User portrait unique identification generation method

Abstract

The present invention provides a method, device, electronic device and readable storage medium for generating a unique identifier for a user portrait. The method comprises: acquiring data from multiple target data sources and adding a unique identifier; determining a target identifier field based on the acquired data; creating a data set based on the target identifier field, and sequentially determining the point, line and graph relationships of the data; and determining the target unique identifier based on the graph relationship of the data. The user identification information reported by the same channel in different time periods is recorded, and converted into a data set through a preset format. The changes in the user identification information are determined through the data set, and the records reported by different channels are identified as the same person, and a target unique identifier is generated. There is no need to write a data query request. The target unique identifier is automatically generated through the input table format and the output table format, and the required development workload and time cost are greatly reduced.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method, device, electronic device and readable storage medium for generating a unique identifier for a user portrait. Background Art

[0002] Customers of the same company may come from different channels. For example, Alibaba has Alipay, Taobao, etc., and Tencent has QQ, WeChat, etc. The types of IDs reported by customers collected by each channel may be different. For example: phone, unionid, openid obtained by WeChat applet; phone, unionid, openid obtained by Alipay applet; phone, feceid obtained by face payment; phone, memberid obtained by CRM system. Users of each system are independent of each other, and there is no way to grasp the full picture of contact with customers, and there is no way to accurately identify the value of customers.

[0003] The existing technical solution formulates rules based on the association of IDs, and then uses SQL to connect the IDs to generate One ID (unique identifier of user portrait). However, the existing technology uses SQL to write, and the rules are relatively complex and require customized development. According to the actual business situation of the company, the types of IDs collected are different, and the corresponding SQL also needs to be modified, resulting in repeated development work; and the maintenance cost is high. Summary of the invention

[0004] The main purpose of the embodiments of the present invention is to propose a method, device, electronic device and readable storage medium for generating a unique identifier for a user portrait, thereby achieving efficient generation of a unique identifier for a user portrait.

[0005] One aspect of the present invention provides a method for generating a unique identifier for a user portrait, characterized in that it includes: in response to a generation request, obtaining first data and second data from multiple target data sources, adding a first unique identifier to the first data and the second data, the first data and the second data being used to represent historical data and new data within a preset time period, respectively; determining a target identification field based on the first data and the second data; determining a first data set based on the target identification field, the first data set being used to represent the point relationship of the target identification field; determining a second data set based on the first data set, the second data set being used to represent the linear relationship of the fields of the first data set; determining a third data set based on the first data set and the second data set, the third data set being used to represent the entity relationship corresponding to the target identification field; determining the target unique identifier based on the first unique identifier in the third data set.

[0006] According to the implementation mode of the present invention, the technical solution of the present invention has at least the following beneficial effects: recording the user identification information reported in different time periods of the same channel, and converting it into a data set through a preset format, determining the change of the user identification information through the data set, identifying the records reported through different channels as the same person, and generating a target unique identifier, without writing a data query request, automatically generating a target unique identifier through the input table format and the output table format, and greatly reducing the required development workload and time cost.

[0007] According to the method for generating a unique identifier for a user portrait, first data and second data are obtained from multiple target data sources, and a first unique identifier is added to the first data and the second data, including: obtaining the first data, adding the first unique identifier to each piece of the first data, and writing it into a target table; obtaining the second data, adding the first unique identifier to each piece of the second data.

[0008] According to the method for generating a unique identifier for a user portrait, determining the target identification field based on the first data and the second data includes: parsing the first data and the second data to obtain corresponding fields; selecting multiple fields as the target identification fields based on the data sources of the fields, and sorting the target identification fields according to the field weights.

[0009] According to the method for generating a unique identifier for a user portrait, a first data set is determined based on the target identification field, including: searching in sequence according to a first preset rule and according to the field weight whether the target identification field is empty, taking the target identification field with the highest weight and not empty as the user identifier, and generating the first data set based on the user identifier, wherein the first data set is a point RDD data set.

[0010] According to the method for generating a unique identifier for a user portrait, determining a second data set based on the first data set includes: obtaining multiple fields of the first data set, generating the second data set according to the first preset rule, wherein the second data set is an edge data set.

[0011] According to the method for generating a unique identifier for a user portrait, a third data set is determined based on the first data set and the second data set, including: the third data set is obtained by drawing the point RDD data set and the edge data set, the third data set is a graph data set, and the fields in the point RDD data set, the edge data set and the graph data set all have corresponding hash codes.

[0012] According to the user portrait unique identifier generation method, the target unique identifier is determined based on the third data set, including: searching whether the first data and the second data include the first unique identifier; if the first unique identifier exists, using the first unique identifier as the target unique identifier; if the first unique identifier does not exist, adding the target unique identifier; if there are multiple first unique identifiers, selecting the target identifier field in the first unique identifier according to the field weight.

[0013] Another aspect of an embodiment of the present invention provides a device for generating a unique identifier of a user portrait, including:

[0014] A data acquisition module, used to acquire first data and second data from multiple target data sources in response to a generation request, and add a first unique identifier to the first data and the second data, wherein the first data and the second data are respectively used to characterize historical data and new data within a preset time period; a target identification field module, used to determine a target identification field based on the first data and the second data; a first data set module, used to determine a first data set based on the target identification field, wherein the first data set is used to characterize the point relationship of the target identification field; a second data set module, used to determine a second data set based on the first data set, wherein the second data set is used to characterize the linear relationship of the fields of the first data set; a third data set module, used to determine a third data set based on the first data set and the second data set, wherein the third data set is used to characterize the entity relationship corresponding to the target identification field; a target unique identification module, used to determine a target unique identification based on the first unique identifier in the third data set.

[0015] Another aspect of an embodiment of the present invention provides an electronic device, including a processor and a memory; the memory is used to store a program; the processor executes the program to implement the method described above.

[0016] The embodiment of the present invention also discloses a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device can read the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the method described above.

[0017] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0019] Figure 1 It is a flowchart of a method for generating a unique identifier for a user portrait according to an embodiment of the present invention.

[0020] Figure 2 It is a schematic diagram of a point data set generation process according to an embodiment of the present invention.

[0021] Figure 3 It is a schematic diagram of the edge data set generation process according to an embodiment of the present invention.

[0022] Figure 4 It is a schematic diagram of a flow chart of generating a graphic data set according to an embodiment of the present invention.

[0023] Figure 5 It is a schematic diagram of the target unique identifier generation process according to an embodiment of the present invention.

[0024] Figure 6 This is a diagram of a user portrait unique identifier generation and analysis device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. In the subsequent description, the use of suffixes such as "module", "component" or "unit" used to represent elements is only for the purpose of facilitating the description of the present invention, and has no specific meaning in itself. Therefore, "module", "component" or "unit" can be used in a mixed manner. "First", "second" and the like are only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features or implicitly indicating the order of the indicated technical features. In this subsequent description, the continuous numbering of the method steps is for the convenience of review and understanding. In combination with the overall technical solution of the present invention and the logical relationship between the various steps, adjusting the implementation order between the steps will not affect the technical effect achieved by the technical solution of the present invention. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0026] Terminology explanation:

[0027] phone: phone number.

[0028] union_id: For example, WeChat has multiple products, the most common of which are official accounts and mini-programs. The subject that needs to be bound when applying for official accounts and mini-programs. There may be multiple mini-programs and official accounts under the same subject (company).

[0029] open_id: Each mini program has a unique openid for different users in different mini programs.

[0030] ep_id: Enterprise ID.

[0031] channel_id: channel ID. For example, the channel may include mini-programs, public accounts, etc. Users reported through different channels have different channel_ids.

[0032] refer to Figure 1 The process of this embodiment includes the following steps:

[0033] S100, in response to a generation request, obtaining first data and second data from multiple target data sources, adding a first unique identifier to the first data and the second data, the first data and the second data being used to represent historical data and newly added data within a preset time period, respectively;

[0034] In some embodiments, the fields that need to be mapped are taken from the data warehouse. Further, all historical data is taken from the full table (idmapping_platform_ids), the data is initialized, a first unique identifier is generated for each piece of data, and the data is written into the target table. The daily incremental data is taken from the dws_idmapping_detail table. And each row of the incremental data is assigned a OneID;

[0035] In some embodiments of the present invention, the first data, the second data and the newly added unique identifier are composed of a 32-bit string.

[0036] S200, determining a target identification field according to the first data and the second data;

[0037] In some embodiments, the data source is a mini program and a public account, wherein the phone number is recorded as: phone, and union_id and open_id are both IDs reported by the mini program and the public account.

[0038] Select the phone, union_id, and open_id of the WeChat applet.

[0039] The phone, union_id, and open_id of the WeChat public account are used as IDs for ID-Mapping.

[0040] The target identification fields selected according to the field weight priority are: phone, union_id, and open_id.

[0041] S300, determining a first data set according to a target identification field, where the first data set is used to characterize a point relationship of the target identification field;

[0042] In some embodiments, reference Figure 2 , which discloses the following process:

[0043] S310, according to the first preset rule and according to the field weight, searching in sequence whether the target identification field is empty, and taking the target identification field with the highest weight and not empty as the user identification;

[0044] S320: Generate a first data set according to the user identifier, where the first data set is a point RDD data set.

[0045] In some embodiments, the first preset rule is to select a field with the highest field weight and which is not empty.

[0046] For example, a point RDD dataset is created based on the selected phone, union_id, and open_id, and its data structure is as follows:

[0047] (userid.hashCode.toLong,(userid,userids,row)), where hashCode is the hash code. The selection rule of userid is based on the field weight, as follows:

[0048] If phone is not empty, userid = phone,

[0049] If phone is empty, userid = union_id,

[0050] If phone and union_id are both empty, user_id = open_id, and so on

[0051] userids is a collection of phone, union_id, and open_id.

[0052] row is a collection of the following ids:

[0053] ep_id,channel_id,phone,union_id,open_id,update_time are the subject ID, channel ID, product subject ID, product ID and data update time respectively.

[0054] S400, determining a second data set according to the first data set, where the second data set is used to characterize a linear relationship of fields of the first data set;

[0055] In some embodiments, reference Figure 3 , which discloses the following process:

[0056] S410, obtaining multiple fields of a first data set, and generating a second data set according to a first preset rule, wherein the second data set is an edge data set.

[0057] For example, an edge dataset is created based on the selected ep_id, channel_id, phone, union_id, open_id, and update_time, and the structure is as follows:

[0058] (userid.hashCode.toLong,id.hashCode.toLong)

[0059] The selection rule of userid is the same as that in the above embodiment.

[0060] The set of selected ids is: (phone,union_id,open_id)

[0061] Then the edge data set formed is:

[0062] phone.hashCode.toLong,union_id.hashCode.toLong;

[0063] phone.hashCode.toLong,open_id.hashCode.toLong.

[0064] The loop executes the process to traverse the sets of ids in the first data and the second data, and finally obtains the edge data set.

[0065] S500, determining a third data set according to the first data set and the second data set, where the third data set is used to characterize the entity relationship corresponding to the target identification field;

[0066] In some embodiments, reference Figure 4 , the process includes:

[0067] S510, a third data set is obtained by drawing the point RDD data set and the edge data set, where the third data set is a graph data set, and fields in the point RDD data set, the edge data set, and the graph data set all have corresponding hash codes.

[0068] Exemplarily, the graph dataset structure formed in this step is as follows:

[0069] (min(id.hashCode.toLong),(userid,userids,row));

[0070] Among them, min(id.hashCode.toLong) is: the smallest hashCode value among the ids of the connected graph formed, (userid,userids,row).

[0071] S600: Determine a target unique identifier according to the first unique identifier in the third data set.

[0072] In some embodiments, reference Figure 5 , which discloses the following process:

[0073] S610, searching whether the first data and the second data include the first unique identifier

[0074] S620, performing corresponding processing according to the number of existing first unique identifiers;

[0075] In some embodiments, if there is a first unique identifier, the first unique identifier is used as the target unique identifier; if there is no first unique identifier, the target unique identifier is added; if there are multiple first unique identifiers, the target identifier field in the first unique identifier is selected according to the field weight.

[0076] Exemplarily, OneID is generated according to the created graph dataset, and the fields in the graph dataset are parsed and input into the target table;

[0077] The following rules need to be followed when generating OneID: In a piece of data, if the OneID has already been generated, it needs to remain unchanged. If there are multiple OneIDs in the association graph, the corresponding OneID is taken out according to the id with higher priority (weight). The newly added OneID is composed of a 32-character string.

[0078] like Figure 6 As shown, the embodiment of the present invention also provides a user portrait unique identification generation and analysis device, which includes a data acquisition module 601, a target identification field module 602, a visualization display module 603, a second data set module 604, a third data set module 605 and a target unique identification module 606;

[0079] Among them, the data acquisition module is used to obtain the first data and the second data from multiple target data sources in response to the generation request, and add a first unique identifier to the first data and the second data, and the first data and the second data are respectively used to represent the historical data and the new data within a preset time period; the target identification field module is used to determine the target identification field according to the first data and the second data; the first data set module is used to determine the first data set according to the target identification field, and the first data set is used to represent the point relationship of the target identification field; the second data set module is used to determine the second data set according to the first data set, and the second data set is used to represent the linear relationship of the fields of the first data set; the third data set module is used to determine the third data set according to the first data set and the second data set, and the third data set is used to represent the entity relationship corresponding to the target identification field; the target unique identification module is used to determine the target unique identification according to the first unique identifier in the third data set.

[0080] Exemplarily, with the cooperation of the data acquisition module, target identification field module, visualization display module, second data set module, third data set module and target unique identification module in the device, the embodiment device can implement any of the aforementioned user portrait unique identification generation methods, namely: acquiring data from multiple target data sources and adding a unique identification; determining the target identification field based on the acquired data; creating a data set based on the target identification field, and sequentially determining the point, line and graph relationship of the data; determining the target unique identification based on the graph relationship of the data. The user identification information reported by the same channel in different time periods is recorded, and converted into a data set through a preset format. The changes in the user identification information are determined through the data set, and the records reported by different channels are identified as the same person, and a target unique identification is generated. There is no need to write a data query request. The target unique identification is automatically generated through the input table format and the output table format, and the required development workload and time cost are greatly reduced.

[0081] An embodiment of the present invention further provides an electronic device, the electronic device comprising a processor and a memory;

[0082] The memory stores a program;

[0083] The processor executes the program to execute the aforementioned method for generating a unique user portrait identifier; the electronic device has the function of carrying and running the software system for generating a unique user portrait identifier provided in an embodiment of the present invention, for example, a personal computer (PC), a mobile phone, a smart phone, a personal digital assistant (PDA), a wearable device, a Pocket PC (PPC), a tablet computer, etc.

[0084] An embodiment of the present invention also provides a computer-readable storage medium, which stores a program, and the program is executed by a processor to implement the method for generating a unique identifier for a user portrait as described above.

[0085] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0086] The embodiment of the present invention also discloses a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device can read the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the aforementioned user portrait unique identifier generation method.

[0087] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified, one or more of the functions and / or features described may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions, and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0088] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0089] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0090] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0091] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0092] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0093] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0094] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the described embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for generating a unique identifier of a user portrait, characterized in that: include: In response to the generation request, first data and second data are obtained from multiple target data sources, and a first unique identifier is added to the first data and the second data, where the first data and the second data are used to represent historical data and new data within a preset time period, respectively; Determine a target identification field according to the first data and the second data; Determine a first data set according to the target identification field, where the first data set is used to characterize the point relationship of the target identification field; Determine a second data set according to the first data set, where the second data set is used to characterize the linear relationship of the fields of the first data set; Determine a third data set according to the first data set and the second data set, wherein the third data set is used to characterize the entity relationship corresponding to the target identification field; Determine a target unique identifier according to the first unique identifier in the third data set; The determining the target unique identifier according to the first unique identifier in the third data set includes: searching whether the first data and the second data include the first unique identifier; If the first unique identifier exists, use the first unique identifier as the target unique identifier; If the first unique identifier does not exist, adding the target unique identifier; If there are multiple first unique identifiers, the target identifier field in the first unique identifier is selected according to the field weight.

2. The method for generating a unique identifier for a user portrait according to claim 1, characterized in that: The acquiring the first data and the second data from the multiple target data sources, and adding a first unique identifier to the first data and the second data, includes: Acquire the first data, add the first unique identifier to each piece of the first data, and write the data into a target table; The second data is obtained, and the first unique identifier is added to each piece of data of the second data.

3. The method for generating a unique identifier for a user portrait according to claim 1, characterized in that: The determining the target identification field according to the first data and the second data includes: Parsing the first data and the second data to obtain corresponding fields; Multiple fields are selected as the target identification fields according to the data sources of the fields, and the target identification fields are sorted according to the field weights.

4. The method for generating a unique identifier for a user portrait according to claim 3, characterized in that: The determining the first data set according to the target identification field includes: According to the first preset rule and according to the field weight, the target identification field is searched in turn to see if it is empty, and the target identification field with the highest weight and not empty is used as the user identifier. The first data set is generated according to the user identifier, and the first data set is a point RDD data set.

5. The method for generating a unique identifier for a user portrait according to claim 4, characterized in that: The determining the second data set according to the first data set includes: A plurality of fields of the first data set are obtained, and the second data set is generated according to the first preset rule, wherein the second data set is an edge data set.

6. The method for generating a unique identifier for a user portrait according to claim 5, characterized in that: The determining a third data set according to the first data set and the second data set includes: The third data set is obtained by drawing the point RDD data set and the edge data set. The third data set is a graph data set. Fields in the point RDD data set, the edge data set and the graph data set all have corresponding hash codes.

7. A device for generating a unique identifier of a user portrait, characterized in that: include: a data acquisition module, configured to acquire first data and second data from multiple target data sources in response to a generation request, and to add a first unique identifier to the first data and the second data, wherein the first data and the second data are respectively used to represent historical data and newly added data within a preset time period; A target identification field module, used to determine a target identification field according to the first data and the second data; A first data set module, used to determine a first data set according to the target identification field, wherein the first data set is used to characterize the point relationship of the target identification field; A second data set module, used to determine a second data set according to the first data set, where the second data set is used to characterize the linear relationship of the fields of the first data set; A third data set module, used to determine a third data set according to the first data set and the second data set, wherein the third data set is used to characterize the entity relationship corresponding to the target identification field; A target unique identification module is used to determine the target unique identification based on the first unique identification in the third data set; it is also used to determine whether the first unique identification is included in the first data and the second data. If the first unique identification exists, the first unique identification is used as the target unique identification; if the first unique identification does not exist, the target unique identification is added; if there are multiple first unique identifications, the target identification field in the first unique identification is selected according to the field weight.

8. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method for generating a unique identifier for a user portrait as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: The storage medium stores a program, and the program is executed by a processor to implement the method for generating a unique identifier for a user portrait as described in any one of claims 1 to 6.

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