CN116862266A - Method and system for improving operation precision of fog gun vehicle based on big data - Google Patents
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Abstract
The invention discloses a method and a system for improving the operation precision of a fog gun vehicle based on big data, and belongs to the technical field of gas pollution source diffusion simulation prediction and application. The method comprises the following steps: data input: predicting weather data of the enterprise location as weather input data; the topographic data in the prediction range is used as topographic input data; prediction and analysis: importing terrain input data and meteorological input data, selecting a coordinate system, confirming a simulation period, running a prediction model, acquiring a simulation result and analyzing the simulation result; and (3) judging operation: and judging the range of fog gun operation according to the prediction and analysis results. Compared with the prior art, the invention has the advantages that: the working precision of the fog gun vehicle is improved by utilizing big data statistics, and the prediction model is applied to fog gun operation for the first time; through mass data simulation result evaluation, accurate operation of fog guns around the emission source is facilitated, and the sedimentation effect of particulate matters in the air is better; depending on the mature model, the prediction and evaluation steps are simple, and the evaluation cost is low.
Description
Technical Field
The invention relates to the technical field of gas pollution source diffusion simulation prediction and application, in particular to a method and a system for improving the operation precision of a fog gun vehicle based on big data.
Background
Fog gun operation has been widely used as a conventional means of treating atmospheric pollution and reducing the concentration of particulate matter in the air. The fog gun vehicle is one of the means of fog gun operation, the service time and the place of the fog gun vehicle are flexible, the concentrated fog gun operation can be carried out on the place with higher local particle concentration according to the air quality monitoring concentration, the local particle concentration is effectively reduced, the dust diffusion is restrained, and the air quality is improved. Fog gun vehicles are widely used for removing, constructing and dust falling in areas with easy dust rising, have remarkable effects, and are increasingly applied to the aspect of PM2.5 and PM10 treatment of urban atmosphere as conventional means in recent years.
The method reduces the concentration of particles around key enterprises, inhibits dust diffusion, and is very beneficial to improving the air quality. In addition, the concentration distribution of the peripheral particulate matters of the enterprise is uncertainty due to the influences of meteorological factors, the height of a pollution source, the flow rate and the like, so that the suppression and the accurate control of the peripheral particulate matters of the enterprise still have certain difficulty.
In the related art, as disclosed in chinese patent document CN218027427U, a new energy fog gun vehicle of small-size jam prevention, including the water tank, fog gun motor, fog gun vehicle, water tank and fog gun motor are installed on fog gun vehicle, fixedly mounted has the alarm on the water tank, electric connection has alarm switch on the alarm, fixedly mounted has the rack on the alarm switch, rack fixed mounting is on the water tank, fixedly mounted has spacing axle on the rack, slidable mounting has the touch-plate on the spacing axle, this scheme passes through setting up of kickboard, alarm etc. realize in fog gun vehicle use, the kickboard moves along with the water level in the water tank to drive the clamp plate motion, when the water level is low to the water level that needs to add water, the clamp plate just contacts the touch-plate, when the water level continues to descend, drive touch-plate contact alarm switch makes the alarm buzzing, thereby realize reminding the staff for the water tank adds water, and then guaranteed the normal work of fog gun vehicle. However, the scheme does not provide any technical teaching for the problem of lower precision of fog gun operation in the related technology.
In summary, the lower precision of fog gun operation is a problem to be solved in the prior art.
Disclosure of Invention
1. Technical problem to be solved
Aiming at the problem of lower fog gun operation precision in the prior art, the invention provides a method and a system for improving the fog gun vehicle operation precision based on big data, which apply a prediction model to evaluate the fog gun operation route range, provide a new thought for pollution control, dust reduction, haze reduction and urban environment management, realize the improvement of the fog gun operation precision, and carry out the fog gun in the optimal operation range as being more beneficial to the sedimentation of particles in the air and have better air purification effect.
2. Technical proposal
The aim of the invention is achieved by the following technical scheme.
A method for improving the operation precision of a fog gun vehicle based on big data comprises the following steps:
data input: predicting weather data of the enterprise location as weather input data; the topographic data in the prediction range is used as topographic input data;
prediction and analysis: importing terrain input data and meteorological input data, selecting a coordinate system, confirming a simulation period, running a prediction model, acquiring a simulation result and analyzing the simulation result;
judging and operating: and judging the range, the strength and the running speed of the fog gun operation according to the result of the big data prediction and analysis.
Further, the specific step of weather data prediction is that based on ground weather data and high-altitude weather data, wind direction, wind speed, air pressure, temperature, relative humidity, dew point air temperature, total cloud amount and low cloud amount detected by weather every hour of a place where an enterprise is located are predicted, and sounding data observed by a weather observation station are stored into weather input data files meeting the requirements of a prediction model format.
Still further, the sonde data includes ground to high altitude air pressure, wind direction and speed, dry bulb temperature and dew point air temperature of the locus daily.
Further, the specific step of topographic data prediction is to process topographic data in a prediction range and store the topographic data into topographic data files meeting the requirements of a prediction model format;
the prediction model divides the diffusion flow field into a two-layer structure, the flow field of the lower layer keeps horizontally bypassing the barrier, the flow field of the upper layer lifts over the barrier, and the Gaussian distribution is satisfied under the stable and convection conditions.
Further, the mass concentration of grid points (x, y, z) on the land topography is ρ (x, y, z), and after simulation by the prediction model, the total mass concentration of the land topography influence ρ T (x, y, z) is expressed as:
ρ T (x,y,z)＝f·ρ(x,y,z)+(1-f)·ρ(x,y,z a )；
f＝0.5·(1+θ)；
z a ＝z-z i ；
(x, y, z) is the coordinates of the grid points; ρ (x, y, z) is the total concentration of plumes; z a Is of effective height; f is a weight function; θ is the ratio of the mass of plume to the total mass of plume; hc is the height of the demarcation streamline; z i A height value for the terrain; q is the pollution source discharge rate; u is the effective wind speed; p (P) y (y,x)、P z (z, x) is a probability distribution function of the concentration distribution in the horizontal direction and the vertical direction, respectively.
Further, the total concentration ρ (x, y, z) of the plume has different expressions in the convective boundary layer and the stable boundary layer; wherein the total concentration ρ (x, y, z) of plumes at the convective boundary layer diffusion formula is expressed as:
ρ(x,y,z)＝ρ d (x,y,z)+ρ r (x,y,z)+ρ p (x,y,z)；
ρ d (x, y, z) is the direct emission concentration of the contaminant; ρ r (x, y, z) is the virtual source emission concentration, as represented by ρ d (x, y, z), only h i There are differences; ρ p (x, y, z) is the nip source discharge concentration; q is the pollution source discharge rate; u is the effective wind speed;is the average wind speed; z j A height value for the terrain; lambda (lambda) j Is a Gaussian distribution weight coefficient; h is a i Is high in effective source; sigma (sigma) y Is the horizontal diffusion coefficient; sigma (sigma) z Is the vertical diffusion coefficient; sigma (sigma) j For ρ d Vertical diffusion coefficient of (x, y, z); sigma (sigma) b Is the diffusion coefficient caused by buoyancy; b j Is the standard deviation proportion; sigma (sigma) w Is the vertical turbulence intensity.
Further, the total concentration ρ (x, y, z) of the plume has different expressions in the convective boundary layer and the stable boundary layer; wherein the total concentration ρ (x, y, z) of the plume is expressed in the stable boundary layer diffusion formula as:
n is a constant and the domain is (- ≡infinity, fact); f (F) z Is a vertical distribution function of smoke plumes; f (F) y Is the horizontal distribution function of the smoke plume; sigma (sigma) y Is the horizontal diffusion coefficient; sigma (sigma) z Is the vertical diffusion coefficient; h is a p Is the height of the smoke plume; h is a z Is the limit height of the vertical mixed layer; sigma (sigma) y 、σ Z The diffusion parameters of the smoke plume in the horizontal direction and the vertical direction are respectively.
Further, the specific steps of prediction and analysis are that a coordinate system is selected, wherein the coordinate system comprises relative coordinates and base map projection coordinates; importing a base map and giving geographic information;
setting double receptor grids with the predicted point as the center, wherein the first step length is 500m,21×21 grids, and the second step length is 100m,21×21 grids;
importing terrain input data, running a prediction model, and endowing altitude values to all input source intensity and receptor grids;
importing meteorological input data, inputting the altitude of a meteorological site, confirming a simulation period, and running a prediction model;
predicting the concentration and diffusion area of pollutants and the concentration of grid points of each receptor under different meteorological conditions, topographic conditions and source intensity parameter conditions to form a big data prediction result;
and analyzing the big data prediction result to obtain the maximum floor concentration value and the distance of the maximum floor concentration value.
Further, the specific steps of operation judgment are to predict parameters such as longitude and latitude of a pollution source, pollutant type, pollutant discharge rate, chimney height and inner diameter, flue gas temperature and the like of an enterprise;
comprehensively obtaining pollution degree t according to parameters such as longitude and latitude of a predicted pollution source, pollutant types, pollutant emission rate, chimney height, inner diameter, flue gas temperature and the like;
setting a coefficient alpha according to the predicted topographic data; setting a coefficient beta according to the predicted weather data;
when the pollution degree t is larger, the fog gun vehicleThe larger the operation intensity p is, the slower the operation speed v of the fog gun carriage is, and the operation intensity and the operation speed of the fog gun carriage are obtained, namely
The system based on the method for improving the working accuracy of the fog gun vehicle based on big data comprises,
and a data input module: the method comprises the steps of predicting weather data of a place where an enterprise is located, and taking the weather data as weather input data; the topographic data in the prediction range is used as topographic input data;
prediction and analysis module: the method comprises the steps of importing topography input data and meteorological input data, selecting a coordinate system, confirming a simulation period, running a prediction model, obtaining a simulation result, analyzing the simulation result, and obtaining a maximum landing concentration value and a distance at which the maximum landing concentration value appears;
the operation judging module is used for: the method is used for judging the optimal fog gun operation range, operation intensity and operation speed according to the maximum floor concentration, the distance of occurrence of the maximum concentration and the predicted concentration distribution condition of pollutants.
3. Advantageous effects
Compared with the prior art, the invention has the advantages that: the method and the system for improving the operation precision of the fog gun vehicle based on big data are used for applying the AERMOD model to fog gun operation for the first time; through big data simulation evaluation, the method is beneficial to more accurate operation of fog guns around the emission source, so that the sedimentation effect of particulate matters in the air is better; depending on the mature model, the prediction and evaluation steps are relatively simple, and the evaluation cost is low and the return rate is high.
Drawings
FIG. 1 is a flow chart of a method for improving accuracy of fog gun carriage operation based on big data in an embodiment of the invention;
fig. 2 is a schematic diagram of a fog gun dust removal operation in an embodiment of the present invention.
Detailed Description
The invention will now be described in detail with reference to the drawings and the accompanying specific examples.
And simulating the diffusion condition of the enterprise particulate matters based on the prediction model. The prediction model in the embodiment is an AERMOD model, is an atmospheric prediction model applicable to a point source, a surface source, a line source and a body source and has a prediction range smaller than 50km, and is mainly applied to the aspects of pollutant diffusion simulation, atmospheric protection distance calculation and the like in atmospheric evaluation at the present stage, and is less common in the field of practical application.
The embodiment provides a method for improving the working accuracy of fog gun trucks based on big data, which predicts the concentration distribution condition of particles around an enterprise based on a small-scale model so as to improve the working accuracy of the fog gun trucks around the enterprise.
As shown in fig. 1 to 2, a method for improving the working accuracy of a fog gun carriage based on big data comprises the following steps:
data input
Based on the ground meteorological data and the high-altitude meteorological data, predicting meteorological data of the places where enterprises are located, and taking the meteorological data as meteorological input data; terrain data within the prediction horizon is used as terrain input data. The method comprises the following steps:
weather data prediction:
weather data of the enterprise site is predicted, the weather data comprise wind directions, wind speeds, air pressures, temperatures, relative humidity, dew point air temperatures, total cloud amount and low cloud amount detected by weather detection of the enterprise site every day for 1 year based on the ground weather data and high-altitude weather data, and sounding data observed by a weather observation station, the sounding data comprise air pressures, wind directions and wind speeds, dry ball temperatures and dew point air temperatures of 2000m from the ground to the high altitude at the time of 07 hours and 19 hours of the site are predicted, and the data are stored into weather input data files meeting the requirements of a prediction model format.
And (3) predicting topographic data:
terrain data within the prediction horizon, elevation data from SRTM, respectively rate 30m. And processing the topographic data by using a topographic data preprocessing module of the prediction model, and storing the topographic data into a topographic data file meeting the format requirement of the prediction model.
When the influence of topography (including ground obstacles) on the concentration distribution of pollutants is considered, the prediction model uses the concept of demarcation streamline, namely a structure that a diffusion flow field is divided into two layers, wherein the flow field of the lower layer keeps horizontal to bypass the obstacles, and the flow field of the upper layer lifts over the obstacles. Which satisfies a gaussian distribution both under steady and convective conditions.
The concentration value of any grid point is the sum of the two smoke plume concentrations after weighting. Assuming that the mass concentration of a grid point (x, y, z) on the platform terrain is ρ (x, y, z), the total mass concentration formula for the terrain effect is considered after simulation by the predictive model:
ρ T (x,y,z)＝f·ρ(x,y,z)+(1-f)·ρ(x,y,z a )；
f＝0.5·(1+θ)；
z a ＝z-z i ；
wherein (x, y, z) is the coordinates of the grid points; ρ T (x, y, z) is the total concentration; ρ (x, y, z) is the total concentration of plumes, and has different expressions in the convection boundary layer and the stable boundary layer; z a Is of effective height; f is a weight function; θ is the ratio of the mass of plume to the total mass of plume; hc is the height of the demarcation streamline; z i A height value for the terrain; q is the pollution source discharge rate; u is the effective wind speed; p (P) y (y, x), pz (z, x) are probability distribution functions of the concentration distribution in the horizontal direction and the vertical direction, respectively.
(1) Convection boundary layer diffusion formula:
ρ(x,y,z)＝ρ d (x,y,z)+ρ r (x,y,z)+ρ p (x,y,z)；
wherein ρ is d (x, y, z) is the direct emission concentration of the contaminant; ρ r (x, y, z) is the virtual source emission concentration, formula and ρ d (x, y, z) are similar, only h i Slightly different; ρ p (x, y, z) is the nip source discharge concentration; q is the pollution source discharge rate; u is the effective wind speed;is the average wind speed; z j A height value for the terrain; lambda (lambda) j Is a Gaussian distribution weight coefficient; h is a i Is high in effective source; sigma (sigma) y Is the horizontal diffusion coefficient; sigma (sigma) z Is the vertical diffusion coefficient; sigma (sigma) j For ρ d Vertical diffusion coefficient of (x, y, z); sigma (sigma) b Is the diffusion coefficient caused by buoyancy; b j Is the standard deviation proportion; sigma (sigma) w Is the vertical turbulence intensity.
(2) Stabilizing the boundary layer diffusion formula:
where n is a constant, the definition field is (- ≡infinity), F z Is a vertical distribution function of smoke plumes; f (F) y Is the horizontal distribution function of the smoke plume; sigma (sigma) y Is the horizontal diffusion coefficient; sigma (sigma) z Is the vertical diffusion coefficient; h is a p Is the height of the smoke plume; h is a z Is the limit height of the vertical mixed layer; sigma (sigma) y 、σ Z The diffusion parameters of the smoke plume in the horizontal direction and the vertical direction are respectively.
Prediction and analysis
Selecting a corresponding coordinate system comprising relative coordinates and base map projection coordinates, importing a corresponding base map and giving geographic information;
setting double receptor grids with predicted points as centers, wherein the first step length is 500m, and 21 multiplied by 21 grids; a second sub-step 100m,21 x 21 grids;
importing terrain input data, operating an AERMAP model, and endowing all input source intensity and receptor grids with altitude values;
meteorological input data (ground meteorological files and high-altitude meteorological files) are imported, the altitude of a meteorological site is input, and a simulation period is confirmed.
The prediction model can be operated;
model simulation can predict the concentration and diffusion area of the pollutant and the concentration of each receptor grid point under the meteorological conditions, the topographic conditions and the conditions of strong parameters. Analyzing the simulation result, drawing equivalent drawing and data statistics, and obtaining a maximum floor concentration value, a distance of occurrence of the maximum floor concentration value and the like;
judging and operating
And judging the range of the fog gun operation according to the distance of the maximum landing concentration, and acquiring the operation intensity and the operation speed of the fog gun operation according to the prediction result.
Predicting parameters such as longitude and latitude of a pollution source, pollutant types, pollutant emission rate, chimney height and inner diameter, flue gas temperature and the like of an enterprise;
comprehensively obtaining pollution degree t according to parameters such as longitude and latitude of a predicted pollution source, pollutant types, pollutant emission rate, chimney height, inner diameter, flue gas temperature and the like;
setting a coefficient alpha according to the predicted topographic data; setting a coefficient beta according to the predicted weather data;
when the pollution degree t is larger, the operation intensity p of the fog gun carriage is larger, the operation speed v of the fog gun carriage is slower, and the operation intensity and the operation speed of the fog gun carriage are obtained, namely
According to the method for improving the working accuracy of the fog gun vehicle, simulation is conducted on the diffusion condition of the discharged enterprise particulate matters based on meteorological data, topographic data and enterprise pollution source intensity data meeting the requirements of a prediction model format, the diffusion condition of the enterprise particulate matters under the meteorological, topographic and source intensity conditions is predicted, and the maximum landing concentration and the distance at which the maximum landing concentration occur under the conditions are obtained. The distance and the range of fog gun operation can be adjusted according to the conditions of each enterprise, the accuracy of fog gun operation is improved, and the dissipation of dust is more effectively restrained.
The system based on the method for improving the working precision of the fog gun vehicle comprises the following steps:
and a data input module:
the method comprises the steps of predicting weather data of a place where an enterprise is located, and taking the weather data as weather input data; terrain data within the prediction horizon is used as terrain input data.
Weather data is predicted, the weather data is processed by using a weather data preprocessing module, the ground weather data can be in the form of CD144, excel and Chinese weather station A files, and the weather data is processed into interpolation by weather data preprocessing of a prediction system of a prediction model or other means, and then the CD144 format which can be identified by an AERMET model is derived.
And predicting the topographic data, and importing a prediction model into a CD 144-format ground meteorological data file. And the module searches the information such as the geographic position, the altitude and the like of the meteorological site in the self database according to the site well information in the ground meteorological data file. And importing the FSL high-altitude data text, automatically identifying site information in the file by a module, and selecting other default options by other settings. Inputting land utilization parameters: and defining the land coverage type by using the contrast ratio, the Boen ratio and the surface roughness. After the AERMET model is operated, an SFC format ground data file which can be directly input into the prediction model and a PFL format high-altitude meteorological file can be obtained. And respectively saving the SFC file and the PFL file to the formulated path.
Prediction and analysis module:
the method is used for importing topography input data and meteorological input data, selecting a coordinate system, confirming a simulation period, running a prediction model, analyzing simulation results, and obtaining a maximum landing concentration value and a distance at which the maximum landing concentration value appears.
A coordinate system is defined, and relative coordinates and map projection coordinates are selected. The map obtained from any map software is imported, and geographic information of the map is given by providing southwest and northeast angular coordinates of the map or adopting a known reference point and bright point distance method.
And selecting a BREEZE program, switching to a multi-pollutant interface, inputting pollutant names according to requirements, and setting 1 or more pollution factors.
Adding pollution sources on an imported map, and selecting different source types according to the pollution source types, wherein the source types comprise a point source, a torch source, a surface source, a circular surface source, a polygonal surface source, a line source, a body source, an open mine pit source and the like; and after the source type is selected, adding a source on the map or setting the position of the source according to coordinates, and inputting source intensity parameters. The point source parameters include name, stack height and inside diameter, flue gas outlet temperature, outlet flow rate, and pollutant emission rate. The surface source parameters include name, discharge height, initial vertical diffusion parameters, length and width of the surface source, angle information, and discharge rate of pollutants.
Building data (if any) is set, a 'building' in the toolbar is selected, a rectangle, a circle or a polygon is selected, the shape of the building is drawn on a map, and parameters such as height are set. And (5) running the BPIP program to obtain the building washing parameters.
The computational grid is designed. Firstly, selecting a factory boundary receptor tool, sketching factory boundary receptors on a map according to the long street shape of a base map, wherein the grid spacing is 100 meters, and clicking to generate factory boundary receptor grids. Secondly, setting double uniform Cartesian grids in an evaluation range, wherein the first layer of grids is thicker, the grid step length is 500m, the number of grids is 21×21, the second layer of grids is finer, the grid step length is 100m, the number of grids is 21×21, and selecting an option for clearing receptors in a factory boundary.
Topography elevation data was imported using elevation data from SRTM at a rate of 30m. The imported terrain elevation file needs to cover a range needing to be predicted, and if the range is too large, a calculation area can be selected, so that the calculation amount is reduced.
The predictive model is run and all sources, recipients and grids are assigned altitude values.
And importing a meteorological data file. Inputting the SFC format ground data file obtained by the pretreatment and the PFL format high-altitude meteorological file, and inputting the meteorological site altitude.
Setting output parameters, clicking on the output options in the menu, and clicking on "calculate analysis settings", "drawing file output settings", and "detailed record file". The "calculate analysis settings", "drawing file output settings" and "detail record file" option interfaces may check 1 hour, 24 hours, and month average time period averages and year averages; and (5) checking and confirming according to the requirements.
And (3) operating the prediction model, after setting the parameters, clicking a prediction model operation button after confirming without errors, and then waiting for the successful operation of the model. If the parameter is set incorrectly or the options are not set, the description column presents prompt and warning information. The model running interface may then show unsuccessful.
After the model is run, the user can view detailed report summary on report tab on the interface of the prediction model. Including input and output profiles, input and output files, and various result files for output tab selection.
And selecting and using a 3D analysis calculation result, and importing the calculation result after operation. And drawing a concentration distribution diagram or a contour diagram, and carrying out statistical analysis on the data result. As shown in table 1, the prediction results revealed the maximum floor concentration and the distance at which the maximum concentration occurred, and the concentration distribution of the contaminants.
TABLE 1 prediction results
And a judging and operating module:
the method is used for judging the optimal fog gun operation range, operation intensity and operation speed according to the maximum floor concentration, the distance of occurrence of the maximum concentration and the predicted concentration distribution condition of pollutants.
The fog gun operation generally uses a fog gun vehicle, and the rear of the vehicle is provided with a fog gun barrel except a water storage tank, so that the spraying distance of the 'gun barrel' can be adjusted according to different road conditions, the water fog is directionally thrown to a set position, spraying coverage is performed above or around a dust source, the humidity in the air is increased, dust in the air is quickly condensed into particle clusters when meeting water, and the dust is quickly reduced to the ground under the action of self gravity, so that the dust removal effect is achieved.
Comprehensively obtaining pollution degree t according to parameters such as longitude and latitude of a predicted pollution source, pollutant types, pollutant emission rate, chimney height, inner diameter, flue gas temperature and the like;
setting a coefficient alpha according to the predicted topographic data; setting a coefficient beta according to the predicted weather data;
when the pollution degree t is larger, the operation intensity p of the fog gun carriage is larger, the operation speed v of the fog gun carriage is slower, and the operation intensity and the operation speed of the fog gun carriage are obtained, namely
According to the method and the system for improving the operation precision of the fog gun vehicle, provided by the invention, meteorological data, terrain elevation data and pollution source parameters are used as input data of a model; and simulating the pollution concentration by using the prediction model to obtain the simulated concentration value of each receptor grid. The maximum landing concentration and the occurrence distance of the pollutants at the predicted point and the distribution condition of the pollutant concentration can be obtained through the simulation result, and a large enterprise peripheral pollutant concentration data set is established. The method can be applied to evaluation of the operation range of fog guns around enterprises. According to the method and the system for improving the operation precision of the fog gun vehicle based on the big data, the distance and the range of the fog gun operation can be adjusted according to the conditions of each enterprise, the precision of the fog gun operation is improved, and the dissipation of dust is effectively restrained.
The foregoing has been described schematically the invention and embodiments thereof, which are not limiting, but are capable of other specific forms of implementing the invention without departing from its spirit or essential characteristics. The drawings are also intended to depict only one embodiment of the invention, and therefore the actual construction is not intended to limit the claims, any reference number in the claims not being intended to limit the claims. Therefore, if one of ordinary skill in the art is informed by this disclosure, a structural manner and an embodiment similar to the technical scheme are not creatively designed without departing from the gist of the present invention, and all the structural manners and the embodiment are considered to be within the protection scope of the present patent. In addition, the word "comprising" does not exclude other elements or steps, and the word "a" or "an" preceding an element does not exclude the inclusion of a plurality of such elements. The various elements recited in the product claims may also be embodied in software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Claims (10)
1. A method for improving the operation precision of a fog gun vehicle based on big data comprises the following steps:
data input: predicting weather data of the enterprise location as weather input data; the topographic data in the prediction range is used as topographic input data;
prediction and analysis: importing terrain input data and meteorological input data, selecting a coordinate system, confirming a simulation period, running a prediction model, acquiring a simulation result and analyzing the simulation result;
judging and operating: and judging the range, the strength and the running speed of the fog gun operation according to the result of the big data prediction and analysis.
2. The method for improving the working accuracy of the fog gun vehicle based on big data according to claim 1, wherein,
the weather data prediction specifically comprises the steps of predicting wind direction, wind speed, air pressure, temperature, relative humidity, dew point air temperature, total cloud amount and low cloud amount of daily hourly weather detection of an enterprise location based on ground weather data and high-altitude weather data, and storing the data into weather input data files meeting the requirements of a prediction model format according to sounding data observed by a weather observation station.
3. The method for improving the working accuracy of the fog gun vehicle based on big data according to claim 2, wherein,
the exploratory data comprise daily ground to high air pressure, wind direction and speed, dry bulb temperature and dew point air temperature of the place.
4. The method for improving the working accuracy of the fog gun vehicle based on big data according to claim 1, wherein,
the specific step of topographic data prediction is that topographic data in a prediction range is processed and stored into a topographic data file meeting the requirements of a prediction model format;
the prediction model divides the diffusion flow field into a two-layer structure, the flow field of the lower layer keeps horizontally bypassing the barrier, the flow field of the upper layer lifts over the barrier, and the Gaussian distribution is satisfied under the stable and convection conditions.
5. The method for improving the working accuracy of the fog gun vehicle based on big data according to claim 4, wherein,
the mass concentration of grid points (x, y, z) on the platform terrain is ρ (x, y, z), and after simulation by a prediction model, the total mass concentration of the terrain influence ρ T (x, y, z) is expressed as:
ρ T (x,y,z)＝·ρ(x,y,z)+(1-)·(x,y,z a )；
f＝0.5·(1+θ)；
z a ＝-z i ；
(x, y, z) is the coordinates of the grid points; (x, y, z) is the total concentration of plumes; z a Is of effective height; f is a weight function; θ is the ratio of the mass of plume to the total mass of plume; hc is the height of the demarcation streamline; z i A height value for the terrain; q is the pollution source discharge rate; u is the effective wind speed; p (P) y (y,x)、P z (, x) is a probability distribution function of the concentration distribution in the horizontal direction and the vertical direction, respectively.
6. The method for improving the working accuracy of the fog gun vehicle based on big data according to claim 5, wherein,
the total concentration rho (x, y, z) of the plume has different expression forms in the convection boundary layer and the stable boundary layer; wherein the total concentration ρ (x, y, z) of plumes at the convective boundary layer diffusion formula is expressed as:
ρ(x,y,z)＝ d (x,y,z)+ r (x,y,z)+ p (x,y,z)；
ρ d (x, y, z) is the direct emission concentration of the contaminant; ρ r (x, y, z) is the virtual source emission concentration, as represented by ρ d (x, y, z), only h i There are differences; ρ p (x, y, z) is the nip source discharge concentration; q is the pollution source discharge rate; u is the effective wind speed;is the average wind speed; z j A height value for the terrain; lambda (lambda) j Is a Gaussian distribution weight coefficient; h is a i Is high in effective source; sigma (sigma) y Is the horizontal diffusion coefficient; sigma (sigma) z Is the vertical diffusion coefficient; sigma (sigma) j For ρ d Vertical diffusion coefficient of (x, y, z); sigma (sigma) b Is the diffusion coefficient caused by buoyancy; b j Is the standard deviation proportion; sigma (sigma) w Is the vertical turbulence intensity.
7. The method for improving the working accuracy of the fog gun vehicle based on big data according to claim 5, wherein,
the total concentration rho (x, y, z) of the plume has different expression forms in the convection boundary layer and the stable boundary layer; wherein the total concentration ρ (x, y, z) of the plume is expressed in the stable boundary layer diffusion formula as:
n is a constant and the domain is (- ≡infinity, fact); f (F) z Is a vertical distribution function of smoke plumes; f (F) y Is the horizontal distribution function of the smoke plume; sigma (sigma) y Is the horizontal diffusion coefficient; sigma (sigma) z Is the vertical diffusion coefficient; h is a p Is the height of the smoke plume; h is a z Is the limit height of the vertical mixed layer; sigma (sigma) y 、σ z The diffusion parameters of the smoke plume in the horizontal direction and the vertical direction are respectively.
8. The method for improving the working accuracy of the fog gun vehicle based on big data according to claim 1, wherein,
the specific steps of prediction and analysis are that a coordinate system is selected, wherein the coordinate system comprises relative coordinates and base map projection coordinates; importing a base map and giving geographic information;
setting double receptor grids with the predicted point as the center, wherein the first step length is 500m,21×21 grids, and the second step length is 100m,21×21 grids;
importing terrain input data, running a prediction model, and endowing altitude values to all input source intensity and receptor grids;
importing meteorological input data, inputting the altitude of a meteorological site, confirming a simulation period, and running a prediction model;
predicting the concentration and diffusion area of pollutants and the concentration of grid points of each receptor under different meteorological conditions, topographic conditions and source intensity parameter conditions to form a big data prediction result;
and analyzing the big data prediction result to obtain the maximum floor concentration value and the distance of the maximum floor concentration value.
9. The method for improving the working accuracy of the fog gun vehicle based on big data according to claim 8, wherein,
predicting parameters such as longitude and latitude of a pollution source, pollutant types, pollutant emission rate, chimney height, inner diameter, flue gas temperature and the like of an enterprise;
comprehensively obtaining pollution degree t according to parameters such as longitude and latitude of a predicted pollution source, pollutant types, pollutant emission rate, chimney height, inner diameter, flue gas temperature and the like;
setting a coefficient alpha according to the predicted topographic data; setting a coefficient beta according to the predicted weather data;
when the pollution degree t is larger, the operation intensity p of the fog gun carriage is larger, the operation speed v of the fog gun carriage is slower, and the operation intensity and the operation speed of the fog gun carriage are obtained, namely
10. A system based on the method for improving the working accuracy of fog gun vehicles based on big data according to any one of the claims 1-9, comprising,
and a data input module: the method comprises the steps of predicting weather data of a place where an enterprise is located, and taking the weather data as weather input data; the topographic data in the prediction range is used as topographic input data;
prediction and analysis module: the method comprises the steps of importing topography input data and meteorological input data, selecting a coordinate system, confirming a simulation period, running a prediction model, obtaining a simulation result, analyzing the simulation result, and obtaining a maximum landing concentration value and a distance at which the maximum landing concentration value appears;
the operation judging module is used for: the method is used for judging the optimal fog gun operation range, operation intensity and operation speed according to the maximum floor concentration, the distance of occurrence of the maximum concentration and the predicted concentration distribution condition of pollutants.
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