A method and system for automatically associating and binding private and public domain attributes

Abstract

The present invention provides a method for automatically associating and binding private-domain and public-domain attributes, comprising: obtaining all member system data of a user; screening member data, and cleaning the data into the form of a main account ID+unionid, wherein unionid is a WeChat ID; splitting the member data, and the split data format is clientid+shoptype+thirdnick+shopid, wherein clientid is a member ID, thirdnick is a platform ID, and shoptype is a platform type; merging data of S2 and S3, and the result set data format is unionid+shoptype+thirdnick+shopid; using the obtained result set as a result set inserted into a member e-commerce account binding table of a private domain; and summarizing and inserting the result set into a private-domain member table.

Description

Technical Field

[0001] The present invention relates to the field of computer data technology, and in particular to a method and system for automatically associating and binding private and public domain attributes. Background Art

[0002] In the PC era, merchants could not directly contact their consumers because users all belonged to the platform. Merchants were just advertisers and product carriers of the platform, and each order was generated only after paying advertising fees, just like a tenant paying rent every month.

[0003] But in the era of mobile Internet, businesses can directly reach consumers through tools such as WeChat, Weibo, Kuaishou, and Douyin, which forms "private domain traffic", meaning that businesses are no longer just tenants of a certain platform, but own their own houses and have property rights.

[0004] Many companies simply use stores on various platforms and operate them as a single store; or use WeChat groups and simple friend methods to operate. Single store operation makes it difficult to reach marketing activity messages, and the operation and publicity costs are high; WeChat group method: user portrait attributes are single, and the lack of sales attribute portraits is not friendly to the design of operation activities, the operation results are not intuitive, and the code development and maintenance costs are high. Summary of the invention

[0005] In view of the above technical problems and in order to overcome the deficiencies of the above-mentioned prior art, the present invention proposes a method and system for automatically associating and binding private-domain and public-domain attributes, which connects private-domain attributes and public-domain attributes to enrich user portraits; enriches the e-commerce attributes of the private domain, and can intuitively perform statistical analysis on the transaction conversion of private-domain customers.

[0006] The present invention provides a method for automatically associating and binding private domain and public domain attributes, comprising:

[0007] S1. Obtain all membership system data of a user;

[0008] S2. Filter member data. The data is cleaned in the form of main account ID + unionid, where unionid is WeChat ID;

[0009] S3. Split the member data into the following format: clientid+shoptype+thirdnick+shopid, where clientid is the member id, thirdnick is the platform id, and shoptype is the platform type;

[0010] S4, merge the data of S2 and S3, the result set data format is: unionid+shoptype+ thirdnick+shopid;

[0011] S5. The obtained result set is used as the result set inserted into the member e-commerce account binding table of the private domain;

[0012] S6. Insert the result set summary into the private domain member table.

[0013] Furthermore, in S1, the membership system data includes e-commerce order data, membership data and enterprise WeChat data of each platform sub-store.

[0014] Furthermore, in S2, the primary account ID is clientid or thirdnick or mobile phone number.

[0015] Furthermore, in the S3, the platform-level platform id does not need the store id, and the store-level platform id needs to be split into the store id field.

[0016] Furthermore, in S4, the platform-level platform id does not need the store id, and the store-level platform id needs to be split into the store id field.

[0017] Furthermore, in S1, the data is uploaded to the DataWorks big data platform.

[0018] In a second aspect, a system for automatically associating and binding private and public domain attributes includes:

[0019] Data acquisition module, used to obtain all membership system data of a user;

[0020] Data screening module, used to screen member data. Data cleaning is in the form of main account ID + unionid, where unionid is WeChat ID.

[0021] A data splitting module is used to split the member data, and the split data format is: clientid+shoptype+thirdnick+shopid, where clientid is the member id, thirdnick is the platform id, and shoptype is the platform type;

[0022] Data merging module, used to merge the data of S2 and S3. The result set data format is: unionid+shoptype+thirdnick+shopid;

[0023] The data result module is used to obtain the result set as the result set inserted into the member e-commerce account binding table of the private domain;

[0024] Data binding module, used to aggregate and insert result sets into the private domain membership table.

[0025] In a third aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor. The memory is used to store computer program instructions executable by the processor, and the processor implements the binding method provided by the embodiment of the present invention when executing the computer program.

[0026] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer program instructions. When the computer program instructions are executed by a computer, the computer executes the steps of the above-mentioned binding method.

[0027] The beneficial effects of the embodiments of the present invention are as follows:

[0028] The present invention improves efficiency, reduces code workload, and reduces development costs and operation and maintenance costs on the basis of completing the same binding work, and effectively and conveniently achieves the goal of big data screening->automatic analysis->intelligent binding, improves the correlation between private and public domain e-commerce attributes, provides favorable assistance for subsequent e-commerce businesses, and greatly helps the diversity, accuracy, and intuitiveness of operational design. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A schematic diagram of a binding method flow chart provided by an embodiment of the present invention;

[0030] Figure 2 A schematic diagram of the composition of a binding device provided by an embodiment of the present invention;

[0031] Figure 3 A schematic diagram of the composition of an electronic device provided by an embodiment of the present invention;

[0032] Figure 4 A schematic diagram of the composition of a computer storage medium provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] Certain embodiments of the present invention will be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all, embodiments are shown. Indeed, various embodiments of the present invention may be implemented in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure may satisfy applicable legal requirements.

[0034] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0035] Data table structure and table and field definitions

[0036] 1. Field literacy:

[0037] (1) Shoptype: The platform type is a fixed value customized by Huibo to distinguish which e-commerce platform it is. 2 represents TB mall, 4 represents JD mall, 13 represents YZ platform, 1 represents offline mobile phone number, etc.

[0038] (2) Thirdnick: E-commerce account IDs are generally divided into platform-level e-commerce account IDs and store-level e-commerce account IDs.

[0039] ① Platform-level e-commerce ID: The e-commerce ID is at the platform level and is unique under the same platform, such as mall JD and platform YZ.

[0040] ② Store-level e-commerce ID: The e-commerce ID is at the store level. The ID is not unique on the same platform. The same person may have different e-commerce IDs when placing orders in different stores, such as TB for malls and DY for platforms.

[0041] 2. Membership Form:

[0042] (1) An omni-channel membership table. One account can create multiple membership cards. One person can join multiple membership cards and generate different membership IDs. The member ID is globally unique, that is, one person can have one or more member IDs, and multiple member IDs must exist under different membership cards.

[0043] (2) Key information includes card ID, member ID, and mobile phone number.

[0044] The data format is: (taking the same person on different cards as an example)

[0045] Membership card ID Member ID Phone number Card 1 123456789 130****0000 Card 2 987654321 130****0000

[0046] 3. Member nickname binding table:

[0047] (1) An extended table of the member table, which specifically stores each member’s e-commerce account on each platform.

[0048] (2) Key information includes: member ID clientid, platform type shoptype, store nickname sellernick, and platform ID thirdnick.

[0049] The data style is the extended data of the member table sample data

[0050] Clientid shoptype sellernick thirdnick 123456789 2 Mall TB Store A Mall TB id_A 123456789 2 Mall TB Shop B Mall TB id_B 123456789 34 Platform DY Store A Platform DY id_A 123456789 35 Unionid_UserA 123456789 4 JD ID 987654321 13 Platform YZ id 987654321 4 JD ID

[0051] 4. Order form:

[0052] (1) The data is the order data of the e-commerce stores owned by the connected merchants. Different e-commerce platforms have different data characteristics.

[0053] (2) Key information: store name, account ID, and mobile phone number.

[0054] The data style is

[0055] Store Name Account ID Phone number Mall TB shop_A Mall TB id_A Mall TB shop_B Mall TB id_B JD Store_A JD ID Platform YZ store_A Platform YZ id 130****0000 Offline store_A 130****0000 130****0000

[0056] Note: Mobile phone numbers are not provided in orders on platforms such as Mall TB and Platform DY. The account ID for orders on some offline niche store platforms is the mobile phone number.

[0057] 5. Private domain member e-commerce account binding table:

[0058] (1) An information table used to store private domain members’ e-commerce account binding information. A unionid is marked as a natural person.

[0059] (2) The key information is unionid, store name sellernick, platform type shoptype, and platform id thirdnick.

[0060] The data style is: (taking a person's binding data as an example)

[0061] Unionid sellernick shoptype thirdnick Unionid_UserA Mall TB Shop A 2 Mall TB id_A Unionid_UserA 4 JD ID Unionid_UserA 13 Platform YZ id Unionid_UserA 1 130****0000

[0062] The Sellernick field stores the specific e-commerce store name only when thirdnick is the store-level id, and is left blank at the platform-level.

[0063] An embodiment of the present invention provides a method for automatically associating and binding private domain and public domain attributes, such as Figure 1 As shown, the steps include:

[0064] S1. Get all the membership system data of a user.

[0065] Users order barrier systems, create their own private domain systems, obtain all membership system data of a user, and upload the data to the DataWorks big data platform.

[0066] S2. Filter member data and clean the data into the form of main account ID + unionid.

[0067] Explanation of main fields: Unionid: WeChat unionid, thirdnick: platform id, clientid: member, shoptype: platform type. There are two types of platform ids: one is at the store level, where the same e-commerce account has different platform ids in different stores on the same platform, such as mall TB and platform DY; the other is at the platform level, where the same e-commerce account is unique on the platform, and different stores have the same platform id, such as mall JD and platform YZ.

[0068] Filter member data and clean the data into the form of main account ID + unionid, which can be divided into several situations:

[0069] A. Processing of member data containing unionid: Filter member data containing unionid. In this case, the primary account ID is clientid. Then clean the data into the format of clientid+unionid.

[0070] B. Process member data without unionid, obtain the member e-commerce account binding table of the corresponding user. At this time, the main account ID is thirdnick, and unionid+thirdnick (platform id) is obtained. Unionid is a unique id that identifies a group of data for one person.

[0071] C. Order data processing, use the mobile phone number to associate with the member table. In this case, the primary account ID is the mobile phone number. Obtain the unionid corresponding to the mobile phone number in the member table. The thirdnick with shoptype=1 in the member e-commerce binding table is the mobile phone number. In this way, the unionid corresponding to the thirdnick obtained in the order is determined.

[0072] S3. Split the member data into the format of clientid+shoptype+thirdnick+shopid.

[0073] Membership, WeChat, order and other data are split according to different processes and nodes.

[0074] Split member data, the split data format is:

[0075] clientid+shoptype+thirdnick+shopid

[0076] The platform-level platform id does not require the store id, and the store-level platform id needs to be split into the store id field. The clientid is a unique id that identifies a person and a group of data.

[0077] Sort out the order data and obtain the key data set: shoptype+thirdnick+mobile number.

[0078] S4. Merge the data of S2 and S3. The result set data format is: unionid+shoptype+ thirdnick+shopid.

[0079] The data obtained in steps S2 and S3 are associated, and the primary account field is used to associate, confirm the relationship between unionid and clientid, and then the unionid in step S2 is distributed to each thirdnick corresponding to the clientid separated in step S3. The format of the merged result set data is unionid+shoptype+thirdnick+shopid. The platform-level platform id does not need the shop id, and the shop-level platform id needs to be separated into the shop id field.

[0080] S5. The obtained result set is used as the result set inserted into the member e-commerce account binding table of the private domain.

[0081] S6. Insert the result set summary into the private domain member table.

[0082] The result set is aggregated and inserted into the private domain member table. When inserting data, the logical unique primary key of the member e-commerce account binding table is judged. If it exists, it will not be inserted. If it does not exist, it will be inserted. For the data set obtained from the order, there is one more layer of judgment. As long as the thirdnick exists in the member table, the thirdnick of the order will not be inserted into the member binding table. The final data structure obtained is a data structure table result with WeChat as the main body, the same WeChat id + multiple platform ids. Automatic binding is completed.

[0083] Another embodiment of the present invention provides a system for automatically associating and binding private domain and public domain attributes, such as Figure 2 As shown, including:

[0084] Data acquisition module, used to obtain all membership system data of a user;

[0085] Data screening module, used to screen member data. Data cleaning is in the form of main account ID + unionid, where unionid is WeChat ID.

[0086] A data splitting module is used to split the member data, and the split data format is: clientid+shoptype+thirdnick+shopid, where clientid is the member id, thirdnick is the platform id, and shoptype is the platform type;

[0087] Data merging module, used to merge the data of S2 and S3. The result set data format is: unionid+shoptype+thirdnick+shopid;

[0088] The data result module is used to obtain the result set as the result set inserted into the member e-commerce account binding table of the private domain;

[0089] Data binding module, used to aggregate and insert result sets into the private domain membership table.

[0090] An embodiment of the present invention further provides an electronic device. Figure 3 As shown, the electronic device 9 includes: a processor 91, a communication interface 92, a memory 93, and a bus 94. The bus 94 is used to realize the connection communication of the components, the communication interface is used to communicate signaling or data with other node devices, and the memory 93 stores machine-readable instructions executable by the processor 91. When the electronic device 9 is running, the processor 91 communicates with the memory 93 through the bus 94, and the above-mentioned binding method is executed when the machine-readable instructions are called by the processor 91.

[0091] The processor 91 includes one or more processors, which are responsible for executing instructions and performing arithmetic, logic and control operations. The processor can be a general-purpose processor, such as a central processing unit, or a dedicated processor, such as a graphics processor.

[0092] The communication interface 92 is an interface for communication and data transmission between different components, and may be a hardware interface, such as a connector, a cable, etc., or a software interface, such as a network protocol.

[0093] The memory 93 is used to store data and programs, and the data and programs used for the timed acquisition model training in the embodiment of the present invention are stored therein. The memory can be a main memory, that is, a main memory, or an auxiliary memory, such as a hard disk, a solid state drive, a USB flash drive, etc.

[0094] Bus 94 is used to transmit data and signals between various components and is divided into a data bus, an address bus and a control bus.

[0095] Figure 4 A computer-readable storage medium provided in an embodiment of the present invention is used to store computer program instructions. The storage medium can be embedded in the electronic device 9. When the computer program instructions are executed by a computer, the computer executes the binding method described in the aforementioned method embodiment.

[0096] The present invention is based on a method for automatically associating and binding private-domain and public-domain attributes, which improves efficiency while completing the same binding work, reduces code workload, and reduces development costs and operation and maintenance costs. It effectively and conveniently achieves the goal of big data screening->automatic analysis->intelligent binding, improves the correlation between private-domain and public-domain e-commerce attributes, provides favorable assistance for subsequent e-commerce businesses, and greatly helps the diversity, accuracy, and intuitiveness of operational design.

[0097] In addition, the above-mentioned figures are only schematic illustrations of the processing included in the method according to the embodiment of the present invention, and are not intended to limit the purpose. The order in the figures does not limit the time sequence of these operations, and these operations can be performed simultaneously or independently in multiple modules. Moreover, multiple steps can be combined into one step for execution, or one step can be disassembled into multiple steps for execution.

[0098] The above description is only a specific implementation mode of the present invention and is not intended to limit the protection scope of the present invention. For those skilled in the art, the above embodiments can be changed or modified within the scope of the present invention, and these changes and modifications also fall within the protection scope of the claims of the present invention.

Claims

1. A method for automatically associating and binding private and public domain attributes. It is characterized in that include: S1. Obtain all membership system data of a user, including e-commerce order data, membership data and enterprise WeChat data of each platform's sub-stores; S2. Filter member data. The data is cleaned in the form of main account ID + unionid, where unionid is WeChat ID and main account ID is member ID or platform ID; The data cleaning is divided into: A. Processing member data containing unionid: filter member data containing unionid. In this case, the primary account ID is clientid, and then clean the data into the format of clientid+unionid. B. Process member data without unionid, obtain the member e-commerce account binding table of the corresponding user. In this case, the primary account ID is thirdnick, and unionid+thirdnick is obtained; S3. Split the member data into the following data format: clientid+shoptype+thirdnick+shopid. The platform-level platform id does not need the shop id, and the shop-level platform id needs to be split into the shop id field, where clientid is the member id, thirdnick is the platform id, and shoptype is the platform type. S4, merge the data of S2 and S3, the result set data format is: unionid+shoptype+ thirdnick+shopid; S5. The obtained result set is used as the result set inserted into the member e-commerce account binding table of the private domain; S6. Insert the result set into the private domain member table; The above member e-commerce account binding table includes unionid, sellernick, shoptype, and thirdnick, where sellernick is the shop id.

2. The binding method according to claim 1, It is characterized in that In S1, the data is uploaded to the DataWorks big data platform.

3. A system for automatically associating and binding private and public domain attributes. It is characterized in that include: The data acquisition module is used to obtain all the membership system data of a user. The membership system data includes e-commerce order data of each platform's sub-stores, membership data, and corporate WeChat data; Data screening module, used to screen member data. Data cleaning is in the form of main account ID + unionid, where unionid is WeChat ID and main account ID is member ID or platform ID. The data cleaning is divided into: A. Processing member data containing unionid: filter member data containing unionid. In this case, the primary account ID is clientid, and then clean the data into the format of clientid+unionid. B. Process member data without unionid, obtain the member e-commerce account binding table of the corresponding user. In this case, the primary account ID is thirdnick, and unionid+thirdnick is obtained; A data splitting module is used to split the member data. The split data format is: clientid+shoptype+thirdnick+shopid. The platform-level platform id does not need the shop id. The shop-level platform id needs to be split into the shop id field, where clientid is the member id, thirdnick is the platform id, and shoptype is the platform type. Data merging module, used to merge the data of S2 and S3. The result set data format is: unionid+shoptype+thirdnick+shopid; The data result module is used to obtain the result set as the result set inserted into the member e-commerce account binding table of the private domain; Data binding module, used to aggregate the result set and insert it into the private domain member table; The above member e-commerce account binding table includes unionid, sellernick, shoptype, and thirdnick, where sellernick is the shop id.

4. A computer-readable storage medium storing computer program instructions, wherein when the computer program instructions are executed by a computer, the computer executes the steps of the binding method according to any one of claims 1 to 2.

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