WO2019056750A1 - Procédé d'identification de caractère unique d'informations, serveur d'application, système et support d'informations - Google Patents

Procédé d'identification de caractère unique d'informations, serveur d'application, système et support d'informations Download PDF

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Publication number
WO2019056750A1
WO2019056750A1 PCT/CN2018/084325 CN2018084325W WO2019056750A1 WO 2019056750 A1 WO2019056750 A1 WO 2019056750A1 CN 2018084325 W CN2018084325 W CN 2018084325W WO 2019056750 A1 WO2019056750 A1 WO 2019056750A1
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Prior art keywords
group
customer
identification
customers
information
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PCT/CN2018/084325
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English (en)
Chinese (zh)
Inventor
王恩贵
项同德
钱慧敏
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平安科技（深圳）有限公司
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Publication of WO2019056750A1 publication Critical patent/WO2019056750A1/fr

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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
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G06F16/284—Relational databases
    • G06F16/285—Clustering or classification

Definitions

  • the present application relates to the field of information recognition technologies, and in particular, to an information uniqueness identification method, an application server, a system, and a storage medium.
  • the purpose of the present application is to provide an information unique identification method, an application server, a system, and a storage medium, which can perform accurate recognition or fuzzy recognition according to different types of group customers, and solve the problem.
  • Some group customers who are incomplete customer information are unable to perform unique identification and data integration issues.
  • An information uniqueness identification method includes the following steps:
  • the group customers in the precise identification category and the fuzzy recognition category are respectively accurately identified and fuzzyly identified, and the group customers who are the same customer are identified;
  • An application server for uniquely identifying information comprising: a processor, a memory, and a communication bus;
  • a computer readable storage medium storing one or more programs, the one or more programs being executable by one or more processors to achieve unique identification of information as described above The steps in the method.
  • An information uniqueness identification system includes a plurality of source databases, which further includes an application server uniquely identified by the information as described above; each source database is configured to store basic information of a group client; and the application server is configured to acquire Basic information of a group customer stored in each source database, wherein the basic information includes a customer name and identification information; and marking each group customer as a precise identification class or a fuzzy recognition class according to the identification information; according to a preset identification rule Accurately identify and fuzzyly identify group customers in the precision recognition class and the fuzzy recognition class, identify group customers who are the same customer; and obtain the recognition results of accurate recognition and fuzzy recognition, and all the same customers according to the recognition result
  • the basic information of the group customers is integrated to obtain uniquely identified group customer information.
  • the information uniqueness identification method obtains basic information of a group client stored in each source database, where The basic information includes the customer name and the identification information; then, each group customer is marked as a precise identification class or a fuzzy recognition class according to the identification information; and then the group customers in the precise recognition class and the fuzzy recognition class are separately performed according to the preset identification rule.
  • Accurate identification and fuzzy recognition identify group customers who are the same customer; then obtain the recognition results of precise identification and fuzzy recognition, and integrate the basic information of all group customers who are the same customer according to the recognition result to obtain unique identification Group customer information.
  • Accurate identification or fuzzy identification can be carried out according to the types of different groups of customers, which solves the problem that some group customers cannot perform unique identification and data integration due to incomplete customer information.
  • FIG. 1 is a schematic diagram of an application environment of an information uniqueness identification method provided by the present application.
  • FIG. 3 is a flowchart of step S20 in the method for uniquely identifying information provided by the present application
  • step S30 is a flowchart of step S30 in the method for uniquely identifying information provided by the present application
  • FIG. 5 is a flowchart of step S32 in the method for uniquely identifying information provided by the present application.
  • FIG. 6 is a flowchart of step S33 in the method for uniquely identifying information provided by the present application.
  • FIG. 7 is a flowchart of step S332 in the method for uniquely identifying information provided by the present application.
  • FIG. 8 is a flowchart of step S333 in the method for uniquely identifying information provided by the present application.
  • FIG. 9 is a schematic diagram of an operating environment of a preferred embodiment of the information uniqueness identification program of the present application.
  • FIG. 10 is a functional block diagram of a preferred embodiment of an application server for installing an information uniqueness identification program of the present application
  • FIG. 11 is a structural block diagram of an information uniqueness identification system provided by the present application.
  • the purpose of the present application is to provide an information unique identification method, an application server, a system and a storage medium, which can be respectively according to different types of group customers.
  • Accurate identification or fuzzy recognition solves the problem that some group customers cannot perform unique identification and data integration due to incomplete customer information.
  • FIG. 1 is a schematic diagram of an application environment of the information uniqueness identification method provided by the present application.
  • one or more applications can be installed in the application server to process related data.
  • the basic information of the group clients stored in the respective source databases may be received by the application server, and the application server marks each group client as a precise identification class or a fuzzy recognition class according to the basic information, and accurately identifies the group.
  • the group customers in the class and fuzzy recognition class respectively perform precise identification and fuzzy recognition, identify group customers who are the same customer, and integrate the basic information of the group customers according to the recognition result, thereby achieving accurate according to the types of different groups of customers. Identify or blur identification to avoid unrecognized customer information.
  • the method for uniquely identifying information includes the following steps:
  • a plurality of source databases may be set to store group customer data of different industrial companies. Since a group customer may have business contacts with different industrial companies under the same company, the same source database stores the same. Group customer data, so it is necessary to uniquely identify and integrate group customers in all source databases to facilitate group customer management and data analysis, specifically to obtain stored group customers from various source databases of different industry companies.
  • Basic information wherein the basic information includes a customer name and identification information, and the specific identification information is information that can identify the identity of the group customer, such as an organization code, a business registration number, a tax registration number, a business license number, and the like.
  • the source database is an oracle database (the oracle database is also named Oracle) RDBMS, or Oracle for short, is a relational database management system from Oracle), MySQL (MySQL is an open source small relational database management system) database or PostgreSQL (PostgreSQL) Is a free object-relational database server database, the target database is hive (hive is a data warehouse tool based on Hadoop) database.
  • Oracle is also named Oracle
  • Oracle is also named Oracle
  • Oracle is a relational database management system from Oracle
  • MySQL MySQL is an open source small relational database management system
  • PostgreSQL PostgreSQL
  • hive is a data warehouse tool based on Hadoop
  • the identification information of different group customers is different, the identification information of different groups of customers is marked as accurate identification or fuzzy recognition, and corresponding to different categories of customers.
  • the uniqueness identification whether the customer identification information is complete or missing, can realize the identification and integration of the group customer, and broaden the application scope of the unique identification of the information. Please refer to FIG. 3 , which is the step S20 of the information uniqueness identification method provided by the present application. Flow chart.
  • the step S20 includes:
  • the identification information of each group customer is analyzed, and the content included in the identification information of each group customer is obtained, and the identification information of each group customer is sequentially determined whether the preset accurate identification information is included. If there is, it is marked as a precise recognition class, otherwise it is marked as a fuzzy recognition class, so as to realize the identification classification of the group customers, and provide a data base for the subsequent targeted identification process.
  • the preset accurate identification information is preferably a unique and always-changing code identifier of the organization code
  • the group customer including the organization code in the identification information is marked as a precise identification class, thereby enabling Fast and accurate accurate identification, and group customers who do not include the organization code are marked as fuzzy recognition, and fuzzy recognition is realized through other customer information comprehensively, which realizes hierarchical recognition and enriches the application scenario of information unique identification.
  • S30 Perform accurate identification and fuzzy recognition on the group customers in the precise identification category and the fuzzy recognition category according to the preset identification rule, and identify group customers that are the same customer.
  • step S30 is a flowchart of step S30 in the method for uniquely identifying information provided by the present application.
  • the step S30 includes:
  • S31 Perform text detection on the customer name of all group customers, and obtain the word number and text content of the customer name;
  • S32 Perform unique identification according to the preset accurate identification information and the customer name of each group customer in the accurate identification class, and identify the group customers in the accurate identification class that are the same customer;
  • S33 Perform unique identification according to the identification information of each group client in the fuzzy recognition class, the number of words of the customer name, and the text content, and identify the group customers in the fuzzy recognition class that are the same customer.
  • the text detection and recognition can adopt the existing OCR character recognition technology, and then According to the customer category, for the customers in the precision identification class, because the identification information includes the preset accurate identification information (in this embodiment, the organization code), according to the preset accuracy of each group customer in the accurate identification class.
  • the identification information and the customer name are uniquely identified, and for the customer in the fuzzy recognition class, since the identification information does not include the preset accurate identification information, it is necessary to combine the other contents in the identification information with the word number and the text of the customer name.
  • the content and the unique identification ensure that the customers in the accurate identification class can accurately and quickly identify, and also ensure that the customers in the fuzzy recognition class can integrate other basic information to identify and satisfy the different information integrity. Identify needs.
  • FIG. 5 is a flowchart of step S32 in the method for uniquely identifying information provided by the present application.
  • the step S32 includes:
  • S322. Determine, according to the comparison result, whether there is a group customer whose preset precision identification information is the same and the customer name is the same, and if yes, the group customer with the same accurate identification information and the same customer name is identified as the group customer with the selected group customer as The same customer; if not, the selected group customer and other group customers in the precision identification category are identified as different customers;
  • the group customers obtained from different source databases there are ten group customers marked as accurate identification classes, which are recorded as customer 1, customer 2, ..., customer 10,
  • accurate identification arbitrarily select one of the group customers to start unique identification, for example, select customer 1, compare its organization code and customer name with other nine group customers, and get the organization of customer 3 and customer 4.
  • the code and customer name are exactly the same as customer 1.
  • customer 1, customer 3 and customer 4 are identified as the same customer, and the customer data of the three can be integrated, and there is no need for customer 3 and customer 4 for subsequent accurate identification.
  • Identify then continue to compare customer 2's organization code and customer name with the other six group customers, and conclude that the organization code and customer name without group customer are exactly the same as customer 2, then customer 2 and other Nine group customers are identified as different customers; according to the above identification process, customers 5, customers 6, ..., and customers 10 are uniquely identified, so that ten customers in the precision identification class are uniquely identified to facilitate group customers. Data analysis and management.
  • step S33 includes:
  • the threshold is greater than or equal to the preset threshold, the uniqueness is determined according to the number of words and the text content of the customer name of the group client;
  • the unique identification is performed according to the identification information of the group customer, the number of words of the customer name, and the text content.
  • the different identification process is performed according to the number of the customer name of the group client, and the number of the customer name of each group client in the fuzzy identification class is determined to be greater than a preset threshold.
  • the preset threshold is preferred. 8 is to judge whether the customer name of each group customer in the fuzzy recognition class is greater than or equal to 8 words. If it is greater than or equal to 8 words, at this time, because the customer name is long, the probability of occurrence of different customer duplicate names is very small, so according to the customer The name word number and the text content are uniquely identified.
  • step S332 includes:
  • S3322 Determine, according to the comparison result, whether there is a group client whose text content of the customer name is completely the same, if yes, identify the group client with the same text content as the same client as the selected group client; if not, the group client will be selected Group customers and all other group customers are identified as different customers;
  • a group client whose number of words in the fuzzy recognition class is greater than or equal to 8 words is arbitrarily selected, and the client is selected.
  • the name is compared with the customer name of all group customers (including the group customers in the precision identification category and the fuzzy recognition category), and the uniqueness is determined based on the comparison result to determine whether there is a group customer whose textual content of the customer name is identical. If there is a group customer who has the same text content and the selected group customer as the same customer, and no matter how many customer accounts with the same text content of the customer name are identified, they are identified as the same customer as the selected group customer.
  • fuzzy recognition For example, among the group customers obtained from different source databases, there are ten group customers marked as fuzzy recognition classes and the number of customer name words is greater than or equal to 8 words, which are recorded as customer 11, customer 12, ..., customer 20, in progress
  • fuzzy recognition arbitrarily select one of the group customers to start unique identification. For example, select customer 11 and compare its customer name with all other group customers. At this time, it may happen that the customer 11 is the same as the group customer in the precision identification class. The customer, but because the organization code is missing from the identification information, it is marked as fuzzy identification. Therefore, when performing fuzzy recognition, it is necessary to compare the selected group customers with the names of all other group customers to ensure the comprehensiveness of the identification.
  • the customer in the precision identification class 2, the customer 13 in the fuzzy recognition class, and the client 14 have the same customer name text content as the customer 11, then the customer 2, the customer 11, the customer 13 and The customer 14 is identified as the same customer, and the customer data of the four can be integrated; after that, the customer of the customer 12 continues to be The name is compared with the other six group customers, and the customer name without the group customer is exactly the same as the customer 12. At this time, the customer 12 and all other group customers are identified as different customers; according to the above identification process, the customer 15 and the customer are sequentially 16.
  • the customer 20 performs unique identification and completes the uniqueness identification process of the group customer whose name word number is greater than 8 words.
  • FIG. 8 is a flowchart of step S333 in the method for uniquely identifying information provided by the present application.
  • the step S333 includes:
  • a group customer whose name is less than 8 words in the fuzzy recognition class is arbitrarily selected, and the customer name is The customer names of all group customers (including group customers in the precision identification category and fuzzy recognition category) are compared with the text content, and uniquely identified based on the comparison results to determine whether there is a group customer whose textual content of the customer name is identical, if any, Since the selected group customer name is short and the name of the group is likely to occur, it is determined whether the group client with the identical text content has the same identification information as the selected group client, such as the business registration number, the tax registration number, At least one of the business license numbers is the same, the customer name text content is identical, and the group customer with any identical identification information is identified as the same customer as the selected group customer; if not, the selected group customer and all others are selected Group customers are identified as different customers and continue to be selected Another group customer with less than 8 words in the fuzzy recognition
  • the recognition results of accurate recognition and fuzzy recognition are obtained at this time, and the recognition results are mutually Integrate the basic information of the group customers of the same customer, specifically to unify the names of all customers who are the same customer, and to complement and integrate the respective identification information to obtain uniquely identified group customer information, according to unique identification.
  • the group customer information can obtain the customer data stored in different source databases by a group of customers at one time, realizes the unified integration analysis of the same customer data in different source databases, and can perform data analysis according to the identification and integration of customer data. Combined with the user data stored in different industrial companies, it can comprehensively analyze the data life cycle, propensity to consume and risk control information of the group customers, which is beneficial to the follow-up and management of group customers.
  • the present application further provides an application server for information unique identification, which includes a processor 10, a memory 20, and a display 30.
  • Figure 9 shows only some of the components of the application server for information uniqueness identification, but it should be understood that not all illustrated components may be implemented, and more or fewer components may be implemented instead.
  • the memory 20 may, in some embodiments, be an internal storage unit of an application server uniquely identified by the information, such as a hard disk or memory of an application server. In other embodiments, the memory 20 may also be an external storage device of the application server uniquely identified by the information, for example, a plug-in hard disk equipped on the application server uniquely identified by the information, and a smart memory card (Smart Media Card, SMC), Secure Digital (SD) card, flash card (Flash) Card) and so on. Further, the memory 20 may also include an internal storage unit of the application server uniquely identified by the information and an external storage device. The memory 20 is configured to store application software and various types of data installed on an application server uniquely identified by the information, such as a program code of an application server uniquely identified by the installation information.
  • a program code of an application server uniquely identified by the installation information such as a program code of an application server uniquely identified by the installation information.
  • the memory 20 can also be used to temporarily store data that has been output or is about to be output.
  • an information uniqueness identification program 40 is stored on the memory 20, and the information uniqueness identification program 40 can be executed by the processor 10 to implement the information uniqueness identification method of various embodiments of the present application.
  • the processor 10 may be a central processing unit (Central Processing Unit) in some embodiments.
  • the display 30 may be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic) in some embodiments. Light-Emitting Diode, organic light emitting diodes), etc.
  • the display 30 is for displaying information of an application server uniquely identified by the information and a user interface for displaying visualization, such as a recognition result interface or the like.
  • the components 10-30 of the application server uniquely identified by the information communicate with one another via a system bus.
  • the processor 10 executes the information uniqueness identification program 40 in the memory 20, the following steps are implemented in the following embodiments of the information uniqueness identification method, and details are not described herein again.
  • FIG. 10 is a functional block diagram of a preferred embodiment of an application server for installing an information uniqueness identification program of the present application.
  • the application server that installs the information uniqueness identification program may be divided into one or more modules, the one or more modules being stored in the memory 20 and being processed by one or more processors ( This embodiment is performed by the processor 10) to complete the application.
  • an application server that installs the information uniqueness identification program may be divided into an acquisition module 21, a classification module 22, an identification module 23, and an integration module 24.
  • a module referred to in this application refers to a series of computer program instruction segments capable of performing a specific function, and is more suitable than the program to describe the execution process of the group customer uniqueness identification program in the application server uniquely identified by the group client. The following description will specifically describe the functions of the modules 21-24.
  • the obtaining module 21 is configured to obtain basic information of a group client stored in each source database, where the basic information includes a customer name and identification information;
  • the classification module 22 is configured to mark each group customer as a precise recognition class or a fuzzy recognition class according to the identification information
  • the identification module 23 is configured to accurately identify and fuzzyly identify the group customers in the precise identification class and the fuzzy recognition class according to the preset identification rule, and identify group customers that are mutually the same customer;
  • the integration module 24 is configured to obtain the recognition result of the accurate identification and the fuzzy recognition, and integrate the basic information of all the group customers who are the same customer according to the recognition result to obtain the uniquely identified group customer information.
  • the classification module 22 includes:
  • a parsing unit for parsing identification information of each group client
  • the classification unit is configured to determine whether the identification information of each group customer includes preset accurate identification information, and if so, the label is a precise recognition class; otherwise, the label is a fuzzy recognition class.
  • the identification module 23 includes:
  • the detecting unit is configured to perform text detection on the customer names of all group customers, and obtain the word number and text content of the customer name;
  • the accurate identification unit is configured to uniquely identify according to the preset accurate identification information and the customer name of each group customer in the accurate identification class, and identify the group customers in the precision identification class that are the same customer;
  • the fuzzy identification unit is configured to perform unique identification according to the identification information of each group client in the fuzzy recognition class, the number of words of the customer name, and the text content, and identify the group customers in the fuzzy recognition class that are mutually the same customer.
  • the precision identification unit includes:
  • the first comparison subunit is configured to arbitrarily select a group customer in the precision identification class, and compare the preset accurate identification information and the customer name with the preset accurate identification information and the customer name of other group customers in the precision identification category;
  • the first identification subunit is configured to determine, according to the comparison result, whether there is a group customer whose preset precision identification information is the same and the customer name is the same, and if yes, the group customer with the same accurate identification information and the same customer name is preset
  • the selected group customers are identified as the same customer; if not, the selected group customers and other group customers in the precision identification category are identified as different customers.
  • the fuzzy recognition unit includes:
  • a first determining sub-unit configured to determine whether a number of customer name words of each group customer in the fuzzy recognition class is greater than or equal to a preset threshold
  • a second identification subunit configured to perform unique identification according to the number of words and text content of the customer name of the group client if the preset threshold is greater than or equal to;
  • the third identification subunit is configured to perform unique identification according to the identification information of the group customer, the number of words of the customer name, and the text content if the threshold is less than a preset threshold.
  • the second identification subunit includes:
  • a second comparison sub-unit configured to arbitrarily select a group customer whose number of customers in the fuzzy recognition class is greater than or equal to a preset threshold, and compare the customer name with the customer name of all group customers;
  • a second determining sub-unit configured to determine, according to the comparison result, whether there is a group client whose text content of the customer name is identical, and if present, identify the group client with the identical text content as the same client as the selected group client; If it exists, the selected group customer and all other group customers are identified as different customers.
  • the third identification subunit includes:
  • the third comparison sub-unit is configured to arbitrarily select a group customer whose number of customers in the fuzzy recognition class is less than a preset threshold, and compare the customer name with the customer name of all group customers;
  • the third determining subunit is configured to determine, according to the comparison result, whether there is a group client whose text content of the customer name is identical, and if yes, continue to determine whether the group client with the identical text content has the same identification as the selected group client.
  • the information is that the group customer with the same text content and having the same identification information is identified as the same customer as the selected group customer; if not, the selected group customer and all other group customers are identified as different customers.
  • the present application further provides an information uniqueness identification system.
  • an information uniqueness identification system Referring to FIG. 11, it includes a plurality of source databases 110 and an application server 120 for uniquely identifying information as described above.
  • Each source database 110 is configured to store basic information of a group client
  • the application server 120 is configured to acquire basic information of a group client stored in each source database, where the basic information includes a customer name and identification information;
  • the identification information marks each group customer as a precision identification class or a fuzzy recognition class; according to the preset identification rule, the group customers in the precise recognition class and the fuzzy recognition class are respectively accurately identified and fuzzyly identified, and the groups that are the same customer are identified.
  • the customer obtains the recognition result of accurate identification and fuzzy recognition, and integrates the basic information of all group customers who are the same customer according to the recognition result, and obtains the uniquely identified group customer information.
  • the workflow of the information uniqueness identification system in this embodiment is the same as the steps in the foregoing embodiments of the information uniqueness identification method, and is not described here.
  • the information uniqueness identification method obtains basic information of a group client stored in each source database, and the basic information includes Customer name and identification information; then, according to the identification information, each group customer is marked as a precise identification class or a fuzzy recognition class; then, according to the preset identification rule, the group customers in the precise recognition class and the fuzzy recognition class are respectively accurately identified and blurred. Identify and identify group customers who are the same customer; then obtain the recognition results of accurate identification and fuzzy recognition, and integrate the basic information of all group customers who are the same customer according to the recognition result, and obtain the uniquely identified group customer information. . Accurate identification or fuzzy identification can be carried out according to the types of different groups of customers, which solves the problem that some group customers cannot perform unique identification and data integration due to incomplete customer information.
  • a computer program to instruct related hardware (such as a processor, a controller, etc.), and the program can be stored in one.
  • the program when executed, may include the processes of the various method embodiments as described above.
  • the storage medium described therein may be a memory, a magnetic disk, an optical disk, or the like.

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Abstract

L'invention concerne un procédé d'identification de caractère unique d'informations, un serveur d'application, un système et un support d'informations. Le procédé d'identification de caractère unique d'informations comprend les étapes qui consistent : à acquérir des informations de base relatives à un client de groupe mémorisées dans chaque base de données source, ces informations de base incluant un nom de client et des informations d'identification ; à marquer chaque client de groupe pour le placer dans une catégorie d'identification précise ou une catégorie d'identification floue en fonction des informations d'identification ; selon une règle d'identification prédéfinie, à effectuer respectivement une identification exacte et une identification floue de clients de groupe dans la catégorie d'identification précise et la catégorie d'identification floue, et à identifier les clients de groupe qui sont le même client ; à acquérir des résultats d'identification exacte et d'identification floue, et à intégrer les informations de base relatives à tous les clients de groupe qui sont le même client conformément aux résultats d'identification, de façon à obtenir les informations de clients de groupe identifiées de manière unique. Une identification exacte ou une identification floue peuvent être réalisées selon les types des différents clients de groupe, et le problème de l'impossibilité d'effectuer une identification unique et une intégration de données en raison d'informations de clients incomplètes est résolu.
PCT/CN2018/084325 2017-09-20 2018-04-25 Procédé d'identification de caractère unique d'informations, serveur d'application, système et support d'informations WO2019056750A1 (fr)

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