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WO2018157392A1 - Procédé et système pour déterminer les entreprises affiliées sur la base de mégadonnées - Google Patents

Procédé et système pour déterminer les entreprises affiliées sur la base de mégadonnées Download PDF

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Publication number
WO2018157392A1
WO2018157392A1 PCT/CN2017/075626 CN2017075626W WO2018157392A1 WO 2018157392 A1 WO2018157392 A1 WO 2018157392A1 CN 2017075626 W CN2017075626 W CN 2017075626W WO 2018157392 A1 WO2018157392 A1 WO 2018157392A1
Authority
WO
WIPO (PCT)
Prior art keywords
enterprise
name
party
user
shareholder
Prior art date
Application number
PCT/CN2017/075626
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/CN2017/075626 priority Critical patent/WO2018157392A1/fr
Publication of WO2018157392A1 publication Critical patent/WO2018157392A1/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 determining an affiliate based on big data.
  • the McKinsey Global Institute defines big data as a collection of data that is large enough to capture, store, manage, and analyze the capabilities of traditional database software tools. It has a large data scale and fast data flow. Four different data types and low value density.
  • 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 present application provides a method for determining an affiliate based on big data. It solves the shortcomings of the prior art technical solution that cannot be automatically judged by the affiliated company.
  • a method for determining an affiliate based on big data comprising the steps of: determining a method for an affiliate based on big data, the method comprising the steps of:
  • the server obtains the name of the enterprise entered by the user
  • the server queries the big data for contract information related to the enterprise name
  • the server determines, according to the contracting party of the contract information, the affiliate company corresponding to the enterprise name.
  • the method further includes:
  • the server extracts the name of the party B from the contract information, and counts the number of the same party name. If the number exceeds the set threshold, it is determined that the enterprise whose number exceeds the set threshold inputs the enterprise associated enterprise for the user.
  • the method further includes:
  • the server queries the shareholder information of Party B in the contract signing party. If the shareholder information is the same as the shareholder information of the company name entered by the user, the enterprise with the same shareholder is regarded as the affiliated enterprise of the enterprise input by the user.
  • a system for determining an affiliate based on big data comprising:
  • An obtaining unit for obtaining a company name input by a user An obtaining unit for obtaining a company name input by a user
  • the processing unit is configured to query the contract information related to the enterprise name from the big data, and determine, according to the contracting party of the contract information, the affiliate enterprise corresponding to the enterprise name.
  • system further includes:
  • a processing unit configured to: the server extracts the name of the party B from the contract information, and counts the number of the same party name, and if the quantity exceeds the set threshold, determines that the enterprise whose quantity exceeds the set threshold enters the enterprise affiliated enterprise for the user .
  • system further includes:
  • the processing unit is configured to query the shareholder information of Party B in the contract signing party. If the shareholder information is the same as the shareholder information of the company name input by the user, the enterprise with the same shareholder is regarded as the affiliated enterprise of the enterprise input by the user.
  • 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 a company name input by a user. ;
  • the processor is configured to query, from the big data, contract information related to the enterprise name, and determine, according to the contractor of the contract information, an affiliate enterprise corresponding to the enterprise name.
  • the processor is configured to: the server extracts the name of the party B from the contract information, and counts the number of the same party name. If the quantity exceeds a set threshold, determining that the number exceeds the set threshold is The user enters the affiliate of the enterprise.
  • the processor is configured to query the shareholder information of the party B in the contract signing party. If the shareholder information is the same as the shareholder information of the enterprise name input by the user, the enterprise with the same shareholder is input as the user. The affiliates of the enterprise.
  • the technical solution provided by the invention realizes the judgment of the affiliated enterprise according to the contracting party of the contract information of the big data, so it has the advantage of realizing the judgment of the affiliated enterprise.
  • FIG. 1 is a flowchart of a method for determining an affiliate based on big data according to a first preferred embodiment of the present invention
  • FIG. 2 is a structural diagram of a system for determining an affiliate based on 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 method for determining an associated enterprise based on big data according to a first preferred embodiment of the present invention. The method is as shown in FIG. 1 and includes the following steps:
  • Step S101 The server acquires the enterprise name input by the user.
  • Step S103 The server determines, according to the contracting party of the contract information, an affiliate enterprise corresponding to the enterprise name.
  • the technical solution provided by the invention realizes the judgment of the affiliated enterprise according to the contracting party of the contract information of the big data, so it has the advantage of realizing the judgment of the affiliated enterprise.
  • the server extracts the name of the party B from the contract information, and counts the number of the same party name. If the quantity exceeds the set threshold, it is determined that the enterprise whose quantity exceeds the set threshold inputs the enterprise associated enterprise for the user.
  • the server queries the shareholder information of Party B in the contract signing party. If the shareholder information is the same as the shareholder information of the company name entered by the user, the enterprise with the same shareholder is regarded as the affiliated enterprise of the enterprise input by the user.
  • FIG. 2 is a system for determining an affiliate based on big data according to a second preferred embodiment of the present invention.
  • the system is as shown in FIG. 2, and includes:
  • the obtaining unit 201 is configured to acquire a company name input by the user;
  • the processing unit 202 is configured to query the contract information related to the enterprise name from the big data, and determine the affiliate enterprise corresponding to the enterprise name according to the contracting party of the contract information.
  • the technical solution provided by the invention realizes the judgment of the affiliated enterprise according to the contracting party of the contract information of the big data, so it has the advantage of realizing the judgment of the affiliated enterprise.
  • the processing unit 202 is configured to: the server extracts the name of the party B from the contract information, and counts the number of the same party name. If the quantity exceeds the set threshold, determining that the number of enterprises exceeding the set threshold is the user. Enter the affiliate of the company.
  • the processing unit 202 is configured to query the shareholder information of the B party in the contract signing party. If the shareholder information is the same as the shareholder information of the enterprise name input by the user, the enterprise with the same shareholder is input as the user. Enterprise affiliates.
  • 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 a business name input by the user.
  • 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 query, from the big data, the contract information related to the enterprise name, and determine, according to the contractor of the contract information, the affiliate enterprise corresponding to the enterprise name.
  • 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

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  • 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)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

L'invention concerne un procédé pour déterminer des entreprises affiliées sur la base de mégadonnées, comprenant les étapes suivantes : un serveur obtient un nom d'entreprise entré par un utilisateur (101) ; le serveur interroge des informations de contrat relatives au nom d'entreprise à partir de mégadonnées (102) ; et le serveur détermine des entreprises affiliées correspondant au nom d'entreprise selon des parties contractantes des informations de contrat (103). Le procédé peut réaliser une interrogation d'entreprises affiliées.
PCT/CN2017/075626 2017-03-03 2017-03-03 Procédé et système pour déterminer les entreprises affiliées sur la base de mégadonnées WO2018157392A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/CN2017/075626 WO2018157392A1 (fr) 2017-03-03 2017-03-03 Procédé et système pour déterminer les entreprises affiliées sur la base de mégadonnées

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2017/075626 WO2018157392A1 (fr) 2017-03-03 2017-03-03 Procédé et système pour déterminer les entreprises affiliées sur la base de mégadonnées

Publications (1)

Publication Number Publication Date
WO2018157392A1 true WO2018157392A1 (fr) 2018-09-07

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PCT/CN2017/075626 WO2018157392A1 (fr) 2017-03-03 2017-03-03 Procédé et système pour déterminer les entreprises affiliées sur la base de mégadonnées

Country Status (1)

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WO (1) WO2018157392A1 (fr)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102005017A (zh) * 2010-12-13 2011-04-06 苏州德融嘉信信用管理技术有限公司 基于资信管理系统的客户信息查询方法
CN102722834A (zh) * 2012-05-18 2012-10-10 苏州万图明电子软件有限公司 企业客户信息管理系统
CN103699645A (zh) * 2013-12-26 2014-04-02 中国人民银行征信中心 企业关联关系识别系统及其识别方法
CN105976078A (zh) * 2016-03-09 2016-09-28 浪潮通用软件有限公司 一种企业客商主数据的形成方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102005017A (zh) * 2010-12-13 2011-04-06 苏州德融嘉信信用管理技术有限公司 基于资信管理系统的客户信息查询方法
CN102722834A (zh) * 2012-05-18 2012-10-10 苏州万图明电子软件有限公司 企业客户信息管理系统
CN103699645A (zh) * 2013-12-26 2014-04-02 中国人民银行征信中心 企业关联关系识别系统及其识别方法
CN105976078A (zh) * 2016-03-09 2016-09-28 浪潮通用软件有限公司 一种企业客商主数据的形成方法

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