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WO2018157391A1 - Procédé et système d'évaluation de mégadonnées en entreprise - Google Patents

Procédé et système d'évaluation de mégadonnées en entreprise Download PDF

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Publication number
WO2018157391A1
WO2018157391A1 PCT/CN2017/075625 CN2017075625W WO2018157391A1 WO 2018157391 A1 WO2018157391 A1 WO 2018157391A1 CN 2017075625 W CN2017075625 W CN 2017075625W WO 2018157391 A1 WO2018157391 A1 WO 2018157391A1
Authority
WO
WIPO (PCT)
Prior art keywords
enterprise
server
information
big data
evaluated
Prior art date
Application number
PCT/CN2017/075625
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/CN2017/075625 priority Critical patent/WO2018157391A1/fr
Publication of WO2018157391A1 publication Critical patent/WO2018157391A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce

Definitions

  • the present invention relates to the field of data processing, and in particular, to a method and system for evaluating big data enterprises.
  • 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 application provides a method for big data enterprise evaluation. It solves the shortcomings that the prior art technical solutions cannot evaluate the enterprise.
  • a method for evaluating a big data enterprise comprising the following steps: a method for evaluating a big data enterprise, the method comprising the following steps:
  • the server obtains the enterprise identifier to be evaluated
  • the server queries, according to the enterprise identifier, information related to the enterprise identifier from the big data;
  • the server evaluates the enterprise credit based on the related information.
  • the method further includes:
  • the server queries the related information for the default amount and the default amount of the contract of the enterprise information. If the default data and the default amount exceed the set threshold, the enterprise evaluates the dish as dishonest.
  • the method further includes:
  • the server queries from the related information whether there is the number and amount of unexecuted cases of the court of the enterprise, and if so, the enterprise is evaluated as dishonest.
  • a system for big data enterprise evaluation comprising:
  • An obtaining unit configured to obtain an enterprise identifier to be evaluated
  • the processing unit is configured to query, according to the enterprise identifier, information related to the enterprise identifier from the big data, and evaluate the enterprise credit according to the related information.
  • system further includes:
  • the processing unit is configured to query, by the server, the default amount and the default amount of the contract of the enterprise information from the related information, and if the default data and the default amount exceed a set threshold, the enterprise is evaluated as dishonest.
  • system further includes:
  • a processing unit configured to query, from the related information, whether there is a number of the unexecuted cases of the enterprise and the amount of the case, and if yes, the enterprise is evaluated as dishonest.
  • 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 an enterprise identifier to be evaluated. ;
  • the processor is configured to query, according to the enterprise identifier, information related to the enterprise identifier from the big data, and evaluate the enterprise credit according to the related information.
  • the processor is configured to query, by the server, the default amount and the default amount of the contract of the enterprise information from the related information, and if the default data and the default amount exceed a set threshold, the enterprise evaluation is performed. For dishonesty.
  • the processor is configured to query, from the related information, whether there is a number of court unexecuted cases and an amount of the enterprise, and if yes, the enterprise is evaluated as dishonest.
  • the technical solution provided by the invention retrieves big data related to the enterprise by searching for big data, and then evaluates the integrity of the enterprise according to the breach information or the court execution information in the big data, so it has the advantages of realizing the evaluation of the enterprise. .
  • FIG. 1 is a flowchart of a method for evaluating big data enterprises according to a first preferred embodiment of the present invention
  • FIG. 2 is a structural diagram of a system for evaluating big data enterprises 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 evaluating a big data enterprise 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 an enterprise identifier to be evaluated.
  • the company logo may specifically be a company name or a corporate unified credit code.
  • Step S102 The server queries, according to the enterprise identifier, information related to the enterprise identifier from the big data.
  • Step S103 The server evaluates the enterprise credit according to the related information.
  • the technical solution provided by the invention retrieves big data related to the enterprise by searching for big data, and then evaluates the integrity of the enterprise according to the breach information or the court execution information in the big data, so it has the advantages of realizing the evaluation of the enterprise. .
  • the server queries, from the related information, the default amount and the default amount of the contract of the enterprise information, and if the default data and the default amount exceed a set threshold, the enterprise is evaluated as dishonest.
  • the server queries, from the related information, whether there is a number of the unexecuted cases of the enterprise and the amount of the case, and if yes, the enterprise is evaluated as dishonest.
  • FIG. 2 is a system for evaluating big data enterprises 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 obtain an enterprise identifier to be evaluated
  • the processing unit 202 is configured to query, according to the enterprise identifier, information related to the enterprise identifier from the big data, and evaluate the enterprise credit according to the related information.
  • the technical solution provided by the invention retrieves big data related to the enterprise by searching for big data, and then evaluates the integrity of the enterprise according to the breach information or the court execution information in the big data, so it has the advantages of realizing the evaluation of the enterprise. .
  • the processing unit 202 is configured to query, by the server, the default amount and the default amount of the contract of the enterprise information from the related information, and if the default data and the default amount exceed a set threshold, the enterprise is evaluated as not honest.
  • the processing unit 202 is configured to query, from the related information, whether the number of the unexecuted cases of the enterprise and the amount of the enterprise are obtained, and if yes, the enterprise is evaluated as dishonest.
  • 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 obtain an enterprise identifier to be evaluated.
  • 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, according to the enterprise identifier, information related to the enterprise identifier from the big data, and evaluate the enterprise credit according to the related information.
  • 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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  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Marketing (AREA)
  • Development Economics (AREA)
  • General Business, Economics & Management (AREA)
  • Educational Administration (AREA)
  • Tourism & Hospitality (AREA)
  • Quality & Reliability (AREA)
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  • Game Theory and Decision Science (AREA)
  • Accounting & Taxation (AREA)
  • Finance (AREA)
  • Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)

Abstract

L'invention a trait à un procédé d'évaluation de mégadonnées en entreprise, comprenant les étapes suivantes : un serveur obtient une identification d'entreprise à évaluer (101) ; le serveur interroge des informations liées à l'identification d'entreprise à partir de mégadonnées et selon l'identification d'entreprise (102) ; et le serveur évalue le crédit d'entreprise en fonction des informations liées (103). Ce procédé permet de réaliser une évaluation d'entreprise.
PCT/CN2017/075625 2017-03-03 2017-03-03 Procédé et système d'évaluation de mégadonnées en entreprise WO2018157391A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/CN2017/075625 WO2018157391A1 (fr) 2017-03-03 2017-03-03 Procédé et système d'évaluation de mégadonnées en entreprise

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2017/075625 WO2018157391A1 (fr) 2017-03-03 2017-03-03 Procédé et système d'évaluation de mégadonnées en entreprise

Publications (1)

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WO2018157391A1 true WO2018157391A1 (fr) 2018-09-07

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112330083A (zh) * 2020-08-19 2021-02-05 深圳市原点参数信息技术有限公司 一种基于大数据的量化评估方法

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030069822A1 (en) * 2001-10-09 2003-04-10 Kunio Ito Corporate value evaluation system
CN101667266A (zh) * 2008-09-03 2010-03-10 山东征信信用管理咨询有限公司 征信评级管理咨询系统
CN105427033A (zh) * 2015-11-10 2016-03-23 广州西麦科技股份有限公司 一种基于大数据的个人诚信系统
CN105740446A (zh) * 2016-02-02 2016-07-06 河南九博科技股份有限公司 招聘网站用于诚信信用评价的企业信息集成方法及装置
CN105787073A (zh) * 2016-03-02 2016-07-20 上海源庐加佳信息科技有限公司 一种基于大数据挖掘技术的企业信用评价方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030069822A1 (en) * 2001-10-09 2003-04-10 Kunio Ito Corporate value evaluation system
CN101667266A (zh) * 2008-09-03 2010-03-10 山东征信信用管理咨询有限公司 征信评级管理咨询系统
CN105427033A (zh) * 2015-11-10 2016-03-23 广州西麦科技股份有限公司 一种基于大数据的个人诚信系统
CN105740446A (zh) * 2016-02-02 2016-07-06 河南九博科技股份有限公司 招聘网站用于诚信信用评价的企业信息集成方法及装置
CN105787073A (zh) * 2016-03-02 2016-07-20 上海源庐加佳信息科技有限公司 一种基于大数据挖掘技术的企业信用评价方法

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112330083A (zh) * 2020-08-19 2021-02-05 深圳市原点参数信息技术有限公司 一种基于大数据的量化评估方法

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