CN113901100A - Member information management method and device - Google Patents
Member information management method and device Download PDFInfo
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- CN113901100A CN113901100A CN202010902978.2A CN202010902978A CN113901100A CN 113901100 A CN113901100 A CN 113901100A CN 202010902978 A CN202010902978 A CN 202010902978A CN 113901100 A CN113901100 A CN 113901100A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
- G06Q30/0226—Incentive systems for frequent usage, e.g. frequent flyer miles programs or point systems
- G06Q30/0229—Multi-merchant loyalty card systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
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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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
Abstract
The embodiment of the invention provides a member information management method and a member information management device, wherein the member label information and member personalized input information acquired by each member input device in a target member merchant are acquired, then the member label information is analyzed according to a member authority state analysis model trained in advance to obtain the member authority state of each member user in the member label information, meanwhile, the member personalized input information is analyzed to obtain a personalized coverage interaction state corresponding to the member personalized input information, and finally, a member statistical report of a target merchant object corresponding to the target member merchant is generated according to the member authority state and the personalized coverage interaction state to obtain a generated result. Therefore, the problems of high randomness, subjectivity, and tedious and time-consuming statistical process in the existing member management process can be effectively solved, and merchants are helped to summarize member management conditions.
Description
Technical Field
The invention relates to the field of information management, in particular to a member information management method and device.
Background
How to effectively solve the problems of great randomness, subjectivity, and tedious and time-consuming statistical process in the existing member management process to help merchants summarize member management conditions is a big problem in the field.
Disclosure of Invention
In view of this, an object of the embodiments of the present invention is to provide a member information management method and apparatus, which can effectively solve the problems of great randomness and subjectivity, and tedious and time-consuming statistical process in the existing member management process, so as to help a merchant to summarize the member management situation.
According to an aspect of an embodiment of the present invention, there is provided a member information management method applied to a server, the method including:
acquiring member configuration information acquired by each member entry device in a target member merchant, wherein the classroom process data comprises member label information and member personalized entry information;
analyzing the member label information according to a pre-trained member authority state analysis model to obtain a member authority state of each member user in the member label information, wherein the member authority state comprises a member grade state and a member time-length state;
analyzing the member personalized input information to obtain a personalized coverage interaction state corresponding to the member personalized input information;
and generating a member statistical report of the target merchant object corresponding to the target member merchant according to the member authority state and the personalized coverage interaction state to obtain a generated result.
In one possible example, the step of acquiring the member configuration information collected by each member entry device in the target member merchant includes:
determining a target merchant object to be evaluated according to the received generation request;
acquiring a target member merchant corresponding to the target merchant object;
sending a data acquisition request to each member logging device in a target member merchant;
and receiving the acquired member configuration information sent by each member logging device in the target member merchant in response to the data acquisition request.
In one possible example, the member authority state analysis model is trained by:
acquiring a member authority state sample set, wherein the member authority state sample set comprises a plurality of member authority state samples and a member authority state label labeled by each member authority state sample;
and inputting a plurality of member authority state samples included in the member authority state sample set and a member authority state label labeled by each member authority state sample into an initially configured deep learning network model, and obtaining the member authority state analysis model through iterative training.
In one possible example, the recognition network model of the personalized coverage interaction state is trained by:
acquiring a member personalized input information sample set, wherein the member personalized input information sample set comprises a plurality of member personalized input information samples and personalized coverage interaction state labels marked on each member personalized input information sample;
extracting the personalized input characteristics corresponding to each member personalized input information sample;
inputting the personalized entry characteristics corresponding to each member personalized entry information sample and the personalized coverage interaction state label marked by each member personalized entry information sample into an initially configured deep learning network model, and obtaining the identification network model of the personalized coverage interaction state through iterative training.
In a possible example, the step of generating a member statistical report of a target merchant object corresponding to the target member merchant according to the member authority state and the personalized coverage interaction state to obtain a generated result includes:
counting the number of first member users in each member level state and the number of second member users in each member duration state according to the member level state and the member duration state of each member user;
determining the duration of each personalized coverage interaction state according to various personalized coverage interaction states counted in a set time period, and determining a corresponding first statistical report unit according to the duration of each personalized coverage interaction state to obtain the sum of the first statistical report units;
respectively counting the sum of second statistical report units corresponding to the first member user quantity in each member level state and the sum of third statistical report units corresponding to the second member user quantity in each member duration state according to the first member user quantity in each member level state and the second member user quantity in each member duration state;
and obtaining the generated result according to the sum of the first statistical report units, the sum of the second statistical report units and the sum of the third statistical report units.
According to another aspect of an embodiment of the present invention, there is provided a member information management apparatus applied to a server, the apparatus including:
the acquisition module is used for acquiring member configuration information acquired by each member entry device in a target member merchant, and the classroom process data comprises member label information and member personalized entry information;
the first analysis module is used for analyzing the member label information according to a pre-trained member authority state analysis model to obtain a member authority state of each member user in the member label information, wherein the member authority state comprises a member grade state and a member time-length state;
the second analysis module is used for analyzing the member personalized input information to obtain a personalized coverage interaction state corresponding to the member personalized input information;
and the generating module is used for generating a member statistical report of a target merchant object corresponding to the target member merchant according to the member authority state and the personalized coverage interaction state to obtain a generating result.
According to another aspect of embodiments of the present invention, there is provided a readable storage medium having stored thereon a computer program, which, when executed by a processor, is capable of performing the steps of the member information management method described above.
Compared with the prior art, the member information management method and the member information management device provided by the embodiment of the invention have the advantages that the member label information and the member personalized input information acquired by each member input device in the target member merchant are acquired, then the member label information is analyzed according to the pre-trained member authority state analysis model, the member authority state of each member user in the member label information is acquired, meanwhile, the member personalized input information is analyzed, the personalized coverage interaction state corresponding to the member personalized input information is acquired, and finally, the member statistical statement of the target merchant object corresponding to the target member merchant is generated according to the member authority state and the personalized coverage interaction state, and the generated result is acquired. Therefore, the problems of high randomness, subjectivity, and tedious and time-consuming statistical process in the existing member management process can be effectively solved, and merchants are helped to summarize member management conditions.
In order to make the aforementioned objects, features and advantages of the embodiments of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 illustrates a component diagram of a server provided by an embodiment of the invention;
fig. 2 is a flow chart illustrating a member information management method according to an embodiment of the present invention;
fig. 3 is a block diagram showing functional blocks of a member information management apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," "third," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Fig. 1 shows an exemplary component schematic of a server 100. The server 100 may include one or more processors 104, such as one or more Central Processing Units (CPUs), each of which may implement one or more hardware threads. The server 100 may also include any storage media 106 for storing any kind of information, such as code, settings, data, etc. For example, and without limitation, storage medium 106 may include any one or more of the following in combination: any type of RAM, any type of ROM, flash memory devices, hard disks, optical disks, etc. More generally, any storage medium may use any technology to store information. Further, any storage medium may provide volatile or non-volatile retention of information. Further, any storage medium may represent a fixed or removable component of server 100. In one case, when the processor 104 executes the associated instructions stored in any storage medium or combination of storage media, the server 100 may perform any of the operations of the associated instructions. The server 100 further comprises one or more drive units 108 for interacting with any storage medium, such as a hard disk drive unit, an optical disk drive unit, etc.
The server 100 also includes input/output 110 (I/O) for receiving various inputs (via input unit 112) and for providing various outputs (via output unit 114)). One particular output mechanism may include a presentation device 116 and an associated Graphical User Interface (GUI) 118. The server 100 may also include one or more network interfaces 120 for exchanging data with other devices via one or more communication units 122. One or more communication buses 124 couple the above-described components together.
The communication unit 122 may be implemented in any manner, such as over a local area network, a wide area network (e.g., the internet), a point-to-point connection, etc., or any combination thereof. The communication unit 122 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers 100, and so forth, governed by any protocol or combination of protocols.
Fig. 2 is a flowchart illustrating a member information management method according to an embodiment of the present invention, which can be executed by the server 100 shown in fig. 1, and the detailed steps of the member information management method are described as follows.
Step S110, acquiring member configuration information acquired by each member entry device in a target member merchant, wherein the classroom process data comprises member label information and member personalized entry information.
Step S120, analyzing the member label information according to a pre-trained member authority state analysis model to obtain the member authority state of each member user in the member label information, wherein the member authority state comprises a member grade state and a member time-length state.
And step S130, analyzing the member personalized input information to obtain a personalized coverage interaction state corresponding to the member personalized input information.
And step S140, generating a member statistical report of the target merchant object corresponding to the target member merchant according to the member authority state and the personalized coverage interaction state to obtain a generation result.
Based on the design, in the embodiment, the member label information and the member personalized input information acquired by each member input device in the target member merchant are acquired, then the member label information is analyzed according to a pre-trained member authority state analysis model to obtain the member authority state of each member user in the member label information, meanwhile, the member personalized input information is analyzed to obtain the personalized coverage interaction state corresponding to the member personalized input information, and finally, a member statistical report of the target merchant object corresponding to the target member merchant is generated according to the member authority state and the personalized coverage interaction state to obtain a generated result. Therefore, the problems of high randomness, subjectivity, and tedious and time-consuming statistical process in the existing member management process can be effectively solved, and merchants are helped to summarize member management conditions.
Optionally, in step S110, a target merchant object to be evaluated may be determined according to the received generation request, then a target member merchant corresponding to the target merchant object is obtained, then a data obtaining request is sent to each member entry device in the target member merchant, and then collected member configuration information sent by each member entry device in the target member merchant in response to the data obtaining request is received.
Optionally, for step S120, the member authority state analysis model may be trained by:
firstly, a member authority state sample set is obtained, wherein the member authority state sample set comprises a plurality of member authority state samples and a member authority state label marked on each member authority state sample.
And then, inputting a plurality of member authority state samples included in the member authority state sample set and a member authority state label labeled by each member authority state sample into an initially configured deep learning network model, and obtaining the member authority state analysis model through iterative training.
Optionally, for step S130, the recognition network model of the personalized coverage interaction state may be trained by:
firstly, acquiring a member personalized input information sample set, wherein the member personalized input information sample set comprises a plurality of member personalized input information samples and personalized coverage interaction state labels marked on each member personalized input information sample.
And then, extracting the personalized entry characteristics corresponding to each member personalized entry information sample.
And finally, inputting the personalized entry characteristics corresponding to each member personalized entry information sample and the personalized coverage interaction state label marked by each member personalized entry information sample into an initially configured deep learning network model, and obtaining the personalized coverage interaction state recognition network model through iterative training.
Alternatively, with respect to step S140, first, the number of first member users in each member level state and the number of second member users in each member duration state may be counted according to the member level state and the member duration state of each member user.
And then, determining the duration of each personalized coverage interaction state according to various personalized coverage interaction states counted in a set time period, and determining a corresponding first statistical report unit according to the duration of each personalized coverage interaction state to obtain the sum of the first statistical report units.
And simultaneously, respectively counting the sum of second statistical report units corresponding to the first member user quantity in each member grade state and the sum of third statistical report units corresponding to the second member user quantity in each member time length state according to the first member user quantity in each member grade state and the second member user quantity in each member time length state.
And finally, obtaining the generated result according to the sum of the first statistical report units, the sum of the second statistical report units and the sum of the third statistical report units.
Fig. 3 is a functional block diagram of the member information management apparatus 200 according to an embodiment of the present invention, where the functions implemented by the member information management apparatus 200 may correspond to the steps executed by the above method. The member information management apparatus 200 may be understood as the server 100 or a processor of the server 100, or may be understood as a component which is independent from the server 100 or the processor and implements the functions of the present invention under the control of the server 100, as shown in fig. 3, and the functions of the functional modules of the member information management apparatus 200 are described in detail below.
The obtaining module 210 is configured to obtain member configuration information collected by each member entry device in the target member merchant, where the classroom process data includes member tag information and member personalized entry information.
The first analysis module 220 is configured to analyze the member tag information according to a pre-trained member permission state analysis model to obtain a member permission state of each member user in the member tag information, where the member permission state includes a member level state and a member duration state.
And the second analysis module 230 is configured to analyze the member personalized entry information to obtain a personalized coverage interaction state corresponding to the member personalized entry information.
And the generating module 240 is configured to generate a member statistical report of the target merchant object corresponding to the target member merchant according to the member authority state and the personalized coverage interaction state, so as to obtain a generating result.
In one possible example, the obtaining module 210 obtains the member configuration information collected by each member entry device in the target member merchant by:
determining a target merchant object to be evaluated according to the received generation request;
acquiring a target member merchant corresponding to the target merchant object;
sending a data acquisition request to each member logging device in a target member merchant;
and receiving the acquired member configuration information sent by each member logging device in the target member merchant in response to the data acquisition request.
In one possible example, the member authority state analysis model is trained by:
acquiring a member authority state sample set, wherein the member authority state sample set comprises a plurality of member authority state samples and a member authority state label labeled by each member authority state sample;
and inputting a plurality of member authority state samples included in the member authority state sample set and a member authority state label labeled by each member authority state sample into an initially configured deep learning network model, and obtaining the member authority state analysis model through iterative training.
In one possible example, the recognition network model of the personalized coverage interaction state is trained by:
acquiring a member personalized input information sample set, wherein the member personalized input information sample set comprises a plurality of member personalized input information samples and personalized coverage interaction state labels marked on each member personalized input information sample;
extracting the personalized input characteristics corresponding to each member personalized input information sample;
inputting the personalized entry characteristics corresponding to each member personalized entry information sample and the personalized coverage interaction state label marked by each member personalized entry information sample into an initially configured deep learning network model, and obtaining the identification network model of the personalized coverage interaction state through iterative training.
In a possible example, the generating module 240 generates a member statistical report of a target merchant object corresponding to the target member merchant by the following manner, so as to obtain a generating result:
counting the number of first member users in each member level state and the number of second member users in each member duration state according to the member level state and the member duration state of each member user;
determining the duration of each personalized coverage interaction state according to various personalized coverage interaction states counted in a set time period, and determining a corresponding first statistical report unit according to the duration of each personalized coverage interaction state to obtain the sum of the first statistical report units;
respectively counting the sum of second statistical report units corresponding to the first member user quantity in each member level state and the sum of third statistical report units corresponding to the second member user quantity in each member duration state according to the first member user quantity in each member level state and the second member user quantity in each member duration state;
and obtaining the generated result according to the sum of the first statistical report units, the sum of the second statistical report units and the sum of the third statistical report units.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus and method embodiments described above are illustrative only, as the flowcharts 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 invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, 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.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
Alternatively, all or part of the implementation may be in software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (10)
1. A member information management method applied to a server, the method comprising:
acquiring member configuration information acquired by each member entry device in a target member merchant, wherein the classroom process data comprises member label information and member personalized entry information;
analyzing the member label information according to a pre-trained member authority state analysis model to obtain a member authority state of each member user in the member label information, wherein the member authority state comprises a member grade state and a member time-length state;
analyzing the member personalized input information to obtain a personalized coverage interaction state corresponding to the member personalized input information;
and generating a member statistical report of the target merchant object corresponding to the target member merchant according to the member authority state and the personalized coverage interaction state to obtain a generated result.
2. The member information management method according to claim 1, wherein the step of acquiring the member configuration information collected by each member entry device in the target member merchant comprises:
determining a target merchant object to be evaluated according to the received generation request;
acquiring a target member merchant corresponding to the target merchant object;
sending a data acquisition request to each member logging device in a target member merchant;
and receiving the acquired member configuration information sent by each member logging device in the target member merchant in response to the data acquisition request.
3. The member information management method according to claim 1, wherein the member authority state analysis model is trained by:
acquiring a member authority state sample set, wherein the member authority state sample set comprises a plurality of member authority state samples and a member authority state label labeled by each member authority state sample;
and inputting a plurality of member authority state samples included in the member authority state sample set and a member authority state label labeled by each member authority state sample into an initially configured deep learning network model, and obtaining the member authority state analysis model through iterative training.
4. The member information management method according to claim 1, wherein the recognition network model of the personalized coverage interaction state is trained by:
acquiring a member personalized input information sample set, wherein the member personalized input information sample set comprises a plurality of member personalized input information samples and personalized coverage interaction state labels marked on each member personalized input information sample;
extracting the personalized input characteristics corresponding to each member personalized input information sample;
inputting the personalized entry characteristics corresponding to each member personalized entry information sample and the personalized coverage interaction state label marked by each member personalized entry information sample into an initially configured deep learning network model, and obtaining the identification network model of the personalized coverage interaction state through iterative training.
5. The member information management method according to claim 1, wherein the step of generating a member statistical report of the target merchant object corresponding to the target member merchant according to the member authority status and the personalized coverage interaction status to obtain a generated result comprises:
counting the number of first member users in each member level state and the number of second member users in each member duration state according to the member level state and the member duration state of each member user;
determining the duration of each personalized coverage interaction state according to various personalized coverage interaction states counted in a set time period, and determining a corresponding first statistical report unit according to the duration of each personalized coverage interaction state to obtain the sum of the first statistical report units;
respectively counting the sum of second statistical report units corresponding to the first member user quantity in each member level state and the sum of third statistical report units corresponding to the second member user quantity in each member duration state according to the first member user quantity in each member level state and the second member user quantity in each member duration state;
and obtaining the generated result according to the sum of the first statistical report units, the sum of the second statistical report units and the sum of the third statistical report units.
6. A member information management apparatus applied to a server, the apparatus comprising:
the acquisition module is used for acquiring member configuration information acquired by each member entry device in a target member merchant, and the classroom process data comprises member label information and member personalized entry information;
the first analysis module is used for analyzing the member label information according to a pre-trained member authority state analysis model to obtain a member authority state of each member user in the member label information, wherein the member authority state comprises a member grade state and a member time-length state;
the second analysis module is used for analyzing the member personalized input information to obtain a personalized coverage interaction state corresponding to the member personalized input information;
and the generating module is used for generating a member statistical report of a target merchant object corresponding to the target member merchant according to the member authority state and the personalized coverage interaction state to obtain a generating result.
7. The member information management apparatus according to claim 6, wherein the obtaining module obtains the member configuration information collected by each member entry device in the target member merchant by:
determining a target merchant object to be evaluated according to the received generation request;
acquiring a target member merchant corresponding to the target merchant object;
sending a data acquisition request to each member logging device in a target member merchant;
and receiving the acquired member configuration information sent by each member logging device in the target member merchant in response to the data acquisition request.
8. The member information management apparatus according to claim 6, wherein the member authority state analysis model is trained by:
acquiring a member authority state sample set, wherein the member authority state sample set comprises a plurality of member authority state samples and a member authority state label labeled by each member authority state sample;
and inputting a plurality of member authority state samples included in the member authority state sample set and a member authority state label labeled by each member authority state sample into an initially configured deep learning network model, and obtaining the member authority state analysis model through iterative training.
9. The member information management apparatus of claim 6, wherein the recognition network model of the personalized overlay interaction state is trained by:
acquiring a member personalized input information sample set, wherein the member personalized input information sample set comprises a plurality of member personalized input information samples and personalized coverage interaction state labels marked on each member personalized input information sample;
extracting the personalized input characteristics corresponding to each member personalized input information sample;
inputting the personalized entry characteristics corresponding to each member personalized entry information sample and the personalized coverage interaction state label marked by each member personalized entry information sample into an initially configured deep learning network model, and obtaining the identification network model of the personalized coverage interaction state through iterative training.
10. The member information management device according to claim 6, wherein the generation module generates the member statistical report of the target merchant object corresponding to the target member merchant in the following manner, and obtains the generation result:
counting the number of first member users in each member level state and the number of second member users in each member duration state according to the member level state and the member duration state of each member user;
determining the duration of each personalized coverage interaction state according to various personalized coverage interaction states counted in a set time period, and determining a corresponding first statistical report unit according to the duration of each personalized coverage interaction state to obtain the sum of the first statistical report units;
respectively counting the sum of second statistical report units corresponding to the first member user quantity in each member level state and the sum of third statistical report units corresponding to the second member user quantity in each member duration state according to the first member user quantity in each member level state and the second member user quantity in each member duration state;
and obtaining the generated result according to the sum of the first statistical report units, the sum of the second statistical report units and the sum of the third statistical report units.
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| CN202010902978.2A CN113901100A (en) | 2020-09-01 | 2020-09-01 | Member information management method and device |
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| CN202010902978.2A CN113901100A (en) | 2020-09-01 | 2020-09-01 | Member information management method and device |
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| CN113901100A true CN113901100A (en) | 2022-01-07 |
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| CN202010902978.2A Withdrawn CN113901100A (en) | 2020-09-01 | 2020-09-01 | Member information management method and device |
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2020
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Application publication date: 20220107 |