CN116975538A - Client tag generation method and device, electronic equipment and readable storage medium - Google Patents
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Abstract
The invention relates to the field of artificial intelligence, and discloses a client tag generation method, a device, electronic equipment and a storage medium, which can be used for generating tags for clients in the field of financial insurance, and are convenient for business personnel to screen target clients, wherein the method comprises the following steps: acquiring basic information data of a historical client, capturing behavior information data of the historical client, and respectively carrying out data screening and cleaning treatment on the basic information data and the behavior information data to obtain standard basic characteristic data and standard behavior characteristic data; clustering the history clients to obtain a client group; predicting the behavior demand of the guest group by using a preset behavior prediction model; and carrying out tag identification on the historical clients according to the standard basic feature data, the standard behavior feature data and the behavior requirements to obtain client tags. The invention can improve the accuracy and efficiency of generating the client labels.
Description
Technical Field
The present invention relates to the field of artificial intelligence, and in particular, to a method and apparatus for generating a client tag, an electronic device, and a readable storage medium.
Background
Customer labels are important media for customer service agents to clearly read customer demands, read customer qualification and identify customer risks. For example, in the financial field, many insurance companies often mine new insurance users through customer labels.
Most of customer label generation methods provided in the market at present are related products of user portraits, the dimension is relatively single, attribute labels accumulated and verified for a long time in the vertical industry are absent, and besides, the requirements of customer service agents on timely observing customer behaviors and favorites cannot be met, so that the efficiency and accuracy of an enterprise in mining new users are low.
Disclosure of Invention
The invention provides a client label generation method, a client label generation device, electronic equipment and a readable storage medium, and aims to improve client label generation accuracy and efficiency.
In order to achieve the above object, the present invention provides a client tag generation method, which includes:
acquiring basic information data of a history client, capturing behavior information data of the history client, and carrying out tag identification on the basic information data and the behavior information data to obtain a basic information tag and a behavior information tag;
according to preset service requirements, respectively carrying out data screening and cleaning treatment on the basic information data and the behavior information data to obtain standard basic characteristic data and standard behavior characteristic data;
According to the basic information tag and the behavior information tag, dimension distinction is respectively carried out on the standard basic feature data and the standard behavior feature data, and basic dimension and behavior dimension information are obtained;
clustering the history clients according to the basic dimension and the behavior dimension information respectively to obtain a client group;
predicting the behavior demand of the guest group by using a preset behavior prediction model;
and carrying out tag identification on the historical clients according to the standard basic feature data, the standard behavior feature data and the behavior requirements to obtain client tags.
Optionally, the predicting the behavioral requirement of the guest group by using a preset behavioral prediction model includes:
generating a guest group portrait of the guest group according to the standard basic feature data and the standard behavior feature data;
extracting the characteristics of the guest group images by utilizing a characteristic extraction network in a preset behavior prediction model to obtain a characteristic sequence set;
calculating the matching degree of the feature sequence set and the behavior demand label in the behavior prediction model by using an operation layer in the behavior prediction model;
and selecting the behavior demand label with the matching degree larger than a preset threshold as the behavior demand of the guest group.
Optionally, in the step of clustering the history clients according to the basic dimension and the behavior dimension to obtain the guest group, clustering the history clients according to the basic dimension to obtain the guest group includes:
selecting a preset number of standard basic feature data from each basic dimension as an initial clustering center;
calculating the initial distance between each standard basic feature data and each initial clustering center by using a Euclidean distance algorithm;
according to the initial distance, distributing the standard basic feature data to an initial clustering center with the nearest initial distance to obtain an initial guest group;
calculating the average value of all standard basic feature data in the initial guest group, and updating the initial clustering center according to the average value to obtain an updated clustering center;
and calculating a second distance between each standard basic feature data and each updated clustering center by using a Euclidean distance algorithm, returning to the initial clustering center closest to the initial distance according to the initial distance, and distributing the standard basic feature data to the initial clustering center closest to the initial distance to obtain an initial guest group until the updated clustering center is not changed any more, and obtaining the guest group of each basic dimension.
Optionally, the data screening and cleaning process is performed on the basic information data and the behavior information data according to a preset service requirement to obtain standard basic feature data and standard behavior feature data, which includes:
according to preset service requirements, respectively carrying out feature selection on the basic information data and the behavior information data to obtain basic feature data and behavior feature data;
respectively carrying out data cleaning on the basic characteristic data and the behavior characteristic data to obtain cleaning basic information data and cleaning behavior information data;
and carrying out normalization processing on the cleaning basic information data and the cleaning behavior information data to obtain standard basic feature data and standard behavior feature data.
Optionally, the normalizing the cleaning basic information data and the cleaning behavior information data to obtain standard basic feature data and standard behavior feature data includes:
calculating normalized data of the cleaning basic information data and the cleaning behavior information data by using the following normalization formula:
wherein the X is ′ Means the standard basic feature data or the standard behavior feature data; the X refers to the cleaning basic information data and the cleaning behavior information data; the L refers to an L1 norm or an L2 norm.
Optionally, the capturing behavior information data of the historical client includes:
recording request response data between a client and a background service when the history client browses a preset webpage by using a packet grabbing tool;
extracting a page browsing request, a product browsing request and a product purchasing request from the request response data according to a preset target field, and arranging the page browsing request, the product browsing request and the product purchasing request in a queue according to a time sequence to obtain behavior information data.
Optionally, the performing tag identification on the historical client according to the standard basic feature data, the standard behavior feature data and the behavior requirement to obtain a client tag includes:
formulating a client tag pool according to preset tag customization rules;
and respectively matching the standard basic feature data, the standard behavior feature data and the behavior requirements of the historical clients with the tags in the client tag pool to obtain client tags.
In order to solve the above-mentioned problems, the present invention also provides a client tag generating apparatus, the apparatus comprising:
the characteristic data screening module is used for acquiring basic information data of a historical client, capturing behavior information data of the historical client, carrying out tag identification on the basic information data and the behavior information data to obtain basic information tags and behavior information tags, and respectively carrying out data screening and cleaning treatment on the basic information data and the behavior information data according to preset service requirements to obtain standard basic characteristic data and standard behavior characteristic data;
The client behavior prediction module is used for respectively carrying out dimension distinction on the standard basic feature data and the standard behavior feature data according to the basic information tag and the behavior information tag to obtain basic dimension and behavior dimension information, clustering the historical clients according to the basic dimension and the behavior dimension information to obtain a guest group, and predicting the behavior requirement of the guest group by using a preset behavior prediction model;
and the client tag generation module is used for carrying out tag identification on the historical client according to the standard basic feature data, the standard behavior feature data and the behavior requirement to obtain a client tag.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
a memory storing at least one computer program; a kind of electronic device with high-pressure air-conditioning system
And a processor executing the computer program stored in the memory to implement the client tag generation method.
In order to solve the above-mentioned problems, the present invention also provides a computer-readable storage medium having stored therein at least one computer program that is executed by a processor in an electronic device to implement the above-mentioned client tag generation method.
According to the embodiment of the invention, the data quality is ensured by cleaning the target information data of the client, the accuracy of client tag generation is improved, further, the standard information data is subjected to feature selection according to the preset service requirement, the influence of invalid features on client tag generation can be effectively reduced, the accuracy of client tag generation is improved, secondly, the client behavior requirement is predicted by utilizing the preset behavior prediction model according to the standard information data, customer service can be helped to obtain user preference and requirement in advance, the success rate of product recommendation is improved, and finally, the client is subjected to tag identification according to the grouping of the client group and the behavior requirement, so that the client tag is obtained, the manual participation degree in the client tag generation process is reduced, and the efficiency of client tag generation is improved. Therefore, the client label generating method, the client label generating device, the client label generating equipment and the storage medium can improve the accuracy and the efficiency of client label generation, and are convenient for customer service to accurately recommend products to clients.
Drawings
FIG. 1 is a flowchart of a client tag generating method according to an embodiment of the present invention;
FIGS. 2 and 3 are flowcharts illustrating a detailed implementation of one of the steps of the client tag generation method according to an embodiment of the present application;
FIG. 4 is a schematic block diagram of a client tag generating apparatus according to an embodiment of the present application;
fig. 5 is a schematic diagram of an internal structure of an electronic device for implementing a client tag generating method according to an embodiment of the present application;
the achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The embodiment of the application provides a client label generation method. The execution subject of the client tag generation method includes, but is not limited to, at least one of a server, a terminal, and the like, which can be configured to execute the method provided by the embodiment of the application. In other words, the client tag generation method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The server may include an independent server, and may also include a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, a flowchart of a client tag generating method according to an embodiment of the present invention is shown, where in the embodiment of the present invention, the client tag generating method includes:
s1, acquiring basic information data of a history client, capturing behavior information data of the history client, and performing tag identification on the basic information data and the behavior information data to obtain a basic information tag and a behavior information tag.
In the embodiment of the invention, the historical clients can be clients which have saved information in the client information database in the past. The basic information data includes age, sex, name, work property, and the like. The behavior information data may be a record of browsing behavior of the history client in a specific web page or specific software. The basic information tag includes an age tag, a sex tag, a work property tag, and the like. The behavior information labels comprise browsing record labels, clicking times labels, browsing time labels and the like.
In an alternative embodiment of the invention, the basic information data of the historical clients can be obtained by calling the data in the client information database of the enterprise storage client information, and the behavior data of the historical clients can be obtained by grabbing the browsing behavior information of the historical clients on the enterprise official network or the enterprise product software through the grabbing tool, so that the manual participation degree in the process of collecting the information data is reduced, the information data collecting speed is improved, and the information data collecting efficiency is improved.
Further, as an optional embodiment of the present application, referring to fig. 2, the capturing behavior information data of the history client includes:
s11, recording request response data between a client and a background service when the history client browses a preset webpage by using a packet grabbing tool;
s12, extracting a page browsing request, a product browsing request and a product purchase request from the request response data according to a preset target field, and arranging the page browsing request, the product browsing request and the product purchase request in a queue according to a time sequence to obtain behavior information data.
In the embodiment of the present application, the package capturing tool is a tool capable of acquiring request response data (uniform resource locator, abbreviated as URL) transmitted between a client and a background service, and the embodiment of the present application may use any package capturing tool, such as a developer tool of a google browser, as the package capturing tool in the present application to perform URL acquisition operation. The URL is an access lesson selection interface sent by a user (an app terminal), and requests a request link for calling background data content, wherein the URL comprises information such as protocol mode, request address, parameter value, label and the like.
According to the request address field, the embodiment of the invention screens the page browsing request, the product browsing request and the product purchasing request, thereby obtaining behavior information data.
Further, in order to ensure the multi-dimension of the client tag, the feature data of the client is required to be classified, so that the embodiment of the invention obtains the basic information tag and the behavior information tag by performing tag identification on the basic information data and the behavior information data, reduces the difficulty of feature data classification, and ensures the multi-dimension of the client tag.
Specifically, the embodiment of the invention carries out tag identification on the basic information data and the behavior information data by identifying the data types of the basic information data and the behavior information data to obtain a basic information tag and a behavior information tag.
S2, respectively carrying out data screening and cleaning treatment on the basic information data and the behavior information data according to preset service requirements to obtain standard basic characteristic data and standard behavior characteristic data.
In the embodiment of the present invention, the preset service requirement may be a data type required by the client tag identifier, for example, gender, age, working property, etc. in the basic information data, where the client in the behavior information data browses a web page times, browses a web page time, and browses a web page type.
In order to ensure the validity and availability of the data, after the basic information data and the behavior information data of the client are obtained, the embodiment of the invention also needs to respectively carry out data screening and cleaning treatment on the basic information data and the behavior information data according to the preset service requirement to obtain standard basic characteristic data and standard behavior characteristic data, thereby reducing the interference of invalid data on the client tag identification and improving the accuracy of client tag generation.
In detail, as an optional embodiment of the present invention, referring to fig. 3, the performing data screening and cleaning processing on the basic information data and the behavior information data according to a preset service requirement to obtain standard basic feature data and standard behavior feature data includes:
s21, respectively carrying out feature selection on the basic information data and the behavior information data according to preset service requirements to obtain basic feature data and behavior feature data;
s22, respectively carrying out data cleaning on the basic characteristic data and the behavior characteristic data to obtain cleaning basic information data and cleaning behavior information data;
s23, carrying out normalization processing on the cleaning basic information data and the cleaning behavior information data to obtain standard basic feature data and standard behavior feature data.
According to the method and the device, the optional embodiment of the invention, through feature selection, the data which can be used as the feature data is selected from the basic information data and the behavior information data, so that the generated client tag can accurately represent the features of the client, and the influence of names and the like on the information data which is not used for the client tag identification is reduced.
Furthermore, in order to ensure the availability of the basic feature data and the behavior feature data, the data cleaning processing such as missing value filling, abnormal value processing, format conversion and the like is required to be carried out on the basic feature data and the behavior feature data, so that the effectiveness of the basic feature data and the behavior feature data is improved.
Finally, the embodiment of the invention performs characteristic standardization processing on the cleaning basic information data and the cleaning behavior information data, so that different cleaning basic information data and cleaning behavior information data have the same scale and importance, and the influence of certain cleaning basic information data or cleaning behavior information data on a clustering result is avoided.
Further, as an optional embodiment of the present invention, the normalizing the cleaning basic information data and the cleaning behavior information data to obtain standard basic feature data and standard behavior feature data includes:
Calculating normalized data of the cleaning basic information data and the cleaning behavior information data by using the following normalization formula:
wherein the X is ′ Means the standard basic feature data or the standard behavior feature data; the X refers to the cleaning basic information data and the cleaning behavior information data; the L refers to an L1 norm or an L2 norm.
The optional embodiment of the invention performs characteristic standardization processing on the cleaning basic information data and the cleaning behavior information data by utilizing a normalization formula so as to better compare and analyze the basic characteristic data and the behavior characteristic data of the client.
And S3, respectively carrying out dimension distinction on the standard basic feature data and the standard behavior feature data according to the basic information tag and the behavior information tag to obtain a basic dimension and a behavior dimension.
In the embodiment of the present invention, the standard basic feature data and the standard behavior feature data both include data in multiple dimensions, for example, the standard basic feature data includes data in dimensions such as a customer age, a customer gender, a customer working property, etc., and the standard behavior feature data includes data in dimensions such as a number of times a customer browses a specific web page or App, a duration of time a customer browses a specific web page or App, and a number of times a customer browses a product in a specific web page or App.
Further, in order to ensure that each dimension can extract a label, thereby improving the dimension of the client label and realizing the multi-dimension of the client label, the embodiment of the invention firstly needs to respectively distinguish the standard basic feature data and the standard behavior feature data to obtain the basic dimension and the behavior dimension, so that the client label becomes more saturated.
In detail, in an alternative embodiment of the present invention, by identifying the basic information tag and the behavior information tag of the standard basic feature data and the standard behavior feature data, dimension discrimination is performed on the standard basic feature data and the standard behavior feature data, so as to obtain a basic dimension and a behavior dimension.
S4, clustering the history clients according to the basic dimension and the behavior dimension respectively to obtain a client group.
In order to reduce the calculation difficulty of the model prediction client behavior requirements, the embodiment of the invention clusters the historical clients according to the basic dimension and the behavior dimension respectively to obtain the client groups, thereby converting the behavior requirements of each client to be predicted into the behavior requirements of each client group, reducing the calculation difficulty of the model prediction client behavior requirements and improving the efficiency of the application model prediction client behavior requirements.
Further, as an optional embodiment of the present invention, in the step of clustering the history clients according to the basic dimension and the behavior dimension to obtain a guest group, clustering the history clients according to the basic dimension to obtain a guest group includes:
selecting a preset number of standard basic feature data from each basic dimension as an initial clustering center;
calculating the initial distance between each standard basic feature data and each initial clustering center by using a Euclidean distance algorithm;
according to the initial distance, distributing the standard basic feature data to an initial clustering center with the nearest initial distance to obtain an initial guest group;
calculating the average value of all standard basic feature data in the initial guest group, and updating the initial clustering center according to the average value to obtain an updated clustering center;
and calculating a second distance between each standard basic feature data and each updated clustering center by using a Euclidean distance algorithm, returning to the initial clustering center closest to the initial distance according to the initial distance, and distributing the standard basic feature data to the initial clustering center closest to the initial distance to obtain an initial guest group until the updated clustering center is not changed any more, and obtaining the guest group of each basic dimension.
In the embodiment of the invention, the preset number can be calculated by an elbow algorithm or a contour coefficient algorithm.
In an alternative embodiment of the invention, the K-means clustering algorithm is utilized to cluster the historical clients to obtain K client groups, so that the similarity among the client groups is ensured, the clients in the client groups are ensured to have consistent behavior demands, the calculation difficulty of predicting the behavior demands of the clients by the behavior prediction model is reduced, and the generation speed of the client labels is improved.
Further, in the embodiment of the present invention, the history clients are clustered according to the standard behavior feature data to obtain a guest group, and the step of clustering the history clients according to the standard basic feature data to obtain the guest group is similar, so that details are omitted.
S5, predicting the behavior requirements of the guest group by using a preset behavior prediction model.
In order to facilitate business personnel to know the demands of clients in advance and improve the satisfaction of the clients, the embodiment of the invention also needs to predict the behavior demands of the client group by using a preset behavior prediction model, so that client labels are plump.
In detail, as an optional embodiment of the present invention, the predicting the behavior requirement of the guest group by using a preset behavior prediction model includes:
Generating a guest group portrait of the guest group according to the standard basic feature data and the standard behavior feature data;
extracting the characteristics of the guest group images by utilizing a characteristic extraction network in a preset behavior prediction model to obtain a characteristic sequence set;
calculating the matching degree of the feature sequence set and the behavior demand label in the behavior prediction model by using an operation layer in the behavior prediction model;
and selecting the behavior demand label with the matching degree larger than a preset threshold as the behavior demand of the guest group.
In the embodiment of the invention, the behavior requirement label can be a label of various customer services provided by enterprises, for example, for insurance promotion companies in the financial field, the behavior requirement label can be various insurance products.
In an alternative embodiment of the present invention, the matching degree between the feature sequence set and the behavior demand label in the behavior prediction model is calculated, so as to implement the prediction of the behavior demand of the guest group.
And S6, carrying out tag identification on the historical clients according to the standard basic feature data, the standard behavior feature data and the behavior requirements to obtain client tags.
In order to ensure the plumpness and multidimensional of the client labels, the embodiment of the invention needs to consider the standard basic characteristic data, the standard behavior characteristic data and the behavior requirement of the behavior requirement client group of the clients when generating the client labels of the historical clients, so that the generated client labels have the predicted behavior characteristics.
In detail, as an optional embodiment of the present invention, the performing tag identification on the historical client according to the standard basic feature data, the standard behavior feature data and the behavior requirement to obtain a client tag includes:
formulating a client tag pool according to preset tag customization rules;
and respectively matching the standard basic feature data, the standard behavior feature data and the behavior requirements of the historical clients with the tags in the client tag pool to obtain client tags.
In the embodiment of the invention, the preset label customization rule can be a label rule compiling text appointed by a technician according to the requirement of a business person. The customer label pool contains the labels required in all services.
In an alternative embodiment of the invention, the standard basic feature data, the standard behavior feature data and the behavior requirements of the historical clients are analyzed, and the analysis result is matched with the tags in the client tag pool to obtain the client tags, so that the client tags can embody the basic information, the behavior information and the predicted behavior requirements of the historical clients, and the client tags are multidimensional.
According to the embodiment of the invention, the data quality is ensured by cleaning the target information data of the client, the accuracy of client tag generation is improved, further, the standard information data is subjected to feature selection according to the preset service requirement, the influence of invalid features on client tag generation can be effectively reduced, the accuracy of client tag generation is improved, secondly, the client behavior requirement is predicted by utilizing the preset behavior prediction model according to the standard information data, customer service can be helped to obtain user preference and requirement in advance, the success rate of product recommendation is improved, and finally, the client is subjected to tag identification according to the grouping of the client group and the behavior requirement, so that the client tag is obtained, the manual participation degree in the client tag generation process is reduced, and the efficiency of client tag generation is improved. Therefore, the client label generating method provided by the invention can improve the accuracy and efficiency of client label generation, and is convenient for customer service to accurately recommend products to clients.
As shown in fig. 4, a functional block diagram of the client tag generating apparatus according to the present invention is shown.
The client tag generation apparatus 100 according to the present invention may be mounted in an electronic device. Depending on the implemented functions, the client tag generation apparatus 100 may include a feature data filtering module 101, a client behavior prediction module 102, and a client tag generation module 103, which may also be referred to as a unit, refers to a series of computer program segments capable of being executed by a processor of an electronic device and performing a fixed function, and stored in a memory of the electronic device.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the feature data filtering module 101 is configured to obtain basic information data of a historical client, capture behavior information data of the historical client, tag the basic information data and the behavior information data to obtain a basic information tag and a behavior information tag, and perform data filtering and cleaning processing on the basic information data and the behavior information data according to preset service requirements to obtain standard basic feature data and standard behavior feature data.
The client behavior prediction module 102 is configured to perform dimension distinction on the standard basic feature data and the standard behavior feature data according to the basic information tag and the behavior information tag, obtain basic dimension and behavior dimension information, cluster the historical clients according to the basic dimension and the behavior dimension information, obtain a guest group, and predict a behavior requirement of the guest group by using a preset behavior prediction model.
The client tag generation module 103 is configured to perform tag identification on the historical client according to the standard basic feature data, the standard behavior feature data and the behavior requirement, so as to obtain a client tag.
According to the embodiment of the invention, the data quality is ensured by cleaning the target information data of the client, the accuracy of client tag generation is improved, further, the standard information data is subjected to feature selection according to the preset service requirement, the influence of invalid features on client tag generation can be effectively reduced, the accuracy of client tag generation is improved, secondly, the client behavior requirement is predicted by utilizing the preset behavior prediction model according to the standard information data, customer service can be helped to obtain user preference and requirement in advance, the success rate of product recommendation is improved, and finally, the client is subjected to tag identification according to the grouping of the client group and the behavior requirement, so that the client tag is obtained, the manual participation degree in the client tag generation process is reduced, and the efficiency of client tag generation is improved. Therefore, the client label generating device provided by the invention can improve the accuracy and efficiency of client label generation, and is convenient for customer service to accurately recommend products to clients.
Fig. 5 is a schematic structural diagram of an electronic device for implementing the client tag generating method according to the present invention.
The electronic device may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program, such as a client tag generation program, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 11 may in other embodiments also be an external storage device of the electronic device, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only for storing application software installed in an electronic device and various types of data, such as a code of a client tag generation program, etc., but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing Unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, and executes various functions of the electronic device and processes data by running or executing programs or modules (e.g., client tag generation programs, etc.) stored in the memory 11, and calling data stored in the memory 11.
The communication bus 12 may be a peripheral component interconnect standard (PerIPheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The communication bus 12 is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.
Fig. 5 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 5 is not limiting of the electronic device and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power source (such as a battery) for supplying power to the respective components, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
Optionally, the communication interface 13 may comprise a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices.
Optionally, the communication interface 13 may further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The client tag generation program stored in the memory 11 in the electronic device is a combination of a plurality of computer programs, which when run in the processor 10, can implement:
Acquiring basic information data of a history client, capturing behavior information data of the history client, and carrying out tag identification on the basic information data and the behavior information data to obtain a basic information tag and a behavior information tag;
according to preset service requirements, respectively carrying out data screening and cleaning treatment on the basic information data and the behavior information data to obtain standard basic characteristic data and standard behavior characteristic data;
according to the basic information tag and the behavior information tag, dimension distinction is respectively carried out on the standard basic feature data and the standard behavior feature data, and basic dimension and behavior dimension information are obtained;
clustering the history clients according to the basic dimension and the behavior dimension information respectively to obtain a client group;
predicting the behavior demand of the guest group by using a preset behavior prediction model;
and carrying out tag identification on the historical clients according to the standard basic feature data, the standard behavior feature data and the behavior requirements to obtain client tags.
In particular, the specific implementation method of the processor 10 on the computer program may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.
Further, the electronic device integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. The computer readable medium may be non-volatile or volatile. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
Embodiments of the present invention may also provide a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, may implement:
acquiring basic information data of a history client, capturing behavior information data of the history client, and carrying out tag identification on the basic information data and the behavior information data to obtain a basic information tag and a behavior information tag;
according to preset service requirements, respectively carrying out data screening and cleaning treatment on the basic information data and the behavior information data to obtain standard basic characteristic data and standard behavior characteristic data;
According to the basic information tag and the behavior information tag, dimension distinction is respectively carried out on the standard basic feature data and the standard behavior feature data, and basic dimension and behavior dimension information are obtained;
clustering the history clients according to the basic dimension and the behavior dimension information respectively to obtain a client group;
predicting the behavior demand of the guest group by using a preset behavior prediction model;
and carrying out tag identification on the historical clients according to the standard basic feature data, the standard behavior feature data and the behavior requirements to obtain client tags.
Further, the computer-usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.
In the embodiments provided in the present invention, it should be understood that the disclosed electronic device, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
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 characteristics 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 signs in the claims shall not be construed as limiting the claim concerned.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A method of generating a customer label, the method comprising:
acquiring basic information data of a history client, capturing behavior information data of the history client, and carrying out tag identification on the basic information data and the behavior information data to obtain a basic information tag and a behavior information tag;
according to preset service requirements, respectively carrying out data screening and cleaning treatment on the basic information data and the behavior information data to obtain standard basic characteristic data and standard behavior characteristic data;
according to the basic information tag and the behavior information tag, dimension distinction is respectively carried out on the standard basic feature data and the standard behavior feature data, and basic dimension and behavior dimension information are obtained;
clustering the history clients according to the basic dimension and the behavior dimension information respectively to obtain a client group;
predicting the behavior demand of the guest group by using a preset behavior prediction model;
and carrying out tag identification on the historical clients according to the standard basic feature data, the standard behavior feature data and the behavior requirements to obtain client tags.
2. The client tag generation method of claim 1, wherein predicting the behavioral requirements of the guest group using a preset behavioral prediction model comprises:
Generating a guest group portrait of the guest group according to the standard basic feature data and the standard behavior feature data;
extracting the characteristics of the guest group images by utilizing a characteristic extraction network in a preset behavior prediction model to obtain a characteristic sequence set;
calculating the matching degree of the feature sequence set and the behavior demand label in the behavior prediction model by using an operation layer in the behavior prediction model;
and selecting the behavior demand label with the matching degree larger than a preset threshold as the behavior demand of the guest group.
3. The client tag generation method as set forth in claim 1, wherein the step of clustering the history clients according to the basic dimension and the behavior dimension, respectively, to obtain a client group includes the steps of:
selecting a preset number of standard basic feature data from each basic dimension as an initial clustering center;
calculating the initial distance between each standard basic feature data and each initial clustering center by using a Euclidean distance algorithm;
according to the initial distance, distributing the standard basic feature data to an initial clustering center with the nearest initial distance to obtain an initial guest group;
Calculating the average value of all standard basic feature data in the initial guest group, and updating the initial clustering center according to the average value to obtain an updated clustering center;
and calculating a second distance between each standard basic feature data and each updated clustering center by using a Euclidean distance algorithm, returning to the initial clustering center closest to the initial distance according to the initial distance, and distributing the standard basic feature data to the initial clustering center closest to the initial distance to obtain an initial guest group until the updated clustering center is not changed any more, and obtaining the guest group of each basic dimension.
4. The method for generating client labels according to claim 1, wherein the step of performing data screening and cleaning processing on the basic information data and the behavior information data according to preset service requirements to obtain standard basic feature data and standard behavior feature data includes:
according to preset service requirements, respectively carrying out feature selection on the basic information data and the behavior information data to obtain basic feature data and behavior feature data;
respectively carrying out data cleaning on the basic characteristic data and the behavior characteristic data to obtain cleaning basic information data and cleaning behavior information data;
And carrying out normalization processing on the cleaning basic information data and the cleaning behavior information data to obtain standard basic feature data and standard behavior feature data.
5. The method for generating client labels according to claim 4, wherein normalizing the cleaning basic information data and the cleaning behavior information data to obtain standard basic feature data and standard behavior feature data comprises:
calculating normalized data of the cleaning basic information data and the cleaning behavior information data by using the following normalization formula:
wherein the X is ′ Means the standard basic feature data or the standard behavior feature data; said X means said cleaning baseInformation data and the cleaning behavior information data; the L refers to an L1 norm or an L2 norm.
6. The client tag generation method of claim 1, wherein the crawling of the behavior information data of the historic client comprises:
recording request response data between a client and a background service when the history client browses a preset webpage by using a packet grabbing tool;
extracting a page browsing request, a product browsing request and a product purchasing request from the request response data according to a preset target field, and arranging the page browsing request, the product browsing request and the product purchasing request in a queue according to a time sequence to obtain behavior information data.
7. The method for generating client labels according to claim 1, wherein said identifying the history client labels according to the standard basic feature data, the standard behavior feature data and the behavior requirement to obtain client labels includes:
formulating a client tag pool according to preset tag customization rules;
and respectively matching the standard basic feature data, the standard behavior feature data and the behavior requirements of the historical clients with the tags in the client tag pool to obtain client tags.
8. A customer label producing apparatus, the apparatus comprising:
the characteristic data screening module is used for acquiring basic information data of a historical client, capturing behavior information data of the historical client, carrying out tag identification on the basic information data and the behavior information data to obtain basic information tags and behavior information tags, and respectively carrying out data screening and cleaning treatment on the basic information data and the behavior information data according to preset service requirements to obtain standard basic characteristic data and standard behavior characteristic data;
the client behavior prediction module is used for respectively carrying out dimension distinction on the standard basic feature data and the standard behavior feature data according to the basic information tag and the behavior information tag to obtain basic dimension and behavior dimension information, clustering the historical clients according to the basic dimension and the behavior dimension information to obtain a guest group, and predicting the behavior requirement of the guest group by using a preset behavior prediction model;
And the client tag generation module is used for carrying out tag identification on the historical client according to the standard basic feature data, the standard behavior feature data and the behavior requirement to obtain a client tag.
9. An electronic device, the electronic device comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores computer program instructions executable by the at least one processor to enable the at least one processor to perform the client tag generation method of any one of claims 1 to 7.
10. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the client tag generation method of any one of claims 1 to 7.
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