CN102722549A - Cluster-based real-time rendering service of remote sensing data set - Google Patents
Cluster-based real-time rendering service of remote sensing data set Download PDFInfo
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Abstract
The invention provides a systemic solution aiming at the rapid clustered rendering of a multisource remote sensing data set, which is especially used for solving distributed organization and storage problems of data and union query problems of a plurality of data sets in mass remote sensing data and different kinds of data sets. The systemic solution comprises the following steps: firstly allocating a cluster environment suitable for a real-time mass remote sensing data rendering system, wherein the cluster environment is formed by a plurality of calculation and data integrating nodes and a central server; sending a tile rendering request of a client side to the central server through a network channel, carrying out the research, the sieving and the post-processing through the central server so as to obtain a visible data list within the tile scope, and determining the optimal node according to the storage position; reading the tiles corresponding to the data by the nodes, rendering the tiles to a canvas in an overlying mode one by one, and sending the results to the central server; and finally sending the results to the client side by the central server. The systemic solution provided by the invention is suitable for rendered grain on the two-dimensional or three-dimensional surfaces, and large scale application fields, such as visualization and the like in the production of the terrain tile production and the large-scale remote sensing data management.
Description
Technical field
The present invention relates to cluster computing technique and remotely-sensed data visualization technique, specifically, the clustered tile data that relate to the remotely-sensed data collection are played up technology.The present invention is applicable to the real-time rendering service of global range mass remote sensing data.
Background technology
Technical processes such as generally comprising data pyramid construction, tile painting canvas are created, data read, order stack drafting are played up in the stack of RS data.Wherein the pyramid construction technology has been used very generally, the ArcSDE of ESRI company for example, and the GeoImageDB of Wuhan University, the SuperMap of hypergraph software etc. have realized the pyramid construction technology of image, have improved the extraction efficiency of data.Yet the data in the global range play up that the extraction and the stack that relate to multiple data source, multi-wavelength data are drawn, data source is upgraded and problems such as interactive operation, to a great extent limit real-time and the actual effect of playing up.Present most systems has adopted strategies such as playing up and set up global metadata tile index system in advance; Like the Google earth, Skyline software etc.; These products exist deficiency on the degree of specialization that ageing and the user of Data Update operates when significantly improving the tile dispatching efficiency and possessing good user experience.Relevant reference comprises ArcSDE-The Universal Spatial Server for ARC/INFO, An ESRI White Paper, and April 1999; Fang Tao; Li Deren, Gong Jianya, Pi Minghong; Development and Implementation of Multiresolution and Seamless Image Database System GeoImageDB; Journal of Wuhan Technical University of Surveying and Mapping, 24 (3), 1999; Http:// earth.google.com/; Http:// www.skylineglobe.com etc.
In order to realize the true remotely-sensed data real-time visual service in the extensive whole world, need support by cluster computing environment, be specifically related to technological, the gordian techniquies such as task scheduling is technological, distributed file system of cluster management.Existing mainstream technology; Cluster management software such as OSCAR, ROCKS, Perceus, xCAT etc.; The task scheduling technology is like LoadLeveler, Gearman, Hadoop, Control Tower, LVS etc.; Distributed file system such as MapReduce, Lustre etc. provide the solution of cluster computing environment.But, play up calculation task and have the macroreticular handling capacity, carefully calculate characteristics such as granularity, be difficult to find a kind of technical scheme that is fit to fully.Relevant reference comprises http://www.rocksclusters.org; Http:// xcat.sourceforge.net/; Http:// hadoop.apache.org/ etc.
Tile request of playing up between client and the clustered node and the answer of tile data need through Network Transmission, and requirement has fast, stable network responds and specific message processing facility.MPI provides standardized network communication mechanism, but not enough with the calculation task support of response fast for fine granularity.Relevant reference comprises http://www.open-mpi.org; Http:// www.mcs.anl.gov/research/projects/mpich2/ etc.
Except the distributed document storage; Also need to set up the index of metadata and the corresponding spatial index of remotely-sensed data through Database Systems; Thereby can when tile is played up in user's request, retrieve corresponding data storage location apace, and confirm optimum computing node.Support at present or part support the OGR standard database to comprise Oracle, MySQL, PostgreSQL etc.Relevant reference comprises http://www.oracle.com; Http:// www.mysql.com/; Http:// www.postgresql.org/.
Playing up service system based on the global range remotely-sensed data of cluster has related to the conjunctive query of multidata collection simultaneously, has carefully calculated contents such as granularity division, fast network response; Research separate at present visible document/patent is more, and proposes also not occurring of solution from the angle of entire system.
Summary of the invention
The purpose of this invention is to provide a kind of magnanimity RS data collection that is directed against and carry out the systemic solution that clustered is played up fast; Particularly, solve the distributed organization storage problem of data, the conjunctive query problem and the multi-mode parallel computation problem of multidata collection towards the remotely-sensed data and the data of different types collection of magnanimity.
Basic ideas of the present invention are: at first dispose the cluster environment that is fit to mass remote sensing data real-time rendering system; Some nodes and a central server by " calculating-data " associating one are formed, and central server is disposed simultaneously and preserved the remotely-sensed data metamessage that all get into cluster; When the request of playing up of the tile of client is mail to central server through network channel; The latter carries out SQL query and aftertreatment; Obtain that the superiors in this tile scope are required plays up data list, confirm to play up order, and confirm optimum node according to the memory location of these data; When node is played up, read each data files, and be written into the data of tile in-scope, level, stack is rendered on the painting canvas one by one, sends back to central server; At last by the central server return results.
Technical scheme of the present invention provides a kind of remotely-sensed data collection real-time rendering system based on cluster, specifically comprises following implementation step:
1) make up cluster environment, set up the node of some calculating and storage one, each node comprises a multinuclear workstation, and carry memory disk array, connects with the high-speed local area network network between node; Dispose a multiple-core server, relevant database is installed, be connected with each node through LAN;
2) after remotely-sensed data gets into cluster,, extract its metamessage simultaneously, store in the data in server storehouse, as data query conditions with making in the as a whole storage array that is placed on a node (or be placed in a plurality of nodes with the mode of redundancy);
3) for a tile, according to the level of viewpoint, the position in space, three kinds of indexs of size of tile, confirm the numbering that it is unique, its represented spatial dimension is also confirmed;
4) the tile request of playing up is issued central server by client through network, and the former sets up priority mechanism and maintenance request formation, and the latter then plays up request with each tile of mode parallel processing of multithreading;
5) server is through support space indexed data storehouse; Data in all data centralizations find out this spatial dimension; Carry out the data filtering algorithm again; According to rules such as data set type, times data are sorted, and reject the data that are coated over lower floor, data list and the corresponding stored position information thereof that have promptly obtained to play up through ordering;
6), choose DATA DISTRIBUTION is maximum in the tabulation that node as computing node (" calculatings-data " are integrated), and will play up Task Distribution and arrive this node according to the data list that generated;
7) render process of a tile is in the computing node: create painting canvas, according to the bottom-up order of data list, read the data block in the tile scope successively, it is transparent not have section data to be changed to, and is rendered in the painting canvas;
8) the tile data back central server that will play up returns to client through central server;
9) step 4)～8) accomplished playing up of single tile; The different rendering task distribution is calculated in each node; Simultaneously with the mode of multithreading carry out service end query manipulation and node side play up calculating; Promptly realized the parallel rendering process of clustered, the real-time service of playing up externally is provided.
Above-mentioned steps is characterised in that:
The data screening algorithm of
step 5); Filtered out the data list that the user of the superiors can see; The data that major part is coated over lower floor have been rejected; Useless data read and draw operation when avoiding playing up, and have saved calculated amount and data throughout.
Data query, the data screening of
step 5), step 6), confirm that node process is independently to operate for each tile, realize the task level parallelization with the mode of multithreading in service end; Equally, the render process of step 7) has also realized parallelization at the computing node end; Integrating step 9) described overall process is the flow process cluster implementation of playing up of the present invention, embodied the integrated multi-mode parallel computation characteristic with " cluster+multinuclear " of fine granularity task division, " calculating-data ".
The present invention compared with prior art has following characteristics: (1) is owing to calculate and data binding; And to carry out with data be the calculation task scheduling of traction; Can effectively avoid the network congestion of handling up and causing, efficient and the performance giving full play to storage and calculate owing to frequent data network; (2) metamessage of data is left concentratedly, has realized fast query, location and screening; (3) data query of multithreading and " cluster+multinuclear " play up computation schema, formed effective and unique parallelization real-time rendering flow process.
Description of drawings
The cluster topology that Fig. 1 calculates and storage is bound
The metamessage field contents of Fig. 2 data
Fig. 3 global range tile of the present invention is divided and numbering
Wherein, Fig. 3-(1) the 0th grade of global range tile of expression divided and numbering, and Fig. 3-(2) the 1st grade of global range tile of expression divided and numbering;
Fig. 4 remotely-sensed data collection parallel rendering flow process
Fig. 5 plays up the Request Priority definition
Fig. 6 tile request queue cache maintenance flow process
Fig. 7 data screening synoptic diagram
Fig. 8 rendering effect figure
Embodiment
Fig. 1 is a cluster environment topological structure used in the present invention: central server installing M ySQL database, and the metamessage of remotely-sensed data in the management cluster is connected with clustered node through common Ethernet (100,000,000); Each clustered node calculates blade by one and a disk memory array is formed, and is linked to each other by Fast Ethernet (gigabit) between node.For each remotely-sensed data in the system, the included content (see figure 2) of its metamessage has: the useful space scope of data set type, data name, acquisition time, data, memory node, path.Preceding four are used to inquire about the data in the tile spatial dimension, and back two are used for resolution data corresponding memory location and path.Wherein, " data set type " identified different data types, and for example OrthorLandsatTM5 representes the orthography data set of TM5; " data name " is the unique identifications of these data in system; " acquisition time " is meant the shooting time of these data; " useful space scopes of data " are meant the latitude and longitude coordinates scope of effective image of these data, represent and are stored in the space field of database with polygon; " memory node " refers to the numbering of this data place memory node; " path " is meant the store path of these data.
When remotely-sensed data is put in storage, can according to its spatial dimension subregion classified and stored in node, for example be divided into 8 zones according to longitude scope (180 °～180 °), the data in the same area scope all will be stored in same node.Such strategy can guarantee as far as possible that when playing up calculating the deposit data of required visit reduces network throughput in local disk.
Fig. 3 has illustrated that tile dividing mode used in the present invention, employed tile dimensions are 256 pixels * 256 pixels, and concrete dividing mode is following:
1. the whole world is divided into 8 tiles of 2 row, 4 row according to longitude and latitude projection (longitude scope-180 °～180 °, latitude scope-90 °～90 °), as the 0th grade (seeing Fig. 3-(1)), promptly visual top layer; (X, Y D) number each tile, and X representes the row number of tile, and Y representes the row number of tile, and D representes the level of tile with one 3 dimensional vector; (2,1,0) expression the 2nd row for example, the 1st row, the 0th grade tile, and according to the 0th grade of division rule, the geographic range that can calculate this tile is (0 °～90 ° of longitudes, latitude-90 °～0 °).
2. divide the 1st grade of tile downwards according to the principle of quaternary tree, i.e. 4 tiles (seeing Fig. 3-(2)) of the identical geographic range of expression in corresponding the 1st grade of the tile in the 0th grade, for example the 0th grade (2,1; 0) tile is corresponding to the 1st grade (4,2,1), (5; 2,1), (4,3; 1), (5,3,1) four tiles; Usually, if certain tile be numbered (d), then 4 of its corresponding next stage tiles numbering is respectively for x, y:
(2x，2y，d+1)(2x+1，2y，d+1)(2x，2y+1，d+1)(2x+1，2y+1，d+1)
And according to above-mentioned rule, the geographic range that this tile covered is:
Longitude scope:
Latitude scope:
3. carry out the 2nd grade～the 19th grade tile numbering according to above-mentioned rule, because the fixed size of tile, so number of levels is big more, and the tile number of this layer is just many more, and its pixel resolution is also just high more, and the image that is shown is also meticulous more.
After having confirmed the coding rule of tile, the service of playing up will provide (see figure 4) with the form that the image tile is provided, and have following three key points in its overall procedure:
(1) client is to safeguard the tile request according to priority policy (seeing below);
(2) central server is safeguarded the buffer memory of a tile request, and handles request according to the order of sequence with the mode of multithreading, for the operation of each request through data query, data screening, selection node, has confirmed the data list that this tile need be played up;
(3) play up buffer memory of playing up task of node maintenance, and handle the task of playing up in the buffer memory one by one, give full play to " calculating-data " incorporate operation efficiency with the mode of multithreading.
Fig. 5 has illustrated the priority variable of the present invention's design, and it produces according to certain strategy, specifically follow three principles:
< 1>the tile priority in the viewpoint of current activation is the highest;
< 2>the tile request is successively decreased according to the time sequencing that gets into buffer memory, and the priority that promptly more early gets into buffer memory is low more;
< 3>all the other are User Defined priority, according to tile level and horizontal dimensional orientation define longitudinally.
According to above principle, definition is the numerical priority value of 24 compositions altogether, the view viewpoint of the current activation of the highest 1 bit representation, middle 18 bit representation timestamps, the user-defined priority of back 5 bit representations.Wherein, As time goes on middle 18 will be successively decreased, until being should ask from buffer memory, to remove in 0 o'clock.
As shown in Figure 6, the processed flow process of tile request is following:
(1) client modules is safeguarded two formations: request queue and playing up tabulation.All requests of importing into during request queue maintain customer end call request interface, the request that priority is the highest will be sent to the service of playing up, and tabulation is being played up in entering.
(2) when new request gets into, if there has been this request in request queue with playing up in the tabulation, then directly end is returned; Otherwise execution in step (3).
(3), be 3000 for newly giving the interval numerical value of time priority level then, and put it into request queue to request if the minimum request of its medium priority that then removes has earlier been expired in request queue.
(4) judge whether arrived largest request number, if do not reach as yet, then server is mail in the highest request of request queue medium priority if playing up tabulation, simultaneously it is removed, and puts into and play up tabulation from request queue; Otherwise finish to return.
(5) when rendering result when server returns, remove this request and return to the upper strata from sending tabulation, then execution in step (4) through the readjustment mode.
Here need to prove, playing up tabulation and confirming that by the ability of playing up of node its concrete numerical value is:
Num wherein
MaxFor playing up the largest request number of tabulation, n is the node number, NThread
iBe i the employed Thread Count of node.Wherein n and NThread
iValue after system start-up and initialization, obtain and real-time update.The tile request of playing up that has guaranteed to mail to server like this is that current needing most obtains, and has embodied real-time.
At central server, all client tile requests will get into buffer queue, handled by multi-threaded parallel ground.At first put down in writing the geographic range of the computing formula acquisition tile of " tile numbering-geographic range " through preamble; Find out all data that cover in this scope more in order; And sort according to data type and data acquisition time; Its rule is to carry out big type of ordering according to data type earlier; Sort from the near to the remote in the homogeneous data internal condition time, wherein putting in order of data type carried out according to its resolution from high to low again, resolution high on the upper strata; Then and the valid data scope of each data carry out screening based on observability; At last, select to have the maximum node of data according to the data list after the screening, and with Query Result with play up mission bit stream and mail to this node.
In above-mentioned query processing flow process, the screening principle of data screening algorithm is seen shown in Figure 7.Dashed rectangle is the scope of tile among the figure, and 1.～5. the result after tentation data inquiry and the ordering is successively, and is according to the visibility ordering, 5. disallowable so, 1.～4. the data that need play up at last of selected conduct.
Playing up in the thread of node; Program obtains appropriate address successively from data list, from image pyramid, read and the tile scope of sampling in data slot, and with invalid partially filled be transparent; Be rendered on the painting canvas; Extract final rendering result at last, and compress and beam back server, and then return to client.
Fig. 8 has enumerated the rendering effect of two tiles and the effect that two/three-dimension terrian is played up; The tile of the picture left above is numbered (380; 67,7), the upper strata is the SPOT5 image data of " near infrared-red-green " band combination; Lower floor is the TM5 image data of " near infrared-red-green " band combination, and it plays up speed is 1353 milliseconds; The tile of bottom-right graph is numbered (1521,262,9), and the upper strata is the QUICKBIRD image data of RGB, and lower floor is the SPOT5 image data of " near infrared-red-green " band combination, and it plays up speed is 745 milliseconds; Middle figure is the view effect of two-dimentional client; Figure below is the view effect of three-dimensional client.
It is in the 1 second/tile that average tile of the present invention is played up speed, and through the cluster parallel processing, the average response speed of tile service is in the 80 milliseconds/tile, has reached real-time effect.The present invention is applicable to that texture that two/three-dimension terrian is played up and landform tile are produced, the large-scale application field such as visual in the extensive remotely-sensed data management.
Claims (4)
1. remotely-sensed data collection real-time rendering service system based on cluster is characterized in that comprising following implementation step:
Step 1: make up cluster environment, set up the node of some calculating and storage one, each node comprises a multinuclear workstation, and carry memory disk array, connects with the high-speed local area network network between node; Dispose a multiple-core server, the relevant database of support space retrieval is installed, be connected with each node through LAN;
Step 2: during remotely-sensed data typing cluster; Will be in the storage array of individual node as a global storage, or be stored in a plurality of nodes with the mode of redundancy, its metamessage extracted simultaneously; Store in the relational database of server, as data query conditions;
Step 3: for the tile that will play up, according to viewpoint level, locus, three kinds of indexs of tile size, confirm the numbering that it is unique, its represented spatial dimension is also confirmed;
Step 4: tile is played up request and is sent to central server by client through network, and the former sets up priority mechanism and maintenance request formation, and the latter plays up request with each tile of mode parallel processing of multithreading;
Step 5: server is through support space indexed data storehouse; Data list in all data centralizations find out this spatial dimension; Regular according to data set type, time etc. to its ordering; Carry out the data filtering algorithm again, reject the data that are coated over lower floor, the superiors' data list that acquisition need be played up and corresponding stored position information thereof;
Step 6: according to the data list that generated, choose DATA DISTRIBUTION is maximum in the tabulation that node, will play up Task Distribution to this node as computing node;
Step 7: the render process of a tile is in the computing node: create painting canvas, according to the bottom-up order of data list, read the data block in the tile scope successively, it is transparent not have section data to be changed to, and is rendered in the painting canvas;
Step 8: the tile data back central server that will play up returns to client through central server;
Step 9: playing up of single tile accomplished in step 4～8; The different rendering task distribution is calculated in each node; Simultaneously with the mode of multithreading carry out service end query manipulation and node side play up calculating; Promptly realized the parallel rendering process of clustered, the real-time service of playing up externally is provided.
2. cluster environment according to claim 1 is characterized by the integrated deployment of calculating with data, and different pieces of information disperses to be stored in each memory node (also being computing node simultaneously), by its metamessage of relevant database unified management of central server; When realizing the rapid data inquiry, be convenient to,, significantly reduced the network throughput of data when calculating for the data-intensive task of playing up with the node of distribution of computation tasks to the data place.
3. tile request processing mechanism according to claim 1 is characterized by client and sets up the priority buffer memory, and priority processing needs most the tile that the obtains request of playing up and rejects obsolete request at any time, has improved real-time; Central server filters out the data list that the user of the superiors can see through the data screening algorithm, rejects the data that major part is coated over lower floor, and useless data read and draw operation when avoiding playing up, and have saved calculated amount and data throughout.
4. parallel computation according to claim 1, the data query, data screening, the selection node process that it is characterized by central server are independently to operate for each tile, realize the task level parallelization in service end with the mode of multithreading; Equally, the render process of node has also realized the task level parallelization; In conjunction with the overall process that multinode is played up, constitute clustered of the present invention and played up the flow process implementation, embodied the integrated multi-mode parallel computation characteristic with " cluster+multinuclear " of fine granularity task division, " calculating-data ".
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