A method and system for predicting and optimizing the spatial distribution of coking soil pollution

Abstract

The present invention provides a method and system for predicting and optimizing the spatial distribution of coking soil pollution. The scheme includes using acquisition equipment to obtain the smoke diffusion range, calculate pollutant coordinates, emission rate, ground meteorological data, emission height, and emission flow; inputting ground meteorological data and sounding meteorological data in sequence as meteorological data sources; obtaining meteorological background initial values, terrain elevation data, and pollution source data, generating site characteristics and comprehensive pollution source data, inputting pollutant propagation models, and generating pollutant concentrations at any point; obtaining pollutant propagation models, obtaining the corresponding target lateral diffusion coefficient and target transverse diffusion coefficient when the error is the lowest, and generating heavy metal pollutant mapping from pollutant results. The scheme calculates the simulation results of particulate matter emissions from enterprises through an atmospheric diffusion model, and applies them as collaborative variables to the collaborative Kriskin interpolation method to improve the prediction accuracy of heavy metal pollutants in the soil around the coking plant.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological forecasting, and more specifically, to a method and system for predicting and optimizing the spatial distribution of coking soil pollution. Background Art

[0002] Coking contaminated soil has strong spatial variability, the difference between the maximum and minimum soil pollutant content can be as high as 730 times, and the coefficient of variation can be as high as 398%. In order to obtain the spatial distribution characteristics of pollutant content, the existing technology mainly obtains the spatial distribution information of pollutant content based on spatial interpolation models.

[0003] Prior to the present invention, existing studies have mainly improved the accuracy of interpolation by screening interpolation models, combining categorical auxiliary variables and geostatistics. However, existing methods are limited to optimizing prediction effects through mathematical analysis, lacking the study of the inherent laws of widespread coking soil pollution, that is, the driving mechanism of soil pollution, resulting in prediction results that are limited to soil pollution characteristics close to specific research enterprises, and are difficult to directly apply to the spatial distribution pattern of soil pollution in other polluting enterprises. There are large differences in the prediction effects obtained by applying it to different research objects.

[0004] The reason for these defects in the existing foundation is that the spatial interpolation model is based on the theory of spatial autocorrelation, that is, values ​​in adjacent spatial positions have correlation and similar characteristics. This assumption is inconsistent with the strong spatial variation characteristics of coking soil pollution, making it difficult to predict its variation form and difficult to provide support for coking soil remediation and management. Summary of the invention

[0005] In view of the above problems, the present invention proposes a method and system for predicting and optimizing the spatial distribution of coking soil pollution. The simulation results of particulate matter emissions from enterprises are calculated through an atmospheric diffusion model, and are applied as synergistic variables in the synergistic Kriskin interpolation method to improve the prediction accuracy of heavy metal pollutants in the soil around the coking plant.

[0006] According to a first aspect of an embodiment of the present invention, a method for predicting and optimizing the spatial distribution of coking soil pollution is provided.

[0007] In one or more embodiments, preferably, the method for predicting and optimizing the spatial distribution of coking soil pollution comprises:

[0008] Use the acquisition equipment to obtain the smoke diffusion range, calculate the pollutant coordinates, emission rate, ground meteorological data, emission height, and emission flow;

[0009] Input ground meteorological data and sounding meteorological data in sequence as meteorological data sources;

[0010] Obtain initial meteorological background values, terrain elevation data and pollution source data to generate site characteristics and comprehensive pollution source data;

[0011] Obtain the meteorological data source and the comprehensive pollution source data, input into the pollutant propagation model, and generate the pollutant concentration at any point;

[0012] Acquire the pollutant propagation model to obtain the target lateral diffusion coefficient and the target transverse diffusion coefficient corresponding to the minimum error;

[0013] The target lateral diffusion coefficient and the target transverse diffusion coefficient are obtained, and the pollutant results are used to generate a heavy metal pollutant map.

[0014] In one or more embodiments, preferably, the method of obtaining the smoke diffusion range by using the acquisition equipment, calculating the pollutant coordinates, emission rate, ground meteorological data, emission height, and emission flow rate, specifically includes:

[0015] Use the collection equipment to obtain the smoke diffusion range;

[0016] According to the smoke diffusion range, the smoke diffusion range is divided into a plurality of small-scale blocks according to a preset division scale;

[0017] Acquiring pollutant coordinates according to the small-scale blocks;

[0018] Obtain the emission rate and emission height of pollutants;

[0019] Obtain the highest value of the pollutant coordinates according to the small-scale block as terrain height data;

[0020] Extracting the overall pollutant volume according to the pollutant coordinates, and then calculating the emission flow rate using a first calculation formula;

[0021] The first calculation formula is:

[0022] S i =V i / T i

[0023] Among them, S i is the discharge flow at the i-th time point, V i is the pollutant volume at the i-th time point, T i is the time value of the i-th time point.

[0024] In one or more embodiments, preferably, the sequentially inputting ground meteorological data and sounding meteorological data as meteorological data sources specifically includes:

[0025] Obtaining precipitation type, precipitation amount, humidity, ground pressure, temperature, wind direction and speed, and cloud cover as the meteorological data;

[0026] Obtaining wind speed, wind direction, dew point, temperature, and geopotential height at each altitude layer under different pressures as the sounding meteorological data;

[0027] The meteorological data and the sounding meteorological data are used as the meteorological data source.

[0028] In one or more embodiments, preferably, the obtaining of meteorological background initial values, terrain elevation data and pollution source data, and generating site characteristics and comprehensive pollution source data, specifically includes:

[0029] According to the hourly values ​​of meteorological elements, the meteorological background initial values ​​are used;

[0030] Generate terrain elevation data from land use types and land classification;

[0031] The corresponding longitude and latitude are used as pollution source data;

[0032] Taking the meteorological background initial value and terrain elevation data as site characteristics;

[0033] The pollutant coordinates, the emission rate, the emission flow rate and the emission height are taken as comprehensive pollution source data.

[0034] In one or more embodiments, preferably, the obtaining of the meteorological data source and the comprehensive pollution source data, inputting into a pollutant propagation model, and generating a pollutant concentration at any point specifically includes:

[0035] Acquiring the meteorological data source and the comprehensive pollution source data;

[0036] Set the initial values ​​of the lateral diffusion coefficient and the vertical diffusion coefficient;

[0037] The pollutant concentration at any point is obtained using the second calculation formula;

[0038] The second calculation formula is:

[0039]

[0040] Where: C represents the pollutant concentration at any point; Q represents the pollutant emission per unit time; σ y , σ z They represent the lateral and vertical diffusion coefficients respectively, which are functions of the distance x; u represents the average wind speed at the emission port; H represents the effective source height, that is, the effective height of the chimney; x represents the distance from the emission point of the pollution source to any point downwind; y represents the distance from the central axis of the flue gas to any point in the right horizontal direction; z represents the height from the ground to any point.

[0041] In one or more embodiments, preferably, the step of acquiring the pollutant propagation model to obtain the target lateral diffusion coefficient and the target transverse diffusion coefficient corresponding to the minimum error specifically includes:

[0042] According to the pollutant propagation model;

[0043] Obtain the current pollutant emission rate and the coordinate position of the pollutants;

[0044] Obtain hourly meteorological data and terrain elevation data;

[0045] inputting the pollutant propagation model;

[0046] The corresponding lateral diffusion coefficient and longitudinal diffusion coefficient are obtained by using the third calculation formula;

[0047] The third calculation formula is:

[0048] (σ ym , σ zm ) = argmin(C(x,y,z,H))

[0049] Among them, σ ym , σ zm These are the lateral and vertical diffusion coefficients of the target obtained respectively.

[0050] In one or more embodiments, preferably, obtaining the target lateral diffusion coefficient and the target transverse diffusion coefficient and generating heavy metal pollutant mapping from the pollutant results specifically includes:

[0051] Obtaining the target lateral diffusion coefficient and the target transverse diffusion coefficient, and obtaining a target meteorological model;

[0052] Obtain a geographic converter to convert the rectangular coordinate system corresponding to the original pollutant results into geographic coordinates;

[0053] Based on the geographic coordinates and the target meteorological model, the soil heavy metal pollutant map is mapped using the co-kriging interpolation method.

[0054] According to a second aspect of an embodiment of the present invention, a system for predicting and optimizing the spatial distribution of coking soil pollution is provided.

[0055] In one or more embodiments, preferably, the coking soil pollution spatial distribution prediction and optimization system comprises:

[0056] The data acquisition module is used to obtain the smoke diffusion range using acquisition equipment, calculate pollutant coordinates, emission rate, ground meteorological data, emission height, and emission flow rate;

[0057] The propagation prediction module is used to sequentially input the ground meteorological data and the sounding meteorological data as the meteorological data source;

[0058] Atmospheric diffusion analysis module, used to obtain meteorological background initial values, terrain elevation data and pollution source data, and generate site characteristics and comprehensive pollution source data;

[0059] A pollutant diffusion prediction module is used to obtain the meteorological data source and the comprehensive pollution source data, input the pollutant propagation model, and generate the pollutant concentration at any point;

[0060] A data acquisition verification module is used to obtain the pollutant propagation model and obtain the target lateral diffusion coefficient and the target transverse diffusion coefficient corresponding to the minimum error;

[0061] The interpolation operation module is used to obtain the target lateral diffusion coefficient and the target transverse diffusion coefficient, and generate heavy metal pollutant mapping from the pollutant results.

[0062] According to a third aspect of an embodiment of the present invention, there is provided a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method as described in any one of the first aspect of the embodiment of the present invention is implemented.

[0063] According to a fourth aspect of an embodiment of the present invention, there is provided an electronic device, comprising a memory and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement any one of the steps described in the first aspect of the embodiment of the present invention.

[0064] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0065] 1) In the embodiment of the present invention, the law of pollutant diffusion with the atmosphere is simulated based on the Gaussian plume model, and the pollutant emission particles of the coking plant cycle are simulated to provide a data basis for collaborative estimation;

[0066] 2) In the embodiment of the present invention, the particulate matter deposition data is simulated based on the parameter input of the prescribed pollution model combined with the pollutant emission data;

[0067] 3) In the embodiment of the present invention, a geographic coordinate converter is used to convert the rectangular coordinates of the corresponding points in the model prediction of pollutant emissions into geographic coordinates, and the point data and pollutant concentration data are imported into the co-kriging interpolation method to map the distribution of heavy metal pollutant concentrations in coking soil to improve the prediction accuracy.

[0068] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0069] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0071] Figure 1 The present invention is a flowchart of a method for predicting and optimizing the spatial distribution of coking soil pollution according to an embodiment of the present invention.

[0072] Figure 2 It is a flowchart of a method for predicting and optimizing the spatial distribution of coking soil pollution in one embodiment of the present invention, which uses collection equipment to obtain the smoke diffusion range, calculate pollutant coordinates, emission rate, ground meteorological data, emission height, and emission flow.

[0073] Figure 3 This is a flowchart of sequentially inputting ground meteorological data and sounding meteorological data as meteorological data sources in a method for predicting and optimizing the spatial distribution of coking soil pollution in one embodiment of the present invention.

[0074] Figure 4 The present invention is a flowchart of obtaining meteorological background initial values, terrain elevation data and pollution source data, and generating site characteristics and comprehensive pollution source data in a method for predicting and optimizing the spatial distribution of coking soil pollution in one embodiment of the present invention.

[0075] Figure 5 It is a flowchart of obtaining the meteorological data source and the comprehensive pollution source data, inputting the pollutant propagation model, and generating the pollutant concentration at any point in a method for predicting and optimizing the spatial distribution of coking soil pollution in one embodiment of the present invention.

[0076] Figure 6 It is a flowchart of obtaining the pollutant propagation model in a method for predicting and optimizing the spatial distribution of coking soil pollution in one embodiment of the present invention, and obtaining the corresponding target lateral diffusion coefficient and target transverse diffusion coefficient when the error is minimum.

[0077] Figure 7It is a flowchart of obtaining the target lateral diffusion coefficient and the target transverse diffusion coefficient in a method for predicting and optimizing the spatial distribution of coking soil pollution in one embodiment of the present invention, and generating heavy metal pollutant mapping from the pollutant results.

[0078] Figure 8 It is a comparison chart of the daily average emission particulate matter concentration and total deposition amount according to an embodiment of the present invention.

[0079] Fig. 9 This is a comparison picture of the interpolation results of an embodiment of the present invention.

[0080] Fig.10 It is a structural diagram of a coking soil pollution spatial distribution prediction and optimization system according to an embodiment of the present invention.

[0081] Fig.11 It is a structural diagram of an electronic device in one embodiment of the present invention. DETAILED DESCRIPTION

[0082] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0083] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0084] Coking contaminated soil has strong spatial variability, the difference between the maximum and minimum soil pollutant content can be as high as 730 times, and the coefficient of variation can be as high as 398%. In order to obtain the spatial distribution characteristics of pollutant content, the existing technology mainly obtains the spatial distribution information of pollutant content based on spatial interpolation models.

[0085] Prior to the present invention, existing studies have mainly improved the accuracy of interpolation by screening interpolation models, combining categorical auxiliary variables and geostatistics. However, existing methods are limited to optimizing prediction effects through mathematical analysis, lacking the study of the inherent laws of widespread coking soil pollution, that is, the driving mechanism of soil pollution, resulting in prediction results that are limited to soil pollution characteristics close to specific research enterprises, and are difficult to directly apply to the spatial distribution pattern of soil pollution in other polluting enterprises. There are large differences in the prediction effects obtained by applying it to different research objects.

[0086] The reason for these defects in the existing foundation is that the spatial interpolation model is based on the theory of spatial autocorrelation, that is, values ​​in adjacent spatial positions have correlation and similar characteristics. This assumption is inconsistent with the strong spatial variation characteristics of coking soil pollution, making it difficult to predict its variation form and difficult to provide support for coking soil remediation and management.

[0087] In an embodiment of the present invention, a method and system for predicting and optimizing the spatial distribution of coking soil pollution is provided. The scheme calculates the simulation results of particulate matter emitted by enterprises through an atmospheric diffusion model, and applies them as collaborative variables to the collaborative Kriskin interpolation method to improve the prediction accuracy of heavy metal pollutants in the soil around the coking plant.

[0088] According to a first aspect of an embodiment of the present invention, a method for predicting and optimizing the spatial distribution of coking soil pollution is provided.

[0089] Figure 1 The present invention is a flowchart of a method for predicting and optimizing the spatial distribution of coking soil pollution according to an embodiment of the present invention.

[0090] like Figure 1 As shown, in one or more embodiments, preferably, the method for predicting and optimizing the spatial distribution of coking soil pollution includes:

[0091] S101. Use the collection equipment to obtain the smoke diffusion range, calculate the pollutant coordinates, emission rate, ground meteorological data, emission height, and emission flow rate;

[0092] S102, inputting ground meteorological data and sounding meteorological data in sequence as meteorological data sources;

[0093] S103, obtaining meteorological background initial values, terrain elevation data and pollution source data, and generating site characteristics and comprehensive pollution source data;

[0094] S104, obtaining the meteorological data source and the comprehensive pollution source data, inputting into the pollutant propagation model, and generating the pollutant concentration at any point;

[0095] S105, acquiring the pollutant propagation model, and obtaining the target lateral diffusion coefficient and the target transverse diffusion coefficient corresponding to the minimum error;

[0096] S106, obtaining the target lateral diffusion coefficient and the target transverse diffusion coefficient, and generating a heavy metal pollutant map from the pollutant results.

[0097] In an embodiment of the present invention, in order to improve the accuracy of interpolation prediction of heavy metals in soil around coking plants and provide more accurate instructions for the prevention and control of surrounding soil pollution and remediation, the simulation results of particulate matter emissions from coking enterprises calculated by the atmospheric diffusion model are applied as collaborative variables to the collaborative Kriging interpolation method, and compared with conventional interpolation methods such as the interpolation method and the inverse distance weighted method. The prediction accuracy of the spatial distribution pattern of coking soil pollution is better, which can provide more accurate guidance for soil remediation and pollution control around coking enterprises.

[0098] Figure 2 It is a flowchart of a method for predicting and optimizing the spatial distribution of coking soil pollution in one embodiment of the present invention, which uses collection equipment to obtain the smoke diffusion range, calculate pollutant coordinates, emission rate, ground meteorological data, emission height, and emission flow.

[0099] like Figure 2 As shown, in one or more embodiments, preferably, the method of using the acquisition equipment to obtain the smoke diffusion range, calculate the pollutant coordinates, emission rate, ground meteorological data, emission height, and emission flow rate specifically includes:

[0100] S201, using a collection device to obtain the smoke diffusion range;

[0101] S202, dividing the smoke diffusion range into a plurality of small-scale blocks according to a preset division scale according to the smoke diffusion range;

[0102] S203, acquiring pollutant coordinates according to the small-scale blocks;

[0103] S204, obtaining the emission rate and emission height of pollutants;

[0104] S205, obtaining the maximum value of the pollutant coordinates according to the small-scale block as terrain height data;

[0105] S206, extracting the overall pollutant volume according to the pollutant coordinates, and then calculating the emission flow rate using a first calculation formula;

[0106] The first calculation formula is:

[0107] S i =V i / T i

[0108] Among them, S i is the discharge flow at the i-th time point, Vi is the pollutant volume at the i-th time point, T i is the time value of the i-th time point.

[0109] In the embodiment of the present invention, specifically, firstly, the corresponding smoke diffusion position is obtained by collecting equipment, and this acquisition process is divided into small scales to generate multiple small scales; further, the pollutant coordinates, emission rate, ground meteorological data, emission height, emission flow, terrain height data are collected by collecting equipment; the terrain elevation data, the background map and the coordinates are used as site features.

[0110] In the embodiment of the present invention, the Gaussian plume model specifically diffuses the plume emitted by continuous point sources, assuming that its concentration distribution obeys the empirical model of normal distribution. The plume is transported downwind at the average wind speed, ignoring the turbulent diffusion along the wind, and the vertical and horizontal lateral diffusion are described by diffusion parameters. Strictly speaking, the normal concentration distribution is only valid under stable and uniform conditions, which does not conform to the actual atmospheric situation. However, due to the relatively simple form and parameters of the formula, it is still widely used in diffusion calculations, and can be approximately applied to small-scale diffusion calculations on open and flat terrain when the wind speed is not too small, and can also be converted into various variant modes according to the particularities of terrain, meteorological conditions and the geometric shape of the emission source.

[0111] Figure 3 This is a flowchart of sequentially inputting ground meteorological data and sounding meteorological data as meteorological data sources in a method for predicting and optimizing the spatial distribution of coking soil pollution in one embodiment of the present invention.

[0112] like Figure 3 As shown, in one or more embodiments, preferably, the sequentially inputting ground meteorological data and sounding meteorological data as meteorological data sources specifically includes:

[0113] S301, obtaining precipitation type, precipitation amount, humidity, ground pressure, temperature, wind direction and speed, and cloud cover as the meteorological data;

[0114] S302, obtaining wind speed, wind direction, dew point, temperature, and geopotential height at each altitude layer under different pressures as the sounding meteorological data;

[0115] S303: Use the meteorological data and the sounding meteorological data as the meteorological data source.

[0116] In the embodiment of the present invention, the Gaussian plume model specifically diffuses the plume emitted by continuous point sources, assuming that its concentration distribution obeys the empirical model of normal distribution. The plume is transported downwind at the average wind speed, ignoring the turbulent diffusion along the wind, and the vertical and horizontal lateral diffusion are described by diffusion parameters. Strictly speaking, the normal concentration distribution is only valid under stable and uniform conditions, which does not conform to the actual atmospheric situation. However, due to the relatively simple form and parameters of the formula, it is still widely used in diffusion calculations, and can be approximately applied to small-scale diffusion calculations on open and flat terrain when the wind speed is not too small, and can also be converted into various variant modes according to the particularities of terrain, meteorological conditions and the geometric shape of the emission source.

[0117] Figure 4 The present invention is a flowchart of obtaining meteorological background initial values, terrain elevation data and pollution source data, and generating site characteristics and comprehensive pollution source data in a method for predicting and optimizing the spatial distribution of coking soil pollution in one embodiment of the present invention.

[0118] like Figure 4 As shown, in one or more embodiments, preferably, the obtaining of meteorological background initial values, terrain elevation data and pollution source data, and generating site characteristics and comprehensive pollution source data specifically includes:

[0119] S401, taking the hourly values ​​of meteorological elements as the initial values ​​of the meteorological background;

[0120] S402, generating terrain elevation data by land use type and land classification;

[0121] S403, taking the corresponding longitude and latitude as pollution source data;

[0122] S404, using the initial value of the meteorological background and the terrain elevation data as site features;

[0123] S405. Use the pollutant coordinates, the emission rate, the emission flow rate and the emission height as comprehensive pollution source data.

[0124] In an embodiment of the present invention, before conducting a meteorological analysis, it is first necessary to combine site characteristics and comprehensive pollution source data, and use the hourly values ​​of meteorological elements as the meteorological background initial value. The specific source of the meteorological background initial value is the MM5 physical quantity field simulation calculation. The terrain data can be selected from the United States Geographical Survey, and the pollution source data can be obtained from the AutoNavi open platform, Google Maps, and the online monitoring website of the coking enterprise.

[0125] Figure 5It is a flowchart of obtaining the meteorological data source and the comprehensive pollution source data, inputting the pollutant propagation model, and generating the pollutant concentration at any point in a method for predicting and optimizing the spatial distribution of coking soil pollution in one embodiment of the present invention.

[0126] like Figure 5 As shown, in one or more embodiments, preferably, the obtaining of the meteorological data source and the comprehensive pollution source data, inputting the pollutant propagation model, and generating the pollutant concentration at any point specifically includes:

[0127] S501, obtaining the meteorological data source and the comprehensive pollution source data;

[0128] S502, setting initial values ​​of the lateral diffusion coefficient and the vertical diffusion coefficient;

[0129] S503, using the second calculation formula to obtain the pollutant concentration at any point;

[0130] The second calculation formula is:

[0131]

[0132] Where: C represents the pollutant concentration at any point; Q represents the pollutant emission per unit time; σ y , σ z They represent the lateral and vertical diffusion coefficients respectively, which are functions of the distance x; u represents the average wind speed at the emission port; H represents the effective source height, that is, the effective height of the chimney; x represents the distance from the emission point of the pollution source to any point downwind; y represents the distance from the central axis of the flue gas to any point in the right horizontal direction; z represents the height from the ground to any point.

[0133] In the embodiment of the present invention, under a stable state, the x-direction changes non-randomly with the wind speed, while the plume is Gaussian distributed in a stable y-direction and a vertical z-direction.

[0134] Figure 6 It is a flowchart of obtaining the pollutant propagation model in a method for predicting and optimizing the spatial distribution of coking soil pollution in one embodiment of the present invention, and obtaining the corresponding target lateral diffusion coefficient and target transverse diffusion coefficient when the error is minimum.

[0135] like Figure 6 As shown, in one or more embodiments, preferably, the step of obtaining the pollutant propagation model to obtain the target lateral diffusion coefficient and the target transverse diffusion coefficient corresponding to the minimum error specifically includes:

[0136] S601, according to the pollutant propagation model;

[0137] S602, obtaining the current pollutant emission rate and the coordinate position of the pollutant;

[0138] S603, obtaining hourly meteorological data and terrain elevation data;

[0139] S604, inputting the pollutant propagation model;

[0140] S605, obtaining corresponding lateral diffusion coefficient and longitudinal diffusion coefficient using a third calculation formula;

[0141] The third calculation formula is:

[0142] (σ ym , σ zm ) = argmin(C(x,y,z,H))

[0143] Among them, σ ym , σ zm These are the lateral and vertical diffusion coefficients of the target obtained respectively.

[0144] In an embodiment of the present invention, the simulation process mainly includes data acquisition, data processing and input, AERSCREEN screening meteorological parameter settings, AERMOD forecast meteorological parameter input and AERMOD forecast point concentration settings and result plotting. In terms of data acquisition of polluted emission particulate matter, according to the detection results of the geographic detector, it can be known that the heavy metal pollution outside the coking contaminated soil mainly comes from coking waste gas. Therefore, when using AERMOD to simulate the external particulate matter concentration of a coking plant, it is necessary to select the coking waste gas emission point in the coking plant as the model input of the pollution source. Combined with the data of waste gas emission points such as coke oven chimneys, coal loading, coke pushing, dry quenching, storage tanks, desulfurization regeneration towers, ammonium sulfate crystallization and drying, and 7 publicly released online monitoring points of a coking plant, the relevant data on its particulate matter emissions are obtained. The above-mentioned acquired data is input into the model software as pollution source data to complete the particulate matter deposition data simulation of the model results.

[0145] Figure 7 It is a flowchart of obtaining the target lateral diffusion coefficient and the target transverse diffusion coefficient in a method for predicting and optimizing the spatial distribution of coking soil pollution in one embodiment of the present invention, and generating heavy metal pollutant mapping from the pollutant results.

[0146] like Figure 7 As shown, in one or more embodiments, preferably, obtaining the target lateral diffusion coefficient and the target transverse diffusion coefficient, and generating heavy metal pollutant mapping from the pollutant results, specifically includes:

[0147] S701, obtaining the target lateral diffusion coefficient and the target transverse diffusion coefficient, and obtaining a target meteorological model;

[0148] S702, obtaining a geographic converter to convert the rectangular coordinate system corresponding to the original pollutant results into geographic coordinates;

[0149] S703: According to the geographic coordinates and the target meteorological model, the soil heavy metal pollutant map is mapped using the co-kriging interpolation method.

[0150] In an embodiment of the present invention, a geographic coordinate converter is used to convert the rectangular coordinates of the corresponding points in the model prediction of pollutant emission results into geographic coordinates, and the point data and pollutant concentration data are imported into ArcGIS 10.2 for mapping the distribution of heavy metal pollutant concentrations in coking soil using the co-kriging interpolation method.

[0151] Specifically, Figure 8 The average daily emission particulate matter concentration and total deposition. Figure 8 As shown in the figure, the daily average concentration and total deposition of particulate matter in the atmosphere vary greatly, among which the maximum value of the daily average concentration is 2.55μg·m-3, the minimum value is 0.11μg·m-3, the coefficient of variation is greater than 0.15, and the concentration difference is more than 20 times; the maximum value of the daily average total deposition is 4.34×10-2μg·m-2, the minimum value is 5.47×10-5μg·m-2, the coefficient of variation is greater than 0.15, and the deposition difference is more than 700 times. And combined with the kurtosis and skewness results of the two sets of data, it can be seen that the daily average concentration and total deposition both show a left-biased distribution, indicating that among the particulate matter emitted on a single day, the concentration and deposition in most of the surrounding areas are low after diffusion, and the concentration and deposition in a small part of the area are high. The area with higher daily particulate matter concentration is mainly in the range of about 2000m northwest of the emission source, and the area with higher deposition is mainly in the range of about 1000m between the emission source and its northwest.

[0152] The results of the AERMOD model simulation can more accurately reflect the diffusion and deposition of coking waste gas around coking enterprises after emission. And through the analysis of pollution sources and driving factors, it can be known that among the sources of heavy metal pollution in the soil around the coking plant, the deposition of coking waste gas is one of its sources, indicating that there is a certain correlation between heavy metals and deposition around the coking plant. Therefore, the simulated total deposition data can be used in the spatial interpolation method below to improve the accuracy of spatial interpolation.

[0153] In order to further determine the difference in the effect of interpolation methods in spatial interpolation, this part of the study uses common interpolation methods (inverse distance weighted method, simple kriging method, ordinary kriging method and universal kriging method) to compare the interpolation effects of 8 heavy metals in coking plants. It can be found that the RMSE of ordinary kriging for interpolation of 8 heavy metals is smaller than that of the other three interpolation methods, and the interpolation accuracy is higher. Therefore, in the subsequent study of interpolation optimization analysis, ordinary kriging interpolation method and co-kriging interpolation method are selected to compare, in order to determine whether the heavy metal interpolation method that eliminates peripheral points has higher interpolation accuracy, and whether the emission particulate matter deposition results simulated by AERMOD can improve the accuracy of the interpolation model.

[0154] Ordinary Kriging interpolation and co-Kriging interpolation based on atmospheric diffusion model simulation were performed using ArcGIS 10.2 to obtain the spatial distribution interpolation results of 8 heavy metals around the coking plant. According to the qualitative judgment of the interpolation results, compared with the ordinary Kriging interpolation method, the interpolation results of the co-Kriging method with the total deposition results of coking particulate matter predicted by AERMOD as the covariate are more accurate than those of the ordinary Kriging method, and the prediction effect of pollutants has been significantly improved.

[0155] Fig. 9 This is a comparison picture of the interpolation result. Fig. 9 As shown in the figure, the accuracy comparison results of OK and COK were obtained after cross-validation. From the RMSE data results, it can be found that after solving the problems existing in the original sampling points, the collaborative Kriging interpolation based on the prediction results of particulate matter emitted by AERMOD coking enterprises can significantly improve the interpolation results of 8 heavy metals. Among them, the RMSE of heavy metal As decreased from 2.050 to 1.887, Cd decreased from 0.179 to 0.161, Cr decreased from 19.071 to 12.968, Cu decreased from 6.921 to 4.721, Hg decreased from 0.323 to 0.281, Ni decreased from 16.918 to 16.096, Pb decreased from 18.285 to 16.622, and Zn decreased from 159.676 to 153.658. The interpolation effects of the 8 heavy metals in the study have been significantly improved.

[0156] According to a second aspect of an embodiment of the present invention, a system for predicting and optimizing the spatial distribution of coking soil pollution is provided.

[0157] Fig.10 It is a structural diagram of a coking soil pollution spatial distribution prediction and optimization system according to an embodiment of the present invention.

[0158] like Fig.10 As shown, in one or more embodiments, preferably, the coking soil pollution spatial distribution prediction and optimization system comprises:

[0159] The data acquisition module 1001 is used to obtain the smoke diffusion range by using the acquisition equipment, and calculate the pollutant coordinates, emission rate, ground meteorological data, emission height, and emission flow rate;

[0160] The propagation prediction module 1002 is used to sequentially input ground meteorological data and sounding meteorological data as meteorological data sources;

[0161] Atmospheric diffusion analysis module 1003, used to obtain meteorological background initial values, terrain elevation data and pollution source data, and generate site characteristics and comprehensive pollution source data;

[0162] The pollutant diffusion prediction module 1004 is used to obtain the meteorological data source and the comprehensive pollution source data, input the pollutant propagation model, and generate the pollutant concentration at any point;

[0163] The acquisition data verification module 1005 is used to obtain the pollutant propagation model and obtain the target lateral diffusion coefficient and the target transverse diffusion coefficient corresponding to the minimum error;

[0164] The interpolation operation module 1006 is used to obtain the target lateral diffusion coefficient and the target transverse diffusion coefficient, and generate heavy metal pollutant mapping from the pollutant results.

[0165] According to a third aspect of an embodiment of the present invention, there is provided a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method as described in any one of the first aspect of the embodiment of the present invention is implemented.

[0166] According to a fourth aspect of an embodiment of the present invention, an electronic device is provided. Fig.11 It is a structural diagram of an electronic device in one embodiment of the present invention. Fig.11The electronic device shown is a general pollution spatial distribution prediction and optimization device, which includes a general computer hardware structure, which at least includes a processor 1101 and a memory 1102. The processor 1101 and the memory 1102 are connected via a bus 1103. The memory 1102 is suitable for storing instructions or programs executable by the processor 1101. The processor 1101 can be an independent microprocessor or a collection of one or more microprocessors. Thus, the processor 1101 executes the instructions stored in the memory 1102, thereby executing the method flow of the embodiment of the present invention as described above to realize the processing of data and the control of other devices. The bus 1103 connects the above-mentioned multiple components together, and at the same time connects the above-mentioned components to the display controller 1104 and the display device and the input / output (I / O) device 1105. The input / output (I / O) device 1105 can be a mouse, a keyboard, a modem, a network interface, a touch input device, a somatosensory input device, a printer, and other devices known in the art. Typically, the input / output device 1105 is connected to the system via an input / output (I / O) controller 1106.

[0167] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0168] 1) In the embodiment of the present invention, the law of pollutant diffusion with the atmosphere is simulated based on the Gaussian plume model, and the pollutant emission particles of the coking plant cycle are simulated to provide a data basis for collaborative estimation;

[0169] 2) In the embodiment of the present invention, the particulate matter deposition data is simulated based on the parameter input of the prescribed pollution model combined with the pollutant emission data;

[0170] 3) In the embodiment of the present invention, a geographic coordinate converter is used to convert the rectangular coordinates of the corresponding points in the model prediction of pollutant emissions into geographic coordinates, and the point data and pollutant concentration data are imported into the co-kriging interpolation method to map the distribution of heavy metal pollutant concentrations in coking soil to improve the prediction accuracy.

[0171] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0172] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0173] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0174] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0175] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for predicting and optimizing the spatial distribution of coking soil pollution, characterized in that: The method includes: Use the acquisition equipment to obtain the smoke diffusion range, calculate the pollutant coordinates, emission rate, ground meteorological data, emission height, and emission flow; Input ground meteorological data and sounding meteorological data in sequence as meteorological data sources; Obtain initial meteorological background values, terrain elevation data and pollution source data to generate site characteristics and comprehensive pollution source data; Obtain the meteorological data source and the comprehensive pollution source data, input into the pollutant propagation model, and generate the pollutant concentration at any point; Acquire the pollutant propagation model to obtain the target lateral diffusion coefficient and the target transverse diffusion coefficient corresponding to the minimum error; Obtaining the target lateral diffusion coefficient and the target transverse diffusion coefficient, and generating heavy metal pollutant mapping from the pollutant results; The step of obtaining the target lateral diffusion coefficient and the target transverse diffusion coefficient and generating heavy metal pollutant mapping from the pollutant results specifically includes: Obtaining the target lateral diffusion coefficient and the target transverse diffusion coefficient, and obtaining a target meteorological model; Obtain a geographic converter to convert the rectangular coordinate system corresponding to the original pollutant results into geographic coordinates; Based on the geographic coordinates and the target meteorological model, the soil heavy metal pollutant map is mapped using the co-kriging interpolation method.

2. A method for predicting and optimizing the spatial distribution of coking soil pollution according to claim 1, characterized in that: The use of acquisition equipment to obtain the smoke diffusion range, calculate pollutant coordinates, emission rate, ground meteorological data, emission height, and emission flow specifically includes: Use the collection equipment to obtain the smoke diffusion range; According to the smoke diffusion range, the smoke diffusion range is divided into a plurality of small-scale blocks according to a preset division scale; Acquiring pollutant coordinates according to the small-scale blocks; Obtain the emission rate and emission height of pollutants; Obtain the highest value of the pollutant coordinates according to the small-scale block as terrain height data; Extracting the overall pollutant volume according to the pollutant coordinates, and then calculating the emission flow rate using a first calculation formula; The first calculation formula is: S i =V i / T i in, S i For the i The emission flow at a point in time, V i For the i The volume of pollutants at each time point, T i For the i The time value of a time point.

3. The method for predicting and optimizing the spatial distribution of coking soil pollution according to claim 1, characterized in that: The sequential input of ground meteorological data and sounding meteorological data as meteorological data sources specifically includes: Obtaining precipitation type, precipitation amount, humidity, ground pressure, temperature, wind direction and speed, and cloud cover as the meteorological data; Obtaining wind speed, wind direction, dew point, temperature, and geopotential height at each altitude layer under different pressures as the sounding meteorological data; The meteorological data and the sounding meteorological data are used as the meteorological data source.

4. The method for predicting and optimizing the spatial distribution of coking soil pollution according to claim 1, characterized in that: The acquisition of meteorological background initial values, terrain elevation data and pollution source data, and the generation of site characteristics and comprehensive pollution source data specifically include: According to the hourly values ​​of meteorological elements, the meteorological background initial values ​​are used; Generate terrain elevation data from land use types and land classification; The corresponding longitude and latitude are used as pollution source data; Taking the meteorological background initial value and terrain elevation data as site characteristics; The pollutant coordinates, the emission rate, the emission flow rate and the emission height are taken as comprehensive pollution source data.

5. The method for predicting and optimizing the spatial distribution of coking soil pollution according to claim 1, characterized in that: The step of obtaining the meteorological data source and the comprehensive pollution source data, inputting the pollutant propagation model, and generating the pollutant concentration at any point specifically includes: Acquiring the meteorological data source and the comprehensive pollution source data; Set the initial values ​​of the lateral diffusion coefficient and the vertical diffusion coefficient; The pollutant concentration at any point is obtained using the second calculation formula; The second calculation formula is: ； In the formula: C represents the pollutant concentration at any point; Q represents the pollutant emission per unit time; They represent the lateral and vertical diffusion coefficients respectively, which are functions of the distance x; u represents the average wind speed at the emission port; H represents the effective source height, that is, the effective height of the chimney; x represents the distance from the emission point of the pollution source to any point downwind; y represents the distance from the central axis of the flue gas to any point in the right horizontal direction; z represents the height from the ground to any point.

6. The method for predicting and optimizing the spatial distribution of coking soil pollution according to claim 1, characterized in that: The step of obtaining the pollutant propagation model to obtain a target lateral diffusion coefficient and a target transverse diffusion coefficient corresponding to the minimum error specifically includes: According to the pollutant propagation model; Obtain the current pollutant emission rate and the coordinate position of the pollutants; Obtain hourly meteorological data and terrain elevation data; inputting the pollutant propagation model; The corresponding lateral diffusion coefficient and longitudinal diffusion coefficient are obtained by using the third calculation formula; The third calculation formula is: ( σ ym 、 σ zm )=argmin(△C(x,y,z,H)) in, σ ym , σ zm are the lateral and vertical diffusion coefficients of the target obtained respectively, and △C(x, y, z, H) is used to represent the error of C(x, y, z, H).

7. A coking soil pollution spatial distribution prediction and optimization system, characterized in that: The system includes: The data acquisition module is used to obtain the smoke diffusion range using acquisition equipment, calculate pollutant coordinates, emission rate, ground meteorological data, emission height, and emission flow rate; The propagation prediction module is used to sequentially input the ground meteorological data and the sounding meteorological data as the meteorological data source; Atmospheric diffusion analysis module, used to obtain meteorological background initial values, terrain elevation data and pollution source data, and generate site characteristics and comprehensive pollution source data; A pollutant diffusion prediction module is used to obtain the meteorological data source and the comprehensive pollution source data, input the pollutant propagation model, and generate the pollutant concentration at any point; A data acquisition verification module is used to obtain the pollutant propagation model and obtain the target lateral diffusion coefficient and the target transverse diffusion coefficient corresponding to the minimum error; An interpolation operation module is used to obtain the target lateral diffusion coefficient and the target transverse diffusion coefficient, and generate heavy metal pollutant mapping from the pollutant results; The step of obtaining the target lateral diffusion coefficient and the target transverse diffusion coefficient and generating heavy metal pollutant mapping from the pollutant results specifically includes: Obtaining the target lateral diffusion coefficient and the target transverse diffusion coefficient, and obtaining a target meteorological model; Obtain a geographic converter to convert the rectangular coordinate system corresponding to the original pollutant results into geographic coordinates; Based on the geographic coordinates and the target meteorological model, the soil heavy metal pollutant map is mapped using the co-kriging interpolation method.

8. A computer-readable storage medium storing computer program instructions, characterized in that: The computer program instructions implement the method according to any one of claims 1 to 6 when executed by a processor.

9. An electronic device, comprising a memory and a processor, characterized in that: The memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method according to any one of claims 1-6.

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