CN104102845B - The interpolation method of dimension self-adaption and the interplotation system of dimension self-adaption - Google Patents
The interpolation method of dimension self-adaption and the interplotation system of dimension self-adaption Download PDFInfo
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Abstract
The present invention relates to area of geographic information.In order to solve the problems, such as that existing interpolation method loses many useful informations so that interpolation result error is larger, the present invention proposes that a kind of interpolation method of dimension self-adaption and the interplotation system of dimension self-adaption, methods described comprise the following steps：Obtain the meteorological data and air contents concentration data of known data point in target area；Sub-zone dividing is carried out to the target area；Determined to enter the subregion space scale of row interpolation respectively according to the spatial distribution characteristic of data point in every sub-regions, and grid is carried out to the subregion based on the space scale；Wind direction and air speed data in meteorological data, choose the known data point for participating in interpolation；The data of selected known data point are utilized to enter row interpolation the grid points obtained after grid in every sub-regions；Each sub-regions result that interpolation obtains respectively will be merged.Make the interpolation result that is obtained by the present invention more accurate, credible.
Description
Technical field
The present invention relates to area of geographic information, and in particular to a kind of interpolation method of dimension self-adaption and a kind of yardstick are adaptive
The interplotation system answered, for entering row interpolation according to the data of known data point to the air contents concentration in target area.
Background technology
Traditional spatial interpolation methods require that the distribution of observation station spatially is as uniform as possible, otherwise can not obtain preferably
Interpolation result.However, for some actual conditions, such as the air pollutant concentration that air monitering station is observed, observation station is in sky
Between on distribution be highly non-uniform, therefore, traditional spatial interpolation algorithm is no longer desirable for these situations.
When carrying out space interpolation to air pollutant concentration, meteorologic factor, such as temperature, humidity, wind speed and direction, to inserting
Value result has critically important influence.Traditional processing method is directly to enter row interpolation using meteorologic factor as the factor, and this causes
Lose the information that many useful informations, particularly wind speed and direction provide.
The content of the invention
The present invention proposes a kind of interpolation method of dimension self-adaption and a kind of interplotation system of dimension self-adaption, for mesh
Air contents concentration in mark region enters row interpolation according to the data of known data point, to solve the interpolation method of prior art
Many useful informations are lost so that the problem of interpolation result error is larger.This method comprises the following steps：
S1：Obtain the meteorological data and air contents concentration data of known data point in target area；
S2：Sub-zone dividing is carried out to the target area；
S3：Determined to enter the subregion in the sky of row interpolation respectively according to the spatial distribution characteristic of data point in every sub-regions
Between yardstick, and based on the space scale to the subregion carry out grid；
S4：Wind direction and air speed data in the meteorological data obtained according to step S1, choose the given data for participating in interpolation
Point；
S5：To the grid points obtained after step S3 grid in every sub-regions using known to step S4 selections
The data of data point enter row interpolation；
S6：Each sub-regions result that interpolation obtains respectively will be merged.
Spatially discrete observation, i.e. the air contents concentration data of known data point are carried out space company by this method
Continuous property analyzing and processing, air contents concentration is carried out to any locus without observation in target area so as to realize
Interpolation calculation, that is, calculate the air contents concentration of any locus.
Compared with prior art, this method is according to the space chi of the space distribution situation adjust automatically interpolation of known data point
Degree, so as to maximally utilise effective information, and evades falling the interference of garbage；In addition, introduce meteorological data (such as wind
Speed and wind direction data) carry out suitable known data point used in intelligent selection interpolation, therefore, interpolation result is more accurate, credible.
According to the space scale of the space distribution situation adjust automatically interpolation of known data point, there are two advantages：When
Target area is divided into subregion to be handled, for the densely distributed subregion of known data point, using less space
Yardstick enters row interpolation, can maximally utilise the existing information of these subregions, improve interpolation result accuracy and can
Reliability；Second, being distributed sparse subregion for known data point, row interpolation is entered using larger space scale, can not subtracted
In the case of small interpolation result accuracy and confidence level, the speed of calculating is greatly improved, so as to realize the real-time height of interpolation algorithm
Effect performs.
The present invention is proposed, sub-zone dividing is carried out using spatial clustering method in the step S2.
The present invention is proposed, sub-district is carried out using Voronoi diagram division methods or triangulation network division methods in the step S2
Domain divides.
The present invention proposes, the space scale be according to space become average value that journey and/or all known points adjust the distance and/
Or minimum value and/or the space density of intermediate value and/or known data point determine.
The present invention proposes that gridization includes rectangular grid networking, triangular mesh, regular polygon grid in the step S3
Change and irregular polygon grid.
The present invention proposes that the method for interpolation includes inverse distance weight, weighted direction method, direction distance in the step S5
Weighting method and ordinary kriging interpolation method, in addition to the factor Kriging regression side using meteorological data as factor participation interpolation
Method.
The present invention proposes, in the step S6 fusion of each sub-regions interpolation result folded using buffer zone analysis and space
The method that adds utilizes the method that interpolation is carried out again to the void area between subregion.
A kind of interplotation system of dimension self-adaption, including data acquisition module, sub-zone dividing module, dimension self-adaption lattice
Networking module, data decimation module, interpolation calculation module, result Fusion Module, visualization model and display module；The data
Acquisition module, for obtaining the meteorological data and air contents of the known data point with geographical location information in target area
Concentration data, and send data to the sub-zone dividing module, the dimension self-adaption grid module and the data
Choose module；The sub-zone dividing module, for the target area to be divided into K sub-regions of different sizes, and will
Division result is transmitted to the dimension self-adaption grid module and the data decimation module；The dimension self-adaption grid
Module, for the every sub-regions obtained to the sub-zone dividing module, utilize the sky of known data point in every sub-regions
Between distribution characteristics automatically determine suitable space scale, and grid is carried out according to the yardstick, and by grid result transmit to
The data decimation module and the interpolation calculation module；The data decimation module, for the sub-zone dividing module
The every sub-regions obtained after being divided to target area, with reference to contained by the meteorological data and air of data acquisition module acquisition
The space scale determined in thing concentration data and the dimension self-adaption grid module chooses the given data for participating in interpolation
Point, and by the data transfer of the known data point of selection to the interpolation calculation module；The interpolation calculation module, using described
The space scale determined in data and the dimension self-adaption grid module that data decimation module obtains is to each sub-regions
Enter row interpolation, obtain the subregion after K interpolation, and interpolation result is transmitted to the result Fusion Module；The result is melted
Matched moulds block, the interpolation result for each sub-regions that the interpolation calculation module is obtained are merged, and obtain whole target area
Interpolation result, and the interpolation result is transmitted to the visualization model；The visualization model, the result is merged
The interpolation result for the whole target area that module obtains be rendered into the forms such as picture, histogram, line chart, video, interaction figure or
Visualized with numeric form, and visualization result is transmitted to the display module；The display module, to described visual
Change the visualization result that module obtains to be shown, display form includes website, smart mobile phone APP, Intelligent flat APP, insertion
Formula program.
The present invention proposes that the interplotation system of the dimension self-adaption also includes locating module, and the reality of terminal is accessed for obtaining
When positional information, and real-time position information is transferred to the display module, the display module is according to real-time position information exhibition
Show the visualization result of current location.
Brief description of the drawings
The embodiment of the method according to the invention and system is described below by way of accompanying drawing.Accompanying drawing is schematically shown：
The flow chart of the interpolation method of the dimension self-adaption of Fig. 1 present invention；
The structured flowchart of the interplotation system of the dimension self-adaption of Fig. 2 present invention.
Embodiment
Fig. 1 is the flow chart of the interpolation method of the dimension self-adaption of the present invention, and the interpolation method is described below in detail.
Step S1, obtain the meteorological data and air contents concentration data of known data point in target area.These numbers
According to being the given data that observes, can be obtained from monitoring station, and all with geographical location information.
Meteorological data includes temperature, humidity, wind speed, wind direction, air pressure etc..Air contents concentration data is wrapped in air
The concentration data of the various materials contained, such as PM2.5、PM10、SO2、CO、NO2、O3, negative oxygen ion, moisture, O2、N2Deng concentration
Data.
These meteorological datas and air contents concentration data can be stored and expressed using relevant database, be divided into
Two tables of data, respectively air contents concentration data table and meteorological data table, referring to Tables 1 and 2.
Table 1：
Table 2：
Air contents concentration data table (table 1) includes data point numbering (station_code), data time stamp
(time_point), place city (area), data point title (position_name), air quality rank (quality),
Air quality index (aqi), primary pollutant (primary_pollutant), PM2.5Concentration (pm2_5), PM10Concentration
(pm10)、NO2Concentration (no2), SO2Concentration (so2), O3Concentration (o3), CO concentration (co)；Meteorological data table (table 2) includes city
Title (city), city numbering (cityid), temperature (temperature), wind direction (wind_direction), wind speed (wind_
Speed), air speed value (wind_speed_value), relative humidity (humidity), data time stamp (time_point).
Step S2, sub-zone dividing is carried out to target area, i.e., target area is divided into K sub-district of different sizes
Domain.Spatial clustering method can be used in sub-zone dividing.It is preferred that use K mean cluster algorithm.By target area (such as China
Domain) using K mean cluster algorithm partition it is K sub-regions of different sizes, wherein, K represents the number of subregion, the number
Value can be adjusted by algorithm, regulation rule is：The N that K is respectively set into 1,2,3 ... ..., the classification for obtaining clustering algorithm miss
Poor curve, wherein, transverse axis is K value, and the longitudinal axis is the overall error of clustering algorithm when K takes analog value.When K constantly increases,
The curve constantly declines, and the absolute value of the slope of consecutive points line also constantly reduces, i.e., curve is constantly smooth.Work as consecutive points
When the slope absolute value of line is less than 0.1, the K selected now is the number of subregion.
As an alternative solution, can also Voronoi diagram division methods or triangulation network division methods be used to divide target area
For K sub-regions, wherein, Voronoi diagram division methods and triangulation network division methods belong to prior art.
Step S3, determine to enter row interpolation to the subregion respectively using the spatial distribution characteristic of data point in every sub-regions
Space scale and based on the space scale carry out grid.Spatial distribution characteristic refers to research object in a certain survey region
Spatial distribution structure, the spatial distribution structure of known data point is refered in particular in the present invention, is calculated using semivariable function
The space come becomes journey, and the average value adjusted the distance of all known points and/or minimum value and/or intermediate value.Calculate spatial distribution
After feature, formula is utilizedSpace scale is calculated, D is space scale in formula, and R is spatial distribution characteristic (such as sky
Anaplasia journey, average of all-pair distance etc.),It is coefficient, its value depends on specific R values, in embodiments of the invention
The middle average with all-pair distance represents R, now,Value is 0.1.Gridding method is included for those skilled in the art
Known rectangular grid networking, triangular mesh, regular polygon grid and irregular polygon grid.
Step S4, wind direction and air speed data in the meteorological data of step S1 acquisitions have been chosen automatically has participated in interpolation
Primary data point.To each grid points of every sub-regions, according to the wind direction of its current location, its upwind region institute is found first
Comprising M (M is model parameter, is determined as the case may be) individual known data point, wherein, upwind region refer to work as
Axle centered on direction representated by preceding wind direction, the region obtained by 45 ° is respectively extended with counter clockwise direction along clockwise direction.As worked as
Preceding wind direction is north wind, and the region that angle between direction northwest and positive northeastward is 90 ° is criticized in its upwind region；M refers to
The number of wind direction region known data point, due to wind direction time to time change, therefore number M also times to time change.Upwind
After M data point that region includes determines, the data time stamp of data using distance, wind speed, known data point can be sentenced
Break and whether these data points influence the grid points of current interpolation, so as to select the final given data for participating in interpolation calculation
Point.Specifically selection rule is：By the known data point X in upwind region and current interpolation grid points G distance S divided by currently
The wind speed V in region, obtain the air contents at data point X and transmit to the time T needed for current interpolation grid points G, if
Data time stamp and the time interval at current time at primary data point X are T0, if T is less than or equal to T0, choose data point X
Step S5 interpolation calculation is participated in, if T is more than T0, rejecting data points X.
Step S5, the grid points obtained after step S3 grid in every sub-regions are chosen using step S4
The data of known data point enter row interpolation, obtain K spatially continuous subregions.Interpolation method includes inverse distance-weighting
Method, weighted direction method, direction distance weighting method and ordinary kriging interpolation method.In addition, interpolation method is also included meteorological number
According to the factor Kriging regression method that interpolation is participated in as the factor.These interpolation methods are prior art.
Step S6, each sub-regions result that interpolation obtains respectively will be merged, by the interpolation of different subregions
As a result the overall interpolation result in target area is merged into.As a result fusion refers to the son of K obtained in step S5 completion interpolation
Region is merged to obtain the process of the overall interpolation result of whole target area.
The difference of sub-zone dividing method in step S2, result fusion herein include two ways.
First way is to be directed to the sub-zone dividing carried out using spatial clustering method, this sub-zone dividing method meeting
Cause space be present between different subregions, therefore, as a result fusion to be distinguished using for well known to a person skilled in the art buffering
Analysis and the method for space overlapping.For carrying out result fusion using the method for buffer zone analysis and space overlapping, first to step
The subregion of the K completion interpolation obtained in S5 carries out buffer zone analysis respectively, their intersecting parts is then obtained, to these
Intersecting part carries out space overlapping, thus obtains the overall interpolation result of target area.As an alternative solution, also can be to each
Void area between subregion carries out interpolation again so that whole target area is completely covered in whole interpolation results.
The second way is to be directed to the subregion carried out using Voronoi diagram division methods or triangulation network division methods to draw
Point, using this sub-zone dividing method, space is not present between each sub-regions, therefore, as a result fusion refers to simply to
The interpolation result of each sub-regions carries out space and merges i.e. splicing, and the processing of area of geographic information spatial data is merged into space
Known method, not it is described in detail herein.
Fig. 2 be the present invention dimension self-adaption interplotation system structured flowchart, the interplotation system bag of the dimension self-adaption
Include data acquisition module 1, sub-zone dividing module 2, dimension self-adaption grid module 3, data decimation module 4, interpolation calculation
Module 5, result Fusion Module 6, visualization model 7, display module 8 and locating module 9.
Data acquisition module 1, for obtaining the meteorological number of the known data point with geographical location information in target area
According to air contents concentration data, and send data to sub-zone dividing module 2, the and of dimension self-adaption grid module 3
Data decimation module 4.
Sub-zone dividing module 2, for target area to be divided into K sub-regions of different sizes, and by division result
Transmit to dimension self-adaption grid module 3 and data decimation module 4.
Dimension self-adaption grid module 3, for the every sub-regions obtained to sub-zone dividing module 2, using each
The spatial distribution characteristic of known data point automatically determines suitable space scale in subregion, and carries out grid according to the yardstick
Change, and grid result is transmitted to data decimation module 4 and interpolation calculation module 5.
Data decimation module 4, for obtained every sub-regions after being divided to sub-zone dividing module 2 to target area,
The meteorological data and air contents concentration data and dimension self-adaption grid module 3 obtained with reference to data acquisition module 1
The space scale of middle determination chooses the known data point for participating in interpolation, and by the data transfer of the known data point of selection to interpolation
Computing module 5.
In interpolation calculation module 5, the data obtained using data decimation module 4 and dimension self-adaption grid module 3 really
Fixed space scale carries out interpolation to each sub-regions, obtains the subregion after K interpolation, and interpolation result is transmitted to result
Fusion Module 6.
As a result Fusion Module 6, the interpolation result for each sub-regions that interpolation calculation module 5 is obtained are merged, obtained
The interpolation result of whole target area, and the interpolation result is transmitted to visualization model 7.
Visualization model 7, the interpolation result of the whole target area that result Fusion Module 6 is obtained are rendered into picture, straight
The forms such as Fang Tu, line chart, video, interaction figure are visualized with numeric form, and visualization result is transmitted to displaying
Module 8.
Display module 8, the visualization result obtained to visualization model 7 are shown, and display form includes website, intelligence
Cell phone application, Intelligent flat APP, embedded program.
Locating module 9, the real-time position information of terminal (such as smart mobile phone, Intelligent flat) is accessed for obtaining, and will be real
When positional information be transferred to display module 8, thus, the real-time position information displaying that display module 8 is transmitted according to locating module 9 is worked as
The visualization result of front position.
Claims (9)
1. a kind of interpolation method of dimension self-adaption, it is characterised in that methods described comprises the following steps：
S1：Obtain the meteorological data and air contents concentration data of known data point in target area；
S2：Sub-zone dividing is carried out to the target area；
S3：Determined to enter the subregion space chi of row interpolation respectively according to the spatial distribution characteristic of data point in every sub-regions
Degree, and grid is carried out to the subregion based on the space scale；
S4：Wind direction and air speed data in the meteorological data obtained according to step S1, choose the known data point for participating in interpolation；
S5：The given data for utilizing step S4 to choose to the grid points obtained after step S3 grid in every sub-regions
The data of point enter row interpolation；
S6：Each sub-regions result that interpolation obtains respectively will be merged.
2. the interpolation method of dimension self-adaption according to claim 1, it is characterised in that space is used in the step S2
Clustering method carries out sub-zone dividing.
3. the interpolation method of dimension self-adaption according to claim 1, it is characterised in that used in the step S2
Voronoi diagram division methods or triangulation network division methods carry out sub-zone dividing.
4. the interpolation method of dimension self-adaption according to any one of claim 1 to 3, it is characterised in that the space
Yardstick is to become average value and/or minimum value and/or intermediate value that journey and/or all known points adjust the distance and/or known according to space
The space density of data point determines.
5. the interpolation method of dimension self-adaption according to any one of claim 1 to 3, it is characterised in that the step
Gridization includes rectangular grid networking, triangular mesh, regular polygon grid and irregular polygon grid in S3.
6. the interpolation method of dimension self-adaption according to any one of claim 1 to 3, it is characterised in that the step
The method of interpolation includes inverse distance weight, weighted direction method, direction distance weighting method and ordinary kriging interpolation method in S5,
Also include the factor Kriging regression method that interpolation is participated in using meteorological data as the factor.
7. the interpolation method of dimension self-adaption according to claim 1 or 2, it is characterised in that each in the step S6
The fusion of subregion interpolation result utilizes the method for buffer zone analysis and space overlapping or using to the interstice coverage between subregion
The method that domain carries out again interpolation.
8. a kind of interplotation system of dimension self-adaption, including data acquisition module (1), sub-zone dividing module (2), yardstick are adaptive
Answer grid module (3), data decimation module (4), interpolation calculation module (5), result Fusion Module (6), visualization model (7)
With display module (8)；
The data acquisition module (1), for obtaining the meteorology of the known data point with geographical location information in target area
Data and air contents concentration data, and send data to the sub-zone dividing module (2), the dimension self-adaption lattice
Networking module (3) and the data decimation module (4)；
The sub-zone dividing module (2), for the target area to be divided into K sub-regions of different sizes, and it will draw
Point result is transmitted to the dimension self-adaption grid module (3) and the data decimation module (4)；
The dimension self-adaption grid module (3), for the every sub-regions obtained to the sub-zone dividing module (2),
Suitable space scale is automatically determined using the spatial distribution characteristic of known data point in every sub-regions, and is entered according to the yardstick
Row grid, and grid result is transmitted to the data decimation module (4) and the interpolation calculation module (5)；
The data decimation module (4) is each for being obtained after being divided to the sub-zone dividing module (2) to target area
Subregion, the meteorological data obtained with reference to the data acquisition module (1) and air contents concentration data and the yardstick
The space scale determined in adaptive lattice networking module (3) chooses the known data point for participating in interpolation, and by the datum of selection
The data transfer at strong point is to the interpolation calculation module (5)；
The interpolation calculation module (5), the data and the dimension self-adaption grid obtained using the data decimation module (4)
Change the space scale determined in module (3) and interpolation is carried out to each sub-regions, obtain the subregion after K interpolation, and by interpolation
As a result transmit to the result Fusion Module (6)；
The result Fusion Module (6), the interpolation result for each sub-regions that the interpolation calculation module (5) is obtained are melted
Close, obtain the interpolation result of whole target area, and the interpolation result is transmitted to the visualization model (7)；
The visualization model (7), the interpolation result for the whole target area that the result Fusion Module (6) is obtained are rendered into
Picture, histogram, line chart, video, the form of interaction figure are visualized with numeric form, and visualization result is transmitted
To the display module (8)；
The display module (8), the visualization result obtained to the visualization model (7) are shown, and display form includes
Website, smart mobile phone APP, Intelligent flat APP, embedded program.
9. the interplotation system of dimension self-adaption according to claim 8, it is characterised in that the interpolation system of the dimension self-adaption
System also includes locating module (9), and the real-time position information of terminal is accessed for obtaining, and described in real-time position information is transferred to
Display module (8), the display module (8) show the visualization result of current location according to real-time position information.
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| "一种针对气象要素的空间插值算法";李彦等;《重庆理工大学学报（自然科学）》;20140630;第28卷(第6期);第94-98、116页 * |
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| CN104102845A (en) | 2014-10-15 |
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