CN114048385A - Data processing method and server based on user portrait analysis - Google Patents

Data processing method and server based on user portrait analysis Download PDF

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Publication number
CN114048385A
CN114048385A CN202111390727.1A CN202111390727A CN114048385A CN 114048385 A CN114048385 A CN 114048385A CN 202111390727 A CN202111390727 A CN 202111390727A CN 114048385 A CN114048385 A CN 114048385A
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value index
target user
initial value
user
log
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CN202111390727.1A
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孙凤英
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Skylight Think Tank Culture Communication Suzhou Co ltd
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Skylight Think Tank Culture Communication Suzhou Co ltd
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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
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • 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
    • G06F16/21—Design, administration or maintenance of databases
    • G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors

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  • Databases & Information Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Quality & Reliability (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

The method can bind more and more initial value indexes determined based on the value indexes of non-abnormal user labels in the implementation process, and when the method deals with different scenes, the pre-collected initial value indexes can reflect the normal values of the user labels to the maximum extent, so that the analysis result of whether the analysis target user label is an abnormal user label can be more accurate and credible according to the determined undetermined value index.

Description

Data processing method and server based on user portrait analysis
Technical Field
The present application relates to the field of user portrait analysis technologies, and in particular, to a data processing method and a server based on user portrait analysis.
Background
At present, continuous progress of science and technology enables the user portrait analysis function to be more and more perfect. Even so, in practical implementation, the inventor still finds that the related user portrait analysis technology has some problems, such as difficulty in guaranteeing the accuracy and reliability of the analysis result of the abnormal user label.
Disclosure of Invention
In order to solve the technical problems in the related art, the application provides a data processing method and a server based on user portrait analysis.
The application provides a data processing method based on user portrait analysis, which is applied to a data processing server based on user portrait analysis, and the method at least comprises the following steps:
determining a first activity characteristic of a target user tag and a value index corresponding to the target user tag; judging whether an initial value index matched with the first activity characteristic exists in a pre-collected user behavior log or not;
if yes, determining an undetermined value index used for determining whether a target user label is an abnormal target user label according to a pre-collected initial value index matched with the first activity feature and the value index, determining whether the target user label is the abnormal target user label according to the undetermined value index, and adding the undetermined value index serving as the initial value index matched with the first activity feature into the user behavior log when the target user label is not the abnormal target user label.
Preferably, the determining the first activity characteristic of the target user tag includes:
acquiring a first operation track and a second operation track of the target user tag determined when the target user tag is in a target log content set;
and mapping a second activity characteristic of the target user tag in the first operation track by combining a set mapping strategy to obtain the first activity characteristic, wherein the mapping strategy is used for mapping the activity characteristic in the first operation track and the activity characteristic in the second operation track mutually.
Preferably, the first activity feature is a keyword of a track node corresponding to the target user tag in the second operation track; the determining the value index corresponding to the target user tag includes: and determining the value index corresponding to the target user label according to the value index on each restrictive log content set in the track node.
Preferably, the determining whether the initial value index matched with the first activity feature exists in the pre-collected user behavior log includes:
determining an index condition for indexing the initial value index matched with the first activity characteristic through the first activity characteristic;
and judging whether the initial value index corresponding to the target restrictive log content set in the index condition exists in the pre-collected user behavior log, if so, determining that the initial value index matched with the first activity characteristic exists in the pre-collected user behavior log, and otherwise, determining that the initial value index matched with the first activity characteristic does not exist in the pre-collected user behavior log.
Preferably, the first activity feature is a keyword of a track node corresponding to the target user tag in the second operation track;
binding the value index as an initial value index matching a first activity characteristic to the user behavior log comprises: correspondingly binding a hot restrictive log content set in a target log content set and the value index to the user behavior log;
adding the pending value index as an initial value index matched with the first activity feature to the user behavior log comprises: if the hot restrictive log content set does not exist in the user behavior log, correspondingly binding the hot restrictive log content set and the undetermined value index to the user behavior log; if the hot restrictive log content set exists in the user behavior log, when the initial value index corresponding to the hot restrictive log content set is different from the pending value index, adjusting the initial value index corresponding to the hot restrictive log content set to be the pending value index.
The application also provides a data processing server based on user portrait analysis, which comprises a memory, a processor and a network module; wherein the memory, the processor, and the network module are electrically connected directly or indirectly; the processor reads the computer program from the memory and runs the computer program to realize the method.
The present application also provides a computer-readable storage medium having stored thereon a computer program which, when run, implements the above-described method.
The technical scheme provided by the embodiment of the application can have the following beneficial effects.
In the embodiment of the application, a first activity feature of a target user tag and a value index corresponding to the target user tag can be determined, the value index can reflect the value of the target user tag, whether an initial value index matched with the first activity feature exists in a pre-collected user behavior log or not can be judged, if the initial value index matched with the first activity feature exists in the pre-collected user behavior log, a pending value index can be determined through the initial value index matched with the pre-collected first activity feature and the value index, whether the target user tag is an abnormal target user tag or not is determined through the pending value index, when the target user tag is not the abnormal target user tag, the pending value index can reflect the normal value of the non-abnormal target user tag, and the pending value index is added to the user behavior log as the initial value index matched with the first activity feature, by continuous implementation, more and more initial value indexes determined by the value indexes based on the non-abnormal user tags can be bound, and when different scenes are dealt with, the pre-collected initial value indexes can reflect the normal values of the user tags to the greatest extent, so that the analysis result of whether the analysis target user tag is the abnormal user tag can be more accurate and credible according to the determined undetermined value indexes.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
FIG. 1 is a flowchart of a data processing method based on user portrait analysis according to an embodiment of the present application.
FIG. 2 is a schematic diagram of a hardware structure of a data processing server based on user portrait analysis according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
Referring to fig. 1, an embodiment of the present application provides a flowchart of a data processing method based on user portrait analysis, which is applied to a data processing server based on user portrait analysis, and further, the method may specifically include the following contents recorded in step S1 and step S2.
Step S1, determining a first activity characteristic of a target user label and a value index corresponding to the target user label; and judging whether the initial value index matched with the first activity characteristic exists in the pre-collected user behavior log.
Step S2, if yes, determining a pending value index for determining whether a target user label is an abnormal target user label according to the initial value index matched with the first activity characteristic and the value index which are collected in advance, determining whether the target user label is the abnormal target user label according to the pending value index, and adding the pending value index into the user behavior log as the initial value index matched with the first activity characteristic when the target user label is not the abnormal target user label.
In some further embodiments, the determining the first activity characteristic of the target user tag comprises: acquiring a first operation track and a second operation track of the target user tag determined when the target user tag is in a target log content set; and mapping a second activity characteristic of the target user tag in the first operation track by combining a set mapping strategy to obtain the first activity characteristic, wherein the mapping strategy is used for mapping the activity characteristic in the first operation track and the activity characteristic in the second operation track mutually.
In some other embodiments, the first activity feature is a keyword of a track node corresponding to the target user tag in the second operation track; the determining the value index corresponding to the target user tag includes: and determining the value index corresponding to the target user label according to the value index on each restrictive log content set in the track node.
In some further embodiments, the determining whether there is an initial value index in the pre-collected user behavior log that matches the first activity characteristic includes: determining an index condition for indexing the initial value index matched with the first activity characteristic through the first activity characteristic; and judging whether the initial value index corresponding to the target restrictive log content set in the index condition exists in the pre-collected user behavior log, if so, determining that the initial value index matched with the first activity characteristic exists in the pre-collected user behavior log, and otherwise, determining that the initial value index matched with the first activity characteristic does not exist in the pre-collected user behavior log.
In some other embodiments, the first activity feature is a keyword of a track node corresponding to the target user tag in the second operation track; binding the value index as an initial value index matching a first activity characteristic to the user behavior log comprises: correspondingly binding a hot restrictive log content set in a target log content set and the value index to the user behavior log; adding the pending value index as an initial value index matched with the first activity feature to the user behavior log comprises: if the hot restrictive log content set does not exist in the user behavior log, correspondingly binding the hot restrictive log content set and the undetermined value index to the user behavior log; if the hot restrictive log content set exists in the user behavior log, when the initial value index corresponding to the hot restrictive log content set is different from the pending value index, adjusting the initial value index corresponding to the hot restrictive log content set to be the pending value index.
To sum up, a first activity feature of a target user tag and a value index corresponding to the target user tag may be determined, the value index may reflect a value of the target user tag, whether an initial value index matching the first activity feature exists in a pre-collected user behavior log may be determined, if the initial value index matching the first activity feature exists, a pending value index may be determined by the initial value index matching the pre-collected first activity feature and the value index, whether the target user tag is an abnormal target user tag is determined by the pending value index, and when the target user tag is not an abnormal target user tag, the pending value index may reflect a normal value of a non-abnormal target user tag, and the pending value index is added to the user behavior log as the initial value index matching the first activity feature, by continuous implementation, more and more initial value indexes determined by the value indexes based on the non-abnormal user tags can be bound, and when different scenes are dealt with, the pre-collected initial value indexes can reflect the normal values of the user tags to the greatest extent, so that the analysis result of whether the analysis target user tag is the abnormal user tag can be more accurate and credible according to the determined undetermined value indexes.
On the basis, please refer to fig. 2 in combination, the present application further provides a schematic diagram of a hardware structure of a data processing server 20 based on user portrait analysis, which specifically includes a memory 21, a processor 22, a network module 23, and a data processing device based on user portrait analysis. The memory 21, the processor 22 and the network module 23 are electrically connected directly or indirectly to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 21 stores a data processing device based on user portrait analysis, the data processing device based on user portrait analysis includes at least one software function module which can be stored in the memory 21 in the form of software or firmware (firmware), and the processor 22 executes the software program and the module stored in the memory 21.
The Memory 21 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 21 is configured to store a program, and the processor 22 executes the program after receiving the execution instruction.
The processor 22 may be an integrated circuit chip having data processing capabilities. The Processor 22 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like. The various methods, steps and logic blocks disclosed in embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The network module 23 is used for establishing a communication connection between the data processing server 20 and other communication terminal devices based on user portrait analysis through a network, so as to realize the transceiving operation of network signals and data. The network signal may include a wireless signal or a wired signal.
Further, a readable storage medium is provided, on which a program is stored, which when executed by a processor implements the method described above.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
It is well known to those skilled in the art that with the development of electronic information technology such as large scale integrated circuit technology and the trend of software hardware, it has been difficult to clearly divide the software and hardware boundaries of a computer system. As any of the operations may be implemented in software or hardware. Execution of any of the instructions may be performed by hardware, as well as by software. Whether a hardware implementation or a software implementation is employed for a certain machine function depends on non-technical factors such as price, speed, reliability, storage capacity, change period, and the like. Accordingly, it will be apparent to those skilled in the art of electronic information technology that a more direct and clear description of one embodiment is provided by describing the various operations within the embodiment. Knowing the operations to be performed, the skilled person can directly design the desired product based on considerations of said non-technical factors.
The present application may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present application.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present application may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry can execute computer-readable program instructions to implement aspects of the present application by utilizing state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present application are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.
Having described embodiments of the present application, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the application is defined by the appended claims.

Claims (7)

1. A data processing method based on user portrait analysis, applied to a data processing server based on user portrait analysis, the method at least comprising:
determining a first activity characteristic of a target user tag and a value index corresponding to the target user tag; judging whether an initial value index matched with the first activity characteristic exists in a pre-collected user behavior log or not;
if yes, determining an undetermined value index used for determining whether a target user label is an abnormal target user label according to a pre-collected initial value index matched with the first activity feature and the value index, determining whether the target user label is the abnormal target user label according to the undetermined value index, and adding the undetermined value index serving as the initial value index matched with the first activity feature into the user behavior log when the target user label is not the abnormal target user label.
2. The user representation analysis-based data processing method of claim 1, wherein the determining a first activity characteristic of a target user tag comprises:
acquiring a first operation track and a second operation track of the target user tag determined when the target user tag is in a target log content set;
and mapping a second activity characteristic of the target user tag in the first operation track by combining a set mapping strategy to obtain the first activity characteristic, wherein the mapping strategy is used for mapping the activity characteristic in the first operation track and the activity characteristic in the second operation track mutually.
3. The user representation analysis-based data processing method of claim 1, wherein the first activity feature is a keyword of a track node corresponding to the target user tag in the second operation track; the determining the value index corresponding to the target user tag includes: and determining the value index corresponding to the target user label according to the value index on each restrictive log content set in the track node.
4. The user representation analysis-based data processing method of claim 1, wherein the determining whether the initial value index matching the first activity feature exists in the pre-collected user behavior log comprises:
determining an index condition for indexing the initial value index matched with the first activity characteristic through the first activity characteristic;
and judging whether the initial value index corresponding to the target restrictive log content set in the index condition exists in the pre-collected user behavior log, if so, determining that the initial value index matched with the first activity characteristic exists in the pre-collected user behavior log, and otherwise, determining that the initial value index matched with the first activity characteristic does not exist in the pre-collected user behavior log.
5. The user representation analysis-based data processing method of claim 1, wherein the first activity feature is a keyword of a track node corresponding to the target user tag in the second operation track;
binding the value index as an initial value index matching a first activity characteristic to the user behavior log comprises: correspondingly binding a hot restrictive log content set in a target log content set and the value index to the user behavior log;
adding the pending value index as an initial value index matched with the first activity feature to the user behavior log comprises: if the hot restrictive log content set does not exist in the user behavior log, correspondingly binding the hot restrictive log content set and the undetermined value index to the user behavior log; if the hot restrictive log content set exists in the user behavior log, when the initial value index corresponding to the hot restrictive log content set is different from the pending value index, adjusting the initial value index corresponding to the hot restrictive log content set to be the pending value index.
6. A data processing server based on user portrait analysis is characterized by comprising a memory, a processor and a network module; wherein the memory, the processor, and the network module are electrically connected directly or indirectly; the processor implements the method of any one of claims 1-5 by reading the computer program from the memory and running it.
7. A computer-readable storage medium, on which a computer program is stored which, when executed, implements the method of any of claims 1-5.
CN202111390727.1A 2021-11-23 2021-11-23 Data processing method and server based on user portrait analysis Withdrawn CN114048385A (en)

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Application Number Priority Date Filing Date Title
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Application Number Priority Date Filing Date Title
CN202111390727.1A CN114048385A (en) 2021-11-23 2021-11-23 Data processing method and server based on user portrait analysis

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