mentioned above, and the meteorological scenario data is standardized and named: input_wd_ws. Among them, wd in the naming represents the above 512 wind direction combinations, which can be integers between 001 and 512, and ws in the naming represents the above 64 wind speed combinations, which can be integers between 01 and 64.

[0172] This completes the establishment of the meteorological scenario database, refer to Figure 4 At this time, a data set formed by various meteorological scenario data input_wd_ws as described above is constructed.

[0173] In some possible implementations, reference Fig.12 Combined with Figure 1 The atmospheric pollution source tracing analysis method provided in the embodiment of the present application further includes step S310: establishing a pollution source tracing library. The establishment of the pollution source tracing library S310 provided in the embodiment of the present application includes the following steps:

[0174] S3101: Acquire multiple weather scenario data. In this step, the above step S210 (such as Fig.10 The various meteorological scenario data (such as the aforementioned 32768 kinds of meteorological scenario data) obtained by the CALPUFF model are input into the CALPUFF model.

[0175] S3102: Determine pollution source tracing data corresponding to a variety of meteorological scenario data according to the CALPUFF model, wherein the CALPUFF model includes chemical component simulations of a variety of pollutants.

[0176] Before executing this step, a simulation analysis has been performed on the chemical components of different pollutants in the target area B (i.e., the chemical component simulation of step S500, such as Fig. 9 In this step, further combined Figure 5 After inputting each meteorological scenario data (such as input_336_28 described above) into the CALPUFF mode, one-to-one corresponding pollution source tracing data can be obtained (such as output_336_28 described above).

[0177] Complete the establishment of the pollution source tracing database, refer to Figure 6 At this time, a data set formed by various pollution source tracing data output_wd_ws as described above is constructed.

[0178] In addition, refer to Figure 1 , continue to execute steps S300 and S400 as described above, analyze the pollution source tracing data, and obtain the concentration contribution value of each emission source to the monitoring point.

[0179] In some possible implementations, the pollution source tracing library of the embodiment of the present application includes multiple pollution source tracing libraries, and the multiple pollution source tracing libraries correspond one-to-one to the multiple emission intensities of the emission sources.

[0180] That is to say, reference Fig. 9 ,for Fig. 9 In step S504 shown in the figure, when obtaining emission source data, the emission intensity obtained is the actual measured value of the above-mentioned 96 emission sources under normal emission conditions. On this basis, steps S505 to S506 are further executed to complete the simulation process of the entire CALPUFF mode of step S500.

[0181] However, in some emergencies, some emission sources will have abnormally high emissions. For example, if the emission intensity is twice the emission intensity under normal emission conditions, that is, the input emission intensity is the measured emission intensity of the emission source under abnormal 2 times emission conditions, a new emission scenario can be formed based on this emission intensity, and a pollution source tracing library under the condition of "some emission sources abnormally 2 times emission" can be formed under various meteorological scenarios.

[0182] For example, the input emission intensity may be 5 times the emission intensity under normal emission conditions, that is, the input emission intensity is the measured emission intensity of the emission source under abnormal 5 times emission conditions. The emission intensity is then input into the CALPUFF model as emission source data to form a pollution source traceability library under the condition of "abnormal 5 times emission from certain emission sources".

[0183] For example, the input emission intensity can also be any multiple of the emission intensity under normal emission conditions. In this way, a single emission intensity scenario is expanded to multiple emission intensity scenarios, thereby forming multiple pollution source tracing libraries, which can achieve the purpose of enriching the pollution source tracing library and responding to various sudden environmental events.

[0184] In addition, the present invention also provides a computer storage medium, including a memory and a processor, the memory is suitable for storing computer instructions, and the processor is suitable for executing the air pollution source tracing analysis method described in any of the above embodiments when running the computer instructions.

[0185] refer to Fig.13 , Fig.13The block diagram of an electronic device 600 according to an embodiment of the present application is exemplarily shown. The electronic device 600 is, for example, a smart mobile terminal (e.g., a display screen, a mobile phone, a laptop computer, a tablet computer, a wearable device, etc.). The electronic device 600 may include one or more processors 601 coupled to a controller hub 603. For at least one embodiment, the controller hub 603 communicates with the processor 601 via a multi-branch bus such as a front-side bus (FSB), a point-to-point interface such as a QuickPath Interconnect (QPI), or a similar connection 606. The processor 601 executes instructions to control general types of data processing operations. In one embodiment, the controller hub 603 includes, but is not limited to, a graphics memory controller hub (GMCH, Graphics & Memory Controller Hub) (not shown) and an input / output hub (IOH, Input Output Hub) (which may be on a separate chip) (not shown), wherein the GMCH includes a memory and a graphics controller and is coupled to the IOH.

[0186] The electronic device 600 may also include a coprocessor 602 and a memory 604 coupled to a controller hub 603. Alternatively, one or both of the memory and the GMCH may be integrated within the processor, with the memory 604 and the coprocessor 602 directly coupled to the processor 601 and the controller hub 603, with the controller hub 603 and the IOH being in a single chip.

[0187] The memory 604 may be, for example, a dynamic random access memory (DRAM), a phase change memory (PCM), or a combination of the two. The memory 604 may include one or more tangible, non-transitory computer-readable media for storing data and / or instructions. The computer-readable storage medium stores instructions, specifically, temporary and permanent copies of the instructions. The instructions may include: when executed by at least one of the processors, causing the electronic device 600 to implement the following: Figure 1 The instructions of the atmospheric pollution source tracing analysis method shown in the figure. When the instructions are executed on a computer, the computer executes the method disclosed in any one of the above embodiments or combined embodiments to assign the current contract to an operator.

[0188] In one embodiment, the coprocessor 602 is a special-purpose processor, such as, for example, a high-throughput MIC (Many Integrated Core) processor, a network or communication processor, a compression engine, a graphics processor, a GPGPU (General-purpose computing on graphics processing units), or an embedded processor. The optional nature of the coprocessor 602 is indicated by a dashed line in FIG. Fig.13 middle.

[0189] In one embodiment, the electronic device 600 may further include a network interface (NIC, Network Interface Controller) 606. The network interface 606 may include a transceiver for providing a radio interface for the electronic device 600, and then communicating with any other suitable device (such as a front-end module, an antenna, etc.). In various embodiments, the network interface 606 may be integrated with other components of the electronic device 600. The network interface 606 may implement the functions of the communication unit in the above-mentioned embodiments.

[0190] The electronic device 600 may further include an input / output (I / O) device 605. The I / O 605 may include: a user interface designed to enable a user to interact with the electronic device 600; a peripheral component interface designed to enable peripheral components to interact with the electronic device 600; and / or a sensor designed to determine environmental conditions and / or location information related to the electronic device 600.

[0191] It is worth noting that Fig.13 This is for illustrative purposes only. Fig.13 It is shown that the electronic device 600 includes multiple devices such as a processor 601, a controller hub 603, a memory 604, etc.; however, in actual applications, the device using the methods of the present application may include only a portion of the devices in the electronic device 600, for example, it may include only the processor 601 and the network interface 606. Fig.13 The properties of optional devices are shown with dashed lines.

[0192] Reference now Fig.14 , Fig.14 The block diagram of a SoC (System on Chip) 700 according to an embodiment of the present application is shown as an example. Fig.14 In the FIG, similar components have the same reference numerals. In addition, the dashed boxes are optional features of more advanced SoCs. Fig.14In the embodiment, the SoC includes: an interconnect unit 750, which is coupled to the processor 710; a system agent unit 780; a bus controller unit 790; an integrated memory controller unit 740; a group or one or more coprocessors 720, which may include integrated graphics logic, image processors, audio processors, and video processors; a static random access memory (SRAM) unit 730; and a direct memory access (DMA) unit 760. In one embodiment, the coprocessor 720 includes a special-purpose processor, such as, for example, a network or communication processor, a compression engine, a GPGPU (General-purpose computing on graphics processing units), a high-throughput MIC processor, or an embedded processor.

[0193] Static random access memory (SRAM) unit 730 may include one or more tangible, non-transitory computer-readable media for storing data and / or instructions. The computer-readable storage medium stores instructions, and more specifically, stores temporary and permanent copies of the instructions. The instructions may include: when executed by at least one of the processors, causing the SoC to implement the following: Figure 1 The instructions of the atmospheric pollution source tracing analysis method shown in the figure. When the instructions are executed on a computer, the computer executes the method disclosed in the above embodiment.

[0194] The embodiments of the present application also provide a computer program product for implementing the air pollution source tracing analysis methods provided in the above embodiments.

[0195] The various embodiments of the mechanism disclosed in the present application can be implemented in hardware, software, firmware or a combination of these implementation methods. The embodiments of the present application can be implemented as a computer program module or module code executed on a programmable system, and the programmable system includes at least one processor, a storage system (including volatile and non-volatile memory and / or storage elements), at least one input device and at least one output device.

[0196] A computer program module or module code can be applied to input instructions to perform the functions described in this application and generate output information. The output information can be applied to one or more output devices in a known manner. For the purposes of this application, a processing system includes any system having a processor such as, for example, a digital signal processor (DSP), a microcontroller, an application specific integrated circuit (ASIC), or a microprocessor.

[0197] Module code can be implemented with high-level modular language or object-oriented programming language to communicate with the processing system. When necessary, module code can also be implemented with assembly language or machine language. In fact, the mechanism described in this application is not limited to the scope of any specific programming language. In either case, the language can be a compiled language or an interpreted language.

[0198] In some cases, the disclosed embodiments may be implemented in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried or stored on one or more temporary or non-temporary machine-readable (e.g., computer-readable) storage media, which may be read and executed by one or more processors. For example, instructions may be distributed over a network or through other computer-readable media. Therefore, a machine-readable medium may include any mechanism for storing or transmitting information in a machine (e.g., computer) readable form, including, but not limited to, a floppy disk, an optical disk, an optical disk, a magneto-optical disk, a read-only memory (ROM), a random access memory (RAM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic card or an optical card, a flash memory, or a tangible machine-readable memory for transmitting information (e.g., a carrier wave, an infrared signal, a digital signal, etc.) using the Internet in an electrical, optical, acoustic, or other form of propagation signal. Accordingly, machine-readable media include any type of machine-readable media suitable for storing or transmitting electronic instructions or information in a form readable by a machine (eg, a computer).

[0199] Although the present invention has been illustrated and described with reference to certain preferred embodiments of the present invention, it should be understood by those skilled in the art that the above is a further detailed description of the present invention in conjunction with specific embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. Those skilled in the art may make various changes in form and details, including making several simple deductions or substitutions, without departing from the spirit and scope of the present invention.

Claims

1. A method for tracing the source of air pollution, characterized in that: include: Obtain the measured meteorological data of the monitoring points in the target area; Determining meteorological scenario data matching the measured meteorological data according to a meteorological scenario library, wherein the meteorological scenario library includes a plurality of meteorological scenario data; Determining pollution source tracing data matching the meteorological scenario data according to a pollution source tracing database, wherein the pollution source tracing database includes a plurality of pollution source tracing data corresponding one-to-one to the plurality of meteorological scenario data; The emission sources of the matching pollution source tracing data and the concentration contribution values ​​corresponding to the emission sources are determined according to the CALPUFF model.

2. The method for tracing the source of air pollution according to claim 1, characterized in that: The step of determining the meteorological scenario data matching the measured meteorological data according to the meteorological scenario library includes: Meteorological scenario data corresponding to the measured meteorological data are determined according to multiple meteorological scenario reference values ​​in the meteorological scenario library.

3. The method for tracing the source of air pollution according to claim 1, characterized in that: The measured meteorological data includes measured wind direction data and measured wind speed data.

4. The method for tracing the source of air pollution according to claim 2, characterized in that: The measured wind direction data includes the measured wind direction at the monitoring point within a plurality of hours, and the measured wind speed data includes the measured wind speed at the monitoring point within a plurality of hours.

5. The method for tracing the source of air pollution according to any one of claims 1 to 4, characterized in that: The atmospheric pollution source tracing analysis method further includes: establishing the meteorological scenario library, wherein the establishing the meteorological scenario library includes: Classify the wind direction and wind speed in the target area according to the CALPUFF mode; Determine multiple wind direction scenario reference values ​​according to the wind direction level, and determine multiple wind speed scenario reference values ​​according to the wind speed level; Based on the three-hour simulation of the CALPUFF model, the multiple wind direction scenario reference values ​​and the multiple wind speed scenario reference values ​​are arranged and combined to obtain the multiple meteorological scenario data.

6. The method for tracing the source of air pollution according to claim 5, characterized in that: The atmospheric pollution source tracing analysis method further includes: establishing the pollution source tracing library, wherein the establishing the pollution source tracing library includes: Acquiring the plurality of meteorological scenario data; Pollution source tracing data corresponding one-to-one to the multiple meteorological scenario data are determined according to the CALPUFF model, wherein the CALPUFF model includes chemical component simulations of multiple pollutants.

7. The method for tracing the source of air pollution according to claim 1, characterized in that: The pollution source tracing database includes multiple ones, and the multiple pollution source tracing databases correspond one-to-one to the multiple emission intensities of the emission sources.

8. The method for tracing the source of air pollution according to claim 7, characterized in that: The multiple emission intensities include the measured emission intensities of the emission source under normal emission conditions and the measured emission intensities of the emission source under abnormal multiple emission conditions.

9. The method for tracing the source of air pollution according to any one of claims 1 to 8, characterized in that: The CALPUFF model includes basic data within the target area, and the basic data includes multiple chemical components of pollutants in the target area.

10. A computer storage medium, characterized in that: It comprises a memory and a processor, wherein the memory is suitable for storing computer instructions, and the processor is suitable for executing the air pollution source tracing analysis method as described in any one of claims 1 to 9 when running the computer instructions.

11. A computer program product, characterized in that It includes computer programs / instructions, which, when executed by a processor, implement the air pollution source tracing analysis method described in any one of claims 1 to 9.

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