A smoke plume pollution early warning method, device, equipment and storage medium

Abstract

The present invention discloses a smoke plume pollution early warning method, device, equipment and storage medium, which are applied to the field of smoke plume pollution early warning, obtain radial wind data and radar signal-to-noise ratio detected by wind laser radar, obtain horizontal wind field based on radial wind data inversion, obtain extinction coefficient of each sampling point based on radar signal-to-noise ratio inversion; determine smoke plume emission rate based on target sampling point extinction coefficient; determine smoke plume concentration of spatial point in nested rectangle through Gaussian smoke plume model based on regional wind field and smoke plume emission rate of each nested rectangle determined by horizontal wind field, and carry out smoke plume pollution early warning. Calculation of Gaussian smoke plume model based on detection data of wind laser radar avoids deviation between prediction result and actual situation caused by constant horizontal wind field and constant emission rate of pollution source, further divides the entire horizontal wind field into multiple regional wind fields, avoids assumption that wind fields in different regions are consistent, is more in line with actual situation, and further improves the accuracy of prediction result.

Description

Technical Field

[0001] The present invention relates to the field of smoke plume pollution early warning, and in particular to a smoke plume pollution early warning method, device, equipment and computer-readable storage medium. Background Art

[0002] When a forest fire occurs, a large amount of smoke plumes are generated, causing serious air pollution in the downwind area, which may pose a threat to human health and have adverse effects on society and the economy. Therefore, it is of great value and significance to issue smoke plume warnings in the downwind area so that the public can take preventive measures in advance. The Gaussian plume model is a commonly used atmospheric pollutant diffusion model that is used to describe the spatial distribution of pollutants in the atmosphere and is widely used to predict and evaluate the impact of pollution sources. In the process of applying the Gaussian plume model, the existing related technologies assume that the assumption of consistent wind fields in different regions is avoided, which is more in line with the actual situation, and there are cases of false alarms and missed alarms in smoke plume pollution warnings. Summary of the invention

[0003] The purpose of the present invention is to provide a smoke plume pollution early warning method, device, equipment and computer-readable storage medium, which are applied to the field of smoke plume pollution early warning. The method calculates a Gaussian smoke plume model based on the detection data of a wind measuring laser radar, thereby avoiding the deviation between the prediction result and the actual situation caused by the unchanged horizontal wind field and the constant emission rate of the pollution source, and further divides the entire horizontal wind field into multiple regional wind fields, avoiding the assumption that the wind fields in different regions are consistent, which is more in line with the actual situation and further improves the accuracy of the prediction results.

[0004] In order to solve the above technical problems, the present invention provides a smoke plume pollution early warning method, comprising:

[0005] Obtain radial wind data and radar signal-to-noise ratio of each sampling point at each azimuth obtained by wind laser radar scanning, obtain horizontal wind field based on the radial wind data, and obtain extinction coefficient of each sampling point based on the radar signal-to-noise ratio inversion;

[0006] Determine the sampling point whose extinction coefficient is greater than the extinction coefficient threshold as the target sampling point, construct a target point cloud based on the target sampling point, intercept the target point cloud through a plane perpendicular to the horizontal wind direction of the horizontal wind field, and determine the maximum cross-sectional area obtained by interception as the plume cross-sectional area;

[0007] Determine an average particle mass concentration based on the extinction coefficient of the target sampling point, and determine the product of the average particle mass concentration, the plume cross-sectional area and the horizontal wind speed of the horizontal wind field as the plume emission rate;

[0008] The sampling point with the largest extinction coefficient is determined as an emission source, an initial rectangle is constructed with the emission source as the center, and two sides of the initial rectangle are expanded multiple times based on the horizontal wind direction to construct multiple nested rectangles;

[0009] The regional wind field of each nested rectangle is determined based on the horizontal wind field, the plume concentration of the spatial point within the nested rectangle is determined through a Gaussian plume model based on the regional wind field and the plume emission rate, and a plume pollution warning is performed based on the plume concentration.

[0010] Optionally, determining the plume concentration of the spatial point within the nested rectangle through a Gaussian plume model based on the regional wind field and the plume emission rate includes:

[0011] Determining a smoke plume path within each of the nested rectangles based on the regional wind direction of the regional wind field;

[0012] Taking the starting point of each of the plume paths as the coordinate origin, the regional wind direction of the regional wind field as the positive direction of the x-axis, the vertical wind direction perpendicular to the regional wind direction on the horizontal plane as the positive direction of the y-axis, and the upward direction perpendicular to the horizontal plane as the positive direction of the z-axis, construct a regional coordinate system for each of the nested rectangles;

[0013] Determine the spatial coordinates of the spatial point in the regional coordinate system, and determine the initial plume concentration of the spatial point in the regional coordinate system through the Gaussian plume model based on the spatial coordinates, the regional wind speed of the regional wind field, and the plume emission rate;

[0014] A weight factor of each of the initial plume concentrations is determined, and a weighted sum is performed on each of the initial plume concentrations based on the weight factor to obtain the plume concentration at the spatial point.

[0015] Optionally, determining the weight factor of each of the initial plume concentrations includes:

[0016] The ratio of the length of the plume path within the nested rectangle to the length of the plume path passed through is determined as the weight factor of the initial plume concentration.

[0017] Optionally, determining the regional wind field of each nested rectangle based on the horizontal wind field includes:

[0018] Decomposing the horizontal wind field in each nested rectangle into a u component in the longitude direction and a v component in the latitude direction;

[0019] Determine the regional wind speed and regional wind direction of the regional wind field in each of the nested rectangles based on the u component and the v component;

[0020] Two adjacent nested rectangles whose deviation of the regional wind direction is less than a preset deviation are merged to obtain the merged nested rectangle, and the regional wind field of the merged nested rectangle is re-determined.

[0021] Optionally, determining the average particle mass concentration based on the extinction coefficient of the target sampling point includes:

[0022] A correlation model between the extinction coefficient and the particle mass concentration is determined, and an average value of the extinction coefficient of the target sampling point is used as an input of the correlation model to obtain the average particle mass concentration.

[0023] Optionally, the inverting the horizontal wind field based on the radial wind data and the inverting the extinction coefficient of each sampling point based on the radar signal-to-noise ratio include:

[0024] The horizontal wind field is obtained by inverting the radial wind data through a VAD algorithm;

[0025] The extinction coefficient of each sampling point is obtained by inverting the radar signal-to-noise ratio through the Klett backward method.

[0026] Optionally, obtaining radial wind data and radar signal-to-noise ratio of each sampling point at each azimuth obtained by scanning a wind laser radar includes:

[0027] Acquire the radial wind data and the radar signal-to-noise ratio of each sampling point at each azimuth angle obtained by scanning a wind laser radar;

[0028] A radar signal-to-noise ratio threshold is determined, the sampling point whose radar signal-to-noise ratio is less than the radar signal-to-noise ratio threshold is determined as a low-credible sampling point, and the radial wind data of the low-credible sampling point is deleted.

[0029] In order to solve the above technical problems, the present invention provides a smoke plume pollution early warning device, comprising:

[0030] The first module is used to obtain radial wind data and radar signal-to-noise ratio of each sampling point at each azimuth obtained by scanning the wind laser radar, obtain the horizontal wind field based on the radial wind data, and obtain the extinction coefficient of each sampling point based on the radar signal-to-noise ratio inversion;

[0031] The second module is used to determine the sampling point whose extinction coefficient is greater than the extinction coefficient threshold as the target sampling point, construct a target point cloud based on the target sampling point, intercept the target point cloud through a surface perpendicular to the horizontal wind direction of the horizontal wind field, and determine the maximum cross-sectional area obtained by interception as the plume cross-sectional area;

[0032] The third module is used to determine the average particle mass concentration based on the extinction coefficient of the target sampling point, and determine the product of the average particle mass concentration, the plume cross-sectional area and the horizontal wind speed of the horizontal wind field as the plume emission rate;

[0033] The fourth module is used to determine the sampling point with the largest extinction coefficient as the emission source, construct an initial rectangle with the emission source as the center, and expand two sides of the initial rectangle multiple times based on the horizontal wind direction to construct multiple nested rectangles;

[0034] The fifth module is used to determine the regional wind field of each nested rectangle based on the horizontal wind field, determine the plume concentration of the spatial point within the nested rectangle through the Gaussian plume model based on the regional wind field and the plume emission rate, and issue a plume pollution warning based on the plume concentration.

[0035] In order to solve the above technical problems, the present invention provides a smoke plume pollution early warning device, comprising:

[0036] Memory, for storing computer programs;

[0037] A processor is used to implement the smoke plume pollution early warning method when executing the computer program.

[0038] In order to solve the above technical problems, the present invention provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, the smoke plume pollution early warning method is implemented.

[0039] It can be seen that the method of the present invention obtains radial wind data and radar signal-to-noise ratio of each sampling point at each azimuth obtained by scanning the wind measuring laser radar, obtains the horizontal wind field based on the inversion of the radial wind data, and obtains the extinction coefficient of each sampling point based on the inversion of the radar signal-to-noise ratio; the sampling point whose extinction coefficient is greater than the extinction coefficient threshold is determined as the target sampling point, and the target point cloud is constructed based on the target sampling point. The target point cloud is intercepted by a surface perpendicular to the horizontal wind direction of the horizontal wind field, and the maximum cross-sectional area obtained by the interception is determined as the plume cross-sectional area; the average particle mass concentration is determined based on the extinction coefficient of the target sampling point, and the product of the average particle mass concentration, the plume cross-sectional area and the horizontal wind speed of the horizontal wind field is determined as the plume emission rate; the sampling point with the largest extinction coefficient is determined as the emission source, and an initial rectangle is constructed with the emission source as the center, and the two sides of the initial rectangle are expanded multiple times based on the horizontal wind direction to construct multiple nested rectangles; the regional wind field of each nested rectangle is determined based on the horizontal wind field, and the plume concentration of the spatial point in the nested rectangle is determined by the Gaussian plume model based on the regional wind field and the plume emission rate, and plume pollution warning is performed based on the plume concentration.

[0040] The method of the present invention calculates the Gaussian plume model based on the detection data of the wind measuring laser radar, avoids the deviation between the prediction result and the actual situation caused by the unchanged horizontal wind field and the constant emission rate of the pollution source, and further divides the entire horizontal wind field into multiple regional wind fields, thereby improving the accuracy of the wind field and further improving the accuracy of the prediction result. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0042] Figure 1 A flow chart of a smoke plume pollution early warning method provided by an embodiment of the present invention;

[0043] Figure 2 An example diagram of nested rectangles provided by an embodiment of the present invention;

[0044] Figure 3 An example diagram of a merged nested rectangle provided by an embodiment of the present invention;

[0045] Figure 4 An example diagram of a smoke plume path provided by an embodiment of the present invention;

[0046] Figure 5 This is a structural block diagram of a smoke plume pollution early warning device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] When a forest fire occurs, a large amount of smoke plumes are generated, which brings serious air pollution to the downwind area, which may pose a threat to human health and have adverse effects on society and the economy. Therefore, it is of great value to issue smoke plume warnings to the downwind area so that the public can take preventive measures in advance.

[0049] The Gaussian plume model is a commonly used atmospheric pollutant diffusion model that is used to describe the spatial distribution of pollutants (such as plumes) in the atmosphere and is widely used to predict and assess the impact of pollution sources (such as fire plumes). The Gaussian plume model assumes that the horizontal wind field is constant and the emission rate of pollution sources is constant, which is different from the actual situation, resulting in a certain deviation in the model results.

[0050] Wind laser radar can detect horizontal wind fields in different spatial regions in real time. It can also invert the extinction coefficient in real time and further obtain the actual emission rate of pollution sources. Therefore, based on Doppler wind laser radar, the prediction results obtained using the Gaussian plume model are more accurate. The prediction results can be used to obtain the concentration of plume pollutants and issue early warnings for areas that exceed the pollutant concentration threshold.

[0051] Furthermore, the present invention divides the wind field of the entire spatial area into regions to obtain the horizontal wind field of each region, thereby avoiding the assumption that the wind fields in different regions are consistent, being more in line with actual conditions, and making the final predicted plume concentration more accurate.

[0052] The following combination Figure 1 , Figure 1 A flowchart of a smoke plume pollution early warning method provided by an embodiment of the present invention, the method may include:

[0053] S101: radial wind data and radar signal-to-noise ratio of each sampling point at each azimuth obtained by wind laser radar scanning are obtained, horizontal wind field is obtained based on radial wind data inversion, and extinction coefficient of each sampling point is obtained based on radar signal-to-noise ratio inversion.

[0054] In this embodiment, a wind measuring laser radar performs a 360° scan at a preset elevation angle. This embodiment does not limit the scanning mode of the wind measuring laser radar, which can generally be a PPI (Plan Position Indicator) scan; this embodiment does not limit the specific type of the wind measuring laser radar, which can generally be a Doppler wind measuring laser radar; this embodiment also does not limit the specific degree of the preset elevation angle, and the wind measuring laser radar can generally be scanned at a low elevation angle. This embodiment does not limit the specific value of the low elevation angle, which can be set based on actual applications.

[0055] This embodiment obtains radial wind data and radar signal-to-noise ratio of each sampling point at each azimuth obtained by scanning the wind measuring laser radar. There are generally multiple sampling points at the azimuth for sampling. Due to the influence of smoke plume particles, the number of sampling points at each azimuth may be different. Radial wind data and radar signal-to-noise ratio are collected at each sampling point.

[0056] To ensure the accuracy of the final predicted plume concentration, this embodiment can perform quality control on the radial wind data and radar signal-to-noise ratio collected by the wind measuring laser radar. Specifically, this embodiment can determine the radar signal-to-noise ratio threshold, determine the sampling points whose radar signal-to-noise ratio is less than the radar signal-to-noise ratio threshold as low-confidence sampling points, and delete the radial wind data of the low-confidence sampling points.

[0057] In this embodiment, the horizontal wind field can be obtained by inverting based on the radial wind data. This embodiment does not limit the specific method of inverting to obtain the horizontal wind field. Generally, the horizontal wind field can be obtained by inverting based on the radial wind data through a VAD (Velocity Azimuth Display) algorithm.

[0058] In this embodiment, the extinction coefficient of each sampling point can be obtained by inverting the radar signal-to-noise ratio. This embodiment does not limit the specific method of inverting the extinction coefficient of each sampling point. Generally, the extinction coefficient of each sampling point can be obtained by inverting the radar signal-to-noise ratio through the Klett back method. The Klett back method is a technology for inverting the atmospheric aerosol extinction coefficient from the lidar data. This method is based on the analysis of the atmospheric backscattering signal, and a mathematical model is established to describe the relationship between the signal received by the lidar and the scattering characteristics of the aerosol particles in the atmosphere.

[0059] Specifically, this embodiment can determine the maximum inversion distance Z of the wind laser radar based on the radar signal-to-noise ratio threshold. m The Collis slope method is a method for inverting the extinction coefficient of atmospheric aerosols or cloud droplets from lidar data. The Collis slope method assumes that the atmosphere is uniformly distributed. The power equation of the wind lidar echo signal in the Collis slope method can be:

[0060] ;

[0061] Where Z is the radial distance between the sampling point and the wind lidar, P(Z) is the wind lidar echo signal power when the radial distance is Z, which can be collected by the wind lidar, β(Z) is the backscattering coefficient, E is the laser pulse emission energy, C is the radar constant, and σ is the extinction coefficient.

[0062] In this embodiment, the intermediate function S(Z)=ln[Z 2 P(Z)], taking the logarithm of both sides of the formula and taking the derivative with respect to the radial distance Z, we can get the initial extinction coefficient σ m :

[0063] ;

[0064] In the formula, σ mis the initial extinction coefficient, Z0 is the initial detection distance of the wind laser radar, and Z m is the maximum inversion distance of the wind laser radar. According to the Klett backward method, the extinction coefficient of each sampling point can be iteratively calculated as shown in the following formula:

[0065] ;

[0066] Where σ(Z) is the extinction coefficient of the sampling point when the radial distance is Z, S(Z)=ln[Z 2 P(Z)], Z0 is the initial detection distance of the wind laser radar, Z m is the maximum inversion distance of the wind lidar, k is a constant, generally 0.67~1, which can be determined based on the lidar wavelength and aerosol properties.

[0067] In this embodiment, the wind laser radar can collect the wind laser radar echo signal power P(Z) of the sampling points at various azimuth angles, substitute P(Z) into S(Z), and iteratively calculate the extinction coefficient of the sampling points at various azimuth angles through the Klett backward method.

[0068] S102: Determine the sampling points whose extinction coefficients are greater than the extinction coefficient threshold as target sampling points, construct a target point cloud based on the target sampling points, intercept the target point cloud through a surface perpendicular to the horizontal wind direction of the horizontal wind field, and determine the maximum cross-sectional area obtained by interception as the plume cross-sectional area.

[0069] The extinction coefficient is a physical quantity that describes the ability of aerosols or cloud droplets in the atmosphere to attenuate light radiation. In this embodiment, the larger the extinction coefficient, the greater the smoke plume concentration at the sampling point. Therefore, in this embodiment, the sampling point whose extinction coefficient is greater than the extinction coefficient threshold can be determined as the target sampling point, and a target point cloud can be constructed based on the target sampling point. The target point cloud can be considered as a smoke puff with a large smoke plume concentration in the area.

[0070] In this embodiment, the target point cloud can be intercepted by a surface perpendicular to the horizontal wind direction of the horizontal wind field, and the maximum cross-sectional area obtained by the interception is determined as the cross-sectional area of ​​the plume. The cross-sectional area of ​​the plume in this embodiment can be regarded as the emission window area of ​​the plume.

[0071] S103: Determine the average particle mass concentration based on the extinction coefficient of the target sampling point, and determine the product of the average particle mass concentration, the plume cross-sectional area and the horizontal wind speed of the horizontal wind field as the plume emission rate.

[0072] This embodiment can determine the average particle mass concentration based on the extinction coefficient of the target sampling point. This embodiment does not limit the specific method of determining the average particle mass concentration. Generally, the particle mass concentration is proportional to the extinction coefficient. Therefore, this embodiment can determine a correlation model between the extinction coefficient and the particle mass concentration, and use the average value of the extinction coefficient of the target sampling point as the input of the correlation model to obtain the average particle mass concentration.

[0073] This embodiment does not limit the specific type of the relevant model. Generally, a linear, exponential or other mathematical model can be established, or a machine learning method can be used to find the relationship between the extinction coefficient and the particle concentration. In this embodiment, the average particle mass concentration can be obtained based on a linear model, and the linear model can be:

[0074] ;

[0075] Where C is the mass concentration of particulate matter, σ is the extinction coefficient, and a and b are model coefficients.

[0076] In this embodiment, the product of the average particulate matter mass concentration, the plume cross-sectional area and the horizontal wind speed of the horizontal wind field can be determined as the plume emission rate, as shown in the following formula:

[0077] ;

[0078] Where q is the plume emission rate, A is the plume cross-sectional area, V is the horizontal wind speed in the horizontal wind field, and C is the particulate matter mass concentration.

[0079] S104: The sampling point with the largest extinction coefficient is determined as the emission source, an initial rectangle is constructed with the emission source as the center, and two sides of the initial rectangle are extended multiple times based on the horizontal wind direction to construct multiple nested rectangles.

[0080] In this embodiment, the sampling point with the largest extinction coefficient can be determined as the emission source, and an initial rectangle is constructed with the emission source as the center. This embodiment does not limit the length and width of the initial rectangle, which can be set based on actual applications.

[0081] In this embodiment, two sides of the initial rectangle can be expanded multiple times based on the horizontal wind direction to construct multiple nested rectangles. That is, it can be understood that multiple nested rectangles are constructed, and the multiple nested rectangles share a diagonal angle. The shared diagonal angle is determined based on the horizontal wind direction, and the emission source is the center of the innermost nested rectangle.

[0082] Specifically, since the smoke plume path propagates along the horizontal wind direction, the present embodiment can sequentially expand the two sides of the initial rectangle based on the horizontal wind direction so that the smoke plume path is contained in the nested rectangle. The specific example of the nested rectangle can be as follows: Figure 2 As shown, Figure 2The middle arrow F may indicate the horizontal wind direction of the horizontal wind field, and the nested rectangle 1 is the initial rectangle in this embodiment. The length and width of the initial rectangle are expanded along the horizontal wind direction to obtain multiple nested rectangles. This embodiment does not limit the multiples of the length and width expansion each time, nor does it limit the number of expansions, which can be set based on actual applications.

[0083] S105: Determine the regional wind field of each nested rectangle based on the horizontal wind field, determine the plume concentration of the spatial point in the nested rectangle through the Gaussian plume model based on the regional wind field and the plume emission rate, and issue a plume pollution warning based on the plume concentration.

[0084] This embodiment can determine the regional wind field of each nested rectangle based on the horizontal wind field. This embodiment does not limit the specific method of determining the regional wind field. Generally, the horizontal wind field in each nested rectangle can be decomposed into a u component in the longitude direction and a v component in the latitude direction; the regional wind speed and regional wind direction of the regional wind field in each nested rectangle are determined based on the u component and the v component; specifically, this embodiment can take the average value of the u component and the v component of each point in the regional wind field to obtain the average u component and the average v component of the regional wind field, and determine the regional wind field of each nested rectangle based on the average u component and the average v component.

[0085] Furthermore, in order to reduce the amount of calculation, the present embodiment may merge two adjacent nested rectangles whose regional wind direction deviation is less than a preset deviation to obtain a merged nested rectangle, and re-determine the regional wind field of the merged nested rectangle; for example, if Figure 2 If the deviation of the regional wind direction between the nested rectangle 3 and the nested rectangle 4 in the image is less than the preset deviation, the nested rectangle 3 and the nested rectangle 4 can be merged, and the merged nested rectangles can be as follows Figure 3 As shown, Figure 3 The nested rectangle 3 in Figure 2 The nested rectangles after merging the nested rectangles 3 and 4 can be re-merged. Figure 3 Calculation of the regional wind field for the nested rectangle 3.

[0086] In this embodiment, the plume concentration of the spatial point within the nested rectangle can be determined by the Gaussian plume model based on the regional wind field and the plume emission rate. This embodiment does not limit the specific method of determining the plume concentration. Generally, the plume path is determined within each nested rectangle based on the regional wind direction of the regional wind field; Figure 4 As shown, X1 is the smoke plume path of nested rectangle 1, X2 is the smoke plume path of nested rectangle 2, X3 is the smoke plume path of nested rectangle 3, and X4 is the smoke plume path of nested rectangle 4.

[0087] In this embodiment, the starting point of each smoke plume path can be used as the coordinate origin, the regional wind direction of the regional wind field as the positive direction of the x-axis, the vertical wind direction perpendicular to the regional wind direction in the horizontal plane as the positive direction of the y-axis, and the upward direction perpendicular to the horizontal plane as the positive direction of the z-axis to construct a regional coordinate system for each nested rectangle; determine the spatial coordinates of the spatial point in the regional coordinate system. The spatial point in this embodiment can be any point in the nested rectangle, that is, the method of this embodiment can predict the smoke plume concentration of any spatial point in the nested rectangle. This embodiment can perform parallel calculations to predict the smoke plume concentrations of multiple spatial points at the same time.

[0088] In this embodiment, the initial plume concentration of a spatial point in the regional coordinate system can be determined by a Gaussian plume model based on the spatial coordinates, the regional wind speed of the regional wind field, and the plume emission rate; the formula of the Gaussian plume model in this embodiment can be shown as follows:

[0089] ;

[0090] In the formula, is the initial plume concentration of the spatial point in the regional coordinate system of the i-th nested rectangle, is the regional wind speed of the regional wind field in the i-th nested rectangle, q is the plume emission rate, is the spatial coordinate of the spatial point in the regional coordinate system of the i-th nested rectangle, is the diffusion coefficient in the y-axis direction, is the diffusion coefficient in the z-axis direction. The two diffusion coefficients can generally be determined by the PG diffusion curve. The PG diffusion curve, also known as the probability density function diffusion curve, is a tool used to describe the distribution characteristics of particles or molecules in the diffusion process.

[0091] In this embodiment, the nested rectangles may be numbered from small to large, the innermost nested rectangle is numbered 1, the outermost nested rectangle is numbered m, and m is the number of all the nested rectangles.

[0092] In this embodiment, after determining the initial plume concentration of the spatial point in the regional coordinate system, the weight factor of each initial plume concentration can be determined, and the weighted sum of each initial plume concentration is performed based on the weight factor to obtain the plume concentration of the spatial point. The weighted summation formula can be shown as follows:

[0093] ;

[0094] Where C is the plume concentration at a spatial point, is the weight factor of the initial plume concentration in the regional coordinate system of the i-th nested rectangle, is the initial smoke plume concentration of the spatial point in the regional coordinate system of the i-th nested rectangle, and n is the number of the smallest nested rectangle where the spatial point is located. For example, Figure 4In the example, if the spatial point is in the non-overlapping area of ​​nested rectangle 2 and nested rectangle 3, then nested rectangle 3 is the minimum nested rectangle where the spatial point is located.

[0095] This embodiment does not limit the method for determining the weight factor. Generally, this embodiment determines the ratio of the length of the smoke plume path within the nested rectangle to the length of the smoke plume path as the weight factor of the initial smoke plume concentration. The specific calculation method can be shown as follows:

[0096] ;

[0097] In the formula, is the weight factor of the initial plume concentration in the regional coordinate system of the i-th nested rectangle, is the length of the plume path within the i-th nested rectangle, is the length of the smoke plume path, and n is the number of the smallest nested rectangle where the spatial point is located.

[0098] In this embodiment, after determining the smoke plume concentration at a spatial point, a smoke plume pollution warning can be issued. This embodiment does not limit the specific method of issuing a smoke plume concentration warning. Generally, a smoke plume concentration threshold can be set. When the smoke plume concentration at a certain spatial point exceeds the smoke plume concentration threshold, a smoke plume pollution warning is issued. Alternatively, a smoke plume pollution warning can be issued when the smoke plume concentration at a preset number of spatial points exceeds the smoke plume concentration threshold. The specific setting can be based on actual applications. In this embodiment, each time a smoke plume pollution warning is issued, the horizontal wind field and the smoke plume emission rate can be updated in real time through the wind measuring laser radar, which improves the accuracy of the smoke plume concentration prediction results and avoids the occurrence of false alarms or missed alarms in the smoke plume pollution warning.

[0099] Based on the above embodiments, the method of the present invention calculates the Gaussian plume model based on the detection data of the wind measuring lidar, thereby avoiding the deviation between the prediction result and the actual situation caused by the unchanged horizontal wind field and the constant emission rate of the pollution source, and further divides the entire horizontal wind field into multiple regional wind fields, avoiding the assumption that the wind fields in different regions are consistent, which is more in line with the actual situation and further improves the accuracy of the prediction results.

[0100] The following combination Figure 5 , Figure 5 This is a structural block diagram of a smoke plume pollution early warning device provided by an embodiment of the present invention, and the device may include:

[0101] The first module 100 is used to obtain radial wind data and radar signal-to-noise ratio of each sampling point at each azimuth obtained by scanning a wind laser radar, obtain a horizontal wind field based on the radial wind data, and obtain an extinction coefficient of each sampling point based on the radar signal-to-noise ratio.

[0102] The second module 200 is used to determine the sampling point whose extinction coefficient is greater than the extinction coefficient threshold as the target sampling point, construct a target point cloud based on the target sampling point, intercept the target point cloud through a surface perpendicular to the horizontal wind direction of the horizontal wind field, and determine the maximum cross-sectional area obtained by interception as the plume cross-sectional area;

[0103] The third module 300 is used to determine the average particle mass concentration based on the extinction coefficient of the target sampling point, and determine the product of the average particle mass concentration, the plume cross-sectional area and the horizontal wind speed of the horizontal wind field as the plume emission rate;

[0104] The fourth module 400 is used to determine the sampling point with the largest extinction coefficient as an emission source, construct an initial rectangle with the emission source as the center, and expand two sides of the initial rectangle multiple times based on the horizontal wind direction to construct multiple nested rectangles;

[0105] The fifth module 500 is used to determine the regional wind field of each nested rectangle based on the horizontal wind field, determine the plume concentration of the spatial point within the nested rectangle through a Gaussian plume model based on the regional wind field and the plume emission rate, and perform plume pollution warning based on the plume concentration.

[0106] Based on the above embodiments, the method of the present invention calculates the Gaussian plume model based on the detection data of the wind measuring lidar, thereby avoiding the deviation between the prediction result and the actual situation caused by the unchanged horizontal wind field and the constant emission rate of the pollution source, and further divides the entire horizontal wind field into multiple regional wind fields, avoiding the assumption that the wind fields in different regions are consistent, which is more in line with the actual situation and further improves the accuracy of the prediction results.

[0107] Based on the above embodiment, the fifth module 500 may include:

[0108] A first unit is used to determine a smoke plume path within each of the nested rectangles based on the regional wind direction of the regional wind field;

[0109] The second unit is used to construct a regional coordinate system of each nested rectangle with the starting point of each plume path as the coordinate origin, the regional wind direction of the regional wind field as the positive direction of the x-axis, the vertical wind direction perpendicular to the regional wind direction on the horizontal plane as the positive direction of the y-axis, and the upward direction perpendicular to the horizontal plane as the positive direction of the z-axis;

[0110] The third unit is used to determine the spatial coordinates of the spatial point in the regional coordinate system, and determine the initial plume concentration of the spatial point in the regional coordinate system through the Gaussian plume model based on the spatial coordinates, the regional wind speed of the regional wind field and the plume emission rate;

[0111] The fourth unit is used to determine the weight factor of each of the initial plume concentrations, and perform weighted summation on each of the initial plume concentrations based on the weight factor to obtain the plume concentration at the spatial point.

[0112] Based on the above embodiments, the fourth unit may include:

[0113] The first subunit is used to determine the ratio of the length of the plume path within the nested rectangle to the length of the plume path passing through as the weight factor of the initial plume concentration.

[0114] Based on the above embodiments, the fifth module 500 may include:

[0115] A fifth unit, used for decomposing the horizontal wind field in each of the nested rectangles into a u component in the longitude direction and a v component in the latitude direction;

[0116] A sixth unit, configured to determine the regional wind speed and the regional wind direction of the regional wind field in each of the nested rectangles based on the u component and the v component;

[0117] The seventh unit is used to merge two adjacent nested rectangles whose regional wind direction deviation is less than a preset deviation to obtain the merged nested rectangle, and redetermine the regional wind field of the merged nested rectangle.

[0118] Based on the above embodiments, the third module 300 may include:

[0119] The eighth unit is used to determine a correlation model between the extinction coefficient and the particle mass concentration, and to use the average value of the extinction coefficient of the target sampling point as an input of the correlation model to obtain the average particle mass concentration.

[0120] Based on the above embodiments, the first module 100 may include:

[0121] A ninth unit, configured to obtain the horizontal wind field by performing inversion based on the radial wind data through a VAD algorithm;

[0122] The tenth unit is used to obtain the extinction coefficient of each sampling point by inverting the radar signal-to-noise ratio through the Klett backward method.

[0123] Based on the above embodiments, the first module 100 may include:

[0124] The eleventh unit is used to obtain the radial wind data and the radar signal-to-noise ratio of each sampling point at each azimuth angle obtained by scanning the wind laser radar;

[0125] The twelfth unit is used to determine a radar signal-to-noise ratio threshold, determine the sampling point whose radar signal-to-noise ratio is less than the radar signal-to-noise ratio threshold as a low-credible sampling point, and delete the radial wind data of the low-credible sampling point.

[0126] Based on the above embodiments, the present invention also provides a smoke plume pollution warning device, which may include a memory and a processor, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the steps provided in the above embodiments can be implemented. Of course, the device may also include various necessary network interfaces, power supplies, and other components.

[0127] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by an execution terminal or a processor, the method provided in the embodiment of the present invention can be implemented; the storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0128] In this article, relational terms such as first and second, etc. are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

Claims

1. A smoke plume pollution early warning method, characterized in that: include: Obtain radial wind data and radar signal-to-noise ratio of each sampling point at each azimuth obtained by wind laser radar scanning, obtain horizontal wind field based on the radial wind data, and obtain extinction coefficient of each sampling point based on the radar signal-to-noise ratio inversion; Determine the sampling point whose extinction coefficient is greater than the extinction coefficient threshold as the target sampling point, construct a target point cloud based on the target sampling point, intercept the target point cloud through a plane perpendicular to the horizontal wind direction of the horizontal wind field, and determine the maximum cross-sectional area obtained by interception as the plume cross-sectional area; Determine an average particle mass concentration based on the extinction coefficient of the target sampling point, and determine the product of the average particle mass concentration, the plume cross-sectional area and the horizontal wind speed of the horizontal wind field as the plume emission rate; The sampling point with the largest extinction coefficient is determined as the emission source, an initial rectangle is constructed with the emission source as the center, and two sides of the initial rectangle are extended multiple times based on the horizontal wind direction to construct multiple nested rectangles; the plume path is contained in the nested rectangles, and the multiple nested rectangles share a diagonal angle, which is determined based on the horizontal wind direction, and the emission source is the center of the innermost nested rectangle; Determine the regional wind field of each nested rectangle based on the horizontal wind field, determine the plume concentration of the spatial point within the nested rectangle through a Gaussian plume model based on the regional wind field and the plume emission rate, and perform plume pollution warning based on the plume concentration; The determining the plume concentration of the spatial point within the nested rectangle by using a Gaussian plume model based on the regional wind field and the plume emission rate includes: Determining a smoke plume path within each of the nested rectangles based on the regional wind direction of the regional wind field; Taking the starting point of each of the plume paths as the coordinate origin, the regional wind direction of the regional wind field as the positive direction of the x-axis, the vertical wind direction perpendicular to the regional wind direction on the horizontal plane as the positive direction of the y-axis, and the upward direction perpendicular to the horizontal plane as the positive direction of the z-axis, construct a regional coordinate system for each of the nested rectangles; Determine the spatial coordinates of the spatial point in the regional coordinate system, and determine the initial plume concentration of the spatial point in the regional coordinate system through the Gaussian plume model based on the spatial coordinates, the regional wind speed of the regional wind field, and the plume emission rate; A weight factor of each of the initial plume concentrations is determined, and a weighted sum is performed on each of the initial plume concentrations based on the weight factor to obtain the plume concentration at the spatial point.

2. The smoke plume pollution early warning method according to claim 1, characterized in that: The determining of the weighting factors of the initial plume concentrations comprises: The ratio of the length of the plume path within the nested rectangle to the length of the plume path passed through is determined as the weight factor of the initial plume concentration.

3. The smoke plume pollution early warning method according to claim 1, characterized in that: The determining of the regional wind field of each nested rectangle based on the horizontal wind field comprises: Decomposing the horizontal wind field in each nested rectangle into a u component in the longitude direction and a v component in the latitude direction; Determine the regional wind speed and regional wind direction of the regional wind field in each of the nested rectangles based on the u component and the v component; Two adjacent nested rectangles whose deviation of the regional wind direction is less than a preset deviation are merged to obtain the merged nested rectangle, and the regional wind field of the merged nested rectangle is re-determined.

4. The smoke plume pollution early warning method according to claim 1, characterized in that: The determining the average particle mass concentration based on the extinction coefficient of the target sampling point includes: A correlation model between the extinction coefficient and the particle mass concentration is determined, and an average value of the extinction coefficient of the target sampling point is used as an input of the correlation model to obtain the average particle mass concentration.

5. The smoke plume pollution early warning method according to claim 1, characterized in that: The inversion based on the radial wind data to obtain the horizontal wind field, and the inversion based on the radar signal-to-noise ratio to obtain the extinction coefficient of each sampling point, include: The horizontal wind field is obtained by inverting the radial wind data through a VAD algorithm; The extinction coefficient of each sampling point is obtained by inverting the radar signal-to-noise ratio through the Klett backward method.

6. The smoke plume pollution early warning method according to claim 1, characterized in that: The step of obtaining radial wind data and radar signal-to-noise ratio of each sampling point at each azimuth angle obtained by wind laser radar scanning includes: Acquire the radial wind data and the radar signal-to-noise ratio of each sampling point at each azimuth angle obtained by scanning a wind laser radar; A radar signal-to-noise ratio threshold is determined, the sampling point whose radar signal-to-noise ratio is less than the radar signal-to-noise ratio threshold is determined as a low-credible sampling point, and the radial wind data of the low-credible sampling point is deleted.

7. A smoke plume pollution early warning device, characterized in that: include: The first module is used to obtain radial wind data and radar signal-to-noise ratio of each sampling point at each azimuth obtained by scanning the wind laser radar, obtain the horizontal wind field based on the radial wind data, and obtain the extinction coefficient of each sampling point based on the radar signal-to-noise ratio inversion; The second module is used to determine the sampling point whose extinction coefficient is greater than the extinction coefficient threshold as the target sampling point, construct a target point cloud based on the target sampling point, intercept the target point cloud through a surface perpendicular to the horizontal wind direction of the horizontal wind field, and determine the maximum cross-sectional area obtained by interception as the plume cross-sectional area; The third module is used to determine the average particle mass concentration based on the extinction coefficient of the target sampling point, and determine the product of the average particle mass concentration, the plume cross-sectional area and the horizontal wind speed of the horizontal wind field as the plume emission rate; The fourth module is used to determine the sampling point with the largest extinction coefficient as the emission source, construct an initial rectangle with the emission source as the center, and expand the two sides of the initial rectangle multiple times based on the horizontal wind direction to construct multiple nested rectangles; the plume path is contained in the nested rectangles, and the multiple nested rectangles share a diagonal angle, which is determined based on the horizontal wind direction, and the emission source is the center of the innermost nested rectangle; A fifth module is used to determine the regional wind field of each nested rectangle based on the horizontal wind field, determine the plume concentration of the spatial point in the nested rectangle through a Gaussian plume model based on the regional wind field and the plume emission rate, and perform plume pollution warning based on the plume concentration; The fifth module comprises: A first unit is used to determine a smoke plume path within each of the nested rectangles based on the regional wind direction of the regional wind field; The second unit is used to construct a regional coordinate system of each nested rectangle with the starting point of each plume path as the coordinate origin, the regional wind direction of the regional wind field as the positive direction of the x-axis, the vertical wind direction perpendicular to the regional wind direction on the horizontal plane as the positive direction of the y-axis, and the upward direction perpendicular to the horizontal plane as the positive direction of the z-axis; The third unit is used to determine the spatial coordinates of the spatial point in the regional coordinate system, and determine the initial plume concentration of the spatial point in the regional coordinate system through the Gaussian plume model based on the spatial coordinates, the regional wind speed of the regional wind field and the plume emission rate; The fourth unit is used to determine the weight factor of each of the initial plume concentrations, and perform weighted summation on each of the initial plume concentrations based on the weight factor to obtain the plume concentration at the spatial point.

8. A smoke plume pollution early warning device, characterized in that: include: Memory, for storing computer programs; A processor, configured to implement the smoke plume pollution early warning method as claimed in any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the plume pollution early warning method according to any one of claims 1 to 6 is implemented.

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