CN110334090A - The association of multi-source heterogeneous pollution of area source big data based on space-time characteristic and search method and supervising platform - Google Patents
The association of multi-source heterogeneous pollution of area source big data based on space-time characteristic and search method and supervising platform Download PDFInfo
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Abstract
A kind of association and search method of the multi-source heterogeneous pollution of area source big data based on space-time characteristic, obtain the temporal characteristics and space characteristics of multi-source heterogeneous pollution of area source data, the geographical space of target area is divided into several sub-spaces, form initial mesh, it is divided step by step on initial mesh and forms sub-grids at different levels, it is encoded for each sub-grid, determine the space encoding of multi-source heterogeneous pollution of area source data, temporal characteristics code is introduced in each subnet trellis coding, increase time dimension, it is formatted tissue and index model using multi-level network, it is matched using time and spatial position, realize data correlation and retrieval, the present invention also provides a kind of multi-source heterogeneous pollution of area source big data supervising platforms, compared with prior art, the present invention comprehensively considers the time of multi-source heterogeneous big data and space characteristics, help to realize data correlation , retrieval is greatly optimized, convenient for realizing efficient retrieval and management using module Real-time Monitoring Data is crawled.
Description
Technical field
The invention belongs to big data processing technology field, in particular to a kind of multi-source heterogeneous face source based on space-time characteristic is dirty
The association of dye big data and search method and the big data supervising platform using this method.
Background technique
Multi-source heterogeneous pollution of area source big data has the spies such as source diversification, data format disunity, data volume difference be big
Point, existing association and search method, which are mostly used, constructs linked character based on body of data, such as: it is led using longitude and latitude as association
Key, or find out based on data content the relevance between different data.It is different that both methods does not account for pollution of area source multi-source
The space-time characteristic that structure data have can not track the contribution degree with dynamic analysis different data sources to pollution of area source.
Summary of the invention
In order to overcome the disadvantages of the above prior art, the purpose of the present invention is to provide a kind of multi-sources based on space-time characteristic
The association of isomery pollution of area source big data and search method and the big data supervising platform for using this method, realize multi-source heterogeneous face
Pollute efficient correlation and the retrieval of big data in source.
To achieve the goals above, the technical solution adopted by the present invention is that:
A kind of association and search method of the multi-source heterogeneous pollution of area source big data based on space-time characteristic are based on space-time uniformity
Geocoding includes the following steps:
The temporal characteristics and space characteristics of multi-source heterogeneous pollution of area source data are obtained, the time is characterized in referring to the acquisition of data
Time, space characteristics refer to the gain location of data；
The geographical space of target area is divided into several sub-spaces, initial mesh is formed, is drawn step by step on initial mesh
Divide and form sub-grids at different levels, the depth of corresponding sub-grid is determined according to the range of multi-source heterogeneous pollution of area source data and resolution ratio,
Each sub-grid after dividing spatially has uniquely determining property；
It is encoded for each sub-grid, the space encoding of multi-source heterogeneous pollution of area source data is determined, in each height
In grid coding introduce temporal characteristics code, increase time dimension, with reflect multi-source heterogeneous pollution of area source data and meanwhile have when
Sequence characteristics introduce index and constitute multi-level network and format tissue and index model；
It is formatted tissue and index model to multi-source heterogeneous pollution of area source data using multi-level network, utilizes time and spatial position
Data correlation and retrieval are realized in matching.
Specifically, whole geographical space recurrence can be divided into 4 rows 4 column, and totally 16 regions are as initial mesh, initial
4 rows 4 column carried out on grid step by step divide, and form sub-grids at different levels, and each sub-grid includes that 4 rows 4 arrange totally 16 next stage
Grid successively divides until the smallest sub-grid size can correspond to sub-meter grade unit, to meet high resolution satellite remote sensing image tissue
The requirement of management.
Specifically, it can be built on the basis of space characteristics according to the data obtaining time of multi-source heterogeneous pollution of area source data
Vertical time index, to reflect that the temporal aspect of spatial data, time index are arranged according to year, month, day, hour, min, second.
Specifically, the range of the multi-source heterogeneous pollution of area source data refers to the ground that multi-source heterogeneous pollution of area source data are covered
The size in region is managed, resolution ratio refers to that area represented by a pixel point, i.e. ground object can be differentiated in remote sensing images
Minimum unit, the depth of corresponding sub-grid refer to that hierachy number of the corresponding sub-grid in grid division, the depth of initial mesh are 1,
The sub-grid depth divided on initial mesh is 2, and so on, multi-source heterogeneous pollution of area source data correspond to the depth of sub-grid
D formula are as follows:
(r≤16 d=argmax-dr0)
R indicates the range of multi-source heterogeneous pollution of area source data, if the space of multi-source heterogeneous pollution of area source data remote sensing images
Resolution ratio is n meters, and image size is M pixel, then the range of multi-source heterogeneous pollution of area source data is r=n × M, if target empty
Between geographic range be r0, then n is natural number.
Specifically, described to be encoded for each sub-grid, coding form is depth code+row code+column code+inter-network lattice
Code, such as the following table 1:
1 subnet trellis coding of table
| Depth code | Row code | Column code | Inter-network trellis code |
| 3bit | 2bit | 2bit | 3bit |
Wherein, depth code is used to record the depth of multi-source heterogeneous pollution of area source big data, and row code is used to record multi-source heterogeneous
The line number of sub-grid corresponding to pollution of area source big data, column code are used to record subnet corresponding to multi-source heterogeneous pollution of area source big data
The columns of lattice, inter-network trellis code be used to record sub-grid corresponding to multi-source heterogeneous pollution of area source big data whether cross over grid and across
More which adjacent mesh increases temporal characteristics code after inter-network trellis code, realizes the increase of time dimension, the temporal characteristics code
For the code+moon in year code+day yard.Introduce form such as the following table 2 after temporal characteristics code:
The subnet trellis coding of the introducing temporal characteristics code of table 2
| Depth code | Row code | Column code | Inter-network trellis code | Year code | Month code | Day code |
| 3bit | 2bit | 2bit | 3bit | The decimal system | The decimal system | The decimal system |
The multi-source heterogeneous pollution of area source data include pollution monitoring point data, remote sensing raster data, point/line/face vector
Geodata, image and video data and survey data and text data.
Further, the present invention also provides a kind of multi-source heterogeneous pollution of area source big data supervising platforms, comprising:
Data acquisition module acquires multi-source heterogeneous pollution of area source data；
Space-time characteristic extraction module extracts the temporal characteristics and space characteristics of multi-source heterogeneous pollution of area source data；
The geographical space of target area is divided into several sub-spaces, initial mesh is formed, initial by grid dividing module
It is divided step by step on grid and forms sub-grids at different levels, corresponding son is determined according to the range of multi-source heterogeneous pollution of area source data and resolution ratio
The depth of grid, each sub-grid after dividing spatially have uniquely determining property；
Coding module is encoded for each sub-grid, determines the space encoding of multi-source heterogeneous pollution of area source data,
Temporal characteristics code is introduced in each subnet trellis coding, increases time dimension, to reflect multi-source heterogeneous pollution of area source data simultaneously
The temporal aspect having introduces index and constitutes multi-level network and formats tissue and index model；
Matching module is formatted tissue model to multi-source heterogeneous pollution of area source data using multi-level network, utilizes time and space
Location matches realize data correlation；
Database stores the associated data of the matching module
Retrieval module is established associated data and is indexed, and according to incidence relation and indexed search data；
The data retrieved are compared by monitoring modular with preset threshold, not in threshold range, then export report
It is alert.
The present invention may also include data and crawl module, crawl data automatically according to keyword, and will crawl data according to volume
Code feeds back to monitoring modular, is realized and is monitored in real time by monitoring modular.
Compared with prior art, the beneficial effects of the present invention are:
1, the present invention comprehensively considers the time of multi-source heterogeneous big data and space characteristics, realizes multi-source heterogeneous data
Space-time uniformity description and discovery, the efficient retrieval and management of data are helped to realize, especially for a certain geographical space
Polluting time-series dynamics monitoring has important directive significance.
2, the present invention constructs a kind of multi-source heterogeneous pollution of area source big data supervising platform, is realized based on the correlating method
Data correlation greatly optimizes retrieval, crawls module Real-time Monitoring Data convenient for utilizing.
Detailed description of the invention
Fig. 1 is flow chart of the present invention.
Fig. 2 is that multi-source data space-time multi-level network of the present invention is formatted schematic diagram.
Fig. 3 is the space-time code case based on remote sensing image and vector data.
Specific embodiment
The embodiment that the present invention will be described in detail with reference to the accompanying drawings and examples.
Agricultural non-point source pollution have large in number and widely distributed, main body is more for pollution, pollution sources dispersion and hidden, pollution generation time and
Space has the characteristics that randomness and uncertain, is related to soil health, farmland quality, rivers and lakes water quality, safe drinking water, agriculture
Product quality and food safety are the very big giant-scale engineerings of a difficulty of governance, need to take subregion, classification, integrate at times
Processing and control.For this purpose, whole nation each department at different levels use multiple technologies means and awareness apparatus, such as global position system GPS, distant
Feel RS, terrestrial wireless Sensor Network WSN etc., obtains pollution monitoring point data, remote sensing raster data, point/line/face vectorial geographical number
According to multi-source heterogeneous big datas such as, image and video datas.How multi-source big data is efficiently associated with and is retrieved, to construct
High-precision monitoring model and supervising platform are directly related to the efficiency and effect of subsequent controlling area-source pollution.For this purpose, of the invention
Patent proposes a kind of association of multi-source heterogeneous agricultural non-point source pollution big data and search method based on space-time characteristic, specific implementation
Case is as follows:
Since the multi-source heterogeneous big data of acquisition has determining data acquisition time and/or affiliated geographical space mostly
Position, and then can to obtain its by data digging method right for the text datas such as survey data, laws and regulations, monitoring specification
The time answered and Spatial Semantics, using space-time uniformity geocoding realize multi-source heterogeneous agricultural non-point source pollution big data association and
Retrieval.
It is to utilize geocoding on the basis of Global Grid model based on the data organization of space-time uniformity geocoding
Express the geospatial location information of vector, grid, monitoring point, image, video and text semantic and the timing of Various types of data
Information realizes the organization of unity of multi-source heterogeneous data, and has unified hierarchical index, specific method of the invention such as Fig. 1 institute
Show.Here by taking the agricultural non-point source pollution for covering Anhui Province monitors as an example, as shown in Figure 3.The multi-source heterogeneous data being related to include
5 remote sensing image of Landsat, Anhui Province's administrative division vector data, soil sampling point data.
Firstly, in ArcGIS software, using the Create Fishnet functional module in ArcToolbox, according to monitoring
The geospatial location of target area is divided into 2 rows by setting grid length/width, grid row/column number by the spatial dimension in region
× 2 arrange totally 4 sub-spaces, form initial mesh；Further according to the lattice number where monitoring region, drawn step by step on initial mesh
Point sub-grids at different levels are formed, the depth of corresponding grid is determined according to the range of multi-source heterogeneous data and resolution ratio, is guaranteed by drawing
Each sub-grid after point spatially has uniquely determining property.
Secondly, multi-source heterogeneous agricultural non-point source pollution data in addition to spatial characteristics, also have time behavior,
For the timing organization and management for realizing spatial data, temporal characteristics code is introduced in each grid coding of each level,
Increase time dimension, with the temporal aspect for reflecting spatial data while having.
It is formatted tissue and index model to space-time data using multi-level network, is matched using time and spatial position, when realization
Empty data correlation and retrieval, as shown in Figure 2.According to quadtree coding rule, each node is divided into 4 regions, according to data
The separability in source and specific goal in research decide whether to continue to divide toward next stage.Wherein, in quadtree coding NW represent upper left,
NE represents upper right, SW represents lower-left, SE represents bottom right.
Finally, according to as shown in figure 3, according to depth code+row code+column code+inter-network trellis code+temporal characteristics code, to target network
Lattice carry out space-time code, to realize the efficient correlation to the multi-source heterogeneous big data of agricultural non-point source pollution and retrieval.
The present invention is encoded using Spatio-temporal modeling, realizes the organization of unity of multi-source heterogeneous pollution of area source big data, and have
Unified hierarchical index realizes quick mark and the retrieval of multi-source heterogeneous big data.After the association and retrieval of realizing data, clothes
It is engaged in providing efficient data retrieval in the association fusion and efficiently management of multi-source heterogeneous pollution big data for data mining analysis service
Basis.For example, can be applied to construct multi-source heterogeneous pollution of area source big data supervising platform, in the present invention, which includes:
Data acquisition module acquires multi-source heterogeneous pollution of area source data；
Space-time characteristic extraction module extracts the temporal characteristics and space characteristics of multi-source heterogeneous pollution of area source data；
The geographical space of target area is divided into several sub-spaces by grid dividing module, the form based on Fig. 2, is formed
Initial mesh divides step by step on initial mesh and forms sub-grids at different levels, according to the range of multi-source heterogeneous pollution of area source data and
Resolution ratio determines the depth of corresponding sub-grid, each sub-grid after dividing spatially has uniquely determining property；
Coding module is encoded for each sub-grid based on the form of aforementioned table 1 and 2, is determined multi-source heterogeneous face source
The space encoding of contamination data introduces temporal characteristics code in each subnet trellis coding, increases time dimension, to reflect multi-source
The isomery pollution of area source data temporal aspect that has simultaneously introduces index and constitutes multi-level network and formats tissue and index model；
Matching module is formatted tissue model to multi-source heterogeneous pollution of area source data using multi-level network, utilizes time and space
Location matches realize data correlation；
Database stores the associated data of the matching module
Retrieval module is established associated data and is indexed, and according to incidence relation and indexed search data；
The data retrieved are compared by monitoring modular with preset threshold, not in threshold range, then export report
It is alert.
Module is crawled when data are arranged in platform, data crawl module and crawl data automatically according to keyword, and will climb
Access is realized by monitoring modular and is monitored in real time according to according to encoder feedback to monitoring modular.What this feature was substantially passively retrieved
It is a kind of.
Claims (9)
1. a kind of association and search method of the multi-source heterogeneous pollution of area source big data based on space-time characteristic, which is characterized in that packet
Include following steps:
The temporal characteristics and space characteristics of multi-source heterogeneous pollution of area source data are obtained, when the time is characterized in referring to the acquisition of data
Between, space characteristics refer to the gain location of data；
The geographical space for studying area is divided into several sub-spaces, initial mesh is formed, divides shape step by step on initial mesh
At sub-grids at different levels, the depth of corresponding sub-grid is determined according to the range of multi-source heterogeneous pollution of area source data and resolution ratio, is passed through
Each sub-grid after division spatially has uniquely determining property；
It is encoded for each sub-grid, the space encoding of multi-source heterogeneous pollution of area source data is determined, in each sub-grid
Temporal characteristics code is introduced in coding, increases time dimension, it is special with the timing for reflecting multi-source heterogeneous pollution of area source data while having
Sign introduces index and constitutes multi-level network and formats tissue and index model；
It is formatted tissue and index model to multi-source heterogeneous pollution of area source data using multi-level network, utilizes time and spatial position
Match, realizes data correlation and retrieval.
2. the association and search method of the multi-source heterogeneous pollution of area source big data based on space-time characteristic according to claim 1,
It is characterized in that, whole geographical space recurrence are divided into 4 rows 4 column, totally 16 regions are as initial mesh, on initial mesh
4 rows 4 carried out step by step arrange division, form sub-grids at different levels, and each sub-grid includes that 4 rows 4 arrange totally 16 next stage sub-grids, according to
It is secondary to divide until the smallest sub-grid size correspond to sub-meter grade unit, to meet wanting for high resolution satellite remote sensing image organization and administration
It asks.
3. the association and search method of the multi-source heterogeneous pollution of area source big data based on space-time characteristic according to claim 1,
It is characterized in that, according to the data obtaining time of multi-source heterogeneous pollution of area source data, settling time on the basis of space characteristics
Index, to reflect that the temporal aspect of spatial data, time index are arranged according to year, month, day, hour, min, second.
4. the association and search method of the multi-source heterogeneous pollution of area source big data based on space-time characteristic according to claim 1,
It is characterized in that, the range of the multi-source heterogeneous pollution of area source data refers to the geographic region that multi-source heterogeneous pollution of area source data are covered
The size in domain, resolution ratio refer to the area represented by a pixel point in remote sensing images, i.e. the minimum that can differentiate of ground object
Unit, the depth of corresponding sub-grid refer to hierachy number of the corresponding sub-grid in grid division, and the depth of initial mesh is 1, first
The sub-grid depth divided on beginning grid is 2, and so on, the depth d that multi-source heterogeneous pollution of area source data correspond to sub-grid is public
Formula are as follows:
(r≤16 d=argmax-dr0)
R indicates the range of multi-source heterogeneous pollution of area source data, if the spatial discrimination of multi-source heterogeneous pollution of area source data remote sensing images
Rate is n meters, and image size is M pixel, then the range of multi-source heterogeneous pollution of area source data is r=n × M, if object space
Geographic range is r0, then n is natural number.
5. the association and search method of the multi-source heterogeneous pollution of area source big data based on space-time characteristic according to claim 1,
It is characterized in that, described encoded for each sub-grid, coding form is depth code+row code+column code+inter-network trellis code,
In, depth code is used to record the depth of multi-source heterogeneous pollution of area source big data, and row code is used to record multi-source heterogeneous pollution of area source big
The line number of sub-grid corresponding to data, column code are used to record the columns of sub-grid corresponding to multi-source heterogeneous pollution of area source big data,
Inter-network trellis code is used to record whether sub-grid corresponding to multi-source heterogeneous pollution of area source big data crosses over grid and which is crossed over
Adjacent mesh increases temporal characteristics code after inter-network trellis code, realizes the increase of time dimension, the temporal characteristics code be year code+
Month code+day code.
6. the association and search method of the multi-source heterogeneous pollution of area source big data based on space-time characteristic according to claim 1,
It is characterized in that, the multi-source heterogeneous pollution of area source data include pollution monitoring point data, remote sensing raster data, point/line/face arrow
Measure geodata, image and video data and survey data and text data.
7. the association and search method of the multi-source heterogeneous pollution of area source big data based on space-time characteristic according to claim 1,
It is characterized in that, the multi-source heterogeneous pollution of area source big data is the multi-source heterogeneous big data of agricultural non-point source pollution, including pollution prison
Measuring point data, remote sensing raster data, point/line/face vector geographic data, image and video data, survey data.
8. a kind of multi-source heterogeneous pollution of area source big data supervising platform characterized by comprising
Data acquisition module acquires multi-source heterogeneous pollution of area source data；
Space-time characteristic extraction module extracts the temporal characteristics and space characteristics of multi-source heterogeneous pollution of area source data；
The geographical space of target area is divided into several sub-spaces, initial mesh is formed, in initial mesh by grid dividing module
On divide form sub-grids at different levels step by step, corresponding sub-grid is determined according to the range of multi-source heterogeneous pollution of area source data and resolution ratio
Depth, through division after each sub-grid spatially there is uniquely determining property；
Coding module is encoded for each sub-grid, the space encoding of multi-source heterogeneous pollution of area source data is determined, each
Temporal characteristics code is introduced in a sub- grid coding, increases time dimension, to reflect multi-source heterogeneous pollution of area source data while have
Temporal aspect, introduce index and constitute multi-level network and format tissue and index model；
Matching module is formatted tissue model to multi-source heterogeneous pollution of area source data using multi-level network, utilizes time and spatial position
Data correlation is realized in matching；
Database stores the associated data of the matching module
Retrieval module is established associated data and is indexed, and according to incidence relation and indexed search data；
The data retrieved are compared by monitoring modular with preset threshold, not in threshold range, then export alarm.
9. multi-source heterogeneous pollution of area source big data supervising platform according to claim 8, which is characterized in that further include:
Data crawl module, crawl data automatically according to keyword, and will crawl data according to encoder feedback to monitoring modular, by
Monitoring modular realizes real time monitoring.
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