CN115705510A - Factory gas pollution distribution prediction method and system, electronic equipment and storage medium - Google Patents
Factory gas pollution distribution prediction method and system, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a plant gas pollution distribution prediction method, which comprises the following steps: s110, acquiring meteorological data of a target area and position and concentration data of a plurality of sampling points, and establishing a static distribution characteristic database, wherein the plurality of sampling points comprise online monitoring points; s130, screening out a most matched static distribution characteristic database according to the current monitoring state; s140, establishing a concentration regulation coefficient model of the target area to obtain a dynamic distribution characteristic database; and S150, forming a gas pollution distribution map of the target area by using a spatial interpolation and visualization method based on the obtained dynamic distribution characteristic database. The invention also discloses a plant area gas pollution distribution prediction system, electronic equipment and a storage medium. According to the method, a static distribution characteristic database is established through actual measurement of a plurality of sampling points, a concentration regulation and control coefficient model of a target area is established in combination with a current monitoring state, two significant influence factors of weather and distance in a plant area are comprehensively considered, and real-time and accurate prediction of plant-level gas pollution distribution is achieved.
Description
Technical Field
The invention relates to the technical field of gas pollution monitoring, in particular to a method and a system for predicting the distribution of plant gas pollution, electronic equipment and a storage medium.
Background
The gas pollution mainly comprises carbon monoxide (CO) and sulfur dioxide (SO) 2 ) Nitrogen Oxide (NO) x ) Ozone (O) 3 ) And PM2.5, PM10 and other atmospheric pollutants, and volatile organic compounds, hydrogen sulfide, ammonia and other toxic and harmful gases concerned by petrochemical enterprises. With the issuance of standard documents such as 'comprehensive volatile organic compound treatment scheme in key industry', 'construction of technology guide for hazardous gas environment risk early warning system' and the like, petrochemical enterprises have added online monitoring equipment and gradually built a gas pollution monitoring network to realize online monitoring of pollutant concentration at monitoring point positions, but no effective means is available for controlling pollution at positions where monitoring points are not arranged. For petrochemical enterprises, how to rely on the real-time data of the existing grid monitoring network to further predict the dynamic distribution form of the pollutants in the plant area has significant meaning for improving the leakage monitoring capability of the enterprises. However, at present, the pollution distribution is predictedThe measuring method mainly aims at large scale ranges of city level and area level, and is not suitable for factory areas with small areas and dense production enterprises and devices.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
One objective of the present invention is to provide a method, a system, an electronic device and a storage medium for predicting a plant gas pollution distribution, so as to solve the problem that the prior art is not suitable for predicting the plant pollution.
Another objective of the present invention is to provide a method, a system, an electronic device and a storage medium for predicting a gas pollution distribution of a plant, so as to improve the accuracy of the gas pollution distribution prediction of the plant.
To achieve the above object, according to a first aspect of the present invention, there is provided a method for predicting a distribution of a gas pollution in a plant, comprising the steps of:
s110, acquiring meteorological data of a target area and position and concentration data of a plurality of sampling points, and establishing a static distribution characteristic database, wherein the plurality of sampling points comprise online monitoring points;
s130, screening out the most matched static distribution characteristic database according to the current monitoring state;
s140, establishing a concentration regulation coefficient model of the target area to obtain a dynamic distribution characteristic database;
and S150, forming a gas pollution distribution map of the target area by using a spatial interpolation and visualization means based on the obtained dynamic distribution characteristic database.
Further, in the above technical solution, the steps S130 to S150 are repeated according to the set frequency, so as to form a dynamically updated target area gas pollution distribution map.
Further, in the above technical solution, the plurality of sampling points uniformly cover the target area.
Further, in the above technical solution, the current monitoring state includes an average wind direction and an average wind speed at a height of 10 meters above the wind direction in the target area within a set time period, and an average pollution concentration at each online monitoring point.
Further, in the above technical solution, the set time period is the current first 1 to 6 hours. Further, in the above technical solution, the average wind direction and the average wind speed are calculated by a vector average method; the average contaminant concentration was calculated by arithmetic mean.
Further, in the above technical solution, when the target area is closed or semi-closed, the terrain is flat, and the shielding of the building is rare, the static distribution characteristic database is a set of coordinates of the sampling points and concentrations of the sampling points.
Further, in the above technical solution, the step of establishing the static distribution feature database includes:
forming a pollution distribution database of a target area by using a spatial interpolation method based on the position and concentration data of a plurality of sampling points;
dividing a target area into well-type grids, and taking a collection of grid points and online monitoring points to form characteristic points;
according to the pollution distribution database, the pollution concentration of each characteristic point is obtained, and a static distribution characteristic database static _ net { x is established s ,y s ,c s In which x s 、y s As coordinates of feature points, c s Is the concentration of contamination at the characteristic point.
Further, in the above technical solution, the step of establishing the static distribution feature database further includes:
carrying out spatial interpolation reduction on the established static distribution characteristic database to obtain a pollution distribution database, and calculating the similarity between the pollution distribution database obtained by reduction and the original pollution distribution database;
setting similarity thresholds M and N, wherein M is more than 0 and more than N and less than 1; and
judging whether the established static distribution characteristic database is qualified according to the similarity:
if the similarity is less than or equal to N and more than or equal to M, the established static distribution characteristic database is qualified;
if the similarity is less than M, increasing the grid density, dividing the well type grid again, and establishing a static distribution characteristic database; and
if the similarity is more than N, reducing the grid density, dividing the well type grid again, and establishing a static distribution characteristic database.
Furthermore, in the technical scheme, the similarity threshold value is more than or equal to 0.7 and less than or equal to 0.75, and the similarity threshold value is more than or equal to 0.8 and less than or equal to 0.85.
Further, in the above technical solution, the meteorological data of the target area is the wind speed and wind direction at a height of 10 meters above the wind direction in the target area, the average value of the meteorological data in the sampling period is the sampling meteorological condition, and the sampling period is the time when all sampling points complete sampling.
Further, among the above-mentioned technical scheme, establish a plurality of static distribution characteristic databases according to the sampling meteorological condition of difference, compare current monitoring state with the sampling meteorological condition, select the static distribution characteristic database of the most matching, include:
a wind direction screening step of calculating a wind direction difference EW between an average wind direction in a current monitoring state and sampling meteorological conditions of a plurality of static distribution characteristic databases, and screening a minimum value min _ EW,
if only one static distribution characteristic database has EW (min _ EW + W) less than or equal to, determining the static distribution characteristic database corresponding to the minimum wind direction difference min _ EW as a best-matched static distribution characteristic database, wherein W is a set angle value and is less than or equal to 15 degrees;
and otherwise, a wind speed screening step is carried out, wind speed differences ES between the average wind speed in the current monitoring state and the sampling meteorological conditions of the plurality of static distribution characteristic databases are calculated, the minimum value min _ ES is screened, and the static distribution characteristic database corresponding to the minimum value min _ ES of the wind speed differences is determined as the best matched static distribution characteristic database.
Further, in the foregoing technical solution, step S140 includes:
s141, establishing a concentration regulation and control coefficient model k of the ith online monitoring point i (x s ,y s )，k i Coordinate (x) with ith online monitoring point i ，y i ) Average wind direction θ and average in the current monitoring stateWind speed s, and pollution variation intensity p i Correlation, p i ＝c i /c i0 Wherein c is i Average pollution concentration of the ith online monitoring point in the current monitoring state, c i0 The pollution concentration of the characteristic point corresponding to the ith online monitoring point in the most matched static distribution characteristic database is obtained;
s142, establishing a concentration regulation and control coefficient model of the target region:
s143 obtains dynamic distribution characteristic database dynamic _ net (x) s ,y s ,c d ) Wherein c is d ＝c s ·K(x s ,y s )。
Further, in the above technical scheme, the concentration regulation and control coefficient model k of the ith online monitoring point i (x s ,y s ) The gas diffusion attenuation law is met, and the following requirements are met:
when p is i When < 1, k i ∈[p i 1), when p is i When > 1, k i ∈(1,p i ]When p is i K when =1 i ＝1；
When D is present i K when =0 i ＝p i I.e. no attenuation; when D is present i ＝D 0 When k is i =1, i.e. complete attenuation; when D is present i ∈(0,D 0 ) When, with D i Increase of k i Gradual change in intensity p from contamination i Decays to 1, wherein D 0 In order to fully attenuate the distance,
wherein d is a feature point (x) s ,y s ) To the ith online monitoring point (x) i ,y i ) The distance of (a) to (b),
θ i is the average wind direction theta and (x) in the current monitoring state s ,y s ) And (x) i ,y i ) Acute angle of line segment included angle between two points; m represents a coefficient relating to wind speed, 1. Ltoreq. M.ltoreq.5, and the larger the average wind speed s in the current monitoring state, the larger m.
Further, in the above technical solution, D 0 Related to the area size of the target area and the number of on-line monitoring points, D 0 Is 100 to 500m.
Further, in the above technical scheme, if s is more than or equal to 0m/s and less than 1m/s, m =1; if s is more than or equal to 1m/s and less than 3m/s, m =2; m =3 if 3 m/s.ltoreq.s < 5m/s, m =4 if 5 m/s.ltoreq.s < 7m/s, m =5 if s.gtoreq.7 m/s.
According to a second aspect of the present invention, there is provided a plant gas pollution distribution prediction system comprising: the data acquisition unit is used for acquiring meteorological data of a target area and position and concentration data of a plurality of sampling points; the data processing unit is used for establishing a static distribution characteristic database, screening out the most matched static distribution characteristic database and establishing a target area concentration regulation coefficient model according to the data acquired by the data acquisition unit to obtain a dynamic distribution characteristic database; and forming a gas pollution distribution map of the target area by utilizing a spatial interpolation and visualization means based on the obtained dynamic distribution characteristic database.
Further, in the above technical solution, the data acquisition unit includes a mobile monitoring device and an online monitoring site.
According to a third aspect of the invention, there is provided an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform a method for predicting a gas pollution distribution on a plant as set forth in any one of the above aspects.
According to a fourth aspect of the present invention, there is provided a non-transitory computer readable storage medium having stored thereon computer executable instructions for causing a computer to perform a method of plant gas pollution distribution prediction as defined in any one of the above claims.
Compared with the prior art, the invention has one or more of the following beneficial effects:
1. the method is suitable for predicting the gas pollution distribution in the plant area. The area of a plant area is small, production enterprises and devices are dense, and the influence of wind direction on pollution distribution is obvious; the distribution of the emission sources in the factory area is complex but the positions are clear and no mobile sources exist. Therefore, the plant-level pollution conditions have the characteristics of complex and variable distribution and similar distribution characteristics under the same meteorological conditions. The invention establishes a static distribution characteristic database through actual measurement of a plurality of sampling points, screens out the most matched static distribution characteristic database by combining and utilizing the data of the existing equipment such as online monitoring points, meteorological stations and the like, establishes a target area concentration regulation and control coefficient model, comprehensively considers two significant influence factors of meteorology and distance in a plant area and realizes real-time accurate prediction of plant-level gas pollution distribution.
2. The frequency is set as required, the gas pollution distribution of the target area which is dynamically updated can be obtained, and the comprehensive display of the enterprise pollution distribution level is facilitated, so that the enterprise pollution control level is improved. The heavy pollution area is conveniently identified, and data support is provided for fine pollution control and treatment.
3. The invention can extract the static distribution characteristic database by dividing the grids, and the dynamic distribution characteristic database obtained by concentration regulation reflects the actual pollution distribution more accurately.
4. The invention can fully utilize the existing online monitoring resources of a plant area, such as online monitoring sites, meteorological stations and the like, only needs to adopt portable or navigation type monitoring equipment to sample a plurality of sampling points in sequence when establishing the static distribution characteristic database, has less total amount of the monitoring equipment and low investment and maintenance cost, and is beneficial to popularization and application in petrochemical enterprises.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical means of the present invention more clearly understood and to make the technical means implementable in accordance with the contents of the description, and to make the above and other objects, technical features, and advantages of the present invention more comprehensible, one or more preferred embodiments are described below in detail with reference to the accompanying drawings.
Drawings
FIG. 1 is a flow chart of a method for predicting a distribution of a plant gas pollution according to an embodiment of the present invention.
FIG. 2 shows k according to an embodiment of the present invention i The decay curve of (1), wherein p i ＞1。
FIG. 3 is k according to an embodiment of the present invention i In which p is i ＜1。
FIG. 4 is a schematic diagram of a hardware configuration of an electronic device that performs a method for predicting a gas pollution distribution on a factory floor, in accordance with an embodiment of the present invention.
Detailed Description
Specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but it should be understood that the scope of the present invention is not limited to the specific embodiments.
Throughout the specification and claims, unless explicitly stated otherwise, the word "comprise", or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated element or component but not the exclusion of any other element or component.
Spatially relative terms, such as "below," "lower," "upper," "above," "upper," and the like, may be used herein for ease of description to describe one element or feature's relationship to another element or feature in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the object in use or operation in addition to the orientation depicted in the figures. For example, if the items in the figures are turned over, elements described as "below" or "beneath" other elements or features would then be oriented "above" the elements or features. Thus, the exemplary term "below" can encompass both an orientation of below and above. The articles may have other orientations (rotated 90 degrees or otherwise) and the spatially relative terms used herein should be interpreted accordingly.
As used herein, the terms "first," "second," and the like are used to distinguish two different elements or regions, and are not intended to define a particular position or relative relationship. In other words, the terms "first," "second," and the like may also be interchanged with one another in some embodiments.
As shown in FIG. 1, the flow of the method for predicting the distribution of the factory gas pollution according to the embodiment of the invention is as follows:
s110, acquiring meteorological data of a target area and position and concentration data of a plurality of sampling points, and establishing a static distribution characteristic database.
The plurality of sampling points comprise online monitoring points, and the online monitoring points can be existing online monitoring sites. The target area is evenly covered at a plurality of sampling points, and portable or walkabout monitoring equipment can be adopted to obtain the position and the concentration data of a plurality of sampling points, so that the quantity of sampling points can be arranged a plurality of. It should be noted that, when sampling, the meteorological conditions are selected as stable as possible, and the sampled meteorological conditions can be approximated by the average value in the sampling period.
Further, in one or more exemplary embodiments of the present invention, the meteorological data of the target area may be wind speed and wind direction 10 meters high above the target area, the average value of the meteorological data in the sampling period is the sampling meteorological condition, and the sampling period is the time for completing sampling of all sampling points, so as to try to select the meteorological condition to be stable and complete the sampling as soon as possible. Under different sampling meteorological conditions, data acquisition of a plurality of sampling points of a target area is completed, and a plurality of static distribution characteristic databases are established.
The establishment of the static distribution characteristic database can adopt different modes according to factory conditions. Illustratively, when the target area is closed or semi-closed, the terrain is flat, and the shielding of buildings is rare, the coordinates of the sampling points and the set of the concentrations of the sampling points can be directly adopted as the static distribution characteristic database.
Illustratively, the static distribution feature database can also be established in a gridding manner, and the steps are as follows:
forming a pollution distribution database of a target area by using a spatial interpolation method based on the position and concentration data of a plurality of sampling points;
dividing a target area into well type grids, and taking a collection of grid points and online monitoring points to form characteristic points;
according to the pollution distribution database, the pollution concentration of each characteristic point is obtained, and a static distribution characteristic database static _ net { x is established s ,y s ,c s In which x s 、y s As coordinates of feature points, c s Is the concentration of contamination at the characteristic point.
Furthermore, in order to ensure that the grid division reflects the actual distribution characteristics, the data processing time can be reduced as much as possible, the efficiency is improved, and the limitation on the flexibility of the dynamic distribution characteristic database is reduced. In one or more exemplary embodiments of the present invention, the step of establishing the static distribution characteristics database may further include a process of checking:
carrying out spatial interpolation reduction on the established static distribution characteristic database to obtain a pollution distribution database, and calculating the similarity between the pollution distribution database obtained by reduction and the original pollution distribution database;
setting similarity thresholds M and N, wherein M is more than 0 and more than N and less than 1; and
judging whether the established static distribution characteristic database is qualified according to the similarity:
if the similarity is less than or equal to N and more than or equal to M, the established static distribution characteristic database is qualified;
if the similarity is less than M, increasing the grid density, dividing the well type grid again, and establishing a static distribution characteristic database; and
if the similarity is larger than N, reducing the grid density, dividing the well type grid again, and establishing a static distribution characteristic database.
Exemplarily, the similarity threshold M =0.75, n =0.85, it should be understood that the invention is not limited thereto. The value range of the similarity threshold can be that M is more than or equal to 0.7 and less than or equal to 0.75, and N is more than or equal to 0.8 and less than or equal to 0.85.
S130, screening out the most matched static distribution characteristic database according to the current monitoring state.
The current monitoring state may include an average wind direction 10 meters above the wind direction over the target area over a set period of time, an average wind speed, and an average pollution concentration at each online monitoring point. Illustratively, the set time period may be the current first 1 to 6 hours, and the present invention is not limited thereto. Further, in one or more exemplary embodiments of the present invention, the average wind direction and the average wind speed are calculated using a vector averaging method; the average contaminant concentration was calculated by arithmetic mean.
Comparing the current monitoring state with the sampling meteorological conditions, and screening out the most matched static distribution characteristic database, which comprises the following steps:
a wind direction screening step of calculating a wind direction difference EW between an average wind direction in a current monitoring state and sampling meteorological conditions of a plurality of static distribution characteristic databases, and screening a minimum value min _ EW,
if only one static distribution characteristic database has EW (min _ EW + W) less than or equal to, determining the static distribution characteristic database corresponding to the minimum wind direction difference min _ EW as a best-matched static distribution characteristic database, wherein W is a set angle value and is less than or equal to 15 degrees;
and otherwise, a wind speed screening step is carried out, wind speed differences ES between the average wind speed in the current monitoring state and the sampling meteorological conditions of the plurality of static distribution characteristic databases are calculated, the minimum value min _ ES is screened, and the static distribution characteristic database corresponding to the minimum value min _ ES of the wind speed differences is determined as the best matched static distribution characteristic database.
S140, establishing a concentration regulation and control coefficient model of the target area to obtain a dynamic distribution characteristic database.
S141, establishing a concentration regulation and control coefficient model k of the ith online monitoring point i (x s ,y s )，k i Coordinate (x) with ith online monitoring point i ，y i ) Average wind direction theta and average wind speed s in the current monitoring state, and pollution change intensity p i Correlation, p i ＝c i /c s Wherein c is i Average pollution concentration of the ith online monitoring point in the current monitoring state, c i0 The pollution concentration of the characteristic point corresponding to the ith online monitoring point in the most matched static distribution characteristic database is obtained;
s142, establishing a concentration regulation and control coefficient model of the target region:
s143 obtains dynamic distribution characteristic database dynamic _ net (x) s ,y s ,c d ) Wherein c is d ＝c s ·K(x s ,y s )。
Further, in one or more exemplary embodiments of the invention, the concentration regulation coefficient model k of the ith online monitoring point i (x s ,y s ) Accords with the attenuation rule of gas diffusion and meets the following requirements:
when p is i When < 1, k i ∈[p i 1), when p is i When > 1, k i ∈(1,p i ]When p is i K when =1 i ＝1；
When D is present i K when =0 i ＝p i I.e. no attenuation, when D i ＝D 0 When k is i =1, i.e. complete attenuation, when D i ∈(0,D 0 ) When, with D i Increase of k i Gradual change in intensity p from contamination i Change to 1 wherein D 0 In order to fully attenuate the distance,
wherein d is a feature point (x) s ,y s ) To the ith on-line monitoring point (x) i ,y i ) The distance of (a) to (b),
θ i is the average wind direction theta and (x) in the current monitoring state s ,y s ) And (x) i ,y i ) Line segment clamp between two pointsAcute angle of the angle; m represents a coefficient relating to wind speed, 1. Ltoreq. M.ltoreq.5, and the larger the average wind speed s in the current monitoring state, the larger m.
Exemplarily, D 0 Related to the area size of the target area and the number of on-line monitoring points, D 0 Typically 100 to 500m. Illustratively, if 0m/s ≦ s < 1m/s, m =1; if s is more than or equal to 1m/s and less than 3m/s, m =2; m =3 if 3 m/s.ltoreq.s < 5m/s, m =4 if 5 m/s.ltoreq.s < 7m/s, m =5 if s.gtoreq.7 m/s.
Exemplarily, if the ith online monitoring point is provided with a meteorological monitoring station, the specific wind direction and wind speed data of the position can be monitored and obtained in real time, and then theta is calculated i The average wind direction theta in the current monitoring state used in the process can be preferably replaced by the average wind direction of the monitoring point; the average wind speed s in the current monitoring state used when calculating the coefficient m can be preferably replaced by the average wind speed of the monitoring point, and if the height of the meteorological monitoring station of the monitoring point is not 10 meters, the wind speed value at the height of 10 meters is obtained through conversion according to a formula.
Illustratively, k may be determined as follows i And D i The relationship of (1):
1) Configuring point source gas leakage, wherein the type of gas is typical gas pollutants in a target area, the height of a leakage source is the emission height of a typical pollution source in the target area, and the source intensity is the normal emission of the typical pollution source; for example, the gas is propylene, the height of a leakage source is 10m, and the source strength is 50kg/h;
2) Configuring meteorological data as a high common wind speed of a leakage source in a target area; for example, 5m/s;
3) Performing a leakage simulation or test to obtain gas leakage diffusion data; recording the maximum concentration value c on the vertical section of each x position (x is more than or equal to 0) by taking the wind direction as the x-axis direction and taking the position 2-10 m below the wind direction as the original point;
4) C, taking the maximum value of the concentration c as 1000, and scaling down all the concentrations c in equal proportion to obtain ck; taking the x value of the position where ck =1 as D 0 Scaling down/up all x to obtain D x (ii) a And establishing [ D ] x ，ck]A correspondence table f, wherein D x Value of equal spacing and D 0 ≥D x Not less than 0, not less than 1000 not less than ck not less than 1, i.e.The correspondence table may be expressed as ck = f (D) x )；
When p is i When > 1, k i ∈(1,p i ]Then k is i And D i The numerical relationship of (a) is k i ＝1+[(p i -1)×f(D i )/1000]；
When p is i When < 1, k i ∈[p i 1), then k i And D i The numerical relationship of (a) is k i ＝1-[(1-p i )×f(D i )/1000]。
And S150, forming a gas pollution distribution map of the target area by using a spatial interpolation and visualization method based on the obtained dynamic distribution characteristic database.
Further, in one or more exemplary embodiments of the invention, the above steps S130 to S150 are repeated according to a set frequency to form a dynamically updated target area gas pollution distribution map.
A system for predicting a distribution of plant gas contamination according to an embodiment of the present invention comprises: the data acquisition unit is used for acquiring meteorological data of a target area and position and concentration data of a plurality of sampling points; the data processing unit is used for establishing a static distribution characteristic database, screening out the most matched static distribution characteristic database and establishing a target region concentration regulation coefficient model according to the data acquired by the data acquisition unit to obtain a dynamic distribution characteristic database; and forming a gas pollution distribution map of the target area by utilizing a spatial interpolation and visualization means based on the obtained dynamic distribution characteristic database.
Further, in one or more exemplary embodiments of the present invention, the data acquisition unit includes a mobile monitoring device and an online monitoring site.
The method, system, electronic device and storage medium for predicting a factory gas pollution distribution according to the present invention are described in more detail by way of specific embodiments, it should be understood that the embodiments are exemplary only and that the invention is not limited thereto.
Example 1
This embodiment specifically describes the step of screening out the best matching static distribution feature database according to the current monitoring state. In this embodiment, the target area includes 6 online monitoring points, and 8 static distribution feature databases have been established under different sampling weather conditions, which are as follows:
(1) a static characteristic database A with wind direction of 345 degrees (northwest wind) and wind speed of 2.5 m/s;
(2) a static characteristic database B with wind direction of 330 degrees (northwest wind) and wind speed of 3 m/s;
(3) a static characteristic database C with wind direction of 320 degrees (northwest wind) and wind speed of 5m/s;
(4) a static characteristic database D with wind direction of 90 degrees (east wind) and wind speed of 3 m/s;
(5) a static characteristic database E of wind direction 120 degrees (southeast wind) and wind speed 1 m/s;
(6) a static characteristic database F with wind direction of 130 degrees (southeast wind) and wind speed of 3 m/s;
(7) a static characteristic database G with wind direction of 170 degrees (south wind) and wind speed of 2 m/s;
(8) a static characteristic database H with wind direction of 190 degrees (south wind) and wind speed of 2m/s.
And if the average wind direction in the current monitoring state is 80 degrees and the average wind speed is 2m/s. And sequentially comparing the current monitoring state with the different sampling meteorological conditions, wherein in the wind direction screening step, the static distribution characteristic database D has the minimum value min _ EW =10 degrees of wind direction difference, and the EW of no other static distribution characteristic database is less than or equal to (min _ EW + W), so that the static distribution characteristic database D is determined as the most matched static distribution characteristic database.
And if the average wind direction in the current monitoring state is 125 degrees, the average wind speed is 1.3m/s. And sequentially comparing the current monitoring state with the different sampling meteorological conditions, wherein in the wind direction screening step, the static distribution characteristic databases E and F have the same wind direction difference of 5 degrees and are the minimum value. Entering a wind speed screening step, wherein the static characteristic database E has the minimum value min _ ES of the wind speed difference, and therefore, the static distribution characteristic database E is determined as the best matching static distribution characteristic database.
Example 2
This example illustrates that p i When > 1, the ith is onlineConcentration regulation and control coefficient model k of monitoring point i (x s ,y s )。k i (x s ,y s ) Should conform to the decay law of gas diffusion, and k i ∈(1,p i ]I.e. attenuation range of p i To 1.
When a certain characteristic point is exactly positioned at the ith online monitoring point, namely D i If not less than 0, then k i ＝p i I.e. no attenuation; when a feature point is far enough away from the ith online monitoring point, namely D i ≥D 0 When k is i =1, i.e. full decay; with D i Increase of k i Gradually from p i The decay is to 1 and the decay curve is shown in figure 2.
Wherein d is a feature point (x) s ,y s ) To the ith on-line monitoring point (x) i ,y i ) The distance of (a) to (b),
Example 3
This example illustrates that when 0 < p i When the concentration is less than 1, the concentration regulation coefficient model k of the ith online monitoring point i (x s ,y s )。k i (x s ,y s ) Should comply with the attenuation law of gas diffusion, and k i ∈[p i ,1]I.e. attenuation range of 1 to p i 。
When a certain characteristic point is exactly positioned at the ith online monitoring point, namely D i =0, then k i ＝p i I.e. byNo attenuation; when a feature point is far enough away from the ith online monitoring point, namely D i When not less than D0, k i =1, i.e. complete attenuation; with D i Increase of (a), k i Gradually from p i Increasing to 1, the decay curve is shown in fig. 3. In the present embodiment, the average wind speed in the current monitoring state is 1.5m/s, and m =2.
Example 4
This example illustrates that p i When the signal value is not less than 1, the concentration regulation and control coefficient model k of the ith online monitoring point i And =1. The total number of the online monitoring points is N, and a concentration regulation and control coefficient model of a target area is as follows:
is 1, the dynamic distribution characteristic database is equal to the static distribution characteristic database, i.e. dynamic _ net (x) s ,y s ,c d )＝static_net{x s ,y s ,c s }。
Example 5
This example specifically illustrates θ in the concentration control coefficient model of the ith online monitoring point i And m's computational problem. If the ith online monitoring point does not have a meteorological monitoring station, firstly, the average wind direction theta in the current monitoring state is obtained as the wind direction state of the monitoring point, and the average wind direction theta and (x) are used s ,y s ) And (x) i ,y i ) The acute angle of the line segment included angle between the two points is theta i ；
Acquiring the average wind speed s in the current monitoring state as the wind speed state of the position of the monitoring point, and if s is more than or equal to 0m/s and less than 1m/s, m =1; if s is more than or equal to 1m/s and less than 3m/s, m =2; if s is 3 m/s.ltoreq.s < 5m/s, m =3, if s is 5 m/s.ltoreq.s < 7m/s, m =4, if s.gtoreq.7 m/s, m =5.
Example 6
This example specifically illustrates θ in the concentration control coefficient model of the ith online monitoring point i And m, if the ith online monitoring point is provided with a meteorological monitoring station, the specific wind direction and wind speed data of the position can be monitored and obtained in real time; firstly, counting all wind direction monitoring data in a set time period of the weather monitoring station,calculating to obtain an average wind direction theta by adopting a vector averaging method; theta i Is the average wind direction theta and (x) s ,y s ) And (x) i ,y i ) Acute angle of line segment included angle between two points.
That is, when both the average wind direction at a height of 10m above the upwind direction of the target area and the average wind direction at the i-th online monitoring point are known, it is preferable to calculate θ using both the average wind directions at the i-th online monitoring point i 。
Similarly, the value of the average wind speed screening coefficient m of the ith online monitoring point is preferably used, and if the height of the meteorological station of the ith online monitoring point is not 10 meters, the height is converted into the wind speed of 10 meters according to a formula.
Example 7
The present embodiments provide a non-transitory (non-volatile) computer storage medium storing computer-executable instructions that can perform the methods of any of the method embodiments described above and achieve the same technical effects.
Example 8
The present embodiments provide a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the method of the above aspects and achieve the same technical effects.
Example 9
FIG. 4 is a schematic diagram of a hardware configuration of an electronic device for performing a method for predicting a factory floor gas pollution distribution according to the present embodiment. The device includes one or more processors 610 and memory 620. Take a processor 610 as an example. The apparatus may further include: an input device 630 and an output device 640.
The processor 610, memory 620, input device 630, and output device 640 may be connected by a bus or other means, such as by bus in fig. 4.
The memory 620, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules. The processor 610 executes various functional applications of the electronic device and data processing, i.e., a processing method implementing the above-described method embodiments, by executing non-transitory software programs, instructions, and modules stored in the memory 620.
The memory 620 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data and the like. Further, the memory 620 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 620 may optionally include memory located remotely from the processor 610, which may be connected to the processing device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 630 may receive input numeric or character information and generate a signal input. The output device 640 may include a display device such as a display screen.
One or more modules are stored in the memory 620 that, when executed by the one or more processors 610, perform:
s110, acquiring meteorological data of a target area and position and concentration data of a plurality of sampling points, and establishing a static distribution characteristic database, wherein the plurality of sampling points comprise online monitoring points;
s130, screening out a most matched static distribution characteristic database according to the current monitoring state;
s140, establishing a concentration regulation coefficient model of the target area to obtain a dynamic distribution characteristic database;
and S150, forming a gas pollution distribution map of the target area by using a spatial interpolation and visualization means based on the obtained dynamic distribution characteristic database.
The product can execute the method provided by the embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to methods provided by other embodiments of the present invention.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment may be implemented by software plus a general hardware platform, and may also be implemented by hardware. With this in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods of the various embodiments or some parts of the embodiments.
The foregoing descriptions of specific exemplary embodiments of the present invention have been presented for purposes of illustration and description. It is not intended to limit the invention to the precise form disclosed, and obviously many modifications and variations are possible in light of the above teaching. The exemplary embodiments were chosen and described in order to explain certain principles of the invention and its practical application to enable one skilled in the art to make and use various exemplary embodiments of the invention and various alternatives and modifications as are suited to the particular use contemplated. Any simple modifications, equivalent changes and modifications made to the above exemplary embodiments shall fall within the scope of the present invention.
Claims (19)
1. A method for predicting the distribution of plant gas pollution is characterized by comprising the following steps:
s110, acquiring meteorological data of a target area and position and concentration data of a plurality of sampling points, and establishing a static distribution characteristic database, wherein the plurality of sampling points comprise online monitoring points;
s130, acquiring a current monitoring state, and screening out a most matched static distribution characteristic database;
s140, establishing a concentration regulation and control coefficient model of the target area to obtain a dynamic distribution characteristic database;
and S150, forming a gas pollution distribution map of the target area by using a spatial interpolation and visualization method based on the obtained dynamic distribution characteristic database.
2. The method of claim 1, wherein the steps S130 to S150 are repeated according to a predetermined frequency to form a dynamically updated target zone gas pollution distribution map.
3. The method of claim 1, wherein the plurality of sampling points uniformly cover a target area.
4. The factory floor gas pollution distribution prediction method of claim 1, wherein the current monitoring state comprises an average wind direction 10 meters high above the upwind direction of the target area within a set time period, an average wind speed, and an average pollution concentration at each online monitoring point.
5. The factory floor gas pollution distribution prediction method according to claim 4, wherein the set time period is the current first 1-6 hours; the average wind direction and the average wind speed are calculated by adopting a vector average method; the average contaminant concentration is calculated using an arithmetic mean method.
6. The method according to claim 4, wherein the static distribution characteristic database is a set of coordinates of the sampling points and concentrations of the sampling points when the target area is closed or semi-closed, the terrain is flat, and the building is poorly shielded.
7. The method of predicting the factory floor gas pollution distribution of claim 4, wherein the step of building a database of static distribution characteristics includes:
forming a pollution distribution database of a target area by using a spatial interpolation method based on the position and concentration data of a plurality of sampling points;
dividing a target area into well type grids, and taking a collection of grid points and online monitoring points to form characteristic points;
according to the pollution distribution database, the pollution concentration of each characteristic point is obtained, and a static distribution characteristic database static _ net { x is established s ,y s ,c s In which x s 、y s As coordinates of feature points, c s Is the concentration of contamination at the characteristic point.
8. The method of predicting the factory floor gas pollution distribution of claim 7, wherein the step of building a database of static distribution characteristics further comprises:
carrying out spatial interpolation reduction on the established static distribution characteristic database to obtain a pollution distribution database, and calculating the similarity between the pollution distribution database obtained by reduction and the original pollution distribution database;
setting similarity thresholds M and N, wherein M is more than 0 and more than N and less than 1; and
judging whether the established static distribution characteristic database is qualified according to the similarity:
if the similarity is less than or equal to N, the established static distribution characteristic database is qualified;
if the similarity is less than M, increasing the grid density, re-dividing the well type grid, and establishing a static distribution characteristic database; and
if the similarity is larger than N, reducing the grid density, dividing the well type grid again, and establishing a static distribution characteristic database.
9. The method of claim 8, wherein the similarity threshold is 0.7M 0.75, 0.8N 0.85.
10. The factory floor gas pollution distribution prediction method of claim 7, wherein the meteorological data of the target area is wind speed and wind direction at 10 meters above the wind direction on the target area, the average value of the meteorological data in the sampling period is the sampling meteorological condition, and the sampling period is the time when all sampling points finish sampling.
11. The method of claim 10, wherein the step of establishing a plurality of static distribution characteristic databases based on different sampled weather conditions, comparing the current monitored state to the sampled weather conditions, and selecting the best matching static distribution characteristic database comprises:
a wind direction screening step of calculating a wind direction difference EW between an average wind direction in a current monitoring state and sampling meteorological conditions of a plurality of static distribution characteristic databases, and screening a minimum value min _ EW,
if only one static distribution characteristic database has EW (min _ EW + W) less than or equal to, determining the static distribution characteristic database corresponding to the minimum wind direction difference min _ EW as a best-matched static distribution characteristic database, wherein W is a set angle value and is less than or equal to 15 degrees;
and otherwise, a wind speed screening step is carried out, wind speed differences ES between the average wind speed in the current monitoring state and the sampling meteorological conditions of the plurality of static distribution characteristic databases are calculated, the minimum value min _ ES is screened, and the static distribution characteristic database corresponding to the minimum value min _ ES of the wind speed differences is determined as the most matched static distribution characteristic database.
12. The method according to claim 11, wherein the step S140 comprises:
s141, establishing a concentration regulation and control coefficient model k of the ith online monitoring point i (x s ,y s )，k i Coordinate (x) with ith online monitoring point i ，y i ) Average wind direction θ and average wind speed s in the current monitoring state, and pollution variation intensity p i Correlation, p i ＝c i /c i0 Wherein c is i Average pollution concentration of the ith online monitoring point in the current monitoring state, c i0 The pollution concentration of the characteristic point corresponding to the ith online monitoring point in the most matched static distribution characteristic database is obtained;
s142, establishing a concentration regulation and control coefficient model of the target region:
s143 obtains dynamic distribution characteristic database dynamic _ net (x) s ,y s ,c d ) Wherein c is d ＝c s ·K(x s ,y s )。
13. The factory floor gas pollution distribution prediction method of claim 12, wherein the concentration regulation coefficient model k of the ith online monitoring point i (x s ,y s ) Accords with the attenuation rule of gas diffusion and meets the following requirements:
when p is i When < 1, k i ∈[p i 1) when p is i When > 1, k i ∈(1,p i ]When p is i K =1 hour i ＝1；
When D is present i K when =0 i ＝p i I.e. no attenuation; when D is present i ＝D 0 When k is i =1, i.e. complete attenuation; when D is i ∈(0,D 0 ) When following D i Increase of (a), k i Gradual change in intensity p from contamination i Decays to 1, wherein D 0 In order to fully attenuate the distance of the beam,
wherein d is a feature point (x) s ,y s ) To the ith online monitoring point (x) i ,y i ) The distance of (a) to (b),
θ i is the average wind direction theta and (x) in the current monitoring state s ,y s ) And (x) i ,y i ) Acute angle of line segment included angle between two points; m represents a coefficient relating to wind speed, 1. Ltoreq. M.ltoreq.5, and the larger the average wind speed s in the current monitoring state, the larger m.
14. The method of predicting a gas pollution distribution of a plant of claim 13, wherein D is 0 Related to the area size of the target area and the number of on-line monitoring points, D 0 Is 100 to 500m.
15. The method of predicting the factory floor gas pollution distribution of claim 13, wherein m =1 if 0m/s ≦ s < 1 m/s; if s is more than or equal to 1m/s and less than 3m/s, m =2; if s is 3 m/s.ltoreq.s < 5m/s, m =3, if s is 5 m/s.ltoreq.s < 7m/s, m =4, if s.gtoreq.7 m/s, m =5.
16. A system for predicting a distribution of plant gas pollution, comprising:
the data acquisition unit is used for acquiring meteorological data of a target area and position and concentration data of a plurality of sampling points;
the data processing unit is used for establishing a static distribution characteristic database, screening out the most matched static distribution characteristic database and establishing a target area concentration regulation coefficient model according to the data acquired by the data acquisition unit to obtain a dynamic distribution characteristic database; and forming a gas pollution distribution map of the target area by utilizing a spatial interpolation and visualization means based on the obtained dynamic distribution characteristic database.
17. The factory floor gas pollution distribution prediction system of claim 16, wherein the data collection unit includes a mobile monitoring device and an online monitoring site.
18. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,
the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method of predicting a plant gas pollution distribution according to any one of claims 1 to 15.
19. A non-transitory computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform the method of predicting a plant gas pollution distribution according to any one of claims 1 to 15.
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