CN109614454B - Vector big data parallel space superposition analysis method based on MPI - Google Patents
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Abstract
The invention provides a vector big data parallel space superposition analysis method based on MPI, which is characterized in that format conversion is carried out on vector element space coordinates in all geographical layers to be processed based on an MPI message communication interface, and GeoHash index codes are correspondingly generated aiming at different vector element types; selecting a partition layer, and writing into the NFS according to partitions; writing the vector elements in the non-partitioned layer into the NFS; establishing a process number consistent with the partition number based on an MPI communication interface, and reading all vector elements in the corresponding partition from the NFS by each process; each process simultaneously reads each vector element of the non-partitioned layer on the NFS one by one, and performs spatial relationship judgment processing according to the vector elements of the corresponding partitions respectively until all the vector elements of the non-partitioned layer are read and processed; finally, combining and outputting the statistical results obtained by the processes; and the spatial relationship judgment processing is carried out according to the GeoHash index code and the circumscribed rectangle. The technical scheme of the invention has high efficiency and feasibility.
Description
Technical Field
The invention belongs to the technical field of network geographic information system application, and relates to a method for quickly and efficiently processing geographic vector big data, in particular to a method capable of realizing overlay analysis of the geographic vector big data in a parallel mode.
Background
In the field of GIS (geographic information system), the application of spatial superposition analysis is wide, including terrain evaluation, land applicability analysis, soil erosion and the like, in recent years, with the rapid development of the geospatial sensor technology, the data acquisition mode with high spatial resolution is simpler, and the geospatial data shows the trend of explosive growth. In the context of massive spatial data, although the spatial overlay analysis method can explain geographic phenomena more accurately, the huge time cost brought by the method often allows a geography learner, particularly some decision makers, to select other solutions with lower precision but in more real time. Therefore, how to quickly and efficiently realize the large data space superposition analysis of the geographic vectors becomes an urgent need at present.
Disclosure of Invention
In order to solve the problems, the invention provides a vector big data parallel space superposition analysis method based on MPI (message communication interface), which realizes the rapid space superposition analysis of massive vector point elements, line elements and plane elements and effectively overcomes the time bottleneck in the process of processing vector big data.
The technical scheme adopted by the invention comprises a vector big data parallel space superposition analysis method based on MPI, and the implementation based on an MPI message communication interface comprises the following steps:
step 1, carrying out format conversion on vector element space coordinates in all geographical layers to be processed, and converting the vector element space coordinates into a WKT format;
step 2, based on the vector element space coordinate in the WKT format, aiming at different vector element types, a GeoHash index code is generated by adopting a corresponding mode, the following steps are realized,
directly adopting GeoHash codes as GeoHash index codes for the point-like elements;
for linear elements, firstly, computing the GeoHash codes of all end points of the elements, and then taking the longest identical prefix of the GeoHash codes of all end points as the GeoHash index codes of the linear elements;
for the planar element, firstly, computing the GeoHash codes of all end points of the element, and then taking the longest identical prefix of the GeoHash codes of all end points as the GeoHash index code of the planar element;
step 3, selecting one layer from the layers to be subjected to superposition analysis as a partition layer, dividing vector elements in the layers according to a uniform load principle according to the number of predefined partitions, and writing the WKT format space coordinates and the GeoHash index codes of the vector elements into a network file system NFS according to the partitions;
step 4, writing the WKT format space coordinates and the GeoHash index codes of the vector elements in the non-partitioned layer into a network file system NFS;
step 5, based on the MPI communication interface, creating the process number consistent with the partition number, and reading all the vector elements in the corresponding partition from the NFS by each process;
step 6, each process simultaneously reads each vector element of the non-partitioned layer on the NFS one by one, and performs spatial relationship judgment processing according to the vector elements of the corresponding partitions respectively until all the vector elements of the non-partitioned layer are read and processed; finally, combining and outputting the statistical results obtained by the processes;
and the spatial relationship judgment processing is carried out according to the GeoHash index code and the circumscribed rectangle.
In step 6, each process performs the following processing,
step 6.1, reading single vector elements in the non-partitioned layer in sequence, and recording the single vector elements as current processing elements a;
step 6.2, the process judges whether the current processing element a and the GeoHash index codes of all the vector elements in the corresponding partitions have inclusion relations one by one, if the inclusion relations exist, all the vector elements which have inclusion relations with the current processing element a in the corresponding partitions of the process are recorded, the vector elements are marked as a set B, the step 6.3 is skipped, otherwise, the step 6.1 is returned, and a single vector element in the non-partitioned layer is continuously read to serve as a new current processing element a until all the vector elements of the non-partitioned layer are processed;
step 6.3, respectively extracting circumscribed rectangles of the vector elements in the currently processed element a and the set B, judging whether the circumscribed rectangles are intersected, if so, acquiring corresponding statistical data according to actual superposition analysis application, and if not, processing;
and after the external rectangular spatial relationship is judged to be completed for all the vector elements in the set B, returning to the step 6.1, and continuously reading a single vector element in the non-partitioned layer as a new current processing element a until each vector element in the non-partitioned layer is processed.
In step 3, a layer containing a large number of vector elements is selected as a partition layer from layers to be subjected to overlay analysis.
In step 3, a file is created on the NFS with the file name corresponding to the partition number, and the divided vector elements are written in the corresponding file according to the partition number.
In step 5, each process reads all the vector elements in the corresponding partition from the NFS into the memory according to the process number.
Compared with the traditional serial vector data space superposition analysis, the parallel vector big data processing method based on MPI is adopted, the performance improvement can realize the breakthrough of magnitude, the execution time of the algorithm is effectively saved, the geoscience application real-time is supported, and the method has important economic value. Moreover, the invention provides a high-precision self-defined GeoHash index coding mode, which can accurately cover the areas represented by the line and the planar elements to the maximum extent; the initial judgment of the spatial relationship of the vector elements is carried out based on GeoHash character string coding, and compared with the method of directly judging the spatial relationship of the vector elements by using the circumscribed rectangle, the method can bring about great performance improvement.
Detailed Description
The present invention will be described in further detail with reference to examples for the purpose of facilitating understanding and practicing the invention by those of ordinary skill in the art, and it is to be understood that the examples described herein are for the purpose of illustration and explanation only and are not intended to limit the invention.
The invention provides a vector big data parallel space superposition analysis method based on MPI (message communication interface), which realizes the rapid space superposition analysis of massive vector point elements, line elements and surface elements and effectively overcomes the time bottleneck in the process of processing vector big data.
The invention provides the following key technologies:
1) the MPI communication interface is adopted to realize the parallel processing method of the vector big data. MPI is a Message Passing Interface. Compared with the currently common parallel processing frameworks such as hadoop and spark, the MPI provides a bottom layer interface, although the programming difficulty is high, the data communication and task scheduling are flexible, and time-consuming operations such as data shuffling and the like in an uncontrollable process do not exist. The invention provides MPI-based vector data distribution and task scheduling from the bottom layer, and effectively controls the time of interprocess data communication and task execution.
2) Aiming at different space vector elements, a high-precision self-defined GeoHash index coding mode is adopted. 3) When the vector element space relationship is judged, a method of judging the GeoHash space relationship firstly and then judging the circumscribed rectangle space is adopted.
The embodiment of the invention adopts the technical scheme that the vector big data parallel space superposition analysis method based on the MPI comprises the following steps based on an MPI communication interface:
step 1, carrying out format conversion on vector elements in all geographical layers to be processed, and converting the spatial coordinates into a WKT (Well-known text) format defined by OGC (International open geospatial information alliance).
Step 2, generating a GeoHash index code by adopting a corresponding mode aiming at different vector element type points, lines and surfaces based on vector element space coordinates in a WKT format;
in the step, the spatial characteristics of the vector data are comprehensively considered, and a GeoHash indexing mode is adopted. For linear elements, the original GeoHash point element coding mode is adopted, and for linear and planar elements, the GeoHash codes of all end points of the elements are firstly calculated, then the longest identical prefix of the GeoHash codes of all end points is used as the GeoHash index codes of the linear and planar elements, and the precision of the space range is guaranteed to the greatest extent. Namely:
directly adopting GeoHash codes as GeoHash index codes for the point-like elements;
for linear elements, firstly, computing the GeoHash codes of all end points of the elements, and then taking the longest identical prefix of the GeoHash codes of all end points as the GeoHash index codes of the linear elements;
for the planar element, firstly, the GeoHash codes of all the end points of the element are calculated, and then the longest identical prefix of the GeoHash codes of all the end points is used as the GeoHash index code of the planar element.
In the prior art, GeoHash is a spatial index, but only faces to point-shaped elements, and for line and planar elements, the traditional method is to use GeoHash of the gravity center to express, but often cannot cover the area represented by the line and planar elements.
Step 3, selecting one of the layers to be subjected to superposition analysis as a partition layer, dividing vector elements in the layers according to a uniform principle according to the partition quantity defined by a user, and writing the WKT format space coordinates and the GeoHash index codes of the vector elements into an NFS (network file system) according to partitions;
the superposition analysis of the embodiment is oriented to two layers, in the step, one of the layers to be subjected to superposition analysis is selected for division, and the principle is that the layer containing more vector elements is selected for division, so that parallelization resources are utilized to the maximum extent; then, dividing according to the partition number defined by the user, wherein the core number which can be used by the user machine can be taken as the partition number in specific implementation, and randomly dividing; in order to ensure the load balance of each process, the vector element quantity of each partition is ensured to be basically consistent as much as possible in the process of dividing; finally, a file, for example, "partition _0. txt", is created on the NFS with the corresponding file name of the partition number, the divided vector elements are written into the corresponding file according to the partition number, for example, the vector element of partition 0 is written into "partition _0. txt", the vector element of partition 1 is written into "partition _1. txt", and the vector element of partition 2 is written into "partition _2. txt" …
Step 4, writing the WKT format space coordinates and the GeoHash index codes of the vector elements in the layer which is not divided into layers into NFS;
step 5, based on the MPI communication interface, creating a process number consistent with the partition number, and reading all vector elements in the corresponding partition file from the NFS by each process according to the process number;
step 6, each process simultaneously reads each vector element of the non-partitioned layer on the NFS one by one, and judges and processes the vector elements according to the vector elements of the corresponding partitions respectively until all the vector elements of the non-partitioned layer are read and processed; finally, combining and outputting the statistical results obtained by the processes; in the specific implementation, the vector elements can be read one by one according to the storage sequence of the vector elements in the file.
In this step, considering the massive data of the map layer, the data in the non-partitioned map layer is not read into the memory at one time, but each process sequentially reads a single vector element in the non-partitioned map layer into the memory and performs spatial relationship judgment with the corresponding partitioned data in the corresponding partitioned map layer, which is equivalent to only storing the data in the partitioned map layer in the memory, thereby effectively saving the use of the memory;
for example, the process 0 sequentially reads a single vector element in the non-partitioned layer to the memory, and performs spatial relationship judgment with each vector element of the partition 0; the process 1 sequentially reads a single vector element in the non-partitioned layer to a memory, and performs spatial relationship judgment with each vector element of the partition 1; and the process 2 sequentially reads a single vector element in the non-partitioned layer to the memory, performs spatial relationship judgment … on the single vector element and each vector element of the partition 2, and finally combines the processing results of the processes to obtain a spatial relationship judgment result of the whole partitioned layer.
In the embodiment, each process performs the following processing:
step 6.1, reading single vector elements in the non-partitioned layer in sequence, and recording the single vector elements as current processing elements a;
step 6.2, the process judges one by one whether the vector elements (current processing elements a) in the non-partitioned layer read in step 6.1 and the GeoHash index codes of all the vector elements in the corresponding partitions have an inclusion relationship, if the inclusion relationship exists, all the vector elements in the corresponding partitions of the process, which have the inclusion relationship with the current processing elements a, are recorded as a set B, and the step 6.3 is skipped, otherwise, the step 6.1 is returned to continue to read a single vector element in the non-partitioned layer as a new current processing element a until all the vector elements in the non-partitioned layer are processed;
in the step 2, the longest identical prefix of the GeoHash codes of all end points is used as the GeoHash index code of the linear and planar elements, and the step can judge whether the GeoHash index code of the vector elements has the containing relation or not by judging whether the identical character string segments exist in the character strings of the GeoHash index codes or not, and the next step of judgment is continued if the identical character string segments exist, and the step of returning is not carried out. Because the judgment of the spatial relationship of the vector elements based on the GeoHash character string coding is time-saving, compared with the method of directly judging the spatial relationship of the vector elements by using the circumscribed rectangle, the method can bring about great performance improvement for the vector elements which are not intersected or not contained in the space.
And 6.3, based on all vector elements which have inclusion relation with the current processing element in the corresponding partition of the process obtained in the step 6.2, respectively calculating the vector elements (current processing elements a) in the non-partitioned layer and the circumscribed rectangles of the vector elements (vector elements in the set B) in the corresponding partition layer, judging whether the circumscribed rectangles of the vector elements (current processing elements a) and the vector elements (vector elements in the set B) in the non-partitioned layer are intersected, if so, obtaining corresponding statistical data according to actual superposition analysis application, and if not, needing no processing. In a specific implementation, if there is an intersection between the current processing element a and the circumscribed rectangle of the multiple vector elements in the set B, corresponding processing may be performed according to specific application needs, for example, a vector element in the corresponding partition map layer with the largest intersection area may be selected for processing.
And after the external rectangular spatial relationship is judged to be completed for all the vector elements in the set B, returning to the step 6.1 to continuously read a single vector element in the non-partitioned layer as a new current processing element a until each vector element in the non-partitioned layer is processed.
In the step, firstly, more accurate external rectangle space relation judgment is carried out on two vector elements contained in the space of the GeoHash index code, and the minimum boundary rectangle can be taken as an external rectangle; and then, assigning and updating the attributes of the vector elements by combining with corresponding superposition analysis applications (which can be specifically set according to user requirements, such as terrain evaluation, land applicability analysis, soil erosion and the like).
The method comprises the following steps of 1-4, preprocessing vector data to be processed, including specification of a spatial format, generation of a GeoHash index and division of layer data; and 5-6, realizing parallel spatial superposition analysis of vector big data based on an MPI communication interface, sequentially judging the GeoHash spatial relationship and the circumscribed rectangle spatial relationship of the vector elements by each established process, and finally realizing superposition analysis application.
In specific implementation, the technical scheme of the invention can adopt a computer software technology to realize an automatic operation process. The above processes are all implemented in the MPI communication interface environment, including initialization operation and termination operation of the MPI environment, which means that the code is executed in the parallel environment, and the final data result merging also calls the MPI communication interface.
It should be understood that parts of the specification not set forth in detail are well within the prior art.
It should be understood that the above description of the preferred embodiments is given for clarity and not for any purpose of limitation, and that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (5)
1. A vector big data parallel space superposition analysis method based on MPI is characterized in that the implementation based on the MPI message communication interface comprises the following steps:
step 1, carrying out format conversion on vector element space coordinates in all geographical layers to be processed, and converting the vector element space coordinates into a WKT format;
step 2, based on the vector element space coordinate in the WKT format, aiming at different vector element types, a GeoHash index code is generated by adopting a corresponding mode, the following steps are realized,
directly adopting GeoHash codes as GeoHash index codes for the point-like elements;
for linear elements, firstly, computing the GeoHash codes of all end points of the elements, and then taking the longest identical prefix of the GeoHash codes of all end points as the GeoHash index codes of the linear elements;
for the planar element, firstly, computing the GeoHash codes of all end points of the element, and then taking the longest identical prefix of the GeoHash codes of all end points as the GeoHash index code of the planar element;
step 3, selecting one layer from the layers to be subjected to superposition analysis as a partition layer, dividing vector elements in the layers according to a uniform load principle according to the number of predefined partitions, and writing the WKT format space coordinates and the GeoHash index codes of the vector elements into a network file system NFS according to the partitions;
step 4, writing the WKT format space coordinates and the GeoHash index codes of the vector elements in the non-partitioned layer into a network file system NFS;
step 5, based on the MPI communication interface, creating the process number consistent with the partition number, and reading all the vector elements in the corresponding partition from the NFS by each process;
step 6, each process simultaneously reads each vector element of the non-partitioned layer on the NFS one by one, and performs spatial relationship judgment processing according to the vector elements of the corresponding partitions respectively until all the vector elements of the non-partitioned layer are read and processed; finally, combining and outputting the statistical results obtained by the processes;
the spatial relationship judgment processing is firstly judged according to the GeoHash index code, and comprises judging whether the GeoHash index codes of the current processing element and all vector elements in the corresponding subarea have inclusion relationship, and then judging according to the circumscribed rectangle.
2. The MPI-based vector big data parallel spatial overlay analysis method of claim 1, wherein: in step 6, each process is subjected to the following processes,
step 6.1, reading single vector elements in the non-partitioned layer in sequence, and recording the single vector elements as current processing elements a;
step 6.2, the process judges whether the current processing element a and the GeoHash index codes of all the vector elements in the corresponding partitions have inclusion relations one by one, if the inclusion relations exist, all the vector elements which have inclusion relations with the current processing element a in the corresponding partitions of the process are recorded, the vector elements are marked as a set B, the step 6.3 is skipped, otherwise, the step 6.1 is returned, and a single vector element in the non-partitioned layer is continuously read to serve as a new current processing element a until all the vector elements of the non-partitioned layer are processed;
step 6.3, respectively extracting circumscribed rectangles of the vector elements in the currently processed element a and the set B, judging whether the circumscribed rectangles are intersected, if so, acquiring corresponding statistical data according to actual superposition analysis application, and if not, processing;
and after the external rectangular spatial relationship is judged to be completed for all the vector elements in the set B, returning to the step 6.1, and continuously reading a single vector element in the non-partitioned layer as a new current processing element a until each vector element in the non-partitioned layer is processed.
3. The MPI-based vector big data parallel spatial overlay analysis method of claim 1, wherein: in step 3, a layer containing more vector elements is selected as a partition layer from layers to be subjected to overlay analysis.
4. The MPI-based vector big data parallel spatial stacking analysis method according to claim 1, 2 or 3, characterized in that: in step 3, a file is created on the NFS with the corresponding file name of the partition number, and the divided vector elements are written into the corresponding file according to the partition number.
5. The MPI-based vector big data parallel spatial overlay analysis method of claim 4, wherein: in step 5, each process reads all the vector elements in the corresponding partition from the NFS into the memory according to the process number.
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