CN112765292A - Method for processing shp data by using tile technology - Google Patents
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Abstract
The invention discloses a method for processing shp data by using a tile technology, which comprises the following steps: step S1, processing the spatial data by using the tile technology; and step S2, processing the mass vector data by utilizing a two-stage parallel processing technology of vector data graph cutting parallel and vector tile uploading parallel. The invention utilizes the two-stage parallel processing technology of vector data parallel processing and vector tile parallel uploading, can effectively improve the overall processing efficiency of the tile map of the server side through reasonable two-stage parallel and serial processing of other links, can integrally migrate the spatial database to the HBsae cluster in consideration of the overall architecture of massive spatial data processing, can continuously optimize in the aspects of vector data reading and writing and the like by utilizing the characteristic of high concurrency of the distributed database, and provides a simpler and more effective generation mode for the generation of the tile map.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method for processing shp data by using a tile technology.
Background
Geographic Information Systems (GIS) are complex information systems involving surveying and mapping science, mathematics, and computer science. At the initial development stage of the GIS, professionals such as mappers mainly pay attention to the qualitative and engineering application of spatial data acquisition, so that the direction of the major research make internal disorder or usurp of the professionals models spatial data and creates a GIS database. With the development of mapping, professionals use and process spatial data in a large number of desktop GIS software applications.
In the prior art, a traditional slicing tool based on a GIS component is adopted for massive spatial data, several weeks or more time is usually needed for slicing the massive vector data, and under the condition of limited hardware conditions (memory limitation and the like), the slicing tool cannot be competent for slicing tasks, so that the overall processing efficiency of the massive spatial data is low.
Disclosure of Invention
The invention aims to provide a method for processing shp data by using a tile technology, which aims to solve the technical problem that the overall processing efficiency of massive spatial data is low in the prior art.
In order to solve the technical problems, the invention specifically provides the following technical scheme:
a method of processing shp data using tile techniques, comprising the steps of:
step S1, processing the spatial data by using the tile technology;
and step S2, processing the mass vector data by utilizing a two-stage parallel processing technology of vector data graph cutting parallel and vector tile uploading parallel.
As a preferred aspect of the present invention, in step S1, the spatial data is used to describe an entity geographic target, and the data is unified to indicate the shape size, position and distribution characteristics of the entity geographic target, and the specific processing is as follows:
s101, loading and storing a spatial database taking a data source as vector element data by using a GIS system storage tool;
step S102, carrying out map cutting on vector original data by utilizing a vector tile slicing technology to generate vector tile map data;
and S103, storing the vector tile map data on a server, and providing tile service in a GIS platform to perform uploading and downloading interaction with the HBase cluster.
As a preferred scheme of the present invention, in step S102, the vector tile slice map is a server-side tile technology, and a specific construction manner is as follows:
and cutting a map on the server to generate a map tile with a pyramid structure, and sending a request to access through the client.
As a preferred scheme of the present invention, in step S103, the uploading interaction is performed in a specific manner:
starting a tile service;
storing tiles finished with the cutting map in a tile service for viewing;
the vector tile map metadata is issued, namely information such as vector tile map metadata files in the tile service is written into a database to play a role of indexing;
and uploading the vector tile map data.
As a preferred aspect of the present invention, in step S2, the specific manner of processing the mass vector data is as follows:
step S201, designing a flow logic of a parallel processing technology according to coordination factors of data processing;
step S202, designing a scheduling mode of a parallel processing technology according to the parallelism analysis of data;
s203, designing a map cutting algorithm of the single tile;
step S204, realizing task segmentation of the parallel graph cutting technology;
and step S205, realizing a parallel uploading technology.
As a preferred scheme of the present invention, in step S201, the flow logic specifically performs parallel processing on the vector diagram according to the range division of the buckets by the read and write threads of the graph cutting process; and the message thread and the sending thread of the tile service carry out concurrent operation according to the partition of the tile bucket.
As a preferred aspect of the present invention, in the step S202, the parallelism analysis includes analysis of data correlation and data parallelism;
the data correlation analysis is specifically as follows: each node represents a task to be completed, an edge e with a direction from one node u to another node v represents that the task u must be completed before the task v, and if no directed edge from the node u to the node v exists, the fact that the task u is not related to the task v is represented, the fact that great parallelism exists is represented;
the data parallelism analysis method comprises the following specific steps: if task u has no relevance to task v, but 2 tasks perform the same processing operation on different elements within the data set, it can be considered that the related nodes exhibit data parallelism.
As a preferred embodiment of the present invention, in step S203, the single-tile map cutting algorithm calculates a tile row-column number range corresponding to the actual slice range according to the obtained feature point coordinates on the actual slice range and the current slice level, and summarizes the slicing operation as performing map cutting processing on a map range uniquely determined by the current slice level, the row number, and the column number in the two-dimensional matrix.
As a preferred embodiment of the present invention, in step S204, the task segmentation of the parallel graph cutting technique uses a partition strategy taking a computation task as a center, and the specific manner is as follows:
putting all tasks to be sliced into a task queue q, wherein each task comprises 2 types of information of slice levels and slice geographic ranges, starting a main Thread mainThread of the slicing task through parallel processing parameter setting, such as main Thread number, sub-Thread number and the like, starting sub-threads Thread1, Thread2, … and Threadn, and when no task exists in the queue q, completing image cutting and returning system resources.
As a preferred scheme of the present invention, in step S205, the tile service is deployed on the server side by the parallel upload technology, and the specific manner is as follows:
when the tile service is started, a thread pool is created, the provided uploading request is decomposed and respectively corresponds to a plurality of threads in the thread pool, the effect of parallel processing is achieved, each sub-thread task is automatically ended and system resources are released after the sub-thread task is ended, the programming interface also adopts multi-thread design, and the tile cache searching efficiency is improved by applying a Hash (Hash) technology
Compared with the prior art, the invention has the following beneficial effects:
the invention utilizes the two-stage parallel processing technology of vector data parallel processing and vector tile parallel uploading, can effectively improve the overall processing efficiency of the tile map of the server side through reasonable two-stage parallel and serial processing of other links, can integrally migrate the spatial database to the HBsae cluster in consideration of the overall architecture of massive spatial data processing, can continuously optimize in the aspects of vector data reading and writing and the like by utilizing the characteristic of high concurrency of the distributed database, and provides a simpler and more effective generation mode for the generation of the tile map.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
Fig. 1 is a flowchart of a method for processing shp data using tile technology according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for processing spatial data using tile technology according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a scheduling policy according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a task dividing thread according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1 and 2, the present invention provides a method for processing shp data using tile technology, comprising the steps of:
step S1, processing the spatial data by using the tile technology;
the spatial database is the core of the GIS system, and the development of the spatial database technology drives the technical innovation of the GIS system. The spatial database not only has the basic characteristics of a general database, but also comprises 5 unique characteristics, namely spatial characteristics, unstructured characteristics, spatial relation characteristics, classification coding characteristics and mass data characteristics.
The method comprises the following steps of dividing a basic geographic space database and a thematic database from the application aspect, wherein the basic (Oracle) geographic database comprises vector element Data (DLG), digital elevation Data (DEM), image Data (DOM) and raster map Data (DRG); the thematic database comprises place name data, soldier essential data, navigation data and the like.
In step S1, the spatial data is used to describe the entity geographic target, and the data is normalized to indicate the shape size, position and distribution characteristics of the entity geographic target, and the specific processing is as follows:
s101, loading and storing a spatial database taking a data source as vector element data by using a GIS system storage tool;
step S102, carrying out map cutting on vector original data by utilizing a vector tile slicing technology to generate vector tile map data;
in step S102, the vector tile slice map is a server-side tile technology, and the specific construction method is as follows:
map tiles with pyramid structures are generated by cutting a map on a server, and then a client sends a request to access the map tiles, which is essentially a data loading process stored by the server, so that the speed of the map tiles is much higher than that of a tile client technology and an original vector map technology.
The vector tile map adopts a pyramid structure, and is a hierarchical data model based on multi-resolution. Under the condition of representing unchanged geographic range, the scale ruler is smaller and smaller from the top layer to the bottom layer of the tile pyramid.
And S103, storing the vector tile map data on a server, and providing tile service in a GIS platform to perform uploading and downloading interaction with the HBase cluster.
In step S103, the specific manner of the uploading interaction is as follows:
starting a tile service;
storing tiles finished with the cutting map in a tile service for viewing;
the vector tile map metadata is issued, namely information such as vector tile map metadata files in the tile service is written into a database to play a role of indexing;
and uploading the vector tile map data.
And step S2, processing the mass vector data by utilizing a two-stage parallel processing technology of vector data graph cutting parallel and vector tile uploading parallel.
In step S2, the specific manner of processing the mass vector data is as follows:
step S201, designing a flow logic of a parallel processing technology according to coordination factors of data processing;
in step S201, the flow logic specifically is that the read and write threads in the graph cutting process perform parallel processing on the vector map according to the range division of the buckets; and the message thread and the sending thread of the tile service carry out concurrent operation according to the partition of the tile bucket.
As shown in fig. 3, step S202 designs a scheduling manner of the parallel processing technology according to the parallelism analysis of the data;
in the step S202, the parallelism analysis includes analysis of data correlation and data parallelism;
the data correlation analysis is specifically as follows: each node represents a task to be completed, an edge e with a direction from one node u to another node v represents that the task u must be completed before the task v, and if no directed edge from the node u to the node v exists, the fact that the task u is not related to the task v is represented, the fact that great parallelism exists is represented;
the data parallelism analysis method comprises the following specific steps: if task u has no relevance to task v, but 2 tasks perform the same processing operation on different elements within the data set, it can be considered that the related nodes exhibit data parallelism.
An application with parallelism is decomposed into a series of small tasks. The parallel small task scheduling mode is divided into 2 types of static scheduling and dynamic scheduling. As shown in FIG. 3, tile parallel task units are divided into 3 types, namely, A bucket, B bucket and C bucket. The task execution time of the bucket A is 2 unit times, the task execution time of the bucket B is 1 unit time, and the task execution time of the bucket C is 3 unit times.
The static scheduling is divided into 4 processes, the tasks are evenly distributed, and the total time for completing all the buckets is 7 unit times. Dynamic scheduling whenever one bucket completes, the next bucket is called out of the task queue for execution immediately, and the total time for completion of all buckets is 5 unit times. The scheduling mode can remarkably improve the vector data processing efficiency and effectively utilize software and hardware resources.
S203, designing a map cutting algorithm of the single tile;
in step S203, the single-tile mapping algorithm calculates a tile row-column number range corresponding to the actual slicing range according to the obtained feature point coordinates on the actual slicing range and the current slicing level, and summarizes the slicing operation as mapping the map width range uniquely determined by the current slicing level, the row number, and the column number in the two-dimensional matrix.
As shown in fig. 4, step S204, implementing task segmentation of the parallel graph cutting technique;
in step S204, the task segmentation of the parallel graph cutting technique is a division strategy taking a computation task as a center, and the specific method is as follows:
putting all tasks to be sliced into a task queue q, wherein each task comprises 2 types of information of slice levels and slice geographic ranges, starting a main Thread mainThread of the slicing task through parallel processing parameter setting, such as main Thread number, sub-Thread number and the like, starting sub-threads Thread1, Thread2, … and Threadn, and when no task exists in the queue q, completing image cutting and returning system resources.
And step S205, realizing a parallel uploading technology.
In step S205, the tile service is deployed on the server side by the parallel upload technology, and the specific method is as follows:
when the tile service is started, a thread pool is created, the provided uploading request is decomposed and respectively corresponds to a plurality of threads in the thread pool, the effect of parallel processing is achieved, each sub-thread task is automatically ended and system resources are released after the sub-thread task is ended, the programming interface also adopts multi-thread design, and the tile cache searching efficiency is improved by applying a Hash (Hash) technology
The invention utilizes the two-stage parallel processing technology of vector data parallel processing and vector tile parallel uploading, can effectively improve the overall processing efficiency of the tile map of the server side through reasonable two-stage parallel and serial processing of other links, can integrally migrate the spatial database to the HBsae cluster in consideration of the overall architecture of massive spatial data processing, can continuously optimize in the aspects of vector data reading and writing and the like by utilizing the characteristic of high concurrency of the distributed database, and provides a simpler and more effective generation mode for the generation of the tile map.
The above embodiments are only exemplary embodiments of the present application, and are not intended to limit the present application, and the protection scope of the present application is defined by the claims. Various modifications and equivalents may be made by those skilled in the art within the spirit and scope of the present application and such modifications and equivalents should also be considered to be within the scope of the present application.
Claims (10)
1. A method for processing shp data using tile technology, comprising the steps of:
step S1, processing the spatial data by using the tile technology;
and step S2, processing the mass vector data by utilizing a two-stage parallel processing technology of vector data graph cutting parallel and vector tile uploading parallel.
2. The method of processing shp data using tile techniques of claim 1, characterized in that: in step S1, the spatial data is used to describe the entity geographic target, and the data is normalized to indicate the shape size, position and distribution characteristics of the entity geographic target, and the specific processing is as follows:
s101, loading and storing a spatial database taking a data source as vector element data by using a GIS system storage tool;
step S102, carrying out map cutting on vector original data by utilizing a vector tile slicing technology to generate vector tile map data;
and S103, storing the vector tile map data on a server, and providing tile service in a GIS platform to perform uploading and downloading interaction with the HBase cluster.
3. The method of processing shp data using tile techniques of claim 2, characterized in that: in step S102, the vector tile slice map is a server-side tile technology, and the specific construction method is as follows:
and cutting a map on the server to generate a map tile with a pyramid structure, and sending a request to access through the client.
4. A method of processing shp data using tile techniques, as claimed in claim 3, wherein: in step S103, the specific manner of the uploading interaction is as follows:
starting a tile service;
storing tiles finished with the cutting map in a tile service for viewing;
the vector tile map metadata is issued, namely information such as vector tile map metadata files in the tile service is written into a database to play a role of indexing;
and uploading the vector tile map data.
5. The method of processing shp data using tile techniques of claim 4, characterized in that: in step S2, the specific manner of processing the mass vector data is as follows:
step S201, designing a flow logic of a parallel processing technology according to coordination factors of data processing;
step S202, designing a scheduling mode of a parallel processing technology according to the parallelism analysis of data;
s203, designing a map cutting algorithm of the single tile;
step S204, realizing task segmentation of the parallel graph cutting technology;
and step S205, realizing a parallel uploading technology.
6. The method of processing shp data using tile techniques of claim 5, characterized in that: in step S201, the flow logic specifically is that the read and write threads in the graph cutting process perform parallel processing on the vector map according to the range division of the buckets; and the message thread and the sending thread of the tile service carry out concurrent operation according to the partition of the tile bucket.
7. The method of processing shp data using tile techniques of claim 6, characterized in that: in the step S202, the parallelism analysis includes analysis of data correlation and data parallelism;
the data correlation analysis is specifically as follows: each node represents a task to be completed, an edge e with a direction from one node u to another node v represents that the task u must be completed before the task v, and if no directed edge from the node u to the node v exists, the fact that the task u is not related to the task v is represented, the fact that great parallelism exists is represented;
the data parallelism analysis method comprises the following specific steps: if task u has no relevance to task v, but 2 tasks perform the same processing operation on different elements within the data set, it can be considered that the related nodes exhibit data parallelism.
8. The method as claimed in claim 7, wherein in step S203, the single-tile mapping algorithm calculates a tile row-column number range corresponding to the actual slice range according to the obtained feature point coordinates on the actual slice range and the current slice level, and summarizes the mapping operation as mapping the map range uniquely determined by the current slice level, row number and column number in the two-dimensional matrix.
9. The method for processing shp data by using tile technology as claimed in claim 8, wherein in step S204, the task segmentation of the parallel graph cutting technology is based on a partition strategy taking a computing task as a center, in a manner that:
putting all tasks to be sliced into a task queue q, wherein each task comprises 2 types of information of slice levels and slice geographic ranges, starting a main Thread mainThread of the slicing task through parallel processing parameter setting, such as main Thread number, sub-Thread number and the like, starting sub-threads Thread1, Thread2, … and Threadn, and when no task exists in the queue q, completing image cutting and returning system resources.
10. The method for processing shp data by using tile technology of claim 9, wherein in step S205, the parallel upload technology deploys tile services on the server side, specifically:
when the tile service is started, a thread pool is created, the provided uploading request is decomposed and corresponds to a plurality of threads in the thread pool respectively, the effect of parallel processing is achieved, each sub-thread task is automatically ended and system resources are released after the sub-thread task is ended, the programming interface also adopts multi-thread design, and the tile cache searching efficiency is improved by applying a Hash (Hash) technology.
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| CN113670295A (en) * | 2021-08-17 | 2021-11-19 | 北京百度网讯科技有限公司 | Data processing method and device, electronic equipment and readable storage medium |
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Application publication date: 20210507 |