CN116631530B - Pollutant diffusion risk identification method, device and equipment - Google Patents
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Abstract
The disclosure provides a pollutant diffusion risk identification method, device and equipment, which can be applied to the field of pollutant diffusion analysis. The method comprises the following steps: acquiring multi-source weather data from a weather system about a target area, wherein the multi-source weather data includes measured weather data, first simulated weather data, and second simulated weather data; assimilating the multi-source meteorological data to obtain assimilated multi-source meteorological data; inputting pollution source emission data and assimilated multisource meteorological data into a computational fluid dynamics model, and outputting pollutant concentration analysis results of a preset respiratory height plane; and determining a pollutant diffusion risk identification result according to the pollutant concentration analysis result and the pollutant attribute characteristics.
Description
Technical Field
The present disclosure relates to the field of contaminant diffusion analysis, and in particular, to a contaminant diffusion risk identification method, apparatus, and device.
Background
In the prior art of analysis of risk of diffusion of atmospheric pollutants in a park scale, an air quality model AERMOD (AMS/EPA REGULATORY MODEL) or CALPUFF (CALPUFF) and the like are mostly adopted for simulation of diffusion of pollutants in the park scale, the AERMOD model and the CALPUFF model are typical Gaussian atmospheric diffusion models at present, meteorological data, geographic data and pollution source data can be input, three-dimensional terrain influence is considered, and concentration distribution after diffusion of the pollutants is obtained through model calculation. However, the land utilization type, altitude and other topographic parameters of the underlying surface input by the model cannot be completely matched with the actual situation, and the resolution of the input meteorological data is thicker, so that the actual circulation and diffusion conditions cannot be fully reflected.
Disclosure of Invention
In view of the foregoing, the present disclosure provides a contaminant diffusion risk identification method, apparatus, device, medium, and program product.
According to a first aspect of the present disclosure, there is provided a contaminant diffusion risk identification method comprising:
acquiring multi-source weather data from a weather system about a target area, wherein the multi-source weather data includes measured weather data, first simulated weather data, and second simulated weather data;
assimilating the multi-source meteorological data to obtain assimilated multi-source meteorological data;
inputting pollution source emission data and assimilated multisource meteorological data into a computational fluid dynamics model, and outputting pollutant concentration analysis results of a preset respiratory height plane; and
and determining a pollutant diffusion risk identification result according to the pollutant concentration analysis result and the pollutant attribute characteristics.
According to an embodiment of the present disclosure, obtaining multi-source weather data from a weather system about a target area includes:
simulating the target area by using a mesoscale meteorological model to obtain first simulated meteorological data;
simulating the target area and the preset area by using the mesoscale meteorological model and the fluid model to obtain second simulated meteorological data;
And obtaining actual measurement meteorological data in the target area from a meteorological database.
According to an embodiment of the present disclosure, a target area is simulated using a mesoscale meteorological model to obtain first simulated meteorological data, comprising:
inputting preset meteorological data into a mesoscale meteorological model;
dividing nested grids according to the geographic position of the target area so as to output first grid information, wherein the first grid information comprises first longitude and latitude information and first meteorological parameter information;
and extracting first simulated meteorological data in the target area according to the first gridding information.
According to an embodiment of the present disclosure, simulating a target area and a preset area using a mesoscale weather model and a fluid model to obtain second simulated weather data includes:
performing three-dimensional modeling on the target area and the preset area to obtain a modeling space;
performing grid division on the modeling space so as to acquire grid boundary meteorological data by utilizing a mesoscale meteorological model;
and simulating a flow field by using a fluid model based on the grid boundary meteorological data to obtain second simulated meteorological data.
According to an embodiment of the present disclosure, the grid boundary weather data includes inflow interface weather data and outflow interface weather data;
The method for meshing the modeling space so as to obtain grid boundary meteorological data by using a mesoscale meteorological model comprises the following steps:
performing grid division on the modeling space to obtain a plurality of grid points;
determining inflow boundary longitude and latitude information and outflow boundary longitude and latitude information according to the grid points;
and extracting inflow interface weather data and outflow interface weather data from first weather parameter information according to the inflow boundary longitude and latitude information and the outflow boundary longitude and latitude information, wherein the first weather parameter information is determined according to a mesoscale weather model.
According to an embodiment of the present disclosure, the grid boundary weather data includes inflow interface weather data and outflow interface weather data;
based on grid boundary meteorological data, simulating a flow field by using a fluid model to obtain second simulated meteorological data, wherein the method comprises the following steps of:
inputting inflow interface weather data into a fluid model;
carrying out flow field simulation by taking outflow interface meteorological data as constraint conditions;
and outputting the second simulated meteorological data.
According to an embodiment of the present disclosure, data assimilation is performed on multi-source weather data to obtain assimilated multi-source weather data, including:
fusing the first simulated meteorological data and the second simulated meteorological data to obtain fused simulated meteorological data;
And carrying out data assimilation on the fused simulated meteorological data and the actually measured meteorological data by adopting a preset assimilation method to obtain assimilated multi-source meteorological data.
According to an embodiment of the present disclosure, determining a contaminant diffusion risk identification result from a contaminant concentration analysis result and a contaminant attribute feature includes:
determining a risk threshold according to the attribute characteristics of the pollutants;
and determining a pollutant diffusion risk identification result according to the pollutant concentration analysis result and the risk threshold value.
A second aspect of the present disclosure provides a contaminant diffusion risk identification device, comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring multi-source weather data from a weather system about a target area, wherein the multi-source weather data comprises actual measurement weather data, first simulation weather data and second simulation weather data;
the assimilation module is used for assimilating the multi-source meteorological data to obtain assimilated multi-source meteorological data;
the analysis module is used for inputting the pollutant source emission data and the assimilated multi-source meteorological data into a computational fluid dynamics model and outputting pollutant concentration analysis results of a preset respiratory height plane; and
and the determining module is used for determining a pollutant diffusion risk identification result according to the pollutant concentration analysis result and the pollutant attribute characteristics.
A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the contaminant spreading risk identification method described above.
A fourth aspect of the present disclosure also provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the above-described contaminant spreading risk identification method.
A fifth aspect of the present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the above-described contaminant spreading risk identification method.
According to the embodiment of the disclosure, the multisource meteorological data comprising the actually measured meteorological data, the first simulated meteorological data and the second simulated meteorological data are adopted for data assimilation to obtain a further optimized meteorological field, the computational fluid dynamics model is adopted for simulating pollutant diffusion, simulation precision is high, the problem that simulation precision of the meteorological field is low due to the fact that the simulation precision of the meteorological field is not compared and verified with real observation data is avoided, and compared with an air quality model, finer horizontal grids and vertical grids are divided, and influence of underlying shielding is fully considered. The pollutant concentration analysis result and the pollutant attribute characteristic are combined, so that the diffusion risk of pollutants can be accurately estimated.
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The foregoing and other objects, features and advantages of the disclosure will be more apparent from the following description of embodiments of the disclosure with reference to the accompanying drawings, in which:
FIG. 1 schematically illustrates an application scenario diagram of contaminant diffusion risk identification methods, apparatus, devices, media, and program products according to embodiments of the present disclosure;
FIG. 2 schematically illustrates a flow chart of a contaminant diffusion risk identification method in accordance with an embodiment of the present disclosure;
FIG. 3 schematically illustrates a flow chart of a method of acquiring multi-source weather data from a weather system regarding a target area in accordance with an embodiment of the disclosure;
FIG. 4 schematically illustrates a flow chart of a method of assimilating multi-source weather data to obtain assimilated multi-source weather data, according to an embodiment of the disclosure;
FIG. 5 schematically illustrates a flow chart of a contaminant diffusion risk identification method in accordance with another embodiment of the present disclosure;
FIG. 6 schematically illustrates a block diagram of a contaminant diffusion risk identification device, in accordance with an embodiment of the present disclosure; and
fig. 7 schematically illustrates a block diagram of an electronic device adapted to implement a contaminant spreading risk identification method in accordance with an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.
Where expressions like at least one of "A, B and C, etc. are used, the expressions should generally be interpreted in accordance with the meaning as commonly understood by those skilled in the art (e.g.," a system having at least one of A, B and C "shall include, but not be limited to, a system having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
In the technical scheme of the disclosure, the related data (such as including but not limited to personal information of a user) are collected, stored, used, processed, transmitted, provided, disclosed, applied and the like, all conform to the regulations of related laws and regulations, necessary security measures are adopted, and the public welcome is not violated.
In the technical scheme of the embodiment of the disclosure, the authorization or consent of the user is obtained before the personal information of the user is obtained or acquired.
In practicing the present disclosure, it has been found that in order to improve the accuracy of meteorological field simulation, there is a prior art technique that uses a mesoscale meteorological model (WRF, weather Research and Forecasting model) to simulate the meteorological field of the boundary surface as a reference parameter for the input and correction of a computational fluid dynamics (CFD, computational Fluid Dynamics) model, and optimizes the meteorological field simulated by the CFD model. The WRF model is a new generation mesoscale numerical atmospheric model commonly developed by scientific research institutions such as the national atmospheric research center (NCAR), the national atmospheric ocean agency Forecast System Laboratory (FSL), the national environmental forecast center (NCEP), the October university storm analysis center and the like. The WRF has a wide application range, and can be used for business numerical weather forecast and also can be used in the field of atmospheric numerical simulation research, including research of data assimilation, research of physical process parameterization, regional climate simulation, air quality simulation, sea-air coupling, ideal experimental model and the like. However, WRF simulation data and CFD data are not fused, and are both simulation data, and are not compared and verified with real observation data, so that the problem of low simulation accuracy of a meteorological field exists.
The embodiment of the disclosure provides a pollutant diffusion risk identification method, which comprises the following steps: acquiring multi-source weather data from a weather system about a target area, wherein the multi-source weather data includes measured weather data, first simulated weather data, and second simulated weather data; assimilating the multi-source meteorological data to obtain assimilated multi-source meteorological data; inputting pollution source emission data and assimilated multisource meteorological data into a computational fluid dynamics model, and outputting pollutant concentration analysis results of a preset respiratory height plane; and determining a pollutant diffusion risk identification result according to the pollutant concentration analysis result and the pollutant attribute characteristics.
Fig. 1 schematically illustrates an application scenario diagram of a contaminant diffusion risk identification method, apparatus, device, medium, and program product according to an embodiment of the present disclosure.
As shown in fig. 1, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is a medium used to provide a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 through the network 104 using at least one of the first terminal device 101, the second terminal device 102, the third terminal device 103, to receive or send messages, etc. Various communication client applications, such as a shopping class application, a web browser application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc. (by way of example only) may be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103.
The first terminal device 101, the second terminal device 102, the third terminal device 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background management server (by way of example only) providing support for websites browsed by the user using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as the user request, and feed back the processing result (e.g., the web page, information, or data obtained or generated according to the user request) to the terminal device.
It should be noted that the contaminant spreading risk identification method provided by the embodiments of the present disclosure may be generally performed by the server 105. Accordingly, the contaminant spreading risk identification means provided by embodiments of the present disclosure may be generally provided in the server 105. The contaminant spreading risk identification method provided by the embodiments of the present disclosure may also be performed by a server or a server cluster that is different from the server 105 and is capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and/or the server 105. Accordingly, the contaminant spreading risk identification means provided by the embodiments of the present disclosure may also be provided in a server or a server cluster different from the server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103 and/or the server 105.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
The contaminant diffusion risk identification method of the disclosed embodiment will be described in detail below with reference to the scenario described in fig. 1 through fig. 2 to 5.
Fig. 2 schematically illustrates a flow chart of a contaminant diffusion risk identification method in accordance with an embodiment of the present disclosure.
As shown in fig. 2, the contaminant diffusion risk identification method 200 of this embodiment includes operations S210 to S240.
In operation S210, multi-source weather data from a weather system regarding a region of interest is acquired, wherein the multi-source weather data includes measured weather data, first simulated weather data, and second simulated weather data.
According to embodiments of the present disclosure, the measured meteorological data may be obtained after in-situ observation by a gas flow parametric meter. The first simulated weather data and the second simulated weather data may be weather data obtained by simulation of a weather model. The target area may be a target campus.
In operation S220, the multi-source weather data is data-assimilated to obtain the assimilated multi-source weather data.
According to the embodiment of the disclosure, the data assimilation is performed on the actually measured meteorological data, the first simulated meteorological data and the second simulated meteorological data to obtain the assimilated multi-source meteorological data.
According to embodiments of the present disclosure, data assimilation is used to reduce errors in various sources of data in multi-source weather data, i.e., errors in measured weather data, first simulated weather data, and second simulated weather data.
In operation S230, the pollutant source emission data and the assimilated multi-source weather data are input into a computational fluid dynamics model, and the pollutant concentration analysis result of the preset respiratory height plane is output.
According to embodiments of the present disclosure, the pollution source emission data may be obtained from a pollution source emission database. The pollution source emission data can be obtained through field investigation and monitoring aiming at pollutants and then stored in a pollution source emission database. The contaminant may be, for example, at least one of: chemical contaminants, physical contaminants, biological contaminants, and radioactive contaminants.
According to the embodiment of the disclosure, the computational fluid dynamics model can perform diffusion simulation and output a pollutant concentration analysis result of a preset respiratory height plane. The preset breath height may be a human breath height, for example, may be 1.5m.
In operation S240, a contaminant diffusion risk identification result is determined according to the contaminant concentration analysis result and the contaminant attribute characteristics.
According to embodiments of the present disclosure, contaminant attribute characteristics may be determined from contaminants within the actual target area. And combining the pollutant concentration analysis result with the pollutant attribute characteristic to determine a pollutant diffusion risk identification result so as to evaluate the diffusion risk of the pollutant.
According to the embodiment of the disclosure, the multisource meteorological data comprising the actually measured meteorological data, the first simulated meteorological data and the second simulated meteorological data are adopted for data assimilation to obtain a further optimized meteorological field, the computational fluid dynamics model is adopted for simulating pollutant diffusion, simulation precision is high, the problem that simulation precision of the meteorological field is low due to the fact that the simulation precision of the meteorological field is not compared and verified with real observation data is avoided, and compared with an air quality model, finer horizontal grids and vertical grids are divided, and influence of underlying shielding is fully considered. The pollutant concentration analysis result and the pollutant attribute characteristic are combined, so that the diffusion risk of pollutants can be accurately estimated.
FIG. 3 schematically illustrates a flow chart of a method of acquiring multi-source weather data from a weather system regarding a target area, in accordance with an embodiment of the disclosure.
As shown in FIG. 3, the method 310 of acquiring multi-source weather data from a weather system regarding a target area of the embodiment may include operations S311-S313.
In operation S311, the target area is simulated using the mesoscale weather model to obtain first simulated weather data.
According to the embodiment of the disclosure, the mesoscale meteorological model (WRF, weather Research and Forecasting model) can divide nested grids according to the geographic position of the target area, adopts a three-layer nested mode, and requires the resolution of a third-layer grid to be not lower than 1km, and simulates the target area to obtain first simulated meteorological data.
In operation S312, the target area and the preset area are simulated using the mesoscale weather model and the fluid model to obtain second simulated weather data.
According to an embodiment of the present disclosure, the fluid model may be a computational fluid dynamics (CFD, computational Fluid Dynamics) model. The mesoscale meteorological model can be utilized to simulate the target area to obtain meteorological data of the boundary surface of the target area. And simulating a flow field for the target area and the preset area by utilizing a computational fluid dynamics model based on the meteorological data of the boundary surface of the target area to obtain second simulated meteorological data.
The preset area may be a peripheral area of the target area. The predetermined area may be determined based on a sensitivity level of contaminant diffusion in a peripheral area of the target area.
In operation S313, measured weather data within the target area is acquired from the weather database.
According to embodiments of the present disclosure, the weather database may periodically store measured weather data within the target area. The measured weather data in the target area may be obtained from a weather database by data acquisition software, such as crawler software.
According to the embodiment of the disclosure, the multi-source meteorological data is formed by the first simulated meteorological data acquired by the mesoscale meteorological model, the second simulated meteorological data acquired by the mesoscale meteorological model and the fluid model and the actually measured meteorological data acquired by the meteorological database, and the pollutant concentration is analyzed by utilizing the real data and the simulated data together, so that the accuracy of the obtained pollutant concentration analysis result is high.
According to an embodiment of the present disclosure, simulating a target area using a mesoscale weather model to obtain first simulated weather data may include:
inputting preset meteorological data into a mesoscale meteorological model; dividing nested grids according to the geographic position of the target area so as to output first grid information, wherein the first grid information comprises first longitude and latitude information and first meteorological parameter information; and extracting first simulated meteorological data in the target area according to the first gridding information.
According to embodiments of the present disclosure, the preset weather data may include prediction data and historical data. Wherein, the prediction data can adopt global forecast system (Global Forecast System, GFS) data of the national ocean and atmosphere administration; the historical data may be in the form of global re-analysis data (Final Operational Global Analysis data, FNL) from the national environmental forecast center (NCEP) and the national atmospheric research center (NCAR).
According to the embodiment of the disclosure, a three-layer nesting mode can be adopted, and the resolution of the third layer of grid is required to be not lower than 1km, so that the target area can be simulated. The first meteorological parameter information may include: wind speed information, wind direction information, air temperature information, relative humidity information, and the like. And according to the first longitude and latitude information and the first weather parameter information, weather parameter information on the boundary surface except the boundary surface can be extracted from the target area (namely, the simulation space) to obtain first simulation weather data.
According to the embodiment of the disclosure, the meteorological data in the simulation space, namely the first simulation meteorological data, is extracted through the mesoscale meteorological model simulation meteorological field, and is favorable for combining the second simulation meteorological data and the actually measured meteorological data to form multi-source meteorological data.
According to an embodiment of the present disclosure, simulating the target area and the preset area using the mesoscale weather model and the fluid model to obtain the second simulated weather data may include:
performing three-dimensional modeling on the target area and the preset area to obtain a modeling space; performing grid division on the modeling space so as to acquire grid boundary meteorological data by utilizing a mesoscale meteorological model; and simulating a flow field by using a fluid model based on the grid boundary meteorological data to obtain second simulated meteorological data.
According to the embodiment of the disclosure, three-dimensional modeling of a real scene can be performed on a target park and a preset area, after modeling, a modeling space is subjected to grid division, and a grid quality parameter (Orthogonal Quality) is controlled to be more than 0.2. And simulating a flow field by using the fluid model by setting boundary conditions to obtain second simulated meteorological data. The boundary condition may be grid boundary weather data, among others. The grid boundary can be determined according to the grid obtained after grid division. And according to the determined grid boundary, combining the mesoscale meteorological model to obtain grid boundary meteorological data. The grid boundaries may include an inflow boundary and an outflow boundary. And simulating the modeling space through a mesoscale meteorological model to obtain inflow interface meteorological data and outflow interface meteorological data.
According to the embodiment of the disclosure, a weather field is simulated by combining a mesoscale weather model in a real scene three-dimensional modeling mode, so that grid boundary weather data are obtained; and then the flow field is simulated by using the fluid model to obtain second simulated meteorological data, and compared with an air quality model, the flow field is divided into finer horizontal grids and vertical grids, and the influence of the underlying shielding is fully considered.
According to embodiments of the present disclosure, grid boundary weather data may include inflow interface weather data and outflow interface weather data.
The grid division is performed on the modeling space so as to obtain grid boundary meteorological data by using a mesoscale meteorological model, and the method can comprise the following steps:
performing grid division on the modeling space to obtain a plurality of grid points; determining inflow boundary longitude and latitude information and outflow boundary longitude and latitude information according to the grid points; and extracting inflow interface weather data and outflow interface weather data from first weather parameter information according to the inflow boundary longitude and latitude information and the outflow boundary longitude and latitude information, wherein the first weather parameter information is determined according to a mesoscale weather model.
According to embodiments of the present disclosure, modeling space may be grid partitioned above 0.2 according to grid quality parameters (Orthogonal Quality) control. The first weather parameter information may be inputting preset weather data into a mesoscale weather model; and dividing the nested grids according to the geographic position of the target area so as to output first meshing information and then determining. The first meshing information comprises first longitude and latitude information and first weather parameter information. The preset weather data may include forecast data and historical data. Wherein, the prediction data can adopt global forecast system (Global Forecast System, GFS) data of the national ocean and atmosphere administration; the historical data may be in the form of global re-analysis data (Final Operational Global Analysis data, FNL) from the national environmental forecast center (NCEP) and the national atmospheric research center (NCAR). The second weather parameter information may include: wind speed information, wind direction information, air temperature information, relative humidity information, and the like.
According to the embodiment of the disclosure, the boundary meteorological data are extracted, so that the boundary meteorological field can be simulated, the simulation is facilitated by combining a real scene, and the influence of the underlying shielding object is fully considered.
According to embodiments of the present disclosure, grid boundary weather data may include inflow interface weather data and outflow interface weather data.
Wherein, based on the grid boundary meteorological data, the flow field is simulated by using the fluid model to obtain second simulated meteorological data, which may include:
inputting inflow interface weather data into a fluid model; carrying out flow field simulation by taking outflow interface meteorological data as constraint conditions; and outputting the second simulated meteorological data.
According to embodiments of the present disclosure, the fluid model may include a Realizable k-epsilon turbulence model. The Realizable k-epsilon turbulence model may perform steady-state flow field simulation.
According to the embodiment of the disclosure, the fluid model is combined with the real scene to simulate the boundary of the meteorological field, so that second simulated meteorological data is obtained, and the combination of the first simulated meteorological data and the actually measured meteorological data is facilitated, so that multi-source meteorological data is formed and used for analyzing the concentration of pollutants.
FIG. 4 schematically illustrates a flow chart of a method of data assimilating multi-source weather data to obtain assimilated multi-source weather data according to an embodiment of the disclosure.
As shown in FIG. 4, the method 420 for assimilating multi-source weather data to obtain the assimilated multi-source weather data according to the embodiment may comprise operations S421 to S422.
In operation S421, the first simulated weather data and the second simulated weather data are fused to obtain fused simulated weather data.
According to the embodiment of the disclosure, the first simulated meteorological data and the second simulated meteorological data can be fused by using a linear weighted fusion method or a cross fusion method and the like, so as to obtain fused simulated meteorological data.
For example, the linear weighted fusion method may be represented by the following formula (1):
wherein M is k May be represented as first simulated weather data and second simulated weather data; sigma (sigma) k The weights of the simulation models for obtaining the first simulation meteorological data and the second simulation meteorological data can be expressed; c (C) sim May be represented as a fusion of simulated meteorological data.
The cross fusion method can be represented by the following formula (2):
wherein C is k May be represented as first simulated weather data or second simulated weather data; c (C) sim May be represented as a fusion of simulated meteorological data.
It should be noted that, the cross fusion method may be to insert results of different recommendation models into the recommendation results by using different training sets, so as to ensure diversity of the results.
In operation S422, the fusion simulation meteorological data and the actually measured meteorological data are subjected to data assimilation by adopting a preset assimilation method, so as to obtain assimilated multi-source meteorological data.
According to embodiments of the present disclosure, the preset assimilation method may comprise a three-dimensional variation assimilation method. The following formula (3):
wherein, C can be expressed as multi-source meteorological data after assimilation; c (C) sim Can be expressed as fusion of simulated meteorological data; c (C) obs May be represented as measured meteorological data; sigma (sigma) sim Can be expressed as a standard deviation of the fused simulated meteorological data; sigma (sigma) obs May be expressed as the standard deviation of the measured meteorological data.
According to the embodiment of the disclosure, the first simulated meteorological data and the second simulated meteorological data are fused to obtain fused simulated meteorological data; and then, the fused simulated meteorological data and the actually measured meteorological data are subjected to data assimilation, so that errors of all source data in the multi-source meteorological data are reduced, the distribution of meteorological parameters in a simulation space can be optimized, and the problem that the simulation accuracy of a meteorological field is low because WRF (radar flash) simulated data and CFD (computational fluid dynamics) data are not fused and are simulated data and are not compared and verified with real observed data is solved.
According to an embodiment of the present disclosure, determining a contaminant diffusion risk identification result from the contaminant concentration analysis result and the contaminant attribute feature may include:
Determining a risk threshold according to the attribute characteristics of the pollutants; and determining a pollutant diffusion risk identification result according to the pollutant concentration analysis result and the risk threshold value.
According to embodiments of the present disclosure, contaminant attribute characteristics may be determined from contaminants within the actual target area. Determining that the pollutant spreading risk identification result can represent the risk according to the condition that the pollutant concentration analysis result meets the risk threshold; in the event that the contaminant concentration analysis results do not meet the risk threshold, determining the contaminant diffusion risk identification result may characterize the absence of risk.
For example, the contaminant attribute feature may characterize the extent to which the contaminant diffuses into the atmosphere to the detriment of the human body. The risk threshold may be determined based on the degree of harm to the human body.
According to the embodiment of the disclosure, the pollutant concentration analysis result and the pollutant attribute characteristic are combined, so that the diffusion risk of pollutants can be accurately estimated.
Fig. 5 schematically illustrates a flow chart of a contaminant diffusion risk identification method in accordance with another embodiment of the present disclosure.
As shown in fig. 5, the pollutant spreading risk identification method of this embodiment may first use WRF mesoscale meteorological field simulation to obtain first simulated meteorological data. The method comprises the steps that preset meteorological data can be input into a mesoscale meteorological model; dividing nested grids according to the geographic position of the target area so as to output first grid information, wherein the first grid information comprises first longitude and latitude information and first meteorological parameter information; and extracting first simulated meteorological data in the target area according to the first gridding information.
And obtaining grid boundary meteorological data by combining real scene three-dimensional modeling with WRF mesoscale meteorological field simulation. The method comprises the steps that three-dimensional modeling can be conducted on a target area and a preset area, and a modeling space is obtained; performing grid division on the modeling space to obtain a plurality of grid points; inputting preset meteorological data into a mesoscale meteorological model so as to output second meshing information according to grid points, wherein the second meshing information comprises second longitude and latitude information and second meteorological parameter information; and extracting grid boundary weather data from the second weather parameter information according to the grid points of the grid boundary and the second longitude and latitude information.
And obtaining second simulation meteorological data according to the grid boundary meteorological data. Wherein inflow interface weather data can be input into the fluid model; carrying out flow field simulation by taking outflow interface meteorological data as constraint conditions; outputting the second simulated meteorological data
And taking the measured meteorological data, the first simulated meteorological data and the second simulated meteorological data as multi-source meteorological data.
And obtaining a pollutant concentration analysis result according to the multi-source meteorological data and the pollutant source emission data. The multi-source meteorological data can be input into a computational fluid dynamics model together with pollution source emission data after data assimilation, and a pollutant concentration analysis result of a preset respiratory height plane is output.
And determining a pollutant diffusion risk identification result according to the pollutant concentration analysis result. Wherein the pollutant concentration analysis result can be compared with the risk threshold value, so as to determine the pollutant diffusion risk identification result.
According to the embodiment of the disclosure, in terms of flow field simulation, the influence of a near-ground shelter is fully considered by utilizing a real scene three-dimensional modeling mode, and the multi-source meteorological data is combined for optimization. In the aspect of pollutant release and diffusion, based on more accurate flow field data, the simulation is carried out by combining a fluid model which is more suitable for small scale and emission source data, and sufficient grids are arranged in the horizontal direction and the vertical direction of the space for calculation, so that a human body height concentration analysis result is obtained, and risk identification is carried out.
Based on the pollutant spreading risk identification method, the disclosure further provides a pollutant spreading risk identification device. The device will be described in detail below in connection with fig. 6.
Fig. 6 schematically illustrates a block diagram of a contaminant spreading risk identification device, according to an embodiment of the present disclosure.
As shown in fig. 6, the contaminant diffusion risk identification device 600 of this embodiment includes an acquisition module 610, an assimilation module 620, an analysis module 630, and a determination module 640.
The acquisition module 610 is configured to acquire multi-source weather data from a weather system regarding a target area, wherein the multi-source weather data includes measured weather data, first simulated weather data, and second simulated weather data. In an embodiment, the obtaining module 610 may be configured to perform the operation S210 described above, which is not described herein.
The assimilation module 620 is configured to assimilate the multi-source weather data to obtain the assimilated multi-source weather data. In an embodiment, the assimilation module 620 may be used to perform the operation S220 described above, which is not described herein.
The analysis module 630 is used for inputting the pollutant source emission data and the assimilated multi-source meteorological data into a computational fluid dynamics model and outputting pollutant concentration analysis results of a preset respiratory height plane. In an embodiment, the analysis module 630 may be configured to perform the operation S230 described above, which is not described herein.
The determining module 640 is configured to determine a contaminant diffusion risk identification result according to the contaminant concentration analysis result and the contaminant attribute feature. In an embodiment, the determining module 640 may be configured to perform the operation S240 described above, which is not described herein.
Any of the acquisition module 610, the assimilation module 620, the analysis module 630, and the determination module 640 may be combined in one module to be implemented, or any of the modules may be split into a plurality of modules, according to embodiments of the present disclosure. Alternatively, at least some of the functionality of one or more of the modules may be combined with at least some of the functionality of other modules and implemented in one module. At least one of the acquisition module 610, the assimilation module 620, the analysis module 630, and the determination module 640 may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware, such as any other reasonable way of integrating or packaging the circuits, or any one of or a suitable combination of any of the three. Alternatively, at least one of the acquisition module 610, the assimilation module 620, the analysis module 630, and the determination module 640 may be at least partially implemented as a computer program module that, when executed, may perform the corresponding functions.
Fig. 7 schematically illustrates a block diagram of an electronic device adapted to implement a contaminant spreading risk identification method in accordance with an embodiment of the present disclosure.
As shown in fig. 7, an electronic device 700 according to an embodiment of the present disclosure includes a processor 701 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. The processor 701 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or an associated chipset and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. The processor 701 may also include on-board memory for caching purposes. The processor 701 may comprise a single processing unit or a plurality of processing units for performing different actions of the method flows according to embodiments of the disclosure.
In the RAM 703, various programs and data necessary for the operation of the electronic apparatus 700 are stored. The processor 701, the ROM 702, and the RAM 703 are connected to each other through a bus 704. The processor 701 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM 702 and/or the RAM 703. Note that the program may be stored in one or more memories other than the ROM 702 and the RAM 703. The processor 701 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in the one or more memories.
According to an embodiment of the present disclosure, the electronic device 700 may further include an input/output (I/O) interface 705, the input/output (I/O) interface 705 also being connected to the bus 704. The electronic device 700 may also include one or more of the following components connected to the I/O interface 705: an input section 706 including a keyboard, a mouse, and the like; an output portion 707 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 708 including a hard disk or the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. The drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read therefrom is mounted into the storage section 708 as necessary.
The present disclosure also provides a computer-readable storage medium that may be embodied in the apparatus/device/system described in the above embodiments; or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, the computer-readable storage medium may include ROM 702 and/or RAM 703 and/or one or more memories other than ROM 702 and RAM 703 described above.
Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowcharts. The program code, when executed in a computer system, causes the computer system to perform the methods provided by embodiments of the present disclosure.
The above-described functions defined in the system/apparatus of the embodiments of the present disclosure are performed when the computer program is executed by the processor 701. The systems, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed over a network medium in the form of signals, downloaded and installed via the communication section 709, and/or installed from the removable medium 711. The computer program may include program code that may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 709, and/or installed from the removable medium 711. The above-described functions defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 701. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
According to embodiments of the present disclosure, program code for performing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, such computer programs may be implemented in high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. Programming languages include, but are not limited to, such as Java, c++, python, "C" or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that the features recited in the various embodiments of the disclosure and/or in the claims may be provided in a variety of combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the disclosure. In particular, the features recited in the various embodiments of the present disclosure and/or the claims may be variously combined and/or combined without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of the present disclosure.
The embodiments of the present disclosure are described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the embodiments cannot be used advantageously in combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be made by those skilled in the art without departing from the scope of the disclosure, and such alternatives and modifications are intended to fall within the scope of the disclosure.
Claims (8)
1. A contaminant diffusion risk identification method comprising:
acquiring multi-source weather data from a weather system about a target area, wherein the multi-source weather data includes measured weather data, first simulated weather data, and second simulated weather data;
Assimilating the multi-source meteorological data to obtain assimilated multi-source meteorological data;
inputting pollution source emission data and the assimilated multisource meteorological data into a computational fluid dynamics model, and outputting pollutant concentration analysis results of a preset respiratory height plane; and
determining a pollutant diffusion risk identification result according to the pollutant concentration analysis result and the pollutant attribute characteristics;
wherein the acquiring of multi-source weather data from the weather system about the target area comprises:
simulating the target area by using a mesoscale meteorological model to obtain first simulated meteorological data;
simulating the target area and a preset area by using the mesoscale weather model and the fluid model to obtain the second simulated weather data, wherein the preset area is a peripheral area of the target area;
acquiring the actually measured meteorological data in the target area from a meteorological database;
the simulating the target area and the preset area by using the mesoscale weather model and the fluid model to obtain the second simulated weather data includes:
Performing three-dimensional modeling on the target area and the preset area to obtain a modeling space;
performing grid division on the modeling space so as to acquire grid boundary meteorological data by using a mesoscale meteorological model;
and simulating a flow field by using a fluid model based on the grid boundary meteorological data to obtain second simulation meteorological data.
2. The method of claim 1, wherein simulating the target area using a mesoscale meteorological model results in the first simulated meteorological data comprising:
inputting preset meteorological data into the mesoscale meteorological model;
dividing nested grids according to the geographic position of the target area so as to output first grid information, wherein the first grid information comprises first longitude and latitude information and first meteorological parameter information;
and extracting first simulation meteorological data in the target area according to the first gridding information.
3. The method of claim 1, wherein the grid boundary weather data comprises inflow interface weather data and outflow interface weather data;
the step of meshing the modeling space so as to obtain grid boundary meteorological data by using a mesoscale meteorological model comprises the following steps:
Performing grid division on the modeling space to obtain a plurality of grid points;
determining inflow boundary longitude and latitude information and outflow boundary longitude and latitude information according to a plurality of grid points;
and extracting the inflow interface weather data and the outflow interface weather data from first weather parameter information according to the inflow boundary longitude and latitude information and the outflow boundary longitude and latitude information, wherein the first weather parameter information is determined according to the mesoscale weather model.
4. The method of claim 1, wherein the grid boundary weather data comprises inflow interface weather data and outflow interface weather data;
the simulating a flow field by using a fluid model based on the grid boundary meteorological data to obtain the second simulated meteorological data comprises the following steps:
inputting the inflow interface weather data into the fluid model;
taking the outflow interface meteorological data as a constraint condition, and performing flow field simulation;
and outputting the second simulation meteorological data.
5. The method of any one of claims 1-4, wherein the assimilating the multi-source weather data to obtain assimilated multi-source weather data comprises:
Fusing the first simulated meteorological data and the second simulated meteorological data to obtain fused simulated meteorological data;
and carrying out data assimilation on the fusion simulation meteorological data and the actually measured meteorological data by adopting a preset assimilation method to obtain the assimilated multisource meteorological data.
6. The method of any one of claims 1-4, wherein the determining a contaminant diffusion risk identification result from the contaminant concentration analysis result and the contaminant attribute feature comprises:
determining a risk threshold according to the attribute characteristics of the pollutants;
and determining the pollutant diffusion risk identification result according to the pollutant concentration analysis result and the risk threshold value.
7. A contaminant diffusion risk identification device, comprising:
the system comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring multi-source weather data from a weather system, wherein the multi-source weather data comprise actual measurement weather data, first simulation weather data and second simulation weather data;
the assimilation module is used for assimilating the multi-source meteorological data to obtain assimilated multi-source meteorological data;
the analysis module is used for inputting the pollutant source emission data and the assimilated multisource meteorological data into a computational fluid dynamics model and outputting pollutant concentration analysis results of a preset respiratory height plane; and
The determining module is used for determining a pollutant diffusion risk identification result according to the pollutant concentration analysis result and the pollutant attribute characteristics;
wherein the acquiring of multi-source weather data from the weather system about the target area comprises:
simulating the target area by using a mesoscale meteorological model to obtain first simulated meteorological data;
simulating the target area and a preset area by using the mesoscale weather model and the fluid model to obtain second simulated weather data, wherein the preset area is a peripheral area of the target area and is determined according to the sensitivity degree of pollutant diffusion in the peripheral area of the target area;
acquiring the actually measured meteorological data in the target area from a meteorological database;
the simulating the target area and the preset area by using the mesoscale weather model and the fluid model to obtain the second simulated weather data includes:
performing three-dimensional modeling on the target area and the preset area to obtain a modeling space;
performing grid division on the modeling space so as to acquire grid boundary meteorological data by using a mesoscale meteorological model;
And simulating a flow field by using a fluid model based on the grid boundary meteorological data to obtain second simulation meteorological data.
8. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-6.
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