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WO2018157333A1 - Procédé et système de traitement de mégadonnées - Google Patents

Procédé et système de traitement de mégadonnées Download PDF

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Publication number
WO2018157333A1
WO2018157333A1 PCT/CN2017/075334 CN2017075334W WO2018157333A1 WO 2018157333 A1 WO2018157333 A1 WO 2018157333A1 CN 2017075334 W CN2017075334 W CN 2017075334W WO 2018157333 A1 WO2018157333 A1 WO 2018157333A1
Authority
WO
WIPO (PCT)
Prior art keywords
processing
big data
processing device
load
server
Prior art date
Application number
PCT/CN2017/075334
Other languages
English (en)
Chinese (zh)
Inventor
马岩
Original Assignee
深圳市博信诺达经贸咨询有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 深圳市博信诺达经贸咨询有限公司 filed Critical 深圳市博信诺达经贸咨询有限公司
Priority to PCT/CN2017/075334 priority Critical patent/WO2018157333A1/fr
Publication of WO2018157333A1 publication Critical patent/WO2018157333A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor

Definitions

  • the present invention relates to the field of data processing, and in particular, to a method and system for processing big data.
  • Big Data The strategic significance of big data technology is not to master huge data information, but to professionalize these meaningful data.
  • big data the key to profitability in this industry is to increase the “processing capability” of the data and “add value” of the data through “processing”.
  • Big data must not be processed by a single computer, and a distributed architecture must be used. It features distributed data mining for massive data. But it must rely on cloud computing for distributed processing, distributed databases and cloud storage, and virtualization technologies. With the advent of the cloud era, big data (Big Data) has also attracted more and more attention.
  • Big Data (Big) Data) is often used to describe a large amount of unstructured data and semi-structured data created by a company that spends too much time and money when downloaded to a relational database for analysis. Big data analytics is often associated with cloud computing because real-time large dataset analysis requires a framework like MapReduce to distribute work to dozens, hundreds, or even thousands of computers.
  • the existing big data processing is limited to the load balancing processing, and the processing of the same type of collecting equipment cannot be classified and processed, and the processing efficiency is low.
  • the present application provides a method of processing big data. It solves the shortcomings of low efficiency of the technical solutions of the prior art.
  • a method of processing big data comprising the following steps: a method of processing big data, the method comprising the steps of:
  • the server obtains big data to be processed
  • the server allocates the big data to the processing device corresponding to the collecting device according to the type of the collecting device of the big data;
  • the server establishes a processing list including: an identification of the processing device and a load amount.
  • the method further includes:
  • the server allocates the load of the processing device according to the load balancing principle.
  • the method further includes:
  • the data of the collection device corresponding to the processing device is stopped.
  • a system for processing big data comprising:
  • An obtaining unit configured to obtain big data to be processed
  • the processing unit is configured to allocate the big data to the processing device corresponding to the collecting device according to the type of the collecting device of the big data, and establish a processing list, where the processing list includes: an identifier of the processing device and a load amount.
  • system further includes:
  • the processing unit is configured to allocate, by the server, the load of the processing device according to a load balancing principle.
  • system further includes:
  • the processing unit is configured to stop receiving data of the collection device corresponding to the processing device if the load of the processing device exceeds a set threshold.
  • a third aspect provides a server, including: a processor, a wireless transceiver, a memory, and a bus, wherein the processor, the wireless transceiver, and the memory are connected by a bus, and the wireless transceiver is configured to acquire big data to be processed. ;
  • the processor is configured to allocate the big data to the processing device corresponding to the collecting device according to the type of the large data collecting device, and establish a processing list, where the processing list includes: an identifier of the processing device and a load.
  • the processor is configured to allocate, by the server, the load of the processing device according to a load balancing principle.
  • the processor is configured to stop receiving data of the collection device corresponding to the processing device if the load of the processing device exceeds a set threshold.
  • the technical solution provided by the invention classifies the processed data according to the category of the collecting device, so that it has the advantage of high processing efficiency.
  • FIG. 1 is a flowchart of a method for processing big data according to a first preferred embodiment of the present invention
  • FIG. 2 is a structural diagram of a system for processing big data according to a second preferred embodiment of the present invention.
  • FIG. 3 is a hardware structural diagram of a server according to a second preferred embodiment of the present invention.
  • FIG. 1 is a schematic diagram of a method for processing big data according to a first preferred embodiment of the present invention. The method is as shown in FIG.
  • Step S101 The server acquires big data to be processed.
  • Step S102 The server allocates the big data to the processing device corresponding to the collection device according to the type of the large data collection device.
  • Step S103 The server establishes a processing list, where the processing list includes: an identifier of the processing device and a load amount.
  • the technical solution provided by the invention classifies the processed data according to the category of the collecting device, so that it has the advantage of high processing efficiency.
  • the server allocates the load of the processing device according to a load balancing principle.
  • the data of the collecting device corresponding to the processing device is stopped.
  • FIG. 2 is a system for processing big data according to a second preferred embodiment of the present invention.
  • the system is as shown in FIG. 2, and includes:
  • An obtaining unit 201 configured to acquire big data to be processed
  • the processing unit 202 is configured to allocate the big data to the processing device corresponding to the collecting device according to the type of the collecting device of the big data, and establish a processing list, where the processing list includes: an identifier of the processing device and a load amount.
  • the technical solution provided by the invention classifies the processed data according to the category of the collecting device, so that it has the advantage of high processing efficiency.
  • the processing unit 202 is configured to allocate, by the server, the load of the processing device according to a load balancing principle.
  • the processing unit 202 is configured to stop receiving data of the collection device corresponding to the processing device if the load of the processing device exceeds a set threshold.
  • FIG. 3 is a server 30, including: a processor 301, a wireless transceiver 302, a memory 303, and a bus 304.
  • the wireless transceiver 302 is configured to send and receive data with and from an external device.
  • the number of processors 301 can be one or more.
  • processor 301, memory 302, and transceiver 303 may be connected by bus 304 or other means.
  • Server 30 can be used to perform the steps of FIG. For the meaning and examples of the terms involved in the embodiment, reference may be made to the corresponding embodiment of FIG. 1. I will not repeat them here.
  • the wireless transceiver 302 is configured to acquire big data to be processed.
  • the program code is stored in the memory 303.
  • the processor 901 is configured to call the program code stored in the memory 903 for performing the following operations:
  • the processor 301 is configured to allocate the big data to the processing device corresponding to the collecting device according to the type of the collecting device of the big data, and establish a processing list, where the processing list includes: an identifier of the processing device and a load.
  • the processor 301 herein may be a processing component or a general term of multiple processing components.
  • the processing element can be a central processor (Central) Processing Unit, CPU), or a specific integrated circuit (Application Specific Integrated) Circuit, ASIC), or one or more integrated circuits configured to implement embodiments of the present application, such as one or more microprocessors (digital singnal Processor, DSP), or one or more Field Programmable Gate Arrays (FPGAs).
  • CPU central processor
  • ASIC Application Specific Integrated Circuit
  • DSP digital singnal Processor
  • FPGAs Field Programmable Gate Arrays
  • the memory 303 may be a storage device or a collective name of a plurality of storage elements, and is used to store executable program code or parameters, data, and the like required for the application running device to operate. And the memory 303 may include random access memory (RAM), and may also include non-volatile memory (non-volatile memory) Memory), such as disk storage, flash (Flash), etc.
  • RAM random access memory
  • non-volatile memory non-volatile memory
  • flash flash
  • Bus 304 can be an industry standard architecture (Industry Standard Architecture, ISA) bus, Peripheral Component (PCI) bus or extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, etc.
  • the bus can be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, only one thick line is shown in Figure 3, but it does not mean that there is only one bus or one type of bus.
  • the terminal may further include input and output means connected to the bus 304 for connection to other parts such as the processor 301 via the bus.
  • the input/output device can provide an input interface for the operator, so that the operator can select the control item through the input interface, and can also be other interfaces through which other devices can be externally connected.
  • the program may be stored in a computer readable storage medium, and the storage medium may include: Flash drive, read-only memory (English: Read-Only Memory, referred to as: ROM), random accessor (English: Random Access Memory, referred to as: RAM), disk or CD.
  • ROM Read-Only Memory
  • RAM Random Access Memory

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Information Transfer Between Computers (AREA)

Abstract

L'invention concerne un procédé de traitement de mégadonnées, comprenant les étapes suivantes : obtention, par un serveur, de mégadonnées à traiter (101) ; attribution, par le serveur, en fonction du type d'un dispositif de collecte de mégadonnées, des mégadonnées à un dispositif de traitement correspondant au dispositif de collecte pour le traitement (102) ; établissement, par le serveur, d'une liste de traitement, la liste de traitement comprenant une identification et la capacité de charge du dispositif de traitement (103). La solution technique fournie par le procédé présente une efficacité de traitement élevée.
PCT/CN2017/075334 2017-03-01 2017-03-01 Procédé et système de traitement de mégadonnées WO2018157333A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/CN2017/075334 WO2018157333A1 (fr) 2017-03-01 2017-03-01 Procédé et système de traitement de mégadonnées

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2017/075334 WO2018157333A1 (fr) 2017-03-01 2017-03-01 Procédé et système de traitement de mégadonnées

Publications (1)

Publication Number Publication Date
WO2018157333A1 true WO2018157333A1 (fr) 2018-09-07

Family

ID=63369591

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2017/075334 WO2018157333A1 (fr) 2017-03-01 2017-03-01 Procédé et système de traitement de mégadonnées

Country Status (1)

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WO (1) WO2018157333A1 (fr)

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103678609A (zh) * 2013-12-16 2014-03-26 中国科学院计算机网络信息中心 一种基于分布式关系-对象映射处理的大数据查询的方法
US20140095505A1 (en) * 2012-10-01 2014-04-03 Longsand Limited Performance and scalability in an intelligent data operating layer system
CN104252528A (zh) * 2014-09-04 2014-12-31 国家电网公司 基于标识符空间映射的大数据二级索引构建方法

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140095505A1 (en) * 2012-10-01 2014-04-03 Longsand Limited Performance and scalability in an intelligent data operating layer system
CN103678609A (zh) * 2013-12-16 2014-03-26 中国科学院计算机网络信息中心 一种基于分布式关系-对象映射处理的大数据查询的方法
CN104252528A (zh) * 2014-09-04 2014-12-31 国家电网公司 基于标识符空间映射的大数据二级索引构建方法

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