WO2023206983A1 - 一种多源大气环境数据融合方法、装置、终端及存储介质 - Google Patents

一种多源大气环境数据融合方法、装置、终端及存储介质 Download PDF

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WO2023206983A1
WO2023206983A1 PCT/CN2022/125576 CN2022125576W WO2023206983A1 WO 2023206983 A1 WO2023206983 A1 WO 2023206983A1 CN 2022125576 W CN2022125576 W CN 2022125576W WO 2023206983 A1 WO2023206983 A1 WO 2023206983A1
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data
grid
geospatial
atmospheric environment
location information
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PCT/CN2022/125576
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English (en)
French (fr)
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马景金
李岳
王春迎
王玮
王建国
武蕾丹
田灵娣
潘本锋
宋艳艳
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河北先河环保科技股份有限公司
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Publication of WO2023206983A1 publication Critical patent/WO2023206983A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • the present application relates to the field of geographic information technology, and in particular to a multi-source atmospheric environment data fusion method, device, terminal and storage medium.
  • the environmental monitoring methods in most cities have expanded from traditional air quality monitoring and online monitoring of pollution sources to grid-encrypted Monitoring, component monitoring, pollution source inventory, flight monitoring, cruise monitoring, satellite remote sensing monitoring and lidar observation.
  • the comprehensive application of multiple environmental monitoring methods provides more comprehensive data for cities to carry out cause analysis, pollution traceability, environmental supervision, early warning and forecasting, and emission reduction assessment and prevention.
  • description subjects are different, types are complex, and formats are diverse. A large amount of data preprocessing work is required before platform visual display and in-depth mining applications.
  • the fusion of atmospheric environment data in the field of air pollution prevention and control is mainly based on one or several types of data sources based on traditional monitoring methods.
  • the fusion of air quality monitoring data and pollution source online monitoring data because they are both fixed-point monitoring data.
  • the parameters are consistent and the update frequency is consistent, and it is widely used in pollution ranking and high-value alarm environmental status analysis.
  • Emerging atmospheric environment data such as gridded density monitoring, component monitoring, pollution source inventory, navigation monitoring, cruise monitoring, satellite remote sensing monitoring and lidar observation data, are an important part of atmospheric environment management at this stage. , but these data from scattered sources have not yet been integrated with traditional monitoring data due to different temporal and spatial changes, non-regular updates, different description subjects and differences in formats.
  • Embodiments of the present application provide a multi-source atmospheric environment data fusion method, device, terminal and storage medium to solve the problem of multi-source atmospheric environment data fusion.
  • embodiments of this application provide a multi-source atmospheric environment data fusion method, including:
  • the atmospheric environment data from the multiple data sources include air pollution source data, environmental receptor monitoring data and meteorological data;
  • the atmospheric environment data include form data and raster data ; Both the form data and the raster data include location information and time information.
  • the form data is converted into feature class data in vector format.
  • the location information of the feature class data and the location information of the raster class data are respectively associated with the geospatial grid to establish a spatial connection relationship.
  • the time information of the feature class data and the time information of the raster class data are correlated with each other to establish a time connection relationship.
  • the feature data, the raster data, the spatial connection relationship and the time connection relationship are stored in the database to establish a multi-source atmospheric environment data fusion database in the geospatial database format.
  • the geospatial grid includes a grid code and grid location information, where the grid code serves as a unique identifier of each geospatial grid, and the grid location information is used to Spatial location information describing the geospatial grid.
  • Associating the location information of the feature class data and the location information of the raster class data with the geospatial grid respectively to establish a spatial connection relationship includes:
  • the location information of the feature class data is associated with the grid code of the geospatial grid to establish a spatial connection relationship between the feature class data and the geospatial grid.
  • the location information of the raster data is associated with the grid code of the geospatial grid to establish a spatial connection relationship between the raster data and the geospatial grid.
  • the location information of the raster type data is associated with the grid code of the geospatial grid to establish a grid.
  • the spatial connection relationship between grid data and geospatial grid it also includes:
  • the raster data is resampled.
  • the method further includes:
  • the geographical space grid is a three-dimensional space grid marked by longitude, latitude and altitude.
  • embodiments of the present application provide a multi-source atmospheric environment data fusion device, including:
  • a data acquisition module is used to obtain atmospheric environment data from multiple data sources in the target area; the atmospheric environment data from the multiple data sources include air pollution source data, environmental receptor monitoring data and meteorological data; the atmospheric environment data includes form classes Data and raster data; the form data and raster data both contain location information and time information.
  • the conversion module is used to convert the form data into feature class data in vector format based on the location information of the form data.
  • a grid acquisition module is used to acquire the geospatial grid of the target area.
  • a spatial connection module configured to associate the location information of the feature type data and the location information of the raster type data with the geospatial grid respectively to establish a spatial connection relationship.
  • a time connection module is used to correlate the time information of the feature class data and the time information of the raster class data to establish a time connection relationship. as well as
  • the storage module is used to store the feature data, the raster data, the spatial connection relationship and the time connection relationship into the database based on the geospatial database, so as to establish a multi-source atmospheric environment in the geospatial database format.
  • Data fusion database is used to store the feature data, the raster data, the spatial connection relationship and the time connection relationship into the database based on the geospatial database, so as to establish a multi-source atmospheric environment in the geospatial database format.
  • the geospatial grid includes a grid code and grid location information, where the grid code serves as a unique identifier of each geospatial grid, and the grid location information is used to Describe the spatial location information of the geospatial grid;
  • the spatial connection module includes:
  • the first spatial connection unit is used to associate the location information of the feature class data with the grid code of the geospatial grid according to the grid location information of each geographical space grid, so as to establish the feature class data and the geographical space.
  • the spatial connection relationship of the grid as well as
  • the second spatial connection unit is used to associate the location information of the raster data with the grid code of the geospatial grid according to the grid location information of each geospatial grid, so as to establish a relationship between the raster data and Spatial connection relationships of geospatial grids.
  • embodiments of the present application provide a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor.
  • the processor executes the computer program, the The steps of the method described in the above first aspect or any possible implementation of the first aspect.
  • embodiments of the present application provide a computer-readable storage medium that stores a computer program.
  • the computer program is executed by a processor, the above first aspect or any of the first aspects is implemented.
  • Embodiments of the present application provide a multi-source atmospheric environment data fusion method, device, terminal and storage medium.
  • the method includes: obtaining atmospheric environment data from multiple data sources in a target area; the atmospheric environment data from multiple data sources includes air pollution source data. , environmental receptor monitoring data and meteorological data; atmospheric environment data includes form data and raster data; both form data and raster data include location information and time information; based on the location information of form data, the form data Convert the data into feature class data in vector format; obtain the geospatial grid of the target area; associate the location information of the feature class data and the location information of the raster class data with the geospatial grid respectively to establish a spatial connection relationship; The time information of class data and the time information of raster class data are related to each other to establish a time connection relationship; based on the geospatial database, feature class data, raster class data, spatial connection relationships and time connection relationships are stored in the database to establish geographic connection relationships.
  • a multi-source atmospheric environment data fusion database in spatial database format By establishing spatial connections and time connections between multiple sources of atmospheric environment data, and based on geospatial databases, a multi-source atmospheric environment data fusion database is established to realize the fusion of multi-source atmospheric environment data and provide model simulation and Big data mining and platform visualization provide standardized and unified interface data sources, reducing the workload and technical difficulty of data preprocessing and data interface writing.
  • Figure 1 is an implementation flow chart of a multi-source atmospheric environment data fusion method provided by an embodiment of the present application
  • Figure 2 is a flow chart for the implementation of step S4 of a multi-source atmospheric environment data fusion method provided by an embodiment of the present application;
  • Figure 3 is a schematic diagram of a method for associating point feature class data with grids provided by an embodiment of the present application
  • Figure 4 is an implementation flow chart of another multi-source atmospheric environment data fusion method provided by an embodiment of the present application.
  • Figure 5 is a schematic structural diagram of a multi-source atmospheric environment data fusion device provided by an embodiment of the present application.
  • Figure 6 is a schematic diagram of a terminal provided by an embodiment of the present application.
  • 11 Target area; 12: Two-dimensional geospatial grid; 13: Point feature data; 2: Multi-source atmospheric environment data fusion device; 21: Data acquisition module; 22: Conversion module; 23: Grid acquisition module; 24 : spatial connection module; 25: time connection module; 26: warehousing module; 3: terminal; 30: processor; 31: memory; 32: computer program.
  • the embodiments of the present application can be applied to a variety of application scenarios, which involve the fusion of multi-source atmospheric environment data.
  • the specific application scenarios are not limited here.
  • multiple atmospheric environment data collection devices are connected to a database server; the collection devices collect atmospheric environment data and transmit it to the database server; the database server uses the method provided by the embodiment of the present application to establish a database and integrate multiple sources. Atmospheric environment data; both regularly updated and real-time updated data are processed, stored and integrated in the same way.
  • the method provided by the embodiment of the present application is used to process atmospheric environment data from multiple sources, establish a database, and integrate the atmospheric environment data from multiple sources.
  • Figure 1 is an implementation flow chart of a multi-source atmospheric environment data fusion method provided by an embodiment of the present application; referring to Figure 1, the method includes:
  • Atmospheric environment data includes form data and raster data. Both form data and raster data contain location information and time information.
  • the atmospheric environment data can be divided according to the description subject.
  • the atmospheric environment data from multiple data sources includes air pollution source data, environmental receptor monitoring data, and meteorological data.
  • Air pollution source data is data used to describe the sources of air pollution.
  • the air pollution source data may include, but is not limited to, one or more of the following: pollution source inventory data, motor vehicle emission data, pollution source online monitoring data, and agricultural non-point source data.
  • Environmental receptor monitoring data is monitoring data used to describe the quality of the atmospheric environment.
  • the environmental receptor monitoring data may include, but is not limited to, one or more of the following: air quality monitoring data, particulate matter component monitoring data, and volatile organic matter component monitoring data.
  • Meteorological data is data used to describe weather.
  • meteorological data may include, but is not limited to, one or more of the following: ground station monitoring data and weather simulation data.
  • the attributes of atmospheric environment data include one or more of the following: subjects, parameters, spatiotemporal changes, real-time data and basic information.
  • subject attributes may include but are not limited to one or more of the following: air pollution source data, environmental receptor monitoring data, and meteorological data.
  • the main attributes of air pollution source data may include but are not limited to one or more of the following: pollution source inventory data, motor vehicle emission data, pollution source online monitoring data, and agricultural non-point source data.
  • the main attributes of environmental receptor monitoring data may include, but are not limited to, one or more of the following: air quality monitoring data, particulate matter component monitoring data, and volatile organic matter component monitoring data.
  • the main attributes of meteorological data may include but are not limited to one or more of the following: ground station monitoring data and meteorological simulation data.
  • parameter attributes of air pollution source data and environmental receptor monitoring data may include but are not limited to one or more of the following: particulate matter, sulfur dioxide, nitrogen oxides, carbon monoxide, and volatile organic compounds.
  • the parameters of particulate matter, nitrogen oxides and volatile organic compounds in parameter attributes can be subdivided into secondary parameters.
  • the secondary parameter component data of particulate matter can be subdivided into ammonium salts, sulfates, nitrates, etc.
  • the secondary parameter component data of nitrogen oxide compounds can be subdivided into nitric oxide, nitrogen dioxide, etc.
  • the secondary parameter component data of volatile organic compounds can be subdivided into alkanes, alkenes, alkynes, etc.
  • the parameter attributes of meteorological data can be divided into temperature, humidity, atmospheric pressure, wind speed and wind direction, etc.
  • spatiotemporal change attributes can be divided into spatial resolution and time resolution.
  • the spatial resolution can be specifically divided into: provinces, cities and counties divided by administrative divisions, or spatial points, lines, surfaces and grids marked by longitude, latitude and altitude, etc.
  • the time resolution can be divided into years, months, days, hours, minutes, etc.
  • data real-time attributes are divided into regular updates and real-time updates.
  • the basic information attributes of air pollution source data may include but are not limited to one or more of the following: pollution source name, industry classification, contact information, product name, pollution type, and treatment facilities.
  • the first-level subject is the air pollution source data
  • the second-level subject is the pollution source inventory data
  • the third-level subject is the process source data.
  • Parameter attributes are particulate matter, sulfur dioxide, nitrogen dioxide, carbon monoxide, volatile organic compounds, ammonia, black carbon or organic carbon.
  • the temporal resolution is years and the spatial resolution is points labeled by longitude, latitude and altitude.
  • the data real-time attribute is marked as updated annually.
  • the basic information attributes are pollution source name, industry classification, contact information, product name and dust removal, desulfurization and denitrification treatment measures.
  • the first-level subject of the description subject attributes of the pollution source online monitoring data is the air pollution source data
  • the second-level subject is the pollution source online monitoring data.
  • the parameter attributes are particulate matter, sulfur dioxide, nitrogen dioxide and carbon monoxide.
  • the time resolution is minutes and hours. Spatial resolution is points labeled by longitude, latitude, and altitude.
  • the data real-time attribute is marked as real-time update.
  • the basic information attributes are pollution source name, industry classification, contact information and installation location.
  • the first-level subject of description subject attributes of lidar air quality monitoring data is environmental receptor monitoring data
  • the second-level subject is lidar air quality monitoring data
  • the parameter attribute is particulate matter.
  • the temporal resolution is hours
  • the spatial resolution is spatial polygons labeled by longitude, latitude, and altitude.
  • the data real-time attribute is marked as real-time update.
  • atmospheric environment data can be divided according to data formats, including: form data and raster data. Both form data and raster data contain location information and time information.
  • form data may include but is not limited to one or more of the following: pollution source information data tables, enterprise online monitoring data, national and provincial control site monitoring data, navigation and cruise monitoring data, and meteorological data.
  • form data can be stored in CSV format. Data sources from the same source have the same file name. Pollution source names for the same pollution source shall be unified. Field names for similar data in the data source are unified.
  • the names of each field in the pollution source list data describing point pollution sources are set uniformly.
  • the fields can be set to: ID number, data code, pollution source type, pollution source name, district and county, address, contact information, product name, product output , dust removal, desulfurization and denitrification treatment measures, various types of pollutant emissions, spatiotemporal change attributes, real-time data and other basic information, etc.
  • data encoding is used to mark the data description subject, data type, data source, pollution source classification and pollution source number, etc. .
  • the data coding in the form data consists of 15 Arabic digits, of which 1 to 3 digits represent the subject classification described, 4 to 6 digits represent the data type in the database, 7 to 9 digits represent the data source, and 10 to 9 digits represent the data source.
  • the 15 digits can be changed according to the definition of different data sources.
  • the 10 to 11 digits of the data code in the air pollution source inventory data represent the classification of pollution sources, such as point pollution sources, line pollution sources or area pollution sources, and the 12 to 15 digits represent each The number of the pollution source.
  • Data encoding is used to set a unique encoding for each piece of data to facilitate later data connection.
  • the point pollution source data describing the process source may include: ID number, data code, pollution source type, pollution source name, location city, district and county, address, longitude, latitude, product name, product output, Control measures and pollutant emissions, etc.
  • line pollution source data describing road dust sources may include: ID number, data code, pollution source type, pollution source name, location city, location county, traffic flow, dust load, watering frequency and pollutants emissions, etc.
  • the area pollution source describing the soil dust source can include: ID number, data code, pollution source type, pollution source name, location city, location county, location town, land use type, soil mechanical composition and pollutants emissions, etc.
  • raster data may include, but is not limited to, one or more of the following: lidar data and satellite inversion data.
  • lidar data For atmospheric environment data whose initial format is raster data, the spatial coordinate system and file name should be unified.
  • the method further includes: analyzing the consistency of the atmospheric environment data and whether there is missing data.
  • missing data may include, but is not limited to, one or more of the following: missing environmental receptor monitoring data on a certain day, missing parameter fields, and missing documents. Analyze missing data to provide preliminary data support for subsequent data filling.
  • the atmospheric environment data from multiple data sources in the target area after obtaining the atmospheric environment data from multiple data sources in the target area, it also includes:
  • Supplement the missing data in the atmospheric environment data based on similar data in each data source adjacent to the missing data and/or based on similar data adjacent to the missing data in each data source at the time.
  • Data that is missing due to special reasons can be compared and supplemented based on other data sources. For example, due to the lack of unorganized emission monitoring data for one hour due to equipment operation and maintenance, it can be compared and analyzed based on the navigation and radar data at the same time and nearby locations. its supplement.
  • the method further includes: analyzing the data form and data volume of the atmospheric environment data. Analyze data form and data volume to provide preliminary data support for subsequent database construction.
  • step S2 based on the location information of the form data, the form data is converted into feature class data in vector format.
  • the location information of the form data is expressed in latitude and longitude.
  • the description body of form data is divided into points, lines and areas. For example, one longitude and one latitude are used to represent the position information of a point; the longitude and latitude of two end points are used to represent the position information of a line segment; multiple line segments are connected end to end and closed to form a surface, and the longitude and latitude of all end points of each line segment are used to represent the position information of the surface.
  • form class data includes point class, line class and area class data, which are converted into point feature class data, line feature class data and area feature class data respectively.
  • Point feature class data describes atmospheric environment data with fixed location information. For example, it can include but is not limited to one or more of the following: air quality monitoring data, pollution source online monitoring data, point source information in pollution source inventory data, and network data. Gridded data, etc.
  • line feature class data describes linear atmospheric environment data, which may include but is not limited to one or more of the following: road source emission information, navigation data, cruise data, etc. in pollution source inventory data.
  • the format of the form data is CSV format
  • the location information of the form data is expressed in longitude and latitude.
  • the ARCGIS platform is used to convert the form data containing the longitude and latitude location information into file geodatabase feature class data or personal geodatabase in vector format. Feature class data. Field names for similar data are unified, and field names for the same data source are unified.
  • step S3 the geospatial grid of the target area is obtained.
  • the geospatial grid is a two-dimensional spatial grid marked by longitude and latitude.
  • the geospatial grid is a three-dimensional spatial grid marked by longitude, latitude, and altitude.
  • the three-dimensional space grid is a cube with a side length of 1 km, 2 km or 3 km.
  • step S4 the location information of the feature class data and the location information of the raster class data are respectively associated with the geospatial grid to establish a spatial connection relationship.
  • FIG. 2 is a flow chart for the implementation of step S4 of a multi-source atmospheric environment data fusion method provided by an embodiment of the present application; refer to Figure 2:
  • the geospatial grid includes grid coding and grid location information, where the grid code serves as the unique identifier of each geospatial grid, and the grid location information is used to describe the geospatial grid.
  • spatial location information includes longitude, latitude, and/or altitude.
  • the grid position information of a two-dimensional geospatial grid includes the latitude and longitude information of the four vertices of the grid.
  • the grid location information of a three-dimensional geospatial grid includes the latitude and longitude information and altitude of the eight vertices of the grid.
  • step S4 includes:
  • step S41 based on the grid location information of each geospatial grid, the location information of the feature class data is associated with the grid code of the geospatial grid to establish a spatial connection relationship between the feature class data and the geospatial grid.
  • a grid encoding field is added to the point feature class data, where the longitude and latitude of the point feature class data are included in the longitude and latitude of the grid location information. within the range.
  • Each point feature class data corresponds to a geospatial grid.
  • Each geospatial grid can correspond to multiple point feature class data.
  • Figure 3 is a schematic diagram of a method for associating point feature class data with geospatial grids provided by an embodiment of the present application; refer to Figure 3:
  • a certain target area 11 is divided into two-dimensional geographical space grids 12 according to longitude and latitude, and each grid is provided with a unique grid code.
  • the point feature class data 13 is associated with the two-dimensional geospatial grid 12.
  • a grid is added to the point feature class data 13. Coding field, the location of the point feature class data is within the two-dimensional geospatial grid 12 corresponding to the grid coding.
  • a grid coding field is added to the line feature class data, where the longitude and latitude of each point of the line feature class data is included in the grid position information. within the latitude and longitude range.
  • Each line feature class data can correspond to one or more geospatial grids.
  • Each geospatial grid can correspond to multiple line feature class data.
  • each grid position information For example, according to the corresponding relationship between each grid position information and the longitude and latitude of each point of the polygon feature class data, a grid encoding field is added to the polygon feature class data, where the longitude and latitude of each point of the polygon feature class data is included in the grid position. within the latitude and longitude range of the information.
  • Each polygon feature class data can correspond to one or more geospatial grids.
  • Each geospatial grid can correspond to multiple polygon feature class data.
  • the SpatialJoint tool in ARCGIS is used to associate the location information of the feature class data with the grid code of the geospatial grid, and attach the spatial grid code of the location to each feature class data to establish the relationship between the feature class data and the geospatial grid.
  • step S42 according to the grid location information of each geospatial grid, the location information of the raster data is associated with the grid code of the geospatial grid to establish a spatial connection between the raster data and the geospatial grid. relation.
  • Raster data is used to describe surface-shaped atmospheric environment data, which can include but is not limited to one or more of the following: lidar data, satellite inversion data.
  • the smallest unit of raster data is the pixel.
  • the pixel is associated with the geospatial grid based on the latitude and longitude information of each pixel and the location information of the geospatial grid.
  • Each pixel corresponds to a geospatial grid, and each geospatial grid corresponds to at least one pixel.
  • the location information of the raster data is associated with the grid code of the geographical space grid to establish the relationship between the raster data and the geographical space.
  • the spatial connection relationship of the grid it also includes:
  • the Resample tool and ExtractValuesToPoints tool in ARCGIS can be used to process raster data, using the geospatial grid as the pixel resolution benchmark, first resampling the data, and then extracting the resampled pixel values. to the corresponding geospatial grid to realize the spatial connection between raster data and geospatial grid.
  • point feature class data are related based on the same pollution source name.
  • the emission point data of a certain enterprise in the pollution source inventory data is connected to the online monitoring data corresponding to the enterprise through its name.
  • the AddJoin tool in ARCGIS is used to connect based on attributes with the same name.
  • a data connection is established by adding data coding of other source data belonging to the same geospatial grid to the feature class data.
  • the first data and the second data belong to the same geographical space grid, a field is added to the first data to mark the data coding of the second data, and a field is added to the second data to mark the data coding of the first data to establish the first data.
  • the connection between the data and the second data is established by adding data coding of other source data belonging to the same geospatial grid to the feature class data. For example, the first data and the second data belong to the same geographical space grid, a field is added to the first data to mark the data coding of the second data, and a field is added to the second data to mark the data coding of the first data to establish the first data.
  • the connection between the data and the second data is established by adding data coding of other source data belonging to the same geospatial grid to the feature class data.
  • the embodiments provided in this application can be used to obtain all relevant atmospheric environment data of a certain target area based on the spatial connection relationship by establishing a spatial connection relationship.
  • step S5 the time information of the feature class data and the time information of the raster class data are correlated with each other to establish a time connection relationship.
  • All atmospheric environment data such as radar, navigation and monitoring station data at the same time can be time phase synchronized according to the time resolution attributes they contain. Furthermore, all relevant atmospheric environment data for a certain time period can be obtained based on the time connection relationship.
  • the AddJoin tool in ARCGIS is used to correlate the time information of feature class data and the time information of raster class data based on the same time information attributes, and perform time phase connection to establish a time connection relationship.
  • step S6 based on the geospatial database, feature data, raster data, spatial connection relationships and time connection relationships are stored in the database to establish a multi-source atmospheric environment data fusion database in the geospatial database format.
  • the data types of geospatial database can include point feature class data, line feature class data, area feature class data and raster class data.
  • the pollution source inventory data is stored in the database in three formats: point feature class, line feature class, and area feature class.
  • process sources and boiler sites are stored in point feature class format
  • lidar data and satellite inversion data are stored in raster class data format.
  • the multi-source atmospheric environment data fusion database in geospatial database format can express dynamically changing atmospheric environment information such as atmospheric environmental air quality and pollution source emissions and transmission in the target area.
  • ARCGIS geospatial database in accordance with the format requirements of the geospatial database, feature data, raster data, spatial connection relationships and time connection relationships are stored in the database to establish a multi-source geospatial database in GDB format. Atmospheric environment data fusion database.
  • Some atmospheric environment data are highly time-sensitive.
  • the atmospheric environment monitoring data of a monitoring site includes minute values and/or hourly values.
  • the atmospheric environment data is updated in real time or regularly according to the demand.
  • Figure 4 is an implementation flow chart of another multi-source atmospheric environment data fusion method provided by the embodiment of the present application; refer to Figure 4:
  • the method includes: acquiring multi-source atmospheric environment data.
  • Atmospheric environment data includes pollution source information data, online detection data, satellite inversion data, lidar data, navigation data, meteorological data, etc.
  • Unify the classification standards of atmospheric environment data from various sources for example, unify the division of field attributes and unify the setting of field names.
  • Form data can be stored in CSV format, and raster data has a unified spatial coordinate system. Supplement missing data for form data and raster data.
  • the form data in CSV format is converted into feature class data in vector format.
  • Feature class data includes point feature class data, line feature class data and area feature class data. Get the geospatial grid.
  • the embodiment of the present application establishes a spatial connection and a time connection between multiple sources of atmospheric environment data, and establishes a multi-source atmospheric environment data fusion database based on a geospatial database, thereby realizing the fusion of multi-source atmospheric environment data, which is required in atmospheric environment management.
  • Model simulation, big data mining and platform visualization provide standardized and unified interface data sources, reducing the workload and technical difficulty of data preprocessing and data interface writing.
  • the multi-source atmospheric environment data fusion database contains all-element atmospheric environment data oriented to atmospheric environment management needs, which may include: air pollution source data describing pollution source emissions, environmental receptor monitoring data describing the current situation of air quality, and impact Meteorological data transmitted and changed from pollution sources to environmental receptors can simultaneously provide comprehensive basic data for big data mining research for different needs.
  • each data source is redefined according to the point, line, and area data formats, which unifies and standardizes the multi-source data in the data type and spatiotemporal distribution dimensions, and realizes the integration of different data formats.
  • the multi-source atmospheric environment data fusion database established based on this method has the advantages of small storage space, unified data storage format, and spatio-temporal correlation. It is suitable for the fusion of multi-source atmospheric environment data and can be used to update emission inventories and update emission inventories in air pollution prevention and control work. Pollution source analysis, refined traceability analysis, pollution transmission early warning, emission reduction measure formulation and effect evaluation management.
  • the multi-source atmospheric environment data fusion database can provide a standard data source for data visualization, air pollution model simulation and platform data display and call, reducing the difficulty of converting a large number of data formats and writing data interfaces.
  • sequence number of each step in the above embodiment does not mean the order of execution.
  • the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.
  • FIG. 5 shows a schematic structural diagram of a multi-source atmospheric environment data fusion device provided by an embodiment of the present application. For ease of explanation, only the parts related to the embodiment of the present application are shown. The details are as follows:
  • a multi-source atmospheric environment data fusion device 2 includes:
  • the data acquisition module 21 is used to acquire atmospheric environment data from multiple data sources in the target area.
  • Atmospheric environment data includes form data and raster data. Both form data and raster data contain location information and time information.
  • the conversion module 22 is used to convert the form data into feature class data in vector format based on the location information of the form data.
  • the grid acquisition module 23 is used to acquire the geospatial grid of the target area.
  • the spatial connection module 24 is used to associate the location information of the feature class data and the location information of the raster class data with the geospatial grid respectively to establish a spatial connection relationship.
  • the time connection module 25 is used to correlate the time information of feature class data and the time information of raster class data to establish a time connection relationship. as well as
  • the storage module 26 is used to store feature data, raster data, spatial connection relationships and time connection relationships into the database based on the geospatial database, so as to establish a multi-source atmospheric environment data fusion database in the geospatial database format.
  • the embodiment of the present application establishes a spatial connection and a time connection between multiple sources of atmospheric environment data, and establishes a multi-source atmospheric environment data fusion database based on a geospatial database, thereby realizing the fusion of multi-source atmospheric environment data, which is required in atmospheric environment management.
  • Model simulation, big data mining and platform visualization provide standardized and unified interface data sources, reducing the workload and technical difficulty of data preprocessing and data interface writing.
  • the geospatial grid includes grid coding and grid location information, where the grid code serves as the unique identifier of each geospatial grid, and the grid location information is used to describe the geospatial grid.
  • spatial location information includes:
  • the first spatial connection unit is used to associate the location information of the feature class data with the grid coding of the geospatial grid according to the grid location information of each geospatial grid, so as to establish the space between the feature class data and the geospatial grid. connection relationship. as well as
  • the second spatial connection unit is used to associate the location information of the raster data with the grid coding of the geospatial grid according to the grid location information of each geospatial grid, so as to establish the raster data and the geospatial grid. spatial connection relationship.
  • FIG. 6 is a schematic diagram of a terminal provided by an embodiment of the present application.
  • the terminal 3 of this embodiment includes: a processor 30 , a memory 31 , and a computer program 32 stored in the memory 31 and executable on the processor 30 .
  • the processor 30 executes the computer program 32
  • the steps in each of the above embodiments of the multi-source atmospheric environment data fusion method are implemented, such as steps S1 to S6 shown in FIG. 1 .
  • the processor 30 executes the computer program 32, it implements the functions of each module/unit in each of the above device embodiments, such as the functions of the data acquisition module 21 to the warehousing module 26 shown in Figure 5.
  • the computer program 32 can be divided into one or more modules/units, the one or more modules/units are stored in the memory 31 and executed by the processor 30 to complete this application.
  • the one or more modules/units may be a series of computer program instruction segments capable of completing specific functions.
  • the instruction segments are used to describe the execution process of the computer program 32 in the terminal 3 .
  • the computer program 32 can be divided into the data acquisition module 21 to the warehousing module 26 shown in FIG. 5 .
  • the terminal 3 may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc.
  • the terminal 3 may include, but is not limited to, a processor 30 and a memory 31 .
  • FIG. 6 is only an example of the terminal 3 and does not constitute a limitation of the terminal 3. It may include more or fewer components than shown in the figure, or combine certain components, or different components, such as
  • the terminal may also include input and output devices, network access devices, buses, etc.
  • the processor 30 may be a central processing unit (Central Processing Unit). Processing Unit (CPU), or other general-purpose processor, Digital Signal Processor (DSP), Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • CPU Central Processing Unit
  • DSP Digital Signal Processor
  • ASIC Application Specific Integrated Circuit
  • FPGA Field-Programmable Gate Array
  • a general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.
  • the memory 31 may be an internal storage unit of the terminal 3 , such as a hard disk or memory of the terminal 3 .
  • the memory 31 may also be an external storage device of the terminal 3, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), or a secure digital (Secure Digital, SD) card equipped on the terminal 3. Flash Card, etc.
  • the memory 31 may also include both an internal storage unit of the terminal 3 and an external storage device.
  • the memory 31 is used to store the computer program and other programs and data required by the terminal.
  • the memory 31 can also be used to temporarily store data that has been output or is to be output.
  • Module completion means dividing the internal structure of the device into different functional units or modules to complete all or part of the functions described above.
  • Each functional unit and module in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.
  • the above-mentioned integrated unit can be hardware-based. It can also be implemented in the form of software functional units.
  • the specific names of each functional unit and module are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present application.
  • For the specific working processes of the units and modules in the above system please refer to the corresponding processes in the foregoing method embodiments, and will not be described again here.
  • the disclosed devices/terminals and methods can be implemented in other ways.
  • the device/terminal embodiments described above are only illustrative.
  • the division of modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or units. Components may be combined or may be integrated into another system, or some features may be ignored, or not implemented.
  • the coupling or direct coupling or communication connection between each other shown or discussed may be through some interfaces, indirect coupling or communication connection of devices or units, which may be in electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional unit in each embodiment of the present application can be integrated into one processing unit, each unit can exist physically alone, or two or more units can be integrated into one unit.
  • the above integrated units can be implemented in the form of hardware or software functional units.
  • the integrated module/unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.
  • the present application can implement all or part of the processes in the methods of the above embodiments, which can also be completed by instructing relevant hardware through a computer program.
  • the computer program can be stored in a computer-readable storage medium, and the computer can When the program is executed by the processor, the steps of each of the above multi-source atmospheric environment data fusion method embodiments can be implemented.
  • the computer program includes computer program code, which may be in the form of source code, object code, executable file or some intermediate form.
  • the computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (Read-Only Memory, ROM) , random access memory (Random Access Memory, RAM), electrical carrier signals, telecommunications signals, and software distribution media, etc.
  • the content contained in the computer-readable medium can be appropriately added or deleted according to the requirements of legislation and patent practice in the jurisdiction.
  • the computer-readable medium Excluded are electrical carrier signals and telecommunications signals.

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Abstract

本申请提供一种多源大气环境数据融合方法、装置、终端及存储介质。该方法包括：获取目标区域多个数据源的大气环境数据；表单类数据转换为矢量格式的要素类数据；将要素类数据的位置信息和栅格类数据的位置信息分别与地理空间网格关联；将要素类数据的时间信息和栅格类数据的时间信息相互关联；基于地理空间数据库，将要素类数据、栅格类数据、空间连接关系和时间连接关系入库，以建立地理空间数据库格式的多源大气环境数据融合数据库。本申请能够基于地理空间数据库，实现多源大气环境数据融合，为大气环境管理中所需的模型模拟、大数据挖掘和平台可视化提供标准化、统一接口的数据源，降低数据预处理和数据接口编写的工作量及技术难度。

Description

一种多源大气环境数据融合方法、装置、终端及存储介质
本专利申请要求于2022年04月27日提交的中国专利申请No.CN202210447453.3的优先权。在先申请的公开内容通过整体引用并入本申请。
技术领域
本申请涉及地理信息技术领域，尤其涉及一种多源大气环境数据融合方法、装置、终端及存储介质。
 背景技术
随着大气环境管理精细化要求的提高，同时新兴环境监测技术也逐渐发展成熟并开始广泛应用，大部分城市的环境监测手段都从传统的空气质量监测和污染源在线监测，拓展到了网格化加密监测、组分监测、污染源清单、走航监测、巡航监测、卫星遥感监测和激光雷达观测。多种环境监测手段的综合应用一方面为城市开展成因分析、污染溯源、环境监管、预警预报和减排评估防治工作提供了更为全面的数据，另一方面也由于这些数据数量巨大、来源分散、描述主体不同、类型庞杂且格式多样，在对其进行平台可视化展示和深度挖掘应用前都需要开展大量的数据预处理工作。
现阶段在大气污染防治领域的大气环境数据融合，主要是基于传统监测手段的一类或者几类数据源，例如，空气质量监测数据与污染源在线监测数据融合，因为同为固定点的监测数据，且参数一致、更新频率一致，广泛应用于污染排名、高值报警环境状况分析中。
新兴的大气环境数据，例如，网格化加密监测、组分监测、污染源清单、走航监测、巡航监测、卫星遥感监测和激光雷达观测的数据，是现阶段大气环境管理所需的重要组成部分，但这些来源分散的数据由于时空伴随变化不同、非定期更新、描述主体不同和格式差异问题，尚未实现与传统监测数据的融合。
 技术问题
本申请实施例提供了一种多源大气环境数据融合方法、装置、终端及存储介质，以解决多源大气环境数据融合的问题。
 技术解决方案
第一方面，本申请实施例提供了一种多源大气环境数据融合方法，包括：
获取目标区域多个数据源的大气环境数据；所述多个数据源的大气环境数据包括大气污染源数据、环境受体监测数据和气象数据；所述大气环境数据包括表单类数据和栅格类数据；所述表单类数据和栅格类数据均包含位置信息和时间信息。
基于表单类数据的位置信息，将表单类数据转换为矢量格式的要素类数据。
获取所述目标区域的地理空间网格。
将所述要素类数据的位置信息和所述栅格类数据的位置信息分别与所述地理空间网格关联，以建立空间连接关系。
将所述要素类数据的时间信息和所述栅格类数据的时间信息相互关联，以建立时间连接关系。
基于地理空间数据库，将所述要素类数据、所述栅格类数据、所述空间连接关系和所述时间连接关系入库，以建立地理空间数据库格式的多源大气环境数据融合数据库。
在一种可能的实现方式中，所述地理空间网格包括网格编码和网格位置信息，其中，所述网格编码作为各地理空间网格的唯一标识，所述网格位置信息用于描述地理空间网格的空间位置信息。
所述将所述要素类数据的位置信息和所述栅格类数据的位置信息分别与所述地理空间网格关联，以建立空间连接关系，包括：
根据各地理空间网格的网格位置信息，将所述要素类数据的位置信息与所述地理空间网格的网格编码关联，以建立要素类数据与地理空间网格的空间连接关系。
根据各地理空间网格的网格位置信息，将所述栅格类数据的位置信息与所述地理空间网格的网格编码关联，以建立栅格类数据与地理空间网格的空间连接关系。
在一种可能的实现方式中，在所述根据各地理空间网格的网格位置信息，将所述栅格类数据的位置信息与所述地理空间网格的网格编码关联，以建立栅格类数据与地理空间网格的空间连接关系之前，还包括：
将所述地理空间网格作为像元分辨率基准，对所述栅格类数据重新采样。
在一种可能的实现方式中，在所述获取目标区域多个数据源的大气环境数据之后，还包括：
根据各数据源中与缺失数据相邻位置的同类数据和/或根据各数据源中与缺失数据相邻时间的同类数据，补充大气环境数据中的缺失数据。
在一种可能的实现方式中，所述地理空间网格为按经度、纬度和海拔高度标记的三维空间网格。
第二方面，本申请实施例提供了一种多源大气环境数据融合装置，包括：
数据获取模块，用于获取目标区域多个数据源的大气环境数据；所述多个数据源的大气环境数据包括大气污染源数据、环境受体监测数据和气象数据；所述大气环境数据包括表单类数据和栅格类数据；所述表单类数据和栅格类数据均包含位置信息和时间信息。
转换模块，用于基于表单类数据的位置信息，将表单类数据转换为矢量格式的要素类数据。
网格获取模块，用于获取所述目标区域的地理空间网格。
空间连接模块，用于将所述要素类数据的位置信息和所述栅格类数据的位置信息分别与所述地理空间网格关联，以建立空间连接关系。
时间连接模块，用于将所述要素类数据的时间信息和所述栅格类数据的时间信息相互关联，以建立时间连接关系。以及
入库模块，用于基于地理空间数据库，将所述要素类数据、所述栅格类数据、所述空间连接关系和所述时间连接关系入库，以建立地理空间数据库格式的多源大气环境数据融合数据库。
在一种可能的实现方式中，所述地理空间网格包括网格编码和网格位置信息，其中，所述网格编码作为各地理空间网格的唯一标识，所述网格位置信息用于描述地理空间网格的空间位置信息；所述空间连接模块包括：
第一空间连接单元，用于根据各地理空间网格的网格位置信息，将所述要素类数据的位置信息与所述地理空间网格的网格编码关联，以建立要素类数据与地理空间网格的空间连接关系。以及
第二空间连接单元，用于根据各地理空间网格的网格位置信息，将所述栅格类数据的位置信息与所述地理空间网格的网格编码关联，以建立栅格类数据与地理空间网格的空间连接关系。
第三方面，本申请实施例提供了一种终端，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现如上第一方面或第一方面的任一种可能的实现方式所述方法的步骤。
第四方面，本申请实施例提供了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现如上第一方面或第一方面的任一种可能的实现方式所述方法的步骤。
 有益效果
本申请实施例提供一种多源大气环境数据融合方法、装置、终端及存储介质，该方法包括：获取目标区域多个数据源的大气环境数据；多个数据源的大气环境数据包括大气污染源数据、环境受体监测数据和气象数据；大气环境数据包括表单类数据和栅格类数据；表单类数据和栅格类数据均包含位置信息和时间信息；基于表单类数据的位置信息，将表单类数据转换为矢量格式的要素类数据；获取目标区域的地理空间网格；将要素类数据的位置信息和栅格类数据的位置信息分别与地理空间网格关联，以建立空间连接关系；将要素类数据的时间信息和栅格类数据的时间信息相互关联，以建立时间连接关系；基于地理空间数据库，将要素类数据、栅格类数据、空间连接关系和时间连接关系入库，以建立地理空间数据库格式的多源大气环境数据融合数据库。通过在多源大气环境数据之间建立空间连接、时间连接，基于地理空间数据库，建立多源大气环境数据融合数据库，实现了多源大气环境数据融合，为大气环境管理中所需的模型模拟、大数据挖掘和平台可视化提供标准化、统一接口的数据源，降低数据预处理和数据接口编写的工作量及技术难度。
 附图说明
为了更清楚地说明本申请实施例中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1是本申请实施例提供的一种多源大气环境数据融合方法的实现流程图；
图2是本申请实施例提供的一种多源大气环境数据融合方法的步骤S4实现流程图；
图3是本申请实施例提供的一种点要素类数据与网格关联方法的示意图；
图4是本申请实施例提供的另一种多源大气环境数据融合方法的实现流程图；
图5是本申请实施例提供的一种多源大气环境数据融合装置的结构示意图；
图6是本申请实施例提供的终端的示意图。
附图标记说明：
11：目标区域；12：二维地理空间网格；13：点要素类数据；2：多源大气环境数据融合装置；21：数据获取模块；22：转换模块；23：网格获取模块；24：空间连接模块；25：时间连接模块；26：入库模块；3：终端；30：处理器；31：存储器；32：计算机程序。
本申请的实施方式
以下描述中，为了说明而不是为了限定，提出了诸如特定系统结构、技术之类的具体细节，以便透彻理解本申请实施例。然而，本领域的技术人员应当清楚，在没有这些具体细节的其它实施例中也可以实现本申请。在其它情况中，省略对众所周知的系统、装置、电路以及方法的详细说明，以免不必要的细节妨碍本申请的描述。
为使本申请的目的、技术方案和优点更加清楚，下面将结合附图通过具体实施例来进行说明。
面对大气环境管理中预警预报、自动溯源、多维监管和决策支持的智能化大气环境管理需求，空气质量模型、大数据算法技术需要多源大气环境数据的支持；而目前对新兴的大气环境数据的应用仍主要通过开展专项分析应用研究，操作繁琐、效率低，缺乏标准的多源大气环境数据源以满足智能工具的开发和业务化应用需求。
本申请实施例可以适用于多种应用场景，这些场景中涉及对多源大气环境数据的融合，具体的应用场景在此不作限定。示例性的，在一个应用场景中，多个大气环境数据采集设备连接数据库服务器；采集设备采集大气环境数据，传输至数据库服务器；数据库服务器采用本申请实施例提供的方法，建立数据库，融合多源大气环境数据；同时定期更新和实时更新的数据，按相同的方法处理、入库融合。
在另一个应用场景中，采用本申请实施例提供的方法，对多个来源的大气环境数据进行处理，建立数据库，融合多源大气环境数据。
图1是本申请实施例提供的一种多源大气环境数据融合方法的实现流程图；参照图1，该方法包括：
在步骤S1中、获取目标区域多个数据源的大气环境数据。大气环境数据包括表单类数据和栅格类数据。表单类数据和栅格类数据均包含位置信息和时间信息。
在一种可能的实现方式中，可以根据描述主体对大气环境数据进行划分，例如，多个数据源的大气环境数据包括大气污染源数据、环境受体监测数据和气象数据。
大气污染源数据是用于描述大气污染来源的数据。示例性的，大气污染源数据可以包括但不限于下述一项或多项：污染源清单数据、机动车排放数据、污染源在线监测数据和农业面源数据。环境受体监测数据是用于描述大气环境质量的监测数据。示例性的，环境受体监测数据可以包括但不限于下述一项或多项：空气质量监测数据、颗粒物组分监测数据和挥发性有机物组分监测数据。气象数据是用于描述天气的数据。示例性的，气象数据可以包括但不限于下述一项或多项：地面站监测数据和气象模拟数据。
在一种可能的实现方式中，大气环境数据的属性包括下述一项或多项：主体、参数、时空变化、数据实时性和基础信息。
示例性的，主体属性可以包括但不限于下述一项或多项：大气污染源数据、环境受体监测数据和气象数据。大气污染源数据的主体属性可以包括但不限于下述一项或多项：污染源清单数据、机动车排放数据、污染源在线监测数据和农业面源数据。环境受体监测数据的主体属性可以包括但不限于下述一项或多项：空气质量监测数据、颗粒物组分监测数据和挥发性有机物组分监测数据。气象数据的主体属性可以包括但不限于下述一项或多项：地面站监测数据和气象模拟数据。
示例性的，大气污染源数据和环境受体监测数据的参数属性可以包括但不限于下述一项或多项：颗粒物、二氧化硫、氮氧化合物、一氧化碳和挥发性有机物。进一步的，参数属性中颗粒物、氮氧化合物和挥发性有机物参数可细分二级参数。示例性的，颗粒物的二级参数组分数据可细分为铵盐、硫酸盐和硝酸盐等。示例性的，氮氧化合物的二级参数组分数据可细分为一氧化氮和二氧化氮等。示例性的，挥发性有机物的二级参数组分数据可细分为烷烃、烯烃和炔烃等。
示例性的，气象数据的参数属性可分为温度、湿度、大气压、风速和风向等。
示例性的，时空变化属性可分为空间分辨率、时间分辨率。示例性的，空间分辨率具体可分为：按行政区划的省、市和县，或按经度、纬度和海拔高度标记的空间点、线、面和网格等。示例性的，时间分辨率可分为年、月、日、小时和分钟等。
示例性的，数据实时性属性分为定期更新和实时更新等。
示例性的，大气污染源数据的基础信息属性可以包括但不限于下述一项或多项：污染源名称、行业分类、联系方式、产品名称、排污类型和治理设施情况。
示例性的，污染源清单数据中某企业数据的描述主体属性一级主体为大气污染源数据，二级主体为污染源清单数据，三级主体为工艺过程源数据。参数属性为颗粒物、二氧化硫、二氧化氮、一氧化碳、挥发性有机物、氨气、黑碳或有机碳。时间分辨率为年，空间分辨率为按经度、纬度和海拔高度标记的点。数据实时性属性标记为按年度更新。基础信息属性为污染源名称、行业分类、联系方式、产品名称和除尘脱硫脱硝处理措施。
示例性的，污染源在线监测数据的描述主体属性一级主体为大气污染源数据，二级主体为污染源在线监测数据。参数属性为颗粒物、二氧化硫、二氧化氮和一氧化碳。时间分辨率为分钟、小时。空间分辨率为按经度、纬度和海拔高度标记的点。数据实时性属性标记为实时更新。基础信息属性为污染源名称、行业分类、联系方式和安装位置。
示例性的，激光雷达空气质量监测数据的描述主体属性一级主体为环境受体监测数据，二级主体为激光雷达空气质量监测数据。参数属性为颗粒物。时间分辨率为小时，空间分辨率为按经度、纬度和海拔高度标记的空间面。数据实时性属性标记为实时更新。
在一种可能的实现方式中，大气环境数据可以根据数据格式进行划分，包括：表单类数据和栅格类数据。表单类数据和栅格类数据均包含位置信息和时间信息。
示例性的，表单类数据可以包括但不限于下述一项或多项：污染源信息数据表、企业在线监测数据、国省控站点监测数据、走航巡航监测数据和气象数据。示例性的，表单类数据可采用CSV格式存储。相同来源的数据源，文件名称统一。相同污染源的污染源名称统一。数据源中同类数据的字段名称统一。
示例性的，描述点状污染源的污染源清单数据各字段的名称设置统一，字段可设置为：ID编号、数据编码、污染源类型、污染源名称、所在区县、地址、联系方式、产品名称、产品产量、除尘脱硫脱硝处理措施、各类污染物排放量、时空变化属性、数据实时性和其他基础信息等，其中，数据编码用于标记数据描述主体、数据类型、数据来源、污染源分类和污染源编号等。
示例性的，表单类数据中数据编码由15位阿拉伯数字组成，其中，1至3位代表其描述的主体分类，4至6位代表数据库中数据类型，7至9位代表数据来源，10至15位根据不同数据源定义可相应变化，示例性的，在大气污染源清单数据中数据编码的10至11位代表污染源的分类，如点污染源、线污染源或面污染源，12至15位代表每个污染源的编号。数据编码用于为每条数据设置唯一的编码，便于后期数据连接。
参照表1，示例性的，描述工艺过程源的点污染源数据可包括：ID编号、数据编码、污染源类型、污染源名称、所在地市、所在区县、地址、经度、纬度、产品名称、产品产量、治理措施和污染物排放量等。
表1：工艺过程源数据：
ID编号 数据编码 污染源类型 污染源名称 地市 区县 地址 经度 纬度 行业分类 产品名称 产品产量 治理设施 污染物排放量 关联网格编码 关联数据编码
1 001001001010001 工艺过程源 XX制药 XX 市 XX区 X路X号 112.3 39.2 化工 XXX XX XX除尘 XX 001001002003003 002001001001001
参照表2，示例性的，描述道路扬尘源的线污染源数据可包括：ID编号、数据编码、污染源类型、污染源名称、所在地市、所在区县、车流量、积尘负荷、洒水频次和污染物排放量等。
表2：道路扬尘源数据：
ID编号 数据编码 污染源类型 地市 区县 污染源名称 车流量 积尘负荷 洒水频次 污染物排放量 关联网格编码 关联数据编码
1 001001001020001 道路扬尘源 XX市 XX区 XX大道 2000 0.3 5 XX 001001002030003 ...
参照表3，示例性的，描述土壤扬尘源的面污染源可包括：ID编号、数据编码、污染源类型、污染源名称、所在地市、所在区县、所在乡镇、土地利用类型、土壤机械组成和污染物排放量等。
表3：土壤扬尘源数据：
ID编号 数据编码 污染源类型 地市 区县 乡镇 土地利用类型 土壤机械组成 污染物排放量 关联网格编码 关联数据编码
1 001001001030001 土壤扬尘源 XX市 XX区 XX镇 农田 壤土 XX 001001002030001 ...
示例性的，栅格类数据可以包括但不限于下述一项或多项：激光雷达数据和卫星反演数据。对于大气环境数据初始格式为栅格类数据的，统一空间坐标系和文件名称。
在一种可能的实现方式中，在获取目标区域多个数据源的大气环境数据之后，还包括：分析大气环境数据的连贯性，是否缺失数据。示例性的，缺失数据可以包括但不限于下述一项或多项：缺失某一天的环境受体监测数据、缺失参数字段和缺失文档。分析缺失数据为后续数据补缺作前期数据支撑。
在一种可能的实现方式中，在获取目标区域多个数据源的大气环境数据之后，还包括：
根据各数据源中与缺失数据相邻位置的同类数据和/或根据各数据源中与缺失数据相邻时间的同类数据，补充大气环境数据中的缺失数据。由于特殊原因缺失的数据，可根据其他数据源进行比对补充，示例性的，因设备运维缺少一个小时无组织排放监测数据，可根据同时间、临近位置走航和雷达数据对比分析后对其补充。
在一种可能的实现方式中，在获取目标区域多个数据源的大气环境数据之后，还包括：分析大气环境数据的数据形式、数据量。分析数据形式、数据量，为后续建库作前期数据支撑。
在步骤S2中、基于表单类数据的位置信息，将表单类数据转换为矢量格式的要素类数据。
示例性的，用经纬度表达表单类数据的位置信息。表单类数据的描述主体分为点、线和面。示例性的，用一个经度和一个纬度表示点的位置信息；用两端点的经纬度表示线段的位置信息；多个线段首尾相连、闭合构成面，用各线段所有端点的经纬度表示面的位置信息。示例性的，表单类数据包括点类、线类和面类数据，分别转换为点要素类数据、线要素类数据和面要素类数据。点要素类数据描述位置信息固定的大气环境数据，示例性的，可以包括但不限于下述一项或多项：空气质量监测数据、污染源在线监测数据、污染源清单数据中的点源信息以及网格化的数据等。
示例性的，线要素类数据描述线型大气环境数据，可以包括但不限于下述一项或多项：污染源清单数据中的道路源排放信息、走航数据和巡航数据等。
示例性的，表单类数据的格式为CSV格式，表单类数据的位置信息用经纬度表达，采用ARCGIS平台将包含经纬度位置信息的表单类数据转换为矢量格式的文件地理数据库要素类数据或个人地理数据库要素类数据。同类数据字段名称统一，相同数据源的字段名称统一。
在步骤S3中、获取目标区域的地理空间网格。
在一种可能的实现方式中，地理空间网格为按经度、纬度标记的二维空间网格。
在一种可能的实现方式中，地理空间网格为按经度、纬度和海拔高度标记的三维空间网格。示例性的，三维空间网格为边长为1公里、2公里或3公里的立方体。
在步骤S4中、将要素类数据的位置信息和栅格类数据的位置信息分别与地理空间网格关联，以建立空间连接关系。
图2是本申请实施例提供的一种多源大气环境数据融合方法的步骤S4实现流程图；参照图2：
在一种可能的实现方式中，地理空间网格包括网格编码和网格位置信息，其中，网格编码作为各地理空间网格的唯一标识，网格位置信息用于描述地理空间网格的空间位置信息。示例性的，网格位置信息包括经度、纬度和/或海拔高度。一个二维地理空间网格的网格位置信息包括网格四个顶点的经纬度信息。一个三维地理空间网格的网格位置信息包括网格八个顶点的经纬度信息和海拔高度。
相应的，步骤S4包括：
在步骤S41中、根据各地理空间网格的网格位置信息，将要素类数据的位置信息与地理空间网格的网格编码关联，以建立要素类数据与地理空间网格的空间连接关系。
示例性的，根据各网格位置信息与各点要素类数据的经纬度的对应关系，在点要素类数据中添加网格编码字段，其中，点要素类数据的经纬度包含于网格位置信息的经纬度范围内。每个点要素类数据对应一个地理空间网格。每个地理空间网格可对应多个点要素类数据。
图3是本申请实施例提供的一种点要素类数据与地理空间网格关联方法的示意图；参照图3：
示例性的，将某一目标区域11按经度和纬度划分为二维地理空间网格12，每个网格设有唯一的网格编码。根据各网格四个顶点的经纬度信息与各点要素类数据的经纬度的关系，将点要素类数据13与二维地理空间网格12进行关联，例如，在点要素类数据13中添加网格编码字段，点要素类数据的位置在网格编码对应的二维地理空间网格12内。
示例性的，根据各网格位置信息与线要素类数据各点的经纬度的对应关系，在线要素类数据中添加网格编码字段，其中，线要素类数据各点的经纬度包含于网格位置信息的经纬度范围内。每个线要素类数据可对应一个或多个地理空间网格。每个地理空间网格可对应多个线要素类数据。
示例性的，根据各网格位置信息与面要素类数据各点的经纬度的对应关系，在面要素类数据中添加网格编码字段，其中，面要素类数据各点的经纬度包含于网格位置信息的经纬度范围内。每个面要素类数据可对应一个或多个地理空间网格。每个地理空间网格可对应多个面要素类数据。
示例性的，采用ARCGIS中的SpatialJoint工具将要素类数据的位置信息与地理空间网格的网格编码关联，为每个要素类数据附加所在位置的空间网格编码，建立要素类数据与地理空间网格的空间连接关系
在步骤S42中、根据各地理空间网格的网格位置信息，将栅格类数据的位置信息与地理空间网格的网格编码关联，以建立栅格类数据与地理空间网格的空间连接关系。
栅格类数据用于描述面型的大气环境数据，可以包括但不限于下述一项或多项：激光雷达数据、卫星反演数据。栅格类数据的最小单元为像元。根据每个像元的经纬度信息与地理空间网格的位置信息，将像元与地理空间网格关联。每个像元对应一个地理空间网格，每个地理空间网格对应至少一个像元。
在一种可能的实现方式中，在根据各地理空间网格的网格位置信息，将栅格类数据的位置信息与地理空间网格的网格编码关联，以建立栅格类数据与地理空间网格的空间连接关系之前，还包括：
将地理空间网格作为像元分辨率基准，对栅格类数据重新采样。
示例性的，可采用ARCGIS中的Resample工具和ExtractValuesToPoints工具对栅格类数据进行数据处理，将地理空间网格作为像元分辨率基准，先进行数据重新采样，再提取重新采样后的像元值到对应的地理空间网格，实现栅格类数据与地理空间网格的空间连接。
在一种可能的实现方式中，点要素类数据根据相同污染源名称进行关联。示例性的，污染源清单数据中某一企业排放点数据通过其名称与该企业对应的在线监测数据建立连接。示例性的，采用ARCGIS中的AddJoin工具，根据相同名称属性进行连接。
在一种可能的实现方式中，通过对要素类数据添加属于同一地理空间网格的其它来源数据的数据编码，建立数据连接。示例性的，第一数据和第二数据属于同一地理空间网格，对第一数据增加字段标记第二数据的数据编码，同时对第二数据增加字段标记第一数据的数据编码，建立第一数据和第二数据的连接。
本申请提供的实施例通过建立空间连接关系，可用于根据空间连接关系获得某一目标区域的所有相关大气环境数据。
在步骤S5中、将要素类数据的时间信息和栅格类数据的时间信息相互关联，以建立时间连接关系。
同一时间的雷达、走航和监测站数据等所有大气环境数据可根据其包含的时间分辨率属性，进行时间相位同步化。进一步的，可根据时间连接关系获得某一时间段的所有相关大气环境数据。
示例性的，采用ARCGIS中的AddJoin工具，根据相同的时间信息属性，将要素类数据的时间信息和栅格类数据的时间信息相互关联，进行时间相位连接，以建立时间连接关系。
在步骤S6中、基于地理空间数据库，将要素类数据、栅格类数据、空间连接关系和时间连接关系入库，以建立地理空间数据库格式的多源大气环境数据融合数据库。
地理空间数据库的数据类型可以包括点要素类数据、线要素类数据、面要素类数据和栅格类数据。示例性的，污染源清单数据在数据库中以点要素类、线要素类和面要素类三种格式储存。示例性的，工艺过程源和锅炉工地以点要素类的格式储存，激光雷达数据和卫星反演数据以栅格类数据的格式储存。地理空间数据库格式的多源大气环境数据融合数据库可以表达目标区域的大气环境空气质量和污染源排放、传输等动态变化的大气环境信息。
示例性的，以ARCGIS地理空间数据库为基础，依照地理空间数据库的格式要求，将要素类数据、栅格类数据、空间连接关系和时间连接关系入库，以建立地理空间数据库GDB格式的多源大气环境数据融合数据库。
部分大气环境数据的时效性较高，示例性的，监测站点的大气环境监测数据包括分钟数值和/或小时数值。示例性的，根据需求实时或者定期对大气环境数据进行数据更新。
图4为本申请实施例提供的另一种多源大气环境数据融合方法的实现流程图；参照图4：
示例性的，该方法包括：获取多源大气环境数据。大气环境数据包括污染源信息数据、在线检测数据、卫星反演数据、激光雷达数据、走航数据和气象数据等。统一各来源大气环境数据的分类标准，例如，统一字段属性划分、统一字段名称的设置。表单类数据可存储为CSV格式，栅格类数据统一空间坐标系。补充表单类数据和栅格类数据的缺失数据。根据表单类数据的经纬度信息，将CSV格式的表单类数据转换为矢量格式的要素类数据。要素类数据包括点要素类数据、线要素类数据和面要素类数据。获取地理空间网格。将要素类数据、栅格类数据分别与地理空间网格进行空间关联，建立空间连接。将要素类数据、栅格类数据中包含的时间信息相互进行关联，建立时间连接。基于地理空间空间数据库，将要素类数据、栅格类数据、空间连接和时间连接统一入库，建立多源融合地理空间数据库。
本申请实施例通过在多源大气环境数据之间建立空间连接、时间连接，基于地理空间数据库，建立多源大气环境数据融合数据库，实现了多源大气环境数据融合，为大气环境管理中所需的模型模拟、大数据挖掘和平台可视化提供标准化、统一接口的数据源，降低数据预处理和数据接口编写的工作量及技术难度。
本申请实施例提供的多源大气环境数据融合数据库包含了面向大气环境管理需求的全要素大气环境数据，可包括：描述污染源排放的大气污染源数据、描述空气质量现状的环境受体监测数据以及影响从污染源到环境受体传输和变化的气象数据，可同时为面向不同需求的大数据挖掘研究提供全面的基础数据。
基于地理空间数据库，对各数据源，按点、线、面数据格式重新定义，使多源数据在数据类型、时空分布维度统一化、标准化，实现了不同数据格式的融合。基于该方法建立的多源大气环境数据融合数据库具有存储空间小、数据存储格式统一、时空关联的优点，适用于多源大气环境数据的融合，能够用于大气污染防治工作中开展排放清单更新、污染来源解析、精细化溯源分析、污染传输预警、减排措施制定和效果评估管理。多源大气环境数据融合数据库可为数据可视化、大气污染模型模拟和平台数据展示调用提供标准数据源，减少大量数据格式的转换处理及数据接口的编写难度。
应理解，上述实施例中各步骤的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本申请实施例的实施过程构成任何限定。
以下为本申请的装置实施例，对于其中未详尽描述的细节，可以参考上述对应的方法实施例。
图5示出了本申请实施例提供的一种多源大气环境数据融合装置的结构示意图，为了便于说明，仅示出了与本申请实施例相关的部分，详述如下：
如图5所示，一种多源大气环境数据融合装置2包括：
数据获取模块21，用于获取目标区域多个数据源的大气环境数据。大气环境数据包括表单类数据和栅格类数据。表单类数据和栅格类数据均包含位置信息和时间信息。
转换模块22，用于基于表单类数据的位置信息，将表单类数据转换为矢量格式的要素类数据。
网格获取模块23，用于获取目标区域的地理空间网格。
空间连接模块24，用于将要素类数据的位置信息和栅格类数据的位置信息分别与地理空间网格关联，以建立空间连接关系。
时间连接模块25，用于将要素类数据的时间信息和栅格类数据的时间信息相互关联，以建立时间连接关系。以及
入库模块26，用于基于地理空间数据库，将要素类数据、栅格类数据、空间连接关系和时间连接关系入库，以建立地理空间数据库格式的多源大气环境数据融合数据库。
本申请实施例通过在多源大气环境数据之间建立空间连接、时间连接，基于地理空间数据库，建立多源大气环境数据融合数据库，实现了多源大气环境数据融合，为大气环境管理中所需的模型模拟、大数据挖掘和平台可视化提供标准化、统一接口的数据源，降低数据预处理和数据接口编写的工作量及技术难度。
在一种可能的实现方式中，地理空间网格包括网格编码和网格位置信息，其中，网格编码作为各地理空间网格的唯一标识，网格位置信息用于描述地理空间网格的空间位置信息。空间连接模块24包括：
第一空间连接单元，用于根据各地理空间网格的网格位置信息，将要素类数据的位置信息与地理空间网格的网格编码关联，以建立要素类数据与地理空间网格的空间连接关系。以及
第二空间连接单元，用于根据各地理空间网格的网格位置信息，将栅格类数据的位置信息与地理空间网格的网格编码关联，以建立栅格类数据与地理空间网格的空间连接关系。
图6是本申请实施例提供的终端的示意图。如图6所示，该实施例的终端3包括：处理器30、存储器31以及存储在所述存储器31中并可在所述处理器30上运行的计算机程序32。所述处理器30执行所述计算机程序32时实现上述各个多源大气环境数据融合方法实施例中的步骤，例如图1所示的步骤S1至步骤S6。或者，所述处理器30执行所述计算机程序32时实现上述各装置实施例中各模块/单元的功能，例如图5所示数据获取模块21至入库模块26的功能。
示例性的，所述计算机程序32可以被分割成一个或多个模块/单元，所述一个或者多个模块/单元被存储在所述存储器31中，并由所述处理器30执行，以完成本申请。所述一个或多个模块/单元可以是能够完成特定功能的一系列计算机程序指令段，该指令段用于描述所述计算机程序32在所述终端3中的执行过程。例如，所述计算机程序32可以被分割成图5所示的数据获取模块21至入库模块26。
所述终端3可以是桌上型计算机、笔记本、掌上电脑及云端服务器等计算设备。所述终端3可包括，但不仅限于，处理器30、存储器31。本领域技术人员可以理解，图6仅仅是终端3的示例，并不构成对终端3的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件，例如所述终端还可以包括输入输出设备、网络接入设备、总线等。
所称处理器30可以是中央处理单元(Central Processing Unit，CPU)，还可以是其他通用处理器、数字信号处理器 (Digital Signal Processor，DSP)、专用集成电路 (Application Specific Integrated Circuit，ASIC)、现场可编程门阵列 (Field-Programmable Gate Array，FPGA) 或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
所述存储器31可以是所述终端3的内部存储单元，例如终端3的硬盘或内存。所述存储器31也可以是所述终端3的外部存储设备，例如所述终端3上配备的插接式硬盘，智能存储卡（Smart Media Card，SMC），安全数字（Secure Digital，SD）卡，闪存卡（Flash Card）等。进一步地，所述存储器31还可以既包括所述终端3的内部存储单元也包括外部存储设备。所述存储器31用于存储所述计算机程序以及所述终端所需的其他程序和数据。所述存储器31还可以用于暂时地存储已经输出或者将要输出的数据。
所属领域的技术人员可以清楚地了解到，为了描述的方便和简洁，仅以上述各功能单元、模块的划分进行举例说明，实际应用中，可以根据需要而将上述功能分配由不同的功能单元、模块完成，即将所述装置的内部结构划分成不同的功能单元或模块，以完成以上描述的全部或者部分功能。实施例中的各功能单元、模块可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中，上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。另外，各功能单元、模块的具体名称也只是为了便于相互区分，并不用于限制本申请的保护范围。上述系统中单元、模块的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述或记载的部分，可以参见其它实施例的相关描述。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
在本申请所提供的实施例中，应该理解到，所揭露的装置/终端和方法，可以通过其它的方式实现。例如，以上所描述的装置/终端实施例仅仅是示意性的，例如，所述模块或单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通讯连接可以是通过一些接口，装置或单元的间接耦合或通讯连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
所述集成的模块/单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请实现上述实施例方法中的全部或部分流程，也可以通过计算机程序来指令相关的硬件来完成，所述的计算机程序可存储于一计算机可读存储介质中，该计算机程序在被处理器执行时，可实现上述各个多源大气环境数据融合方法实施例的步骤。其中，所述计算机程序包括计算机程序代码，所述计算机程序代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。所述计算机可读介质可以包括：能够携带所述计算机程序代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器（Read-Only Memory，ROM）、随机存取存储器（Random Access Memory，RAM）、电载波信号、电信信号以及软件分发介质等。需要说明的是，所述计算机可读介质包含的内容可以根据司法管辖区内立法和专利实践的要求进行适当的增减，例如在某些司法管辖区，根据立法和专利实践，计算机可读介质不包括是电载波信号和电信信号。
以上所述实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的精神和范围，均应包含在本申请的保护范围之内。

Claims (9)

  1. 一种多源大气环境数据融合方法，其特征在于，包括：
    获取目标区域多个数据源的大气环境数据；所述多个数据源的大气环境数据包括大气污染源数据、环境受体监测数据和气象数据；所述大气环境数据包括表单类数据和栅格类数据；所述表单类数据和所述栅格类数据均包含位置信息和时间信息；
    基于所述表单类数据的位置信息，将所述表单类数据转换为矢量格式的要素类数据；
    获取所述目标区域的地理空间网格；
    将所述要素类数据的位置信息和所述栅格类数据的位置信息分别与所述地理空间网格关联，以建立空间连接关系；
    将所述要素类数据的时间信息和所述栅格类数据的时间信息相互关联，以建立时间连接关系；
    基于地理空间数据库，将所述要素类数据、所述栅格类数据、所述空间连接关系和所述时间连接关系入库，以建立地理空间数据库格式的多源大气环境数据融合数据库。
  2. 根据权利要求1所述的一种多源大气环境数据融合方法，其特征在于，所述地理空间网格包括网格编码和网格位置信息，其中，所述网格编码作为各所述地理空间网格的唯一标识，所述网格位置信息用于描述所述地理空间网格的空间位置信息；
    所述将所述要素类数据的位置信息和所述栅格类数据的位置信息分别与所述地理空间网格关联，以建立空间连接关系，包括：
    根据各所述地理空间网格的所述网格位置信息，将所述要素类数据的位置信息与所述地理空间网格的所述网格编码关联，以建立所述要素类数据与所述地理空间网格的空间连接关系；
    根据各所述地理空间网格的所述网格位置信息，将所述栅格类数据的位置信息与所述地理空间网格的所述网格编码关联，以建立所述栅格类数据与所述地理空间网格的空间连接关系。
  3. 根据权利要求2所述的一种多源大气环境数据融合方法，其特征在于，在所述根据各所述地理空间网格的所述网格位置信息，将所述栅格类数据的位置信息与所述地理空间网格的所述网格编码关联，以建立所述栅格类数据与所述地理空间网格的空间连接关系之前，还包括：
    将所述地理空间网格作为像元分辨率基准，对所述栅格类数据重新采样。
  4. 根据权利要求3所述的一种多源大气环境数据融合方法，其特征在于，在所述获取目标区域多个数据源的大气环境数据之后，还包括：
    根据各数据源中与缺失数据相邻位置的同类数据和/或根据各数据源中与缺失数据相邻时间的同类数据，补充所述大气环境数据中的所述缺失数据。
  5. 根据权利要求4所述的一种多源大气环境数据融合方法，其特征在于，所述地理空间网格为按经度、纬度和海拔高度标记的三维空间网格。
  6. 一种多源大气环境数据融合装置，其特征在于，包括：
    数据获取模块，用于获取目标区域多个数据源的大气环境数据；所述多个数据源的大气环境数据包括大气污染源数据、环境受体监测数据和气象数据；所述大气环境数据包括表单类数据和栅格类数据；所述表单类数据和所述栅格类数据均包含位置信息和时间信息；
    转换模块，用于基于所述表单类数据的位置信息，将所述表单类数据转换为矢量格式的要素类数据；
    网格获取模块，用于获取所述目标区域的地理空间网格；
    空间连接模块，用于将所述要素类数据的位置信息和所述栅格类数据的位置信息分别与所述地理空间网格关联，以建立空间连接关系；
    时间连接模块，用于将所述要素类数据的时间信息和所述栅格类数据的时间信息相互关联，以建立时间连接关系；以及
    入库模块，用于基于地理空间数据库，将所述要素类数据、所述栅格类数据、所述空间连接关系和所述时间连接关系入库，以建立地理空间数据库格式的多源大气环境数据融合数据库。
  7. 根据权利要求6所述的一种多源大气环境数据融合装置，其特征在于，所述地理空间网格包括网格编码和网格位置信息，其中，所述网格编码作为各所述地理空间网格的唯一标识，所述网格位置信息用于描述所述地理空间网格的空间位置信息；所述空间连接模块包括：
    第一空间连接单元，用于根据各所述地理空间网格的所述网格位置信息，将所述要素类数据的位置信息与所述地理空间网格的所述网格编码关联，以建立所述要素类数据与所述地理空间网格的空间连接关系；以及
    第二空间连接单元，用于根据各所述地理空间网格的所述网格位置信息，将所述栅格类数据的位置信息与所述地理空间网格的所述网格编码关联，以建立所述栅格类数据与所述地理空间网格的空间连接关系。
  8. 一种终端，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时实现如上的权利要求1至5中任一项所述多源大气环境数据融合方法的步骤。
  9. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现如上的权利要求1至5中任一项所述多源大气环境数据融合方法的步骤。
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