WO2020125290A1 - 数据处理方法、系统及存储介质 - Google Patents

数据处理方法、系统及存储介质 Download PDF

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Publication number
WO2020125290A1
WO2020125290A1 PCT/CN2019/118770 CN2019118770W WO2020125290A1 WO 2020125290 A1 WO2020125290 A1 WO 2020125290A1 CN 2019118770 W CN2019118770 W CN 2019118770W WO 2020125290 A1 WO2020125290 A1 WO 2020125290A1
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platform
file
data processing
server
request information
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PCT/CN2019/118770
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English (en)
French (fr)
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刘土明
胡永禄
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中兴通讯股份有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/10—File systems; File servers
    • G06F16/17—Details of further file system functions
    • G06F16/174—Redundancy elimination performed by the file system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/10—File systems; File servers
    • G06F16/18—File system types
    • G06F16/182—Distributed file systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases

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  • the present disclosure relates to the field of distributed data technology, for example, to a massive raster data processing method, system, and storage medium.
  • the relevant raster data adopts spatial data storage technology to establish more complicated indexes, which greatly increases the storage space of the data, especially for the massive raster data, the equipment cost is higher. Therefore, the related raster processing method has the disadvantages of slow response speed and large storage space of the spatial database when processing large amounts of raster data. Therefore, the user experience is poor and the equipment cost is high.
  • the object of the present invention is to provide a data processing method, system and storage medium, which aim to increase the speed of massive raster data processing and reduce storage costs.
  • a data processing method provided by the present disclosure is applied to a data processing system.
  • the data processing system includes a data processing platform, and the method includes: the data processing platform Obtain the raster data file to be processed; the data processing platform cleans the raster data file to be processed to generate a layer subcontracting file for business needs; the data processing platform compresses the layer subcontracting file , The compressed file generated is stored in the distributed file system.
  • the present disclosure also proposes a data processing system, the data processing system includes a data processing platform, the data processing platform is used to obtain a raster data file to be processed, the raster data to be processed The file is cleaned to generate a layer subcontracting file for business needs, the layer subcontracting file is compressed, and the compressed package file is generated and stored in a distributed file system.
  • the present disclosure also provides a data processing system, including: a memory, a processor, and a data processing program stored on the memory and executable on the processor, the data processing program is When the processor executes, the steps of the data processing method described above are implemented.
  • the present disclosure also proposes a computer-readable storage medium that stores a computer program on the computer-readable storage medium, and when the computer program is executed by a processor, implements the steps of the data processing method described above .
  • FIG. 1 is a schematic flowchart of Embodiment 1 of the data processing method of the present disclosure
  • Embodiment 2 is a schematic flowchart of Embodiment 2 of the data processing method of the present disclosure
  • Embodiment 3 is a schematic flowchart of Embodiment 3 of the data processing method of the present disclosure
  • FIG. 4 is a schematic diagram of a flow direction of raster data processing according to an embodiment of the present disclosure
  • FIG. 5 is a schematic diagram of a system architecture involved in an operating environment according to an embodiment of the present disclosure.
  • the main solution of the embodiment of the present disclosure is that: the data processing platform obtains the raster data file to be processed, performs Spark cleaning on the raster data file to be processed, and generates a layer subcontract file for business needs;
  • the package file is compressed, and the generated compressed package file is stored in the distributed file system. Therefore, through distributed mode and cleaning of massive raster data, it is beneficial to load balance processing of different regions or types of data, reduce the load of each machine, improve the response speed, and solve the shortcomings of slow response of data processing in related technologies.
  • By compressing the file storage method it solves the shortcoming that the related technology massive raster data storage space occupies a large amount.
  • Raster data is array data arranged in rows and columns of grid cells, with different grayscales or colors.
  • a grid structure is an array of picture elements (grid units) that are evenly distributed and closely connected to represent the spatial data or phenomenon distribution. It is a relatively simple and intuitive spatial data structure, which divides the surface of the earth into a grid array of size, uniformity and close proximity. The location of each unit (pixel) is defined by its row and column number, the physical location represented is implied in the raster row and column location, and each data in the data organization represents the non-geometric attribute of the feature or phenomenon or points to its attribute Pointer.
  • the most notable feature of the grid structure is: the data directly records the pointer of the attribute or the attribute itself, and its location is converted into corresponding coordinates according to the row and column numbers. In other words, positioning is based on the position of the data in the data set.
  • Nginx (engine x, Nginx): It is a high-performance HTTP and reverse proxy service with strong stability and good performance.
  • the underlying language is C language.
  • Spark It is an open source parallel computing engine that can reliably process large amounts of data (TB) data in parallel on large-scale clusters. It supports interactive calculations and complex algorithms.
  • the bottom layer is implemented in scala language.
  • Hdfs Hadoop Distributed File System, Hdfs: Hadoop distributed file system is a highly fault-tolerant distributed file system that can provide high-throughput data access and is suitable for deployment on large-scale clusters. Java language implementation.
  • raster data generally adopts spatial data storage technology
  • the establishment of more complex indexes greatly increases the storage space of data, especially for massive raster data.
  • the cost of equipment is high, so it is particularly important to reduce the storage space of massive data .
  • the present disclosure is to solve the above two shortcomings of slow response and high storage cost, the shortcoming of slow response is solved by the spark cleaning massive grid algorithm, and the shortcoming of large storage space is solved by local compressed file storage.
  • FIG. 1 is a schematic diagram of a flow direction of raster data processing according to an embodiment of the present disclosure.
  • the raster data processing link involved in the solution of the embodiment of the present disclosure mainly includes five parts: front-end part, Nginx load balancing, platform back-end, data cleaning and data compression of the data processing platform. among them:
  • the front-end mainly receives the request information sent by the user accessing the visual interface of the platform.
  • the request information mainly includes geographic location information, business layer information, etc.;
  • Nginx load balancing extract the front-end request information, load it to different platform servers as needed, each server can have at least one backup server, store the same data information, and the request distributed to the server will be loaded to the machine again. On its backup machine, multiple servers responding at the same time can not only improve the response speed but also ensure that when one of the servers is down, the request returns the results normally, thus ensuring that the data is not lost;
  • Platform backend Scan the ZIP compressed file on hdfs, download to the corresponding platform server as needed (such as configuration file), accept and process the front-end request, and return the result to the front-end;
  • Data cleaning scan the raster data files on hdfs, and use spark components to process certain layers into required layer files;
  • Data compression Compress the spark cleaned data into ZIP packages according to certain requirements and levels and store them in hdfs.
  • Embodiment 1 of the present disclosure proposes a data processing method.
  • the method is applied to a data processing system.
  • the data processing system includes a data processing platform.
  • the method includes:
  • Step S101 the data processing platform obtains a raster data file to be processed
  • the data processing platform can be set in the distributed file system, or can be set independently of the distributed file system.
  • the to-be-processed raster data file may include various to-be-processed raster data, and the source thereof may be obtained from an external database, server, terminal device, platform, or the like.
  • raster data is mainly obtained by the following ways:
  • Raster method divide the grid cells evenly on the graphics to be input, determine their attribute codes one by one grid, and finally form a grid digital map file.
  • Scanning digitization scanning the thematic map to be input point by point, re-sampling and re-encoding the scanned data to obtain raster data files.
  • each external device may push the raster data file to be processed into the distributed file system to trigger the data processing task.
  • Step S102 the data processing platform cleans the raster data file to be processed to generate a layer subcontracting file for business requirements
  • the data cleaning is used to improve the response speed of raster data processing.
  • performing the Spark cleaning on the to-be-processed raster data file to generate a layer subcontracting file for business requirements specifically adopts the following scheme:
  • Corresponding configuration files are pre-configured according to business requirements for data cleaning.
  • the method of Spark cleaning is specifically adopted during data cleaning.
  • Step S103 The data processing platform compresses the layer sub-package file, and generates a compressed package file to store in a distributed file system.
  • the data processing platform compresses the layer sub-package file to generate the compressed package file and store it in a distributed file system.
  • the layer sub-package file can be compressed and generated according to preset rules (for example, according to certain requirements and levels). ZIP package files are stored in a distributed file system.
  • the distributed mode can effectively solve the defects of slow response of raster data processing and high storage cost, which is beneficial to load balancing processing of data in different regions or types, reducing the load of each machine, and increasing the response speed.
  • the data processing platform obtains the raster data file to be processed, performs Spark cleaning on the raster data file to be processed, and generates a layer subcontracting file for business needs; Compressed, the generated compressed package file is stored in the distributed file system.
  • the massive raster data is cleaned through distributed mode and spark, and load balancing processing is performed on different regions or types of data, reducing the load of each machine, improving the response speed, and solving the shortcomings of slow response of data processing in related technologies.
  • the compressed file storage method solves the shortcoming of the related technology that the massive raster data storage space occupies a large amount.
  • Embodiment 2 of the present disclosure proposes a data processing method.
  • the data processing system further includes: a platform backend, and a number of platform servers are configured on the platform backend ,
  • the method further includes:
  • Step S104 The platform backend scans the corresponding compressed package storage path according to the pre-configured configuration file, and downloads the corresponding compressed package file from the distributed file system to the corresponding platform server.
  • this embodiment further includes a solution in which the platform server downloads the corresponding compressed package file.
  • the platform backend is configured with several platform servers.
  • the platform server can be configured with one or more backup servers.
  • different files or different The platform server is configured with different configuration files, and a corresponding forwarding node is set at the back end of the platform to forward the different configuration files to the corresponding platform server.
  • the configuration file can carry the storage path of the compressed package corresponding to the raster data file.
  • the platform backend or the platform server may scan the corresponding compressed package storage path according to the pre-configured configuration file, and download the corresponding compressed package file from the distributed file system To the corresponding platform server.
  • the data processing platform obtains the raster data file to be processed, performs Spark cleaning on the raster data file to be processed, and generates a layer subcontracting file for business needs; Compressed, the generated compressed package file is stored in the distributed file system.
  • the massive raster data is cleaned through distributed mode and spark, and load balancing processing is performed on different regions or types of data, reducing the load of each machine, improving the response speed, and solving the shortcomings of slow response of data processing in related technologies.
  • the compressed file storage method solves the shortcoming of the related technology that the massive raster data storage space occupies a large amount.
  • the platform server can also download the corresponding compressed package file from the distributed file system to the corresponding platform server to achieve data distribution and distributed storage.
  • Embodiment 3 of the present disclosure proposes a data processing method.
  • the data processing system further includes a platform front end and a load balancing proxy server.
  • the method further includes:
  • Step S105 the platform front end receives the request information sent by the user through accessing the platform visual interface, and sends the request information to the load balancing proxy server;
  • Step S106 the load balancing proxy server performs load balancing according to the request information sent by the front end of the platform, and distributes the request information to a platform server corresponding to the back end of the platform, or to a platform corresponding to the back end of the platform Server and its backup server;
  • step S107 the platform server, or the platform server and its backup server that received the request information, in response to the request information, return the generated picture of the compressed package file on the local machine to the front end of the platform for presentation.
  • this embodiment further includes: a solution for feeding back data according to a front-end request and implementing load balancing.
  • the front end of the platform may provide a visual interface for accessing the platform for users to interact with.
  • the front end of the platform receives the request information sent by the user by accessing the visual interface of the platform, and sends the request information to the load balancing proxy server.
  • the request information may include at least one of geographic location information and business layer information.
  • the load balancing proxy server can use the Nginx load balancing proxy server.
  • the Nginx load balancing proxy server extracts the front-end request information and loads it to different platform servers as needed.
  • Each server can have at least one backup server to store the same data information and distribute to The request of the server will be loaded on the server and its backup server again. Therefore, the simultaneous response of multiple servers can not only improve the data response speed but also ensure that when one of the servers is down, the request can return the result normally, thus Ensure that the data is not lost.
  • the platform server that receives the above request information, or the platform server and its backup server, in response to the request information, returns the compressed package file on the machine to generate PNG and other format pictures to the front end of the platform for presentation.
  • the data processing platform obtains the raster data file to be processed, performs Spark cleaning on the raster data file to be processed, and generates a layer subcontracting file for business needs; Compressed, the generated compressed package file is stored in the distributed file system.
  • the massive raster data is cleaned through distributed mode and spark, and load balancing processing is performed on different regions or types of data, reducing the load of each machine, improving the response speed, and solving the shortcomings of slow response of data processing in related technologies.
  • the compressed file storage method solves the shortcomings of large storage space occupied by massive raster data in the related art.
  • the platform server can also download the corresponding compressed package file from the distributed file system to the corresponding platform server to achieve data distribution and distributed storage.
  • the front end of the platform can also send the request information to the load balancing proxy server according to the user's request.
  • the load balancing proxy server loads the different platform servers as needed, and the corresponding platform server responds to generate the PNG of the compressed package file on the machine.
  • the picture is returned to the front-end of the platform for presentation, so as to meet the front-end request, achieve load balancing processing, reduce the load of each machine, and further improve the data response speed.
  • an embodiment of the present disclosure also proposes a data processing system.
  • the data processing system includes a data processing platform.
  • the data processing platform is configured to obtain a raster data file to be processed.
  • the to-be-processed raster data file is cleaned by Spark to generate a layer subcontracting file for business needs, the layer subcontracting file is compressed, and the generated compressed package file is stored in a distributed file system.
  • the data processing system further includes: a platform backend, a platform frontend, and a load balancing proxy server, and the platform backend is configured with several platform servers;
  • the platform backend is set to scan the corresponding compressed package storage path according to the pre-configured configuration file, and download the corresponding compressed package file from the distributed file system to the corresponding platform server;
  • the platform front end is configured to receive the request information sent by the user through accessing the platform visual interface, and send the request information to the load balancing proxy server;
  • the load balancing proxy server is configured to perform load balancing according to the request information sent by the front end of the platform, and distribute the request information to the corresponding platform server at the back end of the platform, or to the corresponding platform server at the back end of the platform and its backup server;
  • the platform backend is also configured to receive and present the platform server, or the PNG image generated based on the compressed package file on the machine returned by the platform server and its backup server in response to the request information.
  • the present disclosure also proposes a data processing system, including: a memory, a processor, and a data processing program stored on the memory and executable on the processor, the data processing program being executed by the processor To implement the steps of the data processing method as described above.
  • the system in this embodiment may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002.
  • the communication bus 1002 is configured to implement connection communication between these components.
  • the user interface 1003 may include a display (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.
  • the network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).
  • the memory 1005 may be a high-speed RAM memory, or may be a non-volatile memory (non-volatile memory), such as a disk memory.
  • the memory 1005 may optionally be a storage device independent of the foregoing processor 1001.
  • FIG. 5 does not constitute a limitation on the platform, and may include more or less components than those illustrated, or combine certain components, or arrange different components.
  • the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a data processing program.
  • the network interface 1004 is mainly configured to connect to a web server and perform data communication with the web server;
  • the user interface 1003 is primarily configured to connect to a client and perform data communication with the client;
  • the processor 1001 may be configured to Call the data processing program stored in the memory 1005 and perform the following operations:
  • the data processing platform obtains the raster data file to be processed
  • the data processing platform cleans the raster data file to be processed to generate a layer subcontract file for business needs
  • the data processing platform compresses the layer sub-package file, and generates a compressed package file to store in a distributed file system.
  • the processor 1001 may be configured to call a data processing program stored in the memory 1005, and also perform the following operations:
  • the platform backend scans the corresponding compressed package storage path according to the pre-configured configuration file, and downloads the corresponding compressed package file from the distributed file system to the corresponding platform server.
  • the processor 1001 may be configured to call a data processing program stored in the memory 1005, and also perform the following operations:
  • the platform front end receives the request information sent by the user through accessing the platform visual interface, and sends the request information to the load balancing proxy server;
  • the load balancing proxy server performs load balancing according to the request information sent by the front end of the platform, and distributes the request information to the corresponding platform server at the back end of the platform, or to the corresponding platform server at the back end of the platform and its backup server;
  • the platform server receiving the request information, or the platform server and its backup server, in response to the request information, returns a picture generated by the compressed package file on the local machine to the front end of the platform for presentation.
  • the processor 1001 may be configured to call a data processing program stored in the memory 1005, and also perform the following operations:
  • the Spark component is used to perform Spark cleaning on the raster data file to be processed to generate a layer subcontracting file for the business requirements.
  • the processor 1001 may be configured to call a data processing program stored in the memory 1005, and also perform the following operations:
  • an embodiment of the present disclosure also provides a computer-readable storage medium that stores a computer program on the computer-readable storage medium, and when the computer program is executed by a processor, implements the steps of the data processing method described above.
  • a data processing method, system and storage medium proposed in an embodiment of the present disclosure obtain a raster data file to be processed through a data processing platform, perform Spark cleaning on the raster data file to be processed, and generate a layer subcontract of business requirements Files; compress the layer sub-package files, and generate compressed package files to be stored in the distributed file system.
  • the massive raster data is cleaned through distributed mode and spark, and load balancing processing is performed on different regions or types of data, reducing the load of each machine, improving the response speed, and solving the shortcomings of slow response of data processing in related technologies.
  • the compressed file storage method solves the shortcoming of the related technology that the massive raster data storage space occupies a large amount.
  • the platform server can also download the corresponding compressed package file from the distributed file system to the corresponding platform server to achieve data distribution and distributed storage.
  • the front end of the platform can also send the request information to the load balancing proxy server according to the user's request.
  • the load balancing proxy server loads the different platform servers as needed, and the corresponding platform server responds to generate the PNG of the compressed package file on the machine.
  • the picture is returned to the front-end of the platform for presentation, so as to meet the front-end request, achieve load balancing processing, reduce the load of each machine, and further improve the data response speed.

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Abstract

本公开公开了一种数据处理方法、系统及存储介质，该方法包括：数据处理平台获取待处理栅格数据文件；对待处理栅格数据文件进行Spark清洗，生成业务需求的图层分包文件；对图层分包文件进行压缩，生成压缩包文件存储在分布式文件系统。

Description

数据处理方法、系统及存储介质
本申请要求在2018年12月18日提交中国专利局、申请号为201811548403.4的中国专利申请的优先权，该申请的全部内容通过引用结合在本申请中。
技术领域
本公开涉及分布式数据技术领域，例如涉及一种海量栅格数据处理方法、系统及存储介质。
背景技术
随着数据量大幅增长，海量数据处理和服务应用便捷运维的压力日益倍增，使得界面响应速度以及操作流畅度不够，影响用户体验。
相关的栅格数据采用空间数据存储技术，建立较为复杂的索引，大大增加了数据的存储空间，尤其对于海量栅格数据来说设备成本较高。因此，相关的栅格处理方法在处理大数据量的栅格数据上具有响应速度慢、空间数据库存储空间占用大等缺点，因此用户体验较差且设备成本较高。
随着栅格数据量的增加，数据处理速度和存储容量越来越受到限制，因此有必要采用新的数据处理和存储模式。
发明内容
本发明的目的在于提供一种数据处理方法、系统及存储介质，旨在提高海量栅格数据处理的速度，并减少存储成本。
为实现上述目的，在一实施例中，本公开提供的一种数据处理方法，所述方法应用于数据处理系统，所述数据处理系统包括数据处理平台，所述方法包括：所述数据处理平台获取待处理栅格数据文件；所述数据处理平台对所述待处理栅格数据文件进行清洗，生成业务需求的图层分包文件；所述数据处理平台对所述图层分包文件进行压缩，生成压缩包文件存储在分布式文件系统。
在一实施例中，本公开还提出一种数据处理系统，所述数据处理系统包 括数据处理平台，所述数据处理平台，用于获取待处理栅格数据文件，对所述待处理栅格数据文件进行清洗，生成业务需求的图层分包文件，对所述图层分包文件进行压缩，生成压缩包文件存储在分布式文件系统。
在一实施例中，本公开还提出一种数据处理系统，包括：存储器、处理器及存储在所述存储器上并可在所述处理器上运行的数据处理程序，所述数据处理程序被所述处理器执行时实现如上所述的数据处理方法的步骤。
在一实施例中，本公开还提出一种计算机可读存储介质，所述计算机可读存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现如上所述的数据处理方法的步骤。
附图说明
图1是本公开数据处理方法实施例一的流程示意图；
图2是本公开数据处理方法实施例二的流程示意图；
图3是本公开数据处理方法实施例三的流程示意图；
图4是本公开实施例的涉及的栅格数据处理流向示意图；
图5是本公开实施例运行环境涉及的系统架构示意图。
本公开目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。
为了使本公开的技术方案更加清楚、明了，下面将结合附图作进一步详述。
具体实施方式
应当理解，此处所描述的具体实施例仅仅用以解释本公开，并不用于限定本公开。
本公开实施例的主要解决方案是：数据处理平台获取待处理栅格数据文件，对所述待处理栅格数据文件进行Spark清洗，生成业务需求的图层分包文件；对所述图层分包文件进行压缩，生成压缩包文件存储在分布式文件系统。 由此通过分布式模式及清洗海量栅格数据，有利于对不同区域或者类型的数据进行负载均衡处理，降低每台机器的负荷，提高响应速度，解决了相关技术中数据处理响应慢的缺点，通过压缩文件存储方式，解决了相关技术海量栅格数据存储空间占用较大的缺点。
术语解释
栅格数据，栅格数据是按网格单元的行与列排列、具有不同灰度或颜色的阵列数据。栅格结构是大小相等分布均匀、紧密相连的像元(网格单元)阵列来表示空间地物或现象分布的数据组织。是相对简单、直观的空间数据结构，它将地球表面划分为大小、均匀、紧密相邻的网格阵列。每一个单元(象素)的位置由它的行列号定义，所表示的实体位置隐含在栅格行列位置中，数据组织中的每个数据表示地物或现象的非几何属性或指向其属性的指针。栅格结构的最显著特点是：数据直接记录属性的指针或属性本身，而其所在位置则根据行列号转换成相应的坐标给出。也就是说，定位是根据数据在数据集合中的位置得到的。
Nginx(engine x，Nginx)：是一个高性能的HTTP和反向代理服务，具有稳定性强，性能好等特点，底层语言为C语言。
Spark：是一种开源的并行计算引擎，它可在大规模集群上可靠地并行处理大数据量级(TB)数据，支持交互式计算和复杂算法，底层用scala语言实现。
分布式文件系统Hdfs(Hadoop Distributed File System，Hdfs)：Hadoop分布式文件系统，是一种高容错性分布式文件系统，能提供高吞吐量的数据访问，适合部署在大规模集群上，底层由Java语言实现。
由于栅格数据一般采用空间数据存储技术，建立较为复杂的索引，大大增加了数据的存储空间，尤其对于海量栅格数据来说设备成本较高，因此，减少海量数据的存储空间就显得尤为重要。
本公开就是为了解决以上响应慢和存储成本高两大缺点，通过spark清洗海量栅格算法解决响应慢的缺点，通过本地压缩文件存储方式解决存储空间占用较大的缺点。
具体地，如图1所示，图1是本公开实施例方案涉及的栅格数据处理流向示意图。
本公开实施例方案涉及的栅格数据处理环节主要包括：前端部分、Nginx负载均衡、平台后端、数据处理平台的数据清洗和数据压缩五个部分。其中：
前端：前端主要是接收用户访问平台可视化界面发送的请求信息，请求信息主要包括地理位置信息、业务图层信息等；
Nginx负载均衡：提取前端请求信息，根据需要负载到不同的平台服务器，每台服务器可以有至少一台备份服务器，存储相同的数据信息，分发到该台服务器的请求会再次被负载到该机和其备份机器上，因此，多台服务器同时响应不仅可以提高响应速度还能保证当其中一台服务器宕机时，请求正常返回结果，从而保证数据不丢失；
平台后端：扫描hdfs上的ZIP压缩文件，根据需要(比如配置文件)下载到相应的平台服务器，接受并处理前端请求，返回结果给前端；
数据清洗：扫描hdfs上的栅格数据文件，通过spark组件运用一定的算法处理成需要的图层文件；
数据压缩：将spark清洗完的数据按一定的需求和级别压缩成ZIP包存储在hdfs。
如图2所示，本公开实施例一提出一种数据处理方法，所述方法应用于数据处理系统，所述数据处理系统包括数据处理平台，所述方法包括：
步骤S101，所述数据处理平台获取待处理栅格数据文件；
其中，数据处理平台可以设置在分布式文件系统内，也可以独立于分布式文件系统之外设置。
待处理栅格数据文件可以包括各种待处理栅格数据，其来源可以从外部数据库、服务器、终端设备或平台等获取。
通常栅格数据的获取主要由以下几个途径：
⑴栅格法：在待输入的图形上均匀划分栅格单元，逐个栅格地决定其属性代码，最后形成栅格数字地图文件。
⑵转换法：用手扶跟踪数字化或自动跟踪数字化得到矢量结构数据，再转换为栅格结构。
⑶扫描数字化：逐点扫描待输入的专题地图，对扫描数据重新采样与再编码，从而得到栅格数据文件。
⑷分类影像输入：将经过分类解译的遥感影像数据直接或重新采样后输入系统，这是一种高效获取数据的方法。
作为一种实施方式，各外部设备可以将待处理栅格数据文件推送到分布式文件系统中，触发数据处理任务。
步骤S102，所述数据处理平台对所述待处理栅格数据文件进行清洗，生成业务需求的图层分包文件；
本实施例中，是通过数据清洗来提高栅格数据处理响应速度，其中，对所述待处理栅格数据文件进行Spark清洗，生成业务需求的图层分包文件，具体采用如下方案：
根据业务需求预先配置有相应的配置文件，用于进行数据清洗。
进行数据清洗，本实施例中，在数据清洗时具体采用Spark清洗的方式。获取预设的与业务需求相对应的配置文件；根据预设的与业务需求相对应的配置文件，通过spark组件对所述待处理栅格数据文件进行Spark清洗，生成业务需求的图层分包文件。
步骤S103，所述数据处理平台对所述图层分包文件进行压缩，生成压缩包文件存储在分布式文件系统。
数据处理平台对所述图层分包文件进行压缩，生成压缩包文件存储在分布式文件系统，可以按照预设规则(比如：按一定的需求和级别)对图层分包文件进行压缩，生成ZIP包文件存储在分布式文件系统。
本实施例采用分布式模式能够有效解决栅格数据处理响应慢和存储成本高的缺陷，有利于对不同区域或者类型的数据进行负载均衡处理，降低每台机器的负荷，提高响应速度。
本实施例通过上述方案，数据处理平台获取待处理栅格数据文件，对所述待处理栅格数据文件进行Spark清洗，生成业务需求的图层分包文件；对所述图层分包文件进行压缩，生成压缩包文件存储在分布式文件系统。由此通过分布式模式及spark清洗海量栅格数据，对不同区域或者类型的数据进行负 载均衡处理，降低每台机器的负荷，提高响应速度，解决了相关技术中数据处理响应慢的缺点，通过压缩文件存储方式，解决了相关技术海量栅格数据存储空间占用较大的缺点。
如图3所示，本公开实施例二提出一种数据处理方法，基于上述图2所示的实施例，所述数据处理系统还包括：平台后端，所述平台后端配置有若干平台服务器，所述方法还包括：
步骤S104，所述平台后端根据预先配置的配置文件扫描对应的压缩包存储路径，从所述分布式文件系统下载对应的压缩包文件到相应的平台服务器。
相比上述图2所示的实施例一，本实施例还包括平台服务器下载对应的压缩包文件的方案。
在一示例中，作为一种实现方式，平台后端配置有若干平台服务器，为了保证数据不丢失，平台服务器可以配置一个或多个备份服务器，同时在平台后端预先根据不同的文件或者不同的平台服务器配置有的不同配置文件，并在平台后端设置相应的转发节点，将不同配置文件转发至相应的平台服务器。
配置文件中可以携带对应栅格数据文件的压缩包存储路径。
作为一种实施方式，在平台服务器下载栅格数据文件时，平台后端或者平台服务器可以根据预先配置的配置文件扫描对应的压缩包存储路径，从所述分布式文件系统下载对应的压缩包文件到相应的平台服务器。
本实施例通过上述方案，数据处理平台获取待处理栅格数据文件，对所述待处理栅格数据文件进行Spark清洗，生成业务需求的图层分包文件；对所述图层分包文件进行压缩，生成压缩包文件存储在分布式文件系统。由此通过分布式模式及spark清洗海量栅格数据，对不同区域或者类型的数据进行负载均衡处理，降低每台机器的负荷，提高响应速度，解决了相关技术中数据处理响应慢的缺点，通过压缩文件存储方式，解决了相关技术海量栅格数据存储空间占用较大的缺点。
此外，平台服务器也可以从分布式文件系统下载对应的压缩包文件到相应的平台服务器，从而实现数据的分发与分布存储。
如图4所示，本公开实施例三提出一种数据处理方法，基于上述图3所示的实施例，所述数据处理系统还包括：平台前端和负载均衡代理服务器，所述方法还包括：
步骤S105，所述平台前端接收用户通过访问平台可视化界面发送的请求信息，将所述请求信息发送至所述负载均衡代理服务器；
步骤S106，所述负载均衡代理服务器根据平台前端发送的请求信息进行负载均衡，将所述请求信息分发到与所述平台后端相应的平台服务器，或者分发到与所述平台后端相应的平台服务器及其备份服务器；
步骤S107，接收到所述请求信息的平台服务器，或者平台服务器及其备份服务器，响应所述请求信息，将本机上的压缩包文件生成图片返回给平台前端呈现。
相比上述图3所示的实施例，本实施例还包括：根据前端请求反馈数据，并实现负载均衡的方案。
在一示例中，平台前端可以提供访问平台可视化界面给用户进行交互操作。平台前端通过访问平台可视化界面接收用户发送的请求信息，将所述请求信息发送至所述负载均衡代理服务器。
其中，请求信息可以包括地理位置信息和业务图层信息中的至少一种。
负载均衡代理服务器可以采用Nginx负载均衡代理服务器，Nginx负载均衡代理服务器提取前端请求信息，根据需要负载到不同的平台服务器，每台服务器可以有至少一台备份服务器，存储相同的数据信息，分发到该台服务器的请求会再次被负载到该服务器和其备份服务器上，因此，多台服务器同时响应不仅可以提高数据响应速度还能保证当其中一台服务器宕机时，请求能够正常返回结果，从而保证数据不丢失。
在平台后端，接收到上述请求信息的平台服务器，或者平台服务器及其备份服务器，响应该请求信息，将本机上的压缩包文件生成PNG等格式图片返回给所述平台前端呈现。
本实施例通过上述方案，数据处理平台获取待处理栅格数据文件，对所述待处理栅格数据文件进行Spark清洗，生成业务需求的图层分包文件；对所述图层分包文件进行压缩，生成压缩包文件存储在分布式文件系统。由此通过分布式模式及spark清洗海量栅格数据，对不同区域或者类型的数据进行负 载均衡处理，降低每台机器的负荷，提高响应速度，解决了相关技术中数据处理响应慢的缺点，通过压缩文件存储方式，解决了相关技术中海量栅格数据存储空间占用较大的缺点。
此外，平台服务器也可以从分布式文件系统下载对应的压缩包文件到相应的平台服务器，从而实现数据的分发与分布存储。平台前端也可以根据用户的请求，将请求信息发送至负载均衡代理服务器，由负载均衡代理服务器根据需要负载到不同的平台服务器，相应的平台服务器进行响应，将本机上的压缩包文件生成PNG图片返回给平台前端呈现，从而在满足前端请求的同时，实现负载均衡处理，降低每台机器的负荷，进一步的提高数据响应速度。
此外，可以参照图1所示，本公开实施例还提出一种数据处理系统，所述数据处理系统包括数据处理平台，所述数据处理平台，设置为获取待处理栅格数据文件，对所述待处理栅格数据文件进行Spark清洗，生成业务需求的图层分包文件，对所述图层分包文件进行压缩，生成压缩包文件存储在分布式文件系统。
在一示例中，所述数据处理系统还包括：平台后端、平台前端和负载均衡代理服务器，所述平台后端配置有若干平台服务器；
所述平台后端，设置为根据预先配置的配置文件扫描对应的压缩包存储路径，从所述分布式文件系统下载对应的压缩包文件到相应的平台服务器；
所述平台前端，设置为接收用户通过访问平台可视化界面发送的请求信息，将所述请求信息发送至所述负载均衡代理服务器；
所述负载均衡代理服务器，设置为根据平台前端发送的请求信息进行负载均衡，将所述请求信息分发到平台后端相应的平台服务器，或者分发到平台后端相应的平台服务器及其备份服务器；
所述平台后端，还设置为接收并呈现平台服务器，或者平台服务器及其备份服务器响应所述请求信息返回的基于本机上的压缩包文件生成的PNG图片。
本实施例数据处理系统实现数据处理的原理请参照上述各实施例，在此不再赘述。
此外，本公开还提出一种数据处理系统，包括：存储器、处理器及存储在所述存储器上并可在所述处理器上运行的数据处理程序，所述数据处理程序被所述处理器执行时实现如上所述的数据处理方法的步骤。
具体地，如图5所示，本实施例系统可以包括：处理器1001，例如CPU，网络接口1004，用户接口1003，存储器1005，通信总线1002。其中，通信总线1002设置为实现这些组件之间的连接通信。用户接口1003可以包括显示屏(Display)、输入单元比如键盘(Keyboard)，可选用户接口1003还可以包括标准的有线接口、无线接口。网络接口1004可选的可以包括标准的有线接口、无线接口(如WI-FI接口)。存储器1005可以是高速RAM存储器，也可以是稳定的存储器(non-volatile memory)，例如磁盘存储器。存储器1005可选的还可以是独立于前述处理器1001的存储装置。
本领域技术人员可以理解，图5中示出的系统结构并不构成对平台的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置。
如图5所示，作为一种计算机存储介质的存储器1005中可以包括操作系统、网络通信模块、用户接口模块以及数据处理程序。
在图5所示的系统中，网络接口1004主要设置为连接网络服务器，与网络服务器进行数据通信；用户接口1003主要设置为连接客户端，与客户端进行数据通信；而处理器1001可以设置为调用存储器1005中存储的数据处理程序，并执行以下操作：
所述数据处理平台获取待处理栅格数据文件；
所述数据处理平台对所述待处理栅格数据文件进行清洗，生成业务需求的图层分包文件；
所述数据处理平台对所述图层分包文件进行压缩，生成压缩包文件存储在分布式文件系统。
在一示例中，处理器1001可以设置为调用存储器1005中存储的数据处理程序，还执行以下操作：
所述平台后端根据预先配置的配置文件扫描对应的压缩包存储路径，从 所述分布式文件系统下载对应的压缩包文件到相应的平台服务器。
在一示例中，处理器1001可以设置为调用存储器1005中存储的数据处理程序，还执行以下操作：
所述平台前端接收用户通过访问平台可视化界面发送的请求信息，将所述请求信息发送至所述负载均衡代理服务器；
所述负载均衡代理服务器根据平台前端发送的请求信息进行负载均衡，将所述请求信息分发到平台后端相应的平台服务器，或者分发到平台后端相应的平台服务器及其备份服务器；
接收到所述请求信息的平台服务器，或者平台服务器及其备份服务器，响应所述请求信息，将本机上的压缩包文件生成图片返回给平台前端呈现。
在一示例中，处理器1001可以设置为调用存储器1005中存储的数据处理程序，还执行以下操作：
获取预设的与业务需求相对应的配置文件；
根据预设的与业务需求相对应的配置文件，通过spark组件对所述待处理栅格数据文件进行Spark清洗，生成业务需求的图层分包文件。
在一示例中，处理器1001可以设置为调用存储器1005中存储的数据处理程序，还执行以下操作：
按照预设规则对所述图层分包文件进行压缩，生成ZIP包文件存储在分布式文件系统。
此外，本公开实施例还提出一种计算机可读存储介质，所述计算机可读存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现如上所述的数据处理方法的步骤。
本实施例数据处理系统实现数据处理的原理请参照上述各实施例，在此不再赘述。
本公开实施例提出的一种数据处理方法、系统及存储介质，通过数据处理平台获取待处理栅格数据文件，对所述待处理栅格数据文件进行Spark清 洗，生成业务需求的图层分包文件；对所述图层分包文件进行压缩，生成压缩包文件存储在分布式文件系统。由此通过分布式模式及spark清洗海量栅格数据，对不同区域或者类型的数据进行负载均衡处理，降低每台机器的负荷，提高响应速度，解决了相关技术中数据处理响应慢的缺点，通过压缩文件存储方式，解决了相关技术海量栅格数据存储空间占用较大的缺点。
此外，平台服务器也可以从分布式文件系统下载对应的压缩包文件到相应的平台服务器，从而实现数据的分发与分布存储。平台前端也可以根据用户的请求，将请求信息发送至负载均衡代理服务器，由负载均衡代理服务器根据需要负载到不同的平台服务器，相应的平台服务器进行响应，将本机上的压缩包文件生成PNG图片返回给平台前端呈现，从而在满足前端请求的同时，实现负载均衡处理，降低每台机器的负荷，进一步的提高数据响应速度。
以上所述仅为本公开的优选实施例，并非因此限制本公开的专利范围，凡是利用本公开说明书及附图内容所作的等效结构或流程变换，或直接或间接运用在其它相关的技术领域，均同理包括在本公开的专利保护范围内。

Claims (10)

  1. 一种数据处理方法，所述方法应用于数据处理系统，所述数据处理系统包括数据处理平台，所述方法包括：
    所述数据处理平台获取待处理栅格数据文件；
    所述数据处理平台对所述待处理栅格数据文件进行清洗，生成业务需求的图层分包文件；
    所述数据处理平台对所述图层分包文件进行压缩，生成压缩包文件存储在分布式文件系统。
  2. 根据权利要求1所述的方法，其中，所述数据处理系统还包括：平台后端，所述平台后端配置有若干平台服务器，所述方法还包括：
    所述平台后端根据预先配置的配置文件扫描对应的压缩包存储路径，从所述分布式文件系统中下载对应的压缩包文件到相应的平台服务器。
  3. 根据权利要求2所述的方法，其中，所述数据处理系统还包括：平台前端和负载均衡代理服务器，所述方法还包括：
    所述平台前端接收用户通过访问平台可视化界面发送的请求信息，将所述请求信息发送至所述负载均衡代理服务器；
    所述负载均衡代理服务器根据所述请求信息进行负载均衡，将所述请求信息分发到与所述平台后端相应的平台服务器，或者分发到与所述平台后端相应的平台服务器及其备份服务器；
    接收到所述请求信息的平台服务器，或者平台服务器及其备份服务器，响应所述请求信息，将本机上的压缩包文件生成图片返回给所述平台前端呈现。
  4. 根据权利要求3所述的方法，其中，所述请求信息包括地理位置信息和业务图层信息中的至少一种。
  5. 根据权利要求1-4中任一项所述的方法，其中，所述对所述待处理栅格数据文件进行清洗，生成业务需求的图层分包文件包括：
    获取预设的与业务需求相对应的配置文件；
    根据预设的与业务需求相对应的配置文件，通过spark组件对所述待处理栅格数据文件进行Spark清洗，生成业务需求的图层分包文件。
  6. 根据权利要求1-4中任一项所述的方法，其中，所述对所述图层分包文件进行压缩，生成压缩包存储在分布式文件系统包括：
    按照预设规则对所述图层分包文件进行压缩，生成ZIP包文件存储在分布式文件系统。
  7. 一种数据处理系统，其特征在于，所述数据处理系统包括数据处理平台，所述数据处理平台，设置为获取待处理栅格数据文件，对所述待处理栅格数据文件进行清洗，生成业务需求的图层分包文件，对所述图层分包文件进行压缩，生成压缩包文件存储在分布式文件系统。
  8. 根据权利要求7所述的数据处理系统，其特征在于，所述数据处理系统还包括：平台后端、平台前端和负载均衡代理服务器，所述平台后端配置有若干平台服务器；
    所述平台后端，设置为根据预先配置的配置文件扫描对应的压缩包存储路径，从所述分布式文件系统下载对应的压缩包文件到相应的平台服务器；
    所述平台前端，设置为接收用户通过访问平台可视化界面发送的请求信息，将所述请求信息发送至所述负载均衡代理服务器；
    所述负载均衡代理服务器，设置为根据平台前端发送的请求信息进行负载均衡，将所述请求信息分发到与所述平台后端相应的平台服务器，或者分发到与所述平台后端相应的平台服务器及其备份服务器；
    所述平台后端，还设置为接收并呈现平台服务器，或者平台服务器及其备份服务器响应所述请求信息返回的基于本机上的压缩包文件生成的图片。
  9. 一种数据处理系统，其特征在于，包括：存储器、处理器及存储在所述存储器上并可在所述处理器上运行的数据处理程序，所述数据处理程序被所述处理器执行时实现如权利要求1-6中任一项所述的数据处理方法的步骤。
  10. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现如权利要求1-6中任一项所述的数据处理方法的步骤。
PCT/CN2019/118770 2018-12-18 2019-11-15 数据处理方法、系统及存储介质 WO2020125290A1 (zh)

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